msc dissertation

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CCHHAANNGGEE IINN TTRROOPPIICCAALL HHIILLLLSSIIDDEESS:: TTHHEE

IIMMPPAACCTT OOFF PPAATTTTEERRNNSS

JORGE ELIECER RUBIANO MEJIA

1998

THIS DISSERTATION IS SUBMITTED AS PART OF AN MSC DEGREE IN GEOGRAPHY AT KING'S

COLLEGE LONDON

Resumen

El objetivo de esta investigación fue modelar escenarios de cambios en el uso de tierra

utilizando un modelo empírico y evaluar las respuestas hidrológicas en una cuenca

tropical utilizando un modelo distribuido de procesos físicos. Los escenarios fueron

generados con un modelo celular autómata que usó las reglas básicas cambio a áreas

deforestadas si hay cercanía a las carreteras y alrededor de áreas que fueron

previamente deforestadas. La pendiente del terreno fue utilizada como limitante a los

cambios. Estos escenarios fueron utilizados como parte de la información requerida por

el modelo hidrológico con el fin de identificar el impacto potencial que los patrones de

cambio de uso de tierra tienen en la escorrentía, infiltración y evaporación. Los

resultados preliminares muestran las potencialidades del enfoque de modelos basados

en celular autómata para generar patrones de uso/cobertura de la tierra, dependiendo

de las restricciones físicas o socio-económicas. La aplicación de los diferentes

escenarios de uso de tierra en un modelo hidrológico dieron una idea aproximada de

los disturbios ocasionados sobre el balance hidrológico en las cuencas andinas.

2

Acknowledgements

I would like to thank CIAT for the time and support, as well with the Cabuyal

database that they provided to me to carry out this research. I would especially like to

thank Koulla Pallaris and Mauricio Rincon for their help and assistance in the field

and for supplying the digital elevation model of Tambito. Thanks too to Mark Mulligan

for his guidance in the use and development of the models.

I would especially like to mention the support that Alvaro Jose Negret provided to the

King's College team in the Tambito reserve and who left to future generations new

paths to continue in the knowledge of our natural resources in Colombia.

3

Abstract

The objective of the current research was to model the potential impact of different

scenarios of land use change generated by an empirically based model upon

hydrological responses in a tropical watershed. The scenarios were generated with a

cellular automata model that used basic rules concerning deforested areas along the

roads and around previous deforested areas and constrained by terrain attributes.

These scenarios were then used as input to a distributed hydrological model to

identify the potential impacts of patterns of land use change upon hydrological

responses as runoff, infiltration and evaporation. Preliminary results show the

capabilities of the cellular automata model to generate patterns of land use/cover

depending on physical or socio-economical constraints. Application of different

scenarios of land use in an hydrological model gave an approximated idea of the

potential impacts of disturbance in the hydrological balance of Andean watersheds.

4

A. LIST OF CONTENTS

LIST OF CONTENTS...................................................................................... 4

LIST OF FIGURES.......................................................................................... 5

LIST OF TABLES ........................................................................................... 6

1. INTRODUCTION......................................................................................... 7

2. OBJECTIVES.............................................................................................. 8

2.1 Main Objective.................................................................................................8

2.2 Secondary Objectives.....................................................................................8

3. LITERATURE REVIEW............................................................................... 9

3.1 Land use/cover change (LUCC) .....................................................................93.1.1 Land Use/Cover change Modelling ...................................................................10

3.2 Cellular Automata (CA).................................................................................14

3.3 Modelling hydrological processes...............................................................153.3.1 Water Balance Modelling, A Review of Historical Approaches........................163.3.1.1 Empirical Models.............................................................................................173.3.1.2. Source Area concept .....................................................................................173.3.1.3. Distributed Models and LUCC .......................................................................183.3.2 Hydrological impacts of land use change ........................................................18

3.4 The study area ..............................................................................................20

4. METHODOLOGY...................................................................................... 23

4.1 Development of cellular automata (CA) rules - Magnitude and patterns ofland use change..................................................................................................24

4.3 Hydrological model.......................................................................................264.3.1 Parameterisation................................................................................................274.3.2 Calibration and Validation .................................................................................29

5. RESULTS.................................................................................................. 30

5.1 Cellular Automata Model ..............................................................................30

5.2 Hydrological Simulation ...............................................................................35

6. CONCLUSIONS........................................................................................ 40

BIBLIOGRAPHY........................................................................................... 42

APPENDIX 1. CABUYAL WATERSHED MAPS .......................................... 48

APPENDIX 2. PATTERNS CHANGE ANALYSIS ........................................ 51

APPENDIX 3. CROSSTABULATION TABLES............................................ 60

APPENDIX 4. CELLULAR AUTOMATA RULES AND MODEL................... 69

APPENDIX 5. HYDROLOGICAL MODEL CODE......................................... 73

APPENDIX 6. SOIL DATA............................................................................ 77

APPENDIX 7. VEGETATION FIELD MEASUREMENTS ............................. 78

5

B. LIST OF FIGURES

Figure 3.1. Location of Cabuyal and Tambito catchment in Cauca - Colombia..............22Figure 4.1 Methodological framework ..............................................................................23Figure 5.1 Changes in LUC in four time steps generated by the CA model....................32Figure 5.2 a and b (next page) LUC patterns generated in different time steps by the CA

model. .........................................................................................................................33Figure 5.4 Hourly infiltration in Tambito watershed for January 1998. ...........................37Figure 5.5 Hourly bulk density at the water fron in the outlet of Tambito watershed

(January 1998) ............................................................................................................38Figure 5.6 Total fluxes of evaporation and infiltration in Tambito watershed (January

1998)............................................................................................................................39 Figure A.1 Land use series for 1946, 1970 and 1989 in the Cabuyal Watershed - Cauca

- Colombia...................................................................................................................48Figure A.2 Aspect, altitudinal ranges and slope in the Cabuyal Watershed - Cauca -

Colombia.....................................................................................................................49Figure A.3 Proximity to roads and rivers in the Cabuyal Watershed - Cauca - Colombia

.....................................................................................................................................50Figure A2.1. Forest LUC conversion in the higher zone. Dashed ovals = new land uses

and pointed oval = new forest....................................................................................52Figure A2.2. Forest LUC conversion in the lower zone. Dashed ovals = new land uses

and pointed ovals = new forest..................................................................................53Figure A2.3. Forest LUC conversion in the middle zone. Dashed ovals = new land uses

and pointed ovals = new forest..................................................................................54Figure A2.4. Forest LUC conversion in the higher zone between 1970 - 1989. Dashed

ovals = new land uses and pointed ovals = new forest ............................................55Figure A2.5. Forest LUC conversion in the lower zone between 1970 - 1989. Dashed

ovals = new land uses and pointed ovals = new forest ............................................56Figure A2.6. Scrub LUC conversion in the higher zone between 1946 - 1970.

Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = newscrub cover .................................................................................................................57

Figure A2.7. Scrub LUC conversion in the lower zone between 1946 - 1970. Continuousovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover .58

Figure A2.8. Scrub LUC conversion in the middle zone between 1946 - 1970.Continuous ovals same LUC, dashed ovals = new LUC and pointed ovals = newscrub cover .................................................................................................................59

Figure A7.1. Primary forest leaves scanned from pictures taken in Tambito, Cauca -Colombia.....................................................................................................................81

Figure A7.2. Secondary forest leaves scanned from pictures taken in Tambito, Cauca -Colombia.....................................................................................................................82

Figure A7.3. Pasture leaves scanned from pictures taken in Tambito, Cauca - Colombia.....................................................................................................................................82

Figure A7.4. Canopy Forest cover scanned from pictures taken in Tambito, Cauca -Colombia.....................................................................................................................83

6

C. LIST OF TABLES

D. Table 5.1 Conversion from land cover in 1946 towards different land covers in 1970

considering the frequency of the distance to roads (n=40727 pixels). ...................... 31Table 5.2 Conversion from land cover in 1946 towards different land covers in 1970

considering the frequency of the distance to rivers (n=48573 pixels). ...................... 31Table A3.1. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650

MASL and for aspect from 0 to 45 degrees. .................................................................... 60Table A3.2. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650

MASL and for aspect from 45 to 90 degrees................................................................... 61Table A3.3. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650

MASL and for aspect from 90 to 135 degrees................................................................. 62Table A3.4. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650

MASL and for aspect from 135 to 180 degrees............................................................... 63Table A3.5. Crosstabulation table between 1946 and 1970 LUC for altitude below 1650




MASL and for aspect from 315 to 359 degrees............................................................... 67Table A3.9 Conversion from Forest in 1946 towards different land covers in 1970

considering the frequency of the neighbours of the same class (n=8814 pixels). .. 68Table A5.10 Conversion from Pasture in 1946 towards different land covers in 1970

considering the frequency of the neighbours of the same class (n=23116 pixels). 68Table A3.11 Conversion from Scrub in 1946 towards different land covers in 1970

considering the frequency of the neighbours of the same class (n=21047 pixels). 68Table A.1 Soil properties corresponding with the 25 classes of the sampling scheme. 77Table A7.1 Leave measurements in Primary Forest Plot. Tambito, Cauca - Colombia .. 78Table A7.2. Leave measurements in scanned images from Primary Forest in Tambito,

Cauca - Colombia................................................................................................................. 79Table A7.3. Leave measurements in Secondary Forest Plot. Tambito, Cauca - Colombia

................................................................................................................................................. 79Table A7.4. Leave measurements in scanned images from Secondary Forest in

Tambito, Cauca - Colombia................................................................................................ 79Table A7.5. Pasture leave measurements in Tambito, Cauca - Colombia........................ 80Table A7.7 Vegetation Parameters for three different Land Use in Cauca - Colombia ... 80

E.

7

F. 1. INTRODUCTION

There is no doubt that the change produced by human action on the rural landscape

can have a strong impact upon water resources both in terms of their quantity and

their quality. These hydrological changes may influence overland flow, soil erosion,

streamflow and sediment transport. A lot of recent research in these hydrological

processes has shown that it is now possible to model the process change resulting

from this land uses impacts. The results of these models indicate that some parts of

the watershed are more sensitive to a particular type of land use change than others.

In particular it is thought that the 'contributing' areas closest to fluvial zones are

extremely sensitive and that, if left undisturbed, these areas can act as a barrier to

hydrological impact. These buffer zones can be important, but not in all cases. The

size of buffer zone required for protection of hydrological resources against land use

change impacts will vary across the units of sensitivity in the catchment. Indeed, a

buffer zone may not always be necessary. Different patterns of land use change

may lead to different requirements for buffer zones.

The spatial configuration of change is also important because flow paths link

landscape units. Net runoff or erosion is the sum of water and sediment from sinks as

well as sources, and the spatial configuration of land use determines the location of

these sinks relative to the sources. When sources and sinks occupy different flow

paths, net flows of water and sediments may be high, where they occupy the same

flow path re-infiltration and re-sedimentation reduce net losses at the catchment

scale.

8

G. 2. OBJECTIVES

I.

II. 2.1 Main Objective

- To model the potential hydrological impact of scenarios for land use change

generated by empirically based cellular modelling.

III. 2.2 Secondary Objectives

- To analyse patterns of past land use change and their relationship with

environmental parameters.

- To attempt to develop a set of cellular automata rules for land use changes (LUC) in

the hillsides of Colombia to be applied under different environmental and

infrastructure constraints.

- To parameterise an existing PC-raster hydrological model with field data collected

from a tropical catchment in Cauca - Colombia.

- To use the model to identify the potential impact of the different scenarios of LUC

generated with the cellular automata rules over the parameterised tropical catchment

in Cauca - Colombia.

9

H. 3. LITERATURE REVIEW

IV. 3.1 Land use/cover change (LUCC)

"Land use/cover has been recognised by a variety of National and international

bodies as a critical factor mediating between socio-economic, political, and cultural

behaviour and global environmental changes, especially changes in atmospheric

chemistry and potential climatic change" (IGBP, 1988; NRC, 1990; ISSC, 1990, cited

by Rainier et al, 1994 in Turner 1994).

In the past few decades, we have seen rapid land use/cover changes in the form of

afforestation, cropland abandonment and clearance for agriculture in many parts of

the developing world. A global view of this process is not enough to understand its

effect occurring locally. The diversity of socio-economic and biophysical conditions

makes it difficult to find similar changes between one region and another. The

human driving forces involved in the change are clustered in a complex way and their

operation is strongly influenced by the environmental context. The fragility or

robustness of the physical environment mediates the impact of human activities upon

it; similar levels of human pressure may affect different environments to different

degrees. The ways in which social factors define the selection of land use are evident

but are little understood.

