1. business and industrial
2. agriculture and forestry
3. crops and seed

GWRAPPS: A GIS-BASED DECISION SUPPORT SYSTEM FOR AGRICULTURAL
WATER RESOURCES MANAGEMENT
By
SUDHEER REDDY SATTI
A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
UNIVERSITY OF FLORIDA
2002
Copyright 2002
by
Sudheer Reddy Satti
ACKNOWLEDGMENTS
I express my sincere gratitude to Dr. Scot E. Smith and Dr. Ilir Bejleri for their
support and guidance in completing this project. I am most grateful to my co-chair, Dr.
Jennifer M. Jacobs, for her guidance, suggestions, and patience during this project and in
preparing this manuscript.
I am thankful to Mr. Suat Irmak for generously contributing the meteorological
database. I acknowledge the help from SJRWMD personnel for providing the necessary
data and information for the model development. Thanks go to the students of Water
Resources Research Center for the support I received throughout.
Finally, I wish to thank my parents and friends who encouraged me throughout
my academic career.
iii
TABLE OF CONTENTS
page
ACKNOWLEDGMENTS ................................................................................................. iii
LIST OF TABLES............................................................................................................ vii
LIST OF FIGURES ......................................................................................................... viii
ABSTRACT.........................................................................................................................x
CHAPTER
1 INTRODUCTION ...........................................................................................................1
1.1 General...................................................................................................................... 1
1.2 Objectives ................................................................................................................. 3
1.3 Outline of the Thesis................................................................................................. 3
2 HYDROLOGICAL APPLICATIONS OF GIS...............................................................6
2.1 Introduction............................................................................................................... 6
2.2 Decision Support System.......................................................................................... 7
2.3 Geographical Information Systems........................................................................... 8
2.3.1 What is GIS? ................................................................................................... 9
2.3.2 Components of GIS....................................................................................... 10
2.4 Integration of GIS and Hydrology .......................................................................... 12
2.4.1 Coupling........................................................................................................ 14
2.4.1.1 Loose coupling.....................................................................................14
2.4.1.2 Tight coupling......................................................................................14
2.4.2 Embedding .................................................................................................... 15
2.4.2.1 Embedding GIS functionalities into hydrological models...................15
2.4.2.2 Embedding hydrological modeling into GIS packages .......................16
2.5 Applications of GIS in Hydrologic Modeling ........................................................ 17
2.5.1 Coupled Models ............................................................................................ 17
2.5.3 Embedded Models......................................................................................... 26
2.6 Integration Strategy................................................................................................. 27
3 GWRAPPS: SYSTEM DESIGN, IMPLEMENTATION, AND APPLICATION .......29
3.1 Introduction............................................................................................................. 29
3.2 AFSIRS Model........................................................................................................ 30
iv
3.2.1 Soil Water Storage ........................................................................................ 31
3.2.2 Rainfall and Evapotranspiration.................................................................... 33
3.2.3 Drainage ........................................................................................................ 34
3.2.4 Irrigation........................................................................................................ 35
3.2.5 Output Options .............................................................................................. 35
3.3 GWRAPPS Data ..................................................................................................... 36
3.4 GWRAPPS Components ........................................................................................ 38
3.4.1 System Initialization Tool ............................................................................. 38
3.4.2 Climate Interpolation Tool............................................................................ 38
3.4.3 Permitting Tool ............................................................................................. 40
3.4.4 Planning Tool ................................................................................................ 41
3.5 Applications of GWRAPPS.................................................................................... 42
3.5.1 Case Study 1.................................................................................................. 42
3.5.1.1 Study area.............................................................................................43
3.5.1.2 Data ......................................................................................................43
3.5.1.3 Results and discussion .........................................................................44
3.5.2 Case Study 2.................................................................................................. 47
3.5.2.1 Study area.............................................................................................47
3.5.2.2 Results and discussion .........................................................................47
3.6 Conclusion .............................................................................................................. 50
4 GWRAPPS: A SENSITIVITY ANALYSIS TOOL......................................................61
4.1 Introduction............................................................................................................. 61
4.2 AFSIRS Water Budget............................................................................................ 63
4.3 GWRAPPS Sensitivity Analysis ............................................................................ 64
4.3.1 Data ............................................................................................................... 64
4.3.2 Evapotranspiration ........................................................................................ 65
4.3.2.1 Hargreaves method ..............................................................................67
4.3.2.2 IFAS modified Penman method...........................................................68
4.3.2.3 ASCE 1990 Penman Monteith method................................................70
4.3.2.4 FAO Penman Monteith method ...........................................................72
4.3.3 Soil Water Holding Capacity ........................................................................ 74
4.3.4 Crop Root Zone Depth .................................................................................. 74
4.4 Analysis Results and Discussion ............................................................................ 75
4.4.1 Evapotranspiration Analysis Results............................................................. 75
4.4.2 Soil Water Holding Capacity Analysis Results ............................................ 77
4.4.3 Crop Root Zone Depth Analysis Results ...................................................... 78
4.4.4 Discussion ..................................................................................................... 80
4.5 Conclusion .............................................................................................................. 82
5 SUMMARY AND DISCUSSION.................................................................................89
5.1 Summary ................................................................................................................. 89
5.2 Discussion ............................................................................................................... 90
v
APPENDIX
A AFSIRS CROP AND IRRIGATION SYSTEMS.........................................................92
B FORTRAN PROGRAM TO CALCULATE ET USING ASCE 90 PM ......................94
LIST OF REFERENCES...................................................................................................98
BIOGRAPHICAL SKETCH ...........................................................................................105
vi
LIST OF TABLES
page
Table
3-1. Weather stations used in GWRAPPS. ............................................................................52
3-2. Comparison of results from AFSIRS and GWRAPPS simulation runs for a single
farm growing pasture in Alachua County. .....................................................................53
3-3. Summary of the existing soil types in the farm used in case study 1. ............................54
3-4. Comparison of regional crop water use requirements for ferns in Volusia County,
Florida using GWRAPPS’ single soil and multiple soil scenarios. ...............................54
4-1. Summary of the irrigation requirements estimated using GWRAPPS for two crops,
four ET methods, three WHCs and five crop root zone depths. ....................................84
vii
LIST OF FIGURES
page
Figure
3-1. Schematic representation of GWRAPPS........................................................................55
3-2. Study areas and climate stations.....................................................................................55
3-3. System initialization tool graphical user interface. ........................................................56
3-4. Climate interpolation tool graphical user interface. .......................................................56
3-5. Permitting tool graphical user interface for farm-specific information..........................57
3-6. Permitting tool graphical user interface for crop-specific information. .........................57
3-7. Planning tool graphical user interface. ...........................................................................58
3-8. Screen capture of GWRAPPS. .......................................................................................58
3-9. Comparison of 1-in-10 irrigation requirements estimated using AFSIRS and
GWRAPPS....................................................................................................................59
3-10. Comparison of GWRAPPS’ estimated normal irrigation requirements of ferns in
Volusia County............................................................................................................59
3-11. Comparison of individual farm’s single soil vs. multiple soils normal irrigation
requirements in Volusia County..................................................................................60
4-1. Comparison of normal irrigation requirements of ferns for four different ET
methods. .........................................................................................................................85
4-2. Comparison of normal irrigation requirements of potatoes for four different ET
methods. .........................................................................................................................85
4-3. Comparison of normal irrigation requirements of ferns for three different WHC
scenarios. ........................................................................................................................86
4-4. Comparison of normal irrigation requirements of potatoes for three different WHC
scenarios. ........................................................................................................................86
4-5. Comparison of normal irrigation requirements of ferns for five different root zone
depths..............................................................................................................................87
viii
4-6. Comparison of normal irrigation requirements for potatoes for five different root
zone depths .....................................................................................................................87
4-7. Potatoes crop root zone development for five root zone depths considered in St.
Johns County. .................................................................................................................88
ix
Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Science
GWRAPPS: A GIS-BASED DECISION SUPPORT SYSTEM FOR AGRICULTURAL
WATER RESOURCES MANAGEMENT
By
Sudheer Reddy Satti
August 2002
Chair: Dr. Scot E. Smith
Cochair: Dr. Jennifer M. Jacobs
Major Department: Civil and Coastal Engineering
A decision support system, GIS-based Water Resources and Agricultural
Permitting and Planning System (GWRAPPS), was developed for modeling crop water
demand in Florida. GWRAPPS tightly couples ArcGIS with the Agricultural Field Scale
Irrigation Requirements Simulation (AFSIRS) model. Discrepancies often exist between
water quantities predicted by various planning and permitting models due to different
modeling approaches. The variability in crop, climate, and soil made the planning models
use simplified representations of the agricultural water use processes. GWRAPPS
provides a uniform method for modeling consumptive use water requirements in both
permitting and planning applications in humid regions. The system demonstrates the
effects of climate distribution and soil variability on crop water demand. It also functions
as a tool for studying the sensitivity of the AFSIRS model to the environmental, crop and
soil factors in the model.
x
This manuscript presents an overview of the GWRAPPS. Case studies to
investigate the influence of climate and soil variability on regional scale crop water use
requirements are conducted and the results are discussed. A sensitivity analysis was
performed to assess the sensitivity of the AFSIRS model to the crop evapotranspiration,
soil water holding capacity and crop root zone depth. The model was most sensitive to
evapotranspiration and least sensitive to crop root zone depth.
xi
CHAPTER 1
INTRODUCTION
1.1 General
Irrigation scheduling is the application of water to crops only when needed and
only in the amounts needed. Irrigation is practiced to provide water when rainfall is not
sufficient to meet a crop’s water needs. Water available for irrigation is generally limited.
This limitation is imposed by the climate and competition among user sectors.
Precipitation is limited and occurs irregularly, sometimes causing severe water deficit.
Also, irrigation water requirements compete with industrial, commercial, and residential
water use demand. Appropriate planning and application of irrigation are critical for
optimizing water use. Factors such as consumptive use and yield, cost of water and
irrigation technology must be considered while planning irrigation (Singh et al., 1992).
Floridians withdrew about 7.1 billion gallons of fresh water per day in 1995,
slightly more than double the amount withdrawn in 1950 (Burney et al., 1998). The
largest single use of fresh water is agriculture. Agriculture accounted for about 45 percent
of the 1995’s total fresh water use. Despite Florida’s average rainfall of 1.27 to 1.52 m
per year, irrigation is practiced extensively. The non-uniform distribution of rainfall, the
very limited water holding capacities of typical sandy soils, and the extreme sensitivity of
many specialty crops to water stress require that irrigation be scheduled efficiently
(Smajstrla et al., 1997).
Quantitative irrigation scheduling methods are based on three approaches,
namely, crop monitoring, soil monitoring and water balance technique (George et al.,
1
2
2000). Methods based on a crop monitoring approach irrigate based on leaf water
potential or canopy temperature measured at several places in the field. The drawback of
this method is that the decision to irrigate is made after the plant is subjected to some
stress, which may adversely affect the crop yield. Soil moisture monitoring schedules
irrigation based on the measurements of the soil moisture using electrical resistance
blocks or tensiometers. The soil moisture monitoring technique is effective for real-time
irrigation scheduling purposes. However, it is labor-intensive and time consuming.
The most commonly used approach is the soil water balance approach. The soil
water balance approach determines the amount of water available in the soil for a crop.
Numerous computerized simulation models (Fangmeier et al., 1990; Fulton et al., 1990;
Smajstrla, 1990; George et al., 2000) have been developed using this approach. The water
balance approach combines information on the crop, soil and weather to estimate a soil
water deficit. Irrigation is scheduled according to an allowable deficit that may be varied
through the season. The potential drawbacks of this approach are that the models based
on this approach may have high computational requirements, are often not user-friendly,
and the potential evapotranspiration estimates may be difficult to obtain. Also, a
drawback of their application to water supply planning is that many of these models
schedule irrigation for a single field, and their application to multiple fields requires
running the model for each field separately. This is primarily due to the inability of the
models to account for the spatial variability of soil and climate within a larger area. For
effective estimation of crop water use requirements, the variability in soil and climate
must be considered. Thus, there is a need for models that are user-friendly, account for
3
the spatial variability of soil and climate and can be readily used for single and multiple
field cases.
1.2 Objectives
The goal of this research is to develop a user-friendly decision support system,
GIS-based Water Resources and Agricultural Permitting and Planning System
(GWRAPPS), for estimating crop water use requirements. This system should operate at
different scales ranging from single field to local municipalities to County to Water
Management district scale. The GWRAPPS should use the Agricultural Field Scale
Irrigation Requirements Simulations (AFSIRS) model for estimating the crop water
requirements. The GWRAPPS should also account for the spatial variability of soil and
climate.
