Modelling the Hydrological Sensitivity to Land UseChange in a Tropical Mountainous Environment

by

Mauricio Edilberto Rincón Romero

April 2001

A thesis submitted to the University of London for the degreeof Doctor of Philosophy

Department of GeographyKing’s College London

To my lovely wife

And sons

Manuel Felipe and

Miguel Angel

Tambito River (Rincón-Romero, 1997)

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Abstract

The main subject of this thesis is the production of a sensitivity

analysis to land use and land cover change (LUCC) for a tropical

montane cloud forest (TMCF) environment on the basis of flux

responses in a hydrological model. Human pressure is one of the

main causes of LUCC in the TMCF which often results in important

consequences on natural resources like reduction of water quality,

loss of biodiversity, micro-climatic change or ecosystem

degradation (Koning et al., 1998). Deforestation of the tropical

cloud forest is an activity of recent decades that is modifying the

landscape significantly. The impact of this deforestation, rather

than the deforestation itself, is studied here by comparing variation

in fluxes of erosion and overland flow derived from different land

uses within a mountainous tropical forest catchment. A physically

based hydrological model of the Tambito watershed, Cauca-

Colombia and 5 LUCC pattern scenarios are implemented for the

study. A 2.5D dynamic surface hydrological model integrated with

a Geographic Information System (GIS) working on an hourly time

step is designed for the catchment, to assess flux variability in time

and space. The hydrological model includes the following sub-

modules: solar radiation and energy balance, evaporation,

interception and effective precipitation, infiltration, soil

hydrological balance, overland flow, recharge and erosion. Three

hydro-meteorological stations installed on experimental plots

collect basic model information for parameterisation and

validation. Experimental description, methodology, field data,

model implementation and analysed results are presented. Each

LUCC scenario uses 15 to 22 consecutive GIS iterations, which

transform forest to pasture within the catchment. Summaries of

annual average hydrological flux variations are used in the

sensitivity analysis. Multiple linear correlation was carried out for

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flux variations and hydrological sensitivity with landscape physical

properties of the deforested area by iteration for each scenario, in

order to determine the correlation between landscape catchment

physical properties and hydrological flux sensitivities. This process

also facilitated the identification of the topographic characteristics

of the most sensitive areas within the catchment to LUCC. The

model and statistical analysis provides a means of assessing the

contribution of different landscape units to hydrological change in

the face of LUCC. The impact of LUCC is assessed in terms of

catchment hydrological changes and the areas within the

catchment with more hydrological sensitivity to LUCC are

identified.

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ACKNOWLEDGEMENT

This thesis was funded by the Higher Education Programme of the

Office of Presidency of Republic of Colombia, through

COLCIENCIAS, and the “Instituto de Investigaciones Biológicas

Alexander Von Humboldt” for the development and application of

GIS-modelling technologies in Colombia. Additionally some field-

work expedition were possible due to the help of the University of

London Central Research Fund.

I want to express my special thanks to my supervisor Dr. Mark

Mulligan for his unconditional support in all fields including

academic, logistic, personal and moral. Without his direct

assistance this thesis would not have been possible. Also, I would

like to express my gratitude to the late Alvaro Jose Negret, of

Fundacion Proselva and the University of Cauca, Popayan, for his

enthusiastic support and permission to use the Tambito field site

and its facilities. I am also grateful to other Colombian

organisations that provided logistic support, in particular the

International Centre of Tropical Agriculture (CIAT), the Regional

Corporation for Cauca (CRC) and the ‘Instituto de Hidrología

Meteorología y estudios Ambientales’ (IDEAM), this last one who

brought meteorological information of the region.

I would like to make a special mention to the other KCL students

who came to the Tambito Reserve, to provide their assistance in the

field, and additionally, who gave a pleasant touch to the difficult

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experience, making the situation bearable and enjoyable. They

were Koulla Pallaris, Andrew Jarvis, Jorge Rubiano, Robert Stein

Rostaing, Matthew Letts, Juliana Gonzalez, Sim Reaney and Lydia

Bruce-Burgess. Also there were some special people who gave local

support in the campaign activities particularly Quintin and Olga.

This work would not have been possible without constant and

valuable support from my family, particularly my lovely wife who

day by day was behind my shoulders encouraging me and feeding

my hopes to get successful results. Also, to my beautiful and

innocent son Manuel Felipe for his constant stimulation and for

showing me the sense of our life. Also, my father and my brothers

who have been constantly interested in my progress / development

with this thesis. Thanks to all of them.

It is impossible to pass without mentioning the ‘DUNGEON’

friends, Andy, Sotto, Benny, Elias, Matt, Jim, and Christos,

because through the circumstances, we became a family, giving

our support to each other in both personal and academic aspects.

They were the ones who made London a pleasant place to live, and

the dungeon an agreeable palace in the middle of an eighteenth

century building. I would also like to thank the Pallaris family, who

welcomed me in their home during the last stages of the writing up

this thesis.

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Table of contents

Chapter I Introduction

1.1 Land use and cover change (LUCC): a global issue 11.2 LUCC: global impacts 31.3 A review of models for LUCC 5

1.3.1 Methods for identifying the impact of LUCC 71.3.2 Strategies for evaluating hydrological fluxes in the

assessment of LUCC impact 81.4 LUCC: issues and impacts in tropical montane

environments outside Colombia 91.5 LUCC in Colombia: History and impacts in hillside

areas 111.5.1 Historical review of LUCC in Colombia 111.5.2 The hydrological impacts of LUCC in Colombia 16

1.6 Structure of the thesis 20

Chapter II Literature review of hydrological models applied to LUCC impacts research

2.1 Structure of this chapter 232.2 General concepts of hydrological models 232.3 A general classification of hydrological models 242.4 Handling spatial variability in hydrological models 262.5 A review of hydrological models related to LUCC impact 272.6 Hydrological modelling in tropical montane

environments 372.6.1 A review of modelling studies of the hydrologicalimpact of LUCC in Colombia 38

2.7 Research approaches to LUCC impacts 392.8 Main objective 402.9 Specific aims 402.10 Rationale 41

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Chapter III Methodology

3.1 Structure of this chapter 423.2 Description of the study area 433.3 Experimental strategy 493.4 Land use change scenario generation for this thesis 50

3.4.1 Estimating initial vegetation cover for LUCCScenarios 51

3.4.2 Scenario descriptions 543.5 Field methodology 64

3.5.1 Plot scale 643.5.1.1 The hydrological weather stations 673.5.1.2 Data collected from the weather stations 70

3.5.2 Catchment scale 713.5.2.1 Soil data 713.5.2.2 vegetation data 74

3.5.2.2.1 Leaf area index 753.5.2.2.2 Vegetation cover 763.5.2.2.3 Canopy water storage capacity 76

3.5.3 Other spatial data 783.6 Hydrological Modelling methodology 83

3.6.1 Introduction 833.6.2 Strategy 833.6.3 Consideration for modelling process 87Climate3.6.4 Solar Radiation sub-model 90

3.6.4.1 Hourly extraterrestrial solar radiationModel 91

3.6.4.2 Hourly cloud-cover attenuation model 923.6.4.3 Net solar radiation function 98

Hydrology3.6.5 Evaporation sub-model 1003.6.6 Canopy storage, interception and throughfall 1083.6.6.1 The Rutter model 1113.6.7 Sub-surface water sub-model 115

3.6.7.1 Modelling flow of water in porous media 1163.6.7.2 Soil water retention and matric potential 1173.6.7.3 Pedotransfer functions 119

3.6.8 Infiltration sub-model 1243.6.9 Overland flow sub-model 131

3.6.9.1 Sub-model description 1313.6.9.2 Surface component of overland flow at the

catchment scale 1343.6.10 Erosion sub-model 134

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3.7 Integrating the sub-models in the 1D and 2,5D model 1393.7.1 Module sequence 1393.7.2 Data used in the model 1403.7.3 Parameters used in the model 145

Chapter IV Model results, sensitivity analysis andvalidation

4.1 Structure of this chapter 1474.2 Model results 148

4.2.1 Model results at the plot scale 1484.2.2 Model results at the catchment scale 156

4.3 Sensitivity analysis of the hydrological model at theplot scale (1D model) 1604.3.1 Sensitivity to parameter A of net radiation 1624.3.2 Sensitivity to parameter B of net radiation

equation 1654.3.3 Sensitivity to parameter light extinction K 1664.3.4 Sensitivity to parameter leaf area index (LAI) 1684.3.5 Sensitivity to parameter maximum canopy water

storage capacity 1704.3.6 Sensitivity to parameter vegetation cover 1724.3.7 Percent of variation due to soil texture 1764.3.8 Sensitivity to parameter soil porosity 1784.3.9 Sensitivity to parameter soil depth 1804.3.10 Sensitivity to parameter erodability factor, K1 1834.3.11 Sensitivity to parameter m factor of erosion

equation 1844.3.12 Sensitivity to parameter n factor of erosion

equation 1854.4 Summary of 1D sensitivity analysis 1864.5 2.5D model sensitivity analysis 187

4.5.1 Definition of topographic characteristics 1884.5.2 Sensitivity analysis at the catchment scale 189

4.5.2.1 Sensitivity analysis of overland flow to LUCC at the catchment scale 192

4.5.2.2 Sensitivity analysis of erosion toLUCC at the catchment scale 207

4.6 Summary of 2.5D sensitivity analysis 2204.7 Model validation 222

4.7.1 Organisation of this section 2224.7.2 Field data set for validation 2234.7.3 Parameters used in validation 2234.7.4 Validation of net solar radiation 2254.7.5 Validation of soil moisture 228

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Chapter V Summary, conclusions and further work

5.1 Summary of key finding in this thesis 2325.2 Conclusions and their implications 2325.3 Further research and model development 245

Bibliography 248

Appendix I LUCC scenarios 286

Appendix II Collected data from the pasture plot 294

Appendix III Summary of soil analysis samples 298

Appendix IV Summary of vegetation samples for:canopy water storage capacity, vegetationcover, and LAI for grassland 300

Appendix V Tambito daily rainfall data 304

Appendix VI Example of input data file for the model 310

Appendix VII Extraterrestrial solar radiation model 315

Appendix VIII Mean value of cloud cover 326

Appendix IX Hydrological of PCRaster program Code 328

Appendix X Summary of physical variables and modelvariables response for all scenario 336

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List of tables

Table 1.1 Land use census data comparison for Colombia

between 1960 and 1995. 15

Table 3.1 Average NDVI values for classification of land

use classes 54

Table 3.2 Rates of deforestation per iteration of the

different scenarios (values in ha.) 62

Table 3.3 Periods during which data were collected 71

Table 3.4 Classes of slope and land use 73

Table 3.5 Leaf area index samples for grassland 76

Table 3.6 Vegetation parameters 77

Table 3.7 Soil erodability factor (taken from Morgan and

Kirkby, 1980) 137

Table 3.8 Soil parameters used in the physically-based

hydrological model 146

Table 4.1 Parameters used in the physical hydrological

Model 149

Table 4.2 Parameters used in the physical hydrological

model 160

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Table 4.3 Hourly average values of model variables for a

year simulation in 1 m2 161

Table 4.4 Colour code of the degree of sensitivity 162

Table 4.5 Sensitivity to parameter A in the net radiation

equation 163

Table 4.6 Sensitivity to parameter B in the net radiation

equation 165

Table 4.7 Sensitivity to light extinction 166

Table 4.8 Sensitivity to LAI 168

Table 4.9 Sensitivity to maximum canopy storage capacity 170

Table 4.10 Sensitivity to vegetation cover 173

Table 4.11 Soil texture classification classes 175

Table 4.12 Sensitivity to soil porosity 178

Table 4.13 Sensitivity to soil depth 181

Table 4.14 Sensitivity to erodability factor k1 183

Table 4.15 Sensitivity to m factor of erosion equation 184

Table 4.16 Sensitivity to n factor of erosion equation 185

Table 4.17 Summary of 1D sensitivity analysis by classes

with the colour code 186

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Table 4.18 Summary of data used in OF sensitivity

Analysis 205

Table 4.19 Multiple regression analysis of overland flow

for all scenarios. Significant relationships

are highlighted 206

Table 4.20 Summary of data used in Erosion sensitivity

analysis 217

Table 4.21 Multiple regression analysis of erosion for all

scenarios 219

Table 4.22 Parameters used in model validation 224

Table 5.1 Overland flow and erosion model results for the

original vegetation (from Landsat TM, 1989)

comparison with other research 236

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List of figures

Figure 3.1 Location of the Tambito watershed and prevailing land uses 45

Figure 3.2 Monthly average rainfall of the nearest weatherstation to Tambito (3km distance) 47

Figure 3.3 NDVI radiance from Landsat TM for Tambito Catchment 52

Figure 3.4 Transition of LUC by scenarios(a) Scenario 1. Forest conversion to pastures from cellular automata.(b) Scenario 2 Forest conversion with a fixed distance from river channels.(c) Scenario 3. Forest conversion with a fixed distance

toward river channels.(d) Scenario 4. Forest conversion with a fixed altitudinal distance from lower point in up hill

direction.(e) Scenario 5. Forest conversion with a fixed

altitudinal distance from higher point in downhill direction. 55

Figure 3.5 An example of an iteration for SC1 56

Figure 3.6 An example of an iteration for SC2 57

Figure 3.7 An example of an iteration for SC3 59

Figure 3.8 An example of an iteration for SC4 60

Figure 3.9 An example of an iteration for SC5 63

Figure 3.10 Distribution of plots and weather stations 66

Figure 3.11 Location of gutters in plots 66

Figure 3.12 Throughfall collector 68

Figure 3.13 Weather station in deforested areas. 68

Figure 3.14 Classification map for collecting soil samples 72

Figure 3.15 Basic cartography of the area (source from IGAC, 1985) 80

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Figure 3.16 Digital elevation model for the study area derived from digitised contours using Arc/Info 7.3 80

Figure 3.17 Slope map derived the digital elevation model 81

Figure 3.18 Aspect map derived from the digital elevation model 81

Figure 3.19 LUCC map for Tambito watershed from Fundación Proselva (Museo de História Natural 1996) 82

Figure 3.20 Landsat image TM for the study area, false colour (5,4,3) 82

Figure 3.21 Schematic diagram of the hydrological model 86

Figure 3.22 Hourly cloud cover 96

Figure 3.23 Range of modelled cloud cover 97

Figure 3.24 Linear relation between measured and modelled cloud cover 97

Figure 3.25 Regression for computing net radiation in the model. 98

Figure 3.26 Diagram of net solar radiation model 99

Figure 3.27 Evapotranspiration variation with respect to Net Solar Radiation using the Penman-Monteith equation 106

Figure 3.28 Evaporation variation with respect to atmospheric and stomatal conductance 106

Figure 3.29 Flow diagram for potential evaporation 109

Figure 3.30 Diagram of the Rutter model (Jetten, 1994) 112

Figure 3.31 Diagram of interception sub-model 115

Figure 3.32 Soil texture triangle classification (Dingman, 1994) 117

Figure 3.33 Diagram of soil hydrologic characteristics 123

Figure 3.34 Diagram of infiltration sub-model 130

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Figure 3.35 Diagram of runoff sub-model. 133

Figure 3.36 Diagram of erosion sub- model 138

Figure 3.37 A map of simulated rainfall distribution for Tambito watershed 142

Figure 3.38 One year of hourly rainfall from Tambito weather station (1995) 143

Figure 3.39 Histogram distribution for Tambito rainfall using simulated data of 1995 144

Figure 4.1 Modelled evaporation with 1D model for forestand grassland LUCC compared with the rainfallevents 151

Figure 4.2 Modelled canopy interception with 1D model forforest and grassland LUCC, compared withrainfall events 151

Figure 4.3 Modelled matric potential with 1D model forforest and grassland LUCC, compared withrainfall events 152

Figure 4.4 Modelled hydraulic conductivity with 1D modelfor forest and grassland LUCC, compared withrainfall events 152

Figure 4.5 Modelled infiltration with 1D model for forestand grassland LUCC 153

Figure 4.6 Modelled soil moisture with 1D model for forestand grassland LUCC compared with rainfallevents 153

Figure 4.7 Modelled overland flow with 1D model for forestand grassland compared with rainfall events 154

Figure 4.8 Difference between modelled overland flow forboth forest and grassland LUCC, compared withthe rainfall events 154

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Figure 4.9 Modelled erosion with 1D model for forest andgrassland, compared with rainfall events 155

Figure 4.10 Difference between modelled erosion for forestand grassland, compared with rainfall events 155

Figure 4.11 Changes in overland flow due to LUCC (units inmm) for a modelled year. 157

Figure 4.12 Changes in erosion due to LUCC (units inmm m-2) in a modelled year 159

Figure 4.13 Sensitivity to parameter A in the net radiationequation 164

Figure 4.14 Sensitivity to parameter B of the net radiationequation 165

Figure 4.15 Sensitivity to light extinction 167

Figure 4.16 Sensitivity to LAI 169

Figure 4.17 Sensitivity to maximum canopy storagecapacity 171

Figure 4.18 Sensitivity to vegetation cover 174

Figure 4.19 Sensitivity to soil textures 177

Figure 4.20 Sensitivity to soil porosity 179

Figure 4.21 Sensitivity to soil depth 182

Figure 4.22 Sensitivity to erodability factor k1 183

Figure 4.23 Sensitivity to m factor of erosion equation 184

Figure 4.24 Sensitivity to n factor of erosion equation 185

Figure 4.25 Modelled soil moisture with different initialconditions for the same rainfall pattern 191

Figure 4.26 Overland flow sensitivity in scenario 1(deforested pattern with cellular automata) 194

Figure 4.27 Mean topographic variables for deforested areasin SC1 194

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Figure 4.28 Overland flow sensitivity in scenario 2 (forestconversion with a fixed horizontal distance fromriver channel in uphill direction) 195

Figure 4.29 Mean topographic variables of deforested areasin SC2 195

Figure 4.30 Overland flow sensitivity in scenario 3 (forestconversion with a fixed horizontal distancetowards channel rivers in downhill direction) 197

Figure 4.31 Mean topographic variables of deforested areasin SC3 197

Figure 4.32 Overland flow sensitivity in scenario 4 (forestconversion with fixed distance of altitude, inuphill direction from the lowest to the highestpoint) 199

Figure 4.33 Mean topographic variables of deforested areasin SC4 199

Figure 4.34 Overland flow sensitivity Scenario 5 (forestconversion with fixed distance of altitude, indownhill direction from the highest to thelower point) 201

Figure 4.35 Mean topographic variables of deforested areasin SC5 201

Figure 4.36 Erosion sensitivity in scenario 1 (deforestedpattern with cellular automata) 209

Figure 4.37 Mean topographic variables for deforested areasin SC1 209

Figure 4.38 Erosion sensitivity in scenario 2 (forestconversion with horizontal a fixed distancefrom river channel uphill direction) 210

Figure 4.39 Mean topographic variables of deforested areasin SC2 210

Figure 4.40 Erosion sensitivity in scenario 3 (forestconversion with horizontal a fixed distancetowards channel rivers downhill direction) 212

Figure 4.41 Mean topographic variables of deforested areasin SC3 212

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Figure 4.42 Erosion sensitivity in scenario 4 (forestconversion with a fixed distance of altitude,in uphill direction from the lower to the highestpoint) 214

Figure 4.43 Mean topographic variables of deforested areasin SC4 214

Figure 4.44 Erosion sensitivity in scenario 5 (forestconversion with a fixed distance of altitude, indownhill direction from the highest to the lowerpoint) 215

Figure 4.45 Mean topographic variables of deforested areasin SC5 215

Figure 4.46 Modelled and measured solar net radiation forvalidation 226

Figure 4.47 Linear regression between modelled andmeasured net radiation in validation 226

Figure 4.48 Hourly average solar net radiation during theday for validation 227

Figure 4.49 Linear regression between hourly average ofsolar net radiation modelled and measured forvalidation 227

Figure 4.50 Modelled and measured soil moisture forvalidation 230

Figure 4.51 Linear regression of modelled and measured soilmoisture for validation 230

Figure 6.52 Daily soil moisture comparison betweenmodelled and measured, for validation, inJuly of 1999 231

Figure 4.53 Linear regression between measured andmodelled daily soil moisture, for validation 231

1

Chapter I Introduction

Land use and cover change (LUCC) has been recognised as a

modifying agent of landscapes. Some of the effects of LUCC on the

ecosystem are studied in this thesis, particularly those related to

the hydrological cycle.

In this chapter, first LUCC is investigated as one of many globally

important environmental changes. Later, in Colombia is analysed,

as a historical process (including socio-economic and political

factors) concentrating on its effects the hillside areas.

Subsequently, the thesis structure is discussed, indicating briefly

the content of the thesis chapters.

1.1 Land use and cover change (LUCC): a global issue

Of the world’s 12000 million ha. of tropical forest in 1988, 3600

million ha were tropical rain forests, 40% being located in Latin

America (Koning et al., 1998).

The tropical rain forest (TRF) is one of the world’s richest

ecosystems in plant and animal diversity (Jetten, 1994) but is also

one that is threatened by human pressure (Park, 1992; Dale, 1997)

where LUCC is mainly driven by population increase (Sinha, 1997).

Land use and land cover (LUCC) change plays an important role in

this ecosystem when compared with natural events, and can

impact upon water quality, biodiversity, regional climate, and

ecosystem degradation (Koning et al., 1998).

The conversion of TRF to pasture and the subsequent succession of

pasture to secondary forest has a significant effect on canopy

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cover, canopy height, species composition, and biodiversity

(Reiners et al., 1994). Increasing food demand and changes in land

management and land tenure have pushed forward the agricultural

frontier in many tropical countries. Subsistence farmers are

continuously being displaced and forced to clear new areas for

cultivation on steeper slopes.

Throughout history, the land surface has undergone changes in

use. However, over the last decades, these changes have not only

been rapid but also drastic. The forces behind land cover changes

include population growth, which leads to an increased demand for

food and, as result, agricultural expansion, but economic and

technological development are also important (Dale, 1997; FAO,

1997; Sinha, 1997). Most of these processes start at the micro-

level, but because of indiscriminate replication over large areas,

they soon become a global problem (Lambin, 1997). One of the

major expressions of LUCC is deforestation for agriculture and

grazing and to provide wood for housing and fuel (Sinha, 1997). In

most cases, deforestation is the result of complex chains of

causality, originating outside the forestry sector (Lambin, 1997).

Park (1992) reported that 23 million km2 of the earth’s surface was

covered by tropical forest and woodland. In the mid-80s, Latin

America accounted for about 11 million km2 of the world total area.

A historical review of deforestation conducted by Houghton (1994)

revealed that approximately 28% of the forests in Latin America

vanished between 1859 and 1985. During the same period,

croplands and pastures had increased from 3.5 million to 9.2

million km2. FAO reported that 0.15 million km2 of forest are lost

each year (FAO, 1997). Therefore, the area dedicated to agriculture

today is twice that 90 years ago, half of which is accounted for in

the tropics in the last 50 years (Houghton, 1994). Tropical

deforestation can be viewed as a growth process whereby the forest

conversion rate is regulated by the density of deforested areas; the

3

larger the deforested area, the more likely that deforestation will

continue to expand and spread outwards (Lambin, 1997).

This problem has been addressed by FAO’s Global Terrestrial

Observing System (GTOS). The following limitations to the

accurate prediction of LUCC have been found: the lack of data on

terrestrial ecosystems and on the changes occurring within them,

and the lack of technical capacity to identify operative solutions

(FAO, 1997). The knowledge and understanding of the processes

involved in LUCC are fragmented and, in many cases, restricted to

a given area (Watson, 1997). All approaches to the analysis of

LUCC yield only information on specific aspects of the process.

According to Lambin (1997), several essential questions must be

addressed when studying LUCC such as: Why does LUCC occur?

What variables contribute to these changes? Where does LUCC

occur? (In other words, which locations are affected by LUCC?)

When does LUCC occur? , and At what rate does LUCC take place?

1.2 LUCC: global impacts

Land conversion and intensification through human intervention

brings about changes in the ecosystem’s balance, generating a

response in the system (Dale, 1997). System alterations include

increased air temperatures, increased atmospheric CO2, release of

nitrogen to the atmosphere, soil salinity, soil compaction,

pronounced changes in erosion rates, and even soil degradation

and water contamination. In some cases recovery can take from

100 to 500 years, or these effects on the ecosystem may be

irreversible (Dale, 1997).

Variations in vegetation cover in hillside areas generate changes in

hydrological cycles, soil properties and atmospheric fluxes, and

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meso-climatic conditions, as well as the loss of biodiversity.

Researches have shown that tropical forests play a major role in

regulating the earth’s climate, whereby the elimination of forests

can have enormous implications on local, regional and global

climate (O’Brien, 1996).

Several factors determine the impact of deforestation on the

climate. O’Brien (1996) argues that different controls affect the

climatic system in different ways, and that it is not easy to predict,

analytically, just how deforestation will change the climate.

Furthermore, the impact appears to vary depending on local

conditions, such as topography and proximity to oceans. As a

result, neither the magnitude nor the direction of climatic change

associated with deforestation can be considered definite. One

important factor that affects the climate is the change in

concentrations of atmospheric gases. Deforestation increases

atmospheric CO2 because of reduced sequestration of CO2 through

photosynthesis and emissions of CO2 through burning and

decomposition (Melillo et al., 1996; Tinker et al., 1996; Sinha,

1997). Methane (CH4) production, including the variation of

nitrous oxide (N2O), is another significant atmospheric flux that

occurs when forest or grass covers are converted to croplands

(Mosier et al., 1997; Sinha, 1997).

Pitman et al. (1993) evaluated different ways of assessing climate

response to deforestation in South America and Asia. By

comparing the output of six general circulation models (GCM), each

based on different scenarios, whereby forest areas were replaced by

different land uses. The short-term global effect, the global area

affected, and the climate response were all very significant.

Changes in climate variables involved increased air temperature

and reduced annual precipitation and annual evaporation.

5

In contrast to the effects occurring in lowland forests, deforestation

in hillside areas has drastic effects on soil stability and

hydrological cycles, in particular the increasing water runoff and

erosion as well as impacting nutrient cycles (Dale, 1997).

Dale (1997) argues that LUCC has a greater effect on ecological

variables compared with climatic change and LUCC has little to do

with climatic change or even with climate. Man will change the

land use, and especially land management practices, to adjust to

climatic change. The ecological impact of these adaptations is

therefore more significant. However, it can be argued that climatic

and hydrological factors affect, to a certain degree, ecological

factors. Therefore, even small changes in these factors will have a

significant impact on ecosystem ecology.

Both considerations are applicable to long-term LUCC change.

Changes will occur and one way to understand these changes is

through the application of simulation models.

1.3 A review of models for LUCC

Modelling activities on LUCC impacts have taken on more and

more importance in recent decades. Modelling has become an

important tool for understanding physical and hydrological

processes and impacts (Bronstert, 1999). The most common

reasons for applying simulation models are:

1. To monitor and assess potential impact. Impact of LUCC is

assessed by comparing model responses to the incorporation of

different scenarios of land cover (Mosier et al., 1997).

6

2. To conduct sensitivity analysis. To understand which

processes or landscape properties are the key determinants of

hydrological response to land use change (Johnes and Burt,

1990). Sensitivity analysis also illustrates the effects of LUCC

on single model variables or on groups of these variables, giving

the degree of change in the modelled response (LeBlanc et al.,

1997).

3. To predict and forecast. The modelling of hydrological fluxes

and the variation in ecosystem response to LUCC can be used

as a forecasting tool in the short term (Kirkby, 1990; Crohn,

1995; LeBlanc et al., 1997). Forecasting tools are used in the

assessment of water resources for flood risk and hazard.

4. Better understanding of the system. The need for a much

better understanding of the underlying driving forces behind

LUCC and its impact (Turner et al., 1994). Modelling is a mean

of rapid and inexpensive experimentation with model systems to

understand the relationships between variables, especially over

spatially heterogeneous landscapes.

5. Integrate processes. The interaction between climatological

and hydrological mechanisms in hillslope physically-based

models produces an integration of a number of diverse

processes in the disciplines of forest and land management

(Bonell, 1993). Models are a tool for the formal integration of

research applications across disciplines.

7

1.3.1 Methods for identifying the impact of LUCC

A benchmark land cover (LC) must be considered before LUCC can

be modelled. The LC is obtained by direct field observation or

remote sensing, which is clearly defined as a reference point. A

baseline inventory then identifies LC distributions in the recent

past. This initial scenario can be based either on physical or on

biological conditions. The initial hydro-climatological or biological

conditions produced by the reference scenario of LUCC are the

state budget or flux initial conditions, which will be used in the

comparison process.

LUCC can be studied at different scales; global, regional,

watershed, and plot scale (Kirkby, 1990; Dunn and Mackay, 1995;

Johnes, 1996; Leemans et al., 1996). Each scale has related

constraints, such as data availability, and the most appropriate

model type (Kirkby, 1990). Large-scale models and data are

usually integrated with small-scale models for parameterisation

and validation, for example, plot to watershed scale, watershed to

regional scale, regional to global scale (Johnes and Heathwaite,

1997; Bronstert, 1999).

The method most used to determine hydrological LUCC impact

involves the comparison of sequential land-cover maps, which

allows subtle changes to be detected.

Results of spatial statistical models of tropical deforestation show

that single-variable models based on landscape data from a

previous time period provide forecasts information of spatial

deforestation patterns and trends. Predicting the spatial pattern of

deforestation is therefore a much easier task than predicting future

rates of forest clearance (Lambin, 1997). Spatial statistical models

primarily identify location predictors of areas with the greatest

8

propensity for LUCC change. They do not predict when the change

will occur, they only identify proximate causes of LUCC change.

1.3.2 Strategies for evaluating hydrological fluxes in theassessment of LUCC impact

Numerous hydrological models have been adopted to estimate the

hydrological impact of LUCC. Studies on how LUCC affects the

hydrological environment must involve the response of these fluxes

to LUCC. Different approaches can be used to assess the

variations in hydrological fluxes, with respect to initial flux

conditions. Some approaches are summarised below:

1. Real-time LUCC scenarios. The model is used to integrate

scenarios with different land uses; the scenarios can be

generated from measured or remotely-sensed data or from land

use change models. Parameters for each land use must be

previously identified, to be included in the model. The model

includes land cover conversion, with real-time variations in the

scenario during the simulation period (Gustard and Wesselink,

1993).

2. Off-line LUCC scenarios. Remotely-sensed data or models are

used to generate a series of LUCC conditions which are applied

to the hydrological model off-line as alterations of the

equilibrium rather than transient change experiments (Frohn et

al., 1996).

9

1.4 LUCC: Issues and impacts in tropical forestenvironments outside Colombia

About half of all the world’s forests are in the Tropics. Though

there are many different types of tropical forest (moist forest, dry

forest lowland forest, upland forest) some of the characteristics of

this environment are precipitation greater than 1500 mm a year,

dense vegetation, an abundance of epiphytes, and dense under

stories of smaller trees, and shrubs, with harbouring high

biodiversity (Whitmore, 1998). This type of forest can be found in

America, Asia, Africa and Australia.

The main areas of remaining tropical rain forest, particularly

lowland forest are in Brazil and a number of other Latin American

countries, Congo and its neighbours, Indonesia, and Malaysia.

Tropical rain forest are known, as the world’s most productive

plant communities, with giant trees up to 60m in height supporting

thousands of other species of plants and animals.

Montane RainforestMontane rain forest differs in some characteristics growing at

higher elevations, where the climatic and topographic

characteristics are diverse and include extreme wetness

environment and steep slopes. Changes in environmental

characteristics change the forest appearance and structure.

Canopy height decreases with the elevation, reaching up to 35m in

the lower part of the montane zone, but only 9m above 3000m of

elevation (Whitmore, 1998). The structure is simpler than lowland

forest, with large buttresses, branches and epiphytes, which

become more numerous with increasing the elevation (Whitmore,

1998). Temperature can range from 10°C to 25°C according to

elevation (from 1000 to 3000 masl) and latitude. Climatic

conditions are also characterised by low ground level clouds,

10

particularly at different times during the day. The combination of

these creates a particular environment that is known as tropical

montane cloud forest (TMCF).

FAO estimated that for the period 1981-1990 the annual forest loss

in tropical highlands and mountains was 1.1%, much more than

other tropical forest, including lowland forest (Singh, 1994). The

main reason for the disappearance and degradation of this

environment was conversion to grazing land and temperate

vegetable cropping, trimber harvesting and wood production at

unsustainable rates (Bruijnzeel, 2000). Researchers have

expressed that the conversion of TMCF to other land uses could

result in significant declines in overall river flows (Brown et al.,

1996).

LUCC in tropical montane environments and its is becoming

important due to the deleterious nature of its consequences.

Tropical forests are considered a global climate regulator due to the

interaction between land surface processes and atmospheric and

climatic activities (Lambin, 1997).

Some consequences of tropical deforestation have been identified in

the literature including changes in surface and subsurface fluxes,

reduction in infiltration and water retention capacities, ecological

changes and loss of biodiversity, diminished cloud water

interception, increased runoff and thus soil erosion (Scatena and

Larsen, 1991; Brown et al., 1996; Scatena, 1998; Pounds et al.,

1999; Bruijnzeel, 2000; Sperling, 2000),

There are very few well monitored TMCFs in the world. Some of

these important studies areas are: Monte Verde Cloud Forest

reserve in Costa Rica (Pounds et al., 1999) where the forest

conversion to pasture and its effects have been studied by Pounds

11

et al. (1999), and Cusuco National Park in Honduras (Brown et al.

1996). Other notable examples are Sierra de Minas in Guatemala

(Brown et al., 1996) and Mt Kinabalu Sabah in Malaysia for forest

clearance for vegetable cropping. Luquillo Mountains in Puerto

Rico (Scatena, 1998), the Blue Montains in Jamaica (Tanner,

1977), and Talamanca in Costa Rica (Calvo, 1986) among others.

1.5 LUCC in Colombia: History and impacts in hillside areas

Several human activities have affected the vegetation cover in

Colombia, particularly LUCC for subsistence of the majority of the

population. Those activities are driven by socio-political factors

which control land tenancy and land use, and as a consequence

the environment is threatened. A brief historical review of

Colombian agrarian conflicts and land tenancy is presented to

understand the evolution of land use processes in the country.

Then some of the land use activities in the hillside of Colombia are

discussed to provide the context of the national problem and the

importance of a better understanding of hydrological process

affected by LUCC.

1.5.1 Historical review of land use change in Colombia

In Colombia as in most of the Latin American countries the

agrarian problems go back by the time of the great Spanish

conquests in the New world. The conquistadores were amply

rewarded by the Spanish Crown for their efforts on its behalf

through grants of land. The land was granted to the

conquistadores through the system of capitulaciones. Which were a

type of contract in which privileges over lands were granted by the

sovereign to the discoverers (Duff, 1968). By the end of eighteenth

12

century a Spanish system of large landholdings had replaced the

former small areas of communal Indian land tenure system. From

the Independence from the Spanish until the end of the nineteenth

century the land tenure changed ownership with owners,

increasing in number (because the land passed from the Spanish

Crown to the Colombian State, and then to the bourgeoisie

landholders currently in ownership (Duff, 1968).

In the XX Century after the First World War and up to 1920’s the

Colombian bourgeoisie efforts went to build the basis of an

industrial framework for an international open market of

agricultural products. The commercial activities were based in the

latifundio (large extension of land) created through two centuries of

the New Great Colombia, and to take the advantage of increasing

international prices in agricultural products (Bejarano, 1977). The

tendency was for property concentration and land monopoly,

including land holding of good land without production. Those

activities changed the way that the land has been used. In that

time the working population is divided into the workers of the

incipient industrial process in Colombia in the cities and the

farmers that have been attached to small lands and isolated from

the market for the prevailing commercial conditions imposed by the

monopoly (Buitrago, 1977). As consequence, on the one hand the

increasing populations in the cities where the factories improve the

production, and for the other hand, the remaining part of the

landholdings who were expropriated from their own land, were

pushed to colonise new lands meanwhile some of them remain in

the land but becomes land workers (without land) in the big

properties of terratenientes. In this time the government gave the

chance to landowners to make official the land tenancy by owning

property titles through new laws. Using the guise of ‘giving the

owning title to the small farmer’, but in reality they wanted to make

official their big properties which they had appropriated before (Ley

13

de tierras 200 of 1936), where the new structure of land property

were the couple ‘big property – small property’ (latifundio and

minifundio) (Cartier, 1990).

After the Second World War several social and political problems

kept the peoples attention, hiding the agricultural problem. In that

time, the Currie Mission, was assigned to build a program for

Colombia’s development. They highlighted the problem that large

flat extensions allocated on fertile valleys were used for cattle while

people in the hillsides fight for a piece of land to crop their own

food, which meant that the best land was used in the wrong way

(cattle instead of intensive agriculture). In the fifties and sixties the

fast rhythm of agricultural industrialisation and mechanisation,

displaced the field workers from the big farms to the hills, adding

to these hill areas more necessities, been the low yield production a

characteristic these land. This changes the land tenancy stage

from small production to commercial agriculture (as an industry)

and traditional agriculture (survive crops) (Banco de la República,

1951).

As a consequence of those processes the workers displaced from

farming became unemployed in the cities and hillside areas

increasing the social and agricultural problems. The bourgeoisie

hiding behind the government realised the problem and in the

sixties and seventies a project of law (The Agrarian Reform, Law

135 of 1961) was proposed. This law with the face of ‘justness for

the people’ allocated the land redistribution with equal

opportunities, offering the unoccupied lands and the worse types of

land to the population that did not have land (because the better

lands were already occupied by the terratenientes). That law of

land had the purpose to, stop the people that started to invade the

bourgeoisie productive lands. In this way, the agricultural labour

force that were not absorbed by the small industries (because of

14

the mechanisation of agriculture) were neutralised temporally

(Bejarano, 1977). The land problem remained in the country,

displacing the population to colonising new lands, logging the

forest, or on occasion practising slash and burn to increase the soil

fertility for a few years, and then moving to a new forest area and

repeating the procedure.

In the last decades, people that occupied unproductive land were

forced to move to the forest and agricultural frontiers, colonising

and deforesting new land, to supply the necessary food to survive.

However between 1978 and 1992, the proportion of the rural

population in extreme poverty (the countryside farmers) declined

fairly slowly (from 38% to 31%) (The World Bank, 1996), due to the

socio-political conflicts between gerrilla and paramilitares, which

both razing several towns in the countryside and killing people with

the excuse that they were collaborators with the enemy. As

consequence those farmers escape from the countryside to main

cities.

In addition, the most recent problem (illicit cropping of coca leaf

and amapola (opium poppy), as a fast solution to economic

problems), in combination with narco-economy, paramilitaries and

increasing violence, become others factors adding to the problems

of land use change. Also the growth of populations and their

migration to marginal areas, increased the pressure on the forest,

changing the forest to land with low agricultural potential,

producing environmental impacts such as degrading the soil,

natural resources and, vegetation followed by abandonment.

(Fajardo, 1996).

Nowadays these problems are still hitting most of the poor

population and the effects of bad agrarian practices are appearing

markedly on the hillsides areas, characterised by the high density

15

of minifundios (small parcel less than 3 ha), increasing land

degradation and ecosystem instability.

No much information in the country has been published about

deforestation rates. The first agricultural census carried out by the

“Departamento Administrativo Nacional de Estadística” DANE,

which is the national institution with the responsibility of produce

this type of information, was in 1960, covering small parts of the

country, only for the productive area, without including large areas

such as savannas, forest and deserts. The most recent agricultural

census was in 1995, covering less of a half of the country. A

Comparison from those sources is included in table 1.1.

Year 1960 % of area 1995 % of area

Census area 27’337,827 100 51’865,996 100

Agriculture 5’047,088 18.4 4’430,018 8.5

Pasture 14’605,954 53.4 35’527,873 68.5

Forest 6’387,024 23.4 10’088,071 19.4

Other uses 1’297,751 4.7 1’820,034 3.5

Table 1.1 Land use census data comparison for Colombia, between 1960 and

1995. (area values in ha., area total of the country 114’174,887 ha).

Data from table 1.1 show that despite the census area in the 1995

census being twice as much as the 1960 area, the area used for

agricultural exploitation decreases over time. The area used in

pasture increase more than twice, meanwhile the forest area

increases in almost 4 million ha (these reults largely a function of

the different census areas). It is clear that in 35 years 24.5 million

ha. were incorporated to the productive system, of which 85% was

for pasture (20.92 million ha), and just 3.7 million ha were

identified as forest. As the new area came from wild and natural

forest as well the native savannas, the deforestation activities were

significant. The deforested area used in illicit crops is not counted

16

in these assessments, but the rate of deforestation for this activity

is estimated reach up to 60,000 ha per year. Winograd (1995)

reports that the deforestation rate between 1980 to 1990 reached

up to 60% more than previous decade, meanwhile the agriculture

area decrease in a rate of –0.5 % a year and the areas used in

pastures increase in +3.4 % a year.

1.5.2 The hydrological impacts of LUCC in Colombia

Colombia is one of the richest countries in hydrological resources

with abundant rivers and natural resources, which are well

distributed geographically. Colombia occupies the fourth place

after Soviet Union, Canada and Brazil in hydrological richness,

with more than 88% of the total area (1’141,748 km2) with

precipitation over 2000 mm a year, and an average of 3000 mm a

year. The mean evaporation in Colombia is 1150 mm a year, and

the total runoff could average 2,112 km3, which is 67 m3 s-1

approximately (annual values for the whole country area) (Marin-

Ramirez, 1992).

During the last decades water resources have become a problem,

with watershed management in the Andean hillside areas,

producing ecological, social and economical damages due mainly to

population growth, changes in vegetation cover, industrial

development and land use change (Marin-Ramirez, 1992). The

obvious consequences that can be mentioned are, among others:

loss of biodiversity in relation to the rapid loss of natural forest

cover; loss of wild relatives of useful crop species; soil instability

and landslides. Soil erosion, principally loss of topsoil due to water

erosion. Nutrient loss through leaching, with monocultuves and

badly-managed sown pastures. Water quality issues, associated

17

with high sediment load in head waters are also a growing problem

(CIAT-Hillsides Program, 1994).

The World Bank estimated that 45% of the rural Colombian

population were predominantly in hillside areas in the beginning of

the 1990’s, with 23% being the indigenous population. Rural

impoverishment has increased for those areas relative to the

country as a whole (Cepal, 1990).

Poor agricultural practices on the hillsides are used extensively

such as fallow rotation systems in which forest or bush are cleared

for cropping, and then are returned to pasture or bush fallow once

yields decline to a level that is not economically useful.

Deforestation, overgrazing and agricultural activities are also

causes of degradation in the hillside agro-ecosystem.

Environmental degradation in the hillsides has serious implications

not only for the viability of agricultural production in the ecosystem

itself, but for “downstream” lowland agriculture and coastal

ecosystems affected by soil erosion and agrochemical pollution in

the uplands. Soil erosion, sedimentation and major land

degradation caused by deforestation and cropping without use of

soil conservation practices affects watercourses originating in the

hillsides. The most irreversible and potentially damaging with

major social cost caused by hillside environmental degradation, is

the loss of biodiversity due to the disappearance of montane forest

which amounts to 32% of the forest area in the Colombian Andean

Region. The rate of deforestation in hillsides is higher than in the

lowlands. Causing a loss of 90% of the original montane forest

cover by 1990 (CIAT-Hillsides Program, 1994). Montane forest has

very high biodiversity, which is considered important to conserving

wild crop genetic resources in-situ. In ecosystems where the land

use is intensive the most important environmental degradation is

18

the excessive use of agrochemicals which is a characteristic of

agricultural intensification, causing soil and groundwater pollution

(CIAT-Hillsides Program, 1994).

A CIAT study carried out on the hillsides in the Andean Region in

Colombia, was centred in the Rio Ovejas watershed in the Cauca

Department. This watershed covers 100,000 ha. and encompasses

a diverse range of Andean hillside systems ranging from indigenous

slash and burn cultivation to peri-urban, high-input horticulture,

and includes CIAT commodities (Knapp and Buitrago, 1994).

Consequently, the assessment of the location and extent of the

erosion problem in the hillsides was an additional activity

undertaken by CIAT in the study area, as well as the ex-ante

impact assessment of land use change and development of a

diagnostic simulation model of alternative technological

interventions. The model considered impact on soil erosion,

nutrient loss, crop productivity and water quality (Knapp and

Buitrago, 1994).

The relationship between soil erosion and productivity remains

poorly researched and little understood in tropical soils. It is

identified as a need for research focused on improving

methodologies for characterising the extent and cost of soil

degradation. In addition systematising the available data requires

regional collaboration, due to the diversity of the hillside land use

classes found in the country.

To improve crop productivity and forage availability, to enhance

erosion control and soil physical rooting conditions, and to

increase water infiltration, water-holding capacity, and nutrient

retention of the soil, the incorporation into hillside production

systems of practices for soil conservation and regeneration are

being energetically promoted (Knapp and Buitrago, 1994).

19

Knapp and Buitrago (1994) also points out that while farmers

consider the monetary benefits of erosion control, such as yield

increases, they are unlikely to consider non-monetary benefits

such as soil resilience, or downstream benefits which accrue to

others.

Hillside agro-ecosystems are a mosaic of diverse micro-edapho-

climatic regimes, user circumstances and cultures. In any one-

area the results of technological innovation will be location-specific.

An essential task is to develop a replicable approach to innovation,

based on strategic understanding of how to intervene in the hillside

agro-ecosystem and how to make transitions to ecologically-sound

and economically-viable alternatives, acceptable to users.

Determining why some technological options are more acceptable

to farmers than others, and the trade-off between production and

conservation objectives this involves, requires technology testing

which is embedded in a community based participatory framework

(Knapp and Beltran, 1994).

The hillside approach is focused on the effects of soil degradation

that involve diagnostic research to better identify problems and set

priorities amongst them with respect to biophysical and economic

aspects of soil degradation due to agricultural practices and

catchment management. In addition, the design of decision-

support systems incorporating different types of models, including

knowledge-based models drawing on indigenous technical

knowledge and research results that can be introduced into models

to facilitate the understand of LUCC effects in the watershed (CIAT-

Hillsides Program, 1994).

20

1.6 Structure of the thesis

Physical hydrological fluxes are dynamically modelled from the

atmospheric interface to the soil bedrock interface. A 1D dynamic

hydrological model was initially developed at the plot scale for each

type of land cover. The 1D model is parameterised and validated

on the basis of data from hydrological stations in pasture, primary

and secondary forest. Lessons learned from the production and

sensitivity analysis of this model were applied in the development

of a 2.5D distributed hydrological model, integrated within a

Geographic Information Systems (GIS). This was then applied to

understanding the impact of LUCC at the catchment scale. A

sensitivity analysis of the 2.5D model was performed to identify

hydrological flux variation with land use change to determine key

variables of the ecosystem that are affected by different spatial

patterns of LUCC. Five different scenarios of LUCC were used

within the analysis, to assess the hydrological flux sensitivity and

to determine the most sensitive areas in the studied catchment.

Chapter 1 introduces the topic of LUCC in this thesis. First a

discussion about the impact of LUCC in general terms and

additional information about LUCC modelling is provided,

including methods and tools. Then the LUCC impact on tropical

montane forest is discussed in a global context. Subsequently, the

development of LUCC modelling in Colombia are also presented,

and provides brief background of the LUCC in Colombia,

historically and the actual situation of the hillsides research, and

finally the thesis structure is presented. Chapter 2 presents the

literature review of hydrological models applied to LUCC. The

strategy for estimating LUCC is discussed. Then the literature

review of the hydrological models is discussed: characteristics,

classification, types and results, and also a brief review of some of

the best known contemporary hydrological models with their main

21

features. Also hydrological models in tropical montane

environments are reviewed and finally, understanding the problem

and the research approach are presented in the thesis and the

thesis objectives, main goals, and the obtained achievements

discussed.

Chapter 3 describes the methodology used in this thesis. This

chapter has two marked sections: the first is related to the

collection of the information for modelling, the second is related

with the construction of the hydrologically-based model. Initially

the structure of the chapter and the study area are presented. The

research and experimental strategies are provided and detailed

description of the scenarios of LUCC used in combination with the

hydrologically-based model are given. Then the fieldwork

methodology is discussed for plot and catchment scale studies; the

installation of hydrological stations, and the field methods used for

the collection of data are illustrated. The data collected in the field

are presented and additional data used for model parameterisation,

experimentation and for model verification and also for validation

are discussed. Secondly the modelling aspects are discussed. This

section describes the development of the 1D and 2.5D models,

together with a description of the following sub-model components:

solar radiation, energy balance, evaporation, canopy storage and

interception, infiltration, soil water hydrology, overland flow, and

erosion. Each component is explained in detail and source

equations, flow diagrams, and data requirements are indicated.

The inter-relationship between components and information flow is

also indicated. After describing the sub-model, model performance

and initial conditions are explained. Then model integration with

Geographic Information Systems (GIS) is also described.

In Chapter 4 the model results are presented. 1D and 2.5D model

results are shown to discuss the model characteristics and some

22

implication of landscape properties on the hydrological response.

Then 1D model parameterisation and sensitivity analysis is

discussed, and subsequently 2.5D model sensitivity analysis for

overland flow and erosion is shown; the relationship between those

variables and the topographic variables is evaluated. A summary

of TMCF sensitivity to LUCC is presented in terms of overland flow

and erosion sensitivity. Finally, validation of some output variables

is carried out to evaluate the model goodness of fit.

Chapter 5 gives the summary and the conclusions, the objectives

evaluation and the achievements, including the recommendations

for estimating hydrologically sensitive areas to LUCC for the TMCF

environments, and then the conclusions are drawn with further

model applications and future research possibilities elaborated.

23

Chapter II Literature review of hydrological models appliedto LUCC impacts research

2.1 Structure of this chapter

This chapter presents the literature review of hydrological models,

which begins with the general concepts used in the modelling

activities, particularly with the issues related to hydrologically-

based simulations. Then a classification of these models is

presented, including the importance of spatial variability as a

characteristic of modelling the surface water fluxes. A complete

review of the existing commonly used hydrological models related

to LUCC impact is presented, and finally the main objective and

the specific aims of this thesis are numerated.

2.2 General concepts of hydrological models

Hydrological models aim for simplicity by selecting a system’s

fundamental aspects at the expense of incidental detail (Anderson

and Burt, 1985). A number of alternative techniques and

modelling approaches have been developed.

The first integrated hydrological model, called the Stanford

Watershed Model (Singh, 1995), was reported in the literature in

1966 by Crawford and Linsley. During the following decades,

hydrological modelling improved significantly because of advances

in technology and computer hardware.

Better hydrological models are becoming available with these

technological advances and the continuous improvement in

24

modelling strategies, such as inclusion of GIS, remote sensing or

cellular automata (MacMillan et al., 1993; Beven and Moore, 1994;

Robin et al., 1995). Many of these methods are used in

contemporary watershed models, such as TOPMODEL (Beven et

al., 1995); KINEROS, a kinematic runoff and erosion model

developed by Rovey et al. (1977) and described by Smith et al.

(1995), and TOPOG_IRM (CSIRO, 1993).

Many of the latest generation hydrological models use GIS, but, in

many cases, GIS and environmental models are not well integrated,

just used together. GISs are frequently used as post-processors to

display and further analyse model results. In turn, modelling

approaches directly built into a GIS appear rather simple and

restrictive (Fedra, 1993). Dangermond (1993) indicates that the

tendency for integration is to use specialised software systems.

“Such powerful tools without well distributed data are, at best

expensive interpolation tools and, at worst subject to GIGO

(garbage in-garbage out)” (Fedra, 1993). One of the main

restrictions on good spatial (GIS) modelling is a lack of good,

spatially detailed hydrological parameters for model

parameterisation and validation.

2.3 A general classification of hydrological models

Models can be characterised by the type of relations used within

the routines. The relationship between real and model processes

can be represented either empirically or physically.

1. Empirical models. Model relationships are based on empirical

data, not necessarily on physical processes. These models tend

to have a high predictive ability but their physical explanatory

power is often low. They are sometimes called “black box” or

25

“input/output” models. These terms are usually applied to

those models whose internal operation does not aim to directly

represent “real” operative processes, even at an abstract

mathematical level (Kirkby et al., 1993). Successful

applications of this strategy include the unit hydrograph,

extreme frequency analysis, regression analysis, and real time

forecasting models (Anderson and Burt, 1985). Statistical

analysis faces several methodological and interpretative

difficulties, such as measuring complex dependent variables,

and spatial aggregation of data in large units. The existence of a

statistically significant association does not establish a causal

relationship. Moreover, a regression model that fits well in the

region for which it was designed might not function well in other

regions, because it should not be transferred beyond the

physical limits for which it was developed, parameterised and

calibrated.

2. Physically-based models. These models, based on physical

processes, are modelled on the understanding of physical

mechanisms and often make large demands in terms of

computational time and data requirements. Nevertheless, such

models offer increased explanatory and experimental power.

However, because of the higher number of assumptions that are

necessary, their predictive capacity is often equal or worse than

that of empirical models. Beven (1989) argued that highly

complex, physically-based models are possible at smaller scales.

However, larger-scale models must be simple to allow

parameterisation. Woolhiser (1996) pointed out that simpler

models are often more accurate than physically-complex

models, but are difficult to scale up to larger watersheds.

Parameter generalisation within the watershed involves simple

representations of main model elements. Several variables such

as soil characteristics which are important at reduced scales for

26

detailed studies are also important at the watershed level,

increasing model complexity while not necessarily adding

precision to the results.

2.4 Handing spatial variability in hydrological models

Several approaches to represent spatial variability within a

watershed exist. These approaches can be classified as:

1. Lumped modelling, expressed by ordinary differential

equations that describe simple hydraulic laws. These models

do not take into account the spatial variability of processes,

inputs, boundary conditions, or the system’s geometric

characteristics. Instead, a single value for properties and

parameters is applied to the entire watershed. Some examples

are HEC-1 (Hydrologic Engineering Center, 1981) described by

Feldman (1995), RORB (Laurenson and Mein, 1995), and

SSARR (USA Army Engineer, 1972) described by Speers (1995).

2. Distributed modelling, which explicitly accounts for the spatial

variability of processes, inputs, boundary conditions and system

characteristics. The spatial distribution of features and their

spatial inter-relationships are especially important to explaining

physical processes within the watershed. Examples are SHE

(Abbott et al., 1989) described by Bathurst et al. (1995), SWMM

(Metcalf et al., 1971) as described by Huber (1995).

Models can also be classified according to the type of equation used

and the resulting output. Model results can be a singular, or a

population of answers. Processes can be described either by

deterministic or stochastic equations. Deterministic models

have just one possible outcome, whilst stochastic models have a

27

population of answers. In most cases both types of equations

occur within the same model. However, in the cases when the

relevant information for parameterisation is not available, some

processes are better modelled by stochastic equations that could

give an approximation for modelling purposes.

2.5 A Review of hydrological models related to LUCC impact

There are several hydrological models that have been created for

particular purposes or environments. The models have different

abilities, characteristics and type of results, including resolutions

in time and space. Some of the most widely used models are

discussed here, identifying some of their important features related

to the subject of this thesis, and the reasons that the models are

not used in this thesis.

SHE/SHESED

The SHE/SHESED combination is a physically based, spatially

distributed modelling system for water flow and sediment transport

to be applied at a catchment scale. The SHESED model was

developed in the University of Newcastle upon Tyne, UK, and is

based on the SHE (Systeme Hydrologique Europeen) model which

was developed by international collaboration between groups in the

UK, Denmark, and France. SHESED is used to investigate land

management especially the prediction of LUCC and climate change

impacts. SHE was designed as a flexible modelling system,

encompassing several levels of complexity, consisting of sub-

components for evapo-transpiration and interception, overland and

channel flow, unsaturated zone flow, saturated zone flow,

snowmelt and channel/surface aquifer exchange. The SHE model

28

is driven by meteorological inputs and provides inputs to the

sediment transport component (Bathurst et al., 1995).