Changes in land use/cover occurring in tropical hillsides are associated with many

factors and processes ranging from the socio-economical to the physical

characteristics of the landscape. Although the driving forces are more related with

political and socio economical aspects, an understanding of the physical factors is

fundamental to the development of potential scenarios and should therefore be the

key component in future socio-economic models.

10

The effort of many scientists in identifying these key physically based or social

factors has encouraged the development of different approaches to the problem. It

has been necessary to define the terminology used in these studies, to enable

comparisons between studies of different locations and to avoid misinterpretations.

Land use denotes the human employment of the land. Some uses include

settlement, cultivation, pasture, rangeland, and recreation, amongst others. Land use

change at any location may involve either a shift to a different use or an

intensification of the existing one.

Land cover denotes the physical state of the land. It embraces, for example, the

quantity and type of surface vegetation, water, and earth materials. Changes in land

cover driven by land use can occur in two ways: 1) When the land cover changes

completely, e.g. from pasture to crops, a process referred to as conversion; 2) When

the change involves changing the conditions in land use without changing the cover

class; a process referred to as modification. The majority of studies in this field have

been related with the process of conversion, because it is more evident and easy to

identify and does not require the collection of data on land use practices and

economic characteristics of each class. The current study is specifically related with

land cover changes but does not include any socio-economical factors.

IV.1. 3.1.1 Land Use/Cover change Modelling

The approach to study and modelling LUCC, has followed the development of socio-

economical conceptual models operationalised into computer simulations and in

some cases, with the incorporation of biophysical models. The objective in some

cases has been to build descriptive classifications. Others have concentrated on

11

prescription (recommendations) or restoration within planning projects and others still

on projection and/or prediction of future changes given different scenarios.

Conceptual models

Conceptual models have offered a starting point to addressing the problems.

Biophysical and socio-economical factors are included in these models but their full

operation has not been carried out. The reason for this is based specifically in the

complexity of the problem. The Global Change Institute (1991) statement

emphasises the importance of human induced changes:

"Human actions rather than natural forces are the source of most

contemporary changes in the states and flows of the biosphere.

Understanding these actions and the social forces that drive them is thus of

crucial importance for understanding, modelling, and predicting global

environmental change and for managing and responding to such change"(p.

24).

When human forces are included in a model, the uncertainty is increased to the point

at which it becomes impossible to work with. It is generally accepted that socio-

economic factors have more influence in the land use/cover configuration than the

biophysical factors. Unfortunately, as Riebsame et al (1994) state:

"A recurrent problem with land use/cover modelling (and modelling in

general) is that the assumptions and goals of a given study are often

neglected in interpreting the results".

Socio-economical modelling at the macro scale has been dominated by the

identification of "social driven forces" such as population and consumption pressures.

Other important factors that were considered in this study were technological change,

affluence/poverty, political and economical structure, beliefs and attitudes. The

12

general consensus is that the environmental impact of LUCC is directly related to

human numbers and wealth and is either amplified or diminished by technology.

At a meso -scale level the prevailing paradigm is that of optimum economic utility. In

theory, any piece of land, given its physical attributes and spatial location, will be

used in the way that earns the highest rent. The driving forces of change operate

through their integrated effect on the net return of alternative uses of land. Ravnborg

(1998) emphasises that a farmer tends to concern himself, not with the suitability of

soil conditions with respect to the planting of a particular crop but rather with its

marketability and input demands (such as labour, fertilisers, etc).

Land use/cover modelling is one of the most challenging tasks in hillside

environments. The complex pattern of land uses, forming a heterogeneous

landscape, makes the process difficult even for basic descriptive studies, (Langford,

1997). The task is further complicated by the lack of accurate census and historical

information.

Biophysical models

The biophysical approach emerges from general classifications of the vegetation

distribution around the world. Descriptive studies were incorporating normative rules

about the relationship between microclimate, soils, topography and vegetation. The

Holdridge classification is one example of that (Holdridge, 19xx). Biophysical

characteristics, such as the temperature, elevation and latitudinal location, are used

to define an expected vegetation cover.

A new procedure incorporates the theory of probability of transitions. It appears to be

a useful way to deal with uncertainty and complexity in landscape change. The

technique is used to simulate future land use structures considering previous

conditions of biophysical and socio-economic characteristics. This, in turn, is

13

matched to ecological knowledge to simulate the effects of the new use pattern. The

main application has been in the assessment of biodiversity impacts of LUCC.

Although the weight of agent/decision making in comparison with biophysical factors

is greater, there is no doubt that some land use/cover types are restricted by the

landscape. However, biophysical constraints can, to some extent, be overcome with

technological advance and in such circumstances, socio-economic factors became

more significant. A firm understanding of natural resource availability of an area must

be the first step in any framework conducted to model processes like LUCC.

The relations between land use and its driving factors is also dependent on the scale

of observation, (Veldkamp, 1996). Hall et al (1995 in Verburg et al 1997) found that,

at detailed scales, land use in tropical rainforest areas is strongly correlated with

topography. At a coarse scale other factors emerge.

In the application of the CLUE model (Conversion of Land Use and its Effects,

Verburg, 1997) to China and Costa Rica, it was found that, both biophysical and

socio-economical factors are needed to explain the land use structure. Population

density and agricultural labour force were the most important factors, explaining the

land distribution in those areas. However, biophysical conditions, especially soils and

topography, also had an important influence on the distribution of land use.

Cellular automata (CA) modelling approach has been suggested as a new method

for dealing with the complexity of interacting terrestrial and social systems such as

land use/cover change (Waldrup, 1992 in Riebsame, 1994).

V. 3.2 Cellular Automata (CA)

14

Cellular automata are mathematical models applied to a finite set of elements in a

discontinuous space. When these models are applied to a landscape, they consist of

fixed arrays in which each cell represents an area of the land surface. The scale is

defined by the cell size and the time step is set up depending on the process being

simulated. Each cell can be in one of n different states at a given time step. At the

next time step, each cell may change its state, in a way determined by the set of

predefined rules. These rules describe precisely how a given cell should change

states, depending on its current state and the states of its neighbours. Which cells

are in the neighbourhood of a given cell must be specified explicitly (Espericueta,

1997). The rules are generally simple and can be expressed as algebraic statements,

which minimise the need for more complex mathematical operations that are

associated with other modelling approaches involving differential equations. These

algebraic statements are easily translated into command syntax of many raster GIS

packages.

In summary, CA models consist of an array of cells (one or two-dimensional), a

neighbourhood defined for each cell and a set of rules, which specify how the

dynamics of the CA operates both in space and time.

The theory about CA was first introduced in the 1940's by the Hungarian-American

mathematician John von Neumann (1948) whose work in 'self-reproduction' and

Ulam's work on 'cellular auxology', were the first steps in computer development

(Hogeweg, 1988). They were looking for simple mathematical models of biological

systems. The concept was popularised three decades later through John Conway's

work in the Game of Life, which is an infinite class of mathematical systems. CA has

been used to model phenomena from diverse disciplines. Any system can be

analysed from the point of view of large numbers of discrete elements with local

interactions, is posible to being modelled as a CA. Examples of its use include the

15

study of fluid dynamics, plasma physics, chemical systems, growth of dendritic

crystals, economics, two directional traffic flow, image processing and pattern

recognition and geomorphological and ecological modelling (Espericueta 1997 and

White 1993). In spatial and environmental research CA has been used to study the

connectivity and complexity of ecosystems (Green, 1994), spatial urban development

(Camara, 1996), vegetation succession (Hogeweg, 1988), forest fire simulations

(Goncalves, 1994) and rainforest dynamic (Solé, 1995).

VI. 3.3 Modelling hydrological processes

“The study of the water balance is the application in hydrology of the principle of

conservation of mass, often referred to as the continuity equation. This states that,

for any arbitrary volume and during any period of time, the difference between total

input and output will be balanced by the change of water storage within the volume.”

(UNESCO, 1971 in Sokolov et al, 1974).

Water is continuously flowing and distributed in the hydrological cycle. It takes water

from the ocean or land surfaces by evaporation and is transported by winds across

the earth during which condensation occurs, and deposits the water on the Earth's

surfaces in the form of precipitation. Once here, the water runs by gravitational forces

towards the oceans or it is returned to the atmosphere by evaporation and

transpiration (Oki, 1995).

“The Watershed is a natural unit of land which collects precipitation and delivers

runoff to a common outlet” (Black, 1970 in Newson, 1992). First reports considering

the watershed as a unit comes since the 1700's. Philippe Buache (1752) presented a

memoir to the French Academy of Sciences in which he outlined the concept of the

general topographical unity of the drainage basin. In his study of the human

geography of France, Jean Brunhes based his major divisions of the country on the

drainage basins of Geronne, Loire, Seine, and Rhône-Saône and their major towns.

16

His argument for using this method is based partly on convenience and partly on

recognition of water as a link between the earth and man’s activities. ‘Water is the

sovereign wealth of a state and its people. It is nourishment; it is fertiliser; it is power;

it is transport" (Brunhes, 1920 cited in Smith, 1969).

In a variety of ways the drainage basin has formed a framework for human activity: in

guiding the direction of primary settlement, in river navigation and the growth of trade

and towns, in the provision of water-power for industrial concentrations, and in

providing a logical context for irrigation works. (Smith, 1969).

"On the basis of the water balance approach it is possible to make a quantitative

evaluation of water resources and their change under the influence of man’s

activities" (Sokolov et al, 1974). Water balance studies are the first step in the design

of projects for the rational use, control and redistribution of water resources in time

and space. To improve the knowledge of the water balance is usefull to assits the

prediction of the consequences of artificial changes in the regime of streams, lakes,

and ground-water basins.

VI.1. 3.3.1 Water Balance Modelling, A Review of Historical Approaches

"Models - either symbolic (mathematical) or material - are essential to understand

and predict environmental phenomena on agricultural watersheds. The watershed is

an appropriate area element to consider for hydrological models because all

uncontrolled surface water flux out of the system is zero except at the stream

draining it" (Woolhiser, 1975). Summaries of models used in the study of rainfall –

runoff process has been reported in different sources (Woolhiser, 1973; Renard,

1982; Linsley, 1982; Todini, 1982; Boughton, 1988 and Wheater, 1993). There is an

extensive literature of models that are currently used in hydrology for many different

17

purposes. Singh (1995) presents in detail the currently most used computer models

in watershed hydrology.

VI.2. 3.3.1.1 Empirical Models

Hydrologic modelling originated in the latter part of the 19th century as a way to

address design issues for urban sewers, land reclamation drainage systems, and

reservoir spillways (Todini, 1988). Into the early 20th century, empirical formulas

were the primary tool used to estimate runoff. Also during this time, the rational

method, which is based on the concept of concentration time, was developed. The

rational method is probably one of the oldest models used in the rainfall - runoff

relation. Its origins are dated between 1851 to 1889 according to different authors.

(Chow, 1964).

VI.3.

VI.4. 3.3.1.2. Source Area concept

Models of overland flow took a new direction in the early 1970’s by the inclusion of

the source area concept (for instance, Freeze 1971 cited by Engman and Rogowski

(1974). "In order to accommodate the source area concept, Ishaq and Huff (1979)

revised the continuity equation of overland flow and constructed a model, the result

of which are promising and suggest that major portions of runoff are indeed

generated by overland flow originating from small parts of a watershed." (Beven et al

1979 cited by Hugget, 1985). Troendle presents a detailed review of the variable

source area concept in Anderson (1985).

VI.5. 3.3.1.3. Distributed Models and LUCC

The use of computers in LUCC modelling has a short history. The division of the

space covered by a catchment in discrete cells or polygons was possible only when

18

the hydrological distributed models appeared. This made it possible to assign the

correspondent land qualities of each land use/cover class and to compute the

physical relationships between the set of polygons. Hjelmfelt and Amerman (1980),

cited by Woolhiser, (1996) reported a paper written by Merril Bernard and presented

in 1937, in which he used a rectangular grid to represent the topography of a small

watershed and used a routing scheme to represent overland flow. All the

computations had to be done by hand, so his work had little impact and was forgotten

for more than 40 years.

The search for a physically based distributed models was encouraged with the

development of geographical information systems (GIS). Automated procedures are

commonly used to delineate basin geometry and to derive flow pathways from digital

maps of topography. Of more than 100 models reported in a study by the American

Society of Civil Engineering (1985), 28 models quantified major land-use change

effects in the absence of site calibration data. There were eleven models based on

continuous process, eleven based on the soil conservation curve number (SCS) and

six based on statistical regression equations.