The objectives of this research are the following
•
Integrate the AFSIRS model with a geographic information system (GIS) to
incorporate the spatial variability of soil and climate.
•
Provide a user-friendly graphical user interface for the AFSIRS model within a GIS
framework.
•
Provide options for the GWRAPPS to estimate crop water requirements at multiple
scales.
•
Provide a tool for generating climate data nearer to the field using existing data.
•
Perform a sensitivity analysis on the critical parameters in the AFSIRS model using
GWRAPPS.
1.3 Outline of the Thesis
Chapter 1 includes the introduction and objectives of the research. Chapter 2
provides a comprehensive literature review on GIS applications in hydrology. The
chapter provides a brief introduction to the basics of GIS, and then discusses the need for
integrating hydrologic models with GIS. The discussion also covers the integration
4
strategies, coupling and embedding in detail. The discussion is followed by a review of
some models developed by integrating the hydrologic models with GIS. These models
are categorized based on their integration strategy. Finally, the integration strategy
selected for GWRAPPS is introduced and the reasons for the selection of the integration
strategy are highlighted.
Chapter 3 introduces the GWRAPPS and covers the GWRAPPS’ system design
and implementation. The AFSIRS model is explained briefly to provide an overview of
the AFSIRS model and the context in which the GWRAPPS framework is developed.
The data requirements of the GWRAPPS are outlined. The data requirements are
followed by a detailed description of the four tools available in the GWRAPPS. They are
system initialization tool, climate interpolation tool, permitting tool and planning tool. In
addition to the general description of each tool, the description also covers the available
options in each tool, the context in which each tool will be used, and the data
requirements of the tool where applicable. At the end of the chapter, the GWRAPPS is
applied to two irrigation scenarios (single field and multiple fields) and the results are
discussed.
In Chapter 4, a sensitivity analysis is performed on three critical parameters of the
AFSIRS model. This sensitivity analysis is performed using GWRAPPS for estimating
crop water requirements on a regional scale. The three critical parameters are climate,
crop root zone depths and soil water holding capacity (WHC). The sensitivity analysis for
climate includes simulating the crop water requirements for four different reference
evapotranspiration (ETo) methods. The four methods used for estimating ETo are the
Hargreaves method (Hargreaves and Samani, 1985), the Institute of Food and
5
Agricultural Sciences (IFAS) modified Penman method (Jones et al., 1984), the
American Society of Civil Engineers (ASCE) Penman Monteith method (Jensen et al.,
1990), and the Food and Agriculture Organization (FAO) Penman Monteith method
(Allen et al., 1998). Each ET estimation method is described in detail. The soil WHC
sensitivity analysis simulates the crop water requirements for three WHC scenarios. The
scenarios include minimum WHC, average WHC and maximum WHC. The simulations
results for both ET and WHC are presented and discussed.
Chapter 5 summarizes and concludes the work. Some possible uses of GWRAPPS
in other aspects of the hydrologic cycle are presented.
CHAPTER 2
HYDROLOGICAL APPLICATIONS OF GIS
2.1 Introduction
Hydrology is the study of the circulation of water and its constituents through the
hydrologic cycle. It is comprised of precipitation, evaporation, infiltration, groundwater
flow, runoff, streamflow, and the transport of substances dissolved or suspended in
flowing water (Maidment, 1992). Modeling the components of hydrologic cycle draws
upon the geographic tradition that hydrology, catchment and fluvial systems interact
closely in time and space (Clark, 1999). Chow et al. (1988) present taxonomy of
hydrological models based on randomness (deterministic/stochastic), spatial variation
(lumped/distributed; space independent/space dependent) and time variation (steady
flow/unsteady flow; time independent/time correlated). Maidment (1993) states that
hydrologic models have been successful in dealing with time variation, but the spatial
disaggregation of the study area has been relatively simple. The spatial variation of the
model parameters is generally considered to be uniform.
However, hydrologic modeling is increasingly becoming global, both in terms of
spatial scale and depth of treatment. This globalization of hydrology is placing new and
greater demands for data and more sophisticated techniques for managing and processing
them (Singh and Fiorentino, 1996). One of the techniques to manage large amounts of
data is Geographic Information System (GIS). Goodchild (1993) states that the GIS
technology has the ability to perform a variety of tasks like a) pre-processing of data from
large stores into a form suitable for analysis; b) supporting analysis and modeling; and c)
6
7
post-processing of results. The coupling of hydrologic modeling and GIS technology can
offer an improved means to deal with the spatial aspect of hydrology. The integration of
GIS and hydrology can generate more efficient and easier-to-use hydrologic models.
In the past few years, there has been an enormous interest in the application of
GIS to hydrology and water resources. Recently, some hydrologic models have enabled
GIS users to go beyond the data inventory and management to sophisticated modeling
and simulation (Sui and Maggio, 1999). This integration makes various hydrologic
models more transparent and enables the communication of their operations and results to
a large group of users.
This chapter describes some of the basic concepts of GIS. The goal is to provide a
clearer understanding of the development and structure of the GIS-based Water
Resources and Agricultural Permitting and Planning System (GWRAPPS) relative to the
wide scope of available approaches. Towards this end, different model and GIS
integration strategies are reviewed. GIS applications relevant to these integration
strategies are presented. These selected applications exemplify how GIS was integrated
with different hydrologic models. In addition, specific benefits are identified.
2.2 Decision Support System
Water resources management helps in meeting the increasing water demands
through the development of new water resources or by using existing water more
efficiently. Hydrologic models have served as a valuable tool for water resources
management for many years (Greene and Cruise, 1995). The spatial nature of the
hydrological and meteorological data used in these models can be efficiently
accommodated through application of a GIS. Recent advances in computer technology
8
and GIS have facilitated integration of hydrologic models with a GIS resulting in better
and more efficient water resources management.
Because of the complexity of the tools required to fully support an important
hydrologic decision, a system made up of more than a GIS and a simulation model – a
decision support system (DSS) is needed. Reitsma (1996) defines DSS for water
resources application as a computer-based system, which integrates state information,
dynamic or process information, and plan evaluation tools into a single software
implementation. In this definition, state information refers to data that represent the
system’s state at any point of time, process information represents the first principles
governing resource behavior, and evaluation tools refers to software used to transform
raw data into information used for decision making.
Introduction of DSS techniques for water resources planning dates back to the
mid 1980s (e.g., Fedra, 1983). The wide range of applications of DSS techniques for the
study of water resources problems includes surface runoff, river basin management,
urban storm water management, groundwater contamination, have been discussed in
literature (Dunn et al., 1996; Jamieson and Fedra, 1996; Ito et al., 2001; Sample et al.,
2001). With a DSS, the mathematical models are not restricted to predict what may
happen under a given set of conditions and possess the capability of providing expert
advice on the appropriate course of action. Thus, the use of a single integrated modelingdecision-analysis framework can help achieve considerable benefits in effective water
resources management.
2.3 Geographical Information Systems
The use of GIS in hydrologic modeling has increased considerably during the past
two decades. This trend is not a surprise considering the advances in computers and the
9
usage of computers in hydrologic modeling. Early computers were literally ‘number
crunchers’, not handlers of the complex forms of information found on maps (Longley et
al., 1999). New advances in computer technology have enabled computers to handle large
amounts of data and to perform complex operations faster. The price-performance ratio
of computers has increased and the space required for computers has decreased. These
advances have contributed to GIS’s primary benefits to hydrologic modeling, which are
to retrieve, manipulate and analyze spatial data easier and faster. These result benefits in
time saving and cost reductions.
2.3.1 What is GIS?
The term Geographic Information System dates back to the mid sixties and seems
to be rooted in two separate applications, one in Canada and the other in the United States
(Goodchild, 1993). In Canada, it was used describing an application developed to manage
the mapped information collected for the Canada Land Inventory and obtaining estimates
of the area of land available for certain types of uses. In the United States it was used by
large-scale transportation models developed to access many different types of data from
large volumes of information for analysis and presentation of the results in map format
(Coppock and Rhind, 1991).
GIS is a general-purpose computer-based technology for handling geographic
data in digital form. Maidment (1993) describes it as a potential common data and
analysis framework for environmental models. GIS software is developed to capture,
manipulate, process and display spatial and georeferenced data.
Environmental Systems Research Institute (ESRI), a world leader in GIS
technology defines GIS as ”a computer-based tool for mapping and analyzing things that
exist and events that happen on earth”. De Mers (1997) provided a more specific and
10
technical definition for GIS as a computerized data management system designed to
input, store, retrieve, manipulate, analyze, and display spatial data for the purposes of
research and decision-making.
GIS contains both geometry data (coordinates and topological information) and
attribute data (information describing the properties of geometric objects such as points,
lines and areas). GIS technology integrates common database operations such as query
and statistical analysis with the unique visualization and geographic analysis benefits
offered by maps. These abilities distinguish GIS from other information systems and
make it valuable to a wide range of public and private enterprises for explaining events,
predicting outcomes, and planning strategies.
2.3.2 Components of GIS
A successful GIS consists of four principal components: computer hardware,
computer software, data and liveware (Maguire, 1991). The hardware component refers
to the computer platform. Different platforms include relatively modest personal
computers, high performance workstations, minicomputers and mainframe computers.
GIS imposes certain specific requirements, to be met before it can be successful. GIS
needs the system to be interactive with the user. It should allow multiple users to access
the geographical database simultaneously. It should provide graphical input and output of
data and be able to process large volumes of data effectively (Goodchild, 1991). Hence,
many GIS products specify minimum hardware requirements for their product to work
effectively. For example, ArcGIS8.1, a popular GIS product from ESRI recommends
a minimum processor speed of 650 Mhz and RAM of 256 MB.
11
Computer software provides the basic functions and tools to store, analyze and
display geographic information. Powerful GIS software should have the following:
advanced input and manipulation tools, database management systems and tools to
support geographic query, analysis and visualization. There are hundreds of GIS software
packages in use. Some popular packages are ArcGIS8.1, ArcView3.2, Geographic
Resources Analysis Support System (GRASS), IDRISI, AutoCAD Map 2000,
MapInfo, and Microstation MGE. The choice of the GIS package depends on the
features available, technical support provided, price and the customization features
provided.
The third most important element in a GIS is the data. Geographical data are very
expensive to collect, store and manipulate because large volumes are normally required
to solve substantive geographical problems. There are two major ways of collecting
geographic data. Primary data collection involves the design and implementation of a
collection plan and the input of the results into a DBMS. Secondary data consists of
geographic data collected and analyzed by someone else. Recently, there has been
increased use of data collected by other techniques like remote sensing and global
positioning systems. The data collected are generally stored in two formats, vector data
and raster data. Vector data is stored in the form of point, lines and polygons along with
topology. Topology defines the relationship between the features. Raster data stores data
in the form of grids. This data format has gained popularity with the increasing usage of
remote sensing data in GIS.
The final and most significant GIS element is the liveware: the people responsible
for designing, implementing and using GIS. The benefits of GIS technology will be
12
minimal without users adept at managing the information system and formulating real
world applications. They range from GIS specialists who design and maintain the system
to GIS analysts who use the system to create technical maps for presentations and reports
and to help in the everyday work. The designers and developers need access to the most
advanced functionality features a GIS has to offer. The end users might need only some
specific tools. A careful attention to the needs of each user group will lead to higher
utilization rates and therefore a greater return on the investment in GIS technology.
2.4 Integration of GIS and Hydrology
Hydrologic models have been interfaced with GIS since the mid 1980s. However,
most of the models did not emphasize the processes involved. They were used primarily
to provide visual display of data and results. With major advances in computers, the trend
has changed to addressing the hydrologic processes within the GIS framework. Many of
the available GIS packages are equipped with hydrological capabilities. They can process
Digital Elevation Models (DEM) and provide vital topographic characteristics for
hydrologic modeling. ESRI’s products ArcGIS8.1 and ArcView3.2 have tools to
delineate watersheds, generate flow directions, compute contributing areas and
topographic parameters such as slope and aspect.
Hydrologic modeling commonly involves large data sets such as
hydrometeorology, topography, land use, soils, geology, and streamflow. Storage and
maintenance of such datasets often proves to be cumbersome. All these datasets can be
represented in spatial dimension and hence a spatial data analysis and manipulation tool
is desirable for maintaining the data. GIS is an invaluable tool for working with spatial
data.