The interception sub-component is an adaptation of the Rutter

model (1971), and the evapo-transpiration is based on the Penman-

Monteith equation. Some sub-components require more

parameters and input information than are not available for

Tambito such as the atmospheric component, sediment yield and

transport of material within the channels component, as well as

soil matric suction, raindrop impact amongst others (see section

3.6). Also the model has a number of simulation routines that are

not useful for this study. Nevertheless, this model is one of the

investigated models that could be appropriate for this thesis, but

its complexity is too high for the application intended here.

In addition to the lack of information available for parameterisation

and validation of the SHE model, most of the literature consulted

reported the use of the model for short time simulations (days)

providing good simulation results (Wicks and Bathutst, 1996), or

for bigger spatial resolution up to 4000 m of pixel size (Refsgaard,

1997). However, Wicks and Bathust, (1996) used the model for two

small agricultural catchments (5.1 and 6.4 ha) in Iowa, with good

reproduction of the observed temporal variations in sediment yield.

In contrast Refsgaard (1997) applied this model to a catchment of

440 km2 in Denmark, for which calibration and validation

processes were carried out splitting the catchment in seven

sections, and to producing better results at a pixel size resolution

of 500m.

29

SHETRAN

The SHETRAN system was developed by the Water Resource

Systems Research Laboratory (UK), based also on the SHE

(Systeme Hydrologique Europeen). SHETRAN is a 3D, coupled

surface sub-surface physically-based spatially-distributed finite-

difference model for coupled water flow, multi-fraction sediment

transport and multiple, reactive solute transport in river basins

(Parkin, 1996). The model is a powerful tool for studying the

environmental impact of land erosion, pollution, and land use as

well as climate change effects, and also surface and sub-surface

water resources and management. It is integrated in a decision

support system to maximise its usefulness in environmental

impact management. Some of the features of the model are:

- Basin-wide modelling for water resource planning

- 3D solute transport in the surface and sub-surface

- Sediment erosion and transport

- Water balance in large basins (50,000 km2 +)

- Long-term basin evaluation (1,000 years +)

- Coupled hydrological and meteorological modelling

- Impact assessment for land use and climate change

- Risk and pollution assessment for proposed industrial

developments

- Monte Carlo simulation for uncertainty prediction.

SHETRAN is a robust model that simulates more than is needed in

Tambito for the purpose of this thesis for example sediment

transport in the channels, and effects of climate change amongst

others, and also it could have problems due to the lack of input

information for parameterisation for the sub-routines because data

are required which are not available from Tambito (for example

river sediment transport) (see sections 3.6).

30

Lukey (2000) applied the SHETRAN model to assess hydrological

impact of reforestation particularly on runoff and sediment yield,

for a badlands catchment at Draix, France. The semi-arid

environment, shallow slopes and low rainfall rate make it quite

different to the Tambito study area. Lukey (2000) found strong

effects of forestation on decreasing runoff and sediment yield. Also

the SHETRAN model has been integrated into the NELUP Decision-

Support System (Dunn, 1996), to estimate the predictive impact on

water resources and ecological diversity, which has been applied on

Cam river basin.

TOPMODEL

TOPMODEL is a set of conceptual tools that reproduces the

hydrological behaviour of a catchment in a distributed or semi-

distributed way, in particular the dynamic surface or subsurface

contributing areas (Beven et al., 1995).

Despite the fact that TOPMODEL models land-surface-atmosphere

interaction, the main components are centred on the simulation of

subsurface water flow. It could be used as a prediction tool for

catchment hydrology for long time series, based on soil response

using the Topographic Index.

The hydrological processes on the surface that TOPMODEL

simulates are very simple. The level of generalisation of the

atmosphere-soil interface is very high with the model only treating

the evaporation of the surface water, without more attention to

other surface events. Runoff and erosion process are not presented

as an important feature in the model (Beven et al., 1995). These

two features are important for research in Tambito, where the

31

evaporation process is very specific to TMCF environments and

where runoff and erosion processes are the key flux processes in

the model assessment of LUCC impact in hydrological processes for

this thesis.

The main features of TOPMODEL are to produce indices and

parameters of the saturated zone (or storage deficit), the saturated

transmissivity, the root zone parameter, and in large catchments a

channel routing velocity (not available or necessary in Tambito, see

section 3.6).

Most of the evaluation of the TOPMODEL concept has been based

on comparisons of stream flow hydrographs and do not necessarily

provide a test of predictions of subsurface flow, saturated source

areas, runoff-erosion and their effects on the environment (Moore,

1996). Also, most of the applications of the TOPMODEL have been

concentrated on comparison of the predicted water table depth

against the validation data in instrumented catchments from wells

with good results (Beven et al., 1984; Moore, 1996; Saulnier et al.,

1997), but in some cases with poor results (Seibert et al., 1999). In

the same way TOPMODEL has been evaluated to predict the

saturated areas on the basis of topographic information, where the

sensitivity to the spatial resolution takes an important role (Kim et

al, 1999) and different sizes (Blazkova and Beven, 1997).

TOPOG

TOPOG is a terrain analysis-based on a hydrological modelling

package, which describes the topographic attributes of complex

three dimensional landscapes in complex terrain characteristics

and heterogeneous soils and vegetation, predicts the spatial

distribution of steady state water-logging, erosion hazard and

32

landslide indices; it simulates the transient hydrological behaviour

of catchments, and how this is affected by changes in vegetation

cover. Also it models the growth of vegetation and how it impacts

on the water balance; it models solute movement through the soil

and sediment transport over the soil surface (Vertessy et al., 1994).

TOPOG is a robust deterministic distributed-parameter hydrologic

modelling package; this model requires a good set of information as

input data and also works with vector data derived from TAPES-C,

Topographic Analysis Programs for the Environmental Sciences –

Contour data. TAPES-C is a terrain analysis software that

subdivides a catchment into elements using the stream-tube

approach and calculates a variety of topography attributes for each

element (Grayson et al., 1995).

The TOPOG model complexity means that it requires good input

information such as 3D soil characteristics for the Richard’s

equation and vegetation data, information on crop physiology, soil

salinity, fertility, the spatial distribution of rainfall, among others

(Short et al., 1995; Hatton and Dawes, 1993). Despite this many of

the parameters could be extracted from the literature within their

normal range of variation or from a short period of observation

from the field, the long term simulation evaluated monthly or

yearly yields up to +/-10% of variation compared with observed

data. It was the results for 12 year simulation in a 32 ha

catchment size in the central Victorian highlands, Australia

(Vertessy et al., 1993). An important feature of TOPOG is that the

good results are obtained for instrumented catchments, at high

spatial resolution (up to 25m) and small catchment (up to 50ha)

(Hatton T., personal communication in 1993).

Parameterisation of this model in Tambito could be difficult due to

the lack of information on vegetation cover (growth and physiology),

33

surface fluxes and soil characteristics. Also the Tambito

catchment-size could be an impediment to apply this hydrological

model.

THALES

THALES is a dynamic hydrological model based on the element

network created by TAPES-C. THALES is a combination of surface

and sub-surface models that simulate kinematic overland flow

from saturation excess and infiltration excess mechanisms, as well

as sub-surface stormflow. The structural difference of the model is

the partition of the catchment into stream-tubes, beginning at the

contour line of lowest elevation and ending at the highest contour

line (Grayson et al., 1995). Related to hydrological features,

interception and evaporation are considered not relevant, and they

are incorporated only for simulation of long periods and research

applications. Despite the fact that THALES was built as a simple

hydrological model, the surface and sub-surface modules request

parameters not available in Tambito such as cross-sectional area of

tube-element, channel shapes amongst others. Also the model

pays much more attention to within channel processes than is

necessary for this project.

KINEROS

The kinematic runoff and erosion model (KINEROS) is a routing

model for surface runoff over cascades of overland flow planes

contributing lateral inflow to channels with an interactive

infiltration component. Also, it comes with erosion and sediment

transport components, a pond element and spatial variability of the

rainfall. It can be used at several scales (1 to 700 ha.). The

34

interception routine is very simple and works for each runoff

element separated, based on the vegetation or other surface

conditions. Roughness relationships, sediment transport and

channel routines are parts of the model that could be difficult to

adapt to this study, due to the lack of source information for

parameterisation (see sections 3.5 and 3.6) (Smith et al., 1995).

The KINEROS model has been used in semi-arid environments and

different catchment size (3 to 700 ha.) with good results (Goodrich,

1991; Michaud, 1992), however Duru and Hjelmfelt (1994) did not

get the same successful results in a catchment of 30 ha. in Iowa.

A particular reason that this model is not implemented in this

thesis is that the Kineros model transforms the catchment into an

equivalent network composed of runoff surface planes, intercepting

channels, ponds or detention storage; then the flow is analysed in a

1D of network channels, where the runoff surfaces are

encompassed by a cascade of rectangular surfaces of non-uniform

slope, hydraulic resistance or soils (Smith et al., 1995). This

generalises the distributed landscape and topographic

characteristics, which are important in this study. Also the

surface-atmosphere interface does not have the relevant

importance in the process as is requested in Tambito for this

modelling exercise (see section 3.6).

CREAMS

CREAMS are a collection of hydrology components for water quality

models. CREAMS model was developed to assess edge-of-field non-

point source pollutant loading for alternative management systems.

Land use can be changed within a rotation cycle, fertilisation and

pest control practices could be incorporated during the simulation.

The time step used in the routines is daily, but the model was

35

designed to simulate long-term storm by storm differences up to 20

years, as opposed to other models such as ANSWERS that simulate

a single storm (Knisel and Williams, 1995). Also CREAMS model is

not a distributed parameter model, and so can not be applied to

understand the outcomes of spatial variability of distributed LUCC

in Tambito (see section 3.5).

SWAT

The Soil and Water Assessment Tool (SWAT) was developed in the

Grassland, Soil and Water Research Laboratory USDA-ARS Texas,

and is based on the water balance equation. The SWAT model is a

modification of the SWRRB and ROTO models, for application to

large and complex rural basins. It is therefore a distributed version

of the CREAMS model, running simultaneously in several hundred

sub-basins (SWAT website).

The SWAT is to be used only in USA environments. This model use

the USDA geo-database (soil and weather database) and several

other types of information which are the basis of empirical

concepts developed for USA environments such as SCS curve

number for computing overland flow and runoff volume or the

Modified Universal Soil Loss Equation MUSLE, amongst others.

Due to the fact that the SWAT model was designed only for US

environments, it is not readily transferable for use in the Tambito

catchment.

36

HYRROM

HYRROM The Hydrological Rainfall Runoff Model is a computer

model developed by the Software Development Office of the

Institute of Hydrology, UK; it is based on a simple representation of

the physical processes that govern the water flow in a catchment

area. The model incorporates interception, soil, ground water and

runoff stores, and includes some representation of the losses due

to evapo-transpiration. The main use of this rainfall runoff model

is to predict the river flows from rainfall and evaporation data for a

catchment. HYRROM can be used to generate flow records for

periods in which flow data are missing or of doubtful accuracy, but

where rainfall records are complete. It can also be used for quality

control of data, extending historical flow records and generating

synthetic flow sequences for water resources assessments

(Scientific Software Group website, http://www.scisoftware.com).

The reason why HYRROM is not suitable for the analysis is that the

documentation does not mention that the model is spatially-

distributed, this being an important feature in the thesis subject

(see section 3.6).

WATFLOOD

The WATFLOOD hydrologic model is a distributed hydrological

model developed by Dr. Nick Koumen at the University of Waterloo,

Canada. It is an integrated set of computer programs to forecast

flood flows for watersheds having response times ranging from one

hour to several weeks. The emphasis of this model is on making

optimal use of remote sensed data, Radar rainfall data, Landsat

data or Spot data, with incorporation of land cover data directly to

the hydrological system.

37

The reason why this model is not used in the Tambito study is that

the time and the spatial resolution are very different to that which

are needed here, also the remote sensing information are not

available for Tambito (see sections 3.5 and 3.6).

2.6 Hydrological modelling in tropical montaneenvironments

Researchers have paid special attention to the physical hydrology

of forests because previous studies have demonstrated that forests

and, in particular tropical rain forests, have a significant effect on

climate and energy balance (Turner et al., 1994; Dale, 1997) and

catchment hydrology (Copeland et al., 1996). Specialists have

pointed out the importance of canopy storage on all hydrological

processes and its importance for the hydrological balance (Hancock

and Crowther, 1979). Studies have also been conducted on rainfall

interception (Rutter et al., 1971, 1975, 1977; Aston, 1979; Gash,

1979; Gash et al. 1980; Calder, 1986; Calder et al., 1986; Calder,

1996), canopy evaporation (Ford and Deans, 1978; Pearce and

Rowe, 1980), infiltration (Whitehead and Hinkley, 1991), and runoff

(Loukas and Quick, 1996). Their results have been integrated in

studies at the watershed level (Mein and Brown, 1978; Loague and

Freeze, 1985; Jetten, 1994; Sudjono, 1995).

Fewer studies have been conducted on TMCF as compared with

their lowland counterparts. Several studies, for example Golley

(1983), Bonell et al. (1993), Veneklaas (1990; 1991) and Veneklaas

et al. (1990a, 1990b), concentrate on understanding specific

processes such as cloud interception and nutrient budgets rather

than the integrated hydrological system.

38

2.6.1 A review of modelling studies of the hydrological impactof LUCC in Colombia

Little empirical, theoretical or modelling research on land use

change has been carried out in Colombia. This is particularly the

case for the tropical mountains of Colombia.

One of the few examples of modelling LUCC impact in Colombia is

the Andean Amazon River Analysis and Monitoring (AARAM)

project. The project investigates the effects of regional LUCC and

global climatic change on biochemical and hydrological cycles

within riverine ecosystems of the Andean Amazon basin. It also

conducts field research in Colombia, specifically in the Caqueta

watershed (AARAM, 1998). AARAM is a regional research initiative

to develop the scientific understanding necessary for effective

management of aquatic resources in the face of ongoing

development and potential climate change. The research activities

in AARAM examine the fundamental physical and biological

processes controlling the health and the dynamics of Andean

riverine ecosystems, with emphasis on terrestrial-aquatic and

upstream-downstream linkages, with spatial scales ranging from

meso- to micro-scales watersheds (AARAM, 1998).

Another example is the Tropenbos-Colombia programme, which is

oriented toward a land use planning policy centered on the

conservation of biodiversity and the control of deforestation. This

project is located in the Araracuara and Alto Caqueta regions. The

main aims of this project are to: (a) determine the demand for

timber and non-timber products for subsistence use and regional

markets; (b) characterise existing extraction activities in the region

and their impact on biodiversity; (c) establish permanent plots and

monitoring systems to study forest dynamics (Tropenbos

Foundation, 1997). Tropenbos Colombian programme is centred

39

on conservation on biodiversity and control of deforestation as part

of land use planning policy for Colombian Amazonia. In the area

are numerous indigenous people, and the rain forest abounds with

an unmatched diversity of plant and animals. The agricultural

frontier is advancing and a large number of environmental and

social conflicts, such as active agriculture colonisation, coca

plantation, poverty and armed conflicts.

Despite the fact that at governmental level, LUCC impact modelling

is not publicised, The “Departamento Nacional de Planeación”

(DNP) of Colombia is carrying out a programme “Plan Nacional de

Desarrollo Alternativo PLANTE”, in cooperation with the Agriculture

Ministery for rural development alternatives. This is mainly to

encourage farmers to replace illicit crops with productive

plantations (DNP, 1999). In the same way, the DNP has

environmental polices to protect endangered ecosystems, and

improve environmental sustainable mechanisms in order to

increase the quality life and better agricultural production

plantations (DPN, 1999).

2.7 Research approaches to LUCC impacts

Many studies have compared and assessed runoff under different

land uses in coupled catchments but do not take into account the

changes occurring over time due to deforestation (Coles et al.,

1997; Finch, 1998; Rai et al., 1998). Others concentrate on

computer modelling to simulate soil water behaviour, without

modelling surface water (Binley et al., 1992; Faunt et al., 1993) or

the overall runoff, which is assumed to go directly into the river

channel (Taha et al., 1997). In contrast, other studies include very

complex models of water fluxes, providing detailed descriptions of

each process and of fluxes and stores, but do not include the

40

dynamics of LUCC change within the simulation framework

(Speers, 1995; Zhao and Liu, 1995; Singh, 1997; Bronstert, 1999;

Flugel and Smith, 1999). This thesis tries to integrate some of the

hydrological processes with land use and land cover change (LUCC)

to give an approach to understanding the environmental

consequences of LUCC in tropical montane cloud forest (TMCF).

2.8 Main Objective

To determine the importance of LUCC change on the hydrological

cycle in tropical mountainous environments (TMEs) by analysing

the spatial variability of hydrological sensitivity to land use change.

The test watersheds of the Palo Verde and Tambito catchments are

located in a tropical mountainous cloud forests (TMCF) of the

western cordillera of the Colombian Andes.

2.9 Specific aims

1. To compile, for both watersheds, data corresponding to two

years of monitoring of hydrological fluxes at the plot scale

under forest and pasture.

2. To produce a GIS-based hydrological model to monitor and

simulate the impact of LUCC on hydrological processes at

the plot and then at the catchment scale.

3. To parameterise and validate components of the hydrological

model in 1D at the plot scale for different land covers.

4. To develop methods for model parameterisation at the

watershed scale.

5. Through simulation experiments, to determine the spatial

variability of hydrological sensitivity to LUCC using different

scenarios and different patterns of LUCC across the two

41

watersheds within the model, in order to evaluate and

analyse the spatial variability of hydrological sensitivity to

LUCC. To analyse the physiographic properties controlling

this sensitivity and to propose empirical relationships

between physiographic properties and hydrological

sensitivity.

2.10 Rationale

The hydrological implications of LUCC in tropical mountains are

poorly understood. At the same time deforestation in the

mountainous tropics is progressing at a faster rate in comparison

to the tropical lowlands. Since tropical mountains are "water

towers" supplying a volume of high quality water to dependent

lowland populations and infrastructures, it is important to

understand the implications of these rapid changes.

The development of detailed physical hydrological models for all

catchments at risk of LUCC is economically and technically

infeasible. This thesis attempts to further understand the basic

landscape properties, which determine hydrological sensitivity to

deforestation over whole catchments. Such properties can be

derived from basic cartographic data in a GIS and, since such data

are routinely available, reconnaissance studies to prioritise

conservation efforts and hydrological buffering may be possible on

the basis of these results.

42

Chapter III. Methodology

3.1 Structure of this chapter

This chapter has three sections: the first part is a description of the

study area, the research strategy, and the methodology for this

thesis; the second part illustrates the methodology of field activities

and the empirical data collected, and the last part describes the

characteristics of the proposed hydrological model with the

justification for the individual components of that model within the

context of the aims of this thesis.

The study area is described in terms of its location, types of

vegetation, and physical, topographic and climatological

characteristics. Several general characteristics of TMCFs are also

discussed. Then the experimental design is presented highlighting

the importance of LUCC. The vegetation cover in the study area is

described. On the basis of the knowledge of the study area, a

hydrological model is proposed, to evaluate the flux variation

within the catchment, in combination with the land use scenarios

presented in section 3.4. The modelling strategy is described and

includes a combination of 1D and 2.5D models which are used to

generalise the plot hydrology to the catchment level. Finally an

introduction to the sensitivity analysis is presented.

After the research strategy is presented, the field methods for the

collection of basic information for modelling and parameterisation

are described at both plot and watershed scale, and the data

collected for model parameterisation are indicated. Instrumented

plots and the characteristics of the installed weather stations are

also described, and the collection of soil and vegetation samples is

43

discussed. In addition, data from other sources (national and

international) used within this study described.

Subsequently, the identification of the model characteristics for the

study area and for the purpose of this research are mentioned.

Details of the hydrological model (i.e. scale, resolution) are

enumerated, on the basis of the knowledge of the environment of

Tambito and the information collected. These suggest the type of

the models that are most suitable for this study, to represent the

physical hydrological behaviour of the catchment. Also the means

of including LUCC scenarios within the analyses; the types of

scenarios generated, and the type of sensitivity analyses conducted

with both 1D and 2.5D model results are discussed. All the model

subroutines are described in detail along with the way that all of

them are integrated within the model.

3.2 Description of the study area

The study area is located in a natural reserve called ‘Centro de

Estudios Ambientales del Pacífico Tambito’, which is managed by

the Fundación Proselva and the Universidad del Cauca, Colombia.

The area, located in the cloud forests of the Eastern Cordillera of

the Andes, in the Department of Cauca in southwestern Colombia

(Figure 3.1) is about 75 km from the Pacific coast (between 77° 01’

and 76° 58’ W and 2° 28’ and 2° 32’ N). Cartographically, the

study area is included in a window delimited by:

X low left corner 1’008,210.00

Y low left corner 766,088.00

X up right corner 1’012,285.00

Y up right corner 771,538.00

44

These coordinate values are in metres using the Colombian west

origin on Transverse Mercator projection defined by IGAC (IGAC,

1975) (see Figure 3.1).

Elevations in the study area range from 1377 m to 2860 m.a.s.l.

The watershed covers approximately 1500 ha., and contains the

Tambito and the Palo Verde sub-watersheds. Vegetation is largely

tropical montane cloud forest (TMCF), and comprises both primary

and secondary forest, with a high density of epiphytes being

characteristic of the area. About 96% of the watershed is covered

by primary and secondary forest. Approximately 4% is deforested

mainly in areas close to dwellings (Figure 3.1) and in some isolated

patches throughout the watershed, which could be created by tree

fall for strong winds or illegal cultivation by colonists. The

existence of different land uses makes it an ideal watershed for

assessing hydrological impact. The Tambito and Palo Verde sub-

watersheds are paired catchments, with very similar landscape and

terrain properties but differing levels of deforestation.

The climate in TMCF has two main distinguishing features:

relatively constant seasonal temperatures and heavy rainfall with

the presence of persistent ground level clouds for some or all the

day being particularly characteristic. In Tambito the diurnal

temperature ranges from 15 °C to 25 °C, and variations are often

greater from day to night than from month to month. Therefore

dynamic changes vary more over short-term diurnal cycles than for

longer seasonal or annual time-scales, due to latitude.

45

Figure 3.1. Location of the Tambito watershed and prevailing land uses.

45

46

These daily changes in temperature and humidity may be

significant. For example, nocturnal relative humidity in Tambito is

consistently high, almost 100%, but on sunny and dry days

relative humidity can drop to 60% at noon.

The average annual rainfall in Tambito is on average 6560 mm

(annual average 1988-1996 in 20 de Julio IDEAM weather station)

near the higher parts of the catchment (see Figure 3.2) and 4500

mm in the lower part of the catchment (Tambito station for 1999).

Rain frequently occurs in heavy showers and strong convectional

conditions can bring on heavy downpours, with magnitudes as

high as 60 to 80 mm of rainfall per day. Two rainy seasons are

observed, with the highest rainfall occurring between March-April

and October-November. The rest of the year is characterised by

irregular rain showers of varying intensity. A marked dry period,

with very little rain, occurs in July, but often extends until August.

In 1997, El Niño had a significant impact on the rainfall pattern

producing a very dry summer, with no rain during July.

Cloud cover during the day varies greatly. This environment is

characterised by high humidity levels, and the presence of clouds

at ground level is quite normal at any time during the day. Clouds

play an important role in this ecosystem, first because they, in

combination with atmospheric factors, attenuate solar radiation,

absorbing up to 60% of incoming solar radiation (Rincon-Romero,

2000), and second because of cloud interception by vegetation,

which is often termed horizontal or occult precipitation (Jarvis,

1999).

47

47

0100

200

300400

500

600700

800

900

Ranf

all (

mm

)

Jan Feb Mar Apr May June July Aug Sep Oct Nov Dec

Months

Monthly average rainfall in 20 de Julio weather station (1988 - 1996)R

ain

fall

(m

m)

Figure 3.2 Monthly average rainfall of the nearest weather station to Tambito (3km distance)

48

The relative uniformity of climatic conditions throughout the year

allow TMCF plants to grow, flower, and shed leaves all year-

around, and animals continue to reproduce and remain active

throughout the year (Museo de História Natural, 1996). TMCFs

therefore, harbour one of the most abundant sources of life, with

an enormous diversity of species and high density of plants (Museo

de História Natural, 1996). The highly specialised nutrient cycling

systems cause rapid growth rates (Golley, 1983). In addition, the

large number of co-dominant mixed forest species generates

distinctive vegetation patterns (Jetten, 1994), which are difficult to

taxonomically characterise due to the high biodiversity.

Overall, trees present a remarkably uniform appearance. The

TMCF differs from the lowland forest in structure and composition.

Vegetation is lower in height, with dense epiphytes. Tree trunks

are knurled and slender, and the branches form at all the levels

not just close to the canopy top. For this study, foliage can be

stratified into three layers. The top layer encompasses tall trees

(up to 25 m) and heavy vegetation parts, such as trunks, and

predominant light-competing species. The intermediate layer

contains undergrowth species such as shrubs, climbing and

herbaceous plants of all shapes and sizes, as well as a large

number of sapling and seedling trees. Both these layers are used

as a support for the next differentiated layer –epiphytes– that

includes algae, mosses, liverworts, and lichens. The proliferation

of epiphytes could play an important role because of their large

storage capacity for water, but it is not taken into account in this

study, as this involves knowing their plant physiology in some

detail, which is not considered here. The ground is covered with

long standing accumulations of decomposing vegetation.

49

3.3 Experimental strategy

Land use and land cover differentiation plays an important role in

the type of modelling studied in this thesis. Three kinds of land

uses were distinguished within the study area, on the assumption

that these have different hydrological impacts (Bonell and Balek,

1993; Calder et al., 1995): (a) primary forest, (b) secondary forest,

and (c) deforested areas. Different land cover combinations were

found within both the Tambito and Palo Verde sub-watersheds. As

will be shown in section 3.5, the hydrological parameters for both

primary and secondary forest were found on the basis of field

measurement, to be very similar. The analysis will therefore only

differentiate between two classes, since these were quite different

in properties: (a) forests and (b) grasslands.

A physical hydrological model (see section 3.6) is designed and

used in combination with the five scenarios of LUCC designed for

this study (see section 3.4). Hydrological flux variations as a result

of the land use change between individual iterations and different

scenarios are analysed.

The hydrological model proposed in this thesis is a hydrological

budget model. The model is initially implemented as a 1D lumped

model at the plot scale for each type of land use that exists within

the study area. Field hydrological stations are the main source of

data, providing information on collected fluxes and energy. In

order to investigate a) spatial variability of landscape sensitivity

according to the landscape properties, and b) the impact of the

spatial connectivity along the hydrological pathways, the same

routines and equations of 1D model are implemented on 2.5D

physically- based distributed model, which runs within a GIS. A

2.5D model means in this thesis that the fluxes are modelled in a

50

vertical direction and then complemented with lateral surface

fluxes for every time step.

Sensitivity analysis involves comparing hydrological outputs of

model variables, such as overland flow and erosion generated

between different iterations of different land use scenarios

patterns. Sensitivity is assessed as the change in model outputs

(runoff and erosion) per unit change in deforested area. The

hydrological model used was designed to operate on an hourly time

step, and runs with the same parameters and input data (with the

exception of land cover) for all scenarios. Each scenario of LUCC

includes between 15 and 22 iterations before the watershed is

completely deforested.

3.4 Land use change scenario generation for this thesis

Modelling of LUCC is not the topic of this thesis, but LUCC

scenarios are necessary for the hydrological modelling carried out

here. Five different scenarios are used in the hydrological

sensitivity analysis for the study area (see Chapter IV).

LUCC scenarios are used in the analysis to investigate how LUCC

combined with the geographical position of LUCC can affect the

hydrological behaviour of the watershed. The scenarios were

designed using criteria of advancing deforestation fronts rather

than a spatially complex pattern of deforestation. All scenarios

start with the same initial LC, and the LUCC transformation in

each iteration is carried out relative to various physical properties

of the landscape such as change in the vegetation type, then

changes in vegetation parameters. The physical determinant of

advancing deforestation is different for each scenario, leading to

transformations occurring over different parts of the catchment for

51

each scenario. The schemes are designed to produce scenarios,

which will have hydrologically different responses rather than to

simulate spatially realistic deforestation patterns. Each scenario

occurs over a number of iterations, which are not related to

temporal changes, but are used as step changes throughout which

LUCC occurs.

This strategy specifically deals with the significance of:

1. The location where the LUCC occurs within the catchment.

2. Recognition of the sensitive areas where the catchment

hydrological response is significantly affected by different LUCC

trends.

3. The identification of the relationship between areas with

different responses to LUCC with their physical properties.

4. Analysis of how the different LUCC patterns used in the

scenarios affects the model hydrological response.

5. Whether the preceding and current arrangement of LUCC could

affect the catchment hydrological response (Fisher et. al, 1997),

i.e. the importance of the temporal pattern of change.

3.4.1 Estimating initial vegetation cover for LUCC Scenarios

All the scenarios simulate the conversion of forest to pasture,

starting from the Normalised Difference Vegetation Index (NDVI)

image derived initial vegetation cover, obtained from a 1989

Landsat TM image (Figure 3.3).

52

Figure 3.3 NDVI radiance from Landsat TM for Tambito catchment

53

NDVI ratio was selected as land vegetation cover instead of the

most traditional approach normally used (maximum likelihood

classification) because for this type of landscape (high

mountainous and steep slopes), the NDVI minimises the shadow

effects from the topographic variations stress (Cohen, 1991).

The Normalised Difference Vegetation Index (NDVI) is a ratio-based

index applied to a Landsat Thematic Mapper (TM) image using the

near infrared (band 4; 0.76 to 0.9 µm) and the red reflectance ratio

(band 3; 0.63 to 0.69 µm). This index identifies changes in amount

of green biomass, chlorophyll content, and leaf stress (Cohen,

1991), which is defined by,

NDVI = (band4 – band3) / (band4 + band3) (Eq. 3.1)

The NDVI ratio is a relationship which holds both for shadowed

and for directly illuminated pixels (Cohen, 1991). Therefore, a ratio

image shows the radiance information without effects of

topography. This permits the examination of spectral properties of

the surface, without confusing the mixed brightness of the

topography effects and material reflectance (Campbell, 1987).

This NDVI image was reclassified using signature classes extracted

from texture patterns, which were identified with the help of aerial

panchromatic photographs, 1:32,000 scale (approximate resolution

of 0.6X0.6m pixel size). The vegetation texture patterns appears on

the panchromatic photographs as several granules or flat areas,

which produce different textures due to the size of the shadows

and forest trees, showing where the vegetation is forest or

grassland. According to these patterns on the panchromatic

54

photographs, they were compared with the NDVI image to select

the signature areas of the LU used in the classification.

Four reflectance classes of vegetation were distinguished from the

satellite image: two types of forest, grassland and cloud (table 3.1).

LUC class Clouds Primaryforest

Secondaryforest

Grassland

NDVI 0.33 0.59 0.8 0.72

Table 3.1 Average NDVI values for classification of land use classes

3.4.2 Scenario descriptions

The LUCC scenarios are described as follows:

1. Scenario 1 (SC1): The LUCC pattern derived from a cellular

automata, as designed and implemented by Mulligan et al.

(2000). This scenario simulates the conversion of forest to

pasture as spreading from roads and agricultural frontiers, in

an epidemiological fashion or a propagation wave through 22

iterations in this catchment. Figure 3.4 shows the trend and

the area by iteration of land conversion for twenty-two

iterations, and an example of an iteration of this scenario is in

Figure 3.5. Figure A1.1 (Appendix 1) shows the spreading

pattern over the watershed.

2. Scenario 2 (SC2): The conversion pattern is carried out by

applying a fixed horizontal distance from the river channels

taken from ‘Instituto Geográfico Agustín Codazzi’ (IGAC)

cartography to convert forest areas to grassland in an uphill

direction. This pattern was created to understand the effect of

forest buffers and hydrological connectivity on secondary flow

55

path to the major rivers. Deforestation is produced on both

sides of all rivers in 50-m. horizontal distance increments by

iteration. Each 50m is recognised as a single class for a total of

18 classes, and incrementing the same distance for each

iteration until the whole watershed is deforested; this is reached

in iteration 18. Figure 3.4 shows the LUCC pattern in this

scenario and deforested area by iteration, and an example of an

iteration of this scenario is in Figure 3.6. Figure A1.2 (Appendix

1) shows the spreading pattern over the watershed.

Scenario 1 (SC1)

0

500

10001 4 7 10 13 16 19 22Iteration

Are

a (H

ecta

res)

Grassland

Forest

Scenario 2 (SC2)

0

500

1000

1 3 5 7 9 11 13 15 17Iteration

Are

a (H

ecta

res)

Grassland

Forest

Scenario 3 (SC3)

0

500

1000

1 3 5 7 9 11

13

15

17

Iteration

Are

a (

Hec

tare

s)

Grassland

Forest

Scenatio 4 (SC4)

0

500

1000

1 3 5 7 9 11 13 15

Iteration

Are

a (H

ecta

res)

Grassland

Forest

Scenario 5 (SC5)

0

500

1000

1 3 5 7 9 11 13 15Iteration

Are

a (H

ecta

res)

Grassland

Forest

Figure 3.4 Transition of LUC by scenarios(a) Scenario 1. Forest conversion to pastures fromcellular automata. (b) Scenario 2 Forestconversion with a fixed distance from riverchannels (c) Scenario 3. Forest conversion with afixed distance toward river channels. (d) Scenario4. Forest conversion with a fixed altitudinaldistance from lower point in up hill direction. (e)Scenario 5. Forest conversion with a fixedaltitudinal distance from higher point in down hilldirection.

(a)

(b) (c)

(d) (e)

56

Figure 3.5 An example of an iteration for SC1

57

Figure 3.6 An example of an iteration for SC2

58

3. Scenario 3 (SC3): The conversion pattern from forest to pasture

is carried out by applying a 50-m fixed horizontal distance from

the watershed boundary toward river channels in a downhill

direction. Deforestation advances in each iteration by the same

distance (50m.), producing complete deforestation by the 18th

iteration. This scenario combined with SC2 would help to

identify whether or not the direction of deforestation relative to

the rivers could affect the catchment hydrological response.

Figure 3.4 shows the LUCC pattern for this scenario, and an

example of an iteration of this scenario is in Figure 3.7. Figure

A1.3 (Appendix 1) shows the spreading pattern over the

watershed.

SC2 and SC3 are related to deforestation from and toward river

channels because one of the purposes of this experiment is identify

the importance of forested areas close to river channels.

4. Scenario 4 (SC4): Conversion pattern from forest to pasture is

carried out by applying deforestation in 100-m fixed increments

of altitude, in an uphill direction, from the lower to the higher

points of the catchment. Within 15 iterations the catchment is

completely deforested. Deforestation advances with 100-m of

altitude until it reaches the highest points of the watershed.

This scenario was built to identify the elevation effects on the

catchment hydrological response to LUCC conversion. These

may be important since most of the climatic variables change

with elevation in this catchment. Figure 3.4 shows the LUCC

pattern for this scenario, and an example of an iteration of this

scenario is in Figure 3.8. Figure A1.4 (Appendix 1) shows the

spreading pattern over the watershed surface.

59

Figure 3.7 An example of an iteration for SC3

60

Figure 3.8 An example of an iteration for SC4

61

5. Scenario 5 (SC5): Conversion pattern from forest to pasture is

carried out by applying deforestation in 100-m. fixed elevation

distance, from higher to lower points of altitude, in a downhill

direction, through 15 iterations. Higher points in the watershed

are deforested in the first iterations. Figure 3.4 shows the

LUCC pattern for this scenario, and an example of an iteration

of this scenario is in Figure 3.9. Figure A1.5 (Appendix 1)

shows the spreading pattern on the watershed surface.

SC4 and SC5 show the pattern of deforestation related to elevation

change. Deforestation from and towards the highest points are

included in the analysis with the purpose of identifying altitudinal

whether the order of deforestation (top to bottom or bottom to top)

is significant.

Iterations within scenarios are not related to any concept of time

taken for LUCC. Iterations are steps in a pattern of change in

which forest is converted to pasture within the terms that the

scenarios define.

Table 3.2 summarises the extent of deforested areas by scenario

and by iteration. Values in table 3.2 were extracted using GIS

utilities developed for the study area, which is widely explained in

following chapter.

62

Table 3.2 Rates of deforestation per iteration of the different scenarios (values in ha.)

Iteration Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5

1 392 0 1 1 262 160 368 2 52 813 144 349 4 75 964 122 245 5 93 955 100 148 7 119 1086 82 97 9 132 1217 67 58 10 131 1388 63 40 13 133 1339 57 24 16 138 131

10 47 18 18 121 13211 37 16 24 108 11912 30 13 40 95 9313 20 10 58 96 7514 10 9 97 81 5315 6 7 148 26 1316 4 5 24517 4 4 34918 4 2 36819 420 221 122 0

63

Figure 3.9 An example of an iteration for SC5

64

3.5 Field methodology

The hydrological characteristics of each land use type were

sampled using 20-m2 plots (2m x 10m), each with a hydrological

station to collect data of the main elements of the hydrological

cycle to be used for model input and validation. Samples were

collected throughout the watersheds to determine vegetation

biomass, leaf area index, and soil properties for each land use.

Field work was applied at the plots and distributed through the

watershed. Data on vegetation and soils were collected following

the Field Manual Version 3.1 of Medalus (Cammeraat, 1991). In

cases where the topography and tall vegetation made it difficult to

delimit, 1-ha. plots for sample collection (as the manual suggests),

a different strategy was adopted: samples were systematically

collected in the areas surrounding the hydrological plots. Data

gathered on the plots were used to determine and monitor the

physical and hydrological properties, and parameterisation of each

land use type. The field data necessary to parameterise and

validate a 1D lumped model were also collected. The vegetation

and soil samples collected throughout the watershed

complemented the land use parameters used in both the 1D and

2.5D models.

3.5.1 Plot scale

Instruments for parameterisation of meteorological conditions were

installed. Instruments for verification and validation data for

overland flow, throughflow, erosion, soil moisture and recharge

were also installed from 20-m2 (10m x 2m) plots. To ensure that

the areas were representative of each land use, all plots were

located in the middle of a large area that adequately characterised

65

each land use according to the landscape (Figure 3.10). Plot

topographic characteristics are discussed in more detail in this

section, and in the 1D parameterisation section (see chapter 4).

Plot orientation was 10m up-slope, in the direction of the hill slope,

and 2 m perpendicular to the direction of the slope. Vegetation

inside the plots was left undisturbed as much as possible to

prevent alterations in hydrological parameters. A metal sheet was

installed at the upper end of the plots to block any overland flow

from coming into the plot, from up slope. Several instruments

were installed at the lower end of the plot. A 2-m-deep trench was

dug in the soil, into which a vertical plastic sheet was extended to

provide an impermeable border to block the throughflow, allowing

it to be quantified. A series of gutters were positioned at the other

end of the plastic sheet and on the surface. The deep gutter

collected the throughflow, and the surface gutter collect the

overland flow leaving the plot, as illustrated in Figure 3.11. A

funnel with a fine mesh was used to retain and quantify the

amount of soil erosion occurring in the plot. All instruments were

connected to the sensor of the weather station datalogger (© DATA

ELECTRONICS, AUSTRALIA) data. The data were collected at the

weather station installed at the lower end of each plot.

66

Data-logger

Figure 3.11 Location of gutters in plots

Figure 3.10 Distribution of plots and weather stations

66

Scale 1:50,000

67

For primary and secondary forest, collectors of throughfall and

stemflow were installed in an area adjacent to the plots (Figure

3.12). These data were with the intention to be used to

parameterise the interception model for each forest vegetation type.

Rainfall and stemflow data were recorded by tipping buckets of

different capacities: 0.78 mm for throughfall and 0.2 mm for

stemflow.

Vegetation in the deforested plots generally consists of grasses with

small shrubs and palms (Figure 3.13). The forest plot (Palo Verde)

has primary forest vegetation, as described in the previous section.

3.5.1.1 The hydrological weather stations

Hydrological weather stations were installed at every plot and are

equipped with a Datataker 600, and a set of sensors that record

data for each land use. The Datataker 600 is a battery-powered

microprocessor with data logger that measures inputs from most

sensor types. Data were stored in battery-backed RAM removable

memory cards. The Datataker 600 has 10 differential or 30 single-

ended ports, which can be used in any combination (i.e. see Figure

3.13 with grassland weather station). The resolution is 15 bit plus

sign (+/-); accuracy is 0.15 % at full scale, and time resolution can

be set as low as at 1 second per day.

68

Figure 3.12 Throughfall collector.

Figure 3.13 Weather station in deforested areas.

68

69

The data logger was set to record at 15-minute intervals, and

average data for each variable on an hourly basis (writing the mean

and standard deviation). In the case of event variables such as

precipitation, data was recorded on a per event basis. Data used in

the analysis of this thesis is identified in the list with asterisks ***.

The weather station located in the Tambito sub-watershed, which

is considered as being more disturbed, collected the following data

(Figure 3.13):

- Solar radiation received and reflected (Skye Instruments silicon

cell pyranometer) ***- Red/far red radiation received and reflected (Skye Instruments

silicon cell sensor)

- Blue radiation reflected (Skye Instruments silicon cell sensor)

- PAR radiation reflected (Skye instruments PAR sensor)

- Humidity (Skye instruments capacitance based humidity sensor)

- Air temperature (Skye instruments thermistor) ***- Rainfall, using tipping bucket rain gauge (0.02 mm bucket size)-

Environmental Measurements. (UK Ltd) ***- Overland flow, tipping bucket (1 tip 105 ml)

- Throughflow (0.12 mm bucket size)

- Soil erosion (strain gauge-based sediment weighing system)

- Soil matric potential at 40, 80, and 120 cm depth (Soil

Moisture Corporation - gypsum blocks) ***- Soil temperature (thermocouple)

For the primary forest plot, in addition to the instruments

mentioned above, the weather station includes the following:

70

- Stemflow from trees measured using Environmental

Measurements (UK Ltd) tipping buckets (0.2 mm bucket size)

- Throughfall collected on a 6 m x 10 m plastic sheet, measured in

a large 77.5 ml tipping bucket

- Cloud scavenging by epiphytes, measured as grams of water,

using a linear displacement transducer over a 0.8 m x 4 m area

- Drainage from epiphytes, measured using Environmental

Measurements (UK Ltd) tipping buckets (0.2 mm bucket size).

Not all instruments installed in the weather stations generate

information for this research; there are other research projects that

are currently being carried out in the area and which use the

additional information collected by them.

The hydrological station installed in the secondary forest plot has

the same instruments as the primary forest station and several

others:

- Stage for the Tambito and Palo Verde rivers using pressure

transducers to sense water depth.

- PAR sensors throughout the canopy. ***

3.5.1.2 Data collected from the weather stations

The Two first weather stations were installed during the first field

campaign (summer 1997) one in a deforested plot and one in a

primary forest plot. A third weather station, that was assigned to

collect catchment integrated flow data, was installed during the

second field campaign (summer 1998) just before the junction of

the Tambito and Palo-Verde rivers.

71

Unfortunately, all weather stations suffered technical problems due

to the excessive humidity (100%), and lack of maintenance of the

instruments because of their remote location. However, some data

were recovered from the loggers and were subsequently used in the

research. Table 3.3 indicates the periods in which data were

collected at the weather stations.

Plot 1997 1998 1999 2000

Grassland 31st October/97-31st January/98

28th July – 30th

NovemberJuly 11th toNovember 4th

February 14th

To March 11th

Primaryforest

None 8th August –12th August

22nd June to18th September

None

River andsecondary

None 9th August –21st August

None None

Table 3.3. Periods during which data were collected.

An example of data collected at the weather stations is presented in

Appendix 2.

3.5.2 Catchment scale

3.5.2.1 Soil data

In physical distributed modelling (2.5D), soil parameters are

derived from samples collected across the watershed. A

classification map was made to choose the most suitable places for

sampling the soil (Figure 3.14). Soils were classified using

topographic data and a preliminary land use maps from Fundación

Proselva (Museo de História Natural, 1996), assuming that these

would be major controls on soil properties.

72

Figure 3.14 Classification map for collecting soil samples.

Scale 1:33,333

72

73

The geology of the area is relatively uniform with cretaceous

formations and deposits of basaltic and peregnic rocks, which

produce the steep slopes characteristic of the area (Ingeominas,

1999). Topographic characteristics, such as slope, were grouped in

classes. Each class was assigned a number that was to be

combined with the land use map. Classes of slope and land use

are grouped in Table 3.4.

Slope Vegetation type (Land use)

Class Degrees Class Type of vegetation

1 0-30o 1 Primary forest

2 30o-50 o 2 Secondary forest

3 Greater than 50o 3 Grasslands

Table 3.4 Classes of slope and land use.

Soil samples were taken from nine defined classes (Figure 3.14),

using a 1.2-m auger and collected every 10 cm depth up to 80 cm

depth or until bed-rock was found, for a total of 111 samples (204

cm3 each) in the catchment. Soil moisture was measured at the

surface and then at 10-cm intervals using the Theta-probe (Delta T

devices, UK). Hydraulic conductivity was measured using a

minidisk infiltrometer (Decagon devices, USA). After soil samples

were collected, the following parameters were measured: dry bulk

density, soil moisture, texture and organic matter.

Texture and organic matter were extracted at CIAT’s1 soil

laboratory in Cali, Colombia. The Bouyoucous standard method,

amply explained in literature (Avery and Bascomb, 1974), was the

analysis methodology used. Results showed that soils in the

1 CIAT. Centro Internacional de Agricultura Tropical, Apartado aéreo 6713, Cali Colombia.

74

catchment are generally homogeneous as sandy clay loam,

presenting a sampling average of 57% sand, 21% silt, 22% clay and

are explained further in the results section. Percent organic matter

for the first 20-cm depth was 14.4%. These values are used in the

model for soil properties. The soil analysis results are summarised

in Appendix 3.

Stone density was computed from 3.78 kg of stone collected from

several places across the watershed. A stone density value was

computed by measuring the volume of water displaced from a

known beaker capacity. Stone density was found to be 2.51 g cm-3.

This information was used in the computation of soil bulk density

and porosity.

Erosion was intended to be computed from the discharge and

sedimentation data collected at the watershed outflow stations.

However, sensors at both the Tambito and the Palo Verde river

stations failed and no watershed outflow was recorded. As a result,

this variable in the research has not been validated in the models.

3.5.2.2 Vegetation data

Vegetation is one of the most important elements of the landscape.

Its parameters play a decisive role in hydrological models. Each

type of vegetation has its own significant hydrological

characteristics. Leaf area index (LAI), vegetation cover, and canopy

water storage capacity are all derived from vegetation samples

taken for each land use in the Tambito area.

75

3.5.2.2.1 Leaf area index

Leaf area index (LAI) for the forest vegetation class was calculated

by Rubiano (1998) using a network of PAR sensors at different

heights in the canopy at the secondary forest site (to calculate the

light extinction coefficient) and by integrating Beers Law for light

levels measured by the lowest of these sensors (at 1m from the

ground) compared with an open sensor.

The resulting value for LAI are 3.26 which agrees with values

measured in primary and secondary forest at the same site using a

ceptometer (Letts, personal communication); this value is assumed

for both primary and secondary forest. The literature reports LAI

for lower montane cloud forest between 3.4 to 5.5 (Kato et al. 1978;

Yakamura et al 1986; Huttel, 1975).

The same method could not be used to grassland, because grass is

so short and the PAR instruments can not fit under grass leaves.

In the case of grasslands, LAI was calculated from 5 sets of 10-cm2

samples, from which the leaves were separated and superimposed

on a known flat, white area. Monochrome pictures were taken and

then processed. Those pictures were analysed for surface area

using image-processing software (Photoshop 4.0). The relationship

between known area (10 cm2) compared with the dark area from

grass leaves indicate the LAI for grassland. A summary of samples

used for LAI calculation for grassland is in table 3.5.

76

Sample Dark pixels Leaf area index (%)

J41 41865 1.82

J42 36368 1.41

J43 21559 2.91

J44 17914 1.13

J45 25262 1.53

Table 3.5 Leaf area index samples for grassland

Leaf area index for grassland was averaged at 1.67

3.5.2.2.2 Vegetation cover

Vegetation cover in the forest area was calculated using vertical

panchromatic photography below the canopy for areas surrounding

the forest plot. Photographs were scanned and analysed for

shadow pixels (canopy) and the white pixels (sky), where the ratio

between dark and white pixels produces a value for canopy cover.

A summary of sample values used is presented in Appendix 4. The

values used in the analyses are presented in table 3.6.

For the case of the grass type, the value used for vegetation cover

was extracted from the work of Rubiano (1998), which was carried

out in the same area as this study. The value is included in table

3.6.

3.5.2.2.3 Canopy water storage capacity

Fifty random sets of forest vegetation samples were collected in the

area surrounding the forest plots where the weather stations were

installed. Leaves and branches from typical forest vegetation were

collected and brought to the field laboratory for identification and

77

subsequent estimation of canopy storage capacity parameters.

Samples values are summarised in Appendix 4.

Vegetation samples were weighed firstly dry and then were wetted

by submerging in water and shaking, to estimate the weight of the

water they hold per unit leaf area. The area of collected leaves was

measured using the same procedure in the estimation of LAI for

grassland. The density of retained water was assumed 1.0 g.cm-3

to calculate the volume.

Canopy water retention calculation for grassland used the same

procedure as forest samples, using 3 blocks of grassland samples

with an area of 10 cm2. These parameters are summarised in

Table 3.6 and source data are compiled in Appendix 4.

Land use LAIm2 m-2

Maximum canopystorage capacity

Vegetationcover

Primaryforest

3.3 0.18 mm (n=53) 91 %

Secondaryforest

3.3 0.2 mm (n=12) 91 %

Grassland 1. 7 0.03 mm (n=5) 86 %

Table 3.6 Vegetation parameters

The literature reports 1.15mm for maximum canopy water storage

capacity (Schellekens et al., 1999) from Luquillo experimental

forest in Puerto Rico, which was determined using the methods of

Jackson (1975), Gash and Morton (1978) and Rowe (1983). Those

methods were applied for the determination of vegetation

parameters for the evaluation of interception models. Schellekens

et al. (1999) through the literature search compiled values for

canopy water storage capacity ranging from 0.08 mm (from palm-

filled ravine sites) up to 1.3 mm (for well-stocked ridges). Values

78

for canopy storage capacity in table 3.6 were adopted because they

come from Tambito field measurements, despite the fact they are

very different from the mean values reported in the literature.

3.5.3 Other spatial data

Complementary data were also collected from different sources.

1. Basic cartography

From the Instituto Geografico Agustin Codazzi IGAC: the

1:25000 scale, sheets 343 I a, b, and c. All the cartographic

elements were created in a cartographic projection (see section

3.2), according to IGAC’s map normalisation. Contour lines and

rivers were digitised by GIS and Modelling Services Ltda1, and

used to create the Digital Elevation Model (DEM), 25-m. pixel

size. The aspect, slope and topographic index (see section 4.5.1)

were derived from the DEM (see Figures 3.15 to 3.18), using

Geographic Information System software Arc-Info 7.3 (ESRI,

1998) and PCRaster, version 2 (Utrecht University, 1996).

2. Aerial photography of part of the area, scale 1:40000, taken in

1985 by IGAC, for texture characterisation of vegetation and

identification texture for remote sensing image.

3. Distribution map of vegetation cover. A draft made by

Fundación Proselva (Museo de Historia Natural, 1996) based on

field work for characterisation of vegetation, was used for initial

characterisation of study area and for designing soil sampling

(see Figure 3.19).

1 GIS and Modelling Services Ltda. Cartographic and environmental management services, Cali,

Colombia. E-mail gismodelling@uniweb.net.co

79

4. Remotely sensed data of the area, obtained from a Landsat

Image (1989), pixel size of approximately 30m side size, without

geometric correction, supplied by Fundación Proselva (Museo

de História Natural, 1996) (see Figure 3.20).

5. Daily rainfall data from 1987, obtained from the database of the

Hacienda Carpinterias and 20 de Julio weather stations, both

located close to the Tambito watershed (7 and 10 km,

respectively) and belonging to the Instituto de Hidrología,

Meteorología y Estudios Ambientales (IDEAM).

6. Four years of rainfall data that were collected manually at the

Tambito cabin were used to compare data consistency and

analyse rainfall spatial distribution.

7. Additional rain gauges were spread both across and outside the

watershed to compare rainfall distribution (Figure 3.10).

80

Figure 3.15 Basic cartography of the area (source from IGAC, 1985)

Scale 1:50,000

Figure 3.16 Digital elevation model for the study area derived from digitised contours using Arc/Info 7.3

80

Scale 1:50,000

81

Figure 3.17 Slope map derived the digital elevation model Figure 3.18 Aspect map derived from the digital elevation model

81

Scale 1:50,000 Scale 1:50,000

82

Tambito Landsat TM image (bands 5,2,3)

Figure 3.19 LUCC map for Tambito watershed from Fundación Proselva (Museo de História Natural 1996)

Figure 3.20 Landsat image TM for the study area, false colour (5,4,3)

82

Scale 1:50,000

83

3.6 Hydrological Model methodology

3.6.1 Introduction

Although many hydrological models already exist, most have been

designed for (and implemented in) environments that differ

significantly to TMEs and often require input data that are not

always relevant or available for this environment. This was clearly

identified in the section 2.3.3, where several models were described

and their characteristics were evaluated with respect to the

environment of Tambito.

Instead of using an existing model, hydrological processes are

modelled using simple, physical routines that have been

implemented and combined especially for this purpose, with two

important features: a) models should work with minimal data, and

b) models should emphasise the properties of spatial variation and

hydro-connectivity, which are important in this thesis. The

following sections cover the methodology used and model

development.

3.6.2 Strategy

The 1D model was designed to work in a single cell in the vertical

dimension. The main hydrological processes were included to

reproduce the hydrological response for each of the land uses

identified in Tambito catchment. Then the same hydrological

model was implemented in a spatially distributed 2.5D form

produce results over the whole study area and simulate the

hydrological behaviour in the whole catchment simultaneously. A

84

surface component was added to the model to include the surface

fluxes of water between cells simulating overland flow.

Afterwards, the physically-based hydrological model was combined

with the five scenarios of LUCC with 15 to 22 consecutive iterations

for each scenario. The hydrological model was integrated for 1-

hour time steps with data covering 1 year for each iteration of each

land use change scenario. The same weather conditions and

parameters were used to run the model for each LUCC iteration for

a simulated year. A summary of hydrological variables was

extracted from each simulated year as an annual total for

sensitivity analysis.

The 1D hydrological model results were also subjected to sensitivity

analysis to select the most important model parameters and

variables (see section 4.3) for use and application in the 2.5D

model. A 2.5D spatially distributed model for GIS was then

produced, based on the 1D hydrological model, spatial data, and

cartographic and remote sensing data. The sensitivity analysis of

key outputs was also compared against topographic variables of

the forested and deforested areas and their change with the land

use change (see section 4.5), to highlight landscape controls on

hydrological response to land use change.

Hydrological model validation at the plot scale is carried out to

evaluate model results against collected data (see section 4.7), in

order to ensure that the model outputs are reasonable.

In summary, the thesis is based on the premise that land use

change may have different hydrological impacts at the catchment

scale dependent upon where (physiographically speaking) the land

use change occurs. The LUCC scenarios and hydrological

85

modelling used here is an attempt to understand this spatial

variability of hydrological sensitivity.

Figure 3.21 shows the general model structure. All sub-models,

variables, and water and energy fluxes are illustrated. Energy

fluxes are calculated in several stages, starting with the

computation of hourly extraterrestrial energy at the top of the

atmosphere, using the solar radiation sub-model. The effect of

cloud cover is then calculated, producing an energy decrease and

then net radiation is estimated (see Sections 3.6.4.2), which is used

in the evaporation module. Net rainfall, the rain reaching the soil

after interception and evaporation, is also calculated on an hourly

basis, using rainfall and vegetation cover parameters (see Section

3.6.6). Surface and soil water fluxes are determined by calculating

infiltration, overland flow, and recharge from surface to bedrock.

Throughout this process, soil properties are calculated using a

pedo-transfer function that uses several coefficients derived from

hundreds of statistical soil analyses carried out by USDA (see

Section 3.6.7.3). Computed overland flow provides input data for

the erosion sub-model, which estimates the amount of soil removed

from the soil surface during a certain time period. Each sub-model

is discussed in detail in the following sections.

Hourly rainfall data are the only meteorological input data that the

model uses. The model calculates additional data as needed.