VI.6. 3.3.2 Hydrological impacts of land use change

OIES Global Change Institute (1991) presents an overview of the land-cover land-

use change in the environment. On water resources, the report mentions their impact

on water quality and quantity produced by changes in river and groundwater regimes.

The flooding is increased by destruction of vegetation because it promotes

compaction and reduces the soil infiltration capacity. Overgrazing, burning,

deforestation, some agricultural practices and urbanisation can destroy the

vegetation and make less water available for groundwater recharge. “The base flow

19

of perennial rivers in turn could be seriously affected by such a reduction of

groundwater returns to the river, with more of the flow being concentrated in the flood

or peak periods and less during the dry periods. A further consequence of these

impacts on water quantity is the addition or removal of material from the rivers, water

bodies, and groundwater. For example, clearcutting of trees can lead to large

increments of sediment reaching a nearby stream.” (OIES Global Change Institute,

1991).

Effects of Forest Change

The energy balance is affected after clearcutting or afforestation. Processes and

components of the surface system are changed dramatically: the albedo, canopy

interception, the aerodynamic properties of the surface (roughness) and the radiation

available at the ground level, all of these have major impacts upon the energy and

water balances.

The use of water is reduced when forest is changed towards seasonal crops or

pastures and the yield of water is increased and when forest is replaced thus

changing the highest and lowest flow peaks to more extreme levels.

Watershed research studies have empirically confirmed the property of forests to

absorb heavy storms and transmit water to the soil by infiltration through forest litter

(Pereira, 1973 in OIES Global Change Institute, 1991).

A large number of researchers have concentrated their efforts on evaluating the

impact of land use change on water resources, using the forest as their main focus of

interest. In the Proceedings of an International symposium of the International Union

of Geodesy and Geophysics held in Vancouver, British Columbia, a review is made

of different studies in this topic (Swanson, 1987). Field research on clearcutting or

where soil processes were measured (erosion, sediment, and nutrient fluxes) are

summarised in Okunishi et al (1987), Williams et al (1987), Pearce et al (1987), and

20

Troendle (1987). Watershed simulations on clearcutting, reforestation, soil erosion

between other aspects are presented in Hornbeck (1987), Schulze and George

(1987), and Storm et al (1987).

Effects of Grassland Change

“The hydrological effects of grasslands depend entirely upon their management.

Improperly managed grazing and burning can lead to removal of vegetation cover

and the trampling of soils. In many areas where uncontrolled burning and grazing

have been practised, grazing management has a greater impact upon the hydrology

of a watershed than does forest management.

Two opposing hydrological facts are at work with managed grasslands. In order to

control flood flow and soil erosion, control of grazing is essential to preserve the

grass cover, to prevent soil exposure, and to prevent excessive trampling. On the

other hand, increasing crop density and productivity of grasslands, the total water

yield decreases; the vegetation needs the water for evapotranspiration. This leads to

the conclusion that in many cases, rather than looking to afforestation to reduce flood

and erosion damage, maintaining grass cover may be more effective without

reducing the water yield of the watershed as much as forest” (Ives and Messerli,

1989 in OIES Global Change Institute, 1991).

VII. 3.4 The study area

Two similar areas were selected for the current study. Cabuyal watershed in the

municipality of Caldono in Cauca - Colombia and Tambito catchment in the

municipality of Tambo in the same department. Figure 1 shows the location of both

watersheds in Cauca, Colombia. The first one was used to identify land use/cover

change rules. Land use/cover changes studies are very scarce with regards to

Tambito. Its selection was based on the assumption of being affected by similar

21

COLOMBIA

CAUCADEPARTMENT

TAMBO

CABUYAL

CALDONO

socio-economical processes as Cabuyal. Cabuyal is the pilot area of the Hillsides

Project at the International Center for Tropical Agriculture - CIAT and has been the

subject of several agricultural and natural resource researches (CIAT, 1997).

Tambito was selected to take advantage of instrumentation facilities and because it is

preceded for a long fallow period of almost 35 years. Details of the characteristics of

the catchment can be found in Museo de Historia Natural (1996). Tambito catchment

comprises an area of 3000 ha located in altitudes between 1500 and 2900 MASL

irrigated by two rivers: Palo Verde and Tambito. The land cover is represented by

Primary forest (62%), Secondary forest (36%) and Pasture (2%).

22

Figure 3.1. Location of Cabuyal and Tambito catchment in Cauca - Colombia.

I. 4. METHODOLOGY

Figure 4.1 shows the sequence of steps followed to identify the effects of LUC

patterns upon the water resources. Each step is explained with detail in this chapter.

Figure 4.1 Methodological framework

LUC 1946

LUC 1970

LUC 1989

CELLULAR AUTOMATA

RULES

DIGITAL MAPSGIS

SURFACE ANALYSIS

'TAMBITO' CATCHMENT

SCENARIO 1 SCENARIO 2 SCENARIO 3

PC-RASTER HYDROLOGICAL

MODEL

EROSION RUNOFF

RIVER DISCHARGE

ANALYSIS

DIGITAL MAPSGIS

SURFACE ANALYSIS

CANOPY INTERCEPTION

RIVER DISCHARGE (SEDIMENTS)

SOIL PROPERTIES

DE

VE

LOP

ME

NT

OF

CE

LLU

LAR

AU

TO

MA

TA

RU

LES

MO

DE

LLIN

G L

AN

DU

SE

/CO

VE

R S

CE

NA

RIO

SH

YD

RO

LOG

ICA

L M

OD

EL

PA

RA

ME

TE

RIS

AT

ION

(FIE

LD W

OR

K)

SIM

ULA

TIO

NA

ND

AN

ALY

SIS

OF

RE

SU

LTS

23

VIII. 4.1 Development of cellular automata (CA) rules -Magnitude and patterns of land use change

Three LUC time series maps (1946, 1970 and 1989) from the Cabuyal watershed in

Cauca, Colombia were analysed looking for the kind of LUCC patterns present in the

landscape. Appendix 1 contents the maps of this area. Although there are basic

differences between the environment of Cabuyal watershed and the Tambito

catchment, which is the area where the model was applied, this is the closest area

with land use history data available. The analysis consisted of:

1. Identification of shape patterns of land use change, such as linearity or clustering,

between land uses of different series: The coverage of LUC of 1946 was overlaid

with the LUC of 1970 and LUC of 1970 with the LUC of 1989 using the cross-

tabulation command in IDRISI. The area was then divided in three altitudinal zones:

1200 - 1500, 1500 - 1800 and 1800 - 2200 MASL. In each altitudinal zone the shape

and patterns of the main changes were defined. Appendix 1 contains the basic maps

and Appendix 2 the description of crosstabulation map of each zone for the 1946 -

1970 series. Main conclusions derived from this analyses are included in the results

chapter.

2. Identification of neighbourhood relations: To understand whether certain types of

LUC depend on frequency distribution of precedent neighbourhood land uses, or

proximity to rivers and roads, the procedure to obtain this information was as follows:

1. Reclassification of each of the maps from 1946, 1970 and 1989:

Preliminary analyses were carried out with all the land use/cover classes

originally available in the maps but with the purpose of simplifying the data

management, similar land uses were joined. Pine was merged with Forest,

Bare Soil with Scrubs and Crops with Pasture to define three classes of

cover: Forest, Pasture and Scrubs.

24

2. Slope was grouped in six classes: 0 - 3 %, 3 - 12 %, 12 - 30 %, 30 - 50 %,

50 - 75 % and > 75 %.

3. Aspect was organised into eight classes of 45 degrees each, the first class

being between 0 and 45º.

4. Altitude was divided in two ranges: lower and higher than 1650 MASL. This

value was selected considering previous field observations made about the

altitudinal level of LUC differentiation.

5. A 3 by 3 neighbourhood analysis was carried out for each of the LUC

classes in the 1946 series to obtain the frequency of pixels of the same class.

The produced image was then used to make crosstabulation tables with the

1970 series of LUC. This enabled identification of the new land use/cover

depending on the frequency distribution of neighbours of each LUC in the

preceding time step (in this case 1946 series).

6. Preparation of rivers and roads: Roads and rivers vector files were

rasterised and the Euclidean distance to pixels signalling the road was

calculated using the DISTANCE command in IDRISI. The produced images

were reclassified in four evenly distributed classes, with each road class

covering 200 metres and each river class 100 metres. Cross-tabulation

frequency tables were produced between the variables of LUC, slope, altitude

and aspect. The same was done for the neighbourhood images and between

LUC and distance to rivers and roads.

7. Logical rules were written following the frequency distribution of the three

different kinds of tables. Land use changes between series according to

landscapes attributes, neighbourhood relations in 3 by 3 pixels, and proximity

to rivers and roads. In defining the rules, values in tables describing a

change occurring in more than 50 % of the cases were considered with a

weight of 100 %. Those cases where all the options had values below 50 %

25

were solved analysing the 1979/1989 series. Maps are displayed in Appendix

2; with tables in Appendix 3 and the set of rules in Appendix 4.

4.2 CA-model application - Modelling LUC scenarios.

The CA model was then applied to the Tambito catchment under the general

statements representing the physical constraints or river location and infrastructure

investments (location of roads).

As a result, LUC scenarios maps were generated and used as input for the

hydrological model.

IX. 4.3 Hydrological model

A PC-Raster model was used (Mulligan, 1998), which three different modules:

atmospheric, vegetation and soil module. The first calculates evapotranspiration

based on solar net radiation, leaf area index (LAI) and terrain aspect. The vegetation

module calculates the interception rate of vegetation based on the vegetation cover

and leaf area index. The soil module calculates saturated hydraulic conductivity

(Ksat), recharge, bulk density at the water front (BdatWF), infiltration, runoff and

erosion, based on rainfall, soil density, soil texture, depth, stone density and terrain

slope. The three scenarios generated with the CA-model were used as a land use

cover input.

The hydrological model was run for every scenario for January 1998 in 300 time

steps (1 step = 1 hour). A series of maps were produced and displayed as a movie to

follow the changes occurring in each variable. The main focus of attention was on

recharge, erosion and runoff. Time series for recharge and evapotranspiration were

plotted to identify differences in the hydrological response.

contents The code is presented in Appendix 5.

26

IX.1. 4.3.1 Parameterisation

The model required the following list of parameters:

Climatic parameters:

Rainfall: Hourly precipitation was downloaded from the data-loggers currently

installed in the catchment.

Net radiation: The net radiation was computed as the difference between the

measured incoming solar radiation and reflected energy by the surface

(Jetten, 1994; Mulligan, 1996 cited by Rincon, 1998). The equation used to

calculate the net radiation (Rn) in the model was:

Rn = 0.8683 Rt - 8.5931 (MJ)

Where Rt is the terrestrial solar radiation in Meg Jules per day.

Soil parameters:

Soil samples were collected from 16 different points within the catchment at

depths of 10 cms. until the rock bed was reached. Tambito catchment was

classified according to slope, aspect and vegetation cover to produce 25

classes. During the field work the criteria was to collect the maximum samples

number in the most representative classes. Appendix 6 contains the soil

data.

- Soil texture: Was calculated by averaging the values obtained in the first

three soil layers (30 cms). Texture was calculated using the Bouyoucous

Standard method in the Soil Laboratory of the International Center for

Tropical Agriculture CIAT.

- Bulk density: Undisturbed Auger samples of 5.1 CMS were taken at depths

of 10 cms. Wet weight was taken no more than four hours later and dry

weight after drying the samples at 105 C for 48 hours. These were used to

calculate bulk density.

27

- Hydraulic conductivity: This parameter was calculated using a Disk

Infiltrometer (Decagon Devices, Inc.). TheTheory supporting its functioning is

found in Zhang (1997).

- Stone density: 10 random samples were taken from different points within

the catchment and values were calculated measuring its weight and the

volume of water displaced by them.

- Soil erodability (K): The relation between texture and organic matter was

used to identify the K value from tables reported by Kirkby and Morgan

(1984).

Vegetation parameters:

Leaves from every land use in the catchment were collected. Fresh (dry) and

wet weight was measured in the field. To calculate the area, the samples

were photographed in a flat sheet of paper with known area. Pictures were

then scanned and processed to correct visual distortions in commercial

graphic software. Field measurements tables and examples of scanned

images are presented in Appendix 7. Incoming photo-synthetically active

radiation (PAR) was measured with sensors held upright at 1, 3 and 6 meters

above the ground. An exponential relation was used to calculate the radiation

at the top of the canopy and Beer’s Law was used to calculate the Leaf Area

Index (LAI). With this set of data the following parameters were calculated:

- Leaf density (LD): weight of leaves per unit area (g/m2).

