estimation of soil moisture in unsaturated zone and irrigation sceduling

8/8/2019 Estimation of Soil Moisture in Unsaturated Zone and Irrigation Sceduling

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ESTIMATION OF SOIL MOISTURE IN UNSATURATED

ZONE AND IRRIGATION SCHEDULING

A dissertation report submitted

in partial fulfillment of the requirements for the degree of

MASTER OF TECHNOLOGY

(Hydraulics and Water Resources Engineering)

by

Sunil GurrapuRegister No.: 0322667

Under the Guidance of

Dr. K. Varija

Department of Applied Mechanics & Hydraulics

NATIONAL INSTITUTE OF TECHNOLOGY KARNATAKA,(A DEEMED UNIVERSITY)

SURATHKAL, P.O. SRINIVASNAGAR - 575 025MANGALORE, INDIA

JULY - 2005


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i

Department of Applied Mechanics and Hydraulics

NATIONAL INSTITUTE OF TECHNOLOGY KARNATAKA(A DEEMED UNIVERSITY)

SURATHKAL, P.O. SRINIVASNAGAR - 575 025

MANGALORE, INDIA

CERTIFICATE

This is to certify that the dissertation report titled ESTIMATION OF SOIL MOISTURE IN

UNSATURATED ZONE AND IRRIGATION SCHEDULING is being submitted by Mr.

SUNIL GURRAPU, in partial fulfillment of the requirements for the award of the degree of

MASTER OF TECHNOLOGY (Hydraulics and Water Resources Engineering) of N.I.T.K,Surathkal. This is a bonafide record of the work carried out by him under my guidance and

supervision. Further certified that this work has not been submitted for the award of any other

degree or diploma.

(Dr. K. Varija)

Research SupervisorSenior Lecturer


Date

(Dr. A. Vittal Hegde)

Professor & Head


(Round seal of the Department)

This dissertation is accepted/Not accepted

External Examiner Internal Examiner Chairman

Board of Examiners

Date:


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ii

ACKNOWLEDGEMENT

The satisfaction and euphoria, which accompanies the successful completion of any task,

could be incomplete without the expression of gratitude to the people who made it possible with

encouraging guidance. I acknowledge with reverence all those who guided and encouraged me

during this work.

I am deeply indebted to my guide Dr. K. Varija, Sr. Lecturer, Department of Applied

Mechanics and Hydraulics for providing me opportunity to work under her guidance. Her

unflinching support, suggestions and directions have helped in smooth progress of the project

work. She has been a constant source of inspiration in all possible ways for successful completionof my project work.

I acknowledge my sincere gratitude to Dr. A. Vittal Hegde, Professor and Head,

Department of Applied Mechanics and Hydraulics, who has provided me all the facilities of the

department to complete this dissertation work successfully.

Its also my privilege to thank Dr. Lakshman Nandagiri, Assistant Professor,

Department of Applied Mechanics and Hydraulics, for his sincere guidance towards thecompletion of the project.

I also acknowledge the invaluable help rendered by Mr. Balakrishna and all other non-

teaching staff of the Department of Applied Mechanics and Hydraulics, NITK.

Finally, I would like to thank my family and friends for their support extended throughout

my project work. It would have been impossible for me to accomplish this study without their

support.

SUNIL GURRAPU


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iii

ABSTRACT

The vadose zone is an intrinsic part of the hydrological cycle, essentially controllinginterrelationships between precipitation, infiltration, surface runoff, evapo-transpiration and

groundwater recharge. The vadose zone regulates the transfer of water from the land surface togroundwater and vice versa. Vadose zone is a great reservoir of water, where the water isstored in form of soil moisture. This soil moisture is very much essential for proper growth ofcrops or plants.

Estimation of soil moisture content available in the unsaturated zone is very muchessential for efficient use of the available water for irrigation supply. As the water resourcesavailable for mankind are very much limited, utilization of this resource should be properlymanaged. The study of water flow in unsaturated zone helps us in scheduling the irrigationwater application to agricultural fields.

In the present study, efforts have been put to estimate the soil moisture content or soilwater in the unsaturated zone until the maximum root depth. The crops that were considered inthe present study are groundnut and dry beans. Soil moisture content was estimatedsuccessfully using well established agro-hydrological model SWAP developed by Wageningenuniversity, the Netherlands. Soil moisture content was also estimated using water budgettechnique. The obtained results from SWAP model and from water budget technique arecompared with the actual soil moisture content. From this comparison it was observed thatSWAP model can simulate soil moisture effectively with some limitations. These estimatedvalues of soil moisture from SWAP model were in turn used to perform irrigation scheduling.Irrigation water requirement of the crop were simulated using a program written in C basedon water balance. The results from this program are compared with the actual appliedirrigation water.

Keywords: Unsaturated zone, soil moisture content, SWAP model, Irrigation scheduling,water balance.


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iv

CONTENTS

Certificate i

Acknowledgements ii

Abstract iii

Contents iv

List of figures vi

List of tables vii

List of Notations ix

CHAPTER 1 INTRODUCTION

1.1 GENERAL 1

1.2 UNSATURATED ZONE 1

1.3 IRRIGATION SCHEDULING 2

1.3 NEED FOR IRRIGATION SCHEDULING 3

1.4 OBJECTIVES OF THE PRESENT STUDY 3

1.5 ORGANISATION OF THE THESIS 3

CHAPTER 2 LITERATURE REVIEW

2.1 GENERAL 52.2 SWAP MODEL 5

2.2.1 Advantages of SWAP model 7

2.2.2 Disadvantages of SWAP model 7

2.3 IRRIGATION SCHEDULING STRATEGIES 8

2.3.1 Full Irrigation 8

2.3.2 Deficit Irrigation 8

2.4 METHODS TO KNOW WHEN TO IRRIGATE 92.4.1 Plant indicators 9

2.4.2 Soil indicators 10

2.4.3 Water budget technique 11

2.5 REVIEW OF LITERATURE 11


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CHAPTER 3 STUDY AREA AND METHODOLOGY

3.1 GENERAL 16

3.2 DETAILS OF EXPERIMENTAL SITE 16

3.3 INPUT DATA INFORMATION 173.4 METHODOLOGY 18

3.4.1 Determination of Actual evapotranspiration (ETa) 18

3.4.1.1 Determination of reference evapotranspiration (ETo) 18

3.4.1.2 Determination of crop evapotranspiration (ETc) 19

3.4.1.3 Determination of actual evapotranspiration (ETa) 22

3.4.2 Determination of drainage or water flux 23

3.4.2.1 Campbell model 23

3.4.1.2 Van-Genuchten model 24

3.4.1.3 Drainage calculation 25

3.4.3 Soil moisture estimation 25

3.4.3.1 SWAP model 25

3.4.3.2 Water Budget Technique 29

3.4.4 Irrigation Scheduling 32

3.4.4.1 Determination of irrigation water requirement (IWR) 33

CHAPTER 4 RESULTS AND DISCUSSIONS

4.1 GENERAL 34

4.2 SOIL MOISTURE ESTIMATION 34

4.2.1 Dry beans 34

4.2.2 Groundnut 41

4.2.2.1 Modified input data 50

4.3 IRRIGATION SCHEDULING 51

4.31 Example for validation 51

4.3.2 Dry beans crop 52

4.3.3 Groundnut crop 53


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vi

CHAPTER 5 CONCLUSIONS

5.1 GENERAL 55

5.2 OVERALL CONCLUSIONS 55

5.3 LIMITATIONS OF THE PRESENT STUDY 56

5.4 SCOPE FOR THE FUTURE WORK 57

REFERENCES 58

BIBLIOGRAPHY 60

APPENDIX A 62

APPENDIX B 70

APPENDIX C 79

APPENDIX D 81

BIO-DATA 87


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vii

LIST OF FIGURES

Figure

No.Description of the figure

Page

No.