Model input data appears as a flat file of chronologically ordered

data. Hourly data is organised by lines in the input file, and

includes the year, month, day, hour, amount of rainfall, and Julian

day. An example of an input file is presented in Appendix 6.

86

Rainfall Solarradiation

CanopyEvaporation

Loss byevaporation

Canopyinterception

Net rainfall

Soil surface

Infiltration

SoilEvaporation

Recharge

Soil

Runoff

Erosion

Figure 3.21 Schematic diagram of the hydrological model.

Flux

Sub-model

Key

87

3.6.3 Considerations for the modelling process

Technical aspectsModel development occurred in many stages. The initial model 1D

model was highly detailed and, accordingly, difficult to implementin

both plot and catchment scale. Sensitivity analysis of this model

suggested that it should be simplified in order to make it

computationally manageable and parameterisable.

In technical terms, the model was developed in two stages: the first

consists of a 1D hydrological model, implemented in a spreadsheet.

This model was very cumbersome and slow to react for long

simulations because its complexity. This suggested that for this

research activity a complex model with several modules, which

modelled a year in hourly time steps (8760 hours-) was

inappropriate and unworkable within the context of a spreadsheet

approach. The same model was then implemented in PCRaster for

a single cell, which operationally speaking gave good results.

A lumped model is however inappropriate to the spatial modelling

aspects of this thesis so that a second model saw the development

of a spatially distributed 2.5D hydrological model using the same

equations as the 1D hydrological model routines with additional

surface components for lateral flow, which are then applied to the

entire watershed. As in the 1D hydrological model, an

unsuccessful experience in the implementation of this model was

encountered was when the model attempting to implement it in

Arc/Info Macro Language (AML) of Arc/Info 7.3 (ESRI). The

operational problems arose because the temporal information

created by the system throughout the process generates several

files which are stored in the hard drive of the computer. This

produces a very slow simulation process and after several hours

the simulation process stops, due to the fact that the number of

88

subdirectories that are possible to be created is limited (no more

than the maximum available integer number in a computer –

32767-). These is no workaround for this problem in AML. The

2.5D hydrological distributed model was eventually implemented in

PCRaster with good results.

Data availabilityWith regard to the modelling data, unfortunately complete hourly

rainfall data for a single year could not be obtained from the

weather stations because of technical problems, although a broken

record exists. A Monte Carlo simulation technique (Mulligan,

1996) was used to generate hourly data on the basis of daily totals

for both Tambito and 20 de Julio weather stations and probability

distribution function of hourly rainfall measured at the Tambito

station.

Transpiration modellingDespite the fact that plant transpiration is important to take into

account when vegetation is involved in any study, researchers have

pointed out that this process contributes relatively low amount of

water, compared to water evaporation process from the water

canopy and does not contribute significantly to the water balance

in TMCF (Rutter, 1975; Grubb, 1977; Korner, 1983; Cavalier,

1986).

One of the main processes that regulates gas and water exchange

in the plant, is the stomatal conductance, which varies in response

to many meteorological variables. Stomata are sensitive to the

quality and quantity of light, to temperature, to humidity, to plant

water stress and vapour pressure deficit among others.

Temperatures in TMCF are low and relative humidity is high, solar

radiation low and water stress less frequent compared to lowlands

(Jarvis, 1976). Reductions in stomatal conductance have been

89

observed in response to increases in vapour pressure deficit in

tropical and warm temperature of TMCF (Korner et al., 1983; Jane

et al., 1985; Cavalier, 1986). Additionally Bruijnzeel et al. (1993)

argued that transpiration rates in TMCF are low even during

episodes of bright sunshine. Kapos et al. (1985) found that

stomatal responses to changing atmospheric conditions were less

pronounced in Blue Mountains (Jamaica) than other tropical

forests.

The leaf anatomy in TMCF have been shown to have a small

number of stomata per unit surface area (Korner et al., 1983), the

density of which varies between 37 to 299 mm2 in the cloud forest

of Colombia and Venezuela (Cavalier, 1986).

Mulligan and Jarvis (2000b) noted that humidity and atmospheric

saturation for Tambito occurs up to 92 % of the time on an annual

basis, and in the same place, Letts (2000) found the maximum

transpiration rate was 1-4 mmol∙m-2∙s-1.

In addition to these arguments, due to the lack of spatially

distributed instrumentation in Tambito for the collection of data for

transpiration parameterisation, this sub-model is not included in

the proposed hydrological model. Since it is complex to model and

requires spatially distributed meteorological and plant canopy data

that are simply not available for this (and many others

catchments), the process was not modelled though the author

recognises that this is a serious simplification and also limits the

applicability of the model outside of TMCF environments.

Cloud interceptionThe important contribution that fog and cloud deposition on

vegetation surfaces provides an extra source of moisture for the

hydrological cycle in TMCF is broadly acknowledged (Bruijnzeel,

90

2000). However, the difficulty in quantifying this extra input of

moisture has been recognised since 1968 (Kerfoot, 1968).

Bruijnzeel and Proctor (1995) recognised how little is actually

known about hydrological functioning of the vegetation in TMCF

such as epiphytes exposed to cloud impaction, with respect to

cloud water interception and retention. In a literature survey

carried out by Jarvis (1999), the range of cloud deposition on the

vegetation of TMCF, as a contribution to the net rainfall varies

between 2.4 to 60%. The same author reported through modelling

that 73% of annual precipitation resulted from cloud deposition.

Cloud interception is not modelled in this thesis because in the

early stage of understanding the hydrology of Tambito, the purpose

was to produce a model that can be used later on as a framework

for further research, incorporating as yet unquantified additions

such as cloud interception.

3.6.4 Solar Radiation sub-model

Solar energy is the driving force for the Earth’s climate (Brock,

1981; Forseth and Norman, 1993). Therefore, the solar radiation

(SR) sub-model plays a key role in determining the system’s

hydrological balance because it defines the energy available for

evaporation at the Earth surface. SR receipt is highly spatially

variable across steep slopes and over time. Although geographic

location exerts control on SR, the study area under consideration

was too small to yield significant differences in SR because of

latitude and longitude. Accordingly, average geographic co-

ordinates were applied to the whole area to compute an energy

balance.

91

Solar radiation models can be adapted to data produced at any

given time step, whether yearly, monthly, daily or hourly. Model

accuracy depends mainly on the existence of long-term weather

data (20 years or more) (Gansler et al., 1994). It also depends on

the time-step, the resolution and the quality of source data.

However, even if these prerequisites are met, model results do not

always replicate real situations and values accurately.

The SR model includes three stages:

1. Modelling the hourly extraterrestrial SR with physical and

astronomical principles. This involves calculating the amount of

energy available from SR at the top of the atmosphere for any given

point (cell) and time during the day and for any slope, and aspect

(Iqbal, 1983).

2. Application of an hourly cloud-cover attenuation model, with

specific weather conditions for each hour during the day.

3. Development of an empirical net radiation model for specific

surface conditions (Mulligan, 1996).

Each of these components is explained briefly below.

3.6.4.1 Hourly extraterrestrial solar radiation model

According to the literature, extraterrestrial SR is determined by

simplified physical principles, for example solar constant, solar

declination, position on Earth surface and time (Robinson, 1966;

Brock, 1981; Iqbal, 1983; Dobson and Smith, 1988; Dingman,

1994; Forseth and Norman, 1993; Gueymard, 1993). Gueymard,

(1993) analysed several SR models and recommended the Iqbal

(1983) procedure that is physically based with RMS error below 6%

92

for global radiation. This process is outlined in Appendix 7. This

process was used to obtain the extraterrestrial SR for a point (Iobs)

(KJ h-1) with a given aspect and slope, using an hourly time-step.

3.6.4.2 Hourly cloud-cover attenuation model

Clear sky irradiance models have been developed to predict beam,

diffuse, and global radiation on a horizontal surface. A diversity of

models can be found in the literature, ranging from simple

empirical formulae to highly sophisticated spectral codes. Both

empirical and physical model approaches seek to interpret the

physical extinction of energy through the atmosphere (Gueymard,

1993).

The purpose of this sub-model is not to study the atmospheric

components effects on the solar irradiance such as water vapour,

cloud, aerosols, or ozone, but to introduce in the computation a

cloudiness factor, which diminishes the solar radiation reaching

the land surface, in a simple way that takes into account the

temporal and spatial variation in cloud cover in the catchment.

Gueymard (1993) compared 11 physical and empirical models for

atmospheric attenuation that varied in complexity. Gueymard

(1993) compared and analysed all input model parameters in order

to identify model limitations and areas, where their adequate

performance under real conditions, in order to evaluate the models

adaptability. He found that models designed to compute clear sky

irradiance had not been validated against real data, because in

many cases, long term data for parameterisation and validation do

not exist. Hourly irradiation models are more appropriate because

they compute continuously changing cloudiness in short time

steps. Because most of the short-term variability in radiation

93

values can be attributed to clouds, this atmospheric and

meteorological effect must be taken into account. However, model

results have not been statistically verified and the time resolution

for which they predict changes can lead to large errors.

Cloud models require large data sets because they need to account

for optical air mass, aerosol transmittance, Rayleigh scattering,

ozone absorption, water vapour absorptance, extraterrestrial

irradiance (see previous section), and clear sky albedo. Most of

these parameters are not available for the study area, and even if

they were available, the complexity of the relationships between

them makes model implementation difficult. Furthermore,

Gueymard (1993) argued that the use of a large amount of

information does not necessarily give more accurate predictions for

cloudy sky conditions variability, such as those found in the

Tambito area.

Dobson et al. (1988) analysed bulk SR models at sea level and

compared their results. Models that estimate SR from solar

elevation and from hourly cloud amount and type, using empirical

or simple physical formulae, yield poor results in some cases

compared with existing formulae at noon solar elevation and daily

mean cloud amount. He studied models such as that of Budyko

(1974) which calculates monthly clear sky insolation Qc (W m-2)

using a tabulated function of latitude and time with quadratic

formulae to obtain monthly solar insolation:

Where n is the monthly cloud fraction, a is an empirical function of

latitude and b is an empirical constant. Although the monthly

cloud amount varies, cloud factors do not allow the discrimination

of differences in cloud types or seasonal variations with solar

{ }Q Q a bn nc= − +1 ( ) Eq. 3.2

94

elevation. As a result, the path length through clouds does not

take into account regional scale or cloud dynamics which are the

major control on cloud cover in Tambito.

Regarding hourly models, Dobson et al. (1988) expressed the

radiation in terms of the atmospheric factor T, which is defined as

the ratio of downward short-wave radiation Q at the surface with

the radiation incident on the horizontal surface above the

atmosphere:

Where Qo is mean solar flux assumed as 1368 (W m-2) and θ is

solar elevation as a function of time and position.

Dobson et al. (1988) also used a Lumb approach model (1964) that

estimates SR for each hourly cloud observation and can be fitted

into nine categories, based on a combination of cloud amount and

type, expressed as:

Where for each category i, the atmospheric transmission factor T is

a regression function of the sine of solar elevation S. Most models

discussed by Dobson et al. (1988) are expressed as regression

equations, whether linear or exponential, but can be divided into

categories according to cloud height and type. Dobson et al. (1988)

concluded that complex models do not necessarily fit better than

simple models and that complex models require sets of cloud

observation data that are often not available for the study area. A

simple relationship derived from regression values and the sine of

T Q Q Sino= / ( )θ Eq. 3.3

T A B Si i= + Eq. 3.4

95

elevation angle produces an atmospheric transmission factor that

can be used on an hourly basis.

Based on these concepts, the cloud cover model presented herein is

derived from a regression analysis of field data, which is physically

related with the sun elevation angle. Collected SR data from the

deforested plot covering a period of five months in 1997 was used

in the analysis. Cloud cover is the difference between Iobs

irradiance at the top of the atmosphere (calculated as described in

the preliminary section) and SR received at the earth’s surface,

which is expressed by the atmospheric transmission factor T. As

the model is run on an hourly time step, this analysis was carried

out hour by hour.

Ti = IOBS – Robserv (KJ/hour)

The daily analysis carried out with the collected data indicates that

T is heavily dependent on the time of day, meaning that there are

hourly changes in T, which can be related with solar angle

elevation. These hourly relationships are shown in Appendix 8,

which summarises the data used as basis of the proposed model.

Figure 3.22 shows the hourly average cloud cover computed from

the weather station data from 5 months in 1997 (solid line) (n=138

days). During the early hours of the day, T values are higher,

lowering later in the day. During the early morning hours, the

nearby mountain casts a shadow (dark area in Figure 3.22) on the

plot until just before 9 a.m., after which irradiation increases

steadily until noon when cloud cover reaches its minimum value.

Cloud cover then increases, but not to the same magnitude as

morning values.

Eq. 3.5

96

The model proposed reproduces the atmospheric factor for any

aspect and is based on hourly relationships of solar elevation angle

(dotted line, Figure 3.22) (Appendix 8), is:

Where θ is the solar angle elevation (rad) and A is a random value

(between –1:1) to allow spatial variation in T throughout the

catchment. The cubic root is included to decrease the cloud cover

range to 0.5-1 (see Figure 3.22). The sine function applied to the

solar elevation angle is the harmonic function that, with the hourly

variation throughout the day, produces very similar values to the

hourly average of solar radiation measured (Dobson et al., 1988).

The Cos2 θ is included to increase the range of random spatial

variability at noon and decrease it during the earliest and latest

hours of the day. The second term is divided by 10 to reduce the

random values. Seasonal behaviour is already computed in the

extraterrestrial SR function, which is described in the previous

T SinSin

ACos

= − +θθ θ

32

5 10Eq. 3.6

Average Cloud cover in a day

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10 12 14 16 18 20 22 24

Hours

Perc

ent o

f a

ttenu

atio

nCC measured

CC modelled

n=138Shadow effect

Figure 3.22 Hourly cloud cover

97

section. Figure 3.23 shows the range for random values of cloud

cover. The maximum cloud cover value occurs more frequently in

the early morning hours and late afternoon hours.

The regression analysis between average measured values and

those modelled at the Campo station (August to December of 1997)

gives an r2 = 0.82 (Figure 3.24).

The SR at land surface is calculated by the difference between

extraterrestrial SR minus cloud attenuation.

Rt = Iobss (1 – T ) (KJ/hour) Eq. 3.7

Figure 3.23 Range of modelled cloud cover

Modelled cloud cover range

0

0.1

0.20.3

0.4

0.5

0.60.7

0.8

0.9

0 2 4 6 8 10 12 14 16 18 20 22 24Hours a day

Perc

ent)

minimun

medium

Maximunmaximum

Relationship between measured and modelled cloud cover

y = 1.2679x - 0.1934R2 = 0.8203

0

0.25

0.5

0.75

1

0 0.25 0.5 0.75 1Measured values (%)

Mod

elle

d va

lues

(%

)

Figure 3.24 Linear relation between measured and modelled cloud cover

y= 1.27x-0.2r2=0.82

98

3.6.4.3 Net solar radiation function

Net solar radiation is computed as the difference between

measured incoming solar radiation Robserv and energy reflected by

the surface (Jetten, 1994; Mulligan, 1996), whose values were

obtained from hourly recorded incoming and reflected short wave

(solar) radiation data for a grass plot in the last 5 months (N =

1656) in 1997. Net solar radiation was modelled by the solar

radiation regression model, Rt, and net solar radiation calculated

as Rn (Mulligan, 1996). Figure 3.25 shows this relationship.

Coefficients A and B, computed from the linear regression function

are 0.8523 and –16.971, respectively. The expression to calculate

net radiation for the model is:

Rn = 0.85 Rt –16.97 (KJ)

Where Rt is the terrestrial SR calculated by the model in KJ per

day (for deforested area).

Eq. 3.8

Figure 3.25 Regression for computing net radiation in the model.

Net Radiation (KJ)

y = - 16.971+ 0.8523x R2 = 0.9979

0

500

1000

1500

2000

2500

3000

3500

4000

0 1000 2000 3000 4000 5000Received SR (KJ)

SR

net

ref

lect

ed (

KJ)

N = 1656y = 0.85x –16.97

r2 = 0.99

99

The design of the net solar radiation sub-model is illustrated in

Figure 3.26.

Figure 3.26 Diagram of net solar radiation model.

Hourly extraterrestrial solar radiation

InputsDate (year, day, hour)LatitudeLongitudeSlopeAspectt1,t2 time intervals

CalculatedJulian dayTime equationLocal apparent timeSolar declinationSunrise hour angleIncident angleSolar elevation angleExtraterrestrial solar irradiation (Iobs)

OutputsExtraterrestrial solar irradiation (Iobs)Solar elevation angle

Hourly cloud cover model

Calculated

T SinSin

ACos

= − +θθ θ

32

5 10

Inputs- θ Solar elevation angle- A Random value parameter

OutputT Cloud coverattenuation (%)

Net radiation model

InputsExtraterrestrial solar

irradiation (Iobs)T Cloud cover attenuation

Calculated

Rn Net radiation

Output

Rn Net radiation

Parameters A, B

100

3.6.5 The evaporation sub-model

In understanding the hydrological impacts of the land use change,

one of the major factors that may vary between forested and

deforested areas is evaporation. This is thus a key component of

the model and worthy of attention here.

Evaporation E occurs when water in liquid or solid phase, at or just

below the earth’s surface, is converted into water vapour and

transferred in this form to the atmosphere. The process can only

occur if there is an energy input from either the sun or the

atmosphere itself, and is controlled by the rate at which the energy,

in the form of vapour, can diffuse away from the earth’s surface

(Shaw, 1984; Dolman et al., 1991; Maidment, 1993).

The combined process of evaporation Ei, (from various water

surfaces such as water bodies, water intercepted on the vegetation

and moist, bare soil) and transpiration Et, (water vapour escaping

from within plants, mostly via leaves) from a dry canopy will

constitute the total evaporation E (Bonell and Balek, 1993).

The latent heat (λ) of vapourisation is the energy required to

evaporate 1 kg of water under normal conditions (at 10 °C), and is

estimated at 2.47X106 J kg-1 (Shaw, 1984). It changes slightly with

temperature (about 0.1 % per °C), because the initial separation of

the molecules that make up liquid varies with temperature (Ward

et al., 1990). Maidment (1993) expressed this relationship as

λ = 2.501 – 0.002361 Ts MJ kg-1

where Ts is the surface temperature of water in degrees Celsius.

Eq. 3.9

101

The flow of water vapour molecules away from an evaporating

water surface implies a transfer of energy away from the surface, in

the form of latent heat. The energy transferred is numerically

equal to the product of the mass flow, i.e., evaporation E, in mm

h-1, and the latent heat of evaporation (Kramer et al., 1995).

Evaporation is controlled by net radiation, Sn, which is the portion

of incident short-wave radiation captured at ground level, taking

into account losses because of reflection and emission:

Sn = St(1-α)+ Lw(1-Εo) MJ M-2 h-1

where α is the albedo, Lw the long-wave radiation incident, and -Εo

the long- wave radiation emitted (both in MJ m-2 h-1). In equation

3.10, the first term represents the short wave radiation, which is

dependent of the albedo, which, in turn, is a function of the surface

reflectance properties. Atmospheric gases are not very good

absorber of short-wave (0.15 – 3.0 µm) and are much better for

transitivity compared to long-wave radiation, in the band (3 – 100

µm). As a result, the portion of net radiation due to short-wave

radiation is greater than long-wave. The incoming long-wave

radiation emitted by the atmosphere, in absence of cloud, depends

upon the bulk atmospheric temperature and emissivity so incident

long-wave radiation is almost constant through the day. The

outgoing long-wave radiation from the surface depends also on

temperature and emissivity of the land surface. This is usually

greater than the atmospheric counterpart, and because

temperature varies considerably through the day, net long-wave

radiation is usually negatively and relative small (75 – 125 W m-2)

(Oke, 1987) if the surface and air temperature are not significantly

different.

Eq. 3.10

102

Potential evaporation (Ep) is a standard evaporation defined as the

amount of water evaporated per unit area and per unit time from

an idealised, extensive free water surface, under prevailing

atmospheric conditions without any surface resistance (Oke,

1987). Evaporation introduces water into the air and can remove

energy from it, changing atmospheric humidity deficit and possibly

altering evaporation at downwind locations (Maidment, 1993). This

concept measures the meteorological control on evaporation from

an open water surface (Ward et al., 1990).

The Priestley-Taylor (1972) equation provides the basis of an

approximation which keeps strong relation to the first term of

Penman equation (will be discussed later), with the exception of the

inclusion of an empirical coefficient (α) to allow some advection,

which is

Where λ is the latent heat of vaporisation of water (J.Kg-1), EPT is

the rate of evaporation (kg m-2 s-1), Sn is the net energy available for

evaporation (W m-2), γ is the psychrometric constant (J kg-1 °C-1 /

J kg-1) and D is the slope of the saturation vapour pressure curve

(kg kg-1 °C-1), and α theoretically becomes to 1 under advection free

conditions (McNaughton and Jarvis, 1983).

The Penman wet-surface equation (λEp), is the most frequently

used potential evaporation concept, and represents ‘the

evaporation rate from a moist surface exposed to the existing

available energy and atmospheric conditions’ (Granger. 1989). The

Penman equation is totally independent of the surface conditions

and controlled only by energy supply and atmospheric conditions

(Bonell and Balek, 1993).

nPT SEγ

αλ+∆∆⋅= Eq. 3.11

103

The estimation of actual evaporation in vegetation conditions which

are not moist, suggests that the involvement of factors such as

physiology and stomatal resistance of the plants, which also

involves the soil moisture availability in the process.

The energy-balance (Penman-Monteith) approach to determine the

average evaporation rate over a given time period involves

measuring the input and output of energy, as well as the change in

energy storage. The following equation is used to determine

evaporation by this method:

where S is short-wave radiation; L, long-wave radiation, G, ground

conduction and Aw, advected energy which are given in W m-2; ∆, is

the slope of the saturation vapour pressure curve (kg kg-1 °C-1); ρ is

water density (kg m-3); Cp specific heat of air (J kg-1 °C-1); δq is the

vapour pressure deficit (Kpa); ga is the atmospheric conductance

(m s-1); gs is the stomatal conductance (m s-1) and γ is the

psynchrometric constant (0.655) (Dingman, 1994).

The equation assumes the canopy is acting as a “big leaf”

physically. The atmospheric conductance describes the physical

roughness effects of the vegetation on the transfer of energy and

mass from the surface to a reference level in the atmosphere. The

surface resistance describes the biological control over the rate of

transpiration and is particularly linked to the physiological

behaviour of plants expressed through the bulk stomatal resistance

(Stewart, 1989). Normally, this equation in combination with an

interception model yields good results, where the vegetation in the

W m-2 Eq. 3.12[ ]

++∆

++−+∆=

s

a

aqpW

g

g

gCAGLSE

1γ

δρλ

104

area is homogeneous as a particular crop, where the plants belong

to the same species and the stomatal conductance parameter could

be regular (Moran et al., 1996; Cienciala et al., 1997; Gavin and

Agnew, 2000).

Physiological measurements carried out by Letts (table 5.5, 2000)

of stomatal conductance gs (expressed in mmol m-2 s-1) for

individual leaves in the Tambito forest area, for different types of

plants (Cecropiaceae, Guttiferae, Rubiaceae, Melastomataceae,

Palmae, Araceae, Gesnereaceae, Fermeaceae, Flacourteaceae

among others), shows the high variability in both stomatal

response (variation between 0.02 to 0.001 m s-1) and biological

diversity. On the assumption that the bulk physiological

conductance is equal to the conductance of all stomata acting in

parallel (big leaf), the estimation of gs as a product of ‘scaling up’

process, is uncertain due to the landscape biodiversity. The same

assumption was tested earlier by Shuttleworth, (1978) finding a

fairly close agreement. Those difficulties were also identified by

Dolman et al. (1990) in their observations. In addition, Veen and

Dolman (1989) highlight the spatial and temporal variability of gs in

the tropical forest canopy, who suggest the use of stratified

sampling procedures to create sub-layers for modelling gs.

Roberts et al. (1990) using a stratified sampling procedure in the

Amazon tropical rain forest, demonstrated that there is a strong

relationship between gs and the solar radiation, where the

emergent trees had the highest gs (which declined rapidly during

the afternoon), whilst vegetation close to the ground had lower gs,

(with little variation during the day). Dolman et al. (1990)

emphasised that the derived variation in transpiration could be

attributed up to 80% of the gs variation, been accounted for the

variability in solar radiation, which on time varies spatially and

temporally within an area, as it occurs in Tambito area, as has

105

been seen in the previous section. Under such conditions, Dolman

et al. (1990) highlighted that only solar radiation shows the main

variation that, in turn, drives the diurnal variation in temperature

and humidity deficits.

The atmospheric conductance is a function of the wind speed, the

aerodynamic roughness of the vegetation and the stability of the

atmosphere (Bonell and Balek, 1993). Within this context, Wilson

(1989) also argued that using the Penman-Monteith equation for

modelling forest transpiration Et, the ga has only limited sensitivity

to the formulae precision and is data limited.

Daily evapotranspiration response for Tambito was evaluated using

the Penman-Monteith equation (eq. 3.12), varying net solar

radiation, atmospheric and stomatal conductance, in order to

identify the equation’s sensitivity to the variation to these

parameters. Net solar radiation was varied between 0 to 400 W

m-2; atmospheric and stomatal conductance were varied from

0.001 to 0.9 m s-1, covering the range conditions which might be

present in the Tambito area (Letts, 2000).

Figures 3.27 shows the variation of evapotranspiration to changes

in net solar radiation. It is clear from Figure 3.27 that

evapotranspiration is highly sensitive to solar radiation with evapo-

transpiration nearly doubling for a doubling of net solar radiation.

106

Figure 3.28 shows the evapotranspiration change with atmospheric

and stomatal conductance and indicates that except at very low

stomatal and aerodynamic conductances (high resistances) the

impact of these variables on evapo-transpiration is low. This would

indicate that whilst the evaporation module must include spatially

variable solar radiation fluxes, these is little need to include

aerodynamic and stomatal parameters particularly since these is

no way to provide spatially varying measurements for these.

Figure 3.27 Evapotranspiration variation with respect to Net Solar Radiation using Penman-Monteith equation

Evapotranspiration variation with net solar radiation using Penman-Monteith equation

0

1

2

3

4

5

0 100 200 300 400 500

Incoming net solar radiation (W/m2)

Evap

otra

nspi

ratio

n (m

m/d

ay)

Figure 3.28 Evaporation variation with respect to atmospheric and stomatal conductance

Evapotranspiration variation w ith stomata and atmospheric conductance

0

1

2

3

4

5

6

0 0.2 0.4 0.6 0.8 1

Stomata and atmospheric conductance (m s-1)

Evap

orat

ion

(mm

/day

)

Atmospheric

Stomata

107

Thus, there is a need to implement a simplified evaporation model

based on the most sensitive variables. On de basis of the previous

discussion, the evaporation model used in this thesis is a simple

evaporation model based on net solar radiation, which was used by

Mulligan (1996) with good results.

Average annual air temperature in the study area ranges from 14°C

to 25°C. Within this range of temperature, latent heat ranges from

2.467 to 2.443 MJ kg-1; in extreme cases, the difference in

evaporation between these latent heat values is no more than 0.2

mm per day (computed with extreme temperatures recorded with

the weather station in the last 5 months in 1997 at the grass plot,

n=1656). For reasons of simplification, the latent heat parameter

(λ) was assigned an average value 2.445 MJ kg-1 in both 1D and

2.5D models. Latent heat variation with the elevation through the

catchment varies less than 2% across the catchment (temperatures

at the top of the catchment), which would produce an evaporation

variation less than 1%.

As was mentioned in the model presentation at the introduction of

this chapter, normal weather conditions in TMCF environments are

mostly near saturation (92% humidity). This means that

vegetation in the Tambito watershed is unlikely to suffer from

water stress, so net radiation rather than water availability is the

main control on evapotranspiration. The evaporation module can

therefore be simplified to the evaporation over free water as being

the only function of energy available for evaporation from the

energy balance, as follows:

mm h-1 Eq 3.13

=

λρn

p

SE

108

where Ep is the potential evaporation; Sn, net solar

radiation [MJ hour-1] is used because it is the majority of the

energy available and net radiation is not measured; λ, latent heat

as previously calculated; and ρ, water density (in this case is

assumed as 1.0 g cm-3) (Oke, 1987). The model is constrained by

the surface area of water available for evaporation in the canopy.

Net radiation, Sn, is the most important variable for evaporative

energy. In hydrology studies, all the energy available for

evaporation is assumed to be accessible by the plant canopy, and

water vapour first diffuses from the leaves, against surface (or

stomatal) resistance, rs (Maidment, 1993), and then out into the

atmosphere, against an aerodynamic resistance. Meanwhile,

sensible heat, which originates outside rather than inside the

leaves, only has to diffuse upward against aerodynamic resistance

ra.

To include the transpiration process in the model, in the cases

when the canopy is dry, the actual evaporation is calculated from

the potential evaporation applied to the soil moisture availability,

without taking into account any plant physiological activity in the

process. The design of the potential evapotranspiration sub-model

is illustrated in Figure 3.29.

3.6.6 Canopy storage, interception and throughfall

By it’s nature, vegetation has a large influence on hydrological

processes in a TMCF ecosystem (Jetten, 1994; Hafkenscheid,

2000). Trees intercept most of the rainfall, part of which

evaporates (Rutter et al., 1971; Rutter, 1975; Jetten, 1994), so that

the water flux that reaches the soil surface is determined by both

rainfall intensity and the drainage of the canopy (Jetten, 1994).

109

Therefore, when describing the water balance of a forested

watershed, interception cannot be treated as a fraction that is

simply subtracted from the rainfall, because the vegetation is

complex.

Additionally, the microclimate within the canopy produces a

particular set of conditions affecting the evaporation from the

intercepted water. Consequently, water fluxes associated with

wetting and drying processes must be quantified. There are

empirical approaches, which use regression coefficients to estimate

the percent of water loss in the interception process. There are

also physically-based models, which can be adapted for particular

forest conditions (Rutter et al., 1971, 1975). A stochastic

alternative was proposed by Calder (1986) to model the rainfall

interception, which relates the mean number of raindrops retained

on elemental surface areas to the mean number of raindrop strikes

per element, using the Poisson probabilistic distribution. A

simplification of the Rutter interception model was developed by

Gash (1979), focusing on rainfall occurring in a series of discrete

Input

Net radiation at ground level

CalculatedAvailable energy, Potential evaporation

for free water

Parameter

Latent heat

OutputPotential evapotranspiration

Figure 3.29 Flow diagram for potential evaporation.

110

storms, each of which comprises a period of wetting up, a period of

saturation an a period of drying out to empty the canopy storage.

The models of rainfall interception discussed have been applied on

tropical forest environments in combination with the Penman-

Monteith equation to estimate the rainfall interception loss, with

acceptable results. For example, Bruijnzeel and Wiersum (1987)

used the Gash rainfall interception model in West Java, Indonesia.

In contrast, Calder et al. (1986) used the Rutter interception model

in West Java without good results; the model predicted only 50% of

measured interception. Lloyd et al. (1988) compared the Rutter

and Gash models in the rain forest of Amazonas, obtaining similar

and reasonable results.

In relation with tropical montane rain forest, Herwitz (1987) worked

in north-east Queensland, and identified that the assumption of

constant canopy/trunk storage capacities was an

oversimplification, and highlighted the need to take into account

dynamic changes in storage capacities; also it was pointed out that

there is a need for additional studies concerning the effects of

forest structure parameters, interception measurement and

modelling.

In this thesis the Rutter rainfall interception model is adapted to

assess the rainfall intercepted, which combined with the

evaporation module is used to estimate the water lost by

evaporation of intercepted rainfall.

111

3.6.6.1 The Rutter model

The Rutter model (Rutter et al., 1971; 1975; 1977), was designed

as an interception model for a Corsican pine stand in Great Britain

but has been applied successfully to tropical rain forests (Calder et

al., 1986; Lloyd et al., 1988; Veen and Dolman, 1989; Jetten,

1996). Canopy water balance is calculated using empirical forest

stand parameters and potential evaporation.

The Rutter model offers several advantages: the input parameters

are relatively easy to obtain from throughfall measurements and

basic meteorological data. It uses stand characteristics rather

than properties of individual plants and calculates water fluxes on

a small time step basis, making it easy to link to a vertical water

balance model. The Rutter model was expanded by Jetten (1994)

to include canopy structure. In this extended model, called

CASCADE, the canopy is layered but uses virtually the same

parameters as the original model. The “cascade” concept is not

applied in this thesis because parameterisation of the model for

TMCF would be very difficult due to the heterogeneous vegetation

types present in TMCF (including epiphytes, for which hydrological

properties are poorly known). Therefore, vegetation has to be

assumed as a single layer for the purposes of modelling

interception. The processes involved in the Rutter model are

shown in Figure 3.30.

112

Where

P Precipitation (mm)

S Transitory storage (mm) per unit area of canopy

D Drainage (mm) per unit area of canopy

p Rainfall fraction falling directly on the ground (mm)

Th Throughfall (mm) per unit area of canopy

C Canopy storage capacity (mm) per unit area of canopy

The original Rutter model presentation is included here. The water

balance, i.e. the change in transitory storage (S) per unit area of

canopy, is calculated as the sum of the proportion of rainfall (P)

that falls on the canopy minus the drainage (D) and evaporation of

intercepted water (Ei) from the canopy:

dC/dt = (1-p-pt)P – D –Ei

p1-p

Th

D

pP(1-p)P

E

SC Canopy

Figure 3.30 Diagram of the Rutter model (Jetten, 1994)

Eq. 3.14

113

where C is in mm and the other variables in mm hour-1. The

fraction of rainfall intercepted by the canopy is calculated as the

difference between rainfall (P), the fraction of rainfall falling directly

on the ground (p), and the fraction of the rainfall diverted to stem

flow (pt) (Jetten, 1994). The canopy drainage is given by

D = 0 where C < S

D = Do eb(C-S) where C >= S

where S is the storage capacity (in mm), the amount of water

retained by the canopy when rainfall and throughfall have ceased

and the canopy is saturated. The minimum drainage rate, Do, is

the drainage rate when C is equal to S (in mm) and b is a

dimensionless parameter. Because the Rutter model was designed

for small time steps (min), the b parameter could not be adjusted

with an exponential function for this thesis in an hourly time step

because it produces model instability. Increasing the dripping

value produces more net rainfall than the real values; additionally

there are no field data to parameterise this parameter in either an

hourly or minute time step. As Rutter et al. (1971, 1975)

recognised, the canopy storage changes significantly during a 5-

min period (the time step used by Rutter et al. 1971) and this is a

further reason why the b parameter can not be adopted from their

work.

Drainage from the canopy is computed as a water balance with

canopy parameters. Then the drainage function is modified to:

D = 0 where C < S

D = C-S where C >= S

The evaporation from a wet canopy surface is considered equal to

the evaporation from an open water body. The potential

Eq. 3.15

Eq. 3.16

114

evaporation (PE), calculated for the atmospheric conditions

prevailing at the top of the canopy, can therefore be used.

Furthermore, the evaporation of intercepted water (Ei) is

proportional to the area of the wetted surface (Rutter et al., 1971):

Ei = PE * C/S where C < S

Ei = PE where C >= S

Total throughfall (Th) is the sum of direct throughfall and canopy

drainage, and is expressed as:

Th = D + (o –pt) * P

The Rutter model includes routines to compute stemflow, but this

part of the model is not included in this thesis due to the lack of

data for parameterisation. The characteristics of the stemflow

model are included here just for information. Stemflow (Sf) is the

depletion of trunk storage capacity (Ct) as compared with trunk

storage capacity (St). The excess water is completely diverted to

stemflow at the end of each time step, and evaporation is measured

as 0.02*PE (Jetten, 1994). Based on Gash et al. (1978), the model

includes a numerical solution with a finite difference

approximation of the change in canopy water storage (dC/dt).

The evaporation of intercepted water from the canopy depends on

the micro-climate inside the canopy (Jetten, 1994). The energy

available is calculated with an exponential extinction parameter

describing the cumulative leaf area, which was used by Rubiano

(1998) in the Beers Law equation.

The extinction factor (k) was determined using photosynthetically

active radiation (PAR) measured at three different heights (1, 3, and

Eq. 3.17

Eq. 3.18

115

6 m) in the secondary forest plot (Rubiano, 1998). The light

extinction coefficient (k) estimated by Rubiano (1998) was 0.27,

and is used in this module to compute the energy available for

evaporation of intercepted rainfall within the forest. Canopy water

storage capacity (S) was derived from samples of forest vegetation

as is outlined in section 3.5.2.2.

The design for the interception sub-model is illustrated in Figure

3.31.

3.6.7 Sub-surface water sub-model

Soil hydraulic properties are modelled on the basis of measured

structural properties such as texture and bulk density by the pedo-

InputsRainfallPotential evapotranspirationNet radiation at ground level

ParametersLeaf area indexLeaf capacityK, evaporative energy extinction

coefficient for vegetation forestVegetation cover

CalculatedStem interception, storage, evaporation, and drainageCanopy interception, storage, evaporation, and drainageDripThroughfallWater reaching the ground

Outputs- Water reaching the ground- Water lost from canopy by evaporation

Figure 3.31 Diagram of interception sub-model.

116

transfer function of Saxton et al. (1986) that uses the Brooks and

Corey (1964) water retention function. The Saxton et al. (1986)

method is also used to determine soil hydraulic conductivity for

recharge calculation. These model sections are described next.

3.6.7.1 Modelling flow of water in porous media

The size of soil pores through which water flows and pore-size

distribution are mainly determined by grain-size distribution

(Dingman, 1994). For many purposes, particle-size and pore-size

distribution are characterised by soil texture, which is determined

by the proportion per weight of clay, silt, and sand. Figure 3.32

illustrates the scheme for defining soil textures developed by

USDA. Soil texture is determined from soil samples after particles

larger than sand (> 2 mm) have been removed.

The definitions of soil composition, soil classification, and soil

properties, as given by Kutilet and Nielsen (1994), were used in this

study. The routine aims to determine soil hydraulic properties, for

example hydraulic conductivity, matric potential, and soil

moisture. Several attempts have been made to predict moisture

release functions from soil texture data (Van Genuchten, 1980;

Arya and Paris, 1981; Grismer, 1986). Knowledge of particle-size

distribution helps determine pore-size distribution and moisture

retention characteristics. Although this approach presents several

difficulties, it is cheaper and easier than field determination

(Campbell, 1985) but this approach must be used with care

because the soils are extremely complex and variable. The

determination of pore-size, particle-size and pore-size distribution

facilitates the calculation of the space available in the soil for water

storage and movement. Arya and Paris (1981) established a non-

linear relationship between particle-size and pore-size

117

distributions, because water is held within the soil by capillary

binding of water in the pores; then the shape of water-retention

curve depends to a great extent on the pore-size distribution of the

soil (Anderson, 1990).

3.6.7.2 Soil water retention and matric potential

Matric potential is the amount of potential energy per unit of mass

or volume of water in a system, compared to that in pure free water

at a reference elevation point. Because water movement is very

slow through soil micro-pores, kinetic energy is extremely low and

may be neglected. Potential energy therefore dominates and

Figure 3.32. Soil texture triangle classification (Dingman, 1994).

118

results from gravity, capillary, and adsorptive forces. Hence, soil

water potential is the work (energy) needed to overcome forces

acting on soil water, referred from a given datum to the point of

interest.

Darcy’s law describes water infiltration and redistribution of flow in

unsaturated porous media. Campbell (1985) expressed the law as

where fw is the water flux density (kg m-2 s-1), dψ/dx the water

potential gradient, and k the hydraulic conductivity (m s-1). Water

potential is the energy potential per unit mass (or volume) (J kg-1,

J m-3) and is defined as the amount of work per unit mass of water

required to transport a certain quantity of liquid from the soil

matrix, taking into account a reference point.

Brooks and Corey (1964) fitted the following equation to describe

water potential:

where ψ is the soil water potential (J m-2), ψe the soil water

potential at air entry (J m-2), θ the soil water content (mm3 mm-3), θs

the saturated soil water content (mm3 mm-3), and θr the residual

soil water content (mm3 mm-3). This value is empirical to

straighten a curved log-log scale, and B is the fitted value. The

volumetric relationship between soil water content and soil water

potential for ψ < -5 kPa at any spatial location, i, is

Ln[-ψi(θ)] = ai + bi ln(θ)

f kd

dxw = −ψ

( )( )ψ ψθ θθ θ

=−

−

e

r

s r

B

Eq. 3.19

Eq. 3.20

Eq. 3.21

119

Campbell (1985) and others (Ahuja and Williams, 1991) assumed

θr= 0 and parameter b equal to the inverse of the Brooks and Corey

(1964) pore size index, λ (b=1/λ), then

where ψe is the air entry potential and b the slope of lnψ vs lnθ.

According to the Campbell (1985) notation for porosity, ψe

decreases with decreasing mean pore diameter size, and b

increases with increasing standard deviation of pore size.

Campbell (1985) correlated the geometric standard deviation (σg)

with b, approximating the relationship for soils at a bulk density of

1.3 Mg m-3.

3.6.7.3 Pedotransfer functions

An alternative way to determine the soil’s hydraulic characteristics,

for example hydraulic conductivity, soil water content, or soil water

retention other than direct methods with field or laboratory

measurements, is to use a pedotransfer function (PTF). The PTF

includes basic data describing the soil (e.g., particle-size

distribution, bulk density, and organic C content) and yields the

water retention function or the unsaturated hydraulic conductivity

function (Tietje and Tapkenhinrichs, 1993) on the basis of

empirical relationships based on analysis of a wide variety of soils.

Many researchers have performed this approximation using

different tools – algorithmic, empirical or semi-empirical – with the

general idea of producing a tool that can be applied to a broad

range of different soil conditions (Arya and Paris, 1981; Ahula, et

al., 1984; Mulla, 1989; Vereecken et al., 1989, 1990; Vereecken,

( )ψ ψ θ θm e s

b=

−/ Eq. 3.22

120

1992; Tietje and Tapkenhinrichs, 1993; Rawls et al., 1993). In

addition, many reports have been written comparing the results of

several modelled pedotransfer functions (Ahuja et al., 1984;

Vereecken et al., 1990; Tietje and Tapkenhinrichs, 1993).

Three different methods can produce a pedotransfer function: (a)

the point regression method, (b) the physical model method, and (c)

the functional parameter regression method. Tietje and

Tapkenhinrichs (1993) used a point regression method to estimate

retention functions from basic data and predict water content, θi at

certain matric potential, ψI, by regression analysis (generally

multiple linear). Their study included references from methods

developed by Gupta and Larson (1979) and Rawls et al. (1982).

The physical model method consists of three steps: (i) description of

pore-size distribution, (ii) prediction of soil water content from

pore-size distribution via mass conservation, and (iii) prediction of

matric potentials from pore-size distribution by the capillary

equation. This method was followed by Arya and Paris (1981), who

used an empirical relationship that incorporated different particle

forms into the pedo-transfer function and established a non-linear

relationship between particle-size and pore-size distributions.

Parameters were fitted to the data.

In the functional parameter regression method, a certain closed

form function is assumed for the relationship between ψ and θ, and

the parameters are found by regression or other estimators. Most

researchers in the field used the retention function parameter of

Brooks and Corey (1964); one of the most recent approaches by

Van Genuchten (1980) uses the parameters θr, θs (equivalent to the

inverse of ψb), n (equivalent to plus 1), and, in the most cases, m=1-

1/n:

121

(Variables, parameters, and units were defined in the previous

section).

Saxton et al. (1986) described soil moisture characteristics and

water retention functions with constant, linear, or exponential

relationships in specified matric potential sub-ranges, using results

from Rawls et al. (1982). The coefficients used by Saxton et al.

(1986) were derived from the study of Rawls et al. (1982), who

analysed 1323 soil samples from 5350 horizons to develop water

retention parameters. Unfortunately, they do not mention whether

tropical soil samples were included in their data set. Soil water

retention volumes at 0.33 and 15 bars, total porosity, and

saturated hydraulic conductivity classes were developed for major

USDA soil texture classes.

The retention function does not have a continuous derivative and

in simulation models different formulae are used across different

ranges of matric potential.

The Saxton et al. (1986) function is summarised as follows:

Applied tension range, kPa >1500 to 10

( )( )[ ]

θ θθ θ

αψ= +

−

+r

s r

n m

1

[ ]ψ θ=

= + + +

= + + +

A

A a b C c S d S C

B e f C g S g S C

B

exp (% ) (% ) (% ) (% )

(% ) (% ) (% ) (% )

2 2

2 2 2

100

Eq. 3.23

Eq. 3.24

122

Applied tension range, kPa 10 to ψe

Applied tension range, kPa ψe to 0.0

Applied tension range, kPa >1500 to 0.0

Coefficients

a = -4.396 g = -3.484 x 10-6 p = 12.012

b = -0.0715 h = 0.332 q = -7.55 x 10-2

c = -4.88 x 10-4 j = -7.251 x 10-4 r = -3.8950

d = -4.285 x 10-5 k = 0.1276 t = 3.871 x 10-2

e =- 3.140 m = -0.108 u = -0.1103

f = - 2.22 x 10-3 n = 0.341 v = 8.7546 x 10-4

Definitions

ψ = water potential, kPa

ψe = water potential at air entry, kPa

θ = water content, m3 m-3

θs = water content at saturation, m3 m-3

θ10 = water content at 10 kPa, m3 m-3

K = water conductivity, m s-1

(%S) = percent sand

(%C) = percent clay

( )[ ][ ]

ψ θ θ ψ θ θθ

ψ θθ

= − − − −

= −

= += + +

10 0 10 0

2 302

100 0

10 10

10

10

. ( . ) / ( )

exp . ln /

. ( )

(% ) log (% )

e s

e s

s

A B

m n

h j S k C

θ θ= s

[ ]( )[ ]{ }K p q S r t S u C v C= × − + + + + +2 778 10 6 12. exp (% ) (% ) (% ) (% / θ

Eq. 3.25

Eq. 3.26

Eq. 3.27

123

There are many PTFs that offer a good approximation to soil

hydrological properties, using organic matter content or additional

data, such as matric potential at 33 kPa or 1500 kPa (Van den

Berg et al., 1997; Wosten et al., 1989; Wosten et al., 1990; Bell et

al., 1995). This model will use the method of Saxton et al. (1986),

already described. Water movement into the soil will be calculated

using soil hydrological properties, and soil hydraulic parameters

will be calculated using PTFs. Soil texture parameters are

described in Section 3.5.2.1. Figure 3.33 presents a model of soil

moisture characteristics:

ParametersSoil texture

. % Sand

. % Silt

. % ClayBulk densitySoil porosity

Pedotransfer coefficientsa = -4.396 g = -3.484x10-5 p = 12.012b = -0.0715 h = 0.332 q = -7.55X10-2

c = -4.88x10-4 j = -7.251x10-4 r = -3.895d = -4.285x10-5 k = -0.1276 t = 3.671x10-2

e = -3.140 m = -0.108 u = -0.1103f = -2.22x10-3 n = 0.341 v = 87546x10-4

CalculatedGeometric mean particle diameterGeometric standard deviationAir entry potentialB-value porosity coefficientMatric potentialSoil moisture at saturationHydraulic conductivitySaturated hydraulic conductivity

OutputsMatric potentialHydraulic conductivity

Figure 3.33 Diagram of soil hydrologic characteristics.

Inputs- Net infiltrated water- Previous values of soil water content

124

3.6.8 Infiltration sub-model

Infiltration is the process by which rainfall or ponded water enters

the soil surface (Anderson, 1985; Campbell, 1985; Dingman, 1994)

and is one of the most difficult aspects of hydrology to estimate.

Infiltration is controlled by factors governing water movement

through the soil, for example pore size, pore-size distribution, soil

water content, hydrologic conductivity, and soil matric potential.

Both soil properties and vegetation characteristics play an

important role in determining infiltration.

Several attempts have been made to understand infiltration (Smith

et al., 1993; Morin and Kosovsky, 1995; Chu, 1997) and others

have tried to adapt or modify known models (Madramootoo and

Enright, 1990; James et al., 1993; Chu, 1994; Chu, 1995). A wide

range of modelling approaches: physically based, empirical,

stochastic, or mixed, have been used. Most of the previously

mentioned models have been based on Darcy’s Law, mass

conservation, and energy conservation.

Maidment (1993) discusses the following infiltration models:

Horton (1940), Brooks and Corey (1964), Richards (1965), Philip

(1957), and Van Genuchten (1980). A general agreement is that

Richard’s equation represents the closest physical approximation,

but is difficult to implement in numerical terms. Therefore, this

study applies Green and Ampt (1911) formulae, with soil

parameters as calculated by the Saxton et al. (1986) pedotransfer

function.

The main assumptions of the Green-Ampt approach are that a

distinct and precisely definable wetting front exists, and that the

matric suctions at this wetting front remain effectively constant,

125

regardless of time and position (Hillel, 1971). It is also assumed

that the soil behind the wetting front is uniformly wet and of

constant conductivity. The wetting front is thus viewed as a plane

separating a uniformly wetted infiltrated zone from a totally

uninfiltrated zone. This assumes that the relationship between

hydraulic conductivity (k) and soil water content (θ) is

discontinuous (Hillel, 1971).

For Green and Ampt (1911), ideal conditions would be as follows:

The vertical axis (elevation) z indicates the downward direction, f(t)

is the infiltration rate at time t [L T-1], and F(t) is the total amount

of water infiltrated up to time t [L]. The water content just before t

= 0, at the initial value θo <φ. The notation is taken from Dingman

(1994).

Just before water input begins at t = 0, the downward flux of water

given as Vz(z,0) is Kh(θo). Beginning at time t = 0, liquid water

begins arriving at the soil surface at a specified rainfall rate, w, and

continues at this rate until the time tw. Two cases are under

consideration at this point:

Where the water input rate is less than saturated hydraulic

conductivity, w < Khsat. If we assume that w > Kh(θo), then water

will enter the soil, increasing soil water content and, as a result,

both hydraulic conductivity and outward flux will also increase.

However, as long as the water entering the soil is less than the

water content at which conductivity equals water input rate Kh(θo),

soil water content will continue to increase. When soil water

content reaches θw, the hydraulic conductivity is Kh(θw) = w, so the

rate of the outflow from the soil equals the rate of the inflow, and

there is no further change in water content until water input

ceases. So, if

126

If w < Khsat f(t) = w ; 0 < t < tw ,

F(t) = 0 ; t > tw .

The water input rate is greater than saturated hydraulic

conductivity,

w > Khsat.

The process described above will occur in the early stages of

infiltration. Water arrives to the soil faster than it can be

transmitted downward, and will initially go into storage, increasing

the soil water content and hydraulic conductivity. However, soil

water content cannot exceed its value at saturation, φ, and

hydraulic conductivity cannot increase beyond w < Khsat. After the

soil surface is saturated, some rain will continue to infiltrate, but

excess water will accumulate on the surface as ponding or

detention storage. On sloping ground, this excess becomes

potential overland flow. The soil surface to become saturated is

referred to as time to ponding tp.

Until tp, all rain falling infiltrates. Therefore,

F(tp) = w tp

where zf is the depth to the wetting front, all this water occupying

the soil between the surface and zf (tp), so

F(tp) = zf (tp) (φ − θο )

Eq. 3.28

Eq. 3.29

Eq. 3.30

Eq. 3.31

127

To determine zf (tp), Darcy’s law was applied in a finite difference

form, between surface and depth zf (tp)

where ψf is the effective tension at the wetting front. At the time of

ponding, the soil surface is saturated and the tension is 0, the

hydraulic conductivity is equal to its saturation value, and the

infiltration rate is equal to the rainfall rate. When ψf < 0, then

because water input continues after ponding time as does

infiltration, but at a decreasing rate. If zf (t) is the wetting front

depth at the same time t, where tp < t < tw., then the infiltration

equation for this period is a function of infiltration capacity and a

function of the total infiltration that has occurred.

F(t) = zf (t) (φ − θο )

Regarding the relation of t as a function of F, then

V t f t w K kz tz p p hsat hsat

f

f p

( , ) ( )( )

00

= = = −−ψ Eq. 3.32

( )( )

( )f t KF thsat

o f= +−

1φ θ ψ

( ) ( )( )t

F t F t

K

f

K

F t f

F t f

p

hsat

o

hsat

p o

o

=−

+− + −

+ −

( ) ( )ln

( )

( )

ψ φ θ ψ φ θ

ψ φ θ

( )t

K

w w Kp

hsat f

hsat

=−

−

ψ φ θ

( )

Eq. 3.33

Eq. 3.34

Eq. 3.35

Eq. 3.36

128

where:

Khsat effective hydraulic conductivity (L T-1)

φ soil porosity (L3 L-3)

θo initial water content (L3 L-3)

F accumulated infiltration (L)

f infiltration rate (L T)

tp ponding time (T)

t evaluation time (T)

The infiltration sub-model includes the Green and Ampt model

(1911), using bulk density, and soil properties for the watershed,

with a pedo-transfer function, defined in the preliminary sub-

model, integrated into this sub-model. In the Green and Ampt

infiltration model, several variations were made to the final

equation after ponding time. These were:

where:

f(t) infiltration rate (mm h-1)

F(t) cumulative infiltration (mm)

w rainfall rate (mm h-1)

( )( )

( )( )

( )( )

f t ww K

Kt

F t K ww K

w K

Kt

hsat

hsat f o

hsat

f o

hsat

hsat

hsat f o

( )

( )

= +−

−

=−

−× +

−

−

−

−

1 2

1 2 1

21

2

2

21

2

ψ φ θ

ψ φ θ

ψ φ θ

Eq. 3.37

Eq. 3.38

129

ψf pressure head (mm)

φ porosity (mm3 mm-3)

θo initial moisture (mm)

Khsat saturated hydraulic conductivity (mm h-1)

t infiltration evaluation time

Both equations are valid for X < 1, where

for tp < t < tw , where tp is ponding time and tw the rainfall time.

In the model, the sub-routines for calculating ponding time (tp),

rate of infiltration after ponding time (f(tp)) and amount of

infiltration (F(tp)) are created considered to be processes to be used

when needed.

Both initial soil moisture, θo, and soil depth are important

parameters for this sub-model. The initial soil moisture θo was

assumed to be half the porosity. Sampling to determine soil depth

was not carried out because soil depth is very heterogeneous and

difficult to measure.

Though soil thickness is clearly an important hydrological

parameter which controls the soil storage capacity, amongst other

properties, it is extremely difficult to access. The only method

accessible is excavation and, since soil thickness was shown to

vary in a complex way spatially and at a range of scales, providing

a realistic spatially distributed soil thickness is not feasible. In

order to counteract this problem a representative depth of 1m was

chosen and applied uniformly across the catchment.

( )XF t

wt

w

f o p

≡− +

( )

ψ φ θ Eq. 3.39

130

In summary, the infiltration sub-model is illustrated as follows

(Figure. 3.34):

InputsMatric potentialHydraulic conductivityNet rainfallSoil moisture content

ParametersInitial soil moisture conditionsSoil depth

CalculatedSoil moisturePonding timeDistance to wetting frontTotal soil water infiltration at ponding timeInfiltration rate after ponding timeTotal soil water infiltration between ponding time and

end of time stepOverland flowDischarge from soil to subsoil water table or directly to

drainage system

Outputs

Infiltration waterOverland flow (depth)New soil moistureRechargeLoss water by evaporation

Figure 3.34 Diagram of infiltration sub-model.

131

3.6.9 Overland flow sub-model

3.6.9.1 Sub-model description

Overland flow occurs whenever the rate of water application to the

ground surface exceeds the rate of infiltration into the soil (ward

and Elliot, 1995), or on the hillsides during rainstorm events when

surface depression storage is exceeded (Kirkby et al., 1980).

Runoff may result from short, highly intense rainfall, long low-

intensity rainfall, or a combination of both (Maidment, 1993).