- Specific leaf area: area per weight of leaves (m2/g).

- LAI: area of leaves per unit area of ground.

- Specific Water retention (SWR): weight of water per area of leaves (g/m2)

- Cover: Relation of gaps with canopy cover (fraction 0 - 1)

- Canopy Storage capacity: SWR * Cover * LAI (mm)

- Initial Biomass: LAI * LD * 0.5.

28

Data was collected directly from the field between the 1 July and 1 August 1998.

IX.2. 4.3.2 Calibration and Validation

Originally, river discharge was considered as the key variable for calibration and

validation. During the field work pressure sensors were installed in Palo Verde and

Tambito catchments in two homogeneous sections built over 10 meters along the

river. Unfortunately the sensors were not sensitive enough to changes in the amount

of water in the river and no data was recorded. For this reason calibration and

validation are not included in this document. For the purpose of this research, it was

enough to obtain the model response to different land cover scenarios.

29

J. 5. RESULTS

X. 5.1 Cellular Automata Model

From the first set of figures and tables presented in Appendix 2 and 3 it is possible to

conclude that land use change is associated with presence of roads and rivers. The

sequence in almost all images is:

Forest to Scrub and

Scrub to Pasture.

Intact forest is mainly located at great distance from rivers and roads. This processes

were more common in middle altitudes possibly due to the location of the main road

(Panamerican Highway) in this area. In the high altitudes the process is similar but in

a more fragmented way due to limited access relative to the lower altitudes. New

areas of Forest were founded in 1970 where Pasture occurred during 1946. Scrub is

maybe the most consistent land cover over the time series considered in this study

and in some cases is converted into bare soil.

In the neighbourhood analysis presented in Appendix 3 (Tables A3.9 to A3.11) there

are no clear patterns of change that depend on the extent of a specific land use

around each category. These results can be associated with image pixel resolution

(25 meters), which is a small size necessary to outline the neighbourhood relations.

This data clearly shows the tendency of the land to be converted completely into

Pasture. Some areas with Scrub and Pasture turned to Forest again but the overall

proportion that exhibited this is very small. While 80 % of the Forest changed to

Pasture and Scrub, only 11 % of the Scrub and Pasture returned to Forest. Tables

5.1 and 5.2 show the changes in land use with respect to the distance of roads and

rivers. If the Pasture is far from roads, it is more likely to return to Forest and the

most common land cover change is towards Scrub and Pasture again.

30

Table 5.1 Conversion from land cover in 1946 towards different land covers in 1970considering the frequency of the distance to roads (n=40727 pixels).

% LU in 1970Distance to roads LU in 1946 Forest (1) Pasture (2) Scrub (3)

1 - 200 1 3 9 42 2 35 83 3 18 18

200 - 400 1 4 6 42 4 28 113 4 18 21

400 - 600 1 1 6 12 4 41 83 3 25 11

> 600 1 0 6 02 5 69 03 2 18 0

Table 5.2 Conversion from land cover in 1946 towards different land covers in 1970considering the frequency of the distance to rivers (n=48573 pixels).

% LU in 1970Distance to

riversLU in 1946 Forest (1) Pasture (2) Scrub (3)

1 - 100 1 3 8 32 3 36 103 4 17 17

100 - 200 1 4 7 42 2 30 73 2 23 22

200 - 300 1 3 12 82 1 23 83 0 22 22

> 300 1 1 38 22 0 11 83 0 36 3

Although the numerical results obtained from this set of tables are still being

processed, two basic rules were qualitatively considered limited by terrain slope:

1. deforestation around the roads and,

31

2. deforestation in areas adjacent of previous deforested land.

The preliminary cellular automata model was used over the Tambito catchment and

run in fifty time steps. Figure 5.1 shows the change in LUC over time, as generated

by the CA model. Both primary and secondary forest decrease significantly over time

and are replaced by pasture, which increases dramatically from 36 Ha to 1322 Ha

over 1424 Ha in 30 time steps. In 30 time steps the primary forest is almost

completely removed, with very low levels of secondary forest remaining. With time

this is also wholly removed with complete conversion to pasture over the whole

catchment. This can be seen in diagrams of the catchment over time in Figure 5.2

Total deforestation was attained in the time step No. 42. Scenarios for 0, 5, 20 and

30 time step's were used as inputs in the hydrological model.

Figure 5.1 Changes in LUC in four time steps generated by the CA model.

0

250

500

750

1000

1250

1500

VEG0 VEG5 VEG20 VEG30

Land Use/Cover time step

HA

Primary Forest

Secondary Forest

Pasture

Current road Step 0

Step 5Step 10

Figure 5.2 a and b (next page) LUC patterns generated in different time steps by the CA model.

Pasture

Secondary Forest

Primary Forest

33

Figure 5.2 b.

Step 15

Step 30Step 25

Step 20Pasture

Secondary Forest

Primary Forest

XI. 5.2 Hydrological Simulation

In Figure 5.3 the daily pattern of evaporation (EVP) clearly shows a dependence

upon the net radiation. When rain occurs, EVP is depleted temporarily and at the end

of the month the EVP raises almost four fold, relative to the initial condition. EVP is

greater in the presence of more vegetation cover (VEG0 vs. VEG30), according to

reality if we consider the role of vegetation in the interception process. Looking at the

EVP in the motion maps, is possible to appreciate local patterns that are dependent

upon slope, aspect and vegetation cover. Those slopes facing east show higher

values of EVP in comparison with west facing slopes. Cloudiness was observed in

the field to occur mainly during the afternoon, which confirms the result of lower EVP

on west facing slopes.

Figure 5.4 shows the infiltration fluxes in the catchment outlet. Figure 5.5 shows the

patterns of bulk density at the water front measured in the outlet of the catchment.

Note the difference in the scale between graphs a and b which are intended to show

the difference at the end of the two periods.

Due to changes in land use cover shown in Figures 5.1 and 5.2, there are responses

in evaporation rates and level of infiltration. Evaporation decreases from 0.67

m/m/day in the original catchment to 0.45 mm/m/day as deforestation occurs.

Infiltration rates also respond to vegetation loss with an increase from 33.6 to 35.5

mm/m/day. This is as expected as loss of vegetation leads to a reduction in

intercepted evapotranspiration. Similarly reduced interception makes more water

available at ground level as thus increases infiltration levels.

K.

35

Figure 5.3 Hourly evaporation in Tambito watershed upon 4 scenarios of LUC for January 1998.

0

100

200

300

400

500

600

700

800

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

121

127

133

139

145

hour

EV

P (

mm

)0

5

10

15

20

25

30

Rai

nfa

ll (m

m)

(mm/hr)

veg0

veg5

veg20

veg30

0

100

200

300

400

500

600

700

800

1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103

109

115

121

127

133

139

145

151

hour

EV

P (

mm

)

0

5

10

15

20

25

30

Rai

nfa

ll (m

m)

36

Figure 5.4 Hourly infiltration in Tambito watershed for January 1998.

Infiltration

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

1 8

15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

hour

mm

0

2

4

6

8

10

Rai

nfa

ll (m

m)

Infiltration

0

50000

100000

150000

200000

250000

300000

350000

1 8 15 22 29 36 43 50 57 64 71 78 85 92 99 106

113

120

127

134

141

148

hour

mm

0

2

4

6

8

10

12

14

16

18

20R

ain

fall

(mm

)

37

Figure 5.5 Hourly bulk density at the water front in the outlet of Tambito watershed (January1998)

The simplified hydrological model used in the evaluation of different scenarios of land

use/cover reproduced with good approximation the behaviour of parameters like

evaporation and infiltration reported by the literature. Some adjustments are required

in the incorporation of distributed parameters related with the bulk density. The slope

and interception of the equation of bulk density versus depth generated with field

measurements could not be applied into the model. It was necessary to use a

0.910.9110.9120.9130.9140.9150.9160.9170.9180.919

1 11 21 31 41 51 61 71 81 91 101

111

121

131

141

hourly

Bu

lk D

ensi

ty

012345678910

(mm/hr)

veg0

veg5

veg20

veg30

0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

1 10 19 28 37 46 55 64 73 82 91

100

109

118

127

136

145

hourly

Bu

lk D

ensi

ty

0

5

10

15

20

25

30

38

general equation obtained in previous studies in the area supplied by the research

team.

Figure 5.6 Total fluxes of evaporation and infiltration in Tambito watershed (January 1998

L.

M.

)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

Land Use/Cover Time step

mm/day

32.50

33.00

33.50

34.00

34.50

35.00

35.50

36.00

Evaporation 0.68 0.63 0.48 0.45

Infiltration 33.67 34.07 35.19 35.49

VEG0 VEG5 VEG20 VEG30

39

N. 6. CONCLUSIONS

Logical rules can be produced with the study of historical patterns of land use in a

simplified way. The identification of logical and general rules of land use change in

environments like the selected study area is limited by the complexity associated with

the diversity in land use/cover classes. This makes it necessary to simplify the class

numbers with the associated loss of information. Balance between this loss of

information and the reliability of the results appears as a trade off in land use

modelling research.

The simplified hydrological model used in the evaluation of different scenarios of land

use/cover reproduced with good approximation the behaviour of parameters like

evaporation and infiltration reported by the literature. Some adjustments are required

in the incorporation of distributed parameters related with the bulk density.

The integration of empirical models with physically hydrological based models

showed a great potential in the evaluation of 'future' scenarios of land use. Although

the scenarios analysed here are not as complex as the reality is, with the

identification and integration of more factual rules it can be possible to model more

complex landscape structures.

Part of this research involved the use of several geographical information systems

and some modelling GIS software. The problems associated with format conversion

between software formed the most critical components of this study. This problem

can be solve by the develop of more integrated GIS or the more friendly interfaces

between different brands.

40

It is suggested for futures application of the model to try with more complex scenarios

of land use/cover through the incorporation of new rules and extreme hydrological

conditions to establish the range of response of critical variables like erosion, runoff

and evaporation.

O.

41

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Woolhiser, D.A. 1996. Search for Physically Based Runoff Model – A Hydrological El

Dorado? Journal of Hydraulic Engineering. March 122-129.

Woolhiser, D.A. 1975. The Watershed approach to understand out environment.

Journal of Envrion. Quality. Vol.4 No.17-20.

Woolhiser, D.A. 1973 Hydrologic and watershed modeling – State of the art. Trans.

ASAE, 16(3),553-559

Williams, A.G., Ternan, J.L. and Kent, M. 1987 The impact of conifer afforestation on

water quality in an upland catchment in southwest England. In Swanson, R.H.,

Bernier, P.Y. and Woodward, P.D. (editors). Forest Hydrology and Watershed

Management. Proceedings of the Vancouver Symposium, August 1987; IAHS. Publ.

No. 167, 451-464

47

Q. APPENDIX 1. CABUYAL WATERSHED MAPS

Figure A.1 Land use series for 1946, 1970 and 1989 in the Cabuyal Watershed - Cauca - Colombia.

48

Figure A.2 Aspect, altitudinal ranges and slope in the Cabuyal Watershed - Cauca - Colombia

49

Figure A.3 Proximity to roads and rivers in the Cabuyal Watershed - Cauca - Colombia

50

R. APPENDIX 2. PATTERNS CHANGE ANALYSIS

Figures A.2.1 to A.2.17 illustrate the patterns of change between the three series

of land use/cover and some of the observed tendencies are signalled with ovals.

In all figures black lines represent rivers and red lines roads. Explanations of

Figure are presented below eachone.

51

Figure A2.1. Forest LUC conversion in the higher zone. Dashed ovals = new land uses andpointed oval = new forest.

The first four classes in Figure A2.1 correspond with the new classes of LUC than

was occupied in 1946 with forest. The two remaining classes are new areas in

forest than in 1946 were pasture and scrub. It is possible to notice that the forest

changed mainly towards pasture and scrub in areas adjacent to roads and

partially close to river networks protected with forest before. On the other hand,

new forest has re-growth close to river streams. Unchanged areas in forest are

located in between the stream rivers and far from roads.

52

Figure A2.2. Forest LUC conversion in the lower zone. Dashed ovals = new land uses andpointed ovals = new forest

In Figure A2.2 occurs the same pattern as in the higher zone with the exception of

the road influence.

53

Figure A2.3. Forest LUC conversion in the middle zone. Dashed ovals = new land uses andpointed ovals = new forest

In the middle zone, illustrated in Figure 3, changes occur along rivers in a clearer

way than in higher and lower zones perhaps because of the antecedent

conditions or the remaining forest.