2.1 A Schematized overview of the modeled system in SWAP model 6

2.2 Crop production curve 9

3.1 Typical variation of rainfall in the study area for the year 1998 17

3.2 Plot showing the values of crop coeffecient (Kc) for grounnut crop (2nd

June1998 - 12th September1998) for all the growth stages

21

3.3 Plot showing the values of crop coeffecient (Kc) for Dry Beans crop (1st

November1998 - 28th February1999) for all the growth stages

21

3.4 Spatial and temporal discretization used to solve Richards equation 28

3.5 Control volume giving details of input and output components of water

budget

30

4.1 Plot of soil moisture content measured and simulated using SWAP model for

Dry Beans crop (1st November1998 - 28th February1999) at a depth of 20 cm

35


Dry Beans crop (1st November1998 - 28th February1999) at a depth of 35 cm

36

4.3 Plot of soil moisture content measured and simulated using SWAP model andwater budget technique for Dry Beans crop (1st Nov1998 - 28th Feb1999) at a

depth of 50 cm

37

4.4 Variation of soil moisture content simulated using SWAP model at all depths

for the entire crop period of Dry Beans crop (1 st Nov 1998 - 28th Feb1999)

38

4.5 Plot of soil water observed and simulated using SWAP model for Dry Beans

crop (1st Nov 1998 - 28th Feb 1999) at a depth of 20 cm

39



39



40

4.8 Variation of soil water simulated using SWAP model at all depths for the

entire crop period of Dry Beans crop (1st Nov 1998 - 28th Feb 1999)

40


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Groundnut crop (2nd June1998 - 12th September1998) at a depth of 20 cm

42

4.10 Plot of Actual evapotranspiration measured and simulated using SWAP

model for Groundnut crop (2nd June1998 - 12th September1998)

43

4.11 Plot of observed and simulated deep percolation from SWAP model occurring

at a depth of 50 cm for Groundnut crop (2nd June 1998 - 12th September 1998)

44

4.12 Plot showing the rainfall data and simulated drainage values from SWAP

model occurring at a depth of 50 cm for Groundnut crop (2nd June 1998 - 12th

September 1998)

45

4.13 Plot of soil moisture values measured and simulated using SWAP model for

Groundnut crop (2nd June1998 - 12th September1998) at a depth of 35 cm

46

4.14 Plot of soil moisture values measured and simulated using SWAP model andwater budget technique for Groundnut crop (2nd June 1998 - 12th September

1998) at a depth of 50 cm

47

4.15 Variation of soil moisture content simulated using SWAP model at all depths

for the entire crop period of Groundnut crop (2nd June 1998 - 12th September

1998)

47

4.16 Plot of soil water measured and simulated using SWAP model for Groundnut

crop (2nd June1998 - 12th September1998) at a depth of 20 cm

48



49



49

4.19 Variation of soil water simulated using SWAP model at all depths for the

entire crop period of Ground nut crop (2nd June 1998 12th September 1998)

50

4.20 Plot of showing observed and simulated soil moisture from SWAP model for

Groundnut crop (1st June 1998 12th September 1998) at a depth of 50 cm

51

4.21 Plot showing the actual and simulated irrigation water requirement (IWR) by

the dry beans crop (1st Nov 1998 28th Feb 1999) for the entire crop period

53

4.22 Plot showing the actual and simulated irrigation water requirement (IWR) by

the groundnut crop (1st June 1998 12th September 1999) for the entire period

54


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ix

LIST OF TABLES

Table

No.Description of Table

Page

No.

2.1 List of some plant based indicators to know when to irrigate 10

2.2 List of some soil-based indicators to know when to irrigate 10

3.1 Crop coeffecients (Kc) and mean maximum plant heights for non-stressed

crops

20

3.2 Ranges of maximum effective rooting depth and soil water depletion factor

(p) for no stress for common crops

23

4.1 Values of excess rainfall simulated from SWAP model and calculated usingSCS curve number technique

43

4.2 Details of input data for example 52

4.3 Results obtained after running the C program for example 52

A-1 Simulated values of soil moisture, pressure head, water flux etc. from SWAP

model for Groundnut crop at all observed depths (1st June 12th September

1998)

62

B-1 Simulated values of soil moisture, pressure head, water flux etc. from SWAP

model for Dry beans crop at all observed depths (1st November 1998 28th

February 1999)

70

D-1 Details of Irrigation scheduling (Output from C Program) for Groundnut

crop (1st June 12th August 1998)

81

D-2 Details of Irrigation Scheduling (Output from C Program) for Dry Beans

crop (1st November 1998 28th February 1999)

84


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LIST OF NOTATIONS

Notation Description

ETo Reference Evapotranspiration [mm/day]

ETc Crop evapotranspiration [mm/day]

ETa Actual evapotranspiration [mm/day]

Rn Net radiation at the crop surface [MJ/m2/day]

G Soil heat flux density [MJ/m2/day]

T Mean daily air temperature at 2 m height [oC]

u2 Wind speed at 2 m height [m/sec]

es Saturation vapour pressure [kPa]

ea Actual vapour pressure Slope of the saturation vapour pressure temperature relationship [kPa/oC]

Psychometric constant [kPa/oC]

Kun Unsaturated Hydraulic Conductivity [mm/day]

Ksat Saturated Hydraulic Conductivity [mm/day]

Sw Effective saturation

n, m Van-Genuchten model empirical shape factors

Van-Genuchten model shape parameterKc Crop coeffecient

Soil moisture content [cm3/cm3]

i-1 Soil moisture content on previous day [cm3/cm3]

FC Soil moisture content at field capacity [cm3/cm3]

PWP Soil moisture content at permenant wilting point [cm3/cm3]

s Saturated moisture content [cm3/cm3]

r Residual moisture content [cm3

/cm3

]SWa Total available soil water [mm]

St Actual available soil water [mm]

SWt-1 Soil water on previous day [mm]

SWt Soil water on the present day [mm]


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DRZ Depth of root zone [mm]

h Soil matric potential or soil water pressure head [cm]

he Air entry matric potential [cm]

Campbell pore size distribution parameter

q Soil water flux density [cm/day]

K(h) Hydraulic conductivity [cm/day1]

z vertical coordinate [cm]

t Time [days]

Sa Soil water extraction rate by plant roots [cm3/cm3/day]

C Water capacity ( h / ) [cm-1]

p Depletion factor

pTable Depletion factor from table given in FAO Irrigation and Drainage paper No. 56P Precipitation [mm]

Pe Effective rainfall [mm]

SR Surface runoff [mm]

DP Deep percolation [mm]

I Irrigation [mm]

IWR Irrigation water requirement [mm]


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Estimation Of Soil Moisture In Unsaturated Zone And Irrigation Scheduling, NITK 2005

CHAPTER 1

INTRODUCTION

1.1 GENERAL

Knowledge of water and solute movement in the variably saturated soil near the earth

surface is essential to understand man's impact on the environment. Top soils show the largest

concentration of biological activity on earth. Water movement in the upper soil determines the

rate of plant transpiration, soil evaporation, runoff and recharge to the groundwater. In this way

unsaturated soil water flow is a key factor in the hydrological cycle and energy cycle. Due to the

high solubility of water, soil water transports large amounts of solutes, ranging from nutrients to

all kind of contaminations. Therefore an accurate description of unsaturated soil water movement

is essential to derive proper management conditions for vegetation growth and environmental

protection in agricultural and natural systems.

1.2 UNSATURATED (VADOSE) ZONE

Subsurface formations containing water may be divided vertically into several horizontal

zones according to how large a portion of the pore space is occupied by water. Essentially, we

have a zone of saturation in which all the pores are completely filled with water, and an

overlaying zone of aeration in which the pores contain both gases (mainly air and water vapour)

and water. The latter zone is called the unsaturated zone or vadose zone. The vadose zone is an

intrinsic part of the hydrologic cycle, essentially controlling interrelationships between

precipitation, infiltration, surface runoff, evapo-transpiration and groundwater recharge. The

vadose zone serves many functions that are relevant at the regional scale. They can be

summarized as follows:

To separate precipitation and applied irrigation water into infiltration, runoff, evapo-

transpiration, interflow and groundwater recharge;

To store and transfer water in the root zone between the atmosphere above and the

deeper vadose zone or groundwater below, including interflow;


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Estimation Of Soil Moisture In Unsaturated Zone And Irrigation Scheduling, NITK 2005 2

To store and transfer water in the deep vadose zone, that is, between the root zone

above and groundwater below;

To store, transfer, filter, adsorb, retard and attenuate solutes and contaminants before

these reach the ground water.