13
Goodchild (1993) states that the GIS technology has the ability to perform a
variety of tasks some of which are: 1) preprocessing of data from large stores into a form
suitable for analysis exemplified by reformatting, change of projection, resampling, and
generalization; 2) supporting analysis and modeling: forms of analysis, calibration of
models, forecasting and prediction; and 3) post-processing of results through
reformatting, tabulation, report generation, and mapping. All these tasks can simplify
data assimilation for hydrologic models. Furthermore, the use of computers in hydrologic
modeling has become so widespread that the integration of GIS and hydrology is feasible.
The combined spatial capabilities of GIS and the complex hydrologic modeling facilitate
the development of hydrologic models with enhanced spatial capabilities.
GIS has its historical roots in computer cartography and digital image processing,
the representation schemes and analytical functionalities in GIS are designed to work
with map layers and geometric transformations, and not for computationally intensive
tasks. Attempts to perform complex scientific modeling using GIS have met with little
success due to the inability of GIS languages to handle complex algorithms and iterative
processes (Terlien et al., 1993). The primary inhibitor to using a GIS in modeling is the
difficulty of integrating the model with the GIS.
Many approaches have been proposed and implemented for integrating GIS and
hydrological models (Maidment 1993; Abel et al. 1994; Sui and Maggio. 1999). There
are two different approaches to integrate GIS with hydrological models: Coupling, this
provides a common interface or a linkage between the applications, and Embedding, this
involves merging the features of different applications into a single application.
14
2.4.1 Coupling
Coupling is the physical linking of two or more different applications through a
common interface. In coupling, the models are separate and communicate with each other
through data transfer. Coupling can be further classified into loose and tight coupling.
2.4.1.1 Loose coupling
In loose coupling, the GIS and the hydrological model are independent processes.
The software packages do not have a common interface. They are integrated via data
transfer in the form of ASCII or binary data format. An advantage of this method is that it
does not involve additional programming as the packages are treated separately. As
computer programming is minimal, this approach can be the most realistic solution for
most of the GIS-based hydrologic modeling. However, the data conversion between
packages could be tedious and error prone (Sui and Maggio, 1999).
In a loosely coupled system, GIS software is used to construct input files that a
simulation program can read (Panchal et al.). The simulation results are then read back
into the GIS for display and analysis. The loosely coupled system may be developed
using existing technologies, but this integration lacks in providing 1) a consistent user
interface, 2) a consistent data structure, and 3) the support for development and
modification of models. Although loose coupling can be cumbersome, it lets models that
are coded in any programming language use GIS data.
2.4.1.2 Tight coupling
In a tightly coupled system, the GIS user has access to simulation models through
software hooks and/or built-in macro languages (Karimi and Houston, 1996). The models
are developed outside of GIS and have their own data structures. The exchange of data
between the model and GIS is hidden from the user. The GIS and the model remain in
15
distinct executables. However, the simulation models are called and executed from within
a GIS framework. These integration strategies provide access to a consistent user
interface and data structure. This type of coupling provides improved usability from loose
coupling approach as the model is run from the GIS environment. For example,
parameters are defined in the GIS user interface and the data transfer is automatic.
However, tight coupling requires complex programming and data management rather
than simple linkages. GIS issues include the requirement for data structure compatibility
and the extensive development of data translation tools.
2.4.2 Embedding
Embedding is adding an application as an add-on to an existing model to improve
performance. In the present context of GIS based hydrologic modeling, this can be
further classified into two categories: 1) embedding GIS functionalities into hydrological
models and, 2) embedding hydrological modeling into GIS packages. Some basic insight
into these two approaches is provided below.
2.4.2.1 Embedding GIS functionalities into hydrological models
Embedding GIS functionalities in hydrological modeling packages gives the
system developers maximum flexibility. Embedding allows implementing GIS
functionalities in the hydrological applications. This implementation is not constrained by
any existing GIS data structures. The primary drawback with this approach is that the
data management and visualization capabilities of these hydrological modeling packages
are not efficient when compared to those available in commercial GIS packages (Sui and
Maggio, 1999).
16
2.4.2.2 Embedding hydrological modeling into GIS packages
A model is embedded within a commercial GIS software package using either a
GIS macro, or an object-oriented or conventional programming. Most of the leading GIS
software vendors provide macro or scripting capabilities, which allow the user to develop
customized applications. The full functionality of GIS is not being utilized to maintain
the interactivity between the GIS and models. Simpler GIS functionalities are being used
to satisfy the data requirements of the model. This is suitable for models in which the
processes are simple. For complicated processes, the scripting capabilities may not be
sufficient. Several software packages allow user-developed routines to be added to the
GIS software as libraries. This involves more conventional programming and GIS
scripting to have a single user-interface.
Leading GIS software vendors have tried to improve the modeling capabilities of
their products by incorporating modules to perform some basic hydrological modeling
operations. Hydrological modeling functions have been embedded in leading GIS
software packages such as ESRI’s ArcStorm and ArcGrid, and Intergraph’s InRoads.
This approach provides better visualization capabilities. However, these models may not
meet industry standards and may not be validated.
The ideal strategy to link the GIS and the hydrologic model is to fully integrate
one system into the other. A fully integrated system uses the same data structures for both
the GIS and the model to eliminate data transfer between GIS and the model. As this
strategy involves same data structures, the co-development of both the systems may be
necessary. This development requires extensive interaction between the GIS specialist
and the modeler. Although this approach is an effective approach, very little progress has
been made in embedding because the programming is complex and time consumptive.
17
These general approaches have been applied in numerous integrated GIS and
hydrological models. Most have applied a combination of loose and tight coupling as it
involves less programming and costs when compared with embedding. The studies
reported in the literature include simple data pre-processing and hydrological parameter
estimation (Srinivasan and Arnold, 1994; Rao et al., 2000), testing the validity of the
models (Sasowsky and Gardner, 1991), GIS as mapping and visualization tools (Xu et al.,
2001), and comprehensive hydrologic model simulations (Stockle and Nelson, 1993; Van
Duerson et al., 1993; Srinivasan and Engel, 1994; Engel et al., 1997).
2.5 Applications of GIS in Hydrologic Modeling
There are a multitude of applications that integrate GIS and hydrologic models.
Several examples that are highly relevant to, or specifically developed for hydrologic
models are reviewed below. These examples demonstrate how some of the concepts
related to GIS and hydrologic modeling are applied. These systems have demonstrated
the potential of GIS to aid in the development of more efficient water management
systems.
2.5.1 Coupled Models
AEGIS/WIN is a decision support system that links the simulation system
Decision Support System for Agrotechnology Transfer (DSSAT) v3 with the geographic
mapping tool ArcView (Engel et al., 1997). DSSAT v3 is a well-validated crop
simulation system that has been used worldwide in research, teaching, and extension
programs (Tsuji et al., 1994; Bowen et al., 1996). It contains 11 crop models, all of which
use a standard format of input and output. The model framework allows application at a
regional scale. At this scale, coupling with GIS provides efficient data input for the
model.
18
In AEGIS/WIN, DSSAT v3 and ArcView are integrated using a tight coupling
approach. DSSAT v3 has not been altered from its original form. AEGIS/WIN provides a
graphical interface to select the land-use map and management scenarios. The user
interface, which automates data transfer between the simulating system and the mapping
tool, is created using Avenue, an object-oriented macro programming language. DSSAT
can be accessed from within the system and create management inputs for individual
field of the selected land-use map. These details are stored in an experimental details file,
and simulations are run with the crop models.
ArcView acts as both the front-end and back-end tool for DSSAT v3. ArcView
provides input data for the model from the maps. The model results are visualized
spatially in ArcView. The model’s results including simulated crop yield, yield
components, and other related agronomic and environmental variables are displayed as
thematic maps. After the model runs are completed, the user can perform statistical
analysis of the simulation results for all-important agronomic variables, display the
individual variables in thematic maps, and create tables and charts.
Most of the agro-climatic classifications use rainfall and evapotranspiration to
delimit the growth environment of crops. The common and important limitation of this
procedure is that it does not account the variability of the soils and the dynamics of crop
growth in relation to these physical conditions. Use of GIS is an efficient approach to
overcome these problems as it can provide tools to classify and display agro-climatic
datasets obtained from simulation modeling. It allows transformation of data into
information more useful for decision-making. CropSyst is one of the successful attempts
19
to combine GIS and crop-simulating models using the tight coupling approach is
CropSyst.
CropSyst simulates the soil water budget components and multi-crop production
potential, both spatially and temporally on a daily basis. CropSyst couples a model with
databases of soil type, long-term weather, and crop management using a GIS (Badini et
al., 1997). CropSyst, a multi-year and multi-crop simulation model, was developed to
serve as an analytical tool to investigate the effect of cropping systems management on
crop productivity in relation to environmental patterns (Stockle and Nelson, 1993). The
soil water balance model processes include precipitation, canopy and residue
interception, runoff, infiltration, redistribution in the soil, and maximum and actual
evapotranspiration. The CropSyst input data sets are the location, weather, soils, crop,
and management practices. The separation of files allows for an easier link of CropSyst
simulations with GIS software.
A vector-based GIS (Arc/Info) constructs coverages of soils and weather. The
vector-based GIS also provides for the mapping and display of the results. The location
file includes information such as name, latitude, and daily weather database. GIS
generates a location-weather coverage containing attributes related to CropSyst. Using
this information, ArcInfo-CropSyst Cooperator (ARCCS) generates the combined
simulation map. The ARCCS program controls the model execution. ARCCS associates
each polygon with the corresponding CropSyst (soil and location-weather) parameter
files, runs the simulations, and generates CropSyst simulation results. This approach
provides guidelines for resource management and can be extended for crop production
20
forecasting, water management, and comparison of new crop cultivars for introduction in
different agroclimatic zones.
Another example of an integrated GIS with hydrologic models using tight
coupling is EPIC-View. The erosion-productivity impact calculator (EPIC) is a
management-oriented model operating on a daily time step. It consists of physically
based components that simulate erosion, plant growth and related processes, and
economic components. It is used to assess the cost of erosion and to determine optimum
management strategies. The EPIC components include weather simulation, hydrology,
erosion-sedimentation, nutrient cycling, plant growth, tillage, soil temperature,
economics, and plant environmental control. It operates on a daily time step. Its file
structure consists of text files that contain parameter estimates of different modeled
processes.
The integrated system (EPIC-View) links the hydrologic-crop management model
EPIC with a desktop GIS. EPIC-View is applied as a planning tool for implementation of
sustainable farm management practices (Rao et al., 2000). Specifically, the system
framework integrates ArcView, EPIC and a graphical user interface. GIS integrates
diverse spatial data into a comprehensive database allowing easy access and input to the
model. The files required for dataset assembly and model execution are developed using
Avenue and Visual Basic (VB) programs.
The interface accepts the user input and writes the necessary parameter files. The
user interface collects the weather, soil, management and spatial data. The weather and
management files are compiled into a database to provide site-specific information.
Spatial data such as elevation, slope, soil, and land cover are extracted from the GIS
21
coverages. Areas with unique set of spatial characteristics are generated by overlaying the
GIS coverages and are inputs to the EPIC base data file.
The EPIC model uses the EPIC base data file for its simulation. Model results can
be viewed as thematic maps, tables or charts. The simulation results are joined to the
attribute table of the coverage containing areas of unique set of spatial characteristics.
Thematic maps are generated for different model output variables. Visual output in the
form of maps enables the study of farm level response to inputs, which aids in better farm
management. The application of EPIC-View in planning a sustainable farm provides dual
benefits: productivity gains to the farmers and mitigating environmental risks.
Agricultural NonPoint Source Model (AGNPS) is another distributed parameter
model that simulates catchment runoff, erosion and nutrient movement in response to
rainstorm events. AGNPS has several integrated modeling components: a hydrologic
component to estimate runoff and flow, a sediment transport component to estimate
erosion and deposition, and a chemical component to estimate nutrient movement and
concentrations through the catchment.
AGNPS requires twenty-two input parameters for each cell to describe the cell
information, terrain conditions, land cover, management practices, soil and surface
hydrology. The numerous data requirements for each cell make it very difficult to run the
model in a distributed framework. Several, GIS systems have been developed to facilitate
the acquisition of the required data. AGNPS has been integrated with a number of GIS
including GRASS (Srinivasan and Arnold, 1994) and ArcView (Pullar and Springer,
2000).