Several approaches are used to estimate overland flow. Black-box

models have an input-output structure rather than physically

based transfer function. A statistical correspondence needs to be

established between input and output data. The unit hydrograph,

extreme frequency analysis, and regression analyses are examples

of this type of model. Deterministic models are based on complex

physical theory. They include several flow equations, which

produce high computational cost and significant data

requirements. They improve our understanding of the hydrological

system, regardless of their predictive success which is often not as

good as simpler models. In all cases models need to be adapted to

the problem rather than vice-versa. Conceptual models are a

combination of deterministic and black box models. Such models

are formulated on the basis of a simple arrangement of a relative

small number of components with a simplified representation of

elementary system.

To understand the processes that control overland flow, the factors

involved were systematically analysed. Four different processes

were taken in account at different times: Hortonian overland flow,

subsurface flow, saturation overland flow, and ground water

movement. Hortonian overland flow, as discussed in the

132

infiltration model (see Section 3.6.8), refers to the amount of

effective rainfall that reaches the soil at rates higher than soil

infiltration capacity. Subsurface flow or throughflow refers to the

water that infiltrates into the soil and percolates rapidly, mainly

through macropores. Saturation overland flow occurs when the

water table reaches the surface (100% soil saturation) and forms

excess water, thus generating overland flow. Ground water

movement can generally be described in two ways: vertical

movement, which includes raising the ground water table or

pumping water through wells with natural hydraulic

characteristics, and lateral movement, such as throughflow.

Rainfall characteristics exert a strong influence on overland flow

events. Rainfall intensity combined with soil water saturation and

storm characteristics have important implications for flow

generation.

Temporal and spatial variations in runoff, caused by rainfall

properties, may be greatly enhanced by spatial variations in

infiltration capacity of the soil surface. Research conducted in

humid areas indicates that the frequency and magnitude of storm

channel runoff is controlled mainly by the extent and distribution

of saturated areas (Anderson and Burt, 1985). Such areas respond

quickly even to low-intensity rainstorms. Therefore, the spatial

distribution of soil moisture cannot be regarded as a major factor

in the control of storm runoff generation, and spatial non-

uniformity of runoff generation relates significantly to spatial

variations in infiltration capacities.

Overland flow frequency and magnitude therefore depends on

several factors, including geomorphological characteristics (such as

slope, slope distance, aspect, catchment area, among others),

rainfall characteristics (frequency, intensity, duration) and soil

133

properties (hydraulic conductivity, soil texture, porosity, among

others) including the ratio of rocks to soil, on the surface, that in

turn influenced the soil hydrological fluxes. High runoff can be

predicted in those cases where the rock-soil ratio is high, while low

runoff can be expected in those cases where this ratio is low.

Based on Hortonian overland flow, the runoff model indicates water

height at a given time and point, using a simple hydrological

balance in a given time step,

where D is the water depth of overland flow [mm]; P, effective

rainfall (direct rainfall plus throughfall) [mm]; Runin, runoff

contribution from slopes above the point; I, the infiltration for that

period [mm]; E, soil evaporation [mm]; and Runout, the overland

flow outflow. Most values are calculated by other sub-modules or

by the results of previous iterations. Figure 3.35 presents the

diagram of this sub-model.

D P Runin I E Runout= + − + +( ) ( )

Input- Water depth of overland flow,

net rainfall, infiltration, pot-evaporation

OutputOverland flow

CalculatedOverland flow

Figure 3.35 Diagram of runoff sub-model.

Eq. 3.40

134

3.6.9.2 Surface component of overland flow at the catchmentscale

The integration of a surface component in the model produces an

extension of the 1D model at the plot scale to a 2.5 D model at the

catchment scale.

The overland flow sub-model is the only component within the

hydrological model that is changed in this way. Water which ponds

is allowed to flow downslope according to the local drainage

direction (LDD). The overland flow sub-model computes the

amount of outflow surface water in a down slope direction for a

particular area. The inflow water volume is computed by the

UPSTREAM routine used in the GIS component (PCRaster

software), which is the sum of all overland flow values of the

upslope direction areas. This command (upstream) uses as a

parameter the local drainage direction (ldd) network, which is a

direction network connection between areas, which indicates the

flow direction; ldd is derived from the digital elevation model

(DEM). All surface water that is not infiltrated or evaporated

moves down slope direction. The model does not incorporate a

detention storage capacity. LDD is calculated using the 8 point

pour algorithm with flow directions from each cell to its steepest

downslope neighbour. The manner in which PCRaster calculates

the LDD is explained in the user manual (Utrecht University,

1996).

3.6.10 Erosion sub-model

Soil erosion is modelled to identify areas where soil detachment

and loss by natural or anthropogenic causes occurs. The

importance of assessing and quantifying soil loss in TMEs due to

135

LUCC lies in its effects on landscape transformation and

environmental consequences.

Most of the knowledge of soil erosion mechanisms is the result of

studies carried out by the US Soil Conservation Service, which has

emphasised the prediction of erosion rates. Therefore, most of

these approaches are based on empirical equations or on

generalisations for specific scenarios. Kirkby and Morgan (1980)

compiled a number of these developments in detail. The most

common reference point in this field is the Universal Soil Loss

Equation (USLE), which estimates erosion as the product of a

series of terms such as rainfall, slope gradient, slope length, soil

and cropping factors. The equation allows individual factors,

developed from extensive observation of experimental plots in the

US, to be tabulated.

Recent developments in this field are discussed by Boardman and

Favis-Mortlock (1993), who compiled the most commonly used

erosion models and described the different approaches used and

the specific characteristics of each. Although these models often

require significant data for parameterisation and input information,

they describe erosion in detail and the accuracy of the approaches.

Despite technological advances, the development of erosion models

is still largely empirical. Further, research is required because of

the extreme complexity of the physical processes involved in

erosion.

Climatic variables, such as rainfall intensity, have a major effect on

the ecosystem and also exert an effective control on the variables

that determine soil stability. Vegetation cover also has an

important effect because it provides protection against rain splash

and sediment detachment and transportation (Kirkby and Morgan,

1980; Boardman and Favis-Mortlock 1993).

136

Musgrave (1947) developed a relationship between rainfall

characteristics and the amount of soil loss using data from several

stations. He developed a relation which involves slope parameter,

surface runoff and rainfall properties (Thornes and Gilman, 1983;

Thornes, personal communication), and is expressed as follows:

where k1, m and n are parameters (discussed below), and q the

surface overland flow per unit width (mm.h-1) (Thornes, 1990) as

defined in previous section.

Musgrave’s (1947) equation was the basis for Thornes’s model

(1985) which was developed on the basis of results of small

experimental plots (20m2) in Spain and with rainfall records

shorter than an hour, which guarantee that the rainfall properties

are important.

The erosion model proposed by Thornes (1985) was used in this

study, and focuses on the competitive interaction between erosion

and vegetation cover. This routine can be incorporated into the

hydrological model because it is based on physical characteristics

of the soil profile (Thornes, 1990). The spatial variation of erosion

on hillsides can also be examined for the entire watershed. This

method was also chosen because (a) it is a physically-based

approach that uses local data; (b) the spatial resolution of the

proposed model (25m pixel side size) which is related to the

experimental plot size used by Thornes; (c) the input data needed

are available, which uses local information at a good temporal

resolution; and (d) the parameters are easy to estimate or adapt

from the literature.

E k q sm n= 1 Equ. 3.41

137

Parameter k1 is a coefficient that depends, among other things, on

the amount and intensity of rainfall, the effects of lithological

constraints on the availability of materials for erosion within

textural soil properties and organic matter, as related to the size of

material to be transported (Thornes and Gilman, 1983). k1 can be

determined through an experimental combination of rainfall

simulation and collected soil loss in the field, which was not

carried out in this project. Instead of this, Thornes and Gilman

(1983) suggest that k1 be used as a constant value of 0.02, or a

linear coefficient adjusted to empirical data, or use the erodability

factor derived from USLE tables. In the model, the USLE soil

erodability factor is used as the k1 parameter, and is determined by

the soil’s physical properties (texture and organic matter).

Therefore, k1 for sandy loam soil with an organic matter content

higher than 3.5% is 0.19, as derived from the USLE erodability

factor table 3.7 (Kirkby and Morgan, 1980).

Organic matter contentTexture class < 0.5 per cent 2 per cent 4 per centSand 0.05 0.03 0.02Find sand 0.16 0.14 0.10Very fine sand 0.42 0.36 0.28Loamy sand 0.12 0.10 0.08Loamy fine sand 0.24 0.20 0.16Loamy very finesand

0.44 0.38 0.30

Sandy loam 0.27 0.24 0.19Fine sandy loam 0.35 0.30 0.24Very fine sandyloam

0.47 0.41 0.33

Loam 0.38 0.34 0.29Silt loam 0.48 0.42 0.33Silt 0.60 0.52 0.42Sandy clay loam 0.27 0.25 0.21Clay loam 0.28 0.25 0.21Silty clay loam 0.37 0.21 0.26Sandy clay 0.14 0.13 0.12Silty clay 0.25 0.23 0.19Clay 0.13 – 0.9Table 3.7 Soil erodability factor (taken from Morgan and Kirkby, 1980).

138

The parameters m and n are empirically determined as having

values of 2 and 1.66, respectively. Based on the analysis by

Thornes and Gilman (1983), sheet erosion, as related to slope

length and as influenced by vegetation cover on ungullied slopes,

modifies the erosion formula is as follows:

where E is erosion (change in soil depth) [mm]; k1, the previously

defined soil erodability factor, and q, the surface runoff per unit

width; S, the tangent of slope angle; and v, the vegetation cover in

percent units. The advantage of using this model is that it does

not require many parameters nor does it need a lot of data. Figure

3.36 illustrates the sub-model. The erosion model does not include

the analysis of detached soil deposition down slope direction.

E K q S e v= −1

2 1 67 0 07* * *. . *

Figure 3.36 Diagram of erosion sub-model.

Input- Runoff

Parameters- K1 , soil erodability factor- V, vegetation cover- m and n values

Calculated- Erosion

Output- Erosion

Equ3.42

139

3.7 Integrating the sub-models in the 1D and 2.5D models

The 1D model was applied at the plot scale, using the collected

data from the weather stations. The 2.5D model was developed at

catchment scale on the basis of 1D model data and modules, in

order to produce comparable results. However, the 2.5D model

was integrated over the whole Tambito watershed (see Figure

3.21). In GIS concepts, the study area is delimited by the defined

window in section 3.2, which contains 35,534 square regular areas

of 25m pixel side size, organised in 163 columns by 218 rows, and

from which 22577 internal pixels conform the Tambito watershed

that is around 1,411 ha.

3.7.1 Module sequence

Model execution starts with the reading of data from the input file

(see Appendix 6). This file organised by lines, contains two main

data sets: the first, which has year, month, day, and hour, which

all together define the time step, and the second set is the rainfall

occurring within that time step.

With the date and time the solar module computes the incident

solar radiation for each pixel. Then cloud cover is computed

followed by net radiation, which is used in the computation of

potential evaporation.

Intercepted rainfall is computed using an image of vegetation type,

which the first image of the scenarios, derived from TM Landsat

image (Museo de Historia Natural, 1989) (see section 3.4.1), and

the following iterations use the vegetation cover images created for

the processes described in section 3.4.2 as dynamic LUCC

scenarios, which are complemented with vegetation parameters

140

(see section 3.5.2.2). The potential evaporation module is then

used to compute canopy evaporation using the energy extinction

according to LAI (see section 3.5.2.2). Evaporated water from the

canopy and effective rainfall (direct rainfall plus throughfall) (see

section 3.6.5 and 3..6.6) are outputs from the interception module.

This effective rainfall is used in the infiltration module to estimate

the soil water infiltrated (see section 3.6.8), recharge and surface

water as overland flow. The overland flow module computes the

accumulated surface water, which is moved between cells; then

erosion is finally computed.

The units used within the models are KJ∙m-2 for energy and mm∙h-1

for water fluxes. All fluxes for the analysis use this unit in order to

be able to compare and evaluate the results. Erosion is computed

as depth of removed soil (mm∙h-1).

Cumulative images and average values are used to summarise flux

values within the sub-models. The main model outputs variables

are: solar radiation, cloud cover, net radiation, rainfall

interception, effective rainfall, infiltration, matric potential,

hydraulic conductivity, soil moisture, recharge, overland flow and

erosion. Most of these variables are used in the analysis. The

program code of the model can be seen in Appendix 9.

3.7.2 Data used in the model

The only input data variable in the model is rainfall. In the 1D

model the rainfall value is used directly from the input file. In the

2.5D model rainfall it is distributed through the catchment surface

using a rainfall elevation function derived from IDEAM weather

station (20 de Julio – 2200 masl-) and Tambito weather station

(1450 masl), (Mulligan et al., 2000) using the annual rainfall. The

141

derived rainfall distribution function combined with the elevation

and rainfall of Tambito station was

Rainfall = Rainfall(i) * (1 + diff-elevation * 0.001) (mm)

Where,

Rainfall(i) is the input rainfall (mm per hour) (Tambito station

rainfall) and diff-elevation is the difference in elevation between

any point within the catchment from digital elevation model (DEM)

and the Tambito station elevation.

The annual rainfall for the simulated period was 7325 mm in

Tambito station (elev. 1410 masl); the example used here is one of

the wettest years in Tambito, because the normal annual rainfall is

around 4500 mm a year. A map of the estimated rainfall

distribution is shown in Figure 3.37. Hourly rainfall data are

plotted in Figure 3.38 and the frequency distribution in Figure

3.39. 73% of all recorded hours studied (6427) were without

rainfall. 25% of hours (2239) has rainfall less than 20mm. Less

than 1.5% (80) hours have rainfall between 20-50mm. Just 13

hours had rainfall greater than 50mm, with just 4 hours with more

than 80mm, with a maximum of 110.2 mm in one hour.

Equ. 3.43

142

Figure

4000 – 46004601 – 52005201 – 5800

3.37 A map of simulated rainfall distribution for Tambito watershed

5801 – 64006401 – 70007001 – 76007601 – 82008201 – 88008801 – 96009401 – 10000

142

Scale 1 : 50,000

143

A year of hourly rainfall data for Tambito station (1995)

0

20

40

60

80

100

120

1 1001 2001 3001 4001 5001 6001 7001 8001Time (hour)

Rain

fall (

mm

)

Figure 3.38 One year of hourly rainfall from Tambito weather station (1995)

Jun Feb Mar Apr May Jun Jul Ago Sep Oct Nov Dec

143

144144

Frequency Histogram (simulated rainfall for statistics)

Hourly rainfall (mm)

Fre

quen

cy

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������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������������ ��������������� ���������������0

2000

4000

6000

-20 0 20 40 60 80 100 120

Figure 3.39 Histogram distribution for Tambito rainfall using simulated data of 1995

145

The mean hourly rainfall was 0.83mm, with a variance of 19.38

and standard deviation of 4.4.

As seen, the rainfall pattern is typical of rain forests, with a high

precipitation level overall and few, but powerful strong showers,

which can produce very high overland flow and erosion.

3.7.3 Parameters used in the model

Three classes of parameters are used in the model:

Class 1: Parameters used in net radiation equation A and B (see

linear regression equation, section 3.6.4.3) the parameter values

are 0.85 and 16.97 respectively, and those values are used for the

whole catchment. They are assumed to be non-varying with the

surface and are the same for any point within the catchment.

Class 2: Parameters that vary with vegetation type:

- Light extinction for evaporative energy inside of forest canopy,

which is 0.26 for forest. In grassland it is not taken into

account.

- Leaf area index

- Maximum water storage canopy

- Vegetation cover.

Values of the last three parameters are in table 3.6, and a

description of their measurement is given in section 3.5. The

distribution of these parameters over the catchment is in

accordance with LUCC images generated for the LUCC scenarios

(see section 3.4 and Appendix 1).

146

Class 3: Parameters which depend upon soil physical properties.

One soil type was defined on the basis of field data as

representative for the whole catchment: sandy clay loam. There is

no soil type variation throughout the catchment. Parameter values

for sandy clay loam are in table 3.8. In this way, the soil module is

conceptually lumped and the only response to deforestation is

through its effect on the vegetation properties.

Parameter valueSoil texture (sand, clay and silt) 57.03% , 21.8% , 21.23%

Soil porosity 0.61

Soil depth 1000 mm

Erodibility factor K 0.2

M value of erosion equation 2

N value of erosion equation 1.667

Table 3.8 Soil parameters used in the physically-based hydrological

model

Soil parameter definitions are described in section 3.5.2.1.

147

Chapter IV Model results, sensitivity analysis and validation

4.1 Structure of this chapter

Throughout this chapter the results for model experiments are

reported in order to show the model response to hydrological

events. In addition the hydrological model sensitivity are also

presented to illustrate the effects of LUCC on the hydrological cycle

for TMCF environments. First of all, both 1D and 2.5D models

were integrated twice, one with a completely forested catchment

and the other with the catchment completely covered by grassland.

The 1D model was run for a short period (fifteen days) and the

results are shown graphically to identify the model behaviour at

this time resolution (an hourly time step). Also this gives the

manner of model response to rainfall events for the different land

uses. Subsequently results of 2.5D hydrological modelling for one

modelled year are presented, particularly for overland flow and

erosion. Model results are presented in maps to show the

behaviour of the hydrological events for each point within the

catchment, which summarises the yield (difference by m2) between

the runs and the variables for forested and deforested catchment

response.

Sensitivity analyses at both the plot and the catchment scales are

presented. First of all, sensitivity analysis at the plot scale (1D

model) is carried out for all parameters within the model, and

secondly at the catchment scale (2.5D model), analysing the

implications of surface connectivity and the relationships between

hydrological flux changes and the controlling landscape

topographic variables of forested and deforested areas. The

collection of initial parameter values are described in this section,

as well as the data used for model parameterisation.

148

Sensitivity analysis for the 1D model is used to indicate which are

the most important parameters within the model and the most

sensitive variables to parameter change. Sensitivity analysis for

the 2.5D model shows the relationship between overland flow and

erosion sensitivities with respect to landscape topographic

characteristics, and in relation to the location of deforested areas

within the catchment. Sensitivity analysis at the catchment scale

identifies the most sensitive areas to land use change and the

relationship of these with the surface physical properties.

Model validation at the plot scale is discussed in the final section of

the chapter, where the agreement between modelled and measured

data for both an hourly time step and for daily average time step is

shown.

4.2 Model results

Examples of 1D and 2.5D model runs are presented as model

results to illustrate the behaviour of the flux variables during the

hydrological modelling process. The 1D model is presented at

hourly time steps for a short period to show the model skills

required to represent the flux variations at this time resolution.

The 2.5D model results are shown for one modelled year for the

main variables presented here (overland flow and erosion) for the

whole catchment as a surface image.

4.2.1 Model results at the plot scale

As a means of verifying the function of the 1D model, it was run

with collected data for the first fifteen days of the month of April

149

(April 01/95 00:00 AM to April 15/95 23:00 PM). The model was

run twice, once with a complete cover of forest and once with a

complete cover of pasture. A summary of parameters used in this

process is shown in table 4.1. The value used for the slope

parameter in this particular run, was very small (1 degree); as a

result, the amount of overland flow produced remains the same for

the next time step. Graphical comparisons of model results of the

main model variables are in Figures 4.1 to 4.10. The results are

presented as hourly values per m2. The total rainfall in this fifteen

days is 469 mm, with a maximum hourly value of 110 mm. The

mean value is 1.3 mm h-1 with a standard deviation of 0.37mm h-1.

In this period there were 229 hours without rainfall (63%), 125

hours with rainfall less than 20 mm (35%), 5 hours with rainfall

between 20 and 50 mm (1%), and a single large storm of 110 mm

(0.3%).

Parameter Initial values

Forest GrasslandA value in the net radiation equation 0.85

B value in the net radiation equation 16.97

Light extinction in the evaporative energy 0.27 1

Leaf area index 3.26 m2 . m-2 1.7 m2 . m-2

Canopy maximum storage capacity 0.2 mm 0.03 mm

Vegetation cover 91% 86%

Soil texture (sand, clay and silt) 57.03% , 21.8% , 21.23%

Soil porosity 0.61

Soil depth 1000 mm

Initial soil moisture 0.37 %

Erodibility factor K 0.2

m value of erosion equation 2

n value of erosion equation 1.67

Table 4.1 Parameters used in the physical hydrological model

Figure 4.1 shows the difference between total hourly evaporation in

forest and grassland. The maximum value for grassland is 0.13

150

mm h-1 (at noon) whilst for forest it can reach up to 0.54 mm h-1.

From the same graph it is clear that most of the rainfall occurs

during the night. Figure 4.2 shows the rainfall intercepted by the

canopy, which is clearly different between the two types of

vegetation. There are very few hours with dry vegetation (less than

40%) and the rest of the time the vegetation is wet. Figure 4.3

shows the difference in matric potential under the different covers

and during wet and dry periods, and the similarity when the soil is

near saturation (Figure 4.6), and hydraulic conductivity reaches its

maximum values (Figure 4.4). Figure 4.4 shows hydraulic

conductivity, which presents some differences between land cover

with low rainfall, as infiltration does (Figure 4.5). Soil moisture

(Figure 4.6) is consistent with previous variables, and decreases

faster in forested areas during periods of low rainfall. With heavy

rainfall, soil moisture is saturated under both land covers. Under

conditions of heavy rainfall, overland flow (Figure 4.7) describes

very similar behaviour under both land covers. Difference between

overland flow produced under both forest and grass land covers is

shown in Figure 4.8, which is clear that in few times this difference

could reach up to 0.4 mm, but most of the time it remains the

same under both land covers, or it does not exist. The same occurs

with modelled erosion (Figure 4.9), which shows some isolated

differences between both land covers. Figure 4.10 shows the

differences between modelled erosion between land covers. The per

event difference between the land covers is small, up to 0.002 mm

(by hour).

151

Figure 4.1 Modelled evaporation with 1D model for forest and grassland LUCC compared with the rainfall events

Modelled evaporation with 1D model

0

0.1

0.2

0.3

0.4

0.5

0.6

0 50 100 150 200 250 300 350Time (hour)

Eva

pora

tion

(mm

)

0

20

40

60

80

100

120

Rai

nfal

l (m

m)

Rainfall

Grassland

Forest

Figure 4.2 Modelled canopy interception with 1D model for forest and grassland LUCC, compared with rainfall events

Modelled interception w ith 1D model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 50 100 150 200 250 300 350Time (hour)

Inte

rcep

tion

(mm

)

0

20

40

60

80

100

120

Ran

ifall

(mm

)

Grassland

Forest

Rainfall

151

152

Figure 4.3 Modelled matric potential with 1D model for forest and grassland LUCC, compared with rainfall events

Modelled matric potential with 1D model

5000

6000

7000

8000

9000

10000

0 50 100 150 200 250 300 350Time (hour)

Mat

ric P

oten

tial (

KP

a)

0

20

40

60

80

100

120

Rai

nfal

l (m

m)

Grassland

Forest

Rainfall

Figure 4.4 Modelled hydraulic conductivity with 1D model for forest and grassland LUCC, compared with rainfall events

Modelled hydraulic conductivity w ith 1D model

0

1

2

3

4

5

0 50 100 150 200 250 300 350Time (hour)

Hyd

raul

ic c

ondu

ctiv

ity

(mm

hou

r-1)

0

20

40

60

80

100

120

Rai

nfal

l (m

m)

Grassland

Forest

Rainfall

152

153

Modelled infiltration w ith 1D model

0

2

4

6

0 50 100 150 200 250 300 350Time (hour)

Infil

trat

ion

(mm

)

Grassland

Forest

Figure 4.5 Modelled infiltration with 1D model for forest and grassland LUCC

Modelled soil moisture with 1D model

0.3

0.35

0.4

0.45

0 50 100 150 200 250 300 350Time (hour)

Soi

l moi

stur

e (%

)

0

20

40

60

80

100

120

Rai

nfal

l (m

m)

Grassland

Forest

Rainfall

Figure 4.6 Modelled soil moisture with 1D model for forest and grassland LUCC compared with rainfall events

153

154

Difference between modelled overland flow of grassland and forest

0

0.1

0.2

0.3

0.4

0.5

0 50 100 150 200 250 300 350Time (hour)

Diff

. Ove

rland

flo

w

(mm

)

0

20

40

60

80

100

120

Rai

nfal

l (m

m)

Dif ference in OF betw een LUCC

Rainfall

Modelled Overland flow w ith 1D model

0

20

40

60

80

100

120

0 50 100 150 200 250 300 350Time (hour)

Ove

rland

flo

w (

mm

)

0

20

40

60

80

100

120

Rai

nfal

l (m

m)

Forest

grassland

Rainfall

Figure 4.7 Modelled overland flow with 1D model for forest and grassland compared with rainfall events

Figure 4.8 Difference between modelled overland flow for both forest and grassland LUCC, compared with the rainfall events

154

155

Modelled erosion with 1D model

0

0.002

0.004

0.006

0 50 100 150 200 250 300 350Time (hour)

Eros

ion

(mm

)0

25

50

75

100

Rai

nfal

l (m

m)

grass Ero

forest Ero

forest rain

Figure 4.9 Modelled erosion with 1D model for forest and grassland, compared with rainfall events

Figure 4.10 Difference between modelled erosion for forest and grassland, compared with rainfall events

Difference vetween modelled erosion of grassland and forest

0

0.0005

0.001

0.0015

0.002

0 50 100 150 200 250 300 350Time (hour)

Diif

. of e

rosi

on (

mm

)

0

20

40

60

80

100

120

Rai

nfal

l (m

m)Diff erosion betw een LUCC (mm)

Rainfall (mm)

155

156

4.2.2 Model results at the catchment scale

In order to illustrate the model flux variation at the catchment

scale, the 2.5D model was run twice; first with the initial image of

vegetation cover used in the scenarios (NDVI from the Landsat

image TM 1989), and secondly, with the whole catchment

deforested and replaced with the grass cover. Overland flow and

erosion were summarised in the catchment images, which in both

cases show the difference in yield per m2 between the two model

runs, for forested and then deforested catchment. These maps

show the increase in overland flow and erosion due to complete

LUCC in the catchment.

Overland flow (Figure 4.11) increased up to 300mm in a year for

the areas with steepest slopes as a product of deforestation. Areas

within the river channels were not taken into account, because

thesis will not deal with modelling flow in river channels, only

hillslopes. About 15% of the catchment area has less than 46mm

of increment in overland flow with deforestation. Most of these

areas are in the lower part of the catchment (related with altitude

which controls rainfall) with the exception of some high altitude

areas at the northern side of the Palo Verde sub-catchment that

have low slope angles. About 68% of the catchment area has an

increment in overland flow between 46 and 85mm, which occurs

throughout the catchment, with shallow slopes (lower than 11°)

and distance from the river channels. About 14% of the area has

an increase in overland flow of between 85 and 137mm, in the

areas with moderate slope (around 14° of slope) or near to the

areas with steep slopes in an up-slope direction. Just 3% of the

area shows an increase in the overland flow up 300 mm due to

LUCC. These areas have steep slopes, generally with high

elevation. The effects of overland flow connectivity are clearly

identified with the surface water accumulation on downslope areas.

157

Figure 4.11 Changes in overland flow due to LUCC (units in mm) for a modelled year.

Increase in overland flow

due to LUCC using 2.5Dmodel

158

The increment in erosion due to LUCC is strongly related to the

slope of the area. Figure 4.12 shows those erosion increments over

the catchment with a clear relation with the steepest areas (see the

slope map Figure 3.17, pag. 81). Less than 14% of the catchment

has an increment in erosion between 0 and 3mm a year. These

areas are in both the highest and the lowest parts of the mountain

of the catchment (related to altitude) and areas of moderate slope.

About 45% of the area shows erosion increases of between 3 and

37 mm a year; those areas are where the slope is steep. 17% of the

area shows increases in erosion of between 37 to 73 mm a year;

they are in steeper slope areas in the down-slope direction furthest

away from the river channels. Finally, the largest increases in

erosion, due to LUCC, occur in areas nearest to river channels, in

the highest parts of the catchment (related to elevation) with

steepest slopes; those areas represent about 22% of the catchment.

Despite the fact that all processes in the model are affected by the

spatial rainfall function distribution the sensitive areas with

respect to erosion are clearly identified in Figure 4.12 as described

above. Higher areas in the catchment receive a high volume of

rainfall, which produces a large amount of wash erosion in both

land uses, but markedly higher after deforestation. Also as is

commonly assumed based on the topographic index, the areas

close to the river channels have more probability of soil saturation.

159

Figure 4.12 Changes in erosion due to LUCC (units in mm m-2) in a modelled year

Increase in erosion

due to LUCCusing 2.5D model

160

4.3 Sensitivity analysis of the hydrological model at the plotscale (1D model)

The 1D sensitivity analysis was carried out using the PCRaster

hydrological model implemented for a singular cell 1m pixel size.

Since this was designed to test only the sensitivity to model

parameters and not to spatial variation in these parameters.

Twelve parameters were identified as important for this analysis

and eight variables were taken into account in the analysis, in

order to identify the sensitivity of the model. The parameters and

values used in the sensitivity analysis are shown in table 4.2. All

parameters are for forest vegetation. The procedure for collecting

parameters were described in the previous chapter.

Parameter Initial valueA value in the net radiation equation 0.85

B value in the net radiation equation 16.97

Light extinction in the evaporative energy inside of forest canopy 0.27

Leaf area index 3.26 m2 . m-2

Canopy maximum storage capacity 0.2 mm

Vegetation cover 91%

Soil texture (sand, clay and silt) 57.03% , 21.8% , 21.23%

Soil porosity 0.61%

Soil depth 1000 mm

Erodibility factor K1 0.2

m value of erosion equation 2

n value of erosion equation 1.67

Table 4.2 Parameters used in the sensitivity analysis of the model

There are many important model output variables in processes, but

not all of them were used in sensitivity analysis. The following

variables were identified as the most important to the purpose of

this thesis:

161

Soil moisture

Matric potential

Hydrological conductivity

Infiltration

Evaporation

Detention storage

Overland flow (OF)

Erosion (E)

Parameters were varied by plus and minus 10% from the original

value (table 4.2) to +/-100% for the parameter sensitivity analysis

(Fisher et.al, 1997; Saltelli, 1999), where the results compared

between iterations give the ratio of change which is interpreted as

the sensitivity of the evaluated parameter. Model initial conditions

were derived from the model outputs of the previous year-long run

of the model. The model was re-run for each parameter variation

for a year of simulation time.

Results of the first model run using the parameters from table 4.2

are shown in table 4.3. Each variable used in the sensitivity

analysis was summed yearly, and was then divided by 8760 (the

number of hours in a year) to represent the hourly average for that

year by m2.

Table 4.3 Hourly average values of model variables for a year simulation in 1 m2

ErosionEffectiverainfallmm.h-1

SoilMoisture

%

MatricPotential

kPa

Hydraulicconductivity

mm.h-1Infiltration

mm.h-1

Totalevaporation

mm.h-1

Overlandflow

mm.h-1 mm.h-11.34 0.35 8536 0.49 0.51 0.02 0.8 0.1

162

Percent variation (%Δ) in variables was analysed against the

percent of variation (%Δ) of parameter change, to see the resulting

pattern of model sensitivity. In the cases where variables do not

respond, they were not taken into account in the analysis, and so

are not presented in the analysis nor on graphics or tables.

The degree of sensitivity is highlighted with a colour code on the

tables, using the following code of five classes in table 4.4, which

make it easier to identify the parameters sensitivity:

Table 4.4 Colour code of the degree of sensitivity

Five classes were selected in the colour code, with a graphical scale

from light to dark to facilitate the identification where the

sensitivity has a significant change. In some cases the scale of

representation in graphics was varied to allow a good view of the

magnitude of sensitivity.

4.3.1 Sensitivity to parameter A of net radiation

Parameter A in the linear equation is the slope of the line in the

model equation of net solar radiation. This is the most important

parameter in this equation because it has a significant influence on

evaporation (Figure 4.13b). A variation in the parameter causes a

Not sensitive < 2%

Slightly sensitive 2% - 7%

Sensitive 7% - 20%

Moderately sensitive 20% - 100%

Severely sensitive 100% >

163

proportional variation in total evaporation. A decrease of 100% in

the A parameter causes decreases 100% in evaporation sensitivity

and an 11% increment in the sensitivity of hydraulic conductivity

(Figure 4.13d), through the impact of this parameter on soil

moisture. The same behaviour is produced in effective rainfall

(Figure 4.13c) with a maximum increment of 7%. Variation in

sensitivities of Overland flow (OF), detention storage and erosion

(E) is not greater than 1%. Values of sensitivity to parameter A of

net radiation are summarised in table 4.5 and some of them are

drawn in Figure 4.13. Since parameter A represents the ratio of

net solar to incoming solar radiation it reflects the albedo of the

surface and may thus change with LUCC. It is not varied in the

LUCC scenario here because forest and tall grass have very similar

values of A.

% of variation

Effective rainfall

Soil Moisture

Matric Potential

Hydraulic conductivity Infiltration

Total evaporation

Detention storage

Overland flow Erosion

-100 1.56 1.14 -1.44 11.38 7.73 -100.00 0.63 0.71 0.11-90 1.27 0.95 -1.18 9.23 5.96 -90.44 0.56 0.55 0.11-80 0.99 0.75 -1.04 7.45 4.55 -80.32 0.44 0.44 0.08-70 0.76 0.62 -0.78 6.05 3.54 -70.23 0.34 0.33 0.07-60 0.55 0.49 -0.64 4.75 2.62 -60.16 0.26 0.25 0.05-50 0.39 0.39 -0.60 3.65 1.86 -50.11 0.17 0.16 0.04-40 0.26 0.29 -0.35 2.70 1.30 -40.07 0.10 0.11 0.01-30 0.17 0.20 -0.24 1.87 0.81 -30.07 0.07 0.05 0.01-20 0.09 0.13 -0.15 1.13 0.43 -20.03 0.04 0.03 0.00-10 0.04 0.07 -0.07 0.58 0.22 -9.98 0.01 0.00 0.000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0010 -0.06 -0.03 0.08 -0.53 -0.18 9.98 -0.03 -0.03 -0.0120 -0.09 -0.10 0.27 -1.04 -0.31 19.96 -0.04 -0.05 -0.0130 -0.15 -0.16 0.23 -1.64 -0.58 29.94 -0.07 -0.08 -0.0240 -0.17 -0.20 0.28 -2.05 -0.65 39.92 -0.07 -0.08 -0.0250 -0.19 -0.26 0.35 -2.47 -0.72 49.90 -0.09 -0.08 -0.0260 -0.21 -0.29 0.40 -2.88 -0.78 59.81 -0.10 -0.11 -0.0470 -0.24 -0.36 0.47 -3.32 -0.87 67.81 -0.11 -0.14 -0.0780 -0.25 -0.39 0.52 -3.72 -0.92 79.71 -0.13 -0.14 -0.0490 -0.27 -0.42 0.58 -4.11 -0.96 89.69 -0.13 -0.14 -0.05

100 -0.28 -0.49 0.64 -4.50 -1.01 99.60 -0.14 -0.14 -0.05

Table 4.5 Sensitivity to parameter A in the net radiation equation

164

Sensitivity to parameter A of net radiation

-100

-50

0

50

100

-100 -50 0 50 100

Percent variation of param eter A of net radiation

Infiltration

Total evaporation

Sensitivity to parameter A of net radiation

-0.2

0.0

0.2

0.4

0.6

0.8

-100 -50 0 50 100

Percent variation of param eter A of net radiation

Detention storage

Overland flow

Erosion

Sensitivity to parameter A of net radiation

-0.5

0.0

0.5

1.0

1.5

2.0

-100 -50 0 50 100

Percent variation of param eter A of net radiation

Efective rainfall

Sensitivity to parameter A of net radiation

-8

-4

0

4

8

12

16

-100 -50 0 50 100

Percent variation of param eter A of net radiation

Soil m oisture

M atric potential

Hydrological conductivity

%∆

%∆

%∆

%∆

%∆

%∆

%∆

%∆

a b

c d

Figure 4.13. Sensitivity to parameter A in the net radiation equation

164

165

4.3.2 Sensitivity to parameter B of net radiation equation

The B parameter, in the net radiation equation is the intercept of

the linear regression between net solar radiation (Figure 3.25,

page. 98), and where value is 16.9. It has only a small effect in the

hydrological cycle. This parameter variation produces little effect

on the total evaporation sensitivity and the effect is approximately

linear. Increasing parameter B produces a little decrease in total

evaporation sensitivity. The total sensitivity of evaporation is less

than 1% to this parameter. Other variables are not sensitive to

variation in parameter B. Evaporation sensitivity values are in

table 4.6 and are represented in Figure 4.14.

Table 4.6 Sensitivity to parameter B in the net radiation equation

% of variation -100.00 -90.00 -80.00 -70.00 -60.00 -50.00 -40.00 -30.00 -20.00 -10.00 0.00

Total evaporation 0.59 0.59 0.59 0.40 0.40 0.26 0.20 0.20 0.13 0.07 0.00

% of variation 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 100.00

Total evaporation -0.07 -0.13 -0.20 -0.26 -0.26 -0.33 -0.40 -0.53 -0.53 -0.66

Figure 4.14 Sensitivity to parameter B of the net radiation equation

Sensitivity to parameter B of net radiation

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

-100 -50 0 50 100

Percent variation of parameter B of net radiation

Eva

pora

tion

sens

itivi

ty

%∆

%∆

166

4.3.3 Sensitivity to parameter light extinction K

Sensitivity to parameter K, the light extinction parameter, is small

in the variables. The most sensitive variables are hydraulic

conductivity and infiltration, with an extreme value of 8%. This

parameter does not produce important changes in hydrological flux

sensitivities, which means they are not sensitive to this parameter.

Sensitivity values to these parameters are in table 4.7 and are

shown in Figure 4.15. K is function of three canopy forms.

% of variation

Effective rainfall

Soil Moisture

Matric Potential

Hydraulic conductivity Infiltration

Total evaporation

Detention storage

Overland flow Erosion

-100 1.56 0.78 0.00 7.89 7.73 0.73 0.63 0.63 0.11-90 1.25 0.59 0.00 6.00 5.87 0.59 0.54 0.55 0.11-80 0.98 0.46 0.00 4.59 4.50 0.46 0.44 0.44 0.08-70 0.75 0.36 0.00 3.58 3.49 0.33 0.34 0.33 0.07-60 0.54 0.26 0.00 2.63 2.60 0.26 0.24 0.25 0.05-50 0.38 0.20 0.00 1.85 1.81 0.20 0.17 0.16 0.02-40 0.26 0.13 0.00 1.29 1.28 0.13 0.10 0.11 0.01-30 0.17 0.10 0.00 0.81 0.81 0.07 0.07 0.05 0.01-20 0.09 0.07 0.00 0.44 0.43 0.07 0.04 0.03 0.00-10 0.04 0.03 0.00 0.23 0.22 0.00 0.01 0.00 0.000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0010 -0.06 0.00 0.03 -0.18 -0.18 0.00 -0.03 -0.03 -0.0120 -0.09 -0.03 0.04 -0.32 -0.31 -0.07 -0.04 -0.05 -0.0130 -0.15 -0.03 0.04 -0.60 -0.58 -0.07 -0.07 -0.08 -0.0240 -0.17 -0.07 0.04 -0.67 -0.65 -0.07 -0.07 -0.08 -0.0250 -0.19 -0.07 0.04 -0.74 -0.72 -0.07 -0.09 -0.08 -0.0260 -0.21 -0.07 0.04 -0.78 -0.76 -0.07 -0.10 -0.11 -0.0470 -0.24 -0.07 0.04 -0.90 -0.87 -0.07 -0.11 -0.14 -0.0480 -0.25 -0.07 0.04 -0.95 -0.92 -0.07 -0.13 -0.14 -0.0490 -0.26 -0.07 0.04 -0.99 -0.96 -0.13 -0.13 -0.14 -0.05

100 -0.28 -0.10 0.04 -1.04 -1.01 0.13 -0.14 -0.14 -0.05

Table 4.7 Sensitivity to light extinction

167

Sennsitivity to light extinction inside of canopy

-0.25

0.00

0.25

0.50

0.75

-100 -50 0 50 100

Percent of variation of parameter light extinction

Var

iabl

es s

ensi

tivity

EvaporationDetention storageOverland f lowErosion

Sennsitivity to light extinction inside of canopy

-1

1

3

5

7

-100 -50 0 50 100

Percent of variation of parameter light extinction

Var

iabl

es s

ensi

tivity

Effective rainfall

Soil moisture

Matric potentialHydraulic conductivity

Infiltration

b

a

%∆

%∆

%∆

%∆

Figure 4.15 Sensitivity to light extinction

168

4.3.4 Sensitivity to parameter leaf area index (LAI)

Infiltration and hydraulic conductivity are the variables with

significant changes in the sensitivity to LAI variations. The

maximum variation on infiltration sensitivity reaches up to 7% for

a change in LAI of 100%, and in the same proportion in the case of

hydraulic conductivity sensitivity. Other variables do not reach 1%

of sensitivity to this parameter, which are not important in the

analysis. A summary of sensitive values to this parameter is in

table 4.8 and is shown in Figure 4.16. LAI is one of the main

parameters to vary between forested and deforested areas and is

thus a major control on hydrological response.

% of variation

Effective rainfall

Soil Moisture

Matric Potential

Hydraulic conductivity Infiltration

Total evaporation

Detention storeage

Overland flow Erosion

-100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-90 1.31 0.69 -0.84 6.67 6.52 0.66 0.57 0.57 0.11-80 1.07 0.56 -0.70 5.52 5.40 0.53 0.52 0.52 0.09-70 0.83 0.46 -0.56 4.34 4.26 0.40 0.47 0.46 0.09-60 0.69 0.39 -0.47 3.62 3.56 0.33 0.40 0.38 0.08-50 0.55 0.33 -0.39 2.95 2.89 0.26 0.33 0.33 0.07-40 0.43 0.26 -0.29 2.28 2.24 0.20 0.27 0.25 0.06-30 0.30 0.20 -0.21 1.64 1.59 0.13 0.20 0.19 0.05-20 0.21 0.13 -0.15 1.13 1.10 0.13 0.14 0.14 0.04-10 0.10 0.07 -0.07 0.55 0.54 0.07 0.07 0.05 0.01

0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0010 -0.11 -0.03 0.08 -0.55 -0.54 -0.07 -0.09 -0.08 -0.0420 -0.21 -0.10 0.13 -0.97 -0.94 -0.13 -0.14 -0.14 -0.0530 -0.29 -0.13 0.19 -1.34 -1.32 -0.13 -0.20 -0.19 -0.0740 -0.39 -0.16 0.24 -1.75 -1.70 -0.20 -0.26 -0.27 -0.0950 -0.49 -0.23 0.31 -2.19 -2.13 -0.26 -0.33 -0.33 -0.1160 -0.57 -0.26 0.33 -2.42 -2.37 -0.26 -0.39 -0.41 -0.1370 -0.67 -0.29 0.39 -2.75 -2.69 -0.26 -0.46 -0.46 -0.1580 -0.76 -0.33 0.43 -3.09 -3.02 -0.33 -0.52 -0.52 -0.1890 -0.85 -0.33 0.47 -3.30 -3.23 -0.33 -0.59 -0.60 -0.20

100 -0.90 -0.36 0.48 -3.46 -3.38 -0.40 -0.62 -0.63 -0.21

Table 4.8 Sensitivity to LAI

169

Sensitivity to leaf area index

-0.8

-0.4

0.0

0.4

0.8

-100 -50 0 50 100

Percent of variation of parameter leaf area index

Var

iabl

es s

ensi

tivity

Total evaporationDetention storageOverland f lowErosion

Sensitivity to leaf area index

-4

0

4

8

-100 -50 0 50 100

Percent of variation of parameter leaf area index

Var

iabl

es s

ensi

tivity

Ef fective rainfall

Soil moisture

Matric potentialHydraulic conductivity

Inf iltration

b

a

%∆

%∆

%∆

%∆

Figure 4.16 Sensitivity to LAI

170

4.3.5 Sensitivity to parameter maximum canopy water storagecapacity

The model variables infiltration and hydraulic conductivity are the

most sensitive to the variations of parameter maximum canopy

water storage capacity. The maximum variation in these

hydrological variable sensitivities reaches up to 7%. Other

hydrological variables are not sensitive to this parameter variation.

Values of sensitivity to this parameter are in table 4.9 and they are

shown in Figure 4.17.

% of variation

Effective rainfall

Soil Moisture

Matric Potential

Hydraulic conductivity Infiltration

Total evaporation

Detention storage

Overland flow Erosion

-100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-90 1.07 0.69 -0.84 6.67 6.52 0.66 0.57 0.57 0.11-80 0.83 0.56 -0.70 5.52 5.40 0.53 0.52 0.52 0.09-70 0.69 0.46 -0.56 4.34 4.26 0.40 0.47 0.46 0.09-60 0.55 0.39 -0.47 3.62 3.56 0.33 0.40 0.38 0.08-50 0.43 0.33 -0.39 2.95 2.89 0.26 0.33 0.33 0.07-40 0.30 0.26 -0.29 2.28 2.24 0.20 0.27 0.25 0.06-30 0.21 0.20 -0.21 1.64 1.59 0.13 0.20 0.19 0.05-20 0.10 0.13 -0.15 1.13 1.10 0.13 0.14 0.14 0.04-10 0.00 0.07 -0.07 0.55 0.54 0.07 0.07 0.06 0.01

0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0010 -0.11 -0.03 0.08 -0.55 -0.54 -0.07 -0.09 -0.08 -0.0420 -0.21 -0.10 0.13 -0.97 -0.94 -0.13 -0.14 -0.14 -0.0530 -0.29 -0.13 0.19 -1.36 -1.32 -0.13 -0.20 -0.19 -0.0740 -0.39 -0.16 0.24 -1.75 -1.70 -0.20 -0.26 -0.27 -0.0950 -0.49 -0.23 0.31 -2.19 -2.13 -0.26 -0.33 -0.33 -0.1160 -0.57 -0.26 0.33 -2.42 -2.37 -0.26 -0.39 -0.41 -0.1470 -0.67 -0.29 0.39 -2.75 -2.69 -0.26 -0.46 -0.46 -0.1580 -0.76 -0.33 0.43 -3.09 -3.02 -0.33 -0.52 -0.52 -0.1890 -0.85 -0.33 0.47 -3.30 -3.23 -0.33 -0.59 -0.60 -0.20

100 -0.90 -0.36 0.48 -3.46 -3.38 -0.40 -0.62 -0.63 -0.21

Table 4.9 Sensitivity to maximum canopy storage capacity

171

Sensitivity to max. canopy storage capacity

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

-100 -50 0 50 100

Percent of variation of parameter max. canopy storage capacity

Var

iabl

es s

ensi

tivity

Total evaporation

Detention storageOverland f low

Erosion

Sensitivity to max. canopy storage capacity

-4

-2

0

2

4

6

8

-100 -50 0 50 100

Percent of variation of parameter max. canopy storage capacity

Var

iabl

es s

ensi

tivity

Effective rainfallSoil moisture

Matric potentialHydraulic conductivityInfiltration

b

a

%∆

%∆

%∆

%∆

Figure 4.17 Sensitivity to maximum canopy storage capacity

172

4.3.6 Sensitivity to parameter vegetation cover

The parameter vegetation cover is one of the most important in the

model, because his role in the hydrological context introduces the

ratio between vegetation and bare soil. There are two important

physical effects: 1) discriminates according the type of vegetation

the area covered by the vegetation, which associated with LAI

parameter produce the effective area for canopy, and 2) depending

with surface area covered by vegetation determine the exposed soil

for the erosion process. The evaporation and erosion variables are

the most sensitive variables to this parameter (Figure 4.18).

Evaporation sensitivity can decrease up to 86% removing the forest

vegetation cover because the intercepted water available for

evaporation decreases, while the erosion sensitivity can increase up

to 3000% because forest vegetation covers protect the soil surface

and this helps to dismiss the erosion process. The sensitivity of

hydraulic conductivity decreases by about 24% with the same

changes in this parameter. Infiltration and matric potential

sensitivities increase by 8%, while OF and detention storage

sensitivity changes by 0.5% only. These last variables are not

sensitive to this parameter. Despite the fact that changes on this

parameter affects slightly to the soil variables sensitivity, in the

case of OF sensitivity the changes are relatively small compared to

the bit amount of OF produced by the excess of rainfall. A

summary of sensitivities to this parameter is given in table 4.10

and is shown in Figure 4.18.

173

Table 4.10 Sensitivity to vegetation cover

% of variation

Effective rainfall

Soil Moisture

Matric Potential

Hydraulic conductivity Infiltration

Total evaporation

Detention storage

Overland flow Erosion

-100 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-90 1.35 -3.07 5.72 -23.54 6.76 -86.28 0.53 0.52 3150.98-80 1.15 -2.71 4.83 -21.21 5.85 -73.54 0.44 0.44 3135.05-70 0.95 -2.35 4.03 -18.83 4.93 -61.86 0.36 0.36 2943.89-60 0.79 -1.99 3.29 -16.29 4.14 -51.14 0.29 0.27 2383.75-50 0.64 -1.67 2.62 -13.75 3.36 -40.69 0.23 0.22 1630.17-40 0.49 -1.31 1.99 -11.12 2.60 -30.98 0.19 0.16 973.15-30 0.34 -0.98 1.40 -8.49 1.86 -22.63 0.13 0.11 519.96-20 0.22 -0.65 0.88 -5.70 1.23 -14.23 0.07 0.08 243.95-10 0.09 -0.26 0.43 -2.88 0.60 -7.33 0.03 0.03 86.510 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0010 -0.14 0.33 -0.40 2.88 -0.60 6.17 -0.06 -0.05 -67.49

173

174

Sensitivity to vegetation cover

-0.4

0.0

0.4

0.8

1.2

1.6

-100 -70 -40 -10

Percent of variation of param eter vegetation cover

Effective rainfall

Detention storage

Overland flow

Sensitivity to vegetation cover

-30

-20

-10

0

10

-100 -70 -40 -10

Percent of variation of param eter vegetation cover

Soil m oisture

M atric potential

Hydraulic conductivityInfiltration

Sensitivity to vegetation cover

-90

-60

-30

0

-100 -80 -60 -40 -20 0

Percent of variation of param eter vegetation cover

Sensitivity to vegetation cover

-1000

0

1000

2000

3000

4000

-100 -60 -20

Percent of variation of param eter vegetation cover

a b

dc

%∆

%∆

%∆%∆

%∆

%∆

%∆

%∆

Figure 4.18 Sensitivity to vegetation cover

174

175

4.3.7 Percent of variation due to soil texture

Despite the fact that in this study was used just one soil class for

the Tambito study area, because the soil variability from soil

samples was relative uniform, a sensitivity analysis regarding soil

texture is carrying out as a complementary concept for the

analysis. Although soil properties variations were not include in

the model for LUCC, soil texture are one of the soil properties that

determines the hydrology of the area, because soil water is directly

related to available pore size and pore space between soil particles.

The soil texture classes were clustered in 11 main soil texture

groups as per the USGS classification (Figure 3.33, Page 118), and

as shown in table 4.11. These classes are nominal and have no

consecutive order, but they have been used as the bases for the

sensitivity analysis. This classification was selected because it

with relatively small number of classes encompasses the general

classification of existing soil, changing only the soil texture

proportions (sand, silt and clay). In the sensitivity analysis, the

change in the hydrological flux variables sensitivity is expressed as

percent of variation only, in other words, proportional change of

variables with respect to a combination of proportional change of

soil texture (∆ var / %sand, %silt, %clay).

Class Soil type Sand % Silt % Clay %1 Sand 90 5 52 Loamy sand 80 10 103 Sandy loam 60 30 104 Silt loam 25 60 155 Loam 40 40 206 Sandy clay loam 60 10 307 Silty clay loam 10 55 358 Clay loam 30 40 309 Sandy clay 50 10 4010 Silty clay 5 50 4511 Clay 15 15 70

Table 4.11 Soil texture classification classes

176

The percent of variation of soil moisture, hydraulic conductivity

and infiltration reaches up to 50% between soil classes. The

percent variation of OF and detention storage varies with the soil

texture, which the highest value of variation is (160%) between

class 5 and class 6 (Sandy clay loam). This class also is the

dominant soil class of the Tambito watershed, while erosion

percent variation reaches up to 100% in half of the soil classes.

The percent variation in matric potential changes in different scale

of magnitude, with the highest value up to 1000% in soil class 5

(loam). Sensitivity to soil texture is shown in fig. 4.19.

From this analysis is clear the importance of the soil textures in

the hydrological context. Unfortunately soil samples were not

collected with LUCC discrimination, because this parameter could

introduce a relevant information within the land use classes as in

this exercise with forested and deforested areas. Also the

correlation between soil properties and land use classes could be

important and significant in the hydrological response, and needs

to be taken into account within the field expeditions.

177

Variables percent variation due to soil texture

-150

-100

-50

0

50

100

150

200

1 2 3 4 5 6 7 8 9 10 11

Soil texture classes

Detention storage

Overland FLow

Erosion

M atric potential percent variation due to So il

texture

0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8 9 10 11Soil texture classes

Variables percent variation due to soil texture

-80

-60

-40

-20

0

20

40

60

1 2 3 4 5 6 7 8 9 10 11

Soil texture classes

Soil moisture

Hydrological conductivity

Infiltration

%∆ %∆

%∆

Figure 4.19 Sensitivity to soil textures

177

178

4.3.8 Sensitivity to parameter soil porosity

Soil porosity is another important parameter in the model. The

most sensitive variable to soil porosity is OF with its sensitivity

increases up to 367% with a 50% of variation in soil porosity

reduction. The model is not sensitivity for soil porosity variation

greater than -30% in all hydrological flux variables. Below –30%

effective rainfall and then OF and detention storage sensitivities

increase greatly due to an important reduction in the soil

infiltration capacity. Most of the rainfall remains on the surface.

Soil hydrological properties are also sensitive to the variation of

this parameter. A summary of variation of sensitivities to soil

porosity is given in table 4.12 and are shown in Figure 4.20.

% of variation

Soil Moisture

Matric Potential

Hydraulic conductivity Infiltration

Total evaporation

Detention storage

Overland flow Erosion

-50 -13.06 16.85 -82.66 42.55 -13.22 367.02 367.07 16.61-40 -1.05 1.38 -14.98 7.95 -1.12 37.79 37.77 7.70-30 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-20 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00-10 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.000 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0010 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0020 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0030 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0040 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Table 4.12 Sensitivity to soil porosity

179

Sensitivity to soil porosity

-100

0

100

200

300

-60 -40 -20 0 20 40

Percent of variation of parameter porosity

Var

iabl

es s

ensi

tivity

Soil MoistureMatric PotentialHydraulic conductivityInfiltrationTotal evaporationDetention storageOverland f lowErosion

%∆

%∆

Sensitivity to soil porosity

-50

-40

-30

-20

-10

0

10

20

30

40

50

-60 -40 -20 0 20 40

Percent of variation of parameter porosity

Var

iabl

es s

ensi

tivity

Soil MoistureMatric PotentialHydraulic conductivityInfiltrationTotal evaporationDetention storageOverland f lowErosion

%∆

%∆

Figure 4.20 Sensitivity to soil porosity

180

4.3.9 Sensitivity to parameter soil depth

The most sensitive variables to parameter soil depth are OF,

detention storage and E (Figure 4.21). The first two have a similar

behaviour. When soil depth decreases by -90%, sensitivity of OF

and detention storage sensitivities reach their highest value (61%).

Decreasing percent variation of soil depth from –70% up to 0% (in

other words increasing soil depth), OF and detention storage

sensitivities decrease; after that, OF and detention storage

sensitivities do not change with increasing percent of variation of

soil depth.

E sensitivity was highest when soil depth variation was decreased

up to -70%, which produces a soil of 30 cm depth. This is

interesting because it represents an inflection point where the E

sensitivity changes from negative to positive. The highest value of

E sensitivity is not in the higher OF sensitivity (at 10 cm depth).

This could be due to there not being enough soil to detach. This is

why values up to 30 cm of soil depth give the maximum soil

erosion detached from soil surface, and combined with the other

events (OF and detention storage) to produce the maximum E

sensitivity. E is moderately sensitive with soil depth percent

variation greater than 20%. When the percent of soil depth is

increased beyond 1000 mm (initial soil depth), the E sensitivity

increases gradually. This could be due to there being more soil to

detach from the surface than in the previous iteration, while OF

sensitivity remains constant.