Sometimes the term soil water is used for the water in vadose zone. For analytical studies

on soil moisture regime, critical review and accurate assessment of the different controlling

factors is necessary. The controlling factors of soil moisture may be classified under two main

groups viz. climatic factors and soil factors. Climatic factors include precipitation data

containing rainfall intensity, storm duration, inter-storm period, temperature of soil surface,

relative humidity, radiation, evaporation, and evapo-transpiration. The soil factors include soil

matric potential and water content relationship, hydraulic conductivity and water contentrelationship of the soil, saturated hydraulic conductivity, and effective medium porosity. Besides

these factors, the information about depth to water table is also required.

1.3 IRRIGATION SCHEDULING

Irrigation scheduling is the process of determining when to irrigate and how much water

to apply per irrigation. Proper scheduling is essential for the efficient use of water, energy, and

other production inputs, such as fertilizer. It allows irrigations to be coordinated with other

farming activities including cultivation and chemical applications. Among the benefits of proper

irrigation scheduling are: improved crop yield and/or quality, water and energy conservation, and

lower production costs.

Dry land irrigation and agriculture depend on the management of two basic natural

resources, soil and water. Soil is the supporting structure of plant life and water is essential to

sustain plant life. The wise use of these resources requires a basic understanding of soil and water

as well as the crop. The available water capacity and characteristics of soils are critical to water

management planning for irrigation and dry land crops. Soil water holding characteristics are

important for irrigation system selection, irrigation scheduling, crop selection, and ground water

quality. Soil water content in the crop's active root zone and available water capacity are the key

indicators for applying the right amount of irrigation at the right time. Some of the water in soil is

retained and some moves through the soil. It moves readily downward after an irrigation or rain


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and eventually reaches the ground water. It is taken up by plant roots, moves through the plant to

the leaves, and transpires to the atmosphere. Water also moves toward the soil surface where it

evaporates directly into the atmosphere. Textural, structural, and organic matter characteristics

determine how water is held in soils.

1.4 NEED FOR IRRIGATION SCHEDULING

Irrigation scheduling is one of the managerial activities that aim at effective and efficient

utilization of water. The growing competition for water between agricultural and non-agricultural

sectors has increased the concern for the sustainability of the irrigated agricultural systems. The

need for increasing agricultural production demands on increase in the irrigated area regardless of

the water resources availability for irrigation. This necessitates an efficient and effective

utilization of water through various water conserving methods.

Irrigation scheduling is one of the means of conserving water, which helps in decision

making in allocation of quantity and timing of water supply commensurate with crop needs. It is

the key activity that has the potential to improve the performance of the crop productivity, equity

and stability. With increasing adoption of high yielding varieties, which are responsive to

irrigation, interest in irrigation scheduling of crops is growing steadily.

1.5 OBJECTIVES OF THE STUDY

To validate the SWAP Agro hydrological model

To estimate the moisture content and hence the soil water available in unsaturated

zone up to maximum root depth of the crop

To determine irrigation water requirement of two row crops Groundnut and Dry beans

1.6 ORGANIZATION OF THE THESIS

This thesis has been organized in five different chapters.

o Chapter one gives introduction to the present study. It tells us the importance of the

unsaturated zone, processes taking place in this zone etc. It also briefly explains why there


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is a need to study about the processes taking place in unsaturated zone. Objectives of the

present study are also prescribed in this chapter.

o Chapter two gives the details of literature that has been reviewed for the present study. All

the important literatures that are reviewed to clearly understand the field of study and to

finalize the objectives of the present study are cited in this chapter.

o Chapter three gives the details of the present study area. It clearly specifies all the details

of the study area like latitude, longitude, altitude etc. This chapter also describes the

methodology that has been followed for the present study.

o Results and discussions for the present study are given in chapter four.

o Chapter five gives the conclusions made from the present study and scope for the future

work.


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CHAPTER 2

LITERATURE REVIEW

2.1 GENERAL

The water management is being given the top priority in the present era, which would

have been all incomplete without a detailed study of soil water. Several attempts have been done

from the past to contribute towards the estimation of the soil water by the best methods. Some of

these have been reviewed here. SWAP model which has been used in the project is one of the

most sophisticated agro-hydrological models. This model has been used in various parts of the

world and its applications are published in various journals. Those are reviewed and they are

quoted below. And also the literature on various other models that supports irrigation scheduling

has been quoted here. Brief descriptions of SWAP model is also given as follows.

2.2 SWAP MODEL

SWAP is a computer model that simulates vertical transport of water, solutes and heat in

variably saturated top soils and cultivated soils during whole growing seasons. The program is

designed for integrated modeling of Soil Atmosphere Plant System. Transport processes at field

scale level and during whole growing seasons are considered. System boundaries at the top are

defined by the soil surface with or without a crop and the atmospheric conditions. The lateral

boundary simulates the interaction with surface water systems. The bottom boundary is located in

the unsaturated zone or in the upper part of the groundwater and describes the interaction with

regional groundwater.

The program has been developed by Alterra and Wageningen University. The model

offers a wide range of possibilities to address both research and practical questions in the field ofagriculture, water management and environmental protection. SWAP was developed by the

University of Wageningen and the Winand Staring Centre in Wageningen, the Netherlands. The

first version of SWAP, called SWATR, was developed more than 20 years ago (Feddes, Kowalik,

and Zaradny 1978).


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The core of the SWAP model exists of implementations of mathematical descriptions of

soil water flow, solute transport, soil temperatures, with special emphasis on soil heterogeneity.

A schematized overview of the modeled system is given in Fig. 2.1.

Fig. 2.1 A Schematized overview of the modeled system in SWAP model

The theory of the processes simulated by the model is extensively described by Van Dam

et al. (1997) and Van Dam (2000). This model has been applied world wide for obtaining various

objectives some of which are,

o Field scale water and salinity management

o Irrigation scheduling

o Transient drainage conditions

o Plant growth affected by water and salinity

o Pesticide leaching to ground water and surface water

Atmosphere Precipitation

Transpiration

Surface runoff

Soil evaporation

Deep Ground water

Drainage/subsurfaceinfiltration

Drainage/subsurfaceinfiltration

Surface waters

- Transport ofSoil waterSoil heatSoil solute- Influenced byWater repellencySwelling and shrinkingHysteresis

Integrated modeling of Soil

Water Atmosphere Plant

Unsaturated Zone

Saturated zone

Plant


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o Regional drainage from top soils towards different surface water systems

o Optimization of surface water management

o Effects of soil heterogeneity

In the present study latest version of SWAP i.e. version 3.0.3 has been used. The maindifferences between the latest version SWAP 3.0.3 and the previous versions are:

o Source code was restructured (input, output, timing, error handling)

o Snow and frost options were implemented

o Macro Pore flow was extended

o Extended options for interaction with water quality models

o Extended options for bottom boundary conditions

o Interception according to Gash has been added

o Runon is facilitated for sloping areas

2.2.1 Advantages of SWAP model

o SWAP model can simulate soil moisture values, pressure head, water flux, solute

flux simultaneously.

o SWAP model solves Richards equation numerically for simulating soil water

flux.

o Output files obtained after running the model gives explains us clearly about how

each and every component of the water balance vary with respect to time.

o SWAP model can simultaneously be used for obtaining the irrigation scheduling,

given the necessary conditions.

o It has been applied in many parts of the world and almost all its application has

been successful.

o This model runs in different modules some of which are optional. So, estimationof that particular parameter which is not required can be eliminated.

2.2.2 Disadvantages of SWAP model

o This is highly parameterized model, which makes it bit complicated.


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o This model is not user friendly. Giving input to this model is very troublesome, as

the input has to be given in different files in different formats.

o There is no graphical interface for this software which makes it difficult for the

interpretation of results. But these results can be edited as ASCI files and graphs

can be plotted using MS Excel spread sheets.

o This model has been developed in the Netherlands, where the groundwater levels

are very shallow. Hence, there is a chance of underestimation of some of the

quantities such as runoff, soil moisture etc. where the groundwater level is very

deep.