54

Figure A2.4. Forest LUC conversion in the higher zone between 1970 - 1989. Dashed ovals =new land uses and pointed ovals = new forest

Figure A2.4 shows a similar pattern than in the period 1946 - 1970. Moreover it is

fragmented in small patches along the rivers.

55

Figure A2.5. Forest LUC conversion in the lower zone between 1970 - 1989. Dashed ovals =new land uses and pointed ovals = new forest

Figure A2.5 illustrates a similar pattern founded in precedent series, fragmented in

some cases, but with dominance of the scrub instead of pasture as a new land

use after forest. Some streams are almost completely recovered with forest where

pasture was before.

56

Figure A2.6. Scrub LUC conversion in the higher zone between 1946 - 1970. Continuous ovalssame LUC, dashed ovals = new LUC and pointed ovals = new scrub cover

The pattern of scrub is different from forest pattern. In the first place, this LUC

tends to be in its original position and as a second trend, scrub change towards

pasture in both cases in areas randomly distributed between the river channels.

New areas in scrubs are preceded by forest and pasture located before close to

rivers.

57

Figure A2.7. Scrub LUC conversion in the lower zone between 1946 - 1970. Continuous ovalssame LUC, dashed ovals = new LUC and pointed ovals = new scrub cover

Figure A2.7 shows the lower zone, which presents more extended changes

related to scrubs. In first degree, the major part is turned to pastures followed by

bare soil in the lowest zone. Pastures in a bigger extent than in the higher zone

preceded new areas in scrub. Areas with scrub again are present along rivers. In

all cases the direction of the change follow the direction of the rivers.

58

Figure A2.8. Scrub LUC conversion in the middle zone between 1946 - 1970. Continuousovals same LUC, dashed ovals = new LUC and pointed ovals = new scrub cover

In the middle zone, the dynamic of scrub is located closer to rivers. New areas in

scrub coming from pasture are more frequent than from other uses (Figure A2.8).

59

S. APPENDIX 3. CROSSTABULATION TABLES

Table A3.1. Crosstabulation table between 1946 and 1970 LUC for altitude below1650 MASL and for aspect from 0 to 45 degrees.

0 - 3 % 1946 19701970 LUC1 LUC3 LUC6 LUC7 Total 1989 LUC1 LUC3 LUC4 LUC6 LUC7 TotalLUC1 0 3 3 3 LUC1 0 3 0 0 0 2LUC3 62 71 63 70 LUC3 18 58 45 0 37 51LUC4 0 2 8 3 LUC4 64 12 45 0 28 19LUC6 0 0 3 1 LUC5 0 12 9 0 4 9LUC7 38 23 22 23 LUC6 0 0 0 0 0 0

LUC7 18 15 0 100 31 19

3 - 12 % 1946 19701970 LUC1 LUC3 LUC6 LUC7 Total 1989 LUC1 LUC3 LUC4 LUC6 LUC7 TotalLUC1 14 6 0 9 7 LUC1 12 4 0 6 0 4LUC3 75 72 0 64 70 LUC3 21 54 30 36 53 50LUC4 0 4 0 2 4 LUC4 28 16 68 3 16 18LUC6 0 2 100 6 3 LUC5 0 15 3 0 2 11LUC7 11 16 0 18 17 LUC6 0 0 0 6 0 0

LUC7 39 12 0 48 30 17

12 - 30 % 1946 19701970 LUC1 LUC3 LUC6 LUC7 Total 1989 LUC1 LUC3 LUC4 LUC6 LUC7 TotalLUC1 23 5 0 13 8 LUC1 18 3 0 5 2 4LUC3 63 76 0 54 68 LUC3 35 49 26 36 36 44LUC4 0 2 0 1 2 LUC4 16 17 74 27 10 17LUC6 0 1 100 13 6 LUC5 0 10 0 0 1 7LUC7 13 15 0 18 16 LUC6 0 0 0 5 0 0

LUC7 30 22 0 28 52 28


LUC7 48 31 0 51 67 42


LUC7 56 34 0 69 78 56

> 75 % 1946 19701970 LUC1 LUC3 LUC6 LUC7 Total 1989 LUC1 LUC3 LUC4 LUC6 LUC7 TotalLUC1 0 14 11 12 LUC1 36 0 4 2 6LUC3 0 31 27 27 LUC3 29 75 14 26 37LUC4 0 0 0 0 LUC4 0 0 18 0 4LUC6 0 3 34 24 LUC5 0 3 0 0 1LUC7 100 53 28 37 LUC6 0 0 0 0 0

LUC7 36 22 64 72 52

LUC1 Forest LUC2 Pine LUC3 PastureLUC4 Annual crops LUC5 Seasonal crops LUC6 Bare SoilLUC7 Scrub

60


0 - 3 % 1946 1970

1970 LUC1 LUC3 LUC6 LUC7 Total 1989 LUC1 LUC3 LUC4 LUC6 LUC7 Total

LUC1 LUC1

3 - 12 % 1946 1970


LUC1 0 4 6 4 LUC1 5 1 0 25 0 1

LUC3 85 76 70 75 LUC3 5 55 53 25 34 49

LUC4 4 3 2 3 LUC4 68 14 47 0 25 18

LUC6 0 0 3 1 LUC5 0 17 0 0 2 13

LUC7 12 18 19 18 LUC6 0 0 0 0 0 0

LUC7 23 13 0 50 39 18

12 - 30 % 1946 1970


LUC1 3 2 7 4 LUC1 7 3 0 8 2 3

LUC3 79 84 61 77 LUC3 30 60 32 35 37 54

LUC4 0 2 1 2 LUC4 28 14 68 4 13 15

LUC6 14 1 17 7 LUC5 0 8 0 1 1 6

LUC7 3 10 14 11 LUC6 0 0 0 0 0 0

LUC7 35 15 0 51 47 22

30 - 50 % 1946 1970


LUC1 15 2 11 7 LUC1 22 2 0 3 0 3

LUC3 45 80 36 57 LUC3 27 66 33 50 58 58

LUC4 0 1 0 1 LUC4 16 8 67 5 3 7

LUC6 25 3 39 22 LUC5 0 4 0 0 0 2

LUC7 15 14 14 14 LUC6 0 0 0 0 0 0

LUC7 36 20 0 41 39 29

50 - 75 % 1946 1970


LUC1 0 1 2 2 LUC1 0 0 0 2 0 1

LUC3 86 84 32 52 LUC3 57 67 0 32 59 54

LUC4 0 1 0 0 LUC4 29 2 100 4 0 3

LUC6 0 0 55 34 LUC5 0 0 0 1 0 0

LUC7 14 15 10 12 LUC6 0 0 0 0 0 0

LUC7 14 31 0 61 41 42

> 75 % 1946 1970


LUC1 0 4 0 1 LUC1 0 0 0 0 1

LUC3 0 65 25 34 LUC3 100 79 12 46 51

LUC4 0 9 67 52 LUC4 0 0 0 0 1

LUC6 0 0 0 0 LUC5 0 0 0 0 0

LUC7 100 22 8 13 LUC6 0 0 0 0 0

LUC7 0 21 13 54 47

LUC1 Forest LUC2 Pine LUC3 PastureLUC4 Annual crops LUC5 Seasonal crops LUC6 Bare SoilLUC7 Scrub