22
Srinivasan and Engel (1994) developed a Spatial Decision Support System
(SDSS) by loosely coupling the AGNPS and the Geographic Resources Analysis Support
System (GRASS) GIS tool. The integrated system assists with development of AGNPS
input from GIS layers, running of the model, and interpretation of the spatially varying
inputs. The GIS tool serves as the core of the system. The components of the system are
modular and interact through the GIS tool. The hydrologic GIS-based or other generic
tools are used either in the AGNPS-GRASS input interface or the AGNPS-GRASS
output interface.
The AGNPS-GRASS input interface minimizes the user interaction in preparing
the input data. Most of the 22 parameters required by AGNPS are prepared by the
interface from GIS database and spatial layers. A few parameters like rainfall amount and
its corresponding intensity are obtained from the user. The AGNPS model input file is
generated using the parameters. The AGNPS model is run and the AGNPS-GRASS
output interface displays the results. The visualization interface generates 17 GIS layers
from the ASCII output files of an AGNPS run. This model significantly reduces the time
required to obtain the data needed by AGNPS, simplifies operation of AGNPS, and
allows the identification of problem areas quickly.
Pullar et al. (2000) integrated ArcView GIS with AGNPS. Their tightly coupled
GIS and AGNPS system provides an interactive environment that allows decision-makers
to quickly modify parameters and visualize the results of simulation. The system is
provided as an extension for ArcView. The integrated system includes a hydrological
toolbox that supports basic functions necessary for hydrological analysis. A customized
user interface built in ArcView allows selection of sub-catchments. The underlying
23
database contains soil, land cover, land use, and the channel information. The GISAGNPS library uses ArcView’s grid I/O C Application Programming Interface (API)
functions to read and write data files compatible with AGNPS file formats. The input
data is validated before running the analysis. The GIS interface invokes the AGNPS
executable, which runs the simulation using the input data file generated for the subcatchment. The results from the simulation run are subsequently interpreted and
displayed in ArcView. This application demonstrates that GIS can be used to preprocess
information and validates its use in an environmental model.
Another hydrologic model integrated with GIS is Soil and Water Assessment
Tool (SWAT). SWAT is a semi-empirical and semi-physical model used to predict the
effect of agricultural management decisions on water and sediment yields for large
ungauged rural watersheds. It consists of major water budget components such as surface
runoff, return flow, percolation, evapotranspiration, transmission losses, pond and
reservoir storage, crop growth, irrigation water transfer, groundwater flow and channel
routing (Bian et al., 1996). The model runs on a daily time step basis and operates on a
semi-distributed manner to account for the spatial differences in soils, land use, crops,
topography, channel morphology, and weather conditions. Its vast data requirements and
semi-distributed characteristic are well suited for implementation in GIS.
Srinivasan and Arnold (1994) integrated the SWAT water quality model with
GRASS GIS. This integrated tool performs as a continuous-time, distributed-parameter
modeling tool to assist with management of runoff, erosion, pesticide, and nutrient
movement in large basins. Several hydrologic GIS-based and other database access tools
were used in integrating the hydrologic model with GIS and to keep the model structure
24
modular. The input interface was developed using C language. The interface interacts
with the user to collect, prepare, edit, and store basin and sub-basin information to be
formatted into a SWAT input file. Most of the input data is derived from GRASS GIS
layers. The GIS layers needed are a basin layer, an elevation layer, a soils layer, and a
land use layer. The interface allows speedy modification of the various management
practices. Modifying the GIS data layers and/or choosing different aggregation methods
for various input data allows performing some sensitivity analysis.
Bian et al. (1996) created an interface system that streamlines the GIS processes
for preparing spatial parameters required by SWAT. The interface system automates the
link between ArcInfo and SWAT. A user-friendly data entry and editing environment for
the SWAT model replaces DOS based data entry. Owing to the complexity of both the
GIS and the SWAT model, the two systems are independent components. A shared
internal database and an external user interface couples the two systems. An API provides
the communication between the Graphical User Interface (GUI) and the internal database.
The GUI provides data entry, editing, and querying features. The internal database
updates the input data and transfers them into SWAT compatible format. This interface
system facilitates the organization and application of the large GIS dataset for SWAT.
GIS can also be used as a tool for model parameterization. An example of such an
application is the Simulation of Production and Utilization of Rangelands (SPUR) system
(Sasowsky and Gardner, 1991). SPUR is a physically based surface runoff model in
which a watershed is configured as a set of stream segments and contributing areas.
SPUR operates on a daily time-step and was designed for 0.03 to 26 km2 rangeland
watersheds. Within SPUR, watersheds are discretized into a system of channel segments,
25
contributing areas, and ponds, if present. SPUR has climatic, hydrologic, plant, animal
and economic components.
A loosely coupled integration strategy is used to link SPUR with a grid-based
GIS, ERDAS. The GIS and the SPUR model do not share any interface in common. The
GIS provides means of collecting data required for running the simulation. The SPUR
model performs analysis on different watershed sizes using the spatial data collected
using GIS. Specifically, the GIS techniques provide many of the topographic and soil
parameters to the hydrology component. For example, GIS data provides 17 of 34
watershed parameters in the hydrologic component. The GIS enables easy
parameterization for different watershed sizes.
The PDTank model is a comprehensive, deterministic, physically based
distributed model for the simulation of hydrologic processes. The model characterizes the
average response of each cell, rather than capturing variations that might occur within the
grid. In the model, catchment characteristics and input data are represented in the form of
a network of grid squares, and the governing equations are solved using finite-difference
methods.
Xu et al. (2001) integrated the PDTank model with a GIS using a loose coupling
approach. ArcView acts as a front-end tool to compute watershed parameters for the
model and as a back-end tool to display the computed hydrologic simulation results. The
GIS prepares input data by performing complex map overlays and spatial analysis,
provides the linkage between models and spatial representations, and converts digital
landforms of different projections and scales to a standardized format. Finally, it provides
26
post-simulation graphics output display and spatial analysis for evaluating hydrologic
simulation results.
2.5.3 Embedded Models
Embedding is considered to be the efficient means for integrating GIS and
hydrologic models. Programming the GIS functionalities using conventional
programming languages is very complicated and time consumptive. Hence, very few
applications use embedding as the integration strategy. GIS-based RHINEFLOW model
is an example of embedding hydrologic components into a GIS.
RHINEFLOW describes the changes in the water balance compartments of the
river Rhine on a monthly time basis (Van Deursen et al., 1993). The model is built with
the PC RASTER package as an integrated part of GIS. PC RASTER is a set of utilities
for hydrological and geo-morphological modeling that can be linked to a raster GIS. With
these utilities the water balances can be modeled on each of the cells of the raster system.
The model provides an efficient means of estimating different hydrological processes, as
there is no data transfer or translation involved.
A raster-GIS was created for the RHINEFLOW model. The input datasets
represent one calculation element in the model, which is the smallest element in the
spatial connectivity analysis. The input datasets for the model include monthly areal
precipitation and temperature data, DEM, soils, land use. The calculations involved are
evapotranspiration and soil moisture, runoff production and snowfall and snowmelt. The
model produces maps and tables at all calculated time steps for all the hydrological
variables including their spatial and temporal distribution.
This application offers a general approach for modeling different kinds of
processes, and includes the possibility to combine detailed spatial resolution with simple
27
models. This model allows validation of water balance models not only on discharge but
also on the spatial and temporal distribution of other hydrological variables.
2.6 Integration Strategy
The AFSIRS integration strategy was selected based on the advantages and
disadvantages of the methods previously reviewed. This section details issues that
affected the selection.
The Fortran based AFSIRS model simulates agricultural crop water requirements.
The data requirements of AFSIRS can be classified into location-specific and cropspecific data. However, the AFSIRS model reads the data required for running the
simulations in the form of ASCII files only. The model uses historical climate data to
predict the crop water requirements during droughts. The climate data span over a large
number of years. The model simulates a crop water balance on a daily-time step for
twenty or more years. Thus the model run performs numerous complex operations.
Embedding AFSIRS into the GIS would require rewriting the AFSIRS model in a
language compatible with GIS. This was not feasible for two reasons: 1) it is a very time
consuming process and is beyond the scope of this research and 2) when compared with a
Fortran routine which can process hundreds of iterations almost instantaneously, an
embedded GIS routine would significantly longer the iterations. The reason for such
discrepancy in efficiency is that the GIS programming languages are interpreters (Karimi
and Houston, 1996). As discussed above, the AFSIRS model performs many iterations in
its simulation runs. Hence, embedding was considered unsuitable in this scenario.
A common user interface is desired to facilitate the acquisition of both location
and crop specific data and to run the model. This interface suggests the use of a tight
coupling approach. One of the requirements to achieve tight coupling is the elimination
28
of the data conversion between different data structures. As mentioned previously, the
AFSIRS model can read data only in the form of ASCII files. This necessitates data
conversion, which makes tight coupling not completely achievable. However, automation
of the data conversion is possible, and provides a modified version of tight coupling
when coupled with a single user interface.
The applied strategy provides a common interface for the AFSIRS model and the
GIS modules while maintaining separate systems. The common interface automates the
data transfer. This approach resembles tight coupling. However, the data transfer to
ASCII files is representative of loose coupling. The data transfer strategy uses objectoriented technology. ArcObjects are used to access GIS data. The interfacing objects
developed in Visual Basic create the model input files, invoke the Fortran model and
transfer the AFSIRS results back to the interface. The outcome is a common userinterface between the GIS database and the AFSIRS model. The data transfer between the
GIS and the model is hidden by the translation utilities. This strategy overcomes the
deficiencies of a loosely coupled system.
CHAPTER 3
GWRAPPS: SYSTEM DESIGN, IMPLEMENTATION, AND APPLICATION
3.1 Introduction
The GIS-based Water Resources and Agricultural Permitting and Planning
System (GWRAPPS) is a decision support system running in a Windows environment
that tightly couples ArcGIS8.1 (ESRI) with the Agricultural Field Scale Irrigation
Requirements Simulation (AFSIRS) model. The system’s framework integrates
ArcGIS8.1, AFSIRS and a user interface developed using object-oriented technology.
The GWRAPPS handles the user selection of crop-specific and location-specific
information. The GWRAPPS also enables data exchange between the spatial data and the
AFSIRS model. The linkages between ArcGIS8.1 and AFSIRS include automatic data
and control transfer between the two components of the integration system.
The GIS is the front-end tool for pre-processing data and the visualization tool for
analyzing the final results. The GIS provides a Graphical User Interface (GUI) for
assembling the necessary AFSIRS model input. The user interface, developed using
Visual Basic, controls the data and the control flow between the integrated system and
the AFSIRS model. The interface also provides a visual representation of the spatial
distribution of the AFSIRS model results. In addition, the GIS provides access to spatial
and temporal databases that maintain distributed crop-specific data and climate
information.
ArcObjects, a Component Object Model (COM) oriented technology, handles the
interaction between the spatial information and the user. Spatial information, such as the
29
30
soil type and the nearest climate location are acquired from the GIS layers. Temporal
information such as evapotranspiration and rainfall are acquired from a Relational
Database Management System (RDBMS).
A schematic representation of the integrated system is provided in Figure 3-1. The
GIS is the front-end tool for data pre-processing and the visualization tool for analyzing
the final results. The user interface, developed using Visual Basic, resides within the GIS
and interacts with the user for selecting crop-specific and location-specific data. The user
interface accepts input from the user and transfers it to the data access modules. Based on
the user input, the data access modules acquire the necessary spatial and non-spatial data
from GIS layers and a Relational Database Management System (RDBMS) respectively.
Spatial (GIS) and temporal (RDMBS) databases maintain the distributed crop, soils and
climate data. ArcObjects, a Component Object Model (COM) oriented technology,
handles the interaction between the spatial information and the user. Spatial information,
such as the soil type and the nearest climate location are acquired from the GIS layers.
Temporal information such as evapotranspiration and rainfall are acquired from a
RDBMS. The translation modules translate the user selected spatial and non-spatial data
into AFSIRS compatible input datasets. Linkages between ArcGIS and AFSIRS facilitate
automatic control transfer between the user interface and the AFSIRS model. The
AFSIRS model simulates the crop water requirements using the generated input datasets
and the AFSIRS model data files. The visualization modules display the resultant
spatially distributed crop water demand within the GIS framework.