Other variables such as matric potential and hydraulic

conductivity are less sensitive to soil depth variation than previous

variables. A summary of sensitivities to soil depth are in table 4.13

and shown in Figure 4.21.

181

% of variation

Soil Moisture

Matric Potential

Hydraulic conductivity Infiltration

Total evaporation

Detention storage

Overland flow Erosion

-100 0.00 0.00 0.00 0.00 0.00 -89.70 -89.70 22.60-90 -4.08 11.17 3.92 3.40 -4.69 61.40 61.38 -11.70-80 -2.12 4.93 3.83 2.80 -2.05 40.12 40.12 -43.20-70 -1.31 2.78 3.72 2.13 -1.12 23.42 23.42 -64.94-60 -0.88 1.68 3.60 1.46 -0.66 11.16 11.15 -53.32-50 -0.59 1.06 3.48 0.76 -0.46 3.38 3.36 -42.52-40 -0.36 0.62 3.35 0.07 -0.26 0.09 0.08 -32.57-30 -0.20 0.29 2.49 0.00 -0.13 0.00 0.00 -23.33-20 -0.07 0.12 1.66 0.00 -0.07 0.00 0.00 -14.75-10 0.00 0.04 0.83 0.00 0.00 0.00 0.00 -7.02

0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.0010 0.00 0.04 -0.83 0.00 -0.07 0.00 0.00 6.3720 -0.10 0.13 -1.68 0.00 -0.13 0.00 0.00 12.1830 -0.20 0.43 -2.52 0.00 -0.26 0.00 0.00 17.4640 -0.36 1.51 -3.35 0.00 -0.46 0.00 0.00 22.3750 -0.56 3.65 -4.15 0.00 -0.66 0.00 0.00 26.8160 -0.82 7.05 -4.98 0.00 -0.93 0.00 0.00 30.7970 -1.08 12.16 -5.82 0.00 -1.19 0.00 0.00 34.6480 -1.40 19.48 -6.65 0.00 -1.52 0.00 0.00 38.0390 -1.76 30.65 -7.48 0.00 -1.92 0.00 0.00 41.30

100 -2.16 46.43 -8.29 0.00 -2.31 0.00 0.00 44.34

Table 4.13 Sensitivity to soil depth

181

182

Sensitivity to soil depth

-10

5

20

35

50

-100 -50 0 50 100

Percent of variation of parameter soil depth

Var

iabl

es s

ensi

tivity

Soil MoistureMatric PotentialHydraulic conductivityInf iltrationTotal evaporation

Sensitivity to soil depth

-90

-60

-30

0

30

60

-150 -100 -50 0 50 100 150

Percent of variation of parameter soil depth

Var

iabl

es s

ensi

tivity

Detention storageOverland f lowErosion

b %∆

%∆

a

%∆

%∆

Figure 4.21 Sensitivity to soil depth

183

4.3.10 Sensitivity to parameter erodability factor, K1

The erodability factor is an important coefficient in the erosion

module. Change in this parameter produces a proportional linear

change in erosion sensitivity. Other variables used in the analysis

are not sensitive to this parameter. A summary of erosion

sensitivity to the erodability factor is in table 4.14 and is drawn in

Figure 4.22.

% of variation -100 -90 -80 -70 -60 -50 -40 -30 -20 -10erosion -100.0 -89.3 -78.7 -72.1 -57.5 -47.0 -36.5 -26.0 -15.6 -5.2

% of variation 10 20 30 40 50 60 70 80 90 100erosion 9.8 19.6 29.4 39.0 48.8 58.5 68.2 77.5 87.4 96.9

Table 4.14 Sensitivity to erodability factor k1

Erosion sensitivity to erodability factor

-100

-75

-50

-25

0

25

50

75

100

-100 -50 0 50 100

Percent of variation of parameter erodability factor k1

Eros

ion

sens

itivi

ty

%∆

%∆

Figure 4.22 Sensitivity to erodability factor k1

184

4.3.11 Sensitivity to parameter m factor of erosionequation

The sensitivity to the parameter m factor in erosion equation is

strong only on the variable E. E sensitivity increases by more than

367% of the proportion in sensitivity of m factor in an exponential

curve. Decreasing the percent of m factor does not reproduce the

same sensitivity displayed when increasing; sensitivity of erosion

decreases in the almost in same proportion to the m factor of

erosion. Other variables within the model are not sensitive to this

parameter.

This parameter has an exponential effect on E sensitivity, because

it drastically increases or reduces the OF within the E equation. A

summary of the erosion sensitivity to the m factor is given in table

4.15 and is drawn in Figure 4.23.

% of variation Erosion

-100 -92.1-50 -93.750 17.1100 366.6

Table 4.15 Sensitivity to m factor of erosion equation

Erosion sensitivity to m factor of erosion equation

-200

-100

0

100

200

300

400

-100 -50 0 50 100

Percent variation of parameter m factor of erosion

Eros

ion

sens

itivi

ty

%∆

%∆

Figure 4.23 Sensitivity to m factor of erosion equation

185

4.3.12 Sensitivity to parameter n factor of erosionequation

Erosion sensitivity to the n factor of the erosion equation increases

in a lesser proportion when the n factor proportion decreases, and

decreases in greater proportion when the n factor proportion

increases. When the n factor increase, E sensitivity decrease

linearly by –30%. Other variables are not affected by this

parameter. Erosion sensitivity values are summarised in table

4.16 and are shown in Figure 4.24.

% of variation -100 -90 -80 -70 -60 -50 -40 -30 -20 -10Erosion 16.0 15.5 14.9 14.0 12.9 11.5 9.8 7.8 5.5 2.9

% of variation 10 20 30 40 50 60 70 80 90 100Erosion -3.2 -6.6 -10.1 -13.9 -17.7 -21.6 -25.6 -29.6 -33.5 -37.4

Table 4.16 Sensitivity to n factor of erosion equation

Erosion sensitivity to n factor of erosion

-44

-24

-4

16

-100 -50 0 50 100

Percent variation of parameter n of erosion equation

Eros

ion

sens

itivi

ty

%∆

%∆

Figure 4.24 Sensitivity to n factor of erosion equation

186

4.4 Summary of 1D sensitivity analysis

From the parameter sensitivity analysis in the 1D model, the most

important parameters within the model are vegetation cover, soil

texture, soil porosity and soil depth. Erodability factor k1 and m

and n parameters of the erosion equation produce important

changes only in erosion. From this, it is clear that erosion is the

most sensitive variable from the model. Other parameters produce

small changes in the hydraulic variables, which are taken into

account in the analysis. Vegetation cover protects soil from direct

rainfall and according to the type of vegetation, change in OF and E

can be very significant within the watershed. Table 4.17 shows a

summary of the sensitivity of the different variables to parameters

variation.

Variables Parameters

Soil moisture

Matric Potential

Hydraulic conductivity Infiltration Evaporation

Overland flow Erosion

A of Rn eq.B of Rn eq.Light extintionLAIMx. Canopy water storage capacityVeg. CoverSoil textureSoil porositySoil depthK erodability factorm of erosion eq.n of erosion eq.

Not sensitive < 2%

Slightly sensitive 2% - 7%

Sensitive 7% - 20%

Moderately sensitive 20% - 100%

Severely sensitive 100% >

Table 4.17 Summary of 1D sensitivity analysis by classes with the colour code

187

4.5 2.5D model sensitivity analysis

Sensitivity analysis at the catchment scale is carried out to identify

the area characteristics within a given LUCC scenario, which

produce the highest impact on the model hydrological variables.

Based on the sensitivity analysis at the plot scale (1D model), the

most sensitive hydrological variables to LUCC were overland flow

and soil erosion. These two variables are then used in this part of

the analysis process as an indicator of LUCC impact. The

parameter (vegetation cover) was identified from the 1D sensitivity

analysis as one of the drivers of LUCC impact, and in essence,

changes in vegetation cover are the same as changes in LUCC. So

vegetation cover at the catchment scale is going to be included in

the model with the LUCC scenarios designed for this thesis.

Physical soil property parameters (soil texture, soil porosity and

soil depth, as well as the soil parameters in the erosion equation)

are assumed uniform across the whole catchment irrespective of

land cover since the objective of this modelling is to understand

landscape sensitivity resulting from topographical variability and

hydrological connectivity in combination with LUCC. These

topographic variables are used in the sensitivity analysis at the

catchment scale, to identify how they control catchment sensitivity

to LUCC and thus which areas within the catchment are more

sensitive to LUCC.

Further up, the topographic characteristics used, are defined and

described. In addition, overland flow and erosion sensitivity

analyses are presented.

188

4.5.1 Definition of topographic characteristics

In order to analyse the flux variation with changes in scenarios,

physical properties of deforested areas within the watershed

between iterations in the scenarios were summarised and

averaged.

Topographic variables have been used to explain and assess some

physical events that occur in the environment. Quine and Walling

(1993) used topographic variables to assess the landscape

sensitivity to erosion and deposition. Gerrard (1993) used specific

relief values like maximum slope angle, stream density, stream

frequency and stream order to assess the landscape sensitivity.

McKenzie and Ryan (1999) combined environmental variables from

the landscape to predict spatial soil properties with good results.

The variables taken into account in this part of the analysis were:

slope, aspect, topographic index, altitude and proximity of the

deforested area to river channels.

Slope: the degree of rate of change of elevation per unit of

horizontal distance. Slope can be derived from a Digital Elevation

Model1 (DEM, which is a raster2 image whereby each grid cell has

an elevation value).

Aspect is the direction of the maximum slope in a given point with

relation to a geographical north direction (given in degrees). It is

also derived from the DEM.

Altitude or elevation is the vertical distance (m) of a given point in

relation to a reference point, usually mean sea level.

Distance to rivers (m) was computed using the raster image of the

river channels, which was classified into 18 classes using a 50m

buffer of horizontal distance either side of the rivers channels.

1 Interpolated surface derived from elevation points.2 An image surface conformed by pixels or cells of uniform size

189

Topographic index was first proposed by Kirkby (1975) and then

developed as a part of a complete hydrological model by Beven and

Kirkby (1976, 1979). This index represents the propensity of any

point in the catchment to develop saturated conditions. High

values will be caused by either long slopes or upslope contour

convergence, and low slope angles. It can be used as a guide for

water and sediment movement. It has proven a useful index for

predicting soil properties within the landscape (Mckenzie et al.,

1999). It is defined as:

TopIndex = ln (Ac / tan Β)

Where Ac is the specific contributing area expressed in m2 per unit

width orthogonal to the flow direction, and B is the slope angle.

Normally both Ac and B are derived from the analysis of digital

terrain model, in which the evaluation of pixel connectivity is

produced, and integrated by the accumulative area of upslope

direction (Beven et al., 1995).

All variables were calculated with the GIS at a 25-m pixel size and

averaged for each of the deforested areas of each iteration in each

scenario. A summary of average values of topographic variables

from deforested areas, by iteration per scenario, is given in

Appendix 10.

4.5.2 Sensitivity analysis at the catchment scale

Sensitivity analysis was carried out for five LUCC scenarios. Each

iteration of each scenario was run for a year at an hourly time step,

using the 2.5D model developed in PCRaster (Utrecht University,

1996) for the whole catchment. Model initial conditions were

taken from modelled results produced at the end of a one-year pre-

Eq. 4.1

190

run. Overland flow (OF) and erosion (E) were the variables taken

into account in the analysis. The last nine months of the

simulated year were summarised using the one year average by m2

for each of the flux variables. This was done to avoid the inclusion

of data from the period when the model was adjusting to initial

conditions. Three months were shown to be enough time for model

recovery. Three different initial soil moisture conditions were used

to run the model with the same rainfall events; as is shown in

Figure 4.25 the soil moisture takes similar pattern after the first

600 hours. The model was parameterised with the parameters

outlined in the previous section 4.3 and with the initial image of

LUCC for scenarios. The simulated period includes two rainy

seasons and one dry season, and accounts for more than 6000

time steps in the model process.

Graphical analysis of each variable (OF and E) within each scenario

(SC1 to SC5) was undertaken and presented in a set of 6 graphics.

The graphics contain:

1- Pixel average for the catchment of one year total yielded by the

variables (OF in mm and E in mm).

2- Percent variation for each variable between each LUCC iteration,

given as a percentage.

3- Sensitivity of the variable to LUCC, which is the percent of

variation between two consecutive iterations divided by the change

in deforested area between the same iterations. This gives the net

response per unit of deforestation. They are shown on the same

scale for all scenarios to allow comparison of the sensitivities.

4- Total deforested area by iterations (ha.) compared with mean

altitude of deforested area by iteration (masl).

5- Mean slope and aspect of deforested area by iteration. Both are

presented in degrees.

6- Mean topographic index and mean distance to river of the

deforested area.

191

191Figure 4.25. Modelled soil moisture with different initial conditions for the same rainfall pattern

Modelled soil moisture response to different initial conditions

25

27

29

31

33

35

37

39

41

0 100 200 300 400 500 600 700 800 900 1000Time (hour)

So

il m

ois

ture

(%

)

0

10

20

30

40

50

Ria

nfa

ll (m

m h

-1)

Initial soil moisture 25 %

initial soil moisute 37 %

Initial soil moisture 42 %

Rainfall

192

4.5.2.1 Sensitivity analysis of overland flow to LUCC at the catchment scale

Variation in OF between iterations was calculated and then divided

by the deforested area between iterations to get the OF sensitivity

to LUCC by scenario (table 4.18, page 205). Table 4.18

summarises the total OF by iteration for each scenario, the percent

of variation and the sensitivity to LUCC. The sensitivity is

highlighted with colours to identify the highest sensitivities.

Figures 4.26 to 4.35 show total of OF by iteration, the percent

variation of OF between iteration and the OF sensitivity for the

scenarios.

The pattern of deforestation between iterations by scenario is

different. Consequently the OF yield is also different for each

scenario throughout the iterations. Although SC1 (cellular

automata scenario, page 54) has one of the largest deforested areas

in the initial iterations (see table 4.18), it is ranked third due to the

average yield of OF. Also SC1 was the most uniform in

deforestation pattern because the percent variation was the lowest

(83%).

The scenarios with the lowest yield in OF during the simulation

period were SC3 and SC4 (averaging per iteration at 7919 and

7927 mm respectively). The percent variation of these two

scenarios were relatively uniform (132 and 135 %).

Scenarios SC2 and SC5 produce the highest average OF by

iteration (7965 and 7964 mm respectively). Scenarios SC3 and

SC4 both have the same deforestation pattern in opposing

directions, SC2 starts form the lower part of the catchment, and

193

SC5 starts from the top of the catchment. The percent of variation

were similar for both (132 and 125 %).

The LUCC pattern in SC1 is very varied (see Figures 4.26 and

4.27). Deforestation occurs at the beginning in the lowest part of

the catchment (lower mean altitude) and where the slope is small.

More than half of the area (816.4 ha) in SC1 is deforested in the

first four iterations, which produces an additional 35 mm in OF

and 10 mm in E. For this reason, the percentage of variation of

OF and E decreases rapidly. Mean altitude and mean slope of the

deforested area increases gradually through the iterations, but the

area deforested per iteration decreases. A few oscillations of slope

in the deforested area at the end of the scenario, have some

relation with the variations in OF sensitivity. The decreasing trend

of mean topographic values and mean distance to the river also

have some similarities with the OF sensitivity in the last iterations.

Despite those variations, the OF sensitivity in SC1 is very low,

without large changes (range 0.2 to 1.7).

In SC2, 1109 ha (78% of the area) were deforested in the first five

iterations producing 4 mm OF (4% of additional OF generated by

deforestation) and 18 mm in E ( 81% of additional E generated by

deforestation). In these iterations, while OF sensitivity remains

constant, E sensitivity decreases until iteration 2 and then remains

constant. This means that E is affected by other additional

variables compared to OF. The percent variation in OF is related to

the amount of OF. However, it is important to highlight that the

biggest changes in mean altitude, deforested area, and topographic

index occur in the first iterations (see Figures 4.28 and 4.29).

Between iterations 8 and 12, OF sensitivity changes significantly.

These changes are related to a decreasing mean slope in deforested

area, and increasing mean altitude, topographic index, and

distance to rivers of the deforested area.

194

Figure 4.27 Mean topographic variables for deforested areas in SC1

D eforested area and m ean altitude in S C 1

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Figure 4.26 Overland flow sensitivity in scenario 1 (deforested pattern with cellular automata)

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D efo rested area and m ean altitude in S C 2

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Figure 4.29 Mean topographic variables of deforested areas in SC2

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Figure 4.28 Overland flow sensitivity in scenario 2 (forest conversion with a fixed horizontal distance from river channel in uphill direction)

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After iteration 12, OF sensitivity has a big oscillation, decreasing

and then increasing to its maximum value, as a result of the

change in distance to rivers, mean altitude, and topographic index.

This wave on the graphic of OF sensitivity could be explained by

the combination of those factors, in particular the high values of

mean slope and mean altitude, which in the last iterations produce

the highest variation in OF sensitivity. Overall, the highest OF

sensitivity is produced in SC2, and ranged from 0 to 16 (see Figure

4.28).

In the SC3 (see Figure 4.30), the deforested area within the first 12

iterations is 148 ha (10% of the total area), which produces 30 mm

of OF (29% of additional OF generated by deforestation) and 2.8

mm of E (12% of additional E generated by deforestation). The OF

sensitivity in SC3 is a mirror view of the OF sensitivity of SC2 with

small variations. That is expected because the deforested areas are

very similar but in reverse directions. Although the areas are

similar, the OF sensitivity is larger in SC3 than SC2. This could be

due to the biggest change in mean slope and mean topographic

index occurring in the first three iterations, and the longer distance

to rivers. In SC3, the OF sensitivity in the first iterations is high

and extensive, with some variability during the last few iterations.

The big oscillation in the OF sensitivity occurs in iterations 5, 6

and 7, and could be due to changes in percent variation of OF,

because none of the other aspects have the same trends (see Figure

4.31).

197

D eforested area and m ean altitude in S C 3

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Figure 4.31 Mean topographic variables of deforested areas in SC3

197

Figure 4.30 Overland flow sensitivity in scenario 3 (forest conversion with a fixed horizontal distance towards channel rivers in downhill direction)

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The OF sensitivity for SC4 ranges from 0.6 up to 7, which is half

compared to SC2 and SC3. This means that the areas close to

river channels are more hydrologically sensitive to LUCC than the

deforested areas created with the elevation pattern (SC4 and SC5).

The deforested area in SC4 between iterations 1 to 8 was 735 ha

(52% of the catchment), which produces 29.4 mm of OF (27% of OF

generated by deforestation) and 9.7 mm of E (42% of E generated

by deforestation) (see Figures 4.32 and 4.33). The variation of OF

sensitivity in the first eight iterations (see Figure 4.32) is relatively

constant and small. The areas deforested during these iterations

are the lowest (in terms of altitude) in the watershed; the elevation

of these areas ranges between 1400m to 2000m (see Appendix 10).

After the eighth iteration, the OF sensitivity changes in proportion

with a number of oscillations and increases until the fifteenth

iteration. Between iterations 10 and 15 the percentage variation of

OF increases and decreases, but the OF sensitivity in the same

range always increases. Those variations in the OF sensitivity

could be due to a combination of a decrease in mean slope and

mean aspect and an increase in mean distance to rivers (see Figure

4.33) of the deforested area. Also, the mean elevation, which

increases constantly through the simulation, might have some

effects on the OF sensitivity since rainfall is distributed as a

function of elevation. The large variations in OF sensitivity are in

the last 5 iterations, even though the slope values of the deforested

areas in the last iterations are small, and those areas are further

away from rivers channels, but do have high rainfall receipts.

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D eforested area and m ean altitude in S C 4

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Figure 4.33 Mean topographic variables of deforested areas in SC4

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Figure 4.32 Overland flow sensitivity in scenario 4 (forest conversion with fixed distance of altitude, in uphill direction from the lowest to the highest point)

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As in the case for SC2 and SC3, SC5 is the mirror of SC4, but in

this case the range in OF sensitivity of SC5 is a half of SC4. The

deforested area in SC5 in the first 6 iterations was 527 ha (37% of

the catchment), which produces 63 mm of OF (64% of OF produced

by deforestation) and 10.5 mm of E (45% of E produced by

deforestation). The OF sensitivity is highest for SC5 in iteration 2

(Figure 4.34); the increase in mean slope and decrease in mean

distance to rivers combined with decrease in elevation of the

deforested area produce the variation in the OF sensitivity in the

first 5 iterations. Beyond that, values of the topographic attributes

of the deforested areas change, but the OF sensitivity remains low

(see Figure 4.35). It means deforestation in the highest areas of the

watershed at the beginning of the scenario produces more change

in overland flow by area than occurs with SC5, despite the OF yield

being lower in SC4 than in SC5.

Under SC1, areas in most of the iterations are not sensitive to

LUCC, with the exception of the last iteration, where the terrain is

the steepest and highest in elevation. In SC2, despite the

deforested areas at the beginning producing most of the excess of

OF in the catchment, these areas are not particularly sensitive to

LUCC, but the deforested areas between iteration 7 and 13 as well

as the areas in the last 3 iterations are more sensitive. The highest

values of mean topographic index combined with high mean

elevation as well as increasing distance to the rivers with

increasing mean slope values, produce high values in OF

sensitivity. Conversely, in SC3 the most sensitive areas coincide

with the areas in SC2; these areas are the highest in elevation with

the steepest slopes. The same conclusion can be made with SC4

and SC5, which show the most sensitive areas in the last iteration

in SC4, which are the deforested areas at higher elevations with

greater distance to rivers even though mean slope and mean aspect

decrease. These areas coincide with the initial areas of SC5.

201

Figure 4.35 Mean topographic variables of deforested areas in SC5

D eforested area and m ean altitude in S C 5

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Figure 4.34 Overland flow sensitivity Scenario 5 (forest conversion with fixed distance of altitude, in downhill direction from the highest to the lower point)

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This highlights that the areas with highest OF sensitivity to LUCC

within the catchment are at elevations higher than 2200 m.

Despite the fact that high overland flow is produced in the highest

elevations, the stronger OF sensitivity is shown in SC2 and SC3,

which are related with distance to rivers. Clearly, as elevation

increases so does distance from rivers.

To identify whether or not physical variables are involved in the OF

sensitivity explanation, first of all a Kolmogorov-Smirnov test to

determent the topographic variables normal distribution which

they are, and then a multiple regression analysis was performed for

all scenarios, using the statistical package STATISTICA 6.0

(produced by Statsoft Inc., USA). Table 4.19 summarises and

compares the results of the multiple regression analysis for the

scenarios on the basis of data from Appendix 10. The dependent

variable was OF sensitivity and topographic variables were used as

independent variables. The analysis included calculation of the

explanatory coefficient of determination (R2) of the OF sensitivity, in

relation to independent variables, and the t-coefficient for

statistical significance for each variable (Rincon-Romero, 2000).

The multiple linear correlation coefficient (R) shows how dependent

variables (OF sensitivity) can be explained as a function of the

linear combination of independent variables (topographic

variables). Comparing the R values from the five scenarios (see

table 4.19), the R of SC4 (0.98) shows that 98% of the OF

sensitivity can be explained by linear combination of the

topographic variables for the deforested area, while for SC1 the R is

just 51%, only 51% of the OF sensitivity can be explained by linear

combination of topographic variables. The highest coefficients of

determination are in SC4 and SC5, which are the scenarios based

on elevation, followed by SC3 and SC2 (0.84 and 0.83, respectively)

203

and the lowest is SC1. In the same way, R2 is the proportion of the

variation in the dependent variable that can be attributed to the

variation of the combined independent variables. The maximum

value of R2 is in SC4 (R2 = 0.97) and the minimum in SC1 (R2 =

0.26).

The critical value of a statistically significant F value, with 95%

probability with (5,15 in SC1), (5,11 in SC2 and SC3), (5,8 in SC4

and SC5) degrees of freedom is 1.89. From table 4.19, it is clear

that none of the computed F values from all scenarios surpassed

the critical value, and as a result, the null hypothesis was not

rejected in any scenario. This confirms that there is no reason to

believe that the independent variables are correlated with each

other. The probability that R would have fortuitously occurred if

the null hypothesis held true was less than 0.05 in all scenarios

with the exception of SC1. The criteria to argue that each variable

helps in the explanation of dependent variable when its used in

combination with the other variables is the t-critical value.

Assuming α=0.5 the t-critical value for the explanation of OF

sensitivity by the landscape properties discussed is 2.131 for SC1

(N=15), is 2.201 for SC2 and SC3 (N=11) and is 2.306 for SC4 and

SC5 (N=8). If the computed t-value exceeds the t-critical value, it

means that the variable is a significant contributor to explanation

of the dependent variable when it is used in combination with the

other variables. The significant contributor for SC1 is slope, for

SC3 is aspect and for SC4 and SC5 is the distance to rivers. For

SC2 none of the topographic variables appear as a significant

contributor in the explanation of the OF sensitivity. This does not

mean that the other variables are not involved in the explanation of

OF sensitivity; it simply says that the associated probability that

the relationships of those variables could occur by chance, if the

null hypothesis were true, is less than 0.05.

204

From this analysis, it can be concluded that the topographic

variables, which are involved in greater proportion in the

explanation of the OF sensitivity, are slope and distance to rivers.

Aspect is not an important variable, a part of that paradoxically, in

SC3 and also SC2, where is the greatest contributor to the

explanation of the OF sensitivity. Topographic index seems not to

be an important variable in the explanation of the OF sensitivity for

all scenarios with the exception in SC1. Despite the fact that SC2

and SC3 appear as the most sensitive scenarios, the variable

distance to rivers was not the most important in the regression

analysis for those scenarios. Although the degree of correlation

between dependent and independent variables in some models are

not high, further combinations of variables could be tested in order

to produce a better correlation of topographic variables and the OF

sensitivity.

205205

Table 4.18 Summary of data used in OF sensitivity analysis

OverlandOverlandOverlandOverland Flow Flow Flow Flow (mm)(mm)(mm)(mm) Percent Percent Percent Percent of of of of VariationVariationVariationVariation OverlandOverlandOverlandOverland Flow Flow Flow Flow SensitivitySensitivitySensitivitySensitivityScenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 1Scenario 2 Scenario 3Scenario 4Scenario 5 Scenario 1 Scenario 2Scenario 3Scenario 4 Scenario 5

1111 7889 7889 7892 7889 78972222 7910 7908 7893 7891 7913 0.27 0.02 0.24 0.02 0.21 0.97 0.95 14.01 0.65 3.663333 7917 7926 7894 7893 7934 0.09 0.02 0.23 0.03 0.25 0.82 0.96 6.49 0.67 3.764444 7924 7939 7895 7897 7940 0.08 0.01 0.17 0.04 0.08 0.81 1.02 2.43 0.71 1.215555 7929 7947 7895 7901 7951 0.07 0.00 0.10 0.06 0.14 0.75 1.04 0.89 0.75 1.856666 7934 7956 7896 7907 7960 0.06 0.01 0.11 0.07 0.11 0.89 1.67 1.72 0.75 1.397777 7938 7966 7900 7913 7966 0.05 0.05 0.12 0.07 0.08 0.81 3.31 7.55 0.82 0.828888 7941 7973 7902 7918 7972 0.04 0.02 0.08 0.07 0.07 0.82 3.05 2.66 0.83 0.829999 7944 7978 7905 7925 7978 0.04 0.03 0.06 0.08 0.07 0.82 3.88 2.81 0.83 0.82

10101010 7946 7980 7907 7934 7983 0.03 0.03 0.03 0.11 0.07 0.84 2.48 2.48 1.39 0.7511111111 7948 7983 7912 7945 7988 0.03 0.06 0.03 0.14 0.06 1.04 2.82 3.85 1.85 0.7412121212 7950 7985 7919 7951 7992 0.02 0.08 0.02 0.08 0.04 0.69 2.68 3.05 1.21 0.7113131313 7952 7989 7929 7971 7994 0.02 0.13 0.05 0.24 0.03 0.84 7.76 3.28 3.64 0.6614141414 7953 7989 7938 7988 7996 0.01 0.11 0.01 0.21 0.02 0.90 1.72 1.67 3.87 0.6515151515 7953 7990 7946 7996 7996 0.01 0.10 0.00 0.10 0.00 0.95 0.88 1.00 5.73 0.6116161616 7954 7991 7958 0.00 0.16 0.01 0.98 2.46 0.9817171717 7954 7992 7977 0.00 0.24 0.02 1.16 6.72 1.0018181818 7954 7993 7996 0.00 0.24 0.02 0.94 15.45 0.9519191919 7954 0.00 1.2120202020 7954 0.00 0.8021212121 7955 0.00 1.0122222222 7955 0.00 0.30

Sum 142889 143371 142556 118919 119461 82.55 131.87 131.91 134.91 124.90 15.02 58.85 56.82 23.70 18.45average 7941 7965 7920 7928 7964 0.04 0.08 0.08 0.10 0.09 0.87 3.46 3.34 1.69 1.32

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SC1 SC2 SC3 SC4 SC5R2 0.264 0.70 0.71 0.971 0.93R 0.514 0.837 0.840 0.985 0.964

F value (5,15) 1.074 (5,11) 5.145 (5,11) 5.294 (5,8) 53.775 (5,8) 21.002F

significance0.413 0.011 0.010 6E-6 2E-4

Std. Errorof estimate

0.002N = 21

0.024N = 17

0.218N = 17

0.003N = 14

0.004N = 14

t(15) Sig. t t(11) Sig. t t(11) Sig. t t(8) Sig. t t(8) Sig. tAspect -1.18 0.25 2.14 0.06 2.29 0.04 -0.17 0.87 1.89 0.10Slope -2.17 0.04 0.47 0.65 0.34 0.73 1.40 0.20 -0.97 0.36

Altitude -0.17 0.86 0.73 0.48 0.85 0.41 1.15 0.28 -0.69 0.51Topindex -1.34 0.19 0.25 0.80 0.18 0.86 1.49 0.17 -0.54 0.60River dist. 0.39 0.70 .89 0.39 0.69 0.50 4.22 3E-3 3.27 0.01

Table 4.19. Multiple regression analysis of overland flow for all scenarios. Significant relationships are highlighted.

206

207

4.5.2.2 Sensitivity analysis of erosion to LUCC at the catchment scale

Erosion is driven mainly by overland flow, so it can be expected to

have a similar behaviour. SC3 (deforestation pattern in a downhill

direction towards river channels) produces the lowest average

erosion per iteration (77.59 mm in a simulated year) (see table

4.20). While paradoxically the highest average values were

produced by SC2 (complementary to SC3 but in an opposing

direction) with 92.94 mm. This difference is about 1.53 m3 ha-1,

which sums to 2166 m3 for the whole catchment. This value is

similar to the resulted value of the difference between erosion

yields of the iteration at the beginning and at the end of the

simulated scenario i.e. with near-full forest cover and almost totally

deforested. The total deforestation in the catchment produces

3294 m3 of additional erosion for the simulation period throughout

all scenarios. For clarity, these values are of soil transported

within the catchment, which is not necessarily equivalent to soil

removed from the catchment (because of redeposition). Most of

this soil is, in fact, re-deposited in other localities within the

catchment. Redeposition of this removed soil is not calculated here.

In the SC1, the erosion yields with the same trend as the pattern of

LUCC. The minimum erosion is at the beginning of the scenario,

and then increases gradually following the curve of LUCC. From

this, it is clear that erosion is directly related to LUCC, as is the

case in all the scenarios.

In general, erosion variation is much larger in all scenarios than

OF variation, ranging from 2 to 10%. In SC1, percent variation of

E is very similar to the percent variation of OF in the same

208

scenario. The relation between E and OF in SC1 is high, but the

erosion sensitivity changes strongly in the final few iterations of

this scenario. The erosion sensitivity in SC1 ranges from 25 to 60

(Figure 4.36). In this scenario while OF sensitivity decreases in the

last iteration, erosion sensitivity increases markedly. The erosion

sensitivity oscillations indicate that there are some differences

between the physical properties of the deforested areas in each

iteration. The topographic variables most related (see Figures 4.36

and 4.37) with erosion sensitivity in this case (SC1) are slope and

altitude. The magnitude of variation in topographic variables is not

as strong as the apparent variation in erosion sensitivity.

For SC2, the erosion yield is very similar to the LUCC pattern,

which is not the case for OF yield. The percentage variation of E

between iterations is similar to the LUCC pattern, but with a higher

rate of decrease in the first half of the iterations, followed by a

lower rate which then reduces to zero. The erosion sensitivity in

SC2 ranges from 12 to 38; the highest values are at the beginning

of the scenario, and then decrease gradually without significant

changes until iteration 16. For the last two iterations there is an

increase in erosion sensitivity (see Figure 4.38). The differences

between OF and E sensitivities are that OF sensitivity is small at

the beginning of the scenario and increases at the end whilst the

opposite is true for E sensitivity. The sensitivities of OF and E in

SC2 show that the initial deforestation near to the river channels

produces small changes in the percentage variation of OF, though

the sensitivity does not change too much. By comparing E

sensitivity with the mean topographic variables of the deforested

area (Figures 4.38 and 4.39) it can be seen that the most similar

behaviour is produced in slope, which has the same trend

throughout the iterations. The mean aspect of deforested area

does not change very much through the simulation.

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Figure 4.37. Mean topographic variables for deforested areas in SC1

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Figure 4.36 Erosion sensitivity in scenario 1 (deforested pattern with cellular automata)

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Figure 4.39 Mean topographic variables of deforested areas in SC2

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Figure 4.38 Erosion sensitivity in scenario 2 (forest conversion with horizontal a fixed distance from river channel uphill direction)

T o tal ero tion by iteration

70

80

90

100

0 5 10 15 20Iteration

% variatio n o f ero sio n

between iteratio ns

0

2

4

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S ensitivity o f ero sio n

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211

However, there is a large increment with significant changes to

percentage variation early in the LUCC iterations. This is also the

case for erosion sensitivity. The highest erosion sensitivity to

LUCC in this scenario is in deforested areas close to the river

channels (in the first iterations) and this is where most of the

erosion is produced in the catchment. This is the opposite of OF

sensitivity, which is high at the top of the catchment, that can be

due high mean slope values in these deforested areas.

In SC3 the erosion yield by iteration does not increase a lot in the

first 12 iterations (2 mm) and the E percent variation remains

equal (Figure 4.40). Through within these iterations the erosion

sensitivity varies highly (15 to 33). Then it remains low until

iteration 7, then, between iterations 7 to 11, where the E sensitivity

increases again to its highest value (33) after which it oscillates

once more, at a higher level of sensitivity. As in the SC2, the

topographic variable most related with S-E in SC3 is slope,

showing the same trend and pattern but with more exaggerated

changes. Topographic index in the first iteration shows the

opposite trend, but in the last iteration it is the same as erosion

sensitivity (see Figure 4.41). Distance to the rivers is opposite in

trend to erosion sensitivity and this suggests that, erosion

sensitivity is highest when the areas closest to the rivers are

deforested. Mean altitude of deforested area in this case does not

have any bearing on erosion sensitivity. Overall this scenario has

the lowest erosion sensitivity values. By taking into account both

SC2 and SC3 it can be concluded that the areas within 150m of

the rivers are very important for E, in magnitude, variation and

sensitivity. The areas farthest from the river channels do not

increase the E much, but they have a big influence in decreasing

the erosion sensitivity. In the middle areas the erosion sensitivity

increases once more. Slope and topographic index seem to be the

most significant control on erosion sensitivity.

212

D eforested area and m ean altitude in S C 3

0

100

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M ean altitude

M ean slope and aspect of defo rested area in

SC 3

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M ean to p.index and distant to rivers in SC 3

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Top. Index

D istance to river

Figure 4.41 Mean topographic variables of deforested areas in SC3

212

Figure 4.40 Erosion sensitivity in scenario 3 (forest conversion with horizontal a fixed distance towards channel rivers downhill direction)

T otal ero sio n by iteratio n

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% variatio n o f ero sio n

betw een iterations

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213

For SC4, erosion yield remains fairly constant through all

iterations of LUCC. The percent variation ranges between 0 and

3%. It increases constantly through the range until the maximum

value (3%) in the eighth iteration. This then decreases in similar

proportions until the end of the scenario (Figure 4.42). Erosion

sensitivity has similar behaviour to the deforested area pattern

(Figure 4.43), with increments through iterations 1 to 9, and then

remaining high until iteration 12. This then decreases until near

to the initial value within the last three iterations. Erosion

sensitivity ranges between 15 to 36. The maximum values of

erosion sensitivity are in deforested areas between 2000 to 2400 m

of elevation (see Figure 4.43). Despite the OF sensitivity increasing

in the last iteration, erosion sensitivity decreases in the same

iterations. Topographic index and distance to river show opposite

trends to those noted to erosion sensitivity. Erosion sensitivity

decreases after the deforestation occurs up to 2400 m of elevation;

in this part slope, aspect, deforested area, and topographic index

decrease too.

For SC5, erosion yield is almost constant throughout all iterations

(Figure 4.44). The percentage variation is high for the first 5

iterations, reaching the maximum value (3.2%) in iteration 3, then

decreasing consistently to zero. Erosion sensitivity also starts with

high values, increasing up to the third iteration with a maximum

value of 42, then decreasing at the constant rate. This increase is

related to the variation of mean slope and aspect of the deforested

areas (see Figure 4.45), which increase until the same iteration.

Then those topographic variables remain more or less constant.

The mean altitude of the deforested area decreases during the

simulation process, which could relate to the decreasing trend of

erosion sensitivity after the third iteration. In that period, mean

distance to rivers of the deforested area remains more or less low

and constant after it had decreased in the early iterations,

214

D eforested area and m ean altitude in S C 4

0

100

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0 5 10 15Iteration

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1900

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M ean altitude

M ean slope and aspect of defo rested area in

SC 4

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Slope

M ean to p.index and distant to rivers in SC 4

6

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6

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15

18

Top. Index

D istance to river

Figure 4.43 Mean topographic variables of deforested areas in SC4

214

Figure 4.42 Erosion sensitivity in scenario 4 (forest conversion with a fixed distance of altitude, in uphill direction from the lower to the highest point)

T o tal ero sio n by iteratio n

70

80

90

100

0 5 10 15Iteration

% variatio n o f ero sio n

betw een iteratio ns

0

2

4

6

8

10

0 5 10 15Iteration

Sensitivity of erosion

0

20

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0 5 10 15Iteration

215

Figure 4.45 Mean topographic variables of deforested areas in SC5

D eforested area and m ean altitude in S C 5

0

100

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0 5 10 15Iteration

1400

1900

2400

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M ean altitude

M ean slope and aspect of defo rested area in

SC 5

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Slope

M ean to p.index and distant to rivers in SC 5

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Top. Index

D istance to river

215

Figure 4.44 Erosion sensitivity in scenario 5 (forest conversion with a fixed distance of altitude, in downhill direction from the highest to the lower point)

T o tal ero sio n by iteratio n

70

80

90

100

0 5 10 15Iteration

% variatio n o f ero sio n

betw een iteratio ns

0

2.5

5

7.5

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0 5 10 15Iteration

S ensitivity o f ero sio n

0

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0 5 10 15Iteration

216

and mean distance to rivers of the deforested area does change in

the same trend as erosion sensitivity.

Comparing erosion sensitivity with OF sensitivity in SC5, shows

little similarity; OF sensitivity changes drastically at the beginning

of the scenario, with small changes in erosion sensitivity. After

iteration 10, erosion sensitivity shows changes where as OF

sensitivity remains constant.

As in OF sensitivity, multiple linear correlation coefficients were

computed for erosion sensitivity analysis, as the dependent

variable and the topographic variables as the independent

variables. Table 4.21 summarises this analysis. The scenario that

produces the highest coefficient of determination is SC5 (R =

0.995), and the lowest is SC1 (R = 0.88), with slope and altitude

variables as the most correlated with erosion sensitivity. The

variation of erosion sensitivity (modelled statistically with b values)

due to the linear combination of topographic variables (R2) ranged

between 0.998 (SC5) to 0.942 (SC1) (see table 4.21). The critical

values of F significance for erosion sensitivity is the same as the

computed in OF sensitivity for the scenarios (1, 89). None of the

computed F significance in any scenarios surpassed the critical

value, which means that the independent variables are not

correlated between each other. The probability that R would have

fortuitously occurred if the null hypothesis held true was less than

0.05 in all scenarios.

The t-critical value assuming α=0.5 computed for erosion

sensitivity is the same, for SC1 is 2.131with N=15, for SC2 and

SC3 is 2.201 with N=11, and for SC4 and SC5 t-critical is 2.306

with N=8. If the computed t-value exceeds the t-critical value, the

variable is a significant contributor in explaining the dependent

variable when it is used in combination with the other variables.

217

217

Table 4.20 Summary of data used in Erosion sensitivity analysis

Erosion (mm) Percent of Variation Sensitivity of ErosionScenario 1Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5 Scenario 1Scenario 2 Scenario 3 Scenario 4 Scenario 5

1 73.7 73.7 73.7 73.7 73.92 78.8 80.8 73.7 73.7 75.2 6.98 9.69 0.04 0.00 1.72 25.20 38.43 29.84 0.00 30.443 81.3 86.2 73.8 74.8 77.2 3.18 6.62 0.05 1.44 2.72 28.09 27.89 16.53 30.05 40.234 83.8 90.0 73.8 75.9 79.4 3.01 4.45 0.06 1.50 2.84 29.57 26.87 17.00 24.33 42.355 85.9 92.4 73.9 77.3 81.8 2.55 2.70 0.08 1.88 2.99 27.42 27.14 15.24 23.53 39.266 87.7 94.0 74.0 79.2 84.4 2.05 1.67 0.11 2.40 3.18 32.00 25.66 17.15 26.62 38.847 89.2 94.9 74.1 81.1 87.1 1.76 1.02 0.13 2.47 3.21 30.43 27.14 19.52 27.40 33.658 90.4 95.6 74.2 83.4 89.4 1.37 0.71 0.17 2.79 2.59 28.72 25.98 18.19 31.24 29.219 91.6 96.0 74.3 86.1 91.3 1.27 0.36 0.23 3.24 2.19 28.28 22.21 20.51 33.88 24.3610 92.7 96.2 74.6 88.7 93.2 1.17 0.24 0.30 3.02 2.03 29.23 18.67 23.65 36.79 22.6211 93.6 96.4 74.9 91.1 94.6 1.00 0.18 0.47 2.68 1.53 37.40 16.20 28.03 35.31 19.2412 94.4 96.5 75.6 93.3 95.7 0.86 0.13 0.90 2.41 1.18 26.20 14.18 32.34 35.89 19.3013 95.0 96.6 76.5 95.3 96.5 0.66 0.10 1.26 2.19 0.80 30.93 15.58 32.90 32.72 16.8214 95.5 96.7 78.1 96.6 96.8 0.48 0.08 2.02 1.34 0.31 34.55 13.19 30.95 24.12 9.7915 95.7 96.7 80.5 96.9 96.9 0.22 0.06 3.11 0.27 0.05 31.66 11.58 31.36 14.77 6.6116 95.8 96.8 84.4 0.16 0.04 4.76 37.13 12.71 28.7717 96.0 96.8 89.7 0.15 0.03 6.35 48.72 12.54 26.8518 96.1 96.8 96.9 0.12 0.03 7.98 44.97 24.62 31.7519 96.2 0.13 56.5220 96.3 0.10 34.5421 96.4 0.08 44.6822 96.4 0.06 59.34

Sum 1617.12 1672.87 1396.64 1267.19 1313.69 27.01 28.13 28.00 27.63 27.36 550.49 360.58 420.57 376.64 372.72Average 91.02 92.94 77.59 84.48 87.58 1.30 1.65 1.65 1.97 1.95 35.50 21.21 24.74 26.90 26.62

218

For SC1, the significant independent variable is slope. In the rest

of the scenarios all four variables are good contributors in

explanation of erosion sensitivity, with the exception of altitude in

SC2, distance to rivers in SC3 and SC5, and aspect in SC4 and

SC5 (the significants are highlighted with yellow in table 4.21).

Those variables contribute to the explanation of erosion sensitivity

for the respective scenarios.

From this analysis erosion sensitivity is strongly related to

topographic variables, and the mean slope of deforested area is

particularly strongly related to erosion sensitivity.

219

SC1 SC2 SC3 SC4 SC5R2 0.88 0.98 0.94 0.98 0.995R 0.942 0.989 0.969 0.992 0.998

F value (5,15) 23.422 (5,11) 98.267 (5,11) 34.47 (5,8) 96.258 (5,8) 326.52F

significance1.4E-06 9E-09 2.3E-06 6.3E-07 5.1E-09

Std. Errorof estimate

0.038N = 21

0..013N = 17

0.019N = 17

0.012N = 14

0.010N = 14

t(15) Sig. t t(11) Sig. t t(11) Sig. t t(8) Sig. t t(8) Sig. tAspect -0.38 0.71 3.87 2E-3 3.53 4E-3 -0.51 0.07 1.93 0.09Slope 3.97 1E-3 4.25 1E-3 3.68 3E-3 3.19 0.01 5.58 5E-4

Altitude 2E-3 0.99 0.19 0.85 3.92 2E-3 7.56 6E-5 11.56 2E-6Topindex 0.49 0.63 4.19 1E-3 1.15 0.27 -3.63 6E-3 3.84 4E-3River dist. 0.13 0.90 2.56 3E-2 1.11 0.91 -5.22 8E-4 0.23 0.82

Table 4.21 Multiple regression analysis of erosion for all scenarios

219

220

4.6 Summary of 2.5D sensitivity analysis

From the result of the multiple linear regression analysis (tables

4.19 and 4.21), it is clear that OF sensitivity and E sensitivity are

very different within the modelled period. For all scenarios, OF

sensitivity is lower than E sensitivity. The highest OF sensitivity

reaches up to 15 in the last iteration of SC2, when deforestation

occurs in the upper areas of the catchment furthest from the river

channels.

Generally E sensitivity is moderately sensitive (between 20 to 60),

which needs to be considered. The highest E sensitivity occurs in

the last iteration of SC1, where the deforestation occurs in the

highest and steepest part of the catchment. This is also clear for

SC4 and SC5, where the highest E sensitivity occurs when the

deforestation pattern starts from the top of the catchment and

progresses in the downhill direction. Consequently, as was shown

in the regression analysis, the mean slope of the deforested area

has a great effect on OF sensitivity and E sensitivity, as does mean

elevation and mean distance to rivers of the deforested area. Areas

close to river channels also had high E sensitivity values. The

aspect variable was not a significant control, though it appears to

have some effects due to its impact on soil hydrology through

evaporation.

The regression values for erosion sensitivity indicate a strong

relationship with topographic variables, but not in the same form

as for OF sensitivity, though they are related.

Combining this analysis with the parameter sensitivity analysis of

the 1D model, it can be concluded that vegetation cover plays an

important role in perturbations of the hydrological cycle due to

221

LUCC, particularly to overland flow and erosion. Moreover, the

location where the LUCC occurs in relation to the topographic

attributes of those locations within the catchment, produce these

changes on E sensitivity and OF sensitivity. The most sensitive

parts of the catchment are at the highest altitudes with steep

slopes and also areas close to the river channels with steep slopes.

The importance of the change in vegetation cover for flux variation

was made clear with the vegetation parameter analysis in 1D.

Changes in vegetation affect the surface and underground fluxes.

Soil moisture was shown to be particularly sensitive to vegetation

parameter changes.

This catchment analysis, by scenarios, was very useful in

identifying those areas most sensitive to LUCC. From some

scenarios it can be concluded that those areas at the top of the

catchment produce the highest OF sensitivity and E sensitivity.

Those areas nearest to the rivers (within 150 m), which have the

steepest slopes, also produce high OF sensitivity and E sensitivity.

Slope is an important factor because it determines overland flow

and in particular, erosion directly. Altitude is an important factor

because rainfall is highest at high elevations within the catchment

and proximity to rivers is also significant factor because of the

cumulative effect of runoff from large contributing areas.

222

4.7 Model validation

4.7.1 Organisation of this section

Validation is an important part of the modelling process because it

allows determination of the accuracy of process simulation and the

confidence to be engendered in the model results.

Both the 1D and 2.5D hydrological models have been developed on

the basis of plot scale parameters, and the model processes can

only be validated at the plot scale because of data availability.

Validation at the catchment scale was intended by comparison of

model generated runoff and sediment yield with field measured

values near the junction of the Tambito and Palo Verde rivers but,

as a result of instrumental failure, this was not possible.

Validation at the plot scale allows one to judge the accuracy of

process modelling under the two types of land cover (forest and

pasture) but does not allow testing of the accuracy of the runoff

and erosion routing.

A comparison between modelled and measured data of some of the

hydrological fluxes is carried out in order to test the model

accuracy. The agreement between measured and modelled data is

assessed for the variables net solar radiation and soil moisture

produced by the 1D model. The behaviour of those variables plays

a significant role within the model. The importance of those

variables in the hydrological process is: (i) net solar radiation

determines the energy availability for canopy and surface water

evaporation, and (ii) soil moisture is significant as the product of

the balance between evaporation, infiltration, overland flow and

recharge processes.

223

Validation is carried out at the same time step that the model was

designed for (1 hour), but was also evaluated as daily summed

values, in order to compare the results at other time scale. Daily

validation could dismiss some noise variation from small time

resolutions, and as well it could show clearly the model

adjustment. Despite the fact that the model is used to simulate a

year, the validation is carried out for a set of data of 20 days, for

which quality data are available and to facilitate detailed

understanding of the results, not possible for much larger series of

data.

4.7.2 Field data set for validation

Validation is carried out using field data from the pasture plot,

because the weather station of this plot was the most stable in

collecting data. The set of data used in the validation process is for

1999 (from 11th of July at 2 p.m. to 3rd of August at 11 a.m.) in

total 550 hours. This period was selected for validation because it

has no interruption in the records in these variables. This set of

data was used to take advantage of times when the key sensors

had optimum reliability and performance.

The model run was allowed to stabilise for 1000 hours before

entering the validation period to allow adjustment from initial

conditions.

4.7.3 Parameters used in validation

Parameters for validation at the pasture plot were very similar to

the parameters used in the model runs for sensitivity analysis

discussed earlier. The only slight difference is in the soil

224

properties, which were chosen to represent the plot local conditions

better, compared with the soil parameters used in the general

model which were the average of catchment-wide samples. The soil

parameters for the grass plot were collected from the plot area.

Those parameters are summarised in table 4.22.

Parameter Name Value

A Net radiation 0.85

B Net radiation 16.98

Light extinction 0.0 (grass canopy)

Leaf area index 1.7 m2 m-2

Vegetation cover 86 %

Soil texture (sand, clay, silt) 47%, 31%, 22% (plot local conditions)

Soil porosity 0.42

Initial soil moisture 0.38

Soil depth 250 mm (depth of the sensor)

Erodability factor k 0. 02

m in erosion 2

n in erosion 1.67

Table 4.22 Parameters used in model validation

Soil texture parameters and soil porosity for the pasture plot were

extracted from eight field samples of 10cm depth in this plot, until

80cm depth which was the limitation of the instrument. Initial soil

moisture was derived from a previous model run as described. The

value for soil depth was assumed 250 mm of depth because that is

the depth where the sensors that measure the soil matric potential

and soil moisture are located and in order to compare like with

like, one must ensure that the model is simulating a similar

volume to that being measured.

225

4.7.4 Validation of net solar radiation

Modelled net solar radiation shows similarities with measured net

radiation. Figure 4.46 shows both modelled and measured hourly

net radiation for the simulation period. As it was expected, there

are some differences between modelled and measured values,

which occur throughout the simulation because of the effects of

clouds, which are stochastic in the model, however both take the

same pattern. Figure 4.47 shows the agreement between modelled

and measured hourly net radiation. The coefficient of

determination (R2) is 0.71, which means that 71% of measured net

solar radiation is explained with modelled net solar radiation, with

the assumption that the variables have a normal distribution, this

is statistically significant at the 99%. The correlation coefficient is

0.84 (n=550), which shows the level of association between

measured and modelled net solar radiation. Figures 4.48 and 4.49

show the diurnal pattern of hourly average net radiation for the

pasture site (modelled and measured). The relationship between

them, which has a coefficient of determination (R2) is 0.86, which

means that 86% of measured net solar radiation is explained by

the modelled net solar radiation, with the assumption that the

variables are normally distributed, and it is statistically significant

at 99%. The coefficient of correlation is 0.92. The agreement

between measured and modelled net solar radiation is better in the

diurnal hourly average than the simple observations, because on

using the average the stochastic variation in cloud cover is no

longer important.

170

Figure 4.46 Modelled and measured solar netradiation for validation

Hourly net radiation modelled and measured

0

1

2

3

0 100 200 300 400 500

Time (hour)

Net

rad

iatio

n (M

J)

Modelled

Measured

Figure 4.47 Linear regression betweenmodelled and measured net radiation invalidation.

Relationship between modelled and measured hourly net radiation

y = 0.68 x - 0.04R2 = 0.71R=0.84

RMS=0.35n=550

0

0.5

1

1.5

2

2.5

3

3.5

0 0.5 1 1.5 2 2.5 3 3.5

Modelled hourly net radiation (MJ)

Mea

sure

d ho

urly

net

rad

iatio

n (M

J)

226

227

Figure 4.48 Hourly average solar net radiation during the day for validation

Comparison between hourly average of modelled and measured solar net radiation

0

1

2

3

0 5 10 15 20Time (hours during the day)

Sol

ar n

et r

adia

tion

(MJ)

Measured

Modelled

Figure 4.49 Linear regression between hourly average of solar net radiation modelledand measured for validation

Relationship between hourly average net radiation

y = 1.05x + 0.54

R2 = 0.86R = 0.92n = 12

RMS = 0.580

1

2

3

0 1 2

Hourly average measured net radiation (MJ)

Hou

rly a

vera

ge m

odel

led

net

radi

atio

n (M

J)

228

4.7.5 Validation of soil moisture

Soil moisture is one of the most important variables in the

hydrological model, because it is the means of hydrological control

of the atmosphere-vegetation-soil interface and the result of the

processes of the soil water balance. Validating soil moisture

indirectly allows validation of other components of the hydrological

cycle such as net rainfall, evaporation, infiltration, recharge and

overland flow, which exercise control on soil moisture; so the soil

moisture validation could be interpreted as the validation of the

outcome of all of these processes.

Soil moisture was evaluated with the same input data set as net

solar radiation. Parameters for this validation are shown in section

4.7.3. Figure 4.50 shows modelled and measured soil moisture for

the validation period. Shape and trends of both graphs are quite

similar. Rainfall is shown in the same graph and assuming linear

correlation between rainfall events and soil moisture the response

is clear.

Figure 4.51 shows the linear regression between modelled and

measured soil moisture. The coefficient of determination (R2) is

0.84, and is statistically significant at 99%, which means that 84%

of measured soil moisture can be explained by the modelled soil

moisture, assuming that the variables are normally distributed.

The correlation coefficient is 0.91 (n=550), which indicates the

association level between measured and modelled soil moisture are

correlated in 91%. Figure 4.51 shows two important things: (i)

There is an associated dependency between consecutive

observations (consecutive time steps), and (ii) a longer validation

period is required in order to produce a more robust test of model

accuracy. Statistically significant at 99% the soil moisture

229

validation gives enough confidence to conclude that the basic soil

hydrological fluxes are reasonable represented in the model

balance.

Soil moisture validation as well was carried out using daily average

for the same period. Figure 4.52 shows the agreement between

modelled and measured soil moisture in trend and shape. Figure

4.53 shows the agreement between daily average modelled and

measured soil moisture through the simulation period for

validation. The coefficient of determination (R2) is 0.84, which

indicates that the modelled daily average soil moisture can explain

84% of the measured daily average soil moisture, assuming that

the variables are normally distributed. The correlation coefficient

is 0.92 (n=21), indicating the association level between variables.

Comparing hourly and daily analysis, the daily linear regression is

not much better than the regression for hourly time step.