2.3 IRRIGATION SCHEDULING STRATEGIES

Irrigation schedules are designed to either fully or partially provide the irrigation

requirement. These strategies are discussed as follows

2.3.1 Full irrigation

Full irrigation involves providing the entire irrigation requirement and results in

maximum production. Fig 2.2 clearly explains this point. Exceeding full irrigation reduces crop

yields by reducing soil aeration and restricting gas exchange between the soil and atmosphere.

Full irrigation is economically justified when water is readily available and irrigation costs are

low. It is accomplished by irrigating to minimize the occurrence of plant stress.

2.3.2 Deficit irrigation

Partially supplying the irrigation requirement, a practice that has been called deficit

irrigation, reduces yield as smaller amounts of water, energy, and other production inputs are

used to irrigate the crop. Deficit irrigation is economically justified when reducing water

applications below full irrigation causes production costs to decrease faster than revenues

decline. Application levels can be reduced below full irrigation until the slope of the production

function (fig. 2.2) is such that the decrease in revenue due to an incremental reduction in water

application equals the accompanying decline in production costs.


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Normally it is necessary to relate plant parameters to soil water content to determine the amount

of irrigation. Required instruments and/or procedures for several plant indicators are listed in

table 2.1.

Table 2.1 List of some plant based indicators to know when to irrigate

Observed or measured parameter Required instruments or procedures

Appearance Eye

Leaf temperature Non-contacting thermometers

Leaf water potential Pressure chamber or thermocouple

psychrometer

Stomatal resistance Diffusion porometer

2.4.2 Soil indicators

Soil-based irrigation scheduling involves determining the current water content of the

soil, comparing it to predetermined minimum water content and irrigating to maintain soil water

contents above the minimum level. The minimum water content is often varied according to

growth stage, especially for deficit irrigation schedules. Soil indicators of when to irrigate also

provide data for estimating the amount of water to apply per irrigation.

The soil water contents are determined either by direct measurements or inference frommeasurements of other soil parameters such as soil water potential or electrical conductivity.

Several common methods of estimating soil water contents are listed in table 2.2 which also gives

us details of various soil indicators.

Table 2.2 List of some soil-based indicators to know when to irrigate

Observed or measured parameter Required instruments or procedures

Appearance and feel Hand probe

Gravimetric sampling Sample cans, soil agar, scale and oven

Electrical resistance Porous blocks

Soil matric potential Tensiometers

Soil matric potential Porous (ceramic) blocks

Neutron scattering Neutron probes and access tubes


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2.4.3 Water budget technique

The water budget technique of determining when to irrigate is similar to soil indicators

method. This is simply based on the water balance technique. The method was found to be better

and reliable from the studies done. This method is clearly explained in the following chapter 3.

2.5 REVIEW OF THE LITERATURE

T. Hess (1994): A real time irrigation scheduling computer package for use on farms is

described. The package comprises four models: a reference crop evapo-transpiration model, an

actual evapo-transpiration model, a soil water balance model and an irrigation forecast model.

The models used have been shown to produce reliable estimates of the soil water balance.

However, the predictions are sensitive to the accuracy of the input data measured on the farm.This paper summarizes the experience of applying such a program to supplementary irrigation in

the United Kingdom.

W. Trimmer et al. (1994): In this paper, the author described how the knowledge of crop water

use is important for irrigation scheduling. With basic knowledge of soil type and crop water use

information, an irrigator can easily learn to schedule more scientifically and to anticipate

irrigation demands. Computer programs for irrigation scheduling have been developed to help

provide timely and precise scheduling techniques. Irrigation consulting and scheduling services

are available in many areas to perform the technical tasks required to schedule irrigations in order

to save both water and energy.

Amor Valeriano M. Ines et al. (2001): The performance of the decision support system for

agro-technology transfer (DSSAT) and the soil water atmosphere plant (SWAP) was studied

under an acid sulphate soil. The comparison of these models was done as a prerequisite to the

selection of an appropriate model, which is capable of simulating water management scenarios,

water balance and crop growth, to be coupled with an adaptive optimization algorithm that can be

used to explore water management options. The dates of the development stages could be

properly simulated in DSSAT. The model correctly simulated these dates while SWAP

performed well in its prediction. Along the growth process, DSSAT predicted that there was no

water stress while SWAP simulated water and oxygen stress. The soil water balance calculation


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in SWAP is more physically based than in DSSAT. SWAP solves the Richards equation in the

transport of soil water. SWAP simulates the runoff by considering a maximum sill height and a

resistance factor, while DSSAT uses the modified United States Department of Agriculture-Soil

Conservation Service (USDASCS) method. The big advantage of DSSAT over SWAP is its crop-

nitrogen interaction. SWAP however, can simulate the movement and degradation of this element

by assuming it as solute.

Asad Sarwar et al. (2001): Here an attempt to study the long term effects of irrigation water

conservation on crop production and environment in semi arid areas. The agro hydrological

model SWAP is used to investigate possible water reductions for wheat and cotton crops under

shallow water table conditions prevailing in the fourth drainage project in Punjab, Pakistan. The

simulations were performed for both drained and un-drained conditions considering three

different irrigation water qualities. The overall objective was to save good quality irrigation

water. The results indicate that when good-quality canal water is available, a reduced application

to wheat (195mm) and cotton (260mm) will keep the soil healthier under both drained and un-

drained conditions. However, they say that this is only applicable to the areas where proper

subsurface drainage systems are present.

Coen J. Ritsema1 et al. (2001): In this paper authors made an attempt to investigate water flow

and solute transport processes in a water repellent sandy soil, and to introduce and apply newmodeling approaches. Automated TDR measurements revealed that preferential pathways

develop rapidly during severe rain storms, causing infiltrating water to be preferentially

transported to the deeper subsoil. Simulations with a 2-D, numerical finite element flow and

transport model indicate that preferential flow paths will only form during infiltration into dry

water repellent soils, i.e. in the range below the so-called critical soil water content. The process

of preferential flow and transport has been incorporated in the well-known SWAP model also,

and applied to field data of tracer transport through a water repellent sandy soil in the

Netherlands. Results indicate early arrival times of bromide in the subsoil in case preferential

flow is taken into account.

Geoff Kite et al. (2001): In this paper author discusses the integrated basin modeling. Two

models which are integrated are SLURP and SWAP models. SLURP (Semi-Distributed Land


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Use-Based Runoff Processes) is a conceptual model that, although normally used in semi-

distributed form, is capable of being used as a fully distributed hydrological model (Kite 1997).

SWAP (Soil-Water-Atmosphere-Plant) is a one-dimensional physically based model for water,

heat and solute transport in the saturated and unsaturated zones. The SLURP and SWAP models

have been applied at three different scales: basin, irrigation system and field. The main objectives

of applying the models are to understand processes and to evaluate current productivity and

alternative scenarios.The use of these models enabled a more complete investigation of the true

performance of irrigation schemes under various water management and water availability

options. The results of the models could be used to test and apply new methods to increase the

productivity of water through better management of irrigation and water-basin system.

S. Lorentz et al. (2001): In this paper various methods of determining hydraulic characteristics

of soil were discussed. An understanding of hydrological processes is essential for assessing

water resources as well as the changes to the resources caused by changes in the land use or

climate. Moreover, hydrological simulation models which represent hydrological processes can

only be used to predict the consequences of land use and climate change successfully, if they are

built on a sound understanding of the processes. Various methods of finding out the key

components of hydrological cycle are described in this paper. Key components for example can

be mentioned such as like hydraulic conductivity (saturated & unsaturated), matric potential,

infiltration etc. Various methods like Van-Genuchten model, Campbell model etc. are discussed

in this paper for finding unsaturated hydraulic conductivity. Overall, this paper gives us clear

picture of various methods for finding out the hydraulic characteristics of soil.

Peter Droogers et al. (2002): A comparative study of hydrological modeling and remote sensing

was done to check the irrigation performance. Remote sensing and a hydrological model were

applied to an irrigation project in western turkey to estimate the water balance to support water

use productivity analyses. Actual evapo-transpiration for an irrigated area in western turkey was

calculated using the surface energy balance algorithm for land (SEBAL) remote sensing and

algorithm for two land set images. The hydrological model soil-water-atmosphere-plant (SWAP)

was setup to simulate the water balance for the same area, assuming a certain distribution in soil

properties, planting dates and irrigation practices. A comparison between evapo-transpiration

determined from SEBAL and from SWAP was made and differences were minimized by


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adapting the distribution in planting date and irrigation practice. The innovative methodology

diminishes the need of field data and combines the strong points of remotely sensed techniques

and hydrological models.