61


0 - 3 % 1946 1970


LUC1 0 2 6 3 LUC1 0 1 0 0 1

LUC3 45 77 82 74 LUC3 20 64 13 29 54

LUC4 5 4 3 4 LUC4 0 12 75 18 15

LUC6 0 0 0 0 LUC5 0 5 0 3 4

LUC7 50 17 9 19 LUC6 0 0 0 0 0

LUC7 80 18 13 50 26

3 - 12 % 1946 1970


LUC1 0 3 6 3 LUC1 0 4 0 6 1 3

LUC3 84 80 59 76 LUC3 29 58 35 41 43 54

LUC4 0 4 3 4 LUC4 47 12 65 0 30 17

LUC6 0 1 13 3 LUC5 0 14 0 0 3 11

LUC7 16 13 19 14 LUC6 0 0 0 0 0 0

LUC7 24 12 0 53 23 15

12 - 30 % 1946 1970


LUC1 0 2 6 3 LUC1 0 2 0 8 0 2

LUC3 86 83 61 78 LUC3 43 63 31 53 36 58

LUC4 0 5 2 4 LUC4 37 13 69 8 17 16

LUC6 0 2 17 5 LUC5 0 6 0 2 0 5

LUC7 14 9 14 10 LUC6 0 0 0 0 0 0

LUC7 20 16 0 29 47 19

Total 100 100 100 100 100 100

30 - 50 % 1946 1970


LUC1 0 1 7 3 LUC1 0 1 0 1 0 1

LUC3 97 78 58 71 LUC3 38 65 44 57 55 62

LUC4 3 4 0 2 LUC4 33 9 56 4 5 10

LUC6 0 4 28 14 LUC5 0 4 0 3 0 3

LUC7 0 13 6 9 LUC6 0 0 0 0 0 0

LUC7 29 20 0 34 41 24

50 - 75 % 1946 1970


LUC1 0 0 6 3 LUC1 0 1 0 0 0 0

LUC3 86 89 32 62 LUC3 29 70 0 11 76 54

LUC4 0 3 0 2 LUC4 29 1 100 9 0 5

LUC6 14 1 48 23 LUC5 0 1 0 0 0 0

LUC7 0 7 14 10 LUC6 0 1 0 0 0 0

LUC7 43 28 0 80 24 40

> 75 % 1946 1970


LUC1 80 13 44 LUC1 0 0 0 0

LUC3 0 0 0 LUC3 83 34 0 55

LUC4 0 0 0 LUC4 0 7 0 4

LUC6 12 87 53 LUC5 4 0 0 2

LUC7 8 0 4 LUC6 0 0 0 0

LUC7 13 59 100 40

62


0 - 3 % 1946 1970


LUC1 0 0 5 1 LUC1 0 2 0 0 6 3

LUC3 50 84 74 80 LUC3 0 77 0 100 29 69

LUC4 0 1 0 1 LUC4 100 11 100 0 24 14

LUC6 0 1 5 2 LUC5 0 5 0 0 0 4

LUC7 50 14 16 16 LUC6 0 0 0 0 0 0

LUC7 0 5 0 0 41 10

3 - 12 % 1946 1970


LUC1 0 5 3 4 LUC1 0 3 0 0 2 2

LUC3 73 78 62 74 LUC3 69 65 57 25 73 65

LUC4 0 3 0 2 LUC4 31 10 43 13 13 12

LUC6 0 1 8 3 LUC5 0 7 0 0 0 5

LUC7 27 13 27 16 LUC6 0 0 0 0 0 0

LUC7 0 15 0 63 13 15

12 - 30 % 1946 1970


LUC1 10 6 14 8 LUC1 7 2 0 67 0 2

LUC3 86 71 60 69 LUC3 21 58 45 0 46 52

LUC4 0 3 5 3 LUC4 50 18 27 0 5 18

LUC6 0 1 0 1 LUC5 0 7 0 33 0 5

LUC7 5 19 22 19 LUC6 0 0 0 0 0 0

LUC7 21 15 27 0 49 22

30 - 50 % 1946 1970


LUC1 10 2 11 6 LUC1 13 2 0 0 0 2

LUC3 75 75 78 76 LUC3 0 36 17 60 31 33

LUC4 15 2 0 3 LUC4 20 9 83 0 0 11

LUC6 0 2 2 2 LUC5 0 6 0 0 3 5

LUC7 0 17 9 12 LUC6 0 4 0 0 0 3

Total 100 100 100 100 LUC7 67 44 0 40 66 47

50 - 75 % 1946 1970


LUC1 13 0 9 6 LUC1 14 2 0 0 0 3

LUC3 75 93 75 81 LUC3 0 22 100 60 0 21

LUC4 0 2 0 1 LUC4 0 4 0 0 0 4

LUC6 0 0 8 4 LUC5 0 10 0 40 0 10

LUC7 13 5 8 7 LUC6 0 1 0 0 0 1

LUC7 86 61 0 0 100 62

> 75 % 1946 1970


LUC1 0 0 33 19 LUC1 0 0 0 0 0

LUC3 83 100 58 71 LUC3 0 40 100 0 33

LUC4 0 0 0 0 LUC4 0 0 0 0 0

LUC6 0 0 8 5 LUC5 0 13 0 0 10

LUC7 17 0 0 5 LUC6 0 13 0 0 10

LUC7 100 33 0 100 48

63


0 - 3 % 1946 1970


LUC1 0 0 0 0 LUC1 4 0 0 0 3

LUC3 57 87 78 83 LUC3 69 67 50 57 68

LUC4 0 4 7 5 LUC4 9 33 0 29 12

LUC6 0 1 4 2 LUC5 8 0 0 0 7

LUC7 43 8 11 11 LUC6 0 0 0 0 0

LUC7 10 0 50 14 11

3 - 12 % 1946 1970


LUC1 18 4 0 3 4 LUC1 6 1 0 0 1 1

LUC3 55 77 0 68 73 LUC3 41 62 42 14 51 57

LUC4 0 2 0 4 3 LUC4 12 17 58 0 14 17

LUC6 0 1 100 9 3 LUC5 0 3 0 0 1 3

LUC7 27 16 0 16 17 LUC6 0 0 0 7 0 0

LUC7 41 16 0 79 32 22

12 - 30 % 1946 1970


LUC1 13 1 7 4 LUC1 19 1 0 0 7 3

LUC3 75 70 63 67 LUC3 7 53 15 0 35 44

LUC4 0 1 5 3 LUC4 11 20 30 7 5 16

LUC6 0 1 9 4 LUC5 0 2 0 0 2 1

LUC7 13 28 16 22 LUC6 0 1 0 3 1 1

Total 100 100 100 100 LUC7 63 24 55 90 50 35

Total 100 100 100 100 100 100

30 - 50 % 1946 1970


LUC1 18 1 0 5 4 LUC1 24 0 0 2 2 2

LUC3 39 60 0 52 53 LUC3 0 37 0 0 29 29

LUC4 0 0 0 4 2 LUC4 0 7 75 0 7 8

LUC6 3 0 100 11 8 LUC5 0 5 0 0 2 3

LUC7 39 39 0 28 32 LUC6 0 7 0 13 2 6

LUC7 76 43 25 85 57 52

50 - 75 % 1946 1970


LUC1 37 5 3 6 LUC1 38 0 0 3 4 4

LUC3 37 51 39 43 LUC3 0 39 100 0 43 33

LUC4 0 5 0 2 LUC4 0 2 0 0 0 1

LUC6 5 0 26 16 LUC5 0 5 0 0 7 4

LUC7 21 39 32 33 LUC6 0 10 0 5 6 7

LUC7 63 44 0 93 40 51

> 75 % 1946 1970


LUC1 67 12 2 11 LUC1 36 0 0 8 0 6

LUC3 0 48 37 37 LUC3 0 21 100 0 16 14

LUC4 0 6 0 2 LUC4 0 0 0 0 0 0

LUC6 33 0 38 25 LUC5 0 0 0 0 28 7

LUC7 0 33 23 25 LUC6 0 11 0 15 0 8

LUC7 64 68 0 77 56 66

64


0 - 3 % 1946 1970


LUC1 0 6 0 4 LUC1 0 1 0 0 0 1

LUC3 50 70 82 72 LUC3 60 66 25 100 57 63

LUC4 0 3 4 3 LUC4 20 6 75 0 4 8

LUC6 0 2 0 2 LUC5 0 9 0 0 0 7

LUC7 50 19 14 19 LUC6 0 0 0 0 0 0

LUC7 20 18 0 0 39 21

3 - 12 % 1946 1970


LUC1 0 6 0 7 6 LUC1 6 1 0 0 3 1

LUC3 82 71 0 67 70 LUC3 47 60 73 10 43 55

LUC4 0 2 0 4 2 LUC4 8 11 13 5 13 11

LUC6 0 2 100 6 3 LUC5 0 9 7 0 3 7

LUC7 18 21 0 15 19 LUC6 0 0 0 10 0 0

LUC7 39 19 7 75 40 26

12 - 30 % 1946 1970


LUC1 17 3 0 7 5 LUC1 12 2 0 0 5 3

LUC3 66 67 0 62 64 LUC3 26 53 40 7 36 45

LUC4 0 3 0 4 3 LUC4 4 12 43 0 2 10

LUC6 0 1 100 10 5 LUC5 0 6 0 0 7 5

LUC7 17 27 0 17 22 LUC6 0 0 0 12 0 1

LUC7 58 26 17 81 51 36

30 - 50 % 1946 1970


LUC1 55 5 0 12 11 LUC1 11 5 0 0 6 5

LUC3 30 56 0 38 44 LUC3 10 40 30 3 20 26

LUC4 0 1 0 1 1 LUC4 3 4 70 4 6 5

LUC6 5 2 100 20 14 LUC5 0 3 0 0 2 2

LUC7 10 35 0 29 30 LUC6 0 1 0 21 0 4

LUC7 76 47 0 72 65 58

50 - 75 % 1946 1970


LUC1 59 8 0 17 16 LUC1 18 2 2 7 6

LUC3 30 51 0 23 33 LUC3 3 43 0 18 20

LUC4 0 0 0 0 0 LUC4 2 1 0 0 1

LUC6 7 2 100 27 20 LUC5 0 2 0 4 2

LUC7 4 39 0 32 31 LUC6 0 1 18 0 4

LUC7 77 50 80 71 67

> 75 % 1946 1970


LUC1 55 8 0 11 13 LUC1 18 0 7 5 5

LUC3 27 40 0 44 38 LUC3 0 10 0 11 7

LUC4 0 0 0 0 0 LUC4 0 0 0 0 0

LUC6 18 0 100 23 22 LUC5 0 0 0 16 4

LUC7 0 53 0 22 27 LUC6 0 4 37 0 10

LUC7 82 86 57 68 74

65


0 - 3 % 1946 1970


LUC1 LUC1 20 3 0 50 0 3

LUC3 LUC3 20 55 75 0 42 51

LUC4 LUC4 20 9 25 0 11 10

LUC6 LUC5 0 13 0 0 0 9

LUC7 LUC6 0 0 0 0 0 0

LUC7 40 20 0 50 47 27

3 - 12 % 1946 1970


LUC1 6 6 0 6 6 LUC1 5 5 0 5 2 4

LUC3 85 70 0 55 67 LUC3 45 55 79 36 45 53

LUC4 0 3 0 7 4 LUC4 15 13 21 5 16 13

LUC6 0 2 100 5 3 LUC5 0 8 0 0 4 6

LUC7 9 19 0 28 20 LUC6 0 0 0 9 0 0

LUC7 35 19 0 45 34 23

12 - 30 % 1946 1970


LUC1 3 6 0 7 6 LUC1 6 9 0 2 6 8

LUC3 81 70 0 62 67 LUC3 31 45 75 9 35 41

LUC4 0 2 0 5 3 LUC4 4 14 19 2 9 12

LUC6 1 1 100 7 5 LUC5 1 8 0 3 7 7

LUC7 15 21 0 19 20 LUC6 0 1 0 48 0 3

LUC7 58 23 6 36 43 30

30 - 50 % 1946 1970


LUC1 37 13 0 9 12 LUC1 14 14 0 12 12

LUC3 49 64 0 49 55 LUC3 18 37 10 21 28

LUC4 0 0 0 0 0 LUC4 6 5 5 2 5

LUC6 0 2 100 18 11 LUC5 4 8 1 2 5

LUC7 14 22 0 24 21 LUC6 0 3 53 0 7

LUC7 58 34 30 62 43

50 - 75 % 1946 1970


LUC1 12 4 0 18 10 LUC1 5 10 0 3 6

LUC3 88 54 0 25 44 LUC3 0 36 0 26 24

LUC4 0 0 0 0 0 LUC4 0 1 0 0 0

LUC6 0 1 100 28 15 LUC5 0 13 0 2 6

LUC7 0 41 0 29 32 LUC6 0 3 20 0 4

LUC7 95 37 80 69 59

> 75 % 1946 1970


LUC1 14 4 0 14 9 LUC1 0 3 0 0 1

LUC3 86 61 0 38 45 LUC3 17 23 0 21 16

LUC4 0 0 0 0 0 LUC4 0 0 0 0 0

LUC6 0 0 100 28 26 LUC5 0 39 0 0 17

LUC7 0 35 0 21 20 LUC6 0 0 56 0 14

LUC7 83 35 44 79 51

66


0 - 3 % 1946 1970


LUC1 0 2 0 10 4 LUC1 0 1 0 0 7 2

LUC3 73 78 100 71 77 LUC3 14 67 100 60 41 61

LUC4 0 0 0 5 1 LUC4 0 0 0 0 0 0

LUC6 0 2 0 5 3 LUC5 14 9 0 0 17 10

LUC7 27 17 0 10 16 LUC6 0 11 0 0 0 8

LUC7 71 13 0 40 34 19

3 - 12 % 1946 1970


LUC1 6 7 0 11 8 LUC1 9 3 0 0 1 3

LUC3 84 74 0 56 70 LUC3 36 58 37 55 58 55

LUC4 0 4 0 6 4 LUC4 16 11 63 5 15 14

LUC6 0 2 100 5 3 LUC5 0 12 0 0 0 9

LUC7 10 13 0 21 15 LUC6 0 0 0 15 0 0

LUC7 38 15 0 25 26 18

12 - 30 % 1946 1970


LUC1 7 6 0 10 7 LUC1 8 4 0 3 3 4

LUC3 79 70 0 48 64 LUC3 27 60 29 25 36 50

LUC4 0 4 0 3 3 LUC4 10 12 71 3 11 13

LUC6 0 2 100 5 4 LUC5 0 5 0 0 2 4

LUC7 13 18 0 34 22 LUC6 0 0 0 50 0 2

LUC7 55 19 0 20 48 28

30 - 50 % 1946 1970


LUC1 5 9 17 12 LUC1 27 4 0 0 0 5

LUC3 77 54 51 54 LUC3 20 57 100 4 12 37

LUC4 0 1 1 1 LUC4 0 7 0 0 11 7

LUC6 0 3 11 7 LUC5 0 4 0 0 0 2

LUC7 18 33 20 26 LUC6 0 0 0 60 0 4

LUC7 53 28 0 36 78 45

50 - 75 % 1946 1970


LUC1 9 10 27 20 LUC1 35 3 5 0 9

LUC3 73 35 40 40 LUC3 18 45 20 13 28

LUC4 0 0 0 0 LUC4 0 0 5 0 1

LUC6 0 0 19 12 LUC5 0 7 0 0 3

LUC7 18 56 14 27 LUC6 0 0 5 0 1

LUC7 47 45 65 87 59

> 75 % 1946 1970


LUC1 0 0 33 23 LUC1 77 0 0 0 18

LUC3 100 30 28 36 LUC3 0 40 0 6 16

LUC4 0 0 0 0 LUC4 0 0 14 0 2

LUC6 0 0 18 13 LUC5 0 10 0 0 4

LUC7 0 70 23 29 LUC6 0 0 0 0 0

LUC7 23 50 86 94 61

67

Table A3.9 Conversion from Forest in 1946 towards different land covers in 1970considering the frequency of the neighbours of the same class (n=8814 pixels).

% Land Useneighbourhood Forest Pasture Scrub

1 1 5 22 1 4 23 1 5 24 1 3 15 1 4 16 1 5 27 1 4 18 1 4 29 10 22 10

Table A5.10 Conversion from Pasture in 1946 towards different land covers in 1970considering the frequency of the neighbours of the same class (n=23116 pixels).

% Land Useneighbourhood Forest Pasture Scrub

1 0 3 12 0 3 13 0 4 14 0 3 15 0 3 16 0 5 17 0 4 18 0 5 19 4 45 11

Table A3.11 Conversion from Scrub in 1946 towards different land covers in 1970considering the frequency of the neighbours of the same class (n=21047 pixels).

% Land UseNeighbourhood Forest Pasture Scrub

1 1 4 22 0 3 13 1 4 24 0 2 15 0 2 16 0 4 27 0 2 18 0 3 29 5 25 30

68

T. APPENDIX 4. CELLULAR AUTOMATA RULES ANDMODEL

Variables and classes used in the identification of cellular automata rules:

Cover: (Land use/cover)ForestPastureScrub

Probability transition0: = doesn t occur1: >= 50 %2: < 50 %

Slope (Percentage)0 - 3 %3 - 12 %12 - 30 %30 - 50 %50 - 75 %> 75 %

Aspect (Azimut)Asp1: 0 - 45Asp2: 45 - 90Asp3: 90 - 135Asp4: 135 - 180Asp5: 180 - 225Asp6: 225 - 270Asp7: 270 - 315Asp8: 315 - 359

Altitude (meters)Alt1: < 1650 MASLAlt2: > 1650 MASL

*/ altitud < 1650 and aspect between 0 and 45 degrees.

if altitude < 1650 and aspect > 0 and < 45 and slope > 0 and < 3 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 0 and < 45 and slope > 3 and < 12 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 0 and < 45 and slope > 12 and < 30 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 0 and < 45 and slope = > 30 and < 50 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 0 and < 45 and slope = > 50 and < 75 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 0 and < 45 and slope = > 75 and cover = 1 then newcover = 3



*/ altitud < 1650 and aspect between 45 and 90 degrees.

if altitude < 1650 and aspect > 45 and < 90 and slope > 0 and < 3 and cover = 1 then newcover = 0if altitude < 1650 and aspect > 45 and < 90 and slope > 3 and < 12 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 45 and < 90 and slope > 12 and < 30 and cover = 1 then newcover = 2

69

if altitude < 1650 and aspect > 45 and < 90 and slope = > 30 and < 50 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 45 and < 90 and slope = > 50 and < 75 and cover = 1 then newcover = 2if altitude < 1650 and aspect > 45 and < 90 and slope = > 75 and cover = 1 then newcover = 3...