3.2 AFSIRS Model
The Agricultural Field Scale Irrigation Requirements Simulation (AFSIRS) model
is a numerical simulation model that allows the user to simulate the irrigation
31
requirements for a crop based on soil, irrigation system, growing season, climate and
irrigation management practice (Smajstrla, 1990). The model calculates the daily crop
root zone water budget by simulating the dynamic processes of soil water infiltration,
redistribution and extraction by evapotranspiration as steady state processes on a daily
basis. Irrigation requirements are generated by simulating a minimum of 20 years of
historical climate data.
The water balance equation for the crop root zone can be expressed as follows
∆S = P + Inet - QGW - QSR - ET
(3-1)
where ∆S is the change in soil water storage, P the rainfall, Inet the net irrigation
requirement, QGW the ground water drainage, QSR the surface runoff and ET the
evapotranspiration. For Florida’s sandy soils, the AFSIRS model assumes that surface
runoff is negligible or may be combined with drainage.
Rearranging the terms,
Inet = ∆S - P - QGW + ET
(3-2)
3.2.1 Soil Water Storage
The water storage capacity of the soil is defined as the product of the available
water holding capacity of the soil, and the effective root zone depth of the crop. The
water storage capacity of a soil provides a measure of the amount of water that can be
stored within the soil and thus indirectly provides a measure of additional water required
for a crop to grow. The water storage capacity (S) in the crop root zone is given by
S = WHC * z
(3-3)
where WHC is the available water holding capacity of the soil and z is the effective depth
of crop root zone. AFSIRS uses the water holding capacities of 766 soil types obtained
32
from a database of soil series mapped by the Natural Resources Conservation Service
(NRCS) in a cooperative program with Institute of Food and Agricultural Sciences
(IFAS). Available soil water is the water stored between the field capacity and the
permanent wilting point (PWP). Field capacity is defined as the volumetric water content
retained in the soil at a soil water potential of –10 centibars and PWP is the volumetric
water content retained in the soil at a soil water potential of –15 bars. The model provides
the maximum, minimum and average WHC values to account for the natural range in soil
water-holding capacities within a soil series.
The crop root zone for each crop is subdivided into irrigated and non-irrigated
zones. Separate water budgets are maintained for each zone. This division reflects the
routine irrigation practice of irrigating only the upper portion of the crop root zone where
most of the roots are located, rather than irrigating the maximum depth to which
individual roots penetrate. The irrigated root zone is assumed to be the upper 50% of the
maximum expected root depth and the lower 50% is assumed to be the non-irrigated root
zone. 70% and 30% of crop ET is extracted from the irrigated and non-irrigated zones,
respectively, when water is available. As the non-irrigated root zone dries during drought
periods, water becomes less available in this zone, and a greater proportion is extracted
from the irrigated zone in order to meet the total crop ET.
The crop root zone development for annual crops has four growth stages. The
average lengths of the growth stages differ by crop and are given as fractions of the cropgrowing season. The root zone is assumed to be constant at the minimum depth
throughout crop growth stage 1 (establishment of the crop). The root zone then increases
linearly towards the maximum depth throughout growth stage 2 (vegetative growth and
33
development). Finally, the maximum root zone is achieved at the beginning of crop
growth stage 3 (peak growth), and is maintained at that depth throughout growth stages 3
and 4 (maturity to harvest). The crop root zone is assumed to be constant for perennial
crops.
3.2.2 Rainfall and Evapotranspiration
The climate data required by the AFSIRS model are daily evapotranspiration and
rainfall. The AFSIRS model estimates potential crop evapotranspiration (ETc) from the
grass reference evapotranspiration (ETo) and crop water use coefficients (Kc). For
GWRAPPS, the daily reference evapotranspiration (ETo) values were calculated using
the American Society of Civil Engineers (ASCE) 90 version of Penman-Monteith
equation (Jensen et al., 1990). The ASCE PM-90 form of the equation is given by
∆( Rn − G ) + γ (1710 − 6.85T )
c ⋅ ETo =
1
(e s − e a )
ra
r
∆ + γ (1 + s )
ra
(3-4)
where c . ETo is the reference evapotranspiration (mm day-1), c is the conversion factor
used for conversion of MJ m-2 day-1 to mm day-1, Rn is the net radiation at the crop
surface (MJ m-2 day-1), G is the soil heat flux density (MJ m-2 day-1), T is the mean daily
air temperature (oC), es is the saturation vapor pressure (KPa), ea is the actual vapor
pressure (KPa), es-ea is the saturation vapor pressure deficit (KPa), ∆ is the slope vapor
pressure curve (KPa oC-1), γ is the psychrometric constant (KPa oC-1), and rs, ra are the
bulk surface and aerodynamic resistances (s m-1). The bulk surface resistance describes
the resistance of vapor flow through the transpiring crop and evaporating soil surface.
34
The aerodynamic resistance determines the transfer of heat and water vapor from the
evaporating surface into the air above the canopy.
AFSIRS adjusts the reference crop ETo values for application to the crop of
interest using crop coefficients (Kc).
ETc = ETo * Kc
(3-5)
The AFSIRS crop database provides Kc values for 16 perennial and 44 annual crops
(Smajstrla, 1990). For perennial crops, the Kc values are provided on monthly basis and
these values are linearly interpolated to calculate the daily crop water use coefficient. For
annual crops, Kc values are given for the crop growth stages 3 and 4. Kc values for
growth stage 1 are calculated as the ratio of rain to ETo, with a minimum value based on
the soil WHC. Kc values for crop growth stage 2 are linearly interpolated between the Kc
values for growth stages 1 and 3. These values are linearly interpolated to calculate the
daily crop coefficient. When rainfall occurs in amounts exceeding daily ETo, Kc is set to
1.0.
3.2.3 Drainage
The drainage is that portion of rainfall in excess of rain stored in the soil profile to
field capacity or extracted by ET as the water is redistributed in the soil. It is dependant
on the depth required to restore the crop root zone to field capacity (D) and the rainfall. It
is given by
QGW = 0
= P − [(θ − θ max )z + ET ]
if P < D
if P > D
where θ and θmax are the current and maximum soil water contents respectively.
(3-6)
35
3.2.4 Irrigation
The required irrigation is the amount of supplemental water that must be applied
to a crop to prevent yield-reducing water stress. Model irrigation occurs when the
available soil water storage decreases to a minimum allowable level. The minimum
allowable level is calculated as the product of the available soil water storage capacity in
the crop root zone and the allowable soil water depletion (AWD). The AWD is the
fraction of the available soil water storage capacity in the crop root zone that can be
depleted without significant reduction of crop yield. For perennial crops, AWD values are
provided on monthly basis. For annual crops, AWD values are given for the four crop
growth stages.
The irrigation requirement (Inet) is calculated as the depth of water required for
restoring the soil water to field capacity (maximum soil water storage capacity) in the
irrigated crop root zone. The gross irrigation requirement (Igross) is given by
I gross =
I net
η
(3-7)
where η is the irrigation system application efficiency.
3.2.5 Output Options
The output from the AFSIRS model contains the specifications entered by the
user and the model generated values. The model generated values include the irrigation
system design efficiency, fraction of soil surface assumed to be irrigated, soil water
holding capacity of the soil type specified and the simulated irrigation requirement tables.
The simulated irrigation requirement tables list the seasonal and monthly values of
drainage, evapotranspiration, effective rainfall and irrigation. The statistical
characteristics of the simulated irrigation values are also listed in the tables. AFSIRS
36
provides options for presenting the simulation results with daily, weekly, bi-weekly, or
monthly summaries of simulated irrigation values. In addition to the normal irrigation
values, AFSIRS calculates irrigation requirements for different drought conditions based
on the probability of occurrence of drought.
3.3 GWRAPPS Data
The data storage approach applied to GWRAPPS provides for efficient storage of
temporal and spatial data. GIS is efficient in storing and supporting large spatial data.
However, GIS cannot maintain large attribute data, such as long-term climate data, for
single spatial feature. Hence, the point climate data are stored in a RDBMS and the GIS
layer provides a link to the corresponding table in the RDBMS.
The GWRAPPS model requires non-spatial and spatial data. The non-spatial
information includes soil, crop and irrigation system information. The non-spatial
information is maintained in a database consisting of soil, crop and irrigation system
tables. The AFSIRS soils database consists of the 766 soil types mapped in Florida
counties soil surveys by the Soil Conservation Service (SCS). In GWRAPPS, the soilspecific information is provided by the Soil Survey Geographic (SSURGO) database.
SSURGO database is compiled by the soil survey division of the United States
Department of Agriculture (USDA)-Natural Resources Conservation Service (NRCS).
This database has the most detailed level of soil mapping conducted by the NRCS.
SSURGO provides a classification of soil types that is similar to the AFSIRS soil
classification. SSURGO uniquely identifies the soil types using Map Unit Identifier
(MUID). There are 4462 MUID values representing the soils in Florida. The GWRAPPS
soil lookup table provides information necessary to relate the MUID values from the
SSURGO database to the corresponding AFSIRS soil codes. The GWRAPPS lookup
37
table contains the MUID code, the soil series name, and the AFSIRS soil identification
code. The AFSIRS soil database includes the AFSIRS soil identification code, the soil
series name, the textural classification, the soil layer depths, and the lower and upper
available water contents for each layer. The AFSIRS model uses this information to
determine the available soil water content for the crop root zone.
GWRAPPS’ crop-specific database consists of the sixty crops available in
AFSIRS. The crops include sixteen perennial crops including pasture, citrus, alfalfa, and
turf and forty-four annual crops such as beans, cotton, potatoes, and wheat. The AFSIRS
crop database maintains the crop root zone depths and the crop coefficients. The AFSIRS
irrigation system database includes nine irrigation systems ranging from micro-irrigation
to flood irrigation. The AFSIRS irrigation database includes the efficiency of the system,
fraction of the soil surface irrigated, and the fraction of evapotranspiration extracted from
the irrigated zone when water is available in the non-irrigated zone. Appendix A provides
a complete listing of the crops and irrigation systems available in AFSIRS.
The spatial data include location, soil and climate data. A GIS polygon layer
representing the land-use/agricultural use provides the farm location information. The
site’s area and location are used to identify the site-specific soils and climate information.
The SSURGO soils layer provides information about the soil types through MUID. The
generated climate layer provides daily ET and P values at 368 locations spaced at a
resolution of 20 kilometers.
The climate layer was generated using point data from nine weather stations
interpolated to the region using the GWRAPPS’ climate interpolation tool. Table 3-1 lists
the weather stations and figure 3.2 shows their geographic location. Each station
38
measured daily minimum and maximum temperatures, solar radiation, wind-speed,
rainfall and relative humidity data. The GWRAPPS climate database, consisting of 21
years (1970-1990) of daily ETo and P values meets the AFSIRS minimum of twenty
years of historical climate data. These meteorological datasets were assembled from the
NOAA network of weather stations. This data represents the complete set of climate
stations that have long-term records of all the required parameters. Daily reference ET
was calculated by (3-4) using this data (see the Fortran code in Appendix B).
3.4 GWRAPPS Components
The components of the integrated system as shown in figures 3-3 through 3-7 are
system initialization tool, permitting tool, planning tool, and climate generation tool.
Figure 3-8 provides a screen capture of the GWRAPPS system.
3.4.1 System Initialization Tool
The primary goal of the system initialization tool is to reduce the user input
required for each simulation. The default GIS data layers and the system database are
specified with the system initialization tool (Figure 3-3). These default GIS data layers
include the agriculture farm, soils, and climate layers. The fields, which uniquely identify
the farm and the soil characteristics, are also selected with the system initialization tool.
Once established, these variables are maintained within the system for subsequent
simulations. If the layers in the system view are modified such that the default variables
are no longer valid, GWRAPPS alerts the user.
3.4.2 Climate Interpolation Tool
AFSIRS simulations require long term, daily historical climate data. Owing to the
non-uniform distribution of rainfall and differences in weather conditions between inland
and coastal areas in Florida, it is preferred to have the climate data source as near to the
39
farm as possible (Smajstrla et al., 1997). However, because of the cost and the
complexity involved in collecting the climate variables required to estimate ET, the
climate data are generally available only at a few locations. In such situations,
interpolation may be used to estimate the climatic conditions nearer to the farm.
Interpolation techniques provide a systematic means to extrapolate the existing point data
to the region by applying the same method of interpolation to each location. This
facilitates efficient planning of crop water requirements as the climate data used in the
AFSIRS simulations will be representative of the site itself. The GIS provides multiple
built-in interpolation techniques. Available interpolation techniques include inverse
distance weighting (IDW), kriging, and spline interpolation. IDW is not suitable for
locations where there is non-uniform distribution of climate. As described earlier, Florida
has a non-uniform distribution of climate and hence IDW was considered unsuitable.