J. G. Kroes et al. (2003): This is a manual on latest version of SWAP model i.e. SWAP 3.0.3.This manual describes the theoretical background and modeling concepts that were used for soil

water flow, solute transport, heat flow, evapo-transpiration, crop growth, multi-level drainage and

interaction between field water balance and surface water management. The core of the SWAP

model exists of implementations of mathematical descriptions of soil water flow, solute transport

and soil temperatures, with special emphasis on soil heterogeneity. The annexes contain

information on values for input parameters, such as soil hydraulic functions, critical pressure

head values of the root water extraction term and salt tolerance data. Furthermore the annexes

contain printed versions of input and output files that belong to an example which is distributed

with the model.

W. G. M. Bastiaanssen et al. (2003): This paper discusses how far we have progressed in

inserting mans irrigation and drainage wisdom into soil water flow models and bringing it back

out. They discuss about the necessity of computer models to understand the processes taking

place in unsaturated zone for better irrigation scheduling. Unfortunately, computer models for

prediction and better understanding of unsaturated soil water flow processes have low operationalfocus, especially in many irrigation countries where they are most needed. Advanced models

have the potential to contribute to the solution of relatively complex problems, provided that field

data are available to calibrate and run them. Calibration techniques, especially with the help of

GIS and remote sensing, have progressed rapidly, but the required level of expertise tends to

make the application of sophisticated tools highly dependent on modeling experts. The likelihood

of adoption by a broader user community will increase as models become more user- and data-

friendly and heterogeneity-aware. Finally they say that its the time to formulate and market the

unsaturated-zone model as a necessary ingredient to the solution of crop water production

problems and the time to equip users around the globe.

M. T. Van Genuchten et al. (2004): This paper discusses the integrated modeling of vadose-

zone flow and transport processes. A large number of conceptual models are now available to


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make detailed simulations of transient variably-saturated water flow, heat movement and solute

transport in the subsurface. In this paper they have highlighted four examples illustrating such

advances: (1) coupling physical and chemical processes, (2) simulating colloid and colloid-

facilitated transport, (3) integrated modeling of surface and subsurface flow processes, and (4)

modeling of preferential flow in the subsurface. The examples show that improved understanding

of underlying processes, continued advances in numerical methods, and the introduction of

increasingly powerful computers now permit us to make comprehensive simulations of the most

important coupled, nonlinear physical, chemical and biological processes operative in the

unsaturated zone.


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CHAPTER 3

STUDY AREA AND METHODOLOGY

3.1 GENERAL

Two row crops namely Ground nut and dry beans were studied during 1998 -1999 at

Indian Institute of Science campus Bangalore, Karnataka state, India. It lies between Latitude

1258 N, Longitude 7735 E, with an altitude of 930m above M.S.L. The soil in this area is

sandy loam and the climate is sub humid.

3.2 DETAILS OF THE EXPERIMENTAL SITE

The data for the present study was taken from the experiments already carried out in

1998. The experiments were conducted in a plot prepared particularly for the experiments in IISc,

Bangalore campus. This plot is of size 26.6m x 4.8m. Any subsurface lateral flow from the

experimental plot is arrested by constructing a concrete wall on all sides of the plot. Therefore, all

the soil water in the unsaturated zone flows vertically downwards. There might have been lateral

flow within the plot which can be neglected as the field plot is very small. The irrigation water

was supplied from an over head tank which is at a height of 10m. The irrigation was done by

surface spreading roughly on judgment basis of experience. There was no separate arrangement

like tensiometers to know the exact amount of irrigation water to be provided. The crop height

was measured in the field.

The brief details of the experiments are quoted here. Groundnut (monsoon crop) was

grown in an area of 7.03m x 4.3m and dry beans (non-monsoon crop) were grown in an area of

3.39m x 4.3m. The normal annual rainfall of the district calculated for the period 1901-70 is 817

mm (Directorate of Economics and Statistics, 1992). The soil present in the area is sandy loamand the climate of the area is sub-humid.

Crop period of ground nut is from 1st June 1998 to 12th September 1998 where as the crop

period of dry beans is from 1st November 1998 to 3rd March 1999. These two crops were

continuously monitored during their crop periods and the necessary readings were taken. Field


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capacity of Sandy loam soil is 0.27cm3/cm3 and permanent wilting point is 0.11cm3/cm3 and bulk

density is about 1.54g/cc. The plot was initially cleaned, ploughed and manured to required

depths. Before sowing, the soil was watered. The plot was leveled to zero slopes. Manual

weeding was done and insecticides were applied as per requirements at various stages of the crop.

Soil evaporation was obtained from Class A pan installed near the experimental site. Soil

moisture measurements were taken up to 1.35m depth at intervals of 15cm starting from 20cm

depth from the Ground level. The measurements were made using a neutron probe at an interval

of 3 to 4 days.

3.3 INPUT DATA INFORMATION

The measured Daily rainfall figures were obtained from the meteorological station,

Gandhi Krishi Vigyan Kendra (GKVK), University of Agricultural sciences, Bangalore situated

at a distance of 15Km from the site. Typical rainfall variation in the region is shown in fig 3.1.

The data required for the estimation of potential evapo-transpiration (PET) was obtained from the

records of the meteorological station of University of Agricultural sciences, GKVK campus

(Latitude 1258 N, Longitude 7735 E, altitude of 930m above M.S.L.).

Fig 3.1 Typical variation of rainfall in the study area for the year 1998

Rainfall variation

0

50

100

150

200

250

300

350

400

0 1 2 3 4 5 6 7 8 9 10 11 12 13

Month

Rainfall(mm)


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3.4 METHODOLOGY

In the present study, the main objective is to estimate soil moisture present in unsaturated

zone until crop root depth. These soil moisture values were used to perform irrigation scheduling.

For this purposes SWAP model has been used to estimate soil moisture. Knowing the observedsoil moisture content in the field on the first day of sowing, soil moisture content on all the other

days of crop period were simulated using the water budget technique. All these calculations are

done in a Microsoft Excel spread sheet considering all the inputs, outputs and storages in the

control volume. C Program has been written for irrigation scheduling based on water balance

technique. Using this C program the amount of water to be applied per irrigation is obtained.

As discussed above, the data for the present study was collected from an experimental

plot near GKVK, Bangalore. All the required meteorological data was collected from the GKVK

meteorological station, Bangalore. The available observed field data from the experimental site

are soil moisture contents at various depths. These soil moisture values are calculated knowing

the neutron count obtained from neutron probe. These soil moisture values are compared with the

soil moisture values estimated using the SWAP model.

3.4.1 Determination of Actual Evapotranspiration (ETa)

3.4.1.1 Determination of reference evapotranspiration (ETo)

Using the collected meteorological data from GKVK meteorological station reference

evapotranspiration has been calculated using Penman-Montieth equation recommended by FAO.

Equation 3.1 describes the Penman-Montieth equation.

(3.1)

Where,

ETo Reference evapotranspiration [mm/day]

Rn Net radiation at the crop surface [MJ/m2/day]

G Soil heat flux density [MJ/m2/day]

( ) ( )

( )234.012273

900408.0

u

ae

seu

TG

nR

oET ++

+

+

=


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T Mean daily air temperature at 2 m height [C]

u2 Wind speed at 2 m height [m/sec]

es Saturation vapour pressure [kPa]

ea Actual vapour pressure [kPa]

(es-ea) Saturation vapour pressure deficit [kPa]

- Slope of the saturation vapour pressure temperature relationship [kPa/C]

- Psychometric constant [kPa/C]

The (average) daily net radiation expressed in Mega Joules per square meter per day

(MJ/m2 /day) is required. These data are not commonly available but can be derived from the

(average) daily actual duration of bright sunshine [hours/day] measured with a (Campbell-

Stokes) sunshine recorder. The procedure of calculating net radiation from the available netradiation data has been clearly explained in FAO irrigation and drainage paper no. 56.