230

Figure 4.50 Modelled and measured soil moisture for validation

Soil moisture validation

35

36

37

38

39

40

41

42

0 100 200 300 400 500 600Time (hours)

Soi

l moi

stur

e (%

)

0

0.5

1

1.5

2

2.5

3

3.5

4

Rai

nfal

l (m

m)

rainfall

Measured

Modelled

Figure 4.51 Linear regression of modelled and measured soil moisture for validation

Linear regression of measured and modeled soil moisture

y = 1.45x - 0.17R2 = 0.84R = 0.91

RMS = 0.63n = 550

0.34

0.36

0.38

0.4

0.42

0.36 0.38 0.4 0.42

Modelled soil moisture (%)

Mea

sure

d so

il m

oist

ure

(%)

231

Figure 6.52. Daily soil moisture comparison between modelled and measured, forvalidation, in July of 1999

Comparison of daily soil moisture

35

36

37

38

39

40

41

0 5 10 15 20 25 30

Time (days in July of 1999)

Soi

l moi

stur

e (%

)

Modelled

Measured

Figure 4.53 Linear regression between measured and modelled daily soil moisture,for validation

Relationship between daily modelled and measured soil moisture

y = 1.28x - 0.11R2 = 0.84R = 0.92n = 21

RMS = 0.490.34

0.36

0.38

0.4

0.42

0.36 0.38 0.4 0.42

Modelled soil moisture (%)

Mea

sure

d so

il m

oist

ure

(%)

232

Chapter V Summary, conclusions and further work

5.1 Summary of the key findings in the thesis

A few important advances have been made throughout this thesis,

these they are:

- The application of advanced dynamic modelling techniques to

TMCF.

- The development of a new GIS-based 2.5D hydrological

distributed model for tropical montane environments (TMEs).

- The compilation of hydrological flux data from a series of

experimental plots in a TME in Colombia.

- The combination of LUCC scenarios with a distributed

hydrological model to access the impact of LUCC.

- The identification of the importance of topographic properties on

the flux variations with LUCC.

- The strong relationship discovered between the pattern of LUCC

and erosion sensitivity to it.

- The identification of the most sensitive areas to LUCC within the

catchment, and their relationship with the landscape physical

properties.

5.2 Conclusions and their implications

LUCC is recognised as being an important control on hydrological

processes. Therefore this study sets out to determine how LUCC

impacts on hydrological fluxes at the catchment scale in TMCF

environments. This assessment was reached through the

application of a 2.5D GIS-based hydrological model coupled with

233

LUCC scenarios. The overall achievement of this study, is not only

obtaining a better understanding of the spatial distribution of

hydrological sensitivity given the spatial pattern of LUCC, but also

going someway to provide an advanced and robust tool to help

decision makers to develop and protect the environment, and

produce a basis for further research on TMCF hydrology and the

impacts of LUCC.

The importance of LUCC on hydrological fluxes has been

highlighted in this thesis. The sensitivity analysis of the spatial

variability of hydrological sensitivity within the watershed has

identified the importance of the spatial distribution of landscape

physical properties with respect to where the LUCC occurs and the

differing levels of impact if the same LUCC is applied to different

parts of a catchment.

With respect to the aims proposed at the beginning of the thesis

(see chapter 2), all the objectives were realised but to varying

extents. Specifically:

1- Collecting hydrological data in TMCF at both watershed and plot

scale for forest and grassland land uses.

The climatic conditions and the permanent difficulties in collecting

field data impeded the collection from the weather stations of the

two years data that were proposed. Instrumental problems in

humid tropical forest have been addressed also by Manley and

Askew (1993) in a review of hydrological problems for research, and

also those problems have been addressed for TMCF environments

in particular in the reassessment carried out by Bruijnzeel (2000).

Nevertheless, several months of data were collected at the plot

scale with some interruptions for both types of land uses, to be

able to carry out this research and this highly detailed dataset is

234

unusual for TMCF studies (see table 3.3, page 71). The same

kinds of problems affect the weather station that collected the river

flow data for catchment scale analysis. As a consequence no data

were collected in this scale.

2- The development of a physically based hydrological model, which

includes the most important processes of the hydrological cycle at

the plot scale, and the implementation of this model at the catchment

scale for analysis of impacts of various spatial LUCC scenarios.

Chapter 3 discusses the hydro-meteorological and landscape

characteristics of the study area and gives a clear idea of the rather

hydrologically extreme nature (very steep slopes, very high rainfall)

of the environment where the research was carried out. In general

terms, the hydrology in Tambito is very dynamic. The permanently

high rainfall and atmospheric humidity create a climatic condition,

which is unique to TMCF. An almost permanently wet canopy

means that intercepted water is available for evaporation

throughout the day, and the atmosphere is charged with this

water. The catchment has frequent low-level cloud cover but cloud

interception was not studied in this thesis due to lack of data,

though should certainly be a subject for further research in the

area. In the same way, the high rainfall in the area enhances

catchment wetness. At the catchment scale rainfall was

distributed with a function based on data from field stations at

different altitudes. The extrapolation of these data to the extremes

of the catchment meant that these areas received an exaggerated

value of rainfall at more than 10,000 mm a year (see Figure 3.38.

page 144). Whilst the rainfall parameterisation requires

improvement, this is not possible without many more stations.

Overland flow produced by heavy and persistent rainfall is of high

frequency and magnitude in Tambito and this is replicated in the

235

model, as is the resulting potentially high soil loss in non-vegetated

areas on steep slopes.

The model prediction accuracy is very dependent upon the

parameters used for the land use type (see 1D sensitivity analysis,

section 4.3), and the rainfall distribution function used at the

catchment scale. The hydrological model responds to different land

use type through the parameters, which vary between the land

uses. Since soil hydrological properties for forest and pasture were

shown to be very similar, the only parameters varying with land

use were those of the vegetation, in particular vegetation cover, leaf

area index and canopy interception capacity (which may not be as

important if cloud interception were incorporated into the model).

All of these were shown to be important parameters. At the

catchment scale the relationship between the change in land use

parameters and the soil conditions (varying with geology and

geomorphology on the basis of field measurements) was also shown

to be important (see section 4.5).

The hydrological model was validated by comparing soil moisture

at the plot scale with the 1D model results. This indicates that the

model has the ability to reproduce the hydrological balance with

sufficient accuracy. This is discussed in the validation section.

Not much research has been carried out on the impact of LUCC in

TMCF that could have reported data for comparison. Bruijnzeel

(2000) reviews the existing studies in this environment. From a

catchment close to the study area, Restrepo and Kjerfve (2000)

collected water and sediment yield data in the San Juan River

catchment, approximately 200 kilometres to the north of the

Tambito area, in the same side of western cordillera in Colombia).

From this dataset, a small sub-catchment (the Tadó river) has

similar annual rainfall (7410 mm) to Tambito and was selected in

236

order to compare the overland flow and erosion model results (see

table 5.1).

Area

(ha)

Rainfall

(mm m-2 y-1)

Overland flow

(mm m-2 y-1)

Erosion

(t ha-1 y-1)

Tambito 1411 7325 3835 60.7

Tadó 160000 7410 5144 15.7

Table 5.1 Overland flow and erosion model results for the original

vegetation (from Landsat TM, 1989)comparison with other research.

Despite the large difference in catchment size the results, on a unit

area basis, are comparable. Runoff is less for Tambito catchment

that this reflects the fact that there is more forest than in the Tadó

catchment. The soil erosion is much higher in the Tambito

catchment than the Tadó catchment, which reflects the steeper

slopes, despite the fact that the vegetation cover used in Tambito

catchment was the current (from NDVI, Landsat TM, 1989) and

thus has more forest cover than the Tado. Also, and importantly

the soil erosion in the Tambito study is soil flux from cell to cell

with no function for redeposition whereas the field measurements

are for soil loss from the catchment measured as sediment yield so

the two are not directly comparable.

On average by m2, the total increment of overland flow due to

LUCC (total deforestation in the catchment), in a year-long

hydrological simulation for is 100 mm m-2 yr-1 (2% of the total), and

in erosion is 23 mm m-2 yr-1 (22% of the total). In terms of

catchment totals, these are an increments of 14,110 m3 of water

per year in overland flow and 2245 m3 of removed soil by erosion

from the pristine initial position. This indicates that LUCC can

produce very serious consequences within and outside the

catchment because based on the model results, the erosion is

237

much more sensitive to LUCC than overland flow, but that impacts

can be minimised if land use change occurs in areas with low

sensitivity to LUCC.

Although, model results must be used with care, modelling at the

catchment scale allows us to define areas where runoff and erosion

is increased significantly by LUCC. The model limitations mainly

result from the parameters used, because those parameters are

derived from the land use types present in the Tambito watershed.

The model was designed for a TMCF environment, but with several

adjustments it could be used for other landscapes and

environments, although De Roo (1993) and Lorup et al. (1998) in

discussed this idea order to prevent large errors. The 1D model

can more readily be applied to other landscapes. An evident

limitation of 2.5D model is its spatial resolution, because the cell

size of this study was fixed to 25m pixels which according to the

available data and the type of geomorphology was the most suitable

to make the model operational. To improve this spatial resolution

requires more and better data.

In order to apply the model to different areas, several

considerations must be taken into account:

- The scale of integration needs to be related to the landscape

where the model is applied. Spatial resolution has to reflect the

required level of detail in the model in order to produce

reasonable results (see sections 3.3 and 3.6). In the same way,

cell size needs to be considered and adapted to fit the

landscape, and the computationally efficient operation of the

model.

- Vegetation type and land cover need to be parameterised

carefully within the model, in order to be representative and to

reproduce reasonable results (see section 4.3).

238

- The geomorphology of the landscape has to be taken into

account, in order to define the spatial resolution, which must

represent landforms at the level at which the study is carried

out.

- For larger areas, changes in resolution (large pixels) and scale

must be considered carefully, because model adjustments are

required in order to use the model with larger cells (i.e. erosion

sub-model).

There are two important features derived from the catchment scale

model: the effects of cell connectivity and the effects of landscape

variability on the catchment scale response to spatially distributed

LUCC. Connectivity in the model produces an increment in

overland flow in downslope areas due to the accumulation of water

along flow lines. Additionally, this increase depends upon the

distribution of land cover along flow lines and the spatial variability

of landscape properties along the same lines.

3- The parameterisation of the hydrological model at the plot scale

and identification of the most important parameters influencing

hydrological sensitivity to LUCC (see chapter 4).

From modelling at the plot scale (section 3.5.1), parameters, which

change with LUCC and create the greatest sensitivity in runoff and

erosion for this particular catchment and environment were

highlighted. The most sensitive parameters are vegetationcover, soil depth, porosity and the parameters of the erosionequation. Changing land use produces significant changes in

water fluxes (see sensitivity analysis, chapter 4). Soil depth is the

main control on the amount of soil water storage, which plays an

important role in the catchment water balance.

239

Hydraulic soil properties (such as hydraulic conductivity and

matric potential) are linked directly to the most sensitive soil

parameters used in this model, that were also identified by Kirkby

(1978), as having a critical role dominating surface water

processes. Also Ternan et al. (1987) and Elsenbeer and Cassel

(1990) in Grenada and Western Amazonia respectively emphasise

that soil hydraulic properties are an integral part of hillslope

hydrology in the tropical forest. Particularly, the permeability of

the soil in combination with the topography (steep slopes) and the

soil depth are the control of hillslope responses, with a high rate of

infiltration, with the exception of during extreme rainfalls

(Bruijnzeel, 1990), where the generated saturation overland flow is

considered the principal delivery mechanism to the rivers, in

response to steep and concave slopes combined with heavy storms

(Nortcliff and Thornes, 1981), that are a characteristic of TMCF.

The top soil layer (0.2m) is where the soil hydrology controls the

infiltration and consequently the saturated overland flow as was

described in the sensitivity analysis to the parameter soil depth

(see section 4.3.9), as is highlighted by Bonell et al (1983),

Elsenbeer and Cassel (1990) and Bruijzeel (1990). In addition, this

zone is often the most porous of any in the soil profile, with high

available soil water storage capacities. The saturated infiltration

rates ranged from 5 to 12 mm h-1 for undisturbed rain forest, as

was argued by Wierda et al. (1989) in a tropical rain forest in Coté

d’Ivoire, West African, and compares with that calculated for

Tambito (6 mm h-1) with the pedo-transfer function using the soil

texture parameters.

The most sensitive parameter related to the land use type is

vegetation cover (see section 4.3.6). Vegetation cover affects

evaporation increasing it with almost linear trend as forest

vegetation cover increases, and erosion increases in an exponential

240

way as forest vegetation cover decreases (see Figure 4.18). Under

30% forest vegetation cover, erosion sensitivity does not increase at

all, because the remaining forest areas are not significant for

erosion sensitivity as much as the complementary areas of

vegetation cover (between 80% to 30%). Other parameters change

with vegetation type change in the model, but do not produce large

effects on hydrological fluxes as does vegetation cover does.

Burt et al. (1993) came to similar conclusions after analysing

several studies where LUCC can affect the variations in runoff and

erosion, despite the fact that they were carried out on different

environments to this one (North California USA and Plynlimon mid-

Wales), where the experimental catchments were forested rather

than deforested. Those experiments showed that afforestation

reduced the runoff by 25%, this loss being attributed to increasing

water loss by evaporation due to changes in vegetation properties

that are involved in the rainfall interception process. Similarly,

decreases in evapo-transpiration and thus of increasing runoff with

the reduction of forest cover were identified by Bosch and Hewlett

(1982) and Calder (1992).

The increase of erosion with changes in vegetation cover,

particularly deforestation were also identified by Bruijnzeel (1990),

Falkenmark and Chapman, (1989), who argued that forest

conservation can prevent the occurrence of landslides in similar

slopes to those in the study area.

The soil erosion model seems to be very sensitive to the vegetation

cover parameter, and also to the erosion parameters involved in the

erosion equation of the model, such as erodibility factor. This also

has been recognised by Govindaraju (1998) in his study of effective

field scale values for shear stress and soil erodibility and their

spatial variability for physically-based models.

241

Others, mainly soil parameters, were classified as sensitive

parameters, but do not change with change in land use type within

the model (such as the assumption of one type of soil for the whole

catchment, see section 3.5) based on field measurements. Soils are

much more related to the geology and geomorphology of the area.

Within the modelling process those parameters remain constant

between land cover types, so basically the effect of LUCC is applied

in the model at the catchment scale as a change of the vegetation

cover on the catchment surface.

4- To develop methods for model parameterisation at the catchment

scale

One of the difficulties in the generalisation of model parameters at

the catchment scale, is how they vary throughout the surface. In

this respect, the parameters changing on the catchment surface

are the parameters related to the land use type. Land use type in

the catchment was identified and grouped into two classes in this

study (forest and grassland), and for each land use type

parameters were determined by field sampling (see sections 3.2,

3.3 and 3.5). To assign those parameters throughout the

catchment surface the NDVI was extracted from Landsat TM and

was used to distinguish between the main land covers as the most

suitable and economic way to collect land cover information over

the catchment, and with iterations of the scenarios through the

simulation process.

Vegetation parameters such as cover, LAI and canopy storage

capacity also vary within a cover class in response to species

variability and altitudinal change but this level of complexity could

not be taken into account in this study.

242

Model validation at the plot scale

Soil moisture was used in the model validation process, because all

hydrological fluxes contribute to the soil moisture balance. The

agreement between modelled and measured was significant (r2 =

0.83, RMS = 0.63 at one hour time resolution, and r2 = 0.83, RMS

= 0.49 as a daily average), which provides a degree of confidence in

the behaviour of the hydrological model. Unfortunately validation

was carried out over a small period due to a lack of availability of

uninterrupted good quality data.

Validation at the catchment scale was not carried out because data

at this scale were not available; but the modelling suggests that

there are significant effects of LUCC at the catchment level,

necessitating the improvement of the field methodology for data

collection at this level.

5- The application of the model to identify the location of areas

within the Tambito catchment where hydrological processes are most

sensitive to LUCC in relation to physiographic properties, combined

with different scenarios of LUCC.

Burt et al. (1993) recognised the difficulties of identifying the areas

within a catchment that are sensitive to LUCC, and they suggest

the use of hydrological simulation models for this purpose. Such a

simulation model has been developed and used for the stated

purpose here.

The combined process of sensitivity analysis in the 1D and 2.5D

models, helps us to understand first of all, which parameters

within the model are important for surface hydrological fluxes and

secondly, the properties of spatial variability of the sensitive areas.

243

Using a combination of different scenarios indicates how landscape

sensitivity responds to the pattern of deforestation. From the

analysis of the impact of LUCC, the areas within the catchment

that should be protected from LUCC can be identified, because

those areas with greatest sensitivity can, if deforested, lead to more

serious environmental consequences, such as soil degradation,

erosion and sedimentation (see Figures 4.11 and 4.12). In the

same way, hydrological models offer the possibilities to evaluate the

outcome of particular LUCC strategies. The combination of LUCC

scenarios with a distributed hydrological model also was used by

Mulligan et al. (2000) finding serious effects of progressive

deforestation on runoff and erosion yields. A similar process using

the combination of hydrological models and statistical tests was

carried out by Lorup et al. (1998), who highlight the ability to

improve the analysis of the impact of LUCC on the catchment

runoff, compared to using them separately. On the other hand,

Fahey and Jackson (1997) used a comparison approach for

catchments with different vegetation (forest, grasslands, and pine

plantations) to estimate the differences in hydrological properties.

The LUCC scenarios developed in this thesis are not intended to

represent real patterns of land use change, rather they serve as a

means of testing the sensitivity to landscape physical properties

(table 4.18 to 4.21), which the LUCC patterns produce hydrological

variation in the catchment. The relationship between landscape

properties and hydrological flux variation were also studied by

Quine and Walling (1993), where the erosion rate predictions were

derived from a statistical function of a combination of topographic

attributes. Those types of landscape attributes have also been

used to predict the soil properties with good results (McKenzie and

Ryan, 1999).

244

A reduction in the catchment forest cover in the humid tropics

produces an increment in overland flow and a decrease in water

evaporation (see sections 4.3 and 4.5), that also was identified by

Bosch and Hewlett (1982), Bruijnzeel (1990) and Fahey and

Jackson (1997), among others researches. They highlight that

forest acts as a sponge, so that forest conversion increases

flooding. Unfortunately cloud interception was not taken into

account, though this is one of the processes that can affect the

hydrological balance in montane cloud forest, where may

compensate for the extra water evaporation identified in conversion

to forest (Zadroga, 1981). Also with this respect, Calder (1998)

associates the changes in evaporation rates with the variations on

large leaf surface area and the deeper root system of forest.

The pattern of variation in erosion within the modelled scenarios is

very similar to the LUCC pattern, which indicates a strong relation

between LUCC and hydrological change that is driven by vegetation

cover change.

The importance of the landscape physical properties is identified

from modelling at the catchment scale. Slope, aspect, elevation,

and distance to rivers of the deforested area, among others,

produce an important effect on the hydrological fluxes from those

same areas. Those properties, combined with the location of the

deforested areas relative to the hydrological flow-routing network

are critical to the hydrological response to land use change.

Modelling at the catchment scale combined with the sensitivity

analysis helped to identify the topographic characteristics of the

areas most sensitive to LUCC within the catchment (see section

4.5). Those characteristics are:

245

- steep slopes, within 150m distance to rivers and with highelevation (see Slope Map, Figure 3.17, page 81).

- steep slopes furthest away from river channels, which areclose to the boundary of the catchment, where rainfall ishighest (see Elevation Map, Figure 3.16, page 80).

The same land use change can have quite different effects on the

hydrological cycle if it occurs in the high parts of the catchment as

opposed to the lower parts, or at the top of a slope as opposed to its

base.

5.3 Further research

The validation process carried out in this thesis used short periods

of data. It is necessary to collect more field information and re-

validate the model, in order to check the model predictions more

fully.

The initial aim was to produce a simple framework model for this

landscape (TMCF) where the main hydrological events were

represented, and also that the model could operate with little

information. Therefore, there is now a need to incorporate other

modules or improve some of the existing one. The improvements

required include:

- Adjusting the model rainfall equation in order to produce more

realistic results through the acquisition of more spatially

detailed field rainfall data.

- The incorporation of cloud interception by trees to integrate

additional water in the hydrological cycle. Also the interception

module needs to be calculated at shorter time resolutions, in

246

order to assess with more accuracy the loss of intercepted

water.

- Modelling soil deposition and sedimentation. Erosion in the

current module is modelled only as soil detachment; this soil

will be re-deposited or incorporated to the river flux as

suspended sediment.

- Plant transpiration routine. Evapotranspiration could have a

more significant role in the hydrological cycle in TMCFs than

assumed here. As the variation in altitude within the

catchment is significant (more than 1000m) and in the same

way the environmental conditions and vegetation change.

Plants could have a complex and differing response through the

altitudinal range.

- Throughflow may also be more important at the catchment scale

for hydrology in this particular environment (TMCFs), than

assumed here by its non-inclusion.

- Changes in soil properties produced by LUCC could be usefully

included for catchments or LUCC's where LUCC impacts on soil

as well as vegetation properties.

In terms of land use change

- To incorporate dynamic scenarios where the LUCC forces to

change in soil properties (such as porosity and bulk density) as

well as plant properties.

- Allow the capacity for forest growth and regeneration,

incorporating plant physiology processes within the model.

247

In terms of modelling

- There is still a gap between modelling activities and GIS

interfaces. The spatial representations of some processes are

not simple and require better GIS tools.

- The relationships between hydrological fluxes and physical

landscape properties have been shown through this thesis. On

the basis of the analysis, a statistical model could be

developed to identify the sensitive areas (in terms of overland

flow and erosion) within catchments with similar characteristics

and land use to the ones in which the model is developed. On

the basis of catchment physical properties such as those

studied here (slope, aspect, topographic index, altitude, and

distance to rivers) and derived from DEMs should be used in the

statistical model. This statistical model could be a fast

alternative to evaluate the hydrological impact of LUCC in

catchments with similar characteristics without carrying out the

whole process of hydrological modelling, land use

parameterisation and sensitivity analysis developed here, using

only secondary information derived from DEMs. This is the

subject of further papers based on the information and

processes developed in this thesis.

It is recognised that this model is useful to support further

research on TMCF environments. Understanding landscape

sensitivity to land use change could also assist conservation

planning and rural agricultural sustainability. These types of

research can be used by the government and also in more local

areas, by the Corporations who have the responsibility to manage

natural resources within the catchments in Colombia, as a fast

alternative to assess the LUCC impact of proposed plans of

catchment management.

248

Bibliography

AARAM Andean Amazon Rivers Analysis and Monitoring project.

1998. http:boto.ocean.washington.edu/aaram/

Abbott, M. B., Bathurst, J, C., Cunge, J. A., O’Connell, P. E., and

Rasmussen, J. 1989. An Introduction to the European System –

Systeme Hydrologique Europeen, “SHE”, 2. Structure of a

physically-based, distributed modelling system. Journal of

Hydrology, 87: 61 – 77.

Ahuja, L. R., Naney, J. W., and Williams, R. D. 1984. Estimating

soil water characteristics from simpler properties or limited data.

Soil Science Society American Journal, 49: 1100 - 1105.

Ahuja, L. R., and Williams, R. D. 1991. Scaling water

characteristic and hydraulic conductivity based on Gregson-

Hector-McGowan approach. Soil Science Society American Journal,

55: 308 - 319.

Anderson, M. G., and Burt, T. P. 1985. Hydrological Forecasting.

John Wiley & Sons Ltd. Chichester, UK.

Anderson, M. G., and Burt, T. P. 1990. Process studies in hillslope

hydrology. John Wiley & Sons. Chishester, U. K.

249

Arya, L. M., and Paris, J. F. 1981. A physicoempirical model to

predict the soil moisture characteristic from particle-size

distribution and bulk density data. Soil Science Society American

Journal, 45: 1023 - 1030.

Aston A.R. 1979. Rainfall Interception by eight small trees.

Journal of Hydrology, 42: 383 - 396.

Avery B.W., and Bascomb C.L. 1974. Soil survey and laboratory

methods. Harpenden Soil Survey of Great Britain. UK.

Banco de la República, 1951. Bases de un programa de fomento

para Colombia – Informe de una Misión dirigida por Lauchlin Currie.

Banco de la República (eds). Bogotá, Colombia.

Bathurst J. C., Wicks J.M., and O’Connell P. E. 1995. The

SHE/SHESED Basin Scale Water Flow and Sediment Transport

Modelling System. In: Computer models of Watershed Hydrology.

Edited by Vijay P. Singh. Department of Civil and Environment

Engineering Louisiana State University, Baton Rouge, LA 70803-

64405, USA.

Bejarano, J. A. 1977. Contribución al debate sobre el problema

agrario. In: El agro en el desarrollo histórico colombiano. Edutorial

Punta de Lanza, Bogotá, Colombia.

Bell, M. A. and Van Keulen, H. 1995) Division S-5 -Pedology. Soil

Pedotransfer Functions for Four Mexican Soils. Soil Science

Society. American Journal, 59: 865 - 871.

Beven, K. J., and Kirkby, M. J. 1976. Towards a simple

physically-based variable contributing area of catchment

250

hydrology. Working paper 154, School of Geography, University of

Leeds.

Beven, K. J., and Kirkby, M. J. 1979. A physically based variable

contributing area model of basin hydrology. Hydrological Science

Bulletin, 24, 1: 43 – 69.

Beven, K. J., Kirkby, M. J., Schofiled, N., and Tagg, A. F. 1984.

Testing a physically-based flood forecasting model (TOPMODEL) for

three U.K. catchments. Journal of Hydrology, 69: 119 – 143.

Beven, K. J. 1989. Changing ideas in hydrology – the case of

physically-based models. Journal of Hydrology, 105: 157 – 172.

Beven K.J., and Moore I.D. 1994. Advances in Hydrological

Processes. Terrain Analysis and Distributed Modelling in Hydrology.

John Wiley & Sons. New York. USA.

Beven K., Lamb R., Quinn P., Romanowicz R., and Freer J. 1995.

TOPMODEL. In: Computer Models of Watershed Hydrology. Water

Resources Publications. Edited by Vijay P. Singh. Department of

Civil and Environment Engineering Louisiana State University,

Baton Rouge, LA 70803-64405, USA.

Binley, A., and Beven, K. 1992. Three-dimensional modelling of

hillslope hydrology. Hydrological Processes, 6: 347 – 359.

Blazkova, S., and Beven K. 1997. Flood frequency prediction for

data limited catchments in the Czech Republic using a stochastic

rainfall model and TOPMODEL. Journal of Hydrology, 195, 1-4:

256 – 278.

251

Bonell, M. 1993. Progress in the understanding of runoff

generation dynamics in forest. Journal of Hydrology, 150: 217 –

275.

Bonell, M, and Balek, J. 1993. Recent Scientific Developments

and Research Needs in Hydrological Processes of the Humid

Tropics. In: Hydrology and Water Management in the Humid

Tropics. Hydrological research issues and strategies for water

management. Edited by Michael Bonell, Maynard M, Hyfschmidt

and John S. Gladwell. International hydrology series, Cambridge

University press, UK.

Bosch, J.M., and Hewlett, J. D. 1982. A review of catchment

experiments to determine the effect of vegetation changes on water

yield and evapotranspiration. Journal of Hydrology, 55: 3 – 23.

Broadman, J., and Favis-Mortlock, D. T. 1993. Simple methods of

characterizing erosive rainfall with reference to the South Downs,

southern England. In: Farmland Erosion in Temperate Plains,

Wicherek, S. (ed.). Environments and hills. Elsevier, Amsterdam,

The Netherlands: 17 – 29.

Brock, T. D. 1981. Calculating solar radiation for ecological

studies. Ecological Modelling, 14: 1 - 19.

Bronstert, A. 1999. Capabilities and limitations of detailed

hillslope hydrological modelling. Hydrological Processes, 13: 21 –

48.

Brooks, R. H., and Corey, A. T. 1964. Hydraulic properties of

porous media. Hydrology, Paper3. Colorado State University, Fort

Collins, Colorado.

252

Brown, M. B., de la Roca, I., Vallejo, A., Ford, G., Casey, J.,

Aguilar, B., and Haaker, R. 1996. A valuation analysis of the role

of cloud forest in watershed protection, Sierra de Minas Biosphere

Reserve, Guatemala ans Cusuco N. P. Honduras. RARE Center for

Tropical Conservation, Philadelphia, USA.

Bruijnzeel, L. A., and Wiersum, K. F. 1987. Rainfall interception

by a young Acacia auriculiformis A. Cunn. Plantation forest in

West Java, Indonesia: Application of Gash’s analytical model.

Hydrological Processes, 1: 309 – 319.

Bruijnzeel, L. A. 1989. (De)forestation and dry season flow in the

tropics: A closer look. Journal of Tropical Forest Science, 1, 3: 229 –

243.

Bruijnzeel, L. A. 1990. Hydrology of moist tropical forest and

effects of conversion: a state of knowledge review. UNESCO

International Hydrological Programme, A publication of Humid

Tropics Programme, UNESCO, Paris.

Bruijnzeel, L. A., Waterloo, M. J., Proctor, J., Kuiters, A. T., and

Kotterick, B. 1993. Hydrological observations in montane rain

forest on Gunung Silam, Sabah, Malaysia, with special reference to

the “Massenerhebung’ effect. Journal of Ecology, 81: 145 – 167.

Bruijnzeel, L. A., and Proctor, J. 1995. Hydrology and

biogeochemistry of tropical montane cloud forest: what do we really

know? In: L. S. Hamilton, Juvik, J. O., and Scatena, F. N., eds.

Tropical Montane Cloud Forests. Ecological Studies 110, Springer

Verlag, New York: 38 – 78.

Bruijnzeel, L. A. 2000. Hydrology of Tropical Montane Cloud

Forests: A Reassessment. Submitted to Hydrological Processes.

253

Buitrago, F. L. 1977. Desarrollo, Subdesarrollo y Ciencias

Sociales. In: El Agro en el Desarrollo Histórico Colombiano.

Edutorial Punta de Lanza, Bogotá, Colombia.

Budyco, M. I., 1974. Climate and Life. Academic press, London –

UK.

Burt, T. P., Heathwaite, A. L., and Trudgill, S. T. 1993. Catchment

sensitivity to land use controls. In: Landscape Sensitivity. Eds. by

D. S. G. Thomas and R. J. Allison. John Wiley & Sons.

Chichester, UK.

Calder, I. R. 1986. A stochastic model of rainfall interception.

Journal of Hydrology, 89: 65 – 71.

Calder, I. R., Wright, I. R., and Murdiyarso, D. 1986. A study of

evaporation from tropical rainforest – West Java. Journal of

Hydrology, 89, 1 - 2: 13 – 31.

Calder, I. R. 1992. The hydrological impact of land use change

(with special reference to afforestation and deforestation). In:

Proceedings of the Conference on Priorities of Water Resources

Allocation and Management. Southhampton, July 1992. Overseas

Development Administration, London. ISBN: 090 2500 49X.

Calder, I.R., Hall, R.L., Bastable, H.G., Gunston, H.M., Shela, O.,

and Chirwa A. 1995. The impact of land-use change on water

resources in Sub-Saharan Africa. A modelling study of lake

Malawi. Journal of Hydrology. 170, 1-4: 123-135.

254

Calder, I. R. 1996. Dependence of rainfall interception on drop

size: 1. Development of two-layer stochastic model. Journal of

Hydrology, 185: 363 – 378.

Calder, I. R. 1998. Water use by forests: Limits and controls.

Trees Physiology, 18: 625 – 631.

Calvo, J. C. 1986. An evaluation of Thronthwaite’s water balance

technique in predicting stream runoff in Costa Rica. Hydrological

Science Journal, 31: 51 – 60.

Cammeraat, L. H. 1991. MEDALUS, Mediterranean Desertification

and Land Use. Field Manual. Version 3.1.

Campbell, G. S. 1985. Soil physics with basic. Transport models

for soil-plant systems. Elsevier Science Publishers B.V.

Netherlands.

Campbell, J. B. 1987. Introduction to remote sensing. A division of

Guilford publications Inc. 200 Park Avenue south, New York. N.Y.

10003. USA.

Cartier W., and Forero-Alvarez, J. 1990. Planeación Agropecuaria

en Colombia. Cuadernos de Agroindustria y Economía Rural en

Colombia, 24: 9 – 127.

Cavalier, J. 1986. Relaciones Hídricas de Nutrientes en Bosques

Enanos Nublados Tropicales. Magister Scientiae en Ecología

Vegetal, Universidad de los Andes de Merida, Venezuela.

255

CEPAL. 1990. Magnitud de la Pobreza en América Latina en los

Años Ochenta. Comisión económica para América Latina y el

Caribe. Santiago de Chile, Chile.

CIAT - Hillside Program. 1994. Hillsides Program Annual Report

1992 – 1994. Internal document, Ciat, Cali – Colombia.

Cienciala, E., Kucera, J., Lindroth, A., Cermak, J., Grelle, A., and

Halldin, S. 1997. Canopy transpiration from a boreal forest in

Sweden during a dry year. Agricultural and Forest Meteorology, 86:

157 – 167.

Chu, S. T. 1994. Capillary-tube infiltration model with Brooks-

Corey parameters. American Society of Agricultural Engineers, 37,

4: 1205 - 1208.

Chu, S. T. 1995. Effect of initial water content on Green-Ampt

parameters. American Society of Agricultural Engineers, 3, 3: 839 -

841.

Chu, S. T. 1997. Infiltration model for soil profiles with a water

table. American Society of Agricultural Engineers, 4, 4: 1041-1046.

Cohen, W. B. 1991. Response of vegetation indices to changes in

three measures of leaf water stress. Photogrammetric Engineering

& Remote Sensing, 57, 2: 195 – 202.

Coles, N.A., Sivapalan, M., Larsen, J.E., Linnet, P.E. and Fahrner,

C.K. 1997. Modelling runoff generation on small agricultural

catchments: Can real world runoff responses be captured?

Hydrological Processes, 11: 111 – 136.

256

Copeland, J. H., Pielke R. A. and Kittel T. G. F. 1996. Potential

climate impacts of vegetation change: A regional modelling study.

Journal of Geophysical Research, 101, D3: 7409 – 7418.

Crohn, D. M. 1995. Sustainability of sewage sledge land

application to northern hardwood forest. Ecological Applications, 5,

1: 53 - 62.

CSIRO Institute of Natural Resources and Environment. 1993.

TOPOG_IRM, Model description. Division of Water Resources,

Camberra Laboratory, By W. Dawes and T. Hatton. Camberra,

Australia.

Dale, V.H. 1997. The relationship between land use change and

climate change. Ecological Applications, 7, 3: 753 - 769.

Dangermond, J. 1993. The Role of Software Vendors in

Integrating GIS and Environmental Modelling, . Environmental

Modelling with GIS. Oxford University press. New York – Oxford,

USA.

De Roo, A. P. J. 1993. Validation of the ANSWERS catchment

model for runoff and soil erosion simulation in catchments in The

Netherlands and the United Kingdom. In: HydroGIS 93:

Application of Geographic Information Systems in hydrology and

Water Resources. Proceedings of the Vienna Conference, April

1993. IAHS Publ. No. 221.

Digman, S. Lawrence. 1994. Physical Hydrology. Prentice-hall,

Inc. New York, USA.

Dobson, F. and Smith, S. 1988. Bulk models of solar radiation at

sea. Q. J. R. Meteorol. Soc., 114: 165 – 182.

257

Dolman, A. J., Gash, J. H. C., Roberts, J., and Shuttleworth, W. J.

1991. Stomatal and surface conductance of tropical rain-forest.

Agricultural and Forest Meteorology, 54, 2-4: 303 – 318.

DPN, 1999. Planning National Department, Colombia Government.

http://www.dnp.gov.co/plan_nal/LEY_508_1999.pdf

Duff, E. A. 1968. Agrarian Reform in Colombia. Frederick A.

Praeger, Publishers. New York, USA.

Dunn, S. M., and Mackay, R. 1995. Spatial variation on evapo-

transpiration and the influence of land-use on catchment

hydrology. Journal of Hydrology, 171, 1-2: 49-73.

Dunn, S. M. 1996. The hydrological component of the NELUP

decision-support system: an appraisal. Journal of Hydrology, 177,

3-4: 213 – 235.

Duru, J. O., and Hjelmfelt, A. T. 1994. Investigation prediction

capability of HEC-1 and KINEROS kinematic wave runoff models.

Journal of Hydrology, 157, 1-4: 87 – 103.

Elsenbeer, H, and Cassel, D. K. 1990. Surficial processes in the

rainforest of western Amazonia. In : Research Needs and

Applications to Reduce Erosion and Sedimentation in Tropical

Steeplands. R. R. Zimmer, C. L. O’Loughlin, and L. S. Hamilton.

Proc. Fiji Symp. International Association of Hydrological Science

Publications, 192: 289 – 297.

ESRI. 1998. Environmental Systems Research Institute, Inc.

Arc/Info V 7.3. Redlands, California. USA.

258

Falkenmark, M., and Chapman, T. 1989. Comparative hydrology.

An Ecological approach to land and water resources, UNESCO.

Paris.

FAO, Food and Agriculture Organization, 1997.

Http://www.fao.org/gtos/about/gtodbdir/GTOSa001.html.

Faunt, C., D’agnese, F., and Turner, K. 1993. Development of a

three-dimensional hydrological framework model for the Death

Valley region, southern Nevada and California, USA. HydroGIS 93:

Application of Geographic Information Systems. In: Hydrology and

Water Resources. IAHS Publ. No. 211.

Favis-Mortlock, D. 1993. Validation of field scale soil erosion

models using common data sets. Modelling soil erosion by water.

Edited by John Boardman and David Favis-Mortlock. University of

Oxford. UK.

Fedra, Kurt. 1993. GIS and Environmental Modelling.

Environmental Modelling with GIS. Oxford University press. New

York – Oxford, USA.

Fajardo, D. 1996. La Reforma Agraria en la Política Social. In:

Una mirada social al campo. Ministerio de Agricultura y Desarrollo

Rural. Santafé de Bogotá, Colombia.

Fahey, B., and Jackson, R. 1997. Hydrological impacts of

converting native forest and grasslands to pine plantations, South

Island, New Zealand. Agricultural and Forest Meteorology, 84: 69 –

82.

Feldman, A. D. 1995. HEC-1 Flood Hydrograph Package.

Computer models of Watershed Hydrology. Edited by Vijay P.

259

Singh. Department of Civil and Environment Engineering

Louisiana State University, Baton Rouge, LA 70803-64405, USA.

Finch, J. W. 1998. Estimating direct groundwater recharge using

a simple water balance model – Sensitivity to land surface

parameters. Journal of Hydrology, 211: 112 – 125.

Fisher, P., Abrahart R. J, and Herbinger W. 1997. The sensitivity

of two distributed non-point source pollution models to the spatial

arrangement of the landscape. Hydrological Processes, 11: 241 –

252.

Flugel, W. A. and Smith, R. E. 1999. Integrated process studies

and modelling simulations of hillslope hydrology and interflow

dynamics using the HILLS model. Environmental Modelling and

Software, 14: 153 – 160.

Ford, E. D., and Deans, J. D. 1978. The effects of canopy

structure on stemflow, throughfall and interception loss in a young

Sitka Spruce plantation. Journal of Applied Ecology, 15: 905 - 917.

Forseth, I. N., and Norman, J. M. 1993. Modelling of solar

irradiance, leaf energy budget and canopy photosynthesis.

Photosynthesis and Production in a Changing Environment: field and

laboratory manual. Edited by D.O. Hall, J.M.O. Scurlock, H.R.

Bolhar-Nordenkampt, R.C. Leegood and S.P. Long. Chapman &

Hall, London.

Frohn, R. C., McGwire, K. C., Dale, V. H., and Estes, J. E. 1996.

Using remote sensing analysis to evaluate a socio-economic and

ecological model of deforestation in Rondonia, Brazil. International

Journal of Remote Sensing, 17, 16: 3233 - 3255.

260

Gansler, R. A., Klein, S. A., and Beckman, W. A. 1994. Assessment

of the accuracy of generated meteorological data for use in solar

energy simulations studies. Solar Energy, 53, 3: 279 - 287.

Gash, J. H. C., and Morton, A. J. 1978. An application of Rutter

model to the estimation of interception loss from Thetford forest.

Journal of Hydrology, 38: 49 - 58.

Gash, J. H. C. 1979. An analytical model of rainfall interception

in forests. Quart. J. Met. Soc., 105: 43 – 55.

Gash, J. H. C., Wright, I. R., and Lloyd, C. R. 1980. Comparative

estimates of interception loss from three coniferous forest in Great

Britain. Journal of Hydrology, 48: 89 – 105.

Gavin, H., and Agnew, C. T. 2000. Estimating Evaporation and

Surface Resistance from Wet Grassland. Phys. Chem. Earth (B), 25,

7-8: 599 – 603.

Gerrard, A. J. W. 1993. Landscape sensitivity and changes on

Dartmoor. Landscape sensitivity. Edited by D. S. G. Thomas and

R. J. Allison. John Wiley & Sons. Baffins Lane, Chichester,

England.

Golley, F. B. 1983. Ecosystems of the world 14A. ‘Tropical rain

forest ecosystems’. Elsevier Scientific Publishing Company. New

York, U.S.A.

Goodrich, D. C. 1991. Basin scale and runoff model complexity.

Univ. of Arizona, Dept. of Hydrology and Water Resources Tech.

Rep. No. HWR 91010, 361 p.

261

Govindaraju, R. S. 1998. Effective erosion Parameters for slopes

with spatially varying properties. Journal of Irrigation and Drainage

Engineering, 124, 2: 81 – 88.

Granger, R. J. 1989. An examination of the concept of potential

evaporation. Journal of Hydrology, 111: 9 – 19.

Grayson, R. B., Bloschl, G., and Moore, I. D. 1995. Distributed

parameter hydrologic modelling using vector elevation data:

THALES and TAPES-C. In: Computer models of watershed

hydrology. Vijay P. Singh (ed.). Water Resources Publications.

USA.

Green, W., and Ampt, A. 1911. Studies on Soil Physics – Part I.

The flow of air and water through soils. Journal of Agricultural

Science, 4: 1 - 24.

Grismer, M. E. 1986. Pore-size distribution and Infiltration. Soil

Science, 141, 4: 249 - 259.

Grubb, P. J. 1977. Control of forest growth and distribution on

wet tropical mountains: with special reference to the mineral

nutrition. Annual Review of Ecology and Systematics, 8: 83 – 107.

Gueymard, C. 1993. Critical analysis and performance

assessment of clear sky solar irradiance models using theoretical

and measured data. Solar Energy, 51, 2: 121 – 138.

Gupta, S. C., and Larson, W. E. 1979. Estimating soil water

retention characteristics from particle size distribution, organic

matter content, and bulk density. Water Resources Research, 15:

1633 – 1635.

262

Gustard, A. and Wesselink, A. J. 1993. Impact of land-use change

on water resources-Balquhidder catchments. Journal of Hydrology,

145, 3-4: 389 - 401.

Hafkenscheid, R. L. L. J. 2000. Hydrology and biogeochemistry of

tropical montane rainforest of contrasting stature in the Blue

Mountains, Jamaica. Universiteit De Boelelaam, 1105.

Hancock, N. H., and Crowther, J. M. 1979. A Technique for the

direct measurement of water storage on a forest canopy. Journal of

Hydrology, 41: 105 - 122.

Hatton, T. J., and Dawes W. 1993. TOPOG_IRM. 2. Examples

simulation. CSIRO Division of Water Resources, Tech. Memo. 93/6.

Herwitz’s, S. R. 1987. Raindrop impact and water flow on the

vegetative surfaces of trees and the effects of stemflow and

throughfall generation. Earth Surf. Processes Landf., 12: 425 –

432.

Hillel, D. 1971. Soil and Water, Physical principles and processes.

Academic Press, Inc. New York, USA.

Horton, R. E. 1940. An approach toward a physical interpretation

of infiltration-capacity. Soil Science Society American Journal, 5:

399 – 417.

Houghton, R. A. 1994. The worldwide extent of land use change.

Bioscience, 44, 5: 305 - 313.

Huber, W. C. 1995. EPA Storm Water Management Model –

SWMM. Computer Models of Watershed Hydrology. Edited by Vijay

263

P. Singh. Department of Civil and Environment Engineering

Louisiana State University, Baton Rouge, LA 70803-64405, USA.

Huttel, C. 1975. Recherches sur l’écosystème de la fôret

subéquatoriale de basse Côte d’Ivoire. III. Inventaire et structure

de la végétation ligneuse. In: La Terre et la Vie., 29: 178 – 191.

IDEAM, Instituto de Hidrología Meteorología y Estudios

Ambientales. Weather Database. Ministerio del Ambiente,

Colombia.

IGAC- Instituto Geográfico Agustín Codazzi, 1975. Geodésia.

Resultados Definitivos de Parte de las Redes Geodésicas

Establesidas en el País. Publicación epsecial #1. Cuarta Edición.

Santafé de Bogotá, Colombia. Pp 3 - 6.

IGAC Instituto Geografico Agustin Codazzi. 1983. Mapa de Suelos

de Colombia. Ministerio de Agricultura. Bogota, Colombia.

IGAC Instituto Geografico Agustin Codazzi. 1985. Panchromatic

air-photograph scale 1:40,000.

Ingeominas, Insitituto Nacional de Investigaciones Geológico

Mineras, Colombia. 1999. Tambito geomorphology. Verbal

comunication from Patricia Negret.

Iqbal, M. 1983. An Introduction to Solar Radiation. Academic press.

Toronto, Canada.

Jackson, I. J. 1975. Relationships between rainfall parameters

and interception by tropical rainforest. Journal of Hydrology, 24:

215 – 238.

264

James, W., Warinner, J., and Reedy M. 1993. Application of the

Green-Ampt infiltration equation to watershed modelling. Water

Resources Bulletin, 28, 3: 623 - 635.

Jane, G. T., and Green, T. G. A. 1985. Patterns of stomatal

conductance in six evergreen tree species from a New Zealand

cloud forest. Botanical Gazette, 46: 413 – 420.

Jarvis, P. G. 1976. The interpretation of the variations in leaf

water potential and stomatal conductance found in canopies in the

field. Philosophical Transactions of the Royal Society of London B,

273: 543 – 610.

Jarvis, A. 1999. Quantifying the Hydrological Role of Cloud

Deposition onto Epiphytes in a Tropical Montane Cloud Forest,

Colombia. BSc dissertation in Geography at King’s College London,

University of London.

Jetten, V. G. 1994. Modelling the Effects of Logging on the Water

Balance of a Tropical Rainforest. A Study in Guyana. Tropenbos

Series 6. The Tropenbos foundation. Wageningen. Department of

Physical Geography, Faculty of Geographical Sciences. Universiteit

Utrecht, P.O.Box 80.115,3508 TC Utrecht, The Netherlands.

Jetten, 1996. Interception of tropical rain forest: performance of a

canopy water balance model. Hydrological Processes, 10, 5: 671 -

685.

Johnes, P. J., and Burt, T. P. 1990. Modelling the impact of

agriculture upon water quality in the Windrush catchment – an

export coefficient approach in a representative basin. Proceedings

and Information – Committee for Hydrological Research TNO, 44:

245 - 252.

265

Johnes, P. J. 1996. Evaluation and management of the impact of

land use change on nitrogen and phosphorus load derived to

surface waters: the export coefficient modelling approach. Journal

of Hydrology, 183, 3-4: 323 - 349.

Johnes, P. J., and Heathwaite, A. L. 1997. Modelling the impact of

land use change on water quality in agricultural catchments.

Hydrological Processes, 11: 269 - 286.

Kapos, V., and Tanner, E. V. J. 1985. Water relations of Jamaican

upper montane forest trees. Ecology, 66: 241 – 250.

Kato, R., Y. Tadaki, and H. Ogawa. 1978. Plant biomass and

growth increment studies in Pasoh Forest. Malayan Nature

Journal, 30: 211 – 224.

Kerfoot, O. 1968. Mist precipitation on vegetation. Forestry

Abstracts, 29: 8 - 20.

Kim, S., Delleur, J. W., Mitchell, J. K., Engell, B. E., and Walker S.

E. Simulation of runoff in agricultural watersheds with tile drains

using an extended TOPMODEL. Transactions of the ASAE, 42, 3:

639 – 650.

Kinsel, W. G., and Williams, J. R. 1995. Hydrology components of

CREAMS and GLEAMS models. In: Computer Models of Watershed

Hydrology. Vijay P. Singh (ed.). Water Resources Publications.

USA.

266

Kirkby, M.J. 1975. Hydrograph modelling strategies. In R. Peel.

M. Chisholm and P. Haggett (Eds). Process in Physical and Human

Geography. Heinemann, 69 – 90.

Kirkby, M. J. (ed.). 1978. Hillslope Hydrology, Wiley, Chichester,

UK.

Kirkby, M. J., and Morgan, R. P. C. 1980. Soil Erosion. John

Wiley & Sons Ltd. Chichester, UK.

Kirkby, M. J. 1990. Modelling vegetation, water balance and

sediment yield to forecast landscape change due to expected

climate change. Proc. Conference. In: Landscape-Ecological Impact

of Climatic Change.

Kirkby, M. J., Naden, P. S., Burt, T. P., and Butcher, D. P. 1993.

Computer Simulation in Physical Geography. John Wiley & Sons.

Chichester, U.K.

Knapp, E. B., and Buitrago, C. 1994. Improving soil quality

inventory mapping by assessing nutrient retention. In: Hillsides

Program Annual Report 1992 – 1994. Internal document, Ciat, Cali

– Colombia.

Knapp, E. B., and Beltran, J. A. 1994. Soil quality appraisal using

estimates of the irreversible loss of theoretical soil productivity. In:

Hillsides Program Annual Report 1992 – 1994. Internal document,

Ciat, Cali – Colombia.

Koning, H. H. J. de, Vekdkamo, A., Verbulrg, P. H., and Beergsma,

A. R. 1998. CLUE: A Tool for Spatially Explicit and Scale Sensitive

Exploration of Land Use Changes. Wageningen Agricultural

University.

267

Korner, C., Allison A., and Hilscher, H. 1983. Altitudinal variation

of leaf conductance and leaf anatomy in heliophytes of montane

New Guinea and their interrelation with microclimate. Flora, 174:

91 – 135.

Kramer, P. J., and Boyer, J. S. 1995. Water Relations of Plants

and Soils. Academic Press Limited, San Diego, California, USA.

Kutilet, M., and Nielsen, D. R. 1994. Soil Hydrology. Geoecology

Textbook. Catena Verlag, 38162 Cremlingen-Destedt, Germany.

Lambin, E. F. 1997. Modelling and monitoring land-cover change

processes in tropical regions. Progress in Physical Geography, 21,

3: 375 - 393.

Lange, O. L., Losch, R., and Schulze, E. D. 1976. Water and plant

life. Vol 19. Springer-Verlag, Berling.

Laurenson, E. M., and Mein, R. G. 1995. RORB: Hydrograph

Synthesis by Runoff Routing. In: Computer models of Watershed

Hydrology. Edited by Vijay P. Singh. Department of Civil and

Environment Engineering Louisiana State University, Baton Rouge,

LA 70803-64405, USA.

LeBlanc, R. T., Brown, R. D. and FitzGibbon, J. E. 1997.

Modelling the effects of land use change on the water temperature

in unregulated urban streams. Journal of Environmental

Management, 49, 4: 445 - 469.

Leemans, R., Van Amsel, A., Battjes, C., Kreileman, E., and Toel, S.

1996. The land cover and carbon cycle consequences of large-scale

268

utilizations of biomass as an energy source. Global Environmental

Change, 6, 4: 335 - 357.

Letts, M. 2001. Processes of Carbon Assimilation within Tropical

Montane Cloud Forests of the Pacific Occidental Cordillera of Cauca,

Colombia. Unpublished Ph.D. thesis at King’s College London,

University of London, Ph.D. thesis Unpublished.

Lloyd, C. R., Gash, J. H. C., Shuttleworth, W. L. and Marques, F.

1988. The measurement and modelling of rainfall interception by

Amazonian rain forest. Agricultural & Forest Meteorology, 43, 3-4:

277 - 294.

Lloyd, C. R., and Marques Filho, A. de O. 1988. Spatial variability

of throughfall and stemflow measurements in Amazonian

rainforest. Agricultural & Forest Meteorology, 42: 63 – 73.

Loague, K. M., and Freeze, R. A. 1985. A comparison of rainfall-

runoff modelling techniques on small upland catchments. Water

Resources Research, 21, 2: 229 - 248.

Lorup, J. K., Refsgaard, J. C., and Mazvimavi, D. 1998. Assessing

the effects of land use change on catchment runoff by combined

use of statistical tests and hydrological modelling: Case studies

from Zimbabwe. Journal of Hydrology, 205: 147 – 163.

Loukas, A. and, Quick, M. 1996. Physically-based estimation of

lag time for forested mountainous watershed. Hydrological Sciences

Journal, 41, 1: 1 - 19.

Lukey, B. T., Sheffield, J., Bathurst, J. C., Hiley, R. A., and

Mathys, N. 2000. Test of the SHETRAN technology for modelling

269

the impact of reforestation on badlands runoff and sediment yield

at Draix, France. Journal of Hydrology, 235: 44 – 62.

MacMillan, R. A., Furley, P. A., and Healey, R. G. 1993. Using

hydrological models and geographic information systems to assist

with the management of surface water in agricultural landscapes.

Landscape. Taylor & Francis Ltd. London, UK.

Madramootoo, Ch., and Enright, P. 1990. Prediction of surface

runoff using the Green-Ampt. infiltration model and estimation

soils parameters. Canadian Agricultural Engineering, 32, 1: 39 -

45.

Maidment, D. R. 1993. Handbook of Hydrology. McGraw-Hill, Inc.

New York, USA.

Manley, R. E., and Askew, J. A. 1993. Operational hydrology

problems in humid tropics. In: Hydrology and Water Management

in the Humid Tropics. Hydrological research issues and strategies

for water management. Edited by Michael Bonell, Maynard M,

Hyfschmidt and John S. Gladwell. International hydrology series,

Cambridge University press, UK.

Marin-Ramirez, R. 1992. Estadísticas Sobre el Recurso Agua en

Colombia. Ministerio de Agricultura, Instituto Colombiano de

Hidrología, Meteorología y adecuación de Tierras. Santafé de

Bogotá, Colombia.

McKenzie, N. J., and Ryan, P. 1999. Spatial prediction of soil

properties using environmental correlation. Geoderma, 89: 67 –

94.

270

McNaughton, K. G., and Jarvis, P. G. 1983. Predicting effects of

vegetation changes on transpiration and evaporation. In: Water

Deficits and Plant Growth. T. Kozlowski (ed.) Academic Press, New

Yor: 1 – 47.

Mein, R. G., and Brown, B. M. 1978. Sensitivity of optimized

parameters in Watershed Models. Water Resources Research. 4, 2:

299 - 303.

Melillo, J. M., Houghton, R. A., Kicklighter, D. W., and McGuire, A.

D. 1996. Tropical deforestation and the global carbon budget.

Annual Review of Energy and the Environment, 21: 293 - 310.

Michaud, J. D. 1992. Distributed rainfall-runoff modeling of

thunderstorm generated floods: A case study in mid-sized semiarid

watershed in Arizona. PhD. Dissertation, Univ. of Arizona, Tucson.

Moore, R. D. 1996. Are water table variations in a shallow forest

soil consistent with the TOPMODEL concept? Water Resources

Research, 32, 3: 663 – 669.

Moran, M, S,m Rhman, A. F., Wasburne, F. C., Goodrich, D. C.,

Weltz, M. A., and Kustas, W. P. 1996. Combining the Penman-

Monteith equation with measurements of surface temperature and

reflectance to estimate evaporation rates of semiarid grass.

Agricultural and Forest Meteorology, 80: 87 – 109.

Morin, J., and Kosovsky, A. 1995. The surface infiltration model.

Journal of Soil and Water Conservation, 50, 5: 470 - 476.

Mosier, A. R., Parton, W. J., Valentine, D. W., Ojima, D. S.,

Schimel, D. S., and Heinemeyer, O. 1997. CH4 and NO2 fluxes in

271

the Colorado short grass steppe 2. Long-term impact of land use

change. Global Biochemical Cycles, 11, 1: 29 - 42.

Mulla, D. J. 1989. Estimating spatial patterns in water content,

matric suction, and hydraulic conductivity. Soil Science Society

American Journal, 52: 1547 - 1553.