Kriging is a robust interpolation technique and requires more stations to perform the
statistics necessary for interpolation. The number of available stations for GWRAPPS is
less then the required number to perform kriging successfully. Spline interpolation
method fits a minimum curvature surface through the input points. Conceptually, this is
analogous to bending a sheet of rubber to pass through the observation points while
minimizing the total curvature of the surface. The spline fits a mathematical function to a
specified number of the nearest input points while passing through all input points (Mitas
and Mitasova, 1999).
The GWRAPPS’ climate interpolation tool (Figure 3-4) is a utility that generates
regional climate data from point data. Using this utility, gridded climate data can be
generated for a large time span. The climate interpolation tool uses the spline
40
interpolation technique to generate climate data. The climate interpolation tool requires a
climate station point feature layer, polygon feature representing the area of the region,
and the output grid resolution. The polygon feature area delineates the interpolation
region. The output grid resolution sets the cell size of the output climate layer. The point
layer provides the location information of the existing climate stations and their links to
the corresponding climate data tables in the relational database management system
(RDBMS). A RDBMS is created and maintained for each cell in the grid surface. A
climate point layer provides a link to the corresponding grid’s database. The location of
each point in the climate layer is the center of the interpolated surface’s grid cell. Each
cell’s attributes identify the RDBMS containing the cell’s climate data.
The climate interpolation tool reads the daily ET and P values from the RDBMS
linked to the climate feature layer. For each ET and P value in the database, the climate
interpolation tool performs a spline interpolation and generates a temporary interpolated
grid surface with ET or P values. The values from the temporary interpolated grid are
transferred into the RDBMS.
3.4.3 Permitting Tool
The permitting tool analyzes a single farm’s irrigation requirements. The
permitting tool’s input interface has two components. The first interface (Figure 3-5)
prompts the user for site-specific data and the farm soil types. The other interface (Figure
3-6) prompts the user for crop-specific information. The climate, soil and crop data are
converted to the AFSIRS input format. The farm’s crop water requirement is estimated
using the AFSIRS model.
Site-specific data necessary to determine a consumptive use permit are collected
from the appropriate GIS layers. The ArcObjects GIS modules select the climate and soil
41
information based on the farm location. The tool selects the climate point location nearest
to the site. The selected location’s climate data is converted from an RDBMS to an
AFSIRS compatible ASCII file. If a farm consists of multiple soil types, the tool provides
options to use all the existing soil types in the farm or a single soil. If the user selects a
single soil type, the simulation is run for only that soil type and the user specified
acreage. If the user elects to use all the farm’s soils, the area corresponding to each soil
type is determined. The tool also provides the option of choosing the soil type from the
list of 766 soil types available in AFSIRS. The simulation is run for either the single
selected soil or for all the soils within the farm. If the farm has multiple soils, the
resultant crop water demand is calculated as an area weighted average.
The crop-specific information is entered by the user. A GUI facilitates the
selection and entry of crop-specific information. The crop information is validated for the
AFSIRS conditions. The crop and soils information is used to generate an AFSIRS input
file. If there is one soil type, a single AFSIRS input file is created. For multiple soils, a
batch input file is created.
The permitting tool invokes the AFSIRS DLL and passes the AFSIRS input file.
The DLL runs the simulation and generates the crop water requirements for the farm. The
DLL writes the AFSIRS simulation results to an ASCII file and also returns them to
GWRAPPS. The results include monthly and annual crop water requirements for normal
and drought scenarios. The drought conditions include 1-in-5 and 1-in-10 drought
conditions. The results are displayed to the user by the integrated system.
3.4.4 Planning Tool
The planning tool analyzes the irrigation requirements of a region (figure 3-7).
The planning tool is similar to the permitting tool in that the same AFSIRS model and
42
GIS soils and climate data are used to generate the water requirements. However, the
planning tool provides the means to analyze all water permits simultaneously.
In addition to the soils and climate GIS layers, the planning tool requires a permit
layer and the selection of the water holding capacity to be used for estimating the crop
water requirements. The permit layer contains the location and crop information for each
permit. The planning tool sequentially evaluates each permit. The permit’s irrigation
system details and the crop growth season are determined from look up tables. Again, the
selected soil and climate layers provide the soil and climate information for each permit
location. The soils layer and the permit layer are intersected to provide features with
unique site, crop and soil characteristics. An AFSIRS batch file is created for each site.
As with the permitting tool, the batch file is passed to the AFSIRS DLL, which
runs the simulation. ArcObjects GIS modules stores the AFSIRS simulation results by
site as attribute data in the layer. The final output from the planning tool is a layer
providing crop water requirements for each permit site. The layer provides monthly and
annual crop water requirements for three different irrigation scenarios. The scenarios
represent normal conditions, 1-in-5 drought conditions, and 1-in-10 drought conditions.
The output layer provides a graphical representation of the crop water requirements.
Also, the GIS framework may be used to create simple charts and summary tables from
the crop water requirements layer data.
3.5 Applications of GWRAPPS
3.5.1 Case Study 1
In this case study, the net irrigation requirements of a single farm growing pasture
are estimated. The farm in the case study was approximately 100 ha and had eight
43
different soils. The farm’s dominant soil, Sparr, made up approximately 38% of the total
acreage.
3.5.1.1 Study area
The study area is located in North Central Florida (Figure 3.2). It includes the
portion of Alachua County within St. Johns River Water Management District
(SJRWMD). The study area lies between 82o1’W and 82o28’W longitude and 29o24’ and
29o48’ latitude. Temperatures are moderated by the wind from the Gulf, producing mild
winters and relatively cool summer nights. The study area has a mean potential
evapotranspiration of approximately 127 cm and a mean rainfall of about 132 cm. There
are about seventy-nine different soil types within the study area. Approximately 12% of
the study area is agricultural land.
3.5.1.2 Data
The data requirements for the GWRAPPS are described in an earlier section.
Here, the data specific to the study area are described in detail. The non-spatial
information provided in a Microsoft Access database includes the annual and perennial
crops and the irrigation system parameters. The database also provides a lookup table for
the different soil types in the AFSIRS soil database and the GIS layers.
Consumptive Use Permit (CUP) boundary layer maintains the farm location data
for the study area. This layer is created and maintained by SJRWMD. The Alachua
County’s SSURGO soil layer provides the soil-specific information. These GIS layers are
obtained from the SJRWMD’s GIS Export library. The climate layer used for AFSIRS
simulations is generated using the climate interpolation tool.
44
3.5.1.3 Results and discussion
To better understand the impact of spatially distributed climate and soils, three
approaches were used to estimate the crop water requirements. The first approach
determines, the farm’s water requirements using the original Microsoft Disk Operating
System (MS-DOS) based AFSIRS model. Climate data are from the nearest climate
station (Gainesville). The farm’s predominant soil type, Sparr, was used for the AFSIRS
model simulation. Second approach determined the water requirement using GWRAPPS
for the same farm, crop, and the farm’s predominant soil type. In this case, the
GWRAPPS selects the climate data of the location nearest to the farm from the
interpolated climate layer. The third approach is identical to the second approach except
the analysis is conducted using all the soils found at the farm (Table 3-3).
Table 3-2 gives the irrigation requirements for normal, 1-in-5 year, and 1-in-10
year drought irrigation scenarios for pasture using the three approaches outlined above.
The normal irrigation irrigation requirements resulting from the three approaches are
shown in figure 3-9. The annual normal irrigation ranges from 15.04 cm to 17.27 cm with
approximately a 2.23 cm difference among the values from the traditional AFSIRS and
the GWRAPPS simulation results. The monthly differences varied from 0 cm to 0.76 cm.
An intercomparison of the AFSIRS and GWRAPPS simulation results may be
used to examine the effect of climate and soil on crop water requirement estimates. A
comparison between the GWRAPPS single soil scenario and the DOS-based AFSIRS
results is essentially an examination of the influence of distributed climate information on
the irrigation requirement estimation. The DOS-based AFSIRS uses the nearest climate
station to estimate water demand while the GWRAPPS uses an interpolated climate
database. The farm used in this case study was approximately 15 km from the nearest
45
climate station available for the DOS-based AFSIRS simulation. Given this relatively
close proximity, the difference between the models results was approximately 0.76 cm
for both the average irrigation requirement and the 1-in-10 drought irrigation
requirement. These differences appear to be primarily due to the differences in
precipitation. This result is anticipated for a humid climate as large differences in local
water input from effective rainfall result from localized convective systems and tropical
systems. Point rainfall measurements may miss local events all together or extrapolate
local events to inappropriately large scales. When spatial fields display the type of
patchiness typically observed in Florida, point measures are of limited value to regional
scale models. Thus, correctly capturing the distributed precipitation fields and using an
accurate climate data to estimate evapotranspiration is critical to modeling crop water
demand.
The GWRAPPS single soil scenario and the GWRAPPS multiple soil scenario
results may be used to examine the influence of distributed soils information on the
irrigation requirement estimation. The single soil scenario and the multiple soils scenario
differed by approximately 1.47 cm and 2.62 cm for the normal and 1-in-10 irrigation
requirements, respectively. The Sparr soil was used in the single soil scenario. Two other
soils, Wauchula and Lochloosa, had soil properties such as a higher available water
holding capacity (WHC) that resulted in lower irrigation demand. Another soil, Newhan,
had a very high irrigation demand, but comprised a very small part of the total farm
acreage. The soil water storage capacity is a function of the WHC and the crop root zone
depth. The WHC is expressed as a volumetric decimal fraction and the crop root zone
depth is expressed in units of length. Therefore, the response from the same soil will be
46
different for crops with different root zone depths. In this case study, all the simulations
are run using pasture, which has an irrigated crop root zone depth of 91.44 cm. Therefore,
the differences in the soil response for the different irrigation scenarios in this case study
are from the WHC of the soils. A soil with high WHC can store more water when
compared with a soil with low WHC. Hence, soils with high WHC require less water for
irrigation. Table 3-3 gives the acreage and the WHC of the eight soils within the irrigated
crop root zone present in the farm. In the farm used in the case study, two soils Wauchula
and Lochloosa had a high WHC of more than 0.3%, and collectively made up
approximately 27.5% of the total acreage. Newhan soil had the lowest WHC of 0.13 but
comprised only about 3% of total acreage. The predominant soil, Sparr had a WHC of
0.17. The remaining soils had a WHC within the range of 0.16 to 0.22. When all eight
soils were used to generate irrigation demand, the overall demand was reduced.
The GWRAPPS, with multiple soils, is able to provide a comprehensive picture of
the total water demand that is not readily apparent due to the complex interaction of soil
characteristics and their relative contribution to the area of interest. In the present case
study, there was a difference of about 1 cm in the normal irrigation requirement estimated
using the single soil scenario and the multiple soils scenario. The single soil scenario
estimated the normal irrigation requirement to be 16.51 cm and the multiple soils
scenario estimated the same to be 15.04 cm. In the multiple soils scenario, the normal
irrigation requirement ranged from 11.18 cm to 22.10 cm. These values were weighted
based on the percent acreage of each soil to estimate the farm’s overall normal irrigation
requirement. Again, the only difference between single soil and multiple soils scenario
runs was the number of soils used in the model runs.
47
3.5.2 Case Study 2
In this case study, the net irrigation requirements for all the farms growing ferns
in Volusia County, Florida are estimated. There are 161 farms growing ferns comprising
about 4068 ha.
3.5.2.1 Study area
The study area, Volusia County, is located in the east coast of Central Florida
(Figure 3.2). The study area lies between 80o40’W and 81o41’W longitude and 28o37’
and 29o26’ latitude. Volusia County has an average summer temperature of
approximately 27 oC, average winter temperature of approximately 16.4 oC, and a mean
rainfall of about 122 cm. The study area is comprised of seventy-nine different soil types.
Approximately 5% of the total study area is agricultural land. The data used for this case
study were the same as the data used in Case Study 1, except for the soils layer. The soils
layer has been replaced with Volusia County’s SSURGO soil layer using the system
initialization tool.
3.5.2.2 Results and discussion
The crop water requirements for ferns in Volusia County are estimated using the
GWRAPPS planning tool. The AFSIRS engine used for estimating the crop water
demand of a single farm is used for estimating the crop water demand of all the farms
growing ferns in the study area.