3.4.1.2 Determination of crop evapotranspiration

Crops unavoidably use large quantities of water. More than 98% of the water absorbed by

the roots of irrigated crops is transpired as water vapor during the course of the season. This

process is necessary for photosynthesis. Therefore, any measures to reduce water loss through the

leaves (i.e. to reduce transpiration) will also reduce photosynthesis and overall crop yields. Sinceirrigated agriculture uses such a large amount of fresh water, it is essential that water be used

wisely and efficiently. However, irrigation management can only be effective if the amount of

water used by the crop is known. A simple and accurate way to measure crop water usage or crop

evapotranspiration (ETc) is by indirectly using reference evapotranspiration (ETo) from local

weather stations, and a reliable crop coefficient (Kc).

ETo is calculated using Penman-Montieth equation as discussed earlier. Kc values vary for

each and every crop and it also varies with growth stage of the particular crop. Standard Kcvalues for all growth stages for different kinds of crops are suggested by FAO. Some of these

values are listed in Table 3.1. Crop evapotranspiration can be calculated using equation 3.2

ETc = ETo * Kc (3.2)


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Table 3.1 Crop coeffecients (Kc) and mean maximum plant heights for non-stressed crops

(Source: FAO Irrigation and Drainage Paper no. 56)

Crop type Kc ini Kc mid Kc endMaximum Crop

height (h) [m]

Beans, green 0.5 1.05 0.90 0.4

Beans, dry and pulses 0.4 1.15 0.35 0.4

Groundnut (Peanut) 0.4 1.15 0.6 0.4

Peas - Fresh

- Dry/Seed

0.5

0.5

1.15

1.15

1.10

0.30

0.5

0.5

Soyabeans 0.4 1.15 0.5 0.5 1.0

The crop stages used to select a KC value are:o Initial stage planting until 10% ground cover.

o Crop development stage 10% to effective groundcover (around 70-80%).

o Mid-season stage 70-80% groundcover to the start of maturity.

o Late season stage the start of maturity until harvest.

Steps in constructing a crop coefficient curve

Using the crop coeffecient values listed in Table 3.1 crop coeffecient curve has to beconstructed as the Kc values for every crop changes with growth stage. The crop coefecient curve

for the crops under present study are constructed and can be seen in figures 3.2 and 3.3. steps for

constructing the crop coeffecient curve are described below.

o Divide the growing period into the four crop stages as mentioned above, determine their

length and identify the corresponding KC values from Table 3.1.

o Adjust KC values for frequent irrigation or rainfall events, humidity and wind speed.

o Construct the curve by connecting straight lines through each of the growth stages asshown in figures 3.2 and 3.3


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Crop coeffecient (Kc) values (Dry Beans)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

12-Oct-98 1-Nov-98 21-Nov-98 11-Dec-98 31-Dec-98 20-Jan-99 9-Feb-99 1-Mar-99 21-Mar-99

Date

Kc

Crop Coeffecient (Kc) values (Groundnut crop)

0

0.2

0.4

0.6

0.8

1

1.2

1.4

25-May-98 14-Jun-98 4-Jul-98 24-Jul-98 13-Aug-98 2-Sep-98 22-Sep-98

Date

Kc

Fig. 3.2 Plot showing the values of crop coeffecient (Kc) for grounnut crop (2nd June1998 - 12th

September1998) for all the growth stages.

Fig. 3.3 Plot showing the values of crop coeffecient (Kc) for Dry Beans crop (1st November1998 -

28th February1999) for all the growth stages.


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3.4.1.3 Determination of actual evapotranspiration

ca ETET = When St > (1-p) SWa (3.3)

( ) ca

ta ET

SpSET

=

1When St


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=

sehh

p = pTable 3.1 + 0.04 (5 - ETc) (3.6)

Where, the adjusted p is limited to 0.1 p 0.8 and ETc is in mm/day.

Table 3.2 Ranges of maximum effective rooting depth and soil water depletion factor for no

stress (p) for common crops (Source: FAO Irrigation and drainage paper No. 56)

CropMaximum Root

Depth (m)

Depletion Factor (for

ETc = 5 mm/day) p

Beans, green 0.5 0.7 0.45

Beans, dry and pulses 0.6 0.9 0.45

Groundnut (Peanut) 0.5 1.0 0.50

Peas - Fresh

- Dry/Seed

0.6 1.0

0.6 1.0

0.35

0.40

Soyabeans 0.6 1.3 0.50

3.4.2 Determination of drainage (or) water flux

3.4.2.1 Campbell model

Campbell model is widely used all over the world to find out the soil matric potential

knowing the soil moisture content and air entry matric potential (he). he, are the Campbellmodel parameters. A person named Clap-Hernberger has determined these model parameters for

various types of soil. Standard values for these model parameters (S. Lorentz et al., 2001) are also

prescribed to be used as a guide all over the world. For sandy loam soils these parameters are

found to be he=21.8 cm, =4.9.

(3.7)

Where,h Soil matric potential [cm]

he Air entry matric potential [cm]

- Actual soil moisture content

s Saturated moisture content

- Campbell pore size distribution parameter


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nm 11=

rs

rwS

=

3.4.2.2 Van-Genuchten Model

Van-Genuchten model is well established model for finding out the unsaturated hydraulic

conductivity of any type of soil. In the present study this model has been used to find out the

unsaturated hydraulic conductivity. Shape parameter and empirical shape factors m and n arevery important for the solution of this equation. These are also known as the Van Genuchten

model parameters. Standard values of the parameters (S. Lorentz et al., 2001) for all types of

soils are predefined whereas the values of parameter m are calculated knowing the value of n (S.

Lorentz et al., 2001) which is again predefined value for all types of soils. The formula used for

calculating m is given in equation 3.10. In the present study the type of soil is sandy loam for

which the value of is 0.5. The value of n is 1.4.14 from which the value of m is found out to be

0.2928. Saturated hydraulic conductivity Ksat, is found out to be 105 mm/day; effective saturationhas been calculated using equation 3.9at all depths and on all days of crop period.

(3.8)

Where,

Kun Unsaturated Hydraulic Conductivity [mm/day]

Ksat Saturated Hydraulic Conductivity [mm/day]Sw Effective saturation

n, m Empirical shape factors

- Shape parameter

(3.9)

Where,

- Moisture content

s Saturated moisture content

r Residual moisture content

. (3.10)

( ) ( )2

/111

=

mmwwsatun SSKK


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The Van Genuchten function has been used in numerous studies, forms the basis of

several national and international data-bases (e.g. Carsel and Parrish, 1988; Yates et al., 1992;

Leij et al, 1996; Wsten et al., 2001), and is implemented in SWAP.

3.4.2.3 Drainage calculation

Using the matric potential values obtained from Campbell model and unsaturated

hydraulic conductivity obtained from Van-Genuchten model drainage or water flux is calculated

at a depth of 50 cm for both the crops. Darcys flux equation has been used for calculation of

drainage flux which is given in equation 3.11.

(3.11)

Where,

q Water flux [cm/day]

K() Unsaturated hydraulic conductivity [cm/day]

- Moisture content

h Matric potential [cm]

z Depth from the ground surface [cm]

h (h1 h2) z (z1 z2)

3.4.3 Estimation of soil moisture

Soil moisture content for the present study has been estimated in two different ways.

Firstly it is estimated using SWAP model and later it is also estimated using water budget

Technique.

3.4.3.1 SWAP model

Soil water flow

The well known Richards equation is applied integrally for the unsaturated-saturated

zone, with possible presence of transient and perched groundwater levels. Due to its physical

z

HKq

= )(

Z1 = 35 cm

GL

h1

h2

50 cm

Z2 = 65 cm

q


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( )( )

z

zhhKq

+=

( )hSz

q

ta

=

( )( )

( )hSz

z

hhK

t

hhC

ta

+

=

=

1

basis the Richards equation allows the use of soil hydraulic functions from databases and

simulation of all kinds of scenario analysis. Hysteresis of the retention function can be taken into

account. Root water extraction at various depths in the root zone in calculated from potential

transpiration, root length density and possible reductions due to wet, dry or saline conditions.

Spatial differences of the soil water potential induce soil water movement. Darcy's

equation is commonly used to quantify these soil water fluxes. For one-dimensional vertical flow,

Darcy's equation can be written as:

. (3.12)

Where q is soil water flux density (positive upward) [cm/d1], Kis hydraulic conductivity [cm/d1],

h is soil water pressure head [cm] andz is the vertical coordinate [cm], taken positively upward.