Mulligan, M. 1996. Modelling hydrology and vegetation in a

degraded semi-arid environment. Ph.D. thesis, King’s College

London. University of London.

Mulligan, M., Rubiano, J., and, Rincon-Romero, M. 2000.

Hydrological sensitivity to progressive deforestation in a tropical

montane environment. Submitted to Hydrological Processes.

Mulligan, M., and Jarvis, A. 2000a. Monitoring processes of cloud

interception to epiphytes in a tropical montane cloud forest,

Colombia. Submitted to Water Resources Research.

Mulligan, M., and Jarvis, A. 2000b. Laboratory simulation of cloud

interception by mossy epiphytes and implications for the hydrology

of the Tambito experimental cloud forest, Colombia.

Submitted to Water Resources Research.

Museo de Historia Natural, 1989. TM Landsat image for the study

area.

Museo de Historia Natural. 1996. Centro de Estudios Ambientales

Tambito. Universidad del Cauca, Corporacion Autonoma Regional

del Cauca, C.R.C. Biopacifico. Popayan, Cauca –Colombia.

272

Musgrave, G. W. 1947. The quantitative evaluation of factors in

erosion – a first approach. Journal of Soil and Water Conservation,

2: 133 – 138.

Nortcliff, S., and Thornes, J. B. 1981. Seasonal variations in the

hydrology of a small forested catchment near Manaus, Amazonas,

an the implications for its management. In: Tropical Agricultural

Hydrology – Watershed Management and Land Use. R. Lal, and E.

W. Russell (eds). Wiley, Chichester, UK: 37 – 57.

O’Brien, K. L. 1996. Tropical deforestation and climate change.

Progress in Physical Geography. 20, 3: 311 - 335.

Oke, T. R. 1987. Boundary Layer Climate. Cambridge University.

2nd edition. London, Mathuen, UK.

O’Loughlin, E. M. 1981. Saturating regions in catchments and

their relation to soil and topographic properties. Journal of

Hydrology, 53: 229 – 246.

Pack, C. C. 1992. Tropical Rain Forest. Biddles Ltd. Guildford

and King’s Lynn. UK.

Parkin, G. 1996. Validation of catchment models for predicting

land-use and climate change impacts. 2. Case study for a

Mediterranean catchment. Journal of Hydrology, 174, 1-4: 595 –

613.

Pearce, A. J., and Rowe, L. K. 1980. Nighttime wet canopy

evaporation rates and water balance of an evergreen mixed forest.

Water Resources Research, 16, 5: 955 - 959.

273

Philip, J. R. 1957. The theory of infiltration: 4. Sorptivity and

algebraic infiltration equations. Soil Science, 84: 257 – 264.

Pitman, A. J., Durbidge, B., and Henderson-Sellers, A. 1993.

Assessing climate model sensitivity to prescribed deforestation

landscapes. International Journal of Climatology, 13: 879 - 898.

Pounds, J. A., Fogden, M. P. A., and Campbell, J. H. 1999.

Biological response to climate change on a tropical mountain.

Nature, 398: 611 – 615.

Priestley, C. H., and Taylor, R. J. 1972. On the assessment of

surface heat flux and evaporation using large-scale parameters.

Mon. Weather Review, 100: 81 – 92.

Quine, T. A., and Walling, D. E. 1993. Use of Caaesium-137

measurements to investigate relationships between erosion rates

and topography. Landscape Sensitivity. Edited by D. S. G.

Thomas and R. J. Allison. John Wiley & Sons. Baffins Lane,

Chichester, England.

Rai, S. C., and Sharma, E. 1998. Comparative assessment of

runoff characteristics under different land use patterns within a

Himalayan watershed. Hydrological Processes, 12: 2235 – 2248.

Rawls, W. J., Brakensiek, D. L. and K. E. Saxton. 1982.

Estimation of soil water properties. American Society of Agricultural

Engineers, 25: 1316 – 1320.

Rawls, W. J., Brakensiek, D. L., and Logsdon, S. D. 1993.

Predicting saturated hydraulic conductivity utilizing fractal

principles. American Society of Agricultural Engineers, 57: 1193 -

1197.

274

Refsgaard, J. C. 1997. Parameterisation, calibration and

validation of distributed hydrological models. Journal of Hydrology,

198, 1-4: 69 – 97.

Reiners, W. A., Bouwman, A. F., Parsons, W. F. J., and Keller, M.

1994. Tropical rain forest conversion to pasture – Change in

vegetation and soil properties. Ecological Applications, 4, 2: 363-

377.

Resptrepo, J. D., and Kjerfve, D. 2000. Water discharge and

sediment load from the Western slopes of the Colombian Andes

with focus on Rio San Juan. The Journal of Geology, 108: 17 – 33.

Richards, L. A. 1965. Physical condition of water in soil. In:

methods of Soil Analysis, pt 1 C. A. Black (ed.) Agronomy 9,

American Society of Agronomy. Madison, Wis.: 128 - 137.

Richards, P. W. 1996. The Tropical Rain Forest and Ecological

Study. Second edition. Cambridge University press. UK.

Rincon-Romero, M. 2000. Effects of surface hydrological

connectivity on hydrological response to land use change.

Advances in Environmental Monitoring and Modelling.

http://www.kcl.ac.uk/kis/schools/hums/geog/advemm.html

Roberts, J., Cabral, O. M. R., and De Aguilar, L. F. 1990.

Stomatal and boundary-layer conductances in an Amazonian terra

firme rain forest. Journal of Applied Ecology, 27: 336 – 353.

Robin, P. Merot, Ph., Beven, K. 1995. Sensitivity to space and

time resolution of a hydrological model using digital elevation data.

Hydrological Processes, 9: 69 - 85.

275

Robinson, N. 1966. Solar Radiation. Elsevier, London – UK.

Rovey, E. W., Woolhider, D. A., and Smith, R. E. 1977. A

Distributed Kinematic Model of Upland Watersheds. Hydrology

Paper No. 93. Colorado State University.

Rowe, L. K. 1983. Rainfall interception by an evergreen beech

forest Nelson, New Zealand, Journal of Hydrology, 66: 143 – 158.

Rubiano, J. E. 1988. Hydrological impact of land use change in

tropical hillsides: the impact of patterns. M.Sc. dissertation in

Department of Geography, King’s College London, University of

London.

Rutter, A. J., Kershaw, K. A., Robins, P. C., and Morton, A. J.

1971. A predictive model of rainfall interception in forest, 1.

Derivation of the model from observations in a plantation of

Corsican pine. Agricultural Meteorology, 9: 367 - 384.

Rutter, A. J. 1975. The hydrological cycle in vegetation. In:

Vegetation and the Atmosphere. Monteith, J. L. 1975Academic

Press, New York: 111 – 154.

Rutter, A. J., Morton, A. J., and Robins, P. C. 1975. A predictive

model of interception in forest II. Generalization of the model and

comparison with observations in some coniferous and hardwood

stands. Journal of Applied Ecology, 12: 367 - 380.

Rutter, A. J., and Morton, A. J. 1977. A predictive model of

rainfall interception in forest. III. Sensitivity of the model to stand

parameters and meteorological variables. Journal of Applied

Ecology, 14: 567 - 588.

276

Saltelli, A. 1999. Sensitivity analysis: could better methods be

used? Journal of Geophysical Research, 104, D3: 3789 – 3973.

Saulnier, G M., Beven, K, and Obled, C. 1997. Including spatially

variable effective soil depths in TOPMODEL. Journal of Hydrology,

202, 1-4: 158 – 172.

Saxton, K. E., Rawls, W. J., Romberger, J. S. and Papendick, R. I.

1986. Estimating generalized soil-water characteristics from

texture. Soil Science Society American Journal, 50: 1031 - 1036.

Scatena, F. N., and Larsen, M. 1991. Physical aspect of hurricane

Hugo in Puerto Rico. Biotropica, 23: 317 – 323.

Scatena, F. N. 1998. An assessment of climatic change in the

Luquillo Mountains of Puerto Rico. In: Third International

Symposium on Water Resources. American Water Resources

Association. USA.

Seibert, J., Diekkruger, B., Kirkby, M. J., and Schrder, U. 1999.

On TOPMODEL’s ability to simulate groundwater dynamics. In:

Regionalization in hydrology. Proceeding of an International

Conference held at the Technical University of Braunschweig,

Germany, 10 – 14 March 1997, 211 – 220. IAHS Press; Wallingford,

UK.

Shaw, E. M. 1984. Hydrology in Practice. Wokingham, Vam

Nostrand. Reinhold, U. K.

Shellekens, J., Scatena, F. N., Bruijnzeel, L. A., and Wickel, A. J.

1999. Modelling rainfall interception by a lowland rain forest in

northeastern Puerto Rico. Journal of Hydrology, 225: 168 – 184.

277

Short, D., Dawes, W. R., and White, I. 1995. The practicability of

using Richard’s equation for general purpose soil-water dynamics

models. Environmental International, 22, 5: 723 – 730.

Shuttleworth, W. J. 1978. A simplified one-dimensional

theoretical description of the vegetation-atmosphere interaction.

Boundary-Layer Meteorology, 14: 3-27.

Singh, K. D. 1994. Tropical forest resources: an analysis of the

1990. Journal of Forestry, 92: 27 – 31.

Singh, V. P. 1995. Computer Models of Watershed Hydrology.

Vijay P. Singh (ed.). Water Resources Publications. USA.

Singh V. P. 1997. Effect of spatial and temporal variability in

rainfall and watershed characteristics on stream flow hydrograph.

Hydrological Processes, 11: 1649 – 1669.

Sinha, S. K. 1997. Global change scenario: Current and future

with reference to land cover change and sustainable agriculture –

South and South-East Asia context. Current Science, 72, 11: 846 -

854.

Smith, R. E., Goodrich, D. C., Wollhiser, D. A., and Unkrich, C. L.

1995. KINEROS – A kinematic runoff and erosion model. In:

Computer models of watershed hydrology. Vijay P. Singh (ed.).

Water Resources Publications. USA.

278

Smith R., Corrandini C., and Melone F. 1993. Modelling

infiltration for Mltistorm Runoff events. Water Resources Research,

29, 1: 133 - 144.

Speers, D.D. 1995. SSARR model. Computer Models of Watershed

Hydrology. Water Resources Publications. Edited by Vijay P.

Singh. USA.

Sperling, F. 2000. Tropical montane cloud forest – Ecosystems

under the threat of climate change. Unpublished report, World

Conservation Monitoring Centre, Cambridge, UK.

Steward, J. B. 1989. One of the use of the Penman-Monteith

equation for determining areal evapotranspiration. In: Estimation

of Areal evapotranspiration. T. A. Black, D. L. Splittlehouse, M. D.

Novak, and D. T. Price (eds.). Proc. Vancouver Symp., Aug. 1987,

Int’l. Assoc. of Hydrol. Sci. Publ., 177: 3 – 12.

Sudjono, P. 1995. A mathematical concept of runoff prediction

model for small tropical catchment areas. Water Science

Techniques, 31, 9: 27-36.

Taha, A., Gresillon, J. M., and Clothier, B. E. 1997. Modelling the

link between hillslope water movement and stream flow:

Application to a small Mediterranean forest watershed. Journal of

Hydrology, 203: 11 – 20.

Takeuchi, K., Ao, T., and Ishidaira, H. 1999. Introduction of

block-wise use of TOPMODEL and Muxkingum-Cunge method for

the hydro-environmental simulation of a large ungauged basin.

Hydrological Science Journal, 44, 4: 633 - 647.

279

Tanner, E. V. J. 1977. Four montane rain forest of Jamaica: a

quantitative characterization of the floristics, the soils and the

foliar mineral levels, and a discussion of the interrelations. Journal

of Ecology, 65: 883 – 918.

The World Bank. 1996. Review of Colombia’s Agriculture and

Rural Development Strategy. A World Bank Country Study. The

International Bank for Reconstruction and Development / The

World Bank. Washington, USA.

Ternan, J. L., Williams, A. G., and Solman, K. 1987. A preliminary

assessment of soil hydraulic properties, and their implications for

agroforestry management in Granada, West Indies. In : Forest

Hydrology and Watershed Management. R. H. Swanson, P. Y.

Bernier and P. D. Woodard (eds). Proc Vancouver Symp.

International Association of Hydrological Science Publication, 167:

409 – 421.

Thornes, J. B., and Gilman, A. 1983. Potential and actual erosion

around archaeological sites in south east Spain. Rainfall

simulation, runoff and soil erosion. Catena, suplement 4,

Braunschweig. 91 - 113.

Thornes, J. B. 1985. The Ecology of Erosion. Geography 70: 222

– 235.

Thornes, J. B. 1990. The interaction of erosional and vegetation

dynamics in land degradation: spatial outcomes. Vegetation and

Erosion, Processes and Environments. Edited by J. Thornes. John

Wiley & Sons Ltd. London. UK.

280

Tietje, O., and Tapkenhinrichs, M. 1993. Evaluation of Pedo-

Transfer functions. Soil Science Society American Journal, 57:

1088-1095.

Tinker, P. B., Ingram, J. S. I., and Struwe, S. 1996. Effects of

slash and burn agriculture and deforestation on climate change.

Agriculture Ecosystems and Environment, 58, 1: 13 - 22.

Tropenbos Foundation. 1997. Tropenbos - Colombia.

http:www.tropenbos.nl/tropenbos/sites-colombia.html

Turner II, B.L., and Meyer, W. B. 1991. Land use and land cover

in global environmental change: consideration for study.

International Social Science Journal, 43, 130: 669 - 679.

Turner II, B. L., Meyer, W. B. and Skole, D. L. 1994. Global Land-

Use/Land-Cover change: Towards an Integrated Study. Ambio,

23, 1: 91 – 95.

Utrecht University, 1996. PCRaster Environmental Software V.O.F.

Derk-Jan Karssenberg, and the Department of Physical Geography.

The Netherlands.

Van den Berg, M., Klamt, E., van Reeuwijk, L. P. and Sombroek, W.

G. 1997. Pedotransfer functions for the estimation of moisture

retention characteristics of ferralsols and related soils. Geoderma,

78: 161-180.

Van Genuchten, M. Th. 1980. A closed-form equation for

predicting the hydrological conductivity of unsaturated soil. Soil

Science Society American Journal. 44: 892 - 898.

281

Veen, A. W. L., and Dolman, A. J. 1989. Water dynamics of forest:

one-dimensional modelling. Progress in Physical Geography, 13,

4: 474 - 506.

Veneklaas E.J. 1990. Nutrient fluxes in bulk precipitation and

throughfall in two montane tropical rain forest, Colombia. Journal

of Ecology, 78: 974 - 992.

Veneklaas, E. J. 1991. Litterfall and nutrient fluxes in two

montane tropical rain forest, Colombia. Journal of Tropical Ecology,

7: 319 - 336.

Veneklaas, E. J., and Van Ek. R. 19901. Rainfall interception in

two tropical montane rain forest, Colombia. Hydrological

Processes, 4: 311 - 326.

Veneklaas, E. J., Zagt, R. J., Van Leerdam, A., Van Ek, R.,

Broekhoven. A. J., and Van Genderen, M. 19902. Hydrological

properties of the epiphyte mass of a montane tropical rain forest,

Colombia. Vegetation, 89: 183 - 192.

Vereeken, H., Maes J., Feyen J. and Darius P. 1989. Estimation

the soil moisture retention characteristics from texture, bulk

density and carbon content. Soil Science, 148, 6: 389 - 403.

Vereecken, H., Maes, J., and Feyen, J. 1990. Estimating

unsaturated hydraulic conductivity from easily measured soil

properties. The Journal on Soil and Soil-Plant Problems. 149, 1: 1-

12.

Vereeken, H., Diels, J., Van Orshoven, J. F., and Bouma, J. 1992.

Functional evaluation of pedotransfer functions for the estimation

of soil hydraulic properties. Journal of Hydrology, 56: 1371 - 1378.

282

Vertessy, R., Hatton, T. J., O’Shaughnessy, P. J., and Jayasuriya,

M. D. A. 1993. Predicting water yield from a mountain ash forest

catchment using a terrain analysis based catchment model.

Journal of Hydrology, 150: 665 – 700.

Vertessy, R., O’Loughlin, e>, Beverly, e., and Butt, T. 1994.

Australian experiences with the CSIRO TOPOG model in land and

water resources management. In: Proceedings of UNESCO

International Symposium on Water Resources Planning in a

Changing World, Karlsruhe, Germany, June 20-30: III 135 – 144.

Ward, R. C., and Robinson, M. 1990. Principles of Hydrology.

McGraw Hill book company. London, UK.

Ward, A. D., and Elliot, W. J. 1995. Environmental Hydrology.

Lewis Publishers, CRC Press, Inc. USA.

Watson, R. W. 1997. Land Use and Land Cover. Introduction to the

proceedings of the Open Science Meeting. Amsterdam, The

Netherlands, January 29th –31st, 1996. Institut Cartografic de

Catalunya. Barcelona – Catalonia Spain.

Whitehead, D., and Hinckley, T. M. 1991. Models of water flux

through forest stands: critical leaf and stand parameters. Tree

Physiology, 9: 35 - 57.

Whitmore. T. C. 1998. An Introduction to tropical rain forest (2nd

edn), Clarendon Press, Oxford Science Publications ISBN 0 19

850148 X.

Wicks, J. M., and Bathutst, J. C. 1996. SHESED: a physically

based, distributed erosion and sedimentation yield component for

283

the SHE hydrological model system. Journal of Hydrology, 175, 1-

4: 213 – 238.

Wierda, A., Veen, A. W. L., and Hutjes, R. W. A. 1989. Infiltration

at the Tai rain forest (Ivory Coast): Measurements and modelling.

Hydrological Processes, 3: 371 – 382.

Wilson, J. D. 1990. Turbulent transport within the canopy. In:

Estimation of Areal Evapotranspiration. T. A. Black, D. L.

Splittlehouse, M. D. Novak, and D. T. Price (eds). Proc. Vancouver

Symp. Aug. 1987. International Association of Hydrology Science.

Publ. 177: 43 – 80.

Woolhiser, D. A. 1996. Search for Physically-Based Runoff Models

– A hydrologic El Dorado?. Journal or Hydrology, 122, 3: 122 - 129.

Wosten, J. H. M., and Van Genuchten, M. TH. 1988. Division S-6-

Soil and water management and conservation. Using Texture and

other soil properties to predict the unsaturated soil hydraulic

functions. Soil Science Society American Journal, 52: 1762 -

1770.

Wosten, J. H. M., Schuren, C. H. J. E., and Stein, A. 1990.

Functional sensitivity of four methods to generate soil hydraulic

functions. Soil Science Society American Journal, 54: 832 - 836.

Yakamura, T., Hagihara, S., Sukardjo, and H. Ogawa. 1986. Tree

size in a mature dipterocarp forest stand in Sebulu, east

Kalimantan, Indonesia. Southeast Asian Sutdies, 1, 23: 441 – 475.

Zadroga, F. 1981. The hydrological importance of montane cloud

forest area in Costa Rica. In: Tropical Agricultural Hydrology –

284

Watershed Management and Land Use. R Lal, and W. Russelll

(eds.) Wiley, Chichester, UK: 59 –73.

Zhao, R.J., and Liu X. R., 1995. The Xinanjiang model. Computer

Models of Watershed Hydrology. Water Resources Publications.

USA.

285

Appendices

286

Appendix I

LUC ScenariosAnd

Iterations trends

287

Fig. A1-1 SC1. Forest conversion using cellular automata model (Mulligan, 2000)

288

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Fig. A1-2 SC 2. Forest conversion with a fixed distance from river channels up slope

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Fig. A1-3. SC3. Forest conversion with a fixed distance toward river channels in the down slope direction

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Fig. A1-4 SC4. Forest conversion with fix altitude distance from lower point up hill direction

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Fig. A1-5 SC5. LUC pattern iteration of forest conversion to pastures withfix elevation distant down slope direction

294

Appendix No. II

Data collected from grassland weather stationNov 22/97 to Nov 24/97

(Example)

295

Date Time phi 1.2 phi 0.8 phi 0.4 PAR Solar up Solardown

Air temp Rainfall Overlandflow

Troughflow

bar bar bar W/m2 W/m2 W/m2 oC (mm) (mm) (mm)

11/22/97 00:00:00 0.932 0.847 0.921 0.926 -0.202 0.785 15.8 0 0 011/22/97 01:00:00 0.932 0.847 0.92 0.93 -0.109 0.832 15.7 0 0 011/22/97 02:00:00 0.931 0.842 0.917 0.862 -0.18 0.798 15.6 0 0 011/22/97 03:00:00 0.933 0.847 0.923 1.045 -0.044 1.092 15.5 0 0 011/22/97 04:00:00 0.936 0.854 0.928 1.004 0.054 1.018 15.4 0 0 011/22/97 05:00:00 0.936 0.853 0.929 0.968 0.104 0.897 15.2 0 0 011/22/97 06:00:00 0.934 0.85 0.927 1.012 0.04 0.912 15 0 0 011/22/97 07:00:00 0.936 0.859 0.936 27.085 3.058 17.245 15 0 0 011/22/97 08:00:00 0.938 0.868 0.941 84.588 8.672 53.059 15.8 0 0 011/22/97 09:00:00 0.936 0.875 0.943 155.52 16.66 100.48 16.5 0 0 011/22/97 10:00:00 0.938 0.884 0.95 257.38 29.576 174.56 18 0 0 011/22/97 11:00:00 0.939 0.891 0.955 514.38 57.806 371.6 19.1 0 0 011/22/97 12:00:00 0.94 0.898 0.959 689.63 71.688 488.48 21.2 0 0 011/22/97 13:00:00 0.935 0.888 0.946 840.13 81.807 599.14 22.4 0 0 011/22/97 14:00:00 0.936 0.897 0.953 784.81 74.738 548.39 23.3 0 0 011/22/97 15:00:00 0.943 0.904 0.958 358.26 33.727 244.12 21.7 0 0 011/22/97 16:00:00 0.934 0.902 0.956 276.95 26.282 186.29 21.3 0 0 011/22/97 17:00:00 0.928 0.899 0.953 54.264 4.664 35.31 19.5 0 0 011/22/97 18:00:00 0.927 0.898 0.952 11.499 0.391 7.432 18.1 0 0 011/22/97 19:00:00 0.934 0.89 0.95 1.145 -0.477 0.976 17.3 0 0 011/22/97 20:00:00 0.936 0.874 0.94 0.935 -0.516 0.934 17.1 0 0 011/22/97 21:00:00 0.937 0.882 0.947 0.915 -0.55 0.946 17.1 0 0 011/22/97 22:00:00 0.934 0.883 0.946 0.93 -0.379 1.007 16.8 0.4 0 011/22/97 23:00:00 0.936 0.897 0.96 0.886 -0.416 0.75 16.5 0.6 0 011/23/97 00:00:00 0.937 0.895 0.959 0.998 -0.154 0.877 16.2 0.2 0 011/23/97 01:00:00 0.938 0.878 0.948 0.919 -0.293 0.913 15.9 0 0 0

295

296

11/23/97 02:00:00 0.943 0.886 0.956 0.917 -0.183 0.969 15.6 0 0 011/23/97 03:00:00 0.944 0.885 0.956 0.96 -0.002 1.045 15.5 0 0 011/23/97 04:00:00 0.945 0.886 0.956 0.957 0.122 1.103 15.4 0 0 011/23/97 05:00:00 0.943 0.878 0.949 0.844 -0.017 1.012 15.3 0 0 011/23/97 06:00:00 0.943 0.876 0.948 0.978 0.089 1.134 15.2 0 0 011/23/97 07:00:00 0.941 0.872 0.944 19.817 2.087 14.06 15.2 0 0 011/23/97 08:00:00 0.94 0.871 0.943 119.51 12.227 85.258 16.2 0 0 011/23/97 09:00:00 0.937 0.872 0.942 290.76 30.785 210.16 17.7 0 0 011/23/97 10:00:00 0.94 0.885 0.954 674.72 68.881 487.59 20.3 0 0 011/23/97 11:00:00 0.941 0.888 0.956 824.89 76.686 592.16 22.7 0 0 011/23/97 12:00:00 0.948 0.885 0.95 811.83 75.385 566.08 22.5 0 0 011/23/97 13:00:00 0.941 0.895 0.957 1229.1 104.12 858.05 24.2 0 0 011/23/97 14:00:00 0.934 0.883 0.944 557.25 47.036 387.8 23.8 0 0 011/23/97 15:00:00 0.932 0.889 0.95 234.49 21.783 159.07 22.1 0 0 011/23/97 16:00:00 0.934 0.893 0.952 126.74 11.14 84.217 20.9 0 0 011/23/97 17:00:00 0.93 0.893 0.952 56.418 4.107 36.817 19.6 0 0 011/23/97 18:00:00 0.928 0.886 0.947 6.975 -0.394 4.957 18.6 0 0 011/23/97 19:00:00 0.936 0.892 0.956 1.087 -0.617 1.23 17.7 0 0 011/23/97 20:00:00 0.937 0.887 0.953 0.947 -0.521 1.155 17 0.2 0 011/23/97 21:00:00 0.934 0.864 0.935 0.932 -0.314 1.127 16.4 0 0 011/23/97 22:00:00 0.939 0.871 0.943 0.954 -0.288 1.204 16.1 0 0 011/23/97 23:00:00 0.94 0.875 0.947 0.923 -0.394 1.184 15.8 0.2 0 011/24/97 00:00:00 0.942 0.88 0.952 0.908 -0.393 1.211 15.5 0 0 011/24/97 01:00:00 0.942 0.878 0.951 0.95 -0.34 1.124 15 0 0 011/24/97 02:00:00 0.94 0.869 0.943 0.958 -0.28 1.168 14.6 0 0 011/24/97 03:00:00 0.941 0.869 0.943 0.955 -0.161 1.197 14.5 0 0 011/24/97 04:00:00 0.939 0.866 0.941 0.855 -0.294 1.227 14.8 0 0 011/24/97 05:00:00 0.939 0.868 0.942 0.791 -0.525 1.578 15.8 0 0 011/24/97 06:00:00 0.938 0.867 0.941 0.771 -0.869 1.542 16 0 0 011/24/97 07:00:00 0.938 0.869 0.942 50.389 4.208 34.496 15.6 0 0 011/24/97 08:00:00 0.936 0.869 0.941 193.63 18.441 131.34 16.9 0 0 0

296

297

11/24/97 09:00:00 0.936 0.875 0.945 527.75 51.166 382.64 19.2 0 0 011/24/97 10:00:00 0.937 0.88 0.951 1049.9 95.961 755.34 22.3 0 0 011/24/97 11:00:00 0.939 0.878 0.945 1396.6 116.98 989.39 24 0 0 011/24/97 12:00:00 0.934 0.882 0.945 1560.8 125.06 1088.8 24 0 0 011/24/97 13:00:00 0.94 0.889 0.95 1292.9 110 908.44 23.8 0 0 011/24/97 14:00:00 0.934 0.891 0.952 264.38 29.121 183.45 21.1 0 0 011/24/97 15:00:00 0.937 0.901 0.96 87.256 7.717 57.331 19.6 0 0 011/24/97 16:00:00 0.932 0.897 0.957 46.249 3.111 28.425 18.7 0 0 011/24/97 17:00:00 0.93 0.889 0.952 17.074 0.425 10.622 18.1 0 0 011/24/97 18:00:00 0.934 0.888 0.952 4.442 -0.593 3.386 17.7 0 0 011/24/97 19:00:00 0.934 0.881 0.948 0.971 -0.837 1.515 17.6 0 0 011/24/97 20:00:00 0.935 0.882 0.949 0.89 -0.841 1.264 17.5 1.4 0 011/24/97 21:00:00 0.935 0.874 0.944 0.946 -0.576 1.426 17.1 0 0 011/24/97 22:00:00 0.935 0.865 0.938 0.967 -0.431 1.505 16.7 0 0 011/24/97 23:00:00 0.936 0.867 0.939 0.912 -0.444 1.4 16.7 0 0 0

297

298

Appendix No. III

Summary of soil analysis samples

299

Soilmapzone

slopeclass

vegetationclass

Sand Silt Clay BulkDensity

Porosity Numberof

samples1 1 1 0.58 0.19 0.23 0.87 0.65 132 2 1 0.56 0.23 0.21 0.85 0.66 193 3 1 0.58 0.20 0.22 0.74 0.71 74 2 2 0.57 0.24 0.19 0.80 0.68 145 3 2 0.56 0.24 0.20 0.85 0.66 76 1 2 0.63 0.20 0.17 0.69 0.72 157 1 3 0.60 0.19 0.21 0.96 0.62 148 2 3 0.58 0.22 0.20 1.00 0.58 149 3 3 0.56 0.24 0.20 0.83 0.67 8

Table A3.1 Summary of soil properties corresponding of 9 sampledclasses of fig. 4.7.

Soil samples were taken until the bed rock could allow to penetratethe auger, and in some cases more than one set was collected.

Stone density = 2.51 g cm-3

Volume of samples = 204 cm3

This field work collecting samples were carried out with JorgeRubiano1 co-operation.

1 - M.Sc. KCL Geography Student, 1997

300

Appendix IV

Summary of vegetation samples for:

canopy water storage capacity,

vegetation cover

and LAI for grassland

301

Canopy water storage capacity

List of vegetation samples for primary forest

ID number Dry weight(gr)

Wet weight(gr)

water storage(gr)

1.0 13.7 20.6 6.82.0 12.6 17.3 4.73.0 10.3 15.1 4.74.0 15.6 21.4 5.85.0 2.9 3.8 0.96.0 1.4 2.1 0.77.0 1.6 2.4 0.88.0 10.3 14.6 4.49.0 13.8 22.4 8.610.0 169.5 211.1 41.611.0 17.7 28.1 10.412.0 17.0 27.2 10.213.0 27.6 31.9 4.314.0 13.5 19.6 6.115.0 25.6 30.3 4.816.0 54.9 60.3 5.417.0 23.6 30.5 6.918.0 39.6 44.4 4.819.0 11.4 15.7 4.320.0 40.2 45.5 5.421.0 11.4 16.4 5.022.0 4.0 4.5 0.523.0 48.4 62.2 13.824.0 10.7 15.6 4.925.0 36.9 46.7 9.826.0 57.4 75.2 17.830.0 14.1 17.3 3.231.0 34.1 44.6 10.532.0 46.8 76.5 29.733.0 7.4 11.1 3.634.0 18.7 27.5 8.835.0 37.8 74.2 36.436.0 23.7 28.3 4.637.0 34.3 40.1 5.738.0 68.8 95.2 26.439.0 81.3 125.5 44.240.0 24.2 30.2 6.041.0 46.6 56.5 9.950.0 130.3 148.9 18.651.0 45.7 59.3 13.652.0 23.4 31.9 8.553.0 34.2 42.4 8.2

302

List of vegetation samples for secondary forest

SECONDARY FOREST PLOTID number Dry weight

(gr)Wet weight

(gr)Water storage

(gr)1.0 24.1 40.0 15.92.0 13.1 20.2 7.03.0 23.0 42.2 19.24.0 71.4 92.5 21.15.0 140.2 160.3 20.26.0 96.1 111.6 15.57.0 24.5 34.6 10.18.0 42.3 56.7 14.49.0 4.3 5.9 1.610.0 2.1 3.0 0.911.0 25.9 31.9 5.912.0 19.7 32.3 12.5Total 527.2 683.6 156.4

List of vegetation samples for grassland

PASTURE PLOTID number Dry weight

(gr)Wet weight

(gr)Water storage

(gr)1.0 6.6 12.5 5.92.0 7.9 13.4 5.53.0 7.2 19.8 12.64.0 6.1 10.0 3.95.0 4.8 9.6 4.8

Total 32.5 65.3 32.8

303

Vegetation cover.

Using monochromatic vertical pictures below of canopy, wereanalysed the ratio of light – dark pixels on scanned pictures. Totalnumber of pixel by picture was 826 x 550 = 454300.

Forest vegetation cover

Veg. Class Sample Dark pixel Vegetation coverPrimary forest J41 404659 89.07 %Primary forest J42 375475 82.65 %Primary forest J43 445976 98.1 %Primary forest J44 428474 94.31 %Primary forest J45 409424 90.12 %

Secondary forest J46 407287 89.65 %Secondary forest J47 410118 90.27 %Secondary forest J48 410043 90.25 %Secondary forest J49 445732 98.11 %Secondary forest J50 412902 90.88 %

The average value of vegetation cover in primary forest was 90.97%

The average vegetation cover for secondary forest was 91.8%

304

Appendix V

Tambito daily rainfall data

305

Date Rain fall (mm)2/20/95 6.52/21/95 17.52/22/95 19.52/23/95 02/24/95 02/25/95 02/26/95 202/27/95 42/28/95 53/1/95 43/2/95 63/3/95 33/4/95 43/5/95 33/6/95 23/7/95 443/8/95 113/9/95 403/10/95 23/11/95 223/12/95 143/13/95 63/14/95 03/15/95 03/16/95 03/17/95 03/18/95 03/19/95 03/20/95 113/21/95 13

3/22/95 53/23/95 673/24/95 25.53/25/95 23/26/95 03/27/95 83/28/95 03/29/95 43/30/95 123/31/95 04/1/95 2.54/2/95 204/3/95 74/4/95 04/5/95 04/6/95 1.24/7/95 124/8/95 154/9/95 104/10/95 144/11/95 244/12/95 84/13/95 104/14/95 814/15/95 854/16/95 14/17/95 224/18/95 124/19/95 104/20/95 04/21/95 414/22/95 10

4/23/95 24/24/95 84/25/95 24/26/95 04/27/95 544/28/95 04/29/95 284/30/95 75/1/95 405/2/95 05/3/95 05/4/95 05/5/95 65/6/95 305/7/95 275/8/95 185/9/95 405/10/95 45/11/95 05/12/95 05/13/95 05/14/95 16.55/15/95 245/16/95 6.55/17/95 45.55/18/95 75/19/95 85/20/95 225/21/95 55/22/95 05/23/95 15/24/95 10

5/25/95 205/26/95 25/27/95 365/28/95 25/29/95 95/30/95 05/31/95 06/1/95 06/2/95 56/3/95 106/4/95 26/5/95 36/6/95 306/7/95 156/8/95 76/9/95 46/10/95 46/11/95 36/12/95 16/13/95 56/14/95 06/15/95 56/16/95 106/17/95 146/18/95 14.56/19/95 206/20/95 46/21/95 06/22/95 06/23/95 66/24/95 106/25/95 2

6/26/95 36/27/95 06/28/95 06/29/95 46/30/95 37/1/95 07/2/95 07/3/95 07/4/95 77/5/95 77/6/95 107/7/95 87/8/95 07/9/95 07/10/95 07/11/95 07/12/95 27/13/95 17/14/95 17/15/95 07/16/95 107/17/95 637/18/95 87/19/95 127/20/95 87/21/95 167/22/95 47/23/95 167/24/95 167/25/95 147/26/95 107/27/95 32

7/28/95 87/29/95 77/30/95 27/31/95 08/1/95 208/2/95 198/3/95 308/4/95 15.58/5/95 128/6/95 08/7/95 08/8/95 08/9/95 38/10/95 08/11/95 688/12/95 18/13/95 08/14/95 108/15/95 58/16/95 18/17/95 138/18/95 7.58/19/95 48/20/95 78/21/95 08/22/95 18/23/95 98/24/95 08/25/95 58/26/95 208/27/95 28/28/95 0

8/29/95 08/30/95 08/31/95 09/1/95 09/2/95 09/3/95 09/4/95 09/5/95 09/6/95 09/7/95 09/8/95 09/9/95 59/10/95 09/11/95 09/12/95 47.59/13/95 409/14/95 209/15/95 109/16/95 129/17/95 59/18/95 39/19/95 09/20/95 29/21/95 09/22/95 279/23/95 549/24/95 09/25/95 09/26/95 09/27/95 49/28/95 09/29/95 7

9/30/95 27.510/1/95 30.510/2/95 1610/3/95 010/4/95 310/5/95 1510/6/95 210/7/95 2310/8/95 3510/9/95 6410/10/95710/11/951410/12/952010/13/951810/14/954310/15/95710/16/9551.510/17/9565.510/18/951710/19/9516.510/20/95310/21/955010/22/951010/23/95910/24/952010/25/95510/26/951610/27/953510/28/951010/29/952310/30/95410/31/957

Tambito daily rainfall. Recorded data in Cabin weather station (Fundación Proselva)

305

306

11/1/95 1111/2/95 2011/3/95 3511/4/95 1011/5/95 1711/6/95 10.511/7/95 3011/8/95 4011/9/95 011/10/954911/11/9535.511/12/952811/13/951211/14/95011/15/9513.511/16/951011/17/951011/18/952011/19/952511/20/953011/21/952011/22/952011/23/951011/24/952011/25/953011/26/95011/27/95011/28/95011/29/95911/30/95512/1/95 7612/2/95 212/3/95 012/4/95 20

12/5/95 012/6/95 2612/7/95 812/8/95 1212/9/95 512/10/952012/11/953612/12/952512/13/952012/14/952612/15/952012/16/952012/17/951212/18/952012/19/95612/20/951012/21/952612/22/95412/23/951212/24/951112/25/95812/26/951012/27/951012/28/95912/29/951412/30/951012/31/9561/1/96 51/2/96 41/3/96 51/4/96 231/5/96 10.51/6/96 201/7/96 5

1/8/96 41/9/96 31/10/96 21/11/96 71/12/96 19.51/13/96 01/14/96 01/15/96 101/16/96 17.51/17/96 211/18/96 21.51/19/96 91/20/96 101/21/96 101/22/96 51/23/96 121/24/96 51/25/96 171/26/96 6.51/27/96 101/28/96 261/29/96 20.51/30/96 151/31/96 102/1/96 102/2/96 252/3/96 52/4/96 42/5/96 302/6/96 322/7/96 532/8/96 72/9/96 162/10/96 5

2/11/96 102/12/96 11.52/13/96 02/14/96 02/15/96 02/16/96 12/17/96 42/18/96 202/19/96 262/20/96 102/21/96 502/22/96 82/23/96 72/24/96 4.52/25/96 22/26/96 02/27/96 12/28/96 02/29/96 93/1/96 183/2/96 23/3/96 103/4/96 83/5/96 143/6/96 213/7/96 243/8/96 183/9/96 03/10/96 323/11/96 43/12/96 31.53/13/96 183/14/96 203/15/96 32

3/16/96 103/17/96 343/18/96 03/19/96 03/20/96 203/21/96 73/22/96 63/23/96 03/24/96 283/25/96 723/26/96 303/27/96 163/28/96 243/29/96 123/30/96 453/31/96 184/1/96 74/2/96 104/3/96 04/4/96 144/5/96 84/6/96 5.54/7/96 44/8/96 5.54/9/96 104/10/96 5.54/11/96 224/12/96 194/13/96 184/14/96 324/15/96 74/16/96 74/17/96 194/18/96 13

4/19/96 244/20/96 04/21/96 64/22/96 84/23/96 64/24/96 334/25/96 44/26/96 114/27/96 44/28/96 04/29/96 404/30/96 145/1/96 125/2/96 35/3/96 05/4/96 25.55/5/96 115/6/96 1.55/7/96 65/8/96 195/9/96 155/10/96 25/11/96 05/12/96 05/13/96 205/14/96 285/15/96 735/16/96 255/17/96 1.55/18/96 105/19/96 275/20/96 3.55/21/96 05/22/96 9

5/23/96 255/24/96 325/25/96 245/26/96 11.55/27/96 22.55/28/96 05/29/96 18.55/30/96 425/31/96 06/1/96 3.56/2/96 116/3/96 46/4/96 06/5/96 06/6/96 06/7/96 06/8/96 06/9/96 06/10/96 06/11/96 06/12/96 06/13/96 26/14/96 06/15/96 12.56/16/96 196/17/96 216/18/96 31.56/19/96 246/20/96 51.56/21/96 106/22/96 76/23/96 13.56/24/96 36/25/96 7

6/26/96 26/27/96 206/28/96 96/29/96 206/30/96 197/1/96 47/2/96 10.57/3/96 417/4/96 67/5/96 07/6/96 197/7/96 07/8/96 87/9/96 117/10/96 397/11/96 27/12/96 47/13/96 07/14/96 07/15/96 07/16/96 07/17/96 07/18/96 07/19/96 07/20/96 77/21/96 97/22/96 07/23/96 07/24/96 07/25/96 97/26/96 97/27/96 367/28/96 387/29/96 0

306

307

7/30/96 07/31/96 08/1/96 08/2/96 08/3/96 8.58/4/96 08/5/96 68/6/96 58/7/96 48/8/96 08/9/96 08/10/96 118/11/96 28/12/96 08/13/96 08/14/96 108/15/96 08/16/96 08/17/96 08/18/96 08/19/96 08/20/96 08/21/96 08/22/96 1.58/23/96 08/24/96 68/25/96 78/26/96 48/27/96 478/28/96 88/29/96 68/30/96 68/31/96 229/1/96 3

9/2/96 49/3/96 289/4/96 59/5/96 339/6/96 09/7/96 149/8/96 09/9/96 09/10/96 09/11/96 09/12/96 09/13/96 09/14/96 09/15/96 09/16/96 09/17/96 09/18/96 09/19/96 09/20/96 59/21/96 79/22/96 09/23/96 09/24/96 39/25/96 69/26/96 199/27/96 09/28/96 199/29/96 889/30/96 1010/1/96 1710/2/96 2010/3/96 2810/4/96 2710/5/96 14

10/6/96 3010/7/96 2510/8/96 4610/9/96 1310/10/96910/11/962510/12/962910/13/96910/14/965410/15/962010/16/963010/17/963010/18/962110/19/961110/20/96710/21/96910/22/961610/23/965010/24/961010/25/961510/26/96510/27/962610/28/9613.510/29/96510/30/96010/31/96911/1/96 811/2/96 1811/3/96 1011/4/96 1511/5/96 1411/6/96 511/7/96 10.511/8/96 112

11/9/96 33.511/10/963411/11/96411/12/962011/13/9632.511/14/964711/15/96111/16/96011/17/96011/18/96011/19/96011/20/96011/21/96011/22/96011/23/961.511/24/96811/25/961211/26/961511/27/961511/28/961311/29/965311/30/961212/1/96 1012/2/96 1512/3/96 3812/4/96 4012/5/96 3712/6/96 3512/7/96 312/8/96 2612/9/96 2012/10/96912/11/96712/12/9615

12/13/961012/14/961512/15/96412/16/96012/17/96012/18/96012/19/96012/20/96012/21/96612/22/962012/23/96012/24/96412/25/96612/26/96812/27/963212/28/965012/29/962212/30/961612/31/96251/1/97 621/2/97 161/3/97 101/4/97 101/5/97 431/6/97 301/7/97 171/8/97 121/9/97 61/10/97 141/11/97 301/12/97 401/13/97 301/14/97 401/15/97 24

1/16/97 51/17/97 81/18/97 61/19/97 261/20/97 391/21/97 191/22/97 251/23/97 281/24/97 391/25/97 231/26/97 81/27/97 101/28/97 41/29/97 401/30/97 461/31/97 102/1/97 162/2/97 112/3/97 02/4/97 132/5/97 162/6/97 102/7/97 162/8/97 122/9/97 102/10/97 72/11/97 02/12/97 162/13/97 132/14/97 82/15/97 52/16/97 162/17/97 122/18/97 8

2/19/97 52/20/97 02/21/97 02/22/97 02/23/97 02/24/97 02/25/97 02/26/97 02/27/97 02/28/97 03/1/97 313/2/97 243/3/97 253/4/97 453/5/97 703/6/97 03/7/97 03/8/97 03/9/97 03/10/97 03/11/97 03/12/97 33/13/97 83/14/97 23/15/97 83/16/97 53/17/97 303/18/97 203/19/97 103/20/97 43/21/97 2.53/22/97 03/23/97 03/24/97 2

3/25/97 03/26/97 03/27/97 03/28/97 103/29/97 27.53/30/97 173/31/97 154/1/97 174/2/97 64/3/97 04/4/97 04/5/97 04/6/97 04/7/97 04/8/97 104/9/97 74/10/97 54/11/97 234/12/97 204/13/97 304/14/97 94/15/97 44/16/97 84/17/97 04/18/97 104/19/97 44/20/97 24/21/97 264/22/97 244/23/97 194/24/97 54/25/97 544/26/97 324/27/97 35

307

308

4/28/97 104/29/97 204/30/97 05/1/97 05/2/97 05/3/97 05/4/97 05/5/97 05/6/97 05/7/97 115/8/97 05/9/97 25/10/97 05/11/97 215/12/97 95/13/97 125/14/97 75/15/97 85/16/97 65/17/97 05/18/97 05/19/97 05/20/97 05/21/97 05/22/97 45/23/97 105/24/97 65/25/97 05/26/97 305/27/97 105/28/97 165/29/97 05/30/97 55/31/97 0

6/1/97 46/2/97 206/3/97 06/4/97 06/5/97 326/6/97 306/7/97 476/8/97 176/9/97 06/10/97 32.56/11/97 6.56/12/97 66/13/97 10.56/14/97 06/15/97 06/16/97 06/17/97 06/18/97 06/19/97 06/20/97 456/21/97 116/22/97 46/23/97 56/24/97 26/25/97 236/26/97 86/27/97 06/28/97 06/29/97 06/30/97 07/1/97 07/2/97 07/3/97 07/4/97 0

7/5/97 07/6/97 07/7/97 07/8/97 07/9/97 07/10/97 07/11/97 07/12/97 07/13/97 07/14/97 07/15/97 07/16/97 07/17/97 07/18/97 07/19/97 07/20/97 07/21/97 07/22/97 07/23/97 07/24/97 07/25/97 07/26/97 07/27/97 07/28/97 07/29/97 07/30/97 07/31/97 08/1/97 08/2/97 08/3/97 08/4/97 08/5/97 08/6/97 08/7/97 0

8/8/97 08/9/97 08/10/97 08/11/97 08/12/97 08/13/97 08/14/97 08/15/97 08/16/97 08/17/97 08/18/97 08/19/97 08/20/97 08/21/97 08/22/97 08/23/97 08/24/97 08/25/97 08/26/97 08/27/97 08/28/97 08/29/97 08/30/97 08/31/97 09/1/97 09/2/97 09/3/97 89/4/97 169/5/97 09/6/97 449/7/97 229/8/97 119/9/97 69/10/97 0

9/11/97 09/12/97 09/13/97 09/14/97 09/15/97 09/16/97 09/17/97 09/18/97 09/19/97 09/20/97 39/21/97 49/22/97 509/23/97 209/24/97 619/25/97 209/26/97 209/27/97 1.59/28/97 09/29/97 09/30/97 010/1/97 410/2/97 010/3/97 010/4/97 210/5/97 010/6/97 010/7/97 010/8/97 010/9/97 010/10/975010/11/97610/12/971310/13/971710/14/9712

10/15/97810/16/971010/17/9717.510/18/97810/19/971310/20/97010/21/97210/22/97010/23/97110/24/97210/25/97410/26/97710/27/971810/28/972810/29/97310/30/972010/31/971111/1/97 1711/2/97 2311/3/97 1211/4/97 1511/5/97 2211/6/97 2311/7/97 3911/8/97 4211/9/97 2411/10/97711/11/971811/12/97511/13/97611/14/9744.511/15/97011/16/971411/17/978

11/18/972011/19/971211/20/971811/21/971011/22/97011/23/97011/24/971111/25/973311/26/972311/27/97011/28/971011/29/97011/30/97012/1/97 1312/2/97 2012/3/97 3912/4/97 012/5/97 012/6/97 012/7/97 012/8/97 012/9/97 012/10/97212/11/97012/12/97012/13/97312/14/971812/15/974012/16/971412/17/97812/18/97012/19/97012/20/97012/21/970

12/22/97012/23/97012/24/97012/25/97012/26/97012/27/97012/28/97012/29/97012/30/97012/31/9701/1/98 01/2/98 01/3/98 01/4/98 01/5/98 01/6/98 01/7/98 01/8/98 01/9/98 31/10/98 01/11/98 01/12/98 01/13/98 251/14/98 01/15/98 81/16/98 101/17/98 571/18/98 01/19/98 01/20/98 1.51/21/98 21/22/98 11/23/98 01/24/98 0

308

309

1/25/98 01/26/98 01/27/98 01/28/98 01/29/98 21/30/98 11/31/98 02/1/98 02/2/98 02/3/98 02/4/98 02/5/98 82/6/98 52/7/98 82/8/98 322/9/98 552/10/98 19.52/11/98 372/12/98 72/13/98 0

2/14/98 02/15/98 292/16/98 02/17/98 02/18/98 02/19/98 22/20/98 702/21/98 202/22/98 02/23/98 252/24/98 02/25/98 02/26/98 02/27/98 02/28/98 123/1/98 23/2/98 03/3/98 03/4/98 03/5/98 0

3/6/98 03/7/98 63/8/98 33/9/98 03/10/98 73/11/98 15.53/12/98 2.53/13/98 03/14/98 03/15/98 03/16/98 03/17/98 33/18/98 03/19/98 03/20/98 03/21/98 03/22/98 03/23/98 203/24/98 123/25/98 0

3/26/98 143/27/98 503/28/98 20.53/29/98 423/30/98 213/31/98 854/1/98 284/2/98 504/3/98 214/4/98 404/5/98 84/6/98 54/7/98 04/8/98 154/9/98 34/10/98 54/11/98 654/12/98 164/13/98 304/14/98 18

4/15/98 04/16/98 244/17/98 204/18/98 104/19/98 384/20/98 304/21/98 204/22/98 104/23/98 44/24/98 04/25/98 74/26/98 354/27/98 304/28/98 264/29/98 64/30/98 85/1/98 105/2/98 155/3/98 285/4/98 40

5/5/98 35/6/98 75/7/98 05/8/98 205/9/98 155/10/98 105/11/98 165/12/98 75/13/98 165/14/98 05/15/98 05/16/98 05/17/98 05/18/98 05/19/98 05/20/98 05/21/98 05/22/98 05/23/98 05/24/98 0

5/25/98 05/26/98 125/27/98 515/28/98 365/29/98 17.55/30/98 45/31/98 46/1/98 216/2/98 36/3/98 146/4/98 06/5/98 06/6/98 06/7/98 06/8/98 06/9/98 256/10/98 46/11/98 26/12/98 06/13/98 0

6/14/98 06/15/98 06/16/98 06/17/98 06/18/98 56/19/98 26/20/98 06/21/98 06/22/98 06/23/98 26/24/98 16/25/98 06/26/98 26/27/98 36/28/98 06/29/98 0

309

310

Appendix VI

Example of input data file

for use in the

hydrological model

311

7yearmonthdayhourrainfall (mm)

1 1995 9 10 3 2.2 2532 1995 9 10 4 10 2533 1995 9 10 5 0 2534 1995 9 10 6 20 2535 1995 9 10 7 0 2536 1995 9 10 8 3.2 2537 1995 9 10 9 0 2538 1995 9 10 10 1.2 2539 1995 9 10 11 0 25310 1995 9 10 12 30 25311 1995 9 10 13 0 25312 1995 9 10 14 0.2 25313 1995 9 10 15 0 25314 1995 9 10 16 0 25315 1995 9 10 17 1.2 25316 1995 9 10 18 0 25317 1995 9 10 19 0 25318 1995 9 10 20 0.2 25319 1995 9 10 21 0 25320 1995 9 10 22 1.8 25321 1995 9 10 23 0 25322 1995 9 11 0 2.2 25423 1995 9 11 1 0 25424 1995 9 11 2 0.2 25425 1995 9 11 3 0 25426 1995 9 11 4 0 25427 1995 9 11 5 0 25428 1995 9 11 6 0 25429 1995 9 11 7 0 25430 1995 9 11 8 0 25431 1995 9 11 9 0 25432 1995 9 11 10 0 25433 1995 9 11 11 0 25434 1995 9 11 12 0 25435 1995 9 11 13 0 25436 1995 9 11 14 0 25437 1995 9 11 15 0 25438 1995 9 11 16 0 25439 1995 9 11 17 0.2 25440 1995 9 11 18 0.2 25441 1995 9 11 19 0.2 25442 1995 9 11 20 0 25443 1995 9 11 21 1.2 25444 1995 9 11 22 0 25445 1995 9 11 23 36.8 25446 1995 9 12 0 0 25547 1995 9 12 1 17.2 25548 1995 9 12 2 0 25549 1995 9 12 3 0 25550 1995 9 12 4 0 25551 1995 9 12 5 0 25552 1995 9 12 6 0 25553 1995 9 12 7 0 25554 1995 9 12 8 0 25555 1995 9 12 9 0 25556 1995 9 12 10 0.2 25557 1995 9 12 11 0.2 25558 1995 9 12 12 0.2 25559 1995 9 12 13 0 25560 1995 9 12 14 5.2 25561 1995 9 12 15 0.2 25562 1995 9 12 16 0 25563 1995 9 12 17 0 25564 1995 9 12 18 0 25565 1995 9 12 19 0.2 25566 1995 9 12 20 0 25567 1995 9 12 21 0 25568 1995 9 12 22 0 25569 1995 9 12 23 32.6 25570 1995 9 13 0 0 25671 1995 9 13 1 1.2 25672 1995 9 13 2 0 25673 1995 9 13 3 0 25674 1995 9 13 4 0.2 25675 1995 9 13 5 0 25676 1995 9 13 6 0 256

77 1995 9 13 7 1.2 25678 1995 9 13 8 0 25679 1995 9 13 9 0 25680 1995 9 13 10 0.2 25681 1995 9 13 11 0 25682 1995 9 13 12 0 25683 1995 9 13 13 2.2 25684 1995 9 13 14 0 25685 1995 9 13 15 0.2 25686 1995 9 13 16 2.2 25687 1995 9 13 17 0 25688 1995 9 13 18 0 25689 1995 9 13 19 0 25690 1995 9 13 20 0.2 25691 1995 9 13 21 0 25692 1995 9 13 22 0 25693 1995 9 13 23 35.4 25694 1995 9 14 0 0 25795 1995 9 14 1 0 25796 1995 9 14 2 12.2 25797 1995 9 14 3 1.2 25798 1995 9 14 4 0 25799 1995 9 14 5 0 257100 1995 9 14 6 0.2 257101 1995 9 14 7 0 257102 1995 9 14 8 0 257103 1995 9 14 9 0 257104 1995 9 14 10 0.2 257105 1995 9 14 11 0 257106 1995 9 14 12 0 257107 1995 9 14 13 1.2 257108 1995 9 14 14 0 257109 1995 9 14 15 0 257110 1995 9 14 16 0 257111 1995 9 14 17 0 257112 1995 9 14 18 0.2 257113 1995 9 14 19 1.2 257114 1995 9 14 20 0 257115 1995 9 14 21 1.2 257116 1995 9 14 22 0 257117 1995 9 14 23 6.4 257118 1995 9 15 0 1.2 258119 1995 9 15 1 0.2 258120 1995 9 15 2 0 258121 1995 9 15 3 0 258122 1995 9 15 4 0 258123 1995 9 15 5 0 258124 1995 9 15 6 0.2 258125 1995 9 15 7 0 258126 1995 9 15 8 0 258127 1995 9 15 9 1.2 258128 1995 9 15 10 0.2 258129 1995 9 15 11 0 258130 1995 9 15 12 0 258131 1995 9 15 13 0 258132 1995 9 15 14 0 258133 1995 9 15 15 0 258134 1995 9 15 16 0 258135 1995 9 15 17 0 258136 1995 9 15 18 0 258137 1995 9 15 19 0.2 258138 1995 9 15 20 0 258139 1995 9 15 21 0 258140 1995 9 15 22 0 258141 1995 9 15 23 7.8 258142 1995 9 16 0 0 259143 1995 9 16 1 7.2 259144 1995 9 16 2 1.2 259145 1995 9 16 3 0 259146 1995 9 16 4 0.2 259147 1995 9 16 5 0 259148 1995 9 16 6 0.2 259149 1995 9 16 7 1.2 259150 1995 9 16 8 2.2 259151 1995 9 16 9 0 259152 1995 9 16 10 4.2 259153 1995 9 16 11 0.2 259154 1995 9 16 12 0 259155 1995 9 16 13 0 259156 1995 9 16 14 0 259157 1995 9 16 15 0 259158 1995 9 16 16 0 259159 1995 9 16 17 0 259160 1995 9 16 18 0.2 259