*/ Neighbourhood analysis 3 by 3

if neighbourhood > 1 and < 9 and cover = 1 then newcover = 2if neighbourhood > 1 and < 9 and cover = 2 then newcover = 2if neighbourhood > 1 and < 8 and cover = 3 then newcover = 2if neighbourhood > 8 and cover = 3 then newcover = 3

*/road distance

If roaddistance < 200 and cover = 1 then newcover = 2If roaddistance < 200 and cover = 2 then newcover = 2If roaddistance < 200 and cover = 3 then newcover = 3If roaddistance > 200 and < 400 and cover = 1 then newcover = 2If roaddistance > 200 and < 400 and cover = 2 then newcover = 2If roaddistance > 200 and < 400 and cover = 3 then newcover = 2If roaddistance > 400 and < 600 and cover = 1 then newcover = 2If roaddistance > 400 and < 600 and cover = 2 then newcover = 2If roaddistance > 400 and < 600 and cover = 3 then newcover = 3If roaddistance > 600 and cover = 1 then newcover = 2If roaddistance > 600 and cover = 2 then newcover = 2If roaddistance > 600 and cover = 3 then newcover = 2

*/ river distance

If riverdistance < 100 and cover = 1 then newcover = 2If riverdistance < 100 and cover = 2 then newcover = 2If riverdistance < 100 and cover = 3 then newcover = 3If riverdistance > 100 and < 200 and cover = 1 then newcover = 2If riverdistance > 100 and < 200 and cover = 2 then newcover = 2If riverdistance > 100 and < 200 and cover = 3 then newcover = 3If riverdistance > 200 and < 300 and cover = 1 then newcover = 2If riverdistance > 200 and < 300 and cover = 2 then newcover = 2If riverdistance > 200 and < 300 and cover = 3 then newcover = 2If riverdistance > 300 and cover = 1 then newcover = 2If riverdistance > 300 and cover = 2 then newcover = 2If riverdistance > 300 and cover = 3 then newcover = 2

CELLULAR AUTOMATA MODEL

# Cellular Automata Model. Mark Mulligan September 1998# one time slice represent undefined time.

binding

#maps

#inputInitLandUse=veg.map;Roads=roads.map;Rivers=rivers.map;SlopeDeg=slopedeg.map;Pits=pits.map;

#outputLandUse=Landuse;Majority=majority;ProxPit=proxpit.map;ProxRiv=proxriv.map;

70

ProxRoads=Proxroad.map;ProxDefor=proxdef.map;Defor=defor;BoolTrue=booltrue.map;NewX=newx;Newy=newy;NewSec=newsec;#time series

#inputTime=time.tss;Random=random.tss;

#output#tables

#constantscellsize=25;ProbSlope=1;

Nnew=10;

areamap

clone.map;

timer1 50 1; #hours

initial

LandUse=InitLandUse;report ProxRiv=spread(Rivers,0,sin(SlopeDeg));report ProxPit=spread(Pits,0,sin(SlopeDeg));ProxRoads=if(mapmaximum(ordinal(Roads)) gt 0 thenspread(Roads,0,sin(SlopeDeg)) else 0);

dynamic#1 is primary 2 is secondary 3 is deforested

#random seed for regrowth of secondary

Defor=boolean(if(LandUse eq 3 then 1 else 0));

report ProxDefor=spread(Defor,0,sin(SlopeDeg));

Defor=if(ProxDefor le mapminimum(ProxDefor)+cellsize then 1 elseDefor);Defor=if(ProxPit le mapminimum(ProxPit)+cellsize then 1 elseDefor);Defor=if(ProxRoads le mapminimum(ProxRoads)+cellsize then 1 elseDefor);

RandomX=timeinputscalar(Random,1);RandomY=timeinputscalar(Random,2);

71

NewX=boolean(if(xcoordinate(BoolTrue) ge1006600+(RandomX*cellsize)-12.5 and xcoordinate(BoolTrue) le1006600+(RandomX*cellsize)+12.5 then 1 else 0));NewY=boolean(if(ycoordinate(BoolTrue) ge766075+(RandomY*cellsize)-12.5 and ycoordinate(BoolTrue) le766075+(RandomY*cellsize)+12.5 then 1 else 0));LandUse=if(NewX eq 1 and NewY eq 1 and LandUse eq 3 then 2 elseLandUse);

#neighbourhood

LandUse=if(Defor eq 1 then 3 else LandUse);Majority=windowmajority(LandUse,cellsize);report LandUse=if(LandUse ne Majority then Majority else LandUse);

72

U. APPENDIX 5. HYDROLOGICAL MODEL CODE.

# Bendum Hydro Mode l . (C ) Mark MUl l i gan , Depar tmen t o f Geography , K ing ' s Co l l egeLondon .

# December 1997 . Upda ted Augus t 1998 .# one t ime s l i ce rep resen ts one hourb ind ing

# maps#inpu t

Vege ta t i on=veg .map ;Ra inS ta t= ra ins ta t .map ;TopMod= topmod .map ;Poros i t y=poros .map ;In i t= in i t .map ;Sand=sand .map ;S i l t=s i l t .map ;C lay=c lay .map ;Lddmap= ldd .map ;samp lep laces=samp les .map ;S lopedeg=s lopedeg .map ;Aspec tdeg=aspec t .map ; #0 -360 aspec t mapp i t s=p i t s .map ;Spec i f i cWate rRe ten t i on=Specwa t .map ;

# ou tpu tIn te rcEvap= ievap ;Ra in fa l l= ra in fa ;KsA tWF=ksa tw f ;So i lDep th=so i l d .map ;Runo f f= runo f f ;I n f i l = in f i l ;WF=DepWf ;so la rmap=so la r ;ne tmap=net ;Evap=evap ;The ta= the ta ;Lea fArea Index=LAI ;Lea fB iomass= lea fb iom.map ;Roo tB iomass=roo tb iom;Eros ion=eros ion ;BDatWF=bda tw f ;Recharge=recharge ;#Tempsum=tempsum.map ;#Tempsand= tempsand .map ;#Temps i l t= temps i l t .map ;#Tempc lay=Tempc lay .map ;#TempMPd=Tempmpd .map ;#TempSdPd= tempsdpd .map ;#TempPh i= tempph i .map ;Bva lue=bva lue .map ;CanopySto rage=cans to r .map ;

#t ime se r i es

# inpu tRa inF i l e= ra in fa l l . t ss ;T ime= t ime . t ss ;C louds=C loud . t ss ;

# ou tpu tKsa tWFT imeSer ies=minkswf . t ss ;So la rT imeSer ies=sumso la r . t ss ;EvapT imeSer ies=sumevap . t ss ;the t ime ts= the t ime . t ss ;In f i lT imeSer ies=sumin f i l . t ss ;Runo f fT imeSer ies=sumruno f . t ss ;BDa tWFT imeSer ies=mnBda tw f . t ss ;To tRa inT imeSer ies= to t ra in . t ss ;

73

#tab lesIBData= ibda ta . tb l ;

#cons tan tsLa t i tude=2 .5 ; #Long i tude=77 ; #pos i t i ve wes tGMer id ian=0 ;So la rCons t=1367 ;SSTB=0;p i=3 .141592654 ;MaxDep th=3 .94 ; #shou ld ensure tha t t h i s ta l l i es w i th BD dep th func t i on so max BD=2 .6 ;BDs lope=0 .5921 ; #s lope o f BD func t i onBDin te rc=0 .9 ; # in te rc o f BD func t i onRUE=5.5 ;A i rTemp=20 ;Lea fDens i t y=270 ; #g /m2K=0 .2 ; #so i l e rodab i l i t yN=1 .66 ; #MusgraveM=2.0 ; #MusgraveNetRad In te rcep t= -3 .56 ;#based on bendum awsNetRadS lope=0 .719 ;#based on bendum awsRockD =2 .6 ; #Rock dens i t y (g /cm3)#So i lDep th=1 .0 ;#met resareamapdem.map ;

t imer1 300 1 ; #hours

in i t i a lrepor t So i lDep th=1 .0+(TopMod /mapmax imum(TopMod) ) * (MaxDep th -1 .0 ) ;WF=So i lDep th*1000*0 .01 ; #mm - approx 5%The ta=(WF/ (So i lDep th*1000) ) ;S lopeDeg=sca la r (S lopedeg) ;#deg rees -OKAspec tDeg=(180-sca la r (Aspec tdeg ) ) ; #deg rees -OK#so la r .map=0 ;# fo r sums on lyIn i t i a lB iomass= lookupsca la r ( IBDa ta ,Vege ta t i on ) ;Lea fA rea Index=(0 .5 * In i t i a lB iomass ) /Lea fDens i t y ;Lea fB iomass=0 .5* In i t i a lB iomass ;Roo tB iomass=0 .5* In i t i a lB iomass ;Cover=1 ;CanopySto rageCapac i t y=Spec i f i cWate rRe ten t i on*Cover *Lea fArea Index ;CanopySto rage=0 ;

dynamic

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - so la r rad ia t i on - - - - - - - - - - - - - - - - - - - - - - - - - - -

#PCRASTER ca lcu la tes t r i g func t i ons us ing the i npu ts i n deg rees .Ju lDay= t ime inpu tsca la r (T ime ,1 ) ;the t ime= t ime inpu tsca la r (T ime ,2 ) ;DayAng le= (2*p i * ( Ju lDay /365) ) * (180 /p i ) ; #degreesDec l i na t i on=(0 .006918-0 .399912*cos (DayAng le )+0 .070257*s in (DayAng le ) -0 .006758*cos (2*DayAng le )+0 .000907 *s in (2 *DayAng le ) -0 .002697*cos (3*DayAng le )+0 .00148*s in (3 *DayAng le ) ) * (180 /p i ) ;#degrees#repor t t he t ime ts=mapmax imum( the t ime) ;So la r t ime= the t ime+(4* (GMer id ian -Long i tude ) )+ ( (0 .000075+0 .001868*cos(DayAng le )-0 .032077*s in (DayAng le ) -0 .014615*cos (2*DayAng le ) -0 .04089*s in (2 *DayAng le ) ) * (229 .18 ) ) ;#degreesHourAng le= ( ( (1200- (So la r t ime-50 ) ) /100 ) *15 ) ; # {deg rees }Orb i ta lEcc=1+0 .033*cos ( (2 *p i * Ju lDay /365 ) ) ; # rad iansSSTA=So la rCons t *Orb i ta lEcc* ( ( s in (La t i t ude ) *cos (S lopeDeg) -cos (La t i t ude )*s in (S lopeDeg) *cos (Aspec tDeg) ) *s in (Dec l i na t i on )+ (cos (La t i t ude ) *cos (S lopeDeg)+s in (La t i t ude)*s in (S lopeDeg) *cos (Aspec tDeg) ) *cos (Dec l i na t i on ) *cos (HourAng le )+cos (Dec l i na t i on ) *s in (S lopeDeg) *s in (Aspec tDeg) *s in (HourAng le ) ) ;# {W/m2}SSTA= i f (SSTA g t 0 then SSTA e l se 0 ) ;C loud=0 .5 ;#C loud= t ime inpu tsca la r (C louds ,1 ) ;SSTA=SSTA* (1 -C loud ) ; #a tmos a t tenua t ion - mon te car lo

74

so la rmap=SSTA;repor t So la rT imeSer ies=map to ta l (SSTA) ;SSTA=NetRad In te rcep t+ (Ne tRadS lope*SSTA) ; #ne t rad ia t i on (W/m2)ne tmap=SSTA;SSTA=(SSTA*60*60 ) /1000000 ; #ne t rad ia t i on MJPAR=0.5*SSTA;E tF rac=The ta* (1 - (CanopySto rage /CanopySto rageCapac i t y ) ) ;InLFrac=(CanopySto rage /CanopyS to rageCapac i t y ) ;Po tEvap = i f (SSTA g t 0 then (SSTA/2 .45 ) e l se 0 ) ;repor t Evap=Po tEvap* ( ( (1 -Cover ) *The ta )+ (Cover *Lea fA rea Index*E tF rac ) ) ;repor t I n te rcEvap=Po tEvap* (Cover *Lea fArea Index* InLFrac ) ;repor t EvapT imeSer ies=map to ta l (Evap) ;