Water balance considerations of an infinitely small soil volume result in the continuity

equation for soil water:

(3.13)

Where is volumetric water content [cm3/cm3], t is time [days] and Sa is soil water extraction

rate by plant roots [cm3/cm3/d1]

Combination of equations (3.6) and (3.7) provides the general water flow equation in

variably saturated soils, known as the Richards' equation:

. (3.14)

Where, Cis the water capacity [cm-1]

Richards' equation has a clear physical basis at a scale where the soil can be considered to

be a continuum of soil, air and water. SWAP solves equation (3.14) numerically, subject to

specified initial and boundary conditions and with known relations between, h and K.

Numerical solution of soil water flow equation

In SWAP a numerical scheme has been chosen which solves the one-dimensional

Richards' equation with an accurate mass balance and which converges rapidly. This scheme in

( )h


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combination with the top boundary procedure has been shown to handle rapid soil water

movement during infiltration in dry soils accurately. At the same time the scheme is fast,

calculating periods of 40-70 years in a few minutes (Van Dam and Feddes, 2000).

Numerical discretization in the soil profile

A common method to solve Richards' equation has been the implicit, backward, finite

difference scheme with explicit linearization as described by Haverkamp et al. (1977) and

Belmans et al. (1983). Three adaptations to this scheme were made to arrive at the numerical

scheme currently applied in SWAP.

The first adaptation concerns the handling of the differential water capacity C. The old

scheme was limited to the unsaturated zone only. The new numerical scheme enables us to solvethe flow equation in the unsaturated and saturated zone simultaneously. In order to do so, in the

numerical discretization of Richards' equation, the C-term only occurs as numerator (Eqn. 3.14).

The second adaptation concerns the numerical evaluation of the C-term. Because of the

high non-linearity ofC, averaging of C during a time step results in serious mass balance errors

when simulating highly transient conditions. A simple but effective adaptation was suggested by

Milly (1985) and further analyzed by Celia et al. (1990).

The third adaptation concerns the averaging of K between the nodes. Haverkamp and

Vauclin (1979), Belmans et al. (1983) and Hornung and Messing (1983) proposed to use the

geometric mean. In their simulations the geometric mean increased the accuracy of calculated

fluxes and caused the fluxes to be less sensitive to changes in nodal distance. However, when

simulating infiltration in dry soils or high evaporation from wet soils, the geometric mean

severely underestimates the water fluxes. Van Dam and Feddes (2000) show that, although

arithmetic averages at larger nodal distances overestimate the soil water fluxes in case of

infiltration and evaporation events, at nodal distances in the order of 1 cm arithmetic averages are

more close to the theoretically correct solution than geometric averages. Also they show that the

remaining inaccuracy between calculated and theoretically correct fluxes is relatively small

compared to effects of soil spatial variability and hysteresis. Therefore SWAP applies arithmetic

averages ofK.


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( ) ( )

tj

iK

lz

pj

ihpj

ihj

iK

j

iK

uz

pj

ihpj

ihj

iK

iz

jt

j

i

pj

i

pj

ih

pj

ih

pj

iC

+

++

+

+

+

+

+

=

+

+

+

++

2

1

,11

,1

21

21

,1,11

21

1,11,1,11,1

Figure 3.4 Spatial and temporal discretization used to solve Richards equation

The implicit, backward, finite difference scheme of eqn. (3.14) with explicit linearization,

including the three adaptations, yields the following discretization of Richards' equation:

... (3.15)

Where tj= tj+1-tj, zu = zi-1-zi,zl = zi - zi+1 and ziis compartment thickness. Figure 3.4

shows the symbols in the space-time domain. K and S are evaluated at the old time level j

(explicit linearization), which can be shown to give a good approximation at the time steps used.

This numerical scheme applies both to the saturated and unsaturated zone. Starting in the

saturated zone, the groundwater table is simply found at h = 0. Also perched water tables may

occur above dense layers in the soil profile. Calculations show that in order to simulate

infiltration and evaporation accurately, near the soil surface the nodal distance should be in the


41/99


order of centimeters. For this reason the nodal distance in SWAP is made variable. Application of

eqn. (3.15) to each node, subject to the prevailing boundary conditions, results in a tri-diagonal

system of equations which can be solved efficiently (Press et al., 1989).

Top boundary condition

Appropriate criteria for the procedure with respect to the top boundary condition are

important for accurate simulation of rapidly changing soil water fluxes near the soil surface. This

is for instance the case with infiltration/runoff events during intensive rain showers or when the

soil occasionally gets flooded in areas with shallow groundwater tables.

Other boundary condition

The following other boundary conditions are taken into account:

Lateral boundary conditions

Bottom boundary conditions

Initial conditions

Initial conditions are implemented with 2 options:

Input of pressure heads for each compartment; Input of a groundwater level.

The nodal pressure heads will be calculated assuming hydrostatic equilibrium with the

groundwater level, both in the saturated and unsaturated zone.

3.4.3.2 Water budget technique

The term water budget refers to the detailed account of all water inputs and all water

outputs causing storage changes within a given control volume. The general water balance

equation is given as follows:

Input output = change in storage .. (3.16)


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Where inputs are like precipitation (P), irrigation applied (I), capillary rise if any, surface

inflow, subsurface inflow, groundwater inflow etc. Output components are evapotranspiration,

surface outflow, subsurface outflow, groundwater outflow, deep percolation etc. Storage

components are interception, soil moisture, depression storage etc.

Fig 3.5 Control volume giving details of input and output components of water budget

Control volume boundaries have to be defined before starting any type of study. Only

those components that cut across the control volume boundaries need to be accounted for any

type of study. For the present study the control volume is taken up to maximum crop root depth.

It is shown pictorially in fig 3.5.

Assumptions made for the present study

o Amount of soil water in excess of soil water at field capacity is considered to be lost as

deep percolation and surface runoff.

o There is no other input to the field like surface inflow from adjacent field as it is the

controlled experiment. The only inputs considered for the present study are precipitation

and the irrigation applied if any.

o As the field is well ploughed and leveled before planting a crop, the storage of water in

depressions is not considered for the study.


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o As the field plot is very small the storage of water as interception will be very less and is

negligible.

Procedure

All the components shown in fig 3.5 are considered for the water budget equation. The

experiments for the present study were conducted in a plot prepared especially for conducting

experiments in IISc Bangalore campus. As concrete walls are constructed on all sides of the plot

the lateral flow in the control volume is restricted or arrested. As the groundwater water table is

about 200 m below the groundwater table, there is no chance of capillary rise into the control

volume. And there is no any surface flow from the adjacent fields. Coming to the storage

components, only soil moisture is considered. Other components like interception, depression

storage are neglected. As the field plot is very small interception storage would be very less

which can be neglected. And the plot is leveled; there is no chance of depression storage.

Soil water amount available in the crop root zone is found out knowing the soil water

available on the previous day. Known variables of water budget equation and the soil water

available on previous day are provided as input and the actual soil water available in the crop root

zone was obtained. General form of the equation for the present study is as shown in eqn. 3.17.

SWt-1+P+I-ETc-DP-SR = SWt . (3.17)

Where,

SWt-1 Soil water on the previous day [mm]

P Precipitation [mm]

I Irrigation applied if any [mm]

ETc Crop evapotranspiration [mm]

DP Deep percolation [mm]

SR Surface runoff [mm]SWt Soil water on that day [mm]

The moisture content at field capacity is 0.223 or 22.3% for the present study area. From

which it can be said that soil water at field capacity 115mm at a depth of 500 mm. And the soil

moisture content at saturation is 0.4 or 40% from which the soil water is 200mm at a depth of 500


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mm. Soil water more than the soil water at field capacity is considered as the sum of deep

percolation and surface runoff. Soil water crossing soil water at saturation is lost as surface runoff

(SR). Soil water above soil water at field capacity and below soil water at saturation is lost as

deep percolation (DP).