Two approaches were used to determine the crop water requirements. In the first
approach, the crop water requirements were determined using the predominant soil type
in the farm. In the second approach, the crop water requirements were determined using
all the soils existing within the farms. Average soil water holding capacities are used in
both the approaches. The nearest climate data point for each farm is selected from the
48
climate layer. Seven different climate data points were used in estimating the crop water
demand. The farm sizes ranged from 0.5 ha to 307 ha. The spatial variability of soils
ranged from a single soil to fifteen different soils in a single farm. The GWRAPPS’
planning tool simulation results give an estimate of the crop water demand on a monthly
and annual basis. The final output is a GIS layer providing the estimated crop water
requirements for each farm. The crop water requirements include monthly and annual
estimates for normal, 1-in-5 year drought, and 1-in-10 year drought irrigation scenarios.
Table 3-4 gives the summary of the irrigation requirements for normal, 1-in-5
year drought, and 1-in-10 year drought irrigation requirements estimated from the single
soil and multiple soil scenarios. There is about 1 cm difference in the irrigation
requirements estimated from single soil and multiple soil approaches. The normal
irrigation requirement was 16.37 million cubic meters using single soil and 16.16 million
cubic meters using multiple soil approach. Figure 3-10 shows the average irrigation
requirement estimates. The mean absolute error for this case study was 2.16 cm
approximately. Though the combined irrigation requirements of all the farms from both
the approaches were not significantly different, there are significant differences in the
individual farm’s irrigation requirements. Figure 3-11 compares the normal irrigation
requirements by farm for both approaches. Out of the 161 farms under consideration, 80
farms had lower irrigation requirements, 45 had the same irrigation requirements, and the
remaining 36 had higher irrigation requirements when multiple soils within the farm are
considered as opposed to considering the predominant soil in the farm. The magnitude of
the normal irrigation requirement differences ranged from 0 to 22.8 cm. These
differences are largely due to the impact of a farm’s soil variability on the water balance.
49
In case study 1, the soil WHC has a significant influence on the estimated irrigation
requirements.
Several farms within the study area exhibited greater difference in the normal
irrigation requirements from the two approaches. Two sets of farms, identified in Figure
3-11, are studied in detail. The soils within each farm were identified and their WHC
capacities were compared. In the first set of observations (Set 1), the normal irrigation
requirements from single soil scenario were significantly lower than those from multiple
soils scenario. The predominant soil type in most of these farms was of soil texture
‘muck’. In the second set of observations (Set 2), the irrigation requirements were much
higher when all the soils within the farm are considered. In these farms also, soils of
texture type ‘muck’ were present but were not the predominant soils within the farm.
Thus, the large amount of variations between the two approaches can be attributed to the
soil types with texture ‘muck’ and their dominance within the farm. Soils of ‘muck’
texture have a very high WHC when compared to the other soils of sandy texture. Soils
of texture ‘muck’ significantly affect the irrigation requirements and must be given
careful attention while estimating crop water use requirements.
This impact may be better understood by closely examining a farm that exhibited
large differences in the irrigation estimates. A 9.7 ha farm is considered. The farm has
two soils, Tavares and Hontoon. Tavares and Hontoon comprise about 34.5% and 65.5%
of the farm’s total area respectively. The soil texture of Tavares was ‘silt’ and that of
Hontoon was ‘muck’. The WHC of the soils under consideration differed by about 0.08.
The average WHC of Tavares and Hontoon is about 0.13 and 0.05 respectively.
50
When the farm’s irrigation requirements are determined only using the
predominant soil in the farm, Hontoon, the normal irrigation requirement was about
37.59 cm. When GWRAPPS was run for the same farm using Tavares soil, the normal
irrigation requirement was estimated to be about 55.88 cm. GWRAPPS s estimates the
actual irrigation requirements as a weighted average of the irrigation requirements of all
the soils within the farm. The normal irrigation requirement estimated using both the soils
was approximately 49.17 cm. This presents an 11.58 cm of difference in the irrigation
requirements estimated using a single soil (the predominant soil) versus using all the soils
within the farm. The climate and soil parameters used in all the simulation runs for this
particular farm were constant. The same crop root zone depth is used in both the
approaches. Therefore, the difference in the irrigation requirements can be attributed to
the soil variability, and in particular to the WHC, within the farm. Thus, the WHC has a
significant influence on the irrigation requirements estimated using AFSIRS. GWRAPPS
planning tool is able to account for this soil variability in its crop water use estimates.
GWRAPPS also accounts for the climate variability. This makes GWRAPPS an efficient
tool for planning regional scale crop water use.
3.6 Conclusion
Discrepancies often exist between water quantities predicted by various planning and
permitting models due to the different modeling approaches. In general, the farm-scale
permitting models are designed to be applied to a range of crops over a large region. To
allow this flexibility, the models limit the climate and soil variability considered.
However, farm-scale models tend to be more robust in modeling the agricultural water
use processes than those used for planning. Planning models need to consider the climate
and soil variability at regional scales. The complexity involved in modeling the climate
51
and soil variability has required a simplified representation of the agricultural water use
processes. The GWRAPPS is able to provide a consistent tool for planning and
permitting purposes by extending AFSIRS from farm-scale estimation to regional-scale
estimation of irrigation requirements. Again, the GWRAPPS simulations also account for
the spatial variability of soils, climate and the variation in the farm sizes. Thus,
GWRAPPS provides crop water requirement estimate at different scales without
simplification of the agricultural water use processes. The smallest scale remains the farm
scale as required for an individual permit application. The larger scales will range from
local municipalities to County to State scales.
52
Table 3-1. Weather stations used in GWRAPPS.
Latitude
Longitude
Elevation (m) above
Mean Sea Level
Daytona Beach, FL
29o 11'
W 83o 03'
2.0
Gainesville, FL
29o 38'
W 82o 22'
29.3
Jacksonville, FL
30o 30'
W 81o 42'
9.0
Key West, FL
24o 33'
W 81o 45'
1.0
Miami, FL
25o 48'
W 80o 16'
2.0
Mobile, AL
30o 41'
W 88o 15'
7.0
Tallahassee, FL
30o 23'
W 84o 22'
11.0
Tampa, FL
27o 58'
W 82o 32'
3.0
West Palm Beach, FL
26o 41'
W 80o 06'
6.0
Location
Table 3-2. Comparison of results from AFSIRS and GWRAPPS simulation runs for a single farm growing pasture in Alachua County.
Normal irrigation (cm)
1-in-5 year drought irrigation (cm) 1-in-10 year drought irrigation (cm)
GWRAPPS
GWRAPPS
GWRAPPS
GWRAPPS
GWRAPPS
GWRAPPS
Month
Multiple
Multiple
AFSIRS
Multiple
AFSIRS
AFSIRS
Single soil
Single soil
Single soil
soils
soils
soils
January
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
February
0.25
0.25
0.13
N/A
N/A
0.00
N/A
N/A
0.00
0.76
0.76
0.69
0.00
0.00
0.05
4.06
4.06
2.77
April
May
June
July
August
September
October
November
December
2.79
5.59
2.54
1.02
0.76
0.76
2.03
0.76
0.25
2.79
5.59
1.78
1.52
0.51
1.02
1.52
0.76
0.25
2.67
5.05
1.96
1.30
0.33
0.84
1.19
0.69
0.23
4.57
7.87
N/A
0.00
0.00
0.00
4.57
0.00
N/A
4.57
7.87
4.32
1.78
N/A
0.00
4.32
0.00
N/A
4.22
5.23
2.44
0.71
0.00
0.03
3.05
0.10
0.00
5.84
9.40
N/A
1.27
3.81
0.25
4.57
4.06
N/A
5.84
9.65
4.57
3.56
N/A
1.27
4.57
4.06
N/A
5.77
6.38
4.50
3.51
0.46
0.79
3.15
3.02
0.00
Total
17.27
16.51
15.04
25.65
24.89
22.23
31.75
30.99
28.37
53
March
54
Table 3-3. Summary of the existing soil types in the farm used in case study 1.
Farm Acreage
WHC within the
Normal Irrigation
Soil Type
(%)
Irrigated Root Zone
(cm)
LOCHLOOSA
8.67
0.33
11.18
Pomona
7.40
0.21
14.73
Pelham
19.40
0.16
16.00
Bonneau
2.83
0.22
14.22
Wauchula
18.85
0.30
11.68
Surrency
2.23
0.18
16.26
Sparr
37.88
0.17
16.51
Newhan
2.74
0.13
22.10
Table 3-4. Comparison of regional crop water use requirements for ferns in Volusia
County, Florida using GWRAPPS’ single soil and multiple soil scenarios.
Normal
1-in-5 drought
1-in-10 drought
Irrigation (cm)
irrigation (cm)
irrigation (cm)
Month
Single
Multiple
Single
Multiple
Single
Multiple
Soil
soil
Soil
soil
Soil
soil
JANUARY
3.27
3.20
4.55
4.47
5.34
5.27
February
3.31
3.24
4.46
4.37
5.17
5.13
March
6.43
6.35
8.14
8.05
8.99
8.91
April
9.06
8.97
11.40
11.32
12.56
12.50
May
8.68
8.62
11.58
11.54
13.13
13.11
June
5.85
5.79
8.29
8.21
9.78
9.70
July
5.66
5.59
7.66
7.57
8.81
8.74
August
3.86
3.78
5.50
5.39
6.49
6.36
September
3.84
3.75
5.67
5.53
6.93
6.78
October
4.87
4.77
6.73
6.60
7.81
7.68
November
3.98
3.91
5.35
5.29
6.12
6.06
December
3.11
3.05
4.61
4.48
5.66
5.56
Total
61.91
61.03
69.19
68.38
72.22
71.48
55
Figure 3-1. Schematic representation of GWRAPPS.
Figure 3-2. Study areas and climate stations.
56
Figure 3-3. System initialization tool graphical user interface.
Figure 3-4. Climate interpolation tool graphical user interface.
57
Figure 3-5. Permitting tool graphical user interface for farm-specific information.
Figure 3-6. Permitting tool graphical user interface for crop-specific information.
58
Figure 3-7. Planning tool graphical user interface.
Figure 3-8. Screen capture of GWRAPPS.
59
6
AFSIRS
GWRAPPS single soil
Normal Irrigation (cm)
5
GWRAPPS multiple soils
4
3
2
1
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 3-9. Comparison of 1-in-10 irrigation requirements estimated using AFSIRS and
GWRAPPS.
10
Single Soil
9
Multiple soils
Normal Irrigation (cm)
8
7
6
5
4
3
2
1
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 3-10. Comparison of GWRAPPS’ estimated normal irrigation requirements of
ferns in Volusia County.
60
Multiple soils Irrigation (cm)
80
Set 1
60
40
Set 2
20
0
0
20
40
60
80
Single soil Irrigation (cm)
Figure 3-11. Comparison of individual farm’s single soil vs. multiple soils normal
irrigation requirements in Volusia County.
CHAPTER 4
GWRAPPS: A SENSITIVITY ANALYSIS TOOL
4.1 Introduction
Irrigation scheduling is the technique for timely and accurately supplying water to
the crop. Irrigation scheduling is the key to conserving water, improving irrigation
performance and sustainability of irrigated agriculture. A large number of models have
been developed to determine when and how much of water is needed by the crop. These
models range from soil and plant monitoring models to soil water balance and simulation
models. Models based on a soil water balance are the most commonly used models as the
data required for the model are available easily (Hess, 1996; Plauborg et al., 1996).
Irrigation is critical for the crop. Over-irrigation can have negative effects on the
quantitative and qualitative crop yield (Deumier et al., 1996). Under-irrigation subjects
the crop to stress and affects the crop yield adversely. As over-irrigation or underirrigation is undesirable, it is important that the models are accurate in estimating the crop
water requirements. For the models to be accurate, it is necessary to determine the effect
of individual components in the model on the crop water use estimations. Hess (1996)
pointed out that the ‘main source of error is in the measurement (or estimation) of input
data’. Itier (1996) also indicated the need for considering the different terms in the
equation separately and analyze the problems related to each of them. This can be
achieved by performing sensitivity analysis on the critical components of the model.
Performing a sensitivity analysis for a mathematical simulation model is helpful
in identifying key model parameters and simulation errors resulting from parameter
61
62
uncertainty. Sensitivity analysis also validates the model and its applicability for different
environmental, crop and soil conditions. This validation helps in reducing the amount of
site-specific research needed to assess the applicability of the models. Several authors
have studied the effect of the model parameters involved in the water balance models.