Example of Input data filefor modelling in PCraster

312

161 1995 9 16 19 1.2 259162 1995 9 16 20 0.2 259163 1995 9 16 21 1.2 259164 1995 9 16 22 0 259165 1995 9 16 23 2.6 259166 1995 9 17 0 0 260167 1995 9 17 1 0 260168 1995 9 17 2 0 260169 1995 9 17 3 1.2 260170 1995 9 17 4 3.8 260171 1995 9 17 5 0 260172 1995 9 17 6 0 260173 1995 9 17 7 0 260174 1995 9 17 8 0 260175 1995 9 17 9 0 260176 1995 9 17 10 0 260177 1995 9 17 11 0 260178 1995 9 17 12 0 260179 1995 9 17 13 0 260180 1995 9 17 14 0 260181 1995 9 17 15 0 260182 1995 9 17 16 0 260183 1995 9 17 17 0 260184 1995 9 17 18 0 260185 1995 9 17 19 0 260186 1995 9 17 20 0 260187 1995 9 17 21 0 260188 1995 9 17 22 0 260189 1995 9 17 23 0 260190 1995 9 18 0 1.2 261191 1995 9 18 1 1.2 261192 1995 9 18 2 0.6 261193 1995 9 18 3 0 261194 1995 9 18 4 0 261195 1995 9 18 5 0 261196 1995 9 18 6 0 261197 1995 9 18 7 0 261198 1995 9 18 8 0 261199 1995 9 18 9 0 261200 1995 9 18 10 0 261201 1995 9 18 11 0 261202 1995 9 18 12 0 261203 1995 9 18 13 0 261204 1995 9 18 14 0 261205 1995 9 18 15 0 261206 1995 9 18 16 0 261207 1995 9 18 17 0 261208 1995 9 18 18 0 261209 1995 9 18 19 0 261210 1995 9 18 20 0 261211 1995 9 18 21 0 261212 1995 9 18 22 0 261213 1995 9 18 23 0 261214 1995 9 19 0 0 262215 1995 9 19 1 0 262216 1995 9 19 2 0 262217 1995 9 19 3 0 262218 1995 9 19 4 0 262219 1995 9 19 5 0 262220 1995 9 19 6 0.2 262221 1995 9 19 7 0 262222 1995 9 19 8 0 262223 1995 9 19 9 0 262224 1995 9 19 10 0.2 262225 1995 9 19 11 0 262226 1995 9 19 12 0 262227 1995 9 19 13 0 262228 1995 9 19 14 0.2 262229 1995 9 19 15 0.2 262230 1995 9 19 16 0 262231 1995 9 19 17 0 262232 1995 9 19 18 0 262233 1995 9 19 19 0.2 262234 1995 9 19 20 0.2 262235 1995 9 19 21 0 262236 1995 9 19 22 0 262237 1995 9 19 23 6.8 262238 1995 9 20 0 1.2 263239 1995 9 20 1 0 263240 1995 9 20 2 3.2 263241 1995 9 20 3 0 263242 1995 9 20 4 1.2 263243 1995 9 20 5 0.2 263244 1995 9 20 6 1.2 263245 1995 9 20 7 0 263246 1995 9 20 8 0 263

247 1995 9 20 9 0 263248 1995 9 20 10 0 263249 1995 9 20 11 0 263250 1995 9 20 12 0 263251 1995 9 20 13 0 263252 1995 9 20 14 0.2 263253 1995 9 20 15 0 263254 1995 9 20 16 1.2 263255 1995 9 20 17 0 263256 1995 9 20 18 0.2 263257 1995 9 20 19 1.2 263258 1995 9 20 20 0.2 263259 1995 9 20 21 1.2 263260 1995 9 20 22 0 263261 1995 9 20 23 6.8 263262 1995 9 21 0 2.2 264263 1995 9 21 1 2.2 264264 1995 9 21 2 1.2 264265 1995 9 21 3 1.2 264266 1995 9 21 4 0 264267 1995 9 21 5 0 264268 1995 9 21 6 1.2 264269 1995 9 21 7 0.2 264270 1995 9 21 8 0.2 264271 1995 9 21 9 1.2 264272 1995 9 21 10 0 264273 1995 9 21 11 0 264274 1995 9 21 12 2.2 264275 1995 9 21 13 0 264276 1995 9 21 14 0 264277 1995 9 21 15 3.2 264278 1995 9 21 16 0 264279 1995 9 21 17 1.2 264280 1995 9 21 18 2.2 264281 1995 9 21 19 1.2 264282 1995 9 21 20 0 264283 1995 9 21 21 0 264284 1995 9 21 22 0.2 264285 1995 9 21 23 32.2 264286 1995 9 22 0 0 265287 1995 9 22 1 1.2 265288 1995 9 22 2 1.2 265289 1995 9 22 3 0 265290 1995 9 22 4 0 265291 1995 9 22 5 0 265292 1995 9 22 6 0 265293 1995 9 22 7 0.2 265294 1995 9 22 8 0 265295 1995 9 22 9 0 265296 1995 9 22 10 0 265297 1995 9 22 11 0 265298 1995 9 22 12 0 265299 1995 9 22 13 0 265300 1995 9 22 14 0 265301 1995 9 22 15 0 265302 1995 9 22 16 0.2 265303 1995 9 22 17 0 265304 1995 9 22 18 0 265305 1995 9 22 19 1.2 265306 1995 9 22 20 1.2 265307 1995 9 22 21 0 265308 1995 9 22 22 1.2 265309 1995 9 22 23 7.6 265310 1995 9 23 0 4 266311 1995 9 23 1 0 266312 1995 9 23 2 0 266313 1995 9 23 3 0 266314 1995 9 23 4 0 266315 1995 9 23 5 0 266316 1995 9 23 6 0 266317 1995 9 23 7 0 266318 1995 9 23 8 0 266319 1995 9 23 9 0 266320 1995 9 23 10 0 266321 1995 9 23 11 0 266322 1995 9 23 12 0 266323 1995 9 23 13 0 266324 1995 9 23 14 0 266325 1995 9 23 15 0 266326 1995 9 23 16 0 266327 1995 9 23 17 0 266328 1995 9 23 18 0 266329 1995 9 23 19 0 266330 1995 9 23 20 0 266331 1995 9 23 21 0 266332 1995 9 23 22 0 266

313

333 1995 9 23 23 0 266334 1995 9 24 0 0 267335 1995 9 24 1 0 267336 1995 9 24 2 0 267337 1995 9 24 3 0 267338 1995 9 24 4 0 267339 1995 9 24 5 0 267340 1995 9 24 6 0 267341 1995 9 24 7 0 267342 1995 9 24 8 0 267343 1995 9 24 9 0 267344 1995 9 24 10 0 267345 1995 9 24 11 0 267346 1995 9 24 12 0 267347 1995 9 24 13 0 267348 1995 9 24 14 0 267349 1995 9 24 15 0 267350 1995 9 24 16 0 267351 1995 9 24 17 0 267352 1995 9 24 18 0 267353 1995 9 24 19 0 267354 1995 9 24 20 0 267355 1995 9 24 21 0 267356 1995 9 24 22 0 267357 1995 9 24 23 0 267358 1995 9 25 0 1 268359 1995 9 25 1 0 268360 1995 9 25 2 0 268361 1995 9 25 3 0 268362 1995 9 25 4 0 268363 1995 9 25 5 0 268364 1995 9 25 6 0 268365 1995 9 25 7 0 268366 1995 9 25 8 0 268367 1995 9 25 9 0 268368 1995 9 25 10 0 268369 1995 9 25 11 0 268370 1995 9 25 12 0 268371 1995 9 25 13 0 268372 1995 9 25 14 0 268373 1995 9 25 15 0 268374 1995 9 25 16 0 268375 1995 9 25 17 0 268376 1995 9 25 18 0 268377 1995 9 25 19 0 268378 1995 9 25 20 0 268379 1995 9 25 21 0 268380 1995 9 25 22 0 268381 1995 9 25 23 0 268382 1995 9 26 0 0 269383 1995 9 26 1 0 269384 1995 9 26 2 0 269385 1995 9 26 3 0 269386 1995 9 26 4 0 269387 1995 9 26 5 0 269388 1995 9 26 6 0 269389 1995 9 26 7 0 269390 1995 9 26 8 0 269391 1995 9 26 9 0 269392 1995 9 26 10 0 269393 1995 9 26 11 0 269394 1995 9 26 12 0 269395 1995 9 26 13 0 269396 1995 9 26 14 0 269397 1995 9 26 15 0 269398 1995 9 26 16 0 269399 1995 9 26 17 0 269400 1995 9 26 18 0 269401 1995 9 26 19 0 269402 1995 9 26 20 0 269403 1995 9 26 21 0 269404 1995 9 26 22 0 269405 1995 9 26 23 0 269406 1995 9 27 0 0.2 270407 1995 9 27 1 3.2 270408 1995 9 27 2 0 270409 1995 9 27 3 0 270410 1995 9 27 4 0 270411 1995 9 27 5 0 270412 1995 9 27 6 0.2 270413 1995 9 27 7 0 270414 1995 9 27 8 0 270415 1995 9 27 9 3.2 270416 1995 9 27 10 0 270417 1995 9 27 11 6.2 270418 1995 9 27 12 0 270

419 1995 9 27 13 0 270420 1995 9 27 14 0 270421 1995 9 27 15 0 270422 1995 9 27 16 0 270423 1995 9 27 17 0 270424 1995 9 27 18 0 270425 1995 9 27 19 0 270426 1995 9 27 20 0 270427 1995 9 27 21 0 270428 1995 9 27 22 0 270429 1995 9 27 23 0 270430 1995 9 28 0 0 271431 1995 9 28 1 1.2 271432 1995 9 28 2 9.2 271433 1995 9 28 3 0.2 271434 1995 9 28 4 3.2 271435 1995 9 28 5 3.2 271436 1995 9 28 6 0 271437 1995 9 28 7 0 271438 1995 9 28 8 0 271439 1995 9 28 9 0 271440 1995 9 28 10 0 271441 1995 9 28 11 0 271442 1995 9 28 12 0 271443 1995 9 28 13 0 271444 1995 9 28 14 0 271445 1995 9 28 15 0 271446 1995 9 28 16 0 271447 1995 9 28 17 0 271448 1995 9 28 18 0 271449 1995 9 28 19 0 271450 1995 9 28 20 0 271451 1995 9 28 21 0 271452 1995 9 28 22 0 271453 1995 9 28 23 0 271454 1995 9 29 0 0 272455 1995 9 29 1 0 272456 1995 9 29 2 21.2 272457 1995 9 29 3 0 272458 1995 9 29 4 0 272459 1995 9 29 5 0.2 272460 1995 9 29 6 0.2 272461 1995 9 29 7 0.4 272462 1995 9 29 8 0 272463 1995 9 29 9 0 272464 1995 9 29 10 0 272465 1995 9 29 11 0 272466 1995 9 29 12 0 272467 1995 9 29 13 0 272468 1995 9 29 14 0 272469 1995 9 29 15 0 272470 1995 9 29 16 0 272471 1995 9 29 17 0 272472 1995 9 29 18 0 272473 1995 9 29 19 0 272474 1995 9 29 20 0 272475 1995 9 29 21 0 272476 1995 9 29 22 0 272477 1995 9 29 23 0 272478 1995 9 30 0 0.2 273479 1995 9 30 1 0 273480 1995 9 30 2 0 273481 1995 9 30 3 0 273482 1995 9 30 4 0 273483 1995 9 30 5 1.2 273484 1995 9 30 6 0 273485 1995 9 30 7 0 273486 1995 9 30 8 1.2 273487 1995 9 30 9 0 273488 1995 9 30 10 0 273489 1995 9 30 11 0 273490 1995 9 30 12 0.2 273491 1995 9 30 13 0 273492 1995 9 30 14 0 273493 1995 9 30 15 0.2 273494 1995 9 30 16 0 273495 1995 9 30 17 0 273496 1995 9 30 18 0.2 273497 1995 9 30 19 0.2 273498 1995 9 30 20 0 273499 1995 9 30 21 0 273500 1995 9 30 22 0 273501 1995 9 30 23 37.6 273502 1995 10 1 0 2.2 274503 1995 10 1 1 0 274504 1995 10 1 2 0 274

314

505 1995 10 1 3 14.8 274506 1995 10 1 4 0 274507 1995 10 1 5 0 274508 1995 10 1 6 0 274509 1995 10 1 7 0 274510 1995 10 1 8 0 274511 1995 10 1 9 0 274512 1995 10 1 10 0 274513 1995 10 1 11 0 274514 1995 10 1 12 0 274515 1995 10 1 13 0 274516 1995 10 1 14 0 274517 1995 10 1 15 0 274518 1995 10 1 16 0 274519 1995 10 1 17 0 274520 1995 10 1 18 0 274521 1995 10 1 19 0 274522 1995 10 1 20 0 274523 1995 10 1 21 0 274524 1995 10 1 22 0 274525 1995 10 1 23 0 274526 1995 10 2 0 0 275527 1995 10 2 1 0 275528 1995 10 2 2 0 275529 1995 10 2 3 0 275530 1995 10 2 4 3.2 275531 1995 10 2 5 2.2 275532 1995 10 2 6 0 275533 1995 10 2 7 0.2 275534 1995 10 2 8 1.2 275535 1995 10 2 9 0.2 275536 1995 10 2 10 1 275537 1995 10 2 11 0 275538 1995 10 2 12 0 275539 1995 10 2 13 0 275540 1995 10 2 14 0 275541 1995 10 2 15 0 275542 1995 10 2 16 0 275543 1995 10 2 17 0 275544 1995 10 2 18 0 275545 1995 10 2 19 0 275546 1995 10 2 20 0 275547 1995 10 2 21 0 275548 1995 10 2 22 0 275549 1995 10 2 23 0 275550 1995 10 3 0 1.2 276551 1995 10 3 1 0 276552 1995 10 3 2 1.8 276553 1995 10 3 3 0 276554 1995 10 3 4 0 276555 1995 10 3 5 0 276556 1995 10 3 6 0 276557 1995 10 3 7 0 276558 1995 10 3 8 0 276559 1995 10 3 9 0 276560 1995 10 3 10 0 276561 1995 10 3 11 0 276562 1995 10 3 12 0 276563 1995 10 3 13 0 276564 1995 10 3 14 0 276565 1995 10 3 15 0 276566 1995 10 3 16 0 276567 1995 10 3 17 0 276568 1995 10 3 18 0 276569 1995 10 3 19 0 276570 1995 10 3 20 0 276571 1995 10 3 21 0 276572 1995 10 3 22 0 276573 1995 10 3 23 0 276574 1995 10 4 0 0 277575 1995 10 4 1 0 277576 1995 10 4 2 0 277577 1995 10 4 3 0.2 277578 1995 10 4 4 0 277579 1995 10 4 5 0 277580 1995 10 4 6 0 277581 1995 10 4 7 0.2 277582 1995 10 4 8 0 277583 1995 10 4 9 0 277584 1995 10 4 10 0 277585 1995 10 4 11 7.6 277586 1995 10 4 12 0 277587 1995 10 4 13 0 277

588 1995 10 4 14 0 277589 1995 10 4 15 0 277590 1995 10 4 16 0 277591 1995 10 4 17 0 277592 1995 10 4 18 0 277593 1995 10 4 19 0 277594 1995 10 4 20 0 277595 1995 10 4 21 0 277596 1995 10 4 22 0 277597 1995 10 4 23 0 277598 1995 10 5 0 0 278599 1995 10 5 1 0.2 278600 1995 10 5 2 4.2 278601 1995 10 5 3 0 278602 1995 10 5 4 0.2 278603 1995 10 5 5 0 278604 1995 10 5 6 0.2 278605 1995 10 5 7 0.2 278606 1995 10 5 8 0 278607 1995 10 5 9 0.2 278608 1995 10 5 10 0.2 278609 1995 10 5 11 0 278610 1995 10 5 12 0 278611 1995 10 5 13 0 278612 1995 10 5 14 16.2 278613 1995 10 5 15 0 278614 1995 10 5 16 0 278615 1995 10 5 17 0 278616 1995 10 5 18 2.2 278617 1995 10 5 19 0.2 278618 1995 10 5 20 0 278619 1995 10 5 21 0 278620 1995 10 5 22 0 278621 1995 10 5 23 8 278622 1995 10 6 0 0 279623 1995 10 6 1 0.2 279624 1995 10 6 2 0 279625 1995 10 6 3 0 279626 1995 10 6 4 0 279627 1995 10 6 5 0 279628 1995 10 6 6 0.2 279629 1995 10 6 7 0 279630 1995 10 6 8 0 279631 1995 10 6 9 0 279632 1995 10 6 10 0 279633 1995 10 6 11 0 279634 1995 10 6 12 0 279635 1995 10 6 13 2.2 279636 1995 10 6 14 0 279637 1995 10 6 15 0 279638 1995 10 6 16 0 279639 1995 10 6 17 0 279640 1995 10 6 18 0 279641 1995 10 6 19 0 279642 1995 10 6 20 0 279643 1995 10 6 21 0 279644 1995 10 6 22 0 279645 1995 10 6 23 18.4 279646 1995 10 7 0 0.2 280647 1995 10 7 1 0 280648 1995 10 7 2 1.2 280649 1995 10 7 3 0.2 280650 1995 10 7 4 0 280651 1995 10 7 5 1.2 280652 1995 10 7 6 1.2 280653 1995 10 7 7 1.2 280654 1995 10 7 8 1.2 280655 1995 10 7 9 0 280656 1995 10 7 10 1.2 280657 1995 10 7 11 0 280658 1995 10 7 12 0.2 280659 1995 10 7 13 0 280660 1995 10 7 14 0 280661 1995 10 7 15 0 280662 1995 10 7 16 0 280663 1995 10 7 17 3.2 280664 1995 10 7 18 1.2 280665 1995 10 7 19 0 280666 1995 10 7 20 2.2 280667 1995 10 7 21 0 280668 1995 10 7 22 0.2 280669 1995 10 7 23 30.4 280

315

Appendix VII

Extraterrestrial solar radiation

316

Solar Radiation

The “solar constant” is the energy received per unit time, at

Earth’s mean distance from the sun, outside the atmosphere.

The standard value, accepted by the U.S. National Aeronautical

and Space Administration (NASA) and the American Society for

Testing Material, was given by Duffie and Beckman (1980) as

1353 W m-2 or 1940 cal m-2 min-1 or 428 BTU ft-2 h-1 or 4871

MJ m-2 h-1 (Duffie and Benckman, 1980).

The amount of solar radiation reaching the earth is inversely

proportional to the square of its distance from the sun.

Therefore it is important that the value of the sun-earth

distance be accurate.

The mean of sun-earth distance, ro, is called one astronomical

unit:

1 AU = 1.496 x 108 km

The minimum sun-earth distance is about 0.983 AU and the

maximum, 1.017 AU. The distance r is traditionally expressed

in terms of a Fourier series type, with a maximum error of

0.0001. Thus, the eccentricity correction factor of the earth’s

orbit, Eo, is:

E r r Cos Sin

Cos Sino o= = + +

+ +( / ) . . .

. .

2 1000110 0 034221 0 001280

0 000719 2 0 000077 2

Γ ΓΓ Γ

317

where Γ is in radians and called the “day angle”, and is

represented by

and where dn is the day number of the year, ranging from 1 (on

1 January) to 365 (on 31 December. February is always

assumed to have 28 days so the leap year cycle will vary

slightly (Iqbal, 1983).

The solar declination δ is the angle between a line joining the

centres of the sun and the earth to the equatorial plane; this

line changes every day, in fact, every instant. The solar

declination is zero at the vernal and autumnal equinoxes

(literally equal nights) and has a value of approximately +23.5o

at the summer solstice and 23.5o at the winter solstice. Iqbal

(1983) presents the solar declination δ in this equation:

with the maximum error being 0.0035 rad (<3’) (Ibid.).

The time equation Et indicates solar time based on (1) the

rotation of the earth on its polar axis, and (2) its orbit around

the sun. A solar day is the time interval (not necessarily 24 h)

from the moment the sun appears until it completes one cycle

around a stationary observer on earth. The solar day varies in

length throughout the year because the earth sweeps out

unequal areas on the ecliptic plane as it revolves around the

sun. Also, the earth’s axis is tiled with respect to the ecliptic

plane. Again, Iqbal’s time equation is:

Γ = −2 1 365π( ) /dn

δπ

= − + −+ − +

( . . . .

. . . )( / )

0 006918 0 399912 0 070257 0 006758 2

0 000907 2 0 002697 3 0 00148 3 180

Cos Sin Cos

Sin Cos Sin

Γ Γ ΓΓ Γ Γ

E Cos Sin

Cos Sint = + −

− −( . . .

. . )( . )

0 000075 0 001868 0 032077

0 014615 2 0 04089 2 22918

Γ ΓΓ Γ

318

The first right-hand site term in parentheses represents Et in

radians and the multiplier 229.18 converts it to minutes. The

maximum error with this series is 0.0025 rad like 35 sec (Ibid.).

Solar radiation data are often given in terms of local apparent

time (LAT), also called true solar time (TST). The LAT is

expressed as:

LAT = local standard time + longitude correction + time equation

LAT = local standard time + 4(Ls – Le) + Et

where Ls is the standard longitude and Le,, the local longitude.

The longitude correction, 4 min for every degree, accounts for

the difference between the local and the standard meridians

(Ibid.).

The relative position of the sun in reference to a horizontal

surface is shown in Figure A5.1 and Figure A5.2. At any given

time, an observer on the earth’s surface has a corresponding

position on the celestial sphere that is called the “observer’s

zenith”. This is a point of intersection, with the celestial

sphere, of a normal to the earth’s surface at the observer’s

position. The observer’s horizon is the large circle in the

celestial sphere, the plane of which passes through the centre

of the earth normal to the line joining the centre of the earth

and the zenith. The zenith angle θz (or zenith distance) is the

angle (between 0o to 90o) between the local zenith and the line

joining the observer and the sun. The solar altitude α (also

called solar elevation) is the sun’s angular height above the

observer’s celestial horizon and ranges between 0o to 90o. The

solar altitude is the complement of the zenith angle. The solar

319

azimuth ψ is the angle at the local zenith between the plane of

the observer’s meridian and the plane of a great circle passing

through the zenith and the sun, and is measured east positive

and west negative (south zero), thus varying between 0° and +/-

180° (Ibid.).

The hour angle ω is the angle measured at the celestial pole

between the observer’s meridian and the solar meridian,

counting from midday and changing 15o per hour. For a given

geographical position, in absence of the earth’s reflective

atmosphere, the trigonometric relationships between the sun

(the centre of the solar disk) and the horizontal surface are well

known. Their area is as follows:

and where:cos (sin sin sin ) / cos cosψ α θ δ α φ= −

Figure A71. Celestial sphere.

Cos Sin Sin Cos Cos Cos Sinzθ δ φ δ φ ω α= + =. . .

320

then:

θz is the zenith angle, also called zenith distance, in

degrees

α is the solar altitude, also called solar height or solar

elevation, in degrees;

A = 90 – θz

ω is the hour angle, noon zero and morning positive

φ is the geographic latitude, in degrees, north positive

is the solar azimuth, in degrees, south zero, east positive

(Figure A5.2)

δ is the declination, the angular position of the sun at

the solar noon with respect to the plane of the equator,

north positive, in degrees

0 90 0

90 180 0

o o

o o

Cos

Cos

≤ ≤ ≥

≤ ≤ ≤

ψ ψψ ψ

,

,

Figure A7.2 Definition of the sun’s zenith, altitude and azimuth angles.

321

The sunrise angle, Ws, at θz = 90o is

The daylength, Nd, is 2 ω and is expressed in hours as

Nd = (2/15)Cos-1(-Tan φ.Tan δ)

To determine the sun’s position relative to an inclined plane,

the following information is needed:

β is the slope of the surface, measured from horizontal

position, in degrees

γ is the surface azimuth angle; in other words, the

deviation of the normal to the surface regarding the

local meridian, in degrees, east positive

θ is the angle of incidence for an arbitrarily oriented

surface, the angle between normal to the surface and

the sun-earth vector, in degrees

Then, for arbitrarily oriented surface, θ is:

The time of sunrise and sunset are also included:

ωsr is the sunrise hour angle for an arbitrarily oriented

surface, in degrees

ωss is the sunset hour angle for an arbitrarily oriented

ω φ δs Cos Tan Tan= −−1( . )

Cos Sin Cos Cos Cos Sin

Cos Cos Sin Cos Cos Cos Cos

θ φ β φ γ δφ β φ β γ δ ω

= −+ +

( . . )

( . . . ) .

+ Cos Sin Sin Sinδ β γ ω. . .

322

surface, in degrees

The period during which the sun is seen on the surface is ωsr -

ωss, in degrees (Ibid.).

It is clear that the magnitudes of ωsr , ωss are not identical.

Furthermore, each one of these angles should be evaluated

separately for surfaces oriented toward the east and surfaces

oriented toward the west. It is also necessary to watch for two

possible situations: (1) those in which the sunrise hour angle

might be greater than the sunrise angle for the horizontal

surface, or (2) those in which the sunset hour angle is greater

than corresponding angle for the horizontal surface. ωsr can be

obtained numerically, through interaction, by setting θ = 90o.

The following expressions have been developed explicitly for

each one of the two surfaces oriented +/- γ:

0 = A Sin ω + Β Sin ω + C

where

A = Cos δ Sin β Sin φ ,

B = Cos δ Cos φ Cos β + Cos δ Sin φ Sin β Cos γ ,

C = Sin δ Sin φ Cos β - Sin δ Cos φ Sin β Cos γ .

in which ω should be written as:

It should also be expressed as:

x2 = B2/A2 , y2 = C2/A2

CosBC A A B A C

B Aω =

− ± + −+

4 2 2 2 2

2 2

323

so

where

and

When γ > 0, surface oriented toward the east, then:

And when γ < 0, surface oriented toward the west, then (Ibid.):

xCos

Sin Tan

Sin

Tan= +

φγ β

φγ.

y TanSin

Sin Tan

Cos

Tan= −

δ

φγ β

φγ.

ω ωsr s Cosxy x y

x=

− − − ++

−min , 12 2

2

1

1

ω ωss s Cosxy x y

x= −

− + − ++

−min , 12 2

2

1

1

ω ωsr s Cosxy x y

x=

− + − ++

−min , 12 2

2

1

1

ω ωss s Cosxy x y

x= −

− − − ++

−min , 12 2

2

1

1

Cosxy x y

xω =

− − − ++

2 2

2

1

1

324

The extraterrestrial irradiation on a horizontal surface can be

estimated for a different period, for example an hour, a day, a

month, etc. On a given day, IOn is the extraterrestrial irradiance

(energy rate) on a surface normal to sun rays,

where

ISC is the solar constant (1367 Wm-2;ISC without overdot,

in SI energy units (7921 kJm-2h-1)

ro is the mean sun-earth distance (149 597 89Q km)

Eo is the eccentricity correction factor of earth (ro /r)2

The irradiation (amount of energy), dIo, during a short period of

time, dt, will be

dIo = ISC Eo Cos θz.dt

and the irradiation during a 1-hour period is:

If ωi is at noon, then Cos ωi equals zero. In some cases, the

radiation for a period other than an exact hour may be

expressed between hours t1 and t2. Counting the hours from

midnight, and as long as t1 and t2 are during the day, the

radiation on a horizontal surface is given by:

I I r r I EOn SC o SC o= =( / )2

I I E Sin Sin Cos Cos Coso SC o i= +. ( . . . )δ φ δ φ ω

( ) ( ) ( ) ( )[ ]{ }I I E Sin Sin t t Cos Cos Sin t Sin to tt

SC o12

2 1 1 212 15 15= − + −δ φ π δ φ. .

325

And the daily radiation on a horizontal surface is given by:

For the extraterrestrial radiation on an arbitrarily oriented

surface, the hourly irradiation IOβγ includes terms with β

inclination surface from horizontal position (slope) and γ

surface azimuth angle, east positive west negative (aspect):

Daily irradiation is determined by:

( ) ( )I I E

Sin Cos Cos Sin Cos Sin Cos Cos Sin Sin Cos

Cos Cos Cos Sin Sin SinO SC o

i i

βγ

φ β φ β γ δ φ β φ β γδ ω δ β γ ω

=− + ++

. . . . . . .

. . . .

( )H I E

Cos Sin Sin Sin Cos Sin Cos

Cos Cos Cos Sin Sin Cos Cos Sin Sin

Sin Sin Cos Sin Sin Cos Cos

O SC o

ss sr ss sr

ss sr

ss sr ss sr

βγ π

β δ φ ω ω π δ φ β γ ω ω π

φ δ β ω ω δ γ φ β

ω ω δ β γ ω ω

=

− − −

+ − +

− + −

12180 180. . . . .

. . . . . .

. .

( ) ( )[ ]H I E Cos Cos Sin Coso SC o s s s= −24180π φ δ ω π ω ω. .

326

Appendix VIII

Mean value of cloud cover

327

Mean cloud cover calculated from hydrological station data from August to September in 1997.

Column1 6 a.m. 7 a.m. 8a.m. 9 a.m. 10 a.m. 11 a.m. 12 m. 1 p.m. 2 p.m. 3 p.m. 4 p.m. 5 p.m.

Mean (% of solar radiation) 0.992898 0.965122 0.884799 0.708476 0.595014 0.542704 0.590389 0.682293 0.74135 0.758729 0.785783 0.739108Standard Error 0.000957 0.001632 0.00388 0.012917 0.015983 0.018708 0.019341 0.016775 0.014472 0.013722 0.011817 0.015138Median 0.99797 0.969806 0.880874 0.72562 0.555924 0.507698 0.609451 0.729054 0.790236 0.808731 0.816436 0.780958Standard Deviation 0.011205 0.019174 0.045578 0.150633 0.186396 0.218169 0.223883 0.195627 0.166266 0.160021 0.137804 0.175892Sample Variance 0.000126 0.000368 0.002077 0.02269 0.034743 0.047598 0.050124 0.03827 0.027644 0.025607 0.01899 0.030938Range 0.068547 0.112241 0.205501 0.493272 0.613891 0.723389 0.763822 0.783099 0.694036 0.71568 0.710009 0.775447Minimum 0.944183 0.880514 0.768348 0.456014 0.333128 0.220893 0.178159 0.175923 0.2633 0.247883 0.269036 0.196301Maximum 1.012729 0.992756 0.97385 0.949287 0.947018 0.944282 0.941981 0.959023 0.957336 0.963563 0.979045 0.971748Sum 136.0271 133.1868 122.1023 96.35272 80.92186 73.80778 79.11209 92.79181 97.85814 103.1872 106.8665 99.77964Count 137 138 138 136 136 136 134 136 132 136 136 135Confidence Level(95.0%) 0.001893 0.003228 0.007672 0.025545 0.03161 0.036998 0.038255 0.033175 0.028628 0.027137 0.02337 0.029941

Units in % of solar radiation

327

328

Appendix IX

Hydrological PCRaster Program code

329

# MAO PCRaster hydrological model# Release V 1.0# October 03 2000# Dynamic model for surface hydrological fluxes# Basic analysis for Geography Ph.D. degree# King’s College London, London - UK

binding

PI = 3.141592654; # Conversion value data = dry9523.txt ; # file input data AU = 1.49597890E8 ; # {DISTANCIA AL SOL EN KM} SolarKteW = 1373 ; # { W m-2} SolarkteJ = 4921 ; # { kJ m-2 h-1} GR = 0.017453292 ; # Conversion value to rad olat = 2.5 ; # Latitude of the area olong = -76.85 ; # Longitude of the area oslope = slope.map ; # Slope map name oaspect = aspect.map ; # Aspect map name cellnum = 22780; # Number of cell in the catchment

# Parameters of interception

landuse = lucriof0.012; # Land use for this run b_canopy_drip = 0.5; # Parameter empirical value

# for drip from canopy min_drainage = 0.002; # Minimum drainage from

# canopy num_layers = 5; # Layer in the canopy canopy_retention = 0.6; # Canopy retention

# empirical value

# pedotransfer function parameters

av = -4.396; bv = -0.0715; cv = -0.000488; dv = -0.00004285; ev = -3.140; fv = -0.00222; gv = -0.00003484; hv = 0.332; jv = -0.0007251; kv = 0.1276; mv = -0.108; nv = 0.341; pv = 12.012; qv = -0.0755; rv = -3.8950; tv = 0.03671; uv = -0.1103 ; vv = 0.00087546;

# Other parameters

soildepth = mil.map; # soildept.map; soileroda = 0.02;

#---------Salidas---------- IobssSalida = Iobss.new; CloudSalida = Cloud.new; NetRadSalida = NetRad.new; PotEvapoSalida = PotEva.new; InterCanopySal = Intercep.new; EvapocanSal = Evapocan.new; rainsal = rain.new; ThroSal = Thro.new; ThroWSal = ThroW.new; RioSal = RioS.new; TetaSal = Teta.new; TetamSal = Tetamm.new; MatrixPotSal = MatrixPo.new;

330

KSal = K.new; TpSal = Tp.new; InfilSal = Infil.new; EvapoTotSal = evapotot.new; OverlandSal = Overland.new; RunOffSal = Runoff.new; MovedWaterSurfSal = Movedwat.new; WaterSurfSal = SurfWate.new; ErosionSal = Erosion.new; Ero2 = ero2.new; Eroh = eroh.new; qq22 = qq22.new; hortonr = hortonr.new;

#---------Salidas samples points---------- SIobssSalida = Iobss.tss; SCloudSalida = Cloud.tss; SNetRadSalida = NetRad.tss; SPotEvapoSalida = PotEva.tss; SInterCanopySal = Intercep.tss; SEvapocanSal = Evapocan.tss; Srainsal = rain.tss; SThroSal = Thro.tss; SThroWSal = ThroW.tss; STetaSal = Teta.tss; STetamSal = Tetamm.tss; SMatrixPotSal = MatrixPo.tss; SKSal = K.tss; STpSal = Tp.tss; SInfilSal = Infil.tss; SEvapoTotSal = evapotot.tss; SOverlandSal = Overland.tss; SRunOffSal = Runoff.tss; SMovedWaterSurfSal = Movedwat.tss; SWaterSurfSal = SurfWate.tss; SErosionSal = Erosion.tss; SEro2 = ero2.tss; SEroh = eroh.tss; Sqq22 = qq22.tss; Shortonr = hortonr.tss;

areamap ..\data\clone2.map ;

timer 1 8760 1 ;#---------------------------------------------------------------------

initial

Lat = olat ; Long = olong ; rslope = oslope ; raspect = oaspect ; contenido = 0.02*uno.map;statewater = cero.map; infiltration = cero.map;

# Soil initial values

porosity = 0.61*uno.map; #

mxsoilcpm=soildepth*porosity ; # in mm max_cap = mxsoilcpm; # in mm max_soil_cap = max_cap; # in mm

# initial soil moisture; teta_antesmm = 370*uno.map; teta_antes = 0.37*uno.map;

# initial values of soil water flows Q=cero.map; y=cero.map; watersurf=cero.map; diez=diez.map;

331

cien=cien.map; waters=cero.map; waterh=waterh.map; waterhh = waterhh.map; mwaterhh=cero.map; ThroW=cero.map; dnn=1;

p_direct = uno.map-lookupscalar(vegcover.lut,landuse);# Direct precipitation through the canopi

veg_cover = lookupscalar(vegcover.lut,landuse); # Vegetation cover parameter canopi_storage_capacity = lookupscalar(canostor.lut,landuse);

# computed with pictures and grams lai = lookupscalar(lai.lut,landuse); # Leaf area index max_cargag = canopi_storage_capacity*veg_cover*lai; # CANOPY storage CAPACITY vegdrain = uno.map - veg_cover; # direct precipitation; newldd = lddx.map; inflow = cero.map; overland = cero.map; routness = 0.199 * veg_cover; loquequeda = cero.map; mwater = cero.map; mwarter = cero.map; Soilleft = soildepth ; throw500 = rios.map; soilleft2 = cero.map;

#---- inicialization of output accumulated maps.-------

iobss.map = cero.map; cloudcv.map = cero.map; nnn.map = cero.map; evapo.map = cero.map; rain.map = cero.map; thro.map = cero.map; throw.map = cero.map; teta.map = cero.map; mtxpot.map = cero.map; k.map = cero.map; tp.map = cero.map; infil.map = cero.map; oflow.map = cero.map; ero.map = cero.map;

#--------------------------------------------------------------------------------------dynamic

tta = teta_antes; # initial value soil moisture %ttamm = teta_antesmm; # initial value soil moisture mmreport TetaSal = (maptotal(tta))/cellnum;report TetamSal = (maptotal(ttamm))/cellnum;

report STetaSal = timeoutput(samples.map, tta);report STetamSal = timeoutput(samples.map, ttamm);

t1 = timeinputscalar ( data , 4) ; #hour dn = timeinputscalar ( data , 6) ; #julianday

TAO = (2 * PI * (( dn - 1) / 365)) ; E0 = (1.000110 + 0.034221 * (cos(TAO)) + 0.00128 * (sin(TAO)) + 0.000719 * cos(2 * TAO) + 0.000077 * sin(2 * TAO) ) ; v1 = (- 0.002697 * cos (3 * TAO) + 0.0148 * sin (3 * TAO) ) ; OSolDecli = ( (0.006918 - 0.399912 * cos (TAO) + 0.070257 * sin (TAO) - (0.006758 * cos (2 * TAO)) + 0.000907 * sin (2 * TAO + v1)) * (180 / PI ) ) ; SolDecli = (OSolDecli * GR ) ; TAOO = (TAO * GR ) ;

w = ((1150 - t1 * 100) / 100) * 15; A = cos(SolDecli) * sin( rslope ) * sin( raspect ) ; B = cos(SolDecli) * cos(Lat) * cos( rslope ) + cos(SolDecli) * sin(Lat) * sin( rslope ) * cos( raspect ); C = sin(SolDecli) * sin(Lat) * cos( rslope ) - sin(SolDecli) * cos(Lat) * sin( rslope ) * cos( raspect );

iobstmp = ((12 / PI) * SolarkteJ * E0 ) ;

332

wa = 0.26 * A * sin(w) ; wb = 0.26 * B * cos(w) ; wc = 0.26 * C ;

iobsum = iobstmp * ( wa + wb + wc ) ;

iobs = if(iobsum < 0 then cero.map else iobsum) ;

iobs = if(t1 < 5 , cero.map , if(t1 > 17 , cero.map , iobs));

iobss = iobs ; # Solar radiation at the top of the atmospherereport IobssSalida = (maptotal(iobss))/cellnum; # in Kj / m2 / hourreport SIobssSalida = timeoutput(samples.map, iobss);report iobss.map = iobss.map + iobs;

# ____________________________________________________

# Cloud cover compute

w = abs(w); # Sun angle elevation in degrees att = sin(w)**0.3333 + 0.25 * sqr(cos(w)) * normal(bol.map)*uno.map; # random values are included atenuation = att * difdtm.map * 0.0004 ; # in Kj / m2

attenuation = if(atenuation > 1 then 0.98 else atenuation );# Never it takes more than 100 %

cloudate = attenuation; cloudcv = if( (iobss * attenuation) < 0 then cero.map else (iobss * attenuation));report CloudSalida = (maptotal(cloudcv))/cellnum; # Kj / m2report SCloudSalida = timeoutput(samples.map,cloudcv);report cloudcv.map = cloudcv.map + cloudcv;

#______________________________________________________

# Net Radiation compute from simple way

# mjtonm2 = scalar(277.77778) ;

Rad_total = if ((iobss - cloudcv) < 0 then cero.map else (iobss - cloudcv) );

nnn = if ((0.8525 * Rad_total - 16.971) < 0 then cero.map else (0.8525 * Rad_total- 16.971)) / 1000;report nnn.map = nnn.map + nnn;report NetRadSalida = (maptotal(nnn))/cellnum; # W / m2 by hourreport SNetRadSalida = timeoutput(samples.map,nnn);

#______________________________________________________

# Calculus potential evapotranspiration

pot_evapo = nnn / 2.445; # /* < 0 , 0 , nnn / 2.445);

evapo = pot_evapo; # mm by hour

report PotEvapoSalida = (maptotal(pot_evapo))/cellnum;report SPotEvapoSalida = timeoutput(samples.map,pot_evapo);#______________________________________________________

# ******* INTERCEPTION MODEL *************

# rainfall image creation through the catchment

rainfall = timeinputscalar ( data , 5 ); rainfallg = rainfall + difdtm.map * 0.001 * rainfall; rainfallg = if(rainfall <= 0 then cero.map else rainfallg);

rain = rainfallg ; # mm by hourreport rainsal = (maptotal(rain))/cellnum;report Srainsal = timeoutput(samples.map,rain);report rain.map = rain.map + rain;

# --------------------------

direct_rain_soil = rainfallg * vegdrain; #Direct rainfall through canopy mm hour

# Intercepted rainfall by vegetation

333

rain_inter = rainfallg - direct_rain_soil ; # Intercepted rainfall by canopy

rest_empty = max_cargag - contenido ; # empty remanent storage capacity

newcontenido = if(rest_empty > rain_inter then contenido + rain_inter else max_cargag );

# canopy water content after rain

report InterCanopySal = (maptotal(newcontenido))/cellnum;report SInterCanopySal = timeoutput(samples.map,newcontenido);

# Drip function is a simple waterbalance, because the Rutter

# exponential function loss theproportion after 10 mm of rainfall

# by hour. Rutter is for min time step drip = if(rest_empty > rain_inter then cero.map else rain_inter - rest_empty );

dripp = drip ;

#/* Evaporation from canopy

evapo_canopy = if(pot_evapo > newcontenido then newcontenido else pot_evapo *newcontenido / max_cargag ); evapo_canopy = if(evapo_canopy < 0 then cero.map else evapo_canopy );

# Evaporation from canopy storage mm / hour evapocanopy = evapo_canopy;

report EvapocanSal = (maptotal(evapocanopy))/cellnum;report SEvapocanSal = timeoutput(samples.map,evapocanopy);

tempcontenido = if((newcontenido - evapo_canopy) < 0 then 0 else (newcontenido -evapo_canopy));

contenido = tempcontenido; # canopy storage for the next cycle

conte = contenido;

# Throughfall calculus

Throughfall = direct_rain_soil + drip;

Thro = if(Throughfall <= 0 then cero.map else Throughfall);# Net rainfall on the soil surface, including overland# from the neivors cells

report thro.map = thro.map + Thro; ThroW = Thro + loquequeda + inflow - mwarter ;report throw.map = throw.map + ThroW;

report ThroSal = (maptotal(Thro))/cellnum;report ThroWSal = (maptotal(ThroW))/cellnum;

report SThroSal = timeoutput(samples.map,Thro);report SThroWSal = timeoutput(samples.map,ThroW);

# report throw500 = if(ThroW > 500 , throw500, uno.map);# ThroW = if(ThroW > 500 , 0 , ThroW);

# ThroW1 = ThroW*rios.map; # report RioSal = (maptotal(ThroW1))/cellnum;

# report ThroW = ThroW - ThroW1; tetat = teta_antes; # Soil moisture % fi_teta = porosity - teta_antes; # Air space in the soil % fi_tetamm = if(mxsoilcpm-teta_antesmm < 0 , cero.map , mxsoilcpm-teta_antesmm);

# Air space in mm of depth# report fimm=fi_tetamm;# report tetaan=teta_antes;

#/* PEROTRANSFER FUNCTION

teta = teta_antes ;report teta.map = teta.map + teta;

334

ab = (av + (bv * clay.map) + (cv * sand2.map) + (dv * sand2.map * clay.map)); abb = exp(ab) ; app = abb * 100; ap = app; B = ev + fv * clay2.map + gv * sand2.map + gv * sand2.map * clay.map; bp = B; tetapot = teta**bp; matrix_pot = tetapot * (cien * exp(ab)); matrix1 = matrix_pot; # Matrix potential > 1500 Kpa to 10 Kpa#/* ---------------- clay10 = log10(clay.map); moisture_sat = hv + jv * sand.map + kv * clay10; matrix_e = 100 * (mv + nv * moisture_sat); matrixe=matrix_e; # Matrix potential at air entrance teta_10 = exp ((2.302 - ln ( 100 * exp(ab))) / bp) ; matrix_pot10 = diez - (teta - teta_10) * (diez - matrix_e) / (moisture_sat -teta_10); matrix_final = if(matrix_pot10 > matrix_e then matrix_pot10 else moisture_sat ) ; matrix_final = if(matrix_pot > diez then matrix_pot , matrix_final ); matrix_final = matrix_final * 1024 ; mtxpot = matrix_final; # Real MAtrix potential for this moisture KPa or N / m2report mtxpot.map = mtxpot.map + mtxpot;report MatrixPotSal = (maptotal(matrix_final))/cellnum;report SMatrixPotSal = timeoutput(samples.map,matrix_final);

K = 0.000002778 * (exp (pv + qv * sand.map + (rv + tv * sand.map + uv * clay.map+ vv * clay2.map) * (1 / teta))) * 1000 * 3600 ; # k and ksat are in de mm per hour

# Hydrological conductivity mm /hourreport k.map = k.map + K;report KSal = (maptotal(K))/cellnum;report SKSal = timeoutput(samples.map,K);

ksat = 0.000002778 * (exp (pv + qv * sand.map + (rv + tv * sand.map + uv *clay.map + vv * clay2.map) * (1 / moisture_sat))) * 1000 * 3600;

# Saturated hydrological conductivity mm / hour#_________________________________________# Pounding time tp1 = (matrix_final / 1024 * (porosity - teta_antes )) / (ThroW * (ThroW - ksat)); tp2 = if(tp1 <= 0, cero.map, tp1); tp3 = if(ThroW < ksat , cero.map, tp2);

tp = if(ThroW eq 0, cero.map, tp3); # Pounding timereport tp.map = tp.map + tp;report TpSal = (maptotal(tp))/cellnum;report STpSal = timeoutput(samples.map,tp);

infil1 = if(ThroW < fi_tetamm , ThroW , fi_tetamm ); infil11 = if(infil1 > ksat , ksat , infil1); infil12 = ksat + ksat * (uno.map - tp); infil13 = if (infil12 < fi_tetamm , infil12 , fi_tetamm); infil2 = if(tp > 1 ,infil11 , infil13); infil3 = if(ThroW <= ksat , infil1 , ksat);

# After poundig time, infil. is proportional to ksat infiltration = if(ThroW <= 0, cero.map, infil3);

# Infiltration rate of rainfall to the soil surface mm / hour infil=infiltration;report infil.map = infil.map + infil;report InfilSal = (maptotal(infiltration))/cellnum;report SInfilSal = timeoutput(samples.map,infiltration); # Evaporation from soil moisture tot_evapo_soil = teta_antes * pot_evapo * (1 - veg_cover) ; totevapo = tot_evapo_soil + evapo_canopy;report EvapoTotSal = (maptotal(totevapo))/cellnum;report SEvapoTotSal = timeoutput(samples.map,totevapo);

#_________________________________________# Discharge drenage = K * 0.625; # en m3 : k mm/h * .m2 = m3 salida del pixel # es el area de 25 X 25 = 625 m2 * 0.001 m = m3 by cell#_________________________________________#/* Overland Flow

over1 = if(ThroW - infiltration <= 0 then cero.map else ThroW - infiltration);# OVER1 IS THE HORTONIAN OVERLAND FLOW en mm

335

over2 = teta_antesmm + infiltration - K - tot_evapo_soil; over2 = if (over2 < 0 ,cero.map , over2); # Soil water balance mm soilevapo = tot_evapo_soil;

report evapo.map = evapo.map + soilevapo; over3 = if(over2 > max_soil_cap, over2 - max_soil_cap else cero.map );

# in mm Overland_flow = over1 + over3; # in mm of depth overland = cover (Overland_flow*uno.map,cero.map);report oflow.map = oflow.map + overland;report OverlandSal = (maptotal(overland))/cellnum;report SOverlandSal = timeoutput(samples.map,overland);

#/* crear el nuevo moisture en el suelo. Reemplazar el teta_antes aqui.

over4 = if(over2 > max_soil_cap then max_soil_cap else over2); # soil moisture in mm#------------------------------- # Soil water balance tempmoisture = (over4 ) / soildepth ; # in percentage

teta_antes = if (tempmoisture < cero.map then cero.map else tempmoisture); # /* en porcentaje

teta_antesmm = if(tempmoisture * soildepth < cero.map then cero.map elsetempmoisture * soildepth);

# soil water content en mm#_________________________________________

qq = alpha * overland ** m;report RunOffSal = (maptotal(qq))/cellnum;#report hortonr = (maptotal(horton))/cellnum;

report SRunOffSal = timeoutput(samples.map,qq);#report Shortonr = timeoutput(samples.map,horton);

#_________________________________________

#/* creacion de el agua lluvia de overlandflow para completar el set de infitracion#/* para el siguiente ciclo•

movewater2 = overland - uno.map * 0.02;

mwarter = movewater2 ; # accufractionflux (newldd, overland , movido); mwaterrio = mwarter * maskrios.map; mwarter = mwarter - mwaterrio;

loquequeda = uno.map * 0.02; #watersurf2; # overland * routness; inflow = upstream(newldd,mwarter); # cambio de overland por movewater2#_________________________________________#/* calculo de la erosion

#/* y = k Q^m S^n

QP = inflow ** 2; #mwater TPP = tan(oslope) ** 1.66667; expoveg = exp(-0.007*veg_cover); erodable = 0.02 * Soilleft/soildepth; erosion = if(erodable * QP * TPP * expoveg <= 0 , cero.map , erodable * QP * TPP *expoveg); report ero.map = ero.map + erosion;

# erosion = erosion * erodable;

Soilleft = if(erosion >= Soilleft, 0, Soilleft - erosion); eroacu = soildepth - Soilleft; eroreal = if(soilleft2 - Soilleft < 0, 0, soilleft2 - Soilleft) ; soilleft2 = Soilleft;report ErosionSal = (maptotal(erosion))/cellnum; # mm of soil depth by hourreport SErosionSal = timeoutput(samples.map,erosion);dnn = dn;

336

Appendix X

Summary of physical variables

and

model variable response

for all scenarios

337

Values in river distance are multiplied by 50m to give the real distance.

Scenario Iteration Area Aspect Slope AltitudeTopographic

IndexRiver

distance ErosionOverland

flow

Ha. Degrees Degrees M. *50-m. mm. mm.1 1 390.8 197 28 2021 8.89 3.02 74 78891 2 159.9 215 32 2066 8.56 2.98 79 79101 3 143.6 256 32 2095 8.55 3.05 81 79171 4 122.1 224 32 2108 8.67 3.28 84 79241 5 99.8 228 33 2131 8.56 3.35 86 79291 6 81.5 228 33 2159 8.47 3.57 88 79341 7 67.1 217 32 2215 8.57 4.09 89 79381 8 63.3 206 32 2259 8.71 4.31 90 79411 9 56.7 207 32 2272 8.73 4.01 92 79441 10 47.0 208 33 2301 8.74 3.97 93 79461 11 37.1 199 34 2314 8.74 3.63 94 79481 12 30.2 199 34 2357 8.52 3.39 94 79501 13 19.8 187 34 2383 8.79 3.30 95 79521 14 9.8 185 35 2441 8.32 2.97 95 79531 15 7.4 154 35 2492 8.29 2.76 96 79531 16 6.0 154 35 2492 8.29 2.76 96 79541 17 4.4 102 40 2559 8.12 2.68 96 79541 18 3.9 107 45 2583 7.79 2.77 96 79541 19 3.6 146 44 2599 7.71 2.80 96 79541 20 3.6 165 38 2604 7.98 3.06 96 79541 21 2.4 128 40 2597 7.96 2.84 96 79551 22 1.4 109 49 2585 7.75 2.23 96 79552 1 0.0 0 0 0 0.00 0.00 74 78892 2 367.6 221 33 1966 9.44 1.00 81 79082 3 348.6 202 34 2035 8.10 2.00 86 79262 4 244.9 206 32 2100 8.30 3.00 90 79392 5 147.8 203 32 2172 8.48 4.00 92 79472 6 97.0 204 29 2231 8.55 5.00 94 79562 7 57.9 198 28 2291 8.54 6.00 95 79662 8 40.0 202 28 2390 8.52 7.00 96 79732 9 23.6 200 23 2510 8.85 8.00 96 79782 10 18.1 192 21 2596 9.01 9.00 96 79802 11 15.9 185 17 2665 9.41 10.00 96 79832 12 13.2 187 14 2692 9.79 11.00 96 79852 13 9.9 187 14 2692 9.79 12.00 97 79892 14 8.8 180 15 2734 9.36 13.00 97 79892 15 7.4 175 15 2754 9.01 14.00 97 79902 16 5.1 187 15 2773 8.94 15.00 97 79912 17 4.1 191 12 2786 9.06 16.00 97 79922 18 1.8 208 16 2792 9.02 17.00 97 7993

338

Scenario Iteration Area Aspect Slope AltitudeTopographic

IndexRiver

distance ErosionOverland

flow

Ha. Degrees Degrees M. *50-m. mm. mm.3 1 0.6 268 37 2830 7.14 18.00 74 78923 2 1.8 208 16 2792 9.02 17.00 74 78933 3 4.1 191 12 2786 9.06 16.00 74 78943 4 5.1 187 15 2773 8.94 15.00 74 78953 5 7.4 175 15 2754 9.01 14.00 74 78953 6 8.8 180 15 2734 9.36 13.00 74 78963 7 9.9 181 15 2711 9.53 12.00 74 79003 8 13.2 187 14 2692 9.79 11.00 74 79023 9 15.9 185 17 2665 9.41 10.00 74 79053 10 18.1 192 21 2596 9.01 9.00 75 79073 11 23.6 200 23 2510 8.85 8.00 75 79123 12 40.0 202 28 2390 8.52 7.00 76 79193 13 57.9 202 28 2291 8.54 6.00 77 79293 14 97.0 204 29 2231 8.55 5.00 78 79383 15 147.8 203 32 2172 8.48 4.00 81 79463 16 244.9 206 32 2100 8.30 3.00 84 79583 17 348.6 203 34 2035 8.10 2.00 90 79773 18 367.6 221 33 1966 9.44 1.00 97 79964 1 1.3 188 21 1428 10.66 1.24 74 78894 2 52.4 188 28 1506 9.31 1.97 74 78914 3 75.0 184 34 1602 8.71 2.41 75 78934 4 92.7 192 34 1705 8.58 2.71 76 78974 5 119.0 204 32 1801 8.53 2.83 77 79014 6 131.7 200 32 1900 8.57 2.26 79 79074 7 130.7 203 32 2000 8.54 2.20 81 79134 8 132.6 202 33 2099 8.50 2.28 83 79184 9 138.1 214 33 2201 8.51 2.47 86 79254 10 120.9 219 34 2296 8.41 2.93 89 79344 11 108.0 234 32 2401 8.58 2.71 91 79454 12 95.3 237 32 2502 8.80 3.59 93 79514 13 95.9 229 37 2597 8.91 6.09 95 79714 14 80.9 178 21 2701 9.09 8.42 97 79884 15 26.3 158 16 2779 8.87 13.29 97 79965 1 26.4 158 16 2780 8.86 13.05 74 78975 2 80.9 178 21 2701 9.09 8.42 75 79135 3 95.9 229 27 2597 8.91 6.09 77 79345 4 95.2 237 32 2502 8.80 3.59 79 79405 5 108.1 234 32 2401 8.58 2.71 82 79515 6 120.9 219 34 2296 8.41 2.93 84 79605 7 138.1 214 33 2201 8.51 2.47 87 79665 8 132.6 202 33 2099 8.50 2.28 89 79725 9 130.8 203 32 2000 8.54 2.21 91 79785 10 131.7 200 32 1900 8.57 2.26 93 79835 11 119.0 204 32 1801 8.53 2.53 95 79885 12 92.7 192 34 1705 8.58 2.71 96 79925 13 74.9 184 34 1602 8.71 2.41 97 79945 14 52.5 188 28 1506 9.31 1.97 97 79965 15 12.6 188 21 1428 10.66 1.24 97 7996