This way soil water values are obtained on all days of the entire crop period for the two

row crops. The depth at which the soil water values are calculated is 500 mm (maximum root

depth). And thus obtained soil water values are divided with the crop root zone depth which gives

us the soil moisture value at that depth. These values are compared with the actual soil moisture

values observed in the field and the soil moisture values simulated from SWAP model and thus

obtained graphs are discussed in the next chapter Results and Discussions. All these calculations

were done in a Microsoft excel spread sheet.

3.4.4 Irrigation scheduling

Irrigation scheduling is a decision-making process to determine when and how much

water to apply to a growing crop to meet specific management objectives i.e. mainly to maximize

net returns. The maximisation of net returns requires a high level of irrigation efficiency. This

requires the accurate measurement of the volume of water applied or the depth of application.It is

also important to achieve a uniform water distribution across the cultivated land to maximise the

benefits of irrigation scheduling. Accurate water application prevents over or under-irrigation.

Over-irrigation wastes water, energy and labour, leaches nutrients below the root zone and leads

to waterlogging which reduces crop yields. Under-irrigation stresses the plant, resulting in yield

reductions and decreased returns. To benefit from irrigation scheduling you must have an

efficient irrigation system.The factors that contribute to develop a workable and efficient

irrigation schedule are soil properties, soil water relationships, type of crop and its sensitivity to

drought stress, stage of crop development, availability of water supply and climatic factors such

as rainfall and temperature.

Here, for the present study C prgram has been witten to know irrigation water

requirement for the crop. The program has been given at the end of the thesis in Appendix-C.

water balance technique has been made use of for determining the amount of irrigation water

required.


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3.4.4.1 Determination of irrigation water requirement

If the simulated soil moisture on particular day is more than the soil moisture at field

capacity, then there is no need of irrigation on that particular day. And if the simulated soil

moisture is less than the soil moisture at field capacity, then there is a need for irrigation water tobe applied on the particular day. The amount of water to be applied as irrigation water is

calculated on the basis of water balance. It is calculated from equation 3.18.

. (3.18)

Where,

IWR Irrigation water required on that day [mm]

Drz Depth of root zone [mm]fc Soil moisture content at field capacity

- Actual soil moisture on that day

Ei Efficiency of irrigation [%]

If we dont have the daily simulated soil moisture content, it can be calculated easily

using equation 3.19, knowing the soil moisture content on the previous day, rainfall, irrigation if

any on that day etc. This equation is again based on water balance. The equation is given as

follows

(3.19)

Where,

i Soil moisture content on any day

i-1 Soil moisture content on the previous day

Pe Effective rainfall [mm]

( )

i

crz

E

fDIWR

=

=

rz

eii

D

PET1001


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CHAPTER 4

RESULTS AND DISCUSSIONS

4.1 GENERAL

Two crops namely Groundnut and Dry beans were studied. Ground nut is a monsoon

crop, where as Dry beans is non-monsoon crop. The crop period of groundnut is 102 days i.e.

from 1st June 1998 to 12th September 1998. Crop period of dry beans is 120 days i.e. from 1 st

November 1998 to 28th February 1999.

4.2 SOIL MOISTURE ESTIMATION

To use the estimated values of soil moisture obtained from SWAP model; this model

should be validated first. Available data of dry beans crop is used for the validation of the model.

The simulated soil moisture values obtained from SWAP model are compared with actual values.

4.2.1 Dry Beans

Crop period of dry beans is 120 days from 1st November 1998 to 28 February 1999. This

is non-monsoon crop. The whole crop period is divided into 4 different growth stages namelyinitial stage, development stage, middle stage and final stage. The rainfall for the entire period

varied between 0.4 mm and 24.6 mm and rainfall has occurred only during the months of

November and December. As it is non-monsoon crop irrigation is required for the entire crop

period whenever there is no event of rainfall. The requirement for irrigation was very high during

the months of January and February 99. Irrigation requirement varied from 1.37 to 44.25 mm.

Crop height of dry beans crop varied between 0 and 53 cm during its growth period. And

the root depth ranged from 0.05 m to 0.5 m. Reference evapo-transpiration (ETo) values werecalculated using Penman-Montieth equation. All the required meteorological data required for

calculation of ETo are collected from the GKVK meteorological station.

All the available input data was given as input to the model i.e. the crop details,

meteorological data etc. Bottom boundary conditions were also prescribed. The available matric


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potential values near the bottom layer were given as the bottom boundary conditions. And the

matric potential values available near the top layer were given as initial conditions. Coefficients

to be used in Van-Genuchten model were also provided as input. The whole layer was descritised

into 5 different layers each of depth either 10 cm or 20 cm.

Now the model is allowed to run and the output files are obtained. Different files with

output data are obtained with each file describing about different parameters. The main output

file with an extension (*.vap) contains the details of soil moisture, pressure head, and water flux

on required dates and at various depths prescribed earlier before running the model. The soil

moisture values obtained from the model are compared with that of the field observed soil

moisture values by plotting graphs between the date and soil moisture. The correlation between

the two values is quite good as the correlation coefficient and coefficient of determination are

0.907 and 0.878 respectively. The root mean square error is 0.06 which is quite good. Graphs for

all the depths were drawn and they can be discussed as follows.

Fig. 4.1: Plot of soil moisture content measured and simulated using SWAP model for Dry

Beans crop (1st Nov1998 - 28th Feb1999) at a depth of 20 cm

The above fig.4.1 describes the comparison between observed and simulated soil moisture

values for dry beans crop at a depth 20 cm. Though large number of simulated values is matching

0

0.05

0.1

0.15

0.2

0.25

0.3


Date

Soilmoistureco

ntent

Simulated

Observed


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with the observed values, there are some values which dont match properly. To mention some

are the simulated values on 21st, 24th, 28 th, and 31st December 98, 1st, 4th, 6th, 8th, 16th, 18th, and

24th February 1999. On all these days the simulated values are over estimated as compared to the

observed values. Probable reason for over estimation of soil moisture may be either because of

heavy rainfall events or during over irrigation applications. As this period is the dry period there

is no chance of heavy rainfall events. So, over application of irrigation was done that is the

reason why the model is not able estimate the correct values of soil moisture values. Irrigation of

10.23 mm was applied on 19th which might be over application which in turn affected the

estimation of soil moisture on 21st December. Same way on all the days whenever there is over

estimation of soil moisture, there would have been over application of irrigation the previous day.

From the fig. 4.2, it is very clear that all the simulated values are matching with the

observed values of soil moisture. From this it is under stood that the irrigation applications or the

rainfall events did not affect the estimation of soil moisture values at 35 cm depth.

Fig. 4.2: Plot of soil moisture content measured and simulated using SWAP model for DryBeans crop (1st Nov 1998 - 28th Feb 1999) at a depth of 35 cm

Soil moisture content values obtained from SWAP model and from water budget

technique are compared with the actual observed soil moisture content values in fig. 4.3. The

correlation coefficient between the actual observed and SWAP simulated soil moisture content is

0

0.05

0.1

0.15

0.2

0.25

0.3


Date

SoilMois

ture

Simulated

Measured


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0.7808. There is negative correlation between the actual and the simulated soil moisture content

from water budget technique which is -0.3237. This shows that the correlation between the actual

observed and the simulated soil moisture from SWAP model is good.

Simulated soil moisture values from SWAP model on 25th, 26th January 1999 & 1stFebruary 1999 are over estimated. The irrigation water applied on 21 st & 23rd January might have

caused the increase in the estimation of soil moisture on 25th & 26th January. In the same way,

irrigation applied on 30th January might have affected estimation on 1st February.

Simulated soil moisture from water budget technique is underestimated at some points.

This has happened because of the assumptions made earlier. The soil water in excess amount of

soil water at field capacity is considered as the drainage and surface runoff, i.e. it was assumed

that the soil moisture above field capacity and below saturation is considered to be lost as deep

percolation or drainage. And the soil water in excess of saturation soil water is assumed to be lost

as surface runoff. But in reality it may not be true because the deep percolation depends on the

permeability or hydraulic conductivity of the soil. According to this assumption more water is

lost as deep percolation than the actual which caused underestimation of soil moisture.

Fig. 4.3: Plot of soil moisture content measured and simulated using SWAP model and water budget

technique for Dry Beans crop (1st Nov 1998 - 28th Feb 1999) at

estimation of soil moisture in unsaturated zone and irrigation sceduling

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