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -hyd roparamete rs - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Tempsum=(Sand* ln (1 .025 ) )+ (S i l t * l n (0 .026 ) )+ (C lay* ln (0 .001 ) ) ;Tempsand=Sand* ( ln (1 .025 ) * *2 ) - (Tempsum**2 ) ;Temps i l t=S i l t * ( l n (0 .026 ) * *2 ) - (Tempsum**2 ) ;Tempc lay=C lay* ( ln (0 .001 ) * *2 ) - (Tempsum**2 ) ;TempMPd=exp(Tempsum) ;TempSdPd=abs(Tempsand+Temps i l t+Tempc lay ) * *0 .5 ;TempPh i= -0 .5 *TempMPd** -0 .5 ;Bva lue=-2*TempPh i+0 .2 *TempSdPd;#Ph iE toWF=TempPh i * (BD in te rc+ (BDatWF-BDin te rc ) /2 ) ;#Ph iEWho le=TempPh i * (BD in te rc+ ( (BDs lope*So i lDep th ) /2 ) ) ; # i . e ha l fway be tween BD a tsu r face and f i na l BDBDatBedrock=(BDs lope*So i lDep th )+BDin te rc ; #g /cm3BDatBedrock= i f (BDa tBed rock g t RockD then RockD e l se BDa tBedrock ) ;Ksa tBedrock=( (4 *10* * -3 ) * ( (1 .3 /BDa tBedrock ) * * (1 .3 *Bva lue ) ) *exp (6 .9 *C lay -3 .7 *Sand) ) *35280 ;

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -hydro logy- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Ra in fa l l = t ime inpu tsca la r (Ra inF i l e ,Ra inS ta t ) ;CanopySto rageCapac i t y=Spec i f i cWate rRe ten t i on*Cover *Lea fArea Index ;CanopyEmpty=CanopySto rageCapac i t y -CanopySto rage ;repor t Ra in fa l l = i f (Ra in fa l l l e CanopyEmpty then 0 e l se Ra in fa l l -CanopyEmpty ) ;CanopySto rage= i f (Ra in fa l l l e CanopyEmpty t hen CanopySto rage+Ra in fa l l e l seCanopySto rageCapac i t y ) ;BDatWF=(BDs lope* (WF/1000) )+BD in te rc ;#g /cm3 assumes tha t a sha l l ower so i l has a l owerf ina l bd than deep so i lBDa tWF= i f (BDa tWF g t RockD then RockD e l se BDa tWF) ;repor t BDa tWFT imeSer ies=mapmin imum(BDa tWF) ;KsA tWF=( (4 *10* * -3 ) * ( (1 .3 /BDa tWF)* * (1 .3 *Bva lue ) ) *exp( -6 .9 *C lay -3 .7 *Sand) ) *35280 ;#mm/h rrepor t KsA tWF = i f (WF/1000 g t So i lDep th then Ksa tBedrock e l se KsA tWF) ; # lower bdyrepor t Ksa tWFT imeSer ies=mapmin imum(KsAtWF) ;Runo f f , I n f i l =accu th resho ld f l ux ,accu th resho lds ta te (Lddmap ,Ra in fa l l ,KsA tWF) ;repor t Runo f fT imeSer ies= t imeou tpu t (p i t s ,Runo f f ) ;repo r t Runo f f=Runo f f *0 .0004444 ; # conve rs ion to cumecs f o r ce l l s i ze o f 40 m (1h r t imes tep )repor t In f i l = In f i l *1 ;repor t To tRa inT imeSer ies=map to ta l (Ra in fa l l ) ;repor t I n f i l T imeSer ies=map to ta l ( In f i l ) ;repor t Recharge=Ksa tBedrock* ( (The ta ) * * (2 *Bva lue+3) ) ; # mm#Through f l ux=The ta * tan( S lopeDeg) ;#Through f l ow=accu f lux ( l ddmap ,Through f lux ) ;BDAhead=(BDs lope* ( (WF+( In f i l -Evap-Recharge*RockD) ) /1000) )+BDin te rc ;#g /cm3 toin f i l *2 .6 mm ahead #wrong?BDAhead= i f (BDAhead g t RockD then RockD e l se BDAhead) ;PorAhead=1- ( (BDa tWF+( (BDAhead-BDatWF) /2 ) ) /RockD) ; # f rac t i ona lWF=WF+( ( In f i l -Evap-Recharge ) /Po rAhead) ; #mmWF= i f (WF l t 0 then 0 e lse WF) ;WF = i f (WF/1000 g t So i lDep th then So i lDep th *1000 e l se WF) ;The ta=The ta+( ( In f i l -Evap -Recharge ) / (So i lDep th *1000) ) ; #m3wate r /m3so i lrepor t The ta= i f (The ta l t 0 then 0 e l se The ta ) ;repor t CanopyS to rage= i f ( In te rcEvap le CanopySto rage then CanopySto rage- In te rcEvap e l se0) ;

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -g rowth - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

75

Growth=(RUE*The ta ) * (0 .95* (1 -exp ( -0 .7 *Lea fArea Index ) ) *PAR) ;#g ramsGrowth=Growth*0 .8 ; #g rowth respMa in tenance=(0 .015* ( ( (A i rTemp-15) /10 ) * *1 .5 ) ) /24 ; #g /g /h rLea fB iomass=Lea fB iomass+(The ta *Growth ) - (Ma in tenance*Lea fB iomass ) ;Roo tB iomass=Roo tB iomass+( (1 -The ta ) *Growth ) - (Ma in tenance*Roo tB iomass ) ;Lea fB iomass= i f (Lea fB iomass le 0 then 0 .001 e lse Lea fB iomass ) ;#g ramsrepor t Roo tB iomass= i f (Roo tB iomass l e 0 t hen 0 .001 e l se Lea fB iomass ) ;#g ramsrepor t Lea fA rea Index=Lea fB iomass /Lea fDens i t y ;Cover= i f (Lea fA rea Index ge 1 then 1 e lse Lea fArea Index ) ;#T ree fa l l=1 - (S lopedeg /90 ) * ;#Cover=1 - (S lopeDeg /90 ) ;# t ree fa l l con t ro l l ed cove r

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -E ros ion - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

repor t E ros ion=K* (Runo f f * *M) * (S lopeDeg**N) * (2 .71* * ( -0 .07*Cover ) ) ; #mm#So i lDep th=So i lDep th - (E ros ion /1000) ;

76

V. APPENDIX 6. SOIL DATA

Table A.1 Soil properties corresponding with the 25 classes of the sampling scheme.

Soil mapzone

slopeclass

vegetationclass

aspectclass

Ksat(cm/s)

BulkDensity

Clay Silt Sand Porosity Erodability

1 1 4 2 0.0012 0.69 0.63 0.20 0.17 0.72 0.192 1 4 1 0.0012 0.69 0.63 0.20 0.17 0.72 0.193 2 4 2 0.0028 0.80 0.57 0.24 0.19 0.68 0.194 2 4 1 0.0028 0.80 0.57 0.24 0.19 0.68 0.195 3 4 2 0.0028 0.85 0.56 0.24 0.20 0.66 0.296 3 4 1 0.0028 0.85 0.56 0.24 0.20 0.66 0.197 3 6 2 0.0028 0.83 0.56 0.24 0.20 0.67 0.218 2 6 2 0.0027 1.00 0.58 0.22 0.20 0.58 0.199 1 6 2 0.0006 0.96 0.60 0.19 0.21 0.62 0.19

10 3 6 1 0.0020 0.94 0.53 0.23 0.24 0.63 0.2111 1 6 1 0.0006 0.96 0.60 0.19 0.21 0.62 0.2112 3 2 2 0.0071 0.74 0.58 0.20 0.22 0.71 0.1913 3 2 0 0.0071 0.74 0.58 0.20 0.22 0.71 0.2114 2 6 1 0.0027 1.00 0.58 0.22 0.20 0.58 0.2115 2 2 2 0.0047 0.85 0.56 0.23 0.21 0.66 0.1916 2 2 1 0.0047 0.85 0.56 0.23 0.21 0.66 0.2117 3 2 1 0.0071 0.74 0.58 0.20 0.22 0.71 0.2118 1 2 2 0.0077 0.87 0.58 0.19 0.23 0.65 0.2119 1 2 1 0.0077 0.87 0.58 0.19 0.23 0.65 0.2120 3 6 0 0.0020 0.94 0.53 0.23 0.24 0.63 0.2921 1 4 0 0.0012 0.69 0.63 0.20 0.17 0.72 0.2922 2 2 0 0.0047 0.85 0.56 0.23 0.21 0.66 0.2923 2 4 0 0.0028 0.80 0.57 0.24 0.19 0.68 0.2924 3 4 0 0.0028 0.85 0.56 0.24 0.20 0.66 0.2925 2 6 0 0.0027 1.00 0.58 0.22 0.20 0.58 0.29

77

W. APPENDIX 7. VEGETATION FIELD MEASUREMENTS

Table A7.1 Leave measurements in Primary Forest Plot. Tambito, Cauca - Colombia

FOREST PLOT

ID number Dry weight Wet weight water storage1.0 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 8.2Total 1363.0 1794.1 431.2

78

Table A7.2. Leave measurements in scanned images from Primary Forest in Tambito,Cauca - Colombia.

FOREST PLOTFile Name Area (cm2) Pixel No. Pixels on leaves Area of leaves in cm2

jrub11a 5550.0 354460.0 105480.0 1651.6jrub13 4644.0 278880.0 144884.0 2412.7jrub9 4654.5 356547.0 137515.0 1795.2

jrub15 7149.0 225330.0 73541.0 2333.2jrub14 7149.0 326849.0 62328.0 1363.3jrub16a 7149.0 320661.0 191354.0 4266.2jrub12 7149.0 225375.0 66752.0 2117.4jrub17 7149.0 299837.0 209934.0 5005.4Total 50593.5 2387939.0 991788.0 21013.1

Table A7.3. Leave measurements in Secondary Forest Plot. Tambito, Cauca - Colombia

SECONDARY FOREST PLOTID number Dry weight Wet weight Water storage

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 1.6

10.0 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

Table A7.4. Leave measurements in scanned images from Secondary Forest inTambito, Cauca - Colombia.

SECONDARY FOREST PLOTFile Name Area (cm2) Pixel No. Pixels on leaves Area of leaves

in cm2Total Dry

weightjrub28 7149.0 346480.0 224872.0 4639.8 271.8jrub27 7149.0 393451.0 137927.0 2506.1 214.9

7146.0 486.7

79

Table A7.5. Pasture leave measurements in Tambito, Cauca - Colombia.

PASTURE PLOTsample Dry weight Wet weight Water storage Area Leaves

(cm2)1.0 6.6 12.5 5.9 184.22.0 7.9 13.4 5.5 140.93.0 7.2 19.8 12.6 291.24.0 6.1 10.0 3.9 112.85.0 4.8 9.6 4.8 153.5

Total 32.5 65.3 32.8 882.5

Table A7.6 Vegetation Parameters for three different Land Use in Cauca - Colombia

Land Use Type Leaves area(m2)

Leavesweight (g)

Water weight (g) Leaf AreaIndex

Leaf Density(g/m2)

Primary Forest 2.1 1363.0 431.2 1.4 649.0Secondary Forest 0.7 486.7 144.4 1.4 685.5Pasture 0.1 32.5 32.8 2.1 368.5

Specific LeafArea (m2/g)

InitialBiomass(g/m2)

Specific WaterRetention (g/m2)

Cover Canopy StorageCapacity (mm)

Primary Forest 0.0015 1804.4 205.3 0.9 0.25Secondary Forest 0.0015 1905.7 203.4 0.9 0.25Pasture 0.0027 59.0 371.9 1.0 0.03

80

Figure A7.1. Primary forest leaves scanned from pictures taken in Tambito, Cauca - Colombia

81

Figure A7.2. Secondary forest leaves scanned from pictures taken in Tambito, Cauca - Colombia

Figure A7.3. Pasture leaves scanned from pictures taken in Tambito, Cauca - Colombia

82

Figure A7.4. Canopy Forest cover scanned from pictures taken in Tambito, Cauca - Colombia

msc dissertation

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