Hagenmann and Kleidon (1999) found that the rooting depth has a substantial effect on
the hydrology of the tropical river catchments. Stockle et al. (1997) performed sensitivity
analysis on CropSyst using different evapotranspiration (ET) and soil water transport
models. They concluded that the model performance was the best when using the most
detailed ET method and soil water transport model. They attributed the variation in the
model performance to the differences in the vapor pressure deficit estimation by the
different ET methods. McCabe and Wolock (1992) and Singh et al. (1993) conducted
similar sensitivity analysis on water balance based models. They concluded that the
models were most sensitive to the stomatal resistance of the crop. McCown (1973)
studied the influence of available soil water storage capacity (AWSC) on the growing
season length and yield of tropical pastures. His study indicated that differences among
soils in the AWSC have a substantial influence on the growing season length and thus on
the irrigation demand.
This chapter describes a sensitivity analysis performed using GWRAPPS to
determine the influence of the environmental, soil and crop factors on the net irrigation
requirements estimated by AFSIRS. The databases available as input datasets for the
AFSIRS model are limited in detail. Therefore, it is necessary that the sensitivity of the
AFSIRS model to the critical variables be ascertained. Capece et al. (2000) attempted to
determine the relative impact of crop, environmental, soil and management factors on the
63
irrigation estimates generated by the AFSIRS model. They concluded that the water
holding capacity of the soil had the most significant influence on the irrigation estimates.
However, the impacts of climate and soil variability within the farm were not accounted
for due to the lack of the necessary data. GWRAPPS extends the use of the AFSIRS
model for estimating regional scale crop water use. Thus, GWRAPPS provides an
opportunity to perform sensitivity analysis of climate and soil variability on the AFSIRS
model when applied on a regional scale.
4.2 AFSIRS Water Budget
The AFSIRS model is based on a water budget of the crop root zone. Water
budget irrigation scheduling models the physical process of water movement into the soil,
through the soil, and through the plant. This approach uses the crop and soil
characteristics, rainfall and evapotranspiration events to determine the amount of water
available for the crop in the soil. Some assumptions are made about the interactions of
water in soil-plant-atmosphere systems to simplify the AFSIRS model (Smajstrla, 1990).
The conceptual basis of the simplified soil water balance used in AFSIRS is explained
below.
The soil water balance is an account for all the quantities of water added or stored
in a given volume of soil during a given period of time (George et al., 2000). Soil water
refers to the water held in the crop root zone at a given time. In general terms, the change
in soil water can be expressed as the difference in the water inputs to the soil and the
water losses from the soil. Water can be added to the soil in two ways: precipitation, and
irrigation. Water is removed from the soil in the form of evaporation through the soil
surface and transpiration through plant. In addition, water is also removed through
drainage and surface runoff. Surface runoff occurs following a significant rain event.
64
Thus, the water budget equation for irrigation scheduling on a daily basis can be written
as
∆S = R + I − ET − (D + RO )
(4-1)
where ∆S is the change in soil water storage, R is the rainfall measured at the field site, I
is the irrigation applied, ET is the crop evapotranspiration, D is the drainage, and RO is
the runoff (Smajstrla et al., 1997). AFSIRS does not separate runoff from drainage. Also,
the soil water storage is determined based on the crop root zone depth and the water
holding capacity of the soil. The AFSIRS model determines the crop evapotranspiration
using the potential evapotranspiration and crop coefficients.
4.3 GWRAPPS Sensitivity Analysis
This section describes an analysis performed to determine the sensitivity of the
AFSIRS model’s net irrigation requirement estimates to different environmental, soil and
crop factors. The critical variables in the environmental, soil and crop components of the
AFSIRS model are the ET, the water holding capacity of the soil and the crop root zone
depth respectively.
The sensitivity analysis is performed on a regional scale using GWRAPPS. This
analysis is performed on two crops, ferns and potatoes. For each crop, the sensitivity
analysis involved four ET methods, three water holding capacities, and five crop root
zone depths. This resulted in a total of twenty simulation runs of GWRAPPS planning
tool.
4.3.1 Data
Two study areas are considered for performing the sensitivity analysis described
above. For the GWRAPPS model runs involving ferns, the study area described in case
65
study 2 of chapter 3 is used. Ferns are a perennial crop and therefore the irrigation
requirements are estimated for the whole year.
For the GWRAPPS model runs on potatoes, St. Johns County, Florida is chosen
as the study area. St. Johns County is located in the east coast of Florida. The study area
lies between 81o10’W and 81o42’W longitude and 29o36 and 30o16’ latitude. The study
area comprises of sixty-six different soil types. About 37% of the study area is either
agricultural land or golf courses. St. Johns County, along with Flagler and Putnam
County, comprise about 85% of the total potato production of Florida (Hochmuth and
Cordasco, 2000). There are 109 farms growing potatoes in St. Johns County and have a
total acreage of 18,173 ha. In St. Johns County, potatoes are planted in DecemberJanuary and are harvested in May-June in what is considered the spring planting. In the
present scenario, the planting and harvesting dates are chosen to be January 1 and May
15, respectively.
All the GIS layers excluding the climate layers are obtained from SJRWMD GIS
data repository. The climate layers were generated using the climate interpolation tool.
4.3.2 Evapotranspiration
The evapotranspiration (ET) from vegetated surfaces is a critical component in
the computation of water balances to estimate soil water availability and irrigation
requirements. Evapotranspiration is a combination of two processes: evaporation and
transpiration (Dingman, 1994). Evaporation is the direct vaporization of water from a free
water surface, such as a lake or any wet or moist surface. Transpiration is the flow of
water vapor from the interior of the plant to the atmosphere. Direct measurement of ET is
difficult. Hence, numerous methods for estimating ET were developed by hydrologists.
These methods were based on the physics of evaporation, conservation of mass and
66
energy and other basic principles. According to Dingman (1994), the choice of the
method depends on: (1) the purpose of the analysis i.e., determination of the amount of
ET that has actually occurred in a given situation, incorporation in a hydrologic model,
reservoir design, etc., (2) the available data and (3) the time period of interest i.e., hourly,
daily, monthly. The Penman Monteith method is the most widely accepted method for
estimating ET when sufficient data are available. However, no method is considered the
most appropriate when data are limited (Itier, 1996).
Jacobs and Satti (2001) conducted a detailed literature review on the existing
methods to estimate reference crop ET. The methods for estimating ET can be divided
into three general approaches: temperature methods, radiation methods and combination
methods. The temperature methods are empirical equations that rely on air temperatures
as a surrogate for the amount of energy that is available to the reference crop for
evapotranspiration. The generalization of the temperature methods is limited owing to the
absence of a direct, unique relationship between temperature and energy. Radiation
methods use a measure of solar radiation and air temperature to estimate ET. The solar
radiation can be used directly to estimate ET or indirectly to provide a measure of the net
available radiation. The combination methods are based on the original Penman (1948)
combination equation consisting of two terms, the radiation term and the aerodynamic
term. The combination methods require more data than other methods including net
radiation, air temperature, wind speed and relative humidity. The combination methods
give the best results for a variety of vegetated surfaces and climates.
Based on the analysis performed by Jacobs and Satti (2001) on fourteen different
ET methods and Itenfisu et al. (2000), four methods of estimating ET were considered for
67
performing the sensitivity analysis of ET in the AFSIRS model. They are the Hargreaves
method, the Institute of Food and Agricultural Sciences (IFAS) modified Penman
method, the American Society of Civil Engineers (ASCE) 1990 Penman-Monteith
Method, and the Food and Agriculture Organization (FAO) Penman-Monteith method.
4.3.2.1 Hargreaves method
The Hargreaves method (Hargreaves and Samani, 1985) of computing daily grass
reference evapotranspiration is an empirical approach used where the availability of
weather data is limited. The original Hargreaves equation is:
ETo = 0.0135
Rs
(T + 17.8)
λ
(4-2)
where ETo is the reference evapotranspiration (mm day-1), λ is the latent heat of
vaporization (MJ kg-1) = 2.45 MJ kg-1, Rs is the solar radiation (MJ m2 d-1), and T is the
mean air temperature (oC).
Often, solar radiation data are not available. Therefore, an alternate approach is
available that requires only measurements of maximum and minimum temperature, with
extra terrestrial radiation (Ra). Ra is dependant on the latitude and the day of the year. The
relationship between Rs and Ra is given by
R s = k rs R a (Tmax − Tmin )0.5
(4-3)
where krs is the adjustment coefficient based on mean monthly relative humidity. krs is
0.16 for interior regions not influenced by a large water body and 0.19 for coastal
locations. Tmax is the mean monthly maximum temperature (oC), and Tmin is the mean
monthly minimum temperature (oC).
With this estimate, the method becomes a temperature-based method. The
working Hargreaves equation is given by
68
ETo = 0.0023(T + 17.8)( Tmax − Tmin )0.5 R a
(4-4)
where Ra is in mm day-1.
4.3.2.2 IFAS modified Penman method
The IFAS modified Penman method (Jones et. al, 1984) is based on four major
climatic factors: net radiation, air temperature, and wind speed and vapor pressure deficit.
The potential evapotranspiration after taking into account all the above factors can be
expressed as
ETo =
γ
∆ Rn
+
Ea
∆ +γ λ ∆ +γ
(4-5)
where Rn is the net radiation (cal cm-2 day-1), ∆ is the slope of saturated vapor pressure
curve of air (mb oC-1), γ is the psychrometric constant (0.66 mb oC-1), and λ is the latent
heat of vaporization of water (cal cm-2 mm-1).
From Bosen (1960), saturated air vapor pressure as a function of temperature,
e(T), and the slope of the saturated vapor pressure-temperature function, ∆, can be
computed as follows
[
e(T ) = 33.8639 (0.00738T + 0.8072 )8 − 0.000019(1.8T + 48) + 0.001316
[
∆ = 33.8639 0.05904(0.00738T + 0.8072 )7 − 0.0000342
]
]
(4-6)
(4-7)
Penman proposed couple of relationships for calculating the net radiation. They
are
Rn = (1 − α )Rs − Rb
(
(4-8)
)


R
Rb = σT 4 0.56 − 0.08 ed 1.42 s − 0.42 
Rso


(4-9)
69
where Rn is the net radiation (cal cm-2 day-1), Rs is the total incoming solar radiation (cal
cm-2 day-1), Rb is the net outgoing thermal or long wave radiation (cal cm-2 day-1), α is the
albedo or reflectivity of surface for Rs. α is 0.23 for green vegetated surfaces, σ is the
Stefan-Boltzmann constant (11.71 x 10-8 cal cm-2 day-1 oK-1), T is the average air
temperature (oK), Rso is the total daily cloudless sky radiation, Rs is the total incoming
solar radiation. Rs = (0.35 + 0.61 S) Rso, and S is the percent shine hours.
The empirical equation to calculate Ea is given by
Ea = 0.263(ea − ed )(0.5 + 0.006u2 )
(4-10)
where ea = vapor pressure of air and is given by (emax + emin)/2 (mb), emax is the maximum
vapor pressure of air during a day (mb), emin is the minimum vapor pressure of air during
a day (mb), ed is the vapor pressure at dewpoint temperature (mb), and u2 is the wind
speed at a height of 2 m (km day-1).
Wind speed is measured at many different heights above the ground surface. The
Penman equation requires wind speed at a height of 2 m. Wind speed can be adjusted to a
height of 2 m using
2
u2 = u z  
z
0.2
(4-11)
where uz is the wind speed at height z (km day-1), and z is the height of wind
measurement (m).
The latent heat of vaporization of water is given by
λ = (59.59 − 0.055Tavg )
(4-12)
where Tavg is the average daily temperature oC. Tavg = (Tmax + Tmin)/2, where Tmax is the
maximum daily temperature (oC), and Tmin is the minimum daily temperature (oC).
70
Combining all the above equations into a single equation, the working Penman
equation is given by
(
)


Rs
∆ 
4
− 0.42 
(1 − α )Rs − σT 0.56 − 0.08 ed 1.42
Rso
∆ +γ 


ETo =
λ
+
γ
∆ +γ
[0.263(0.5 + 0.0062u 2 )(ea − ed )]
(4-13)
4.3.2.3 ASCE 1990 Penman Monteith method
The original Penman-Monteith method has been modified by many researchers
and extended to crop surfaces by introducing resistance factors. The "full" version of the
Penman-Monteith (PM) equation is described in ASCE Manual 70 (Jensen et al., 1990).
The ASCE 1990 Penman-Monteith (ASCE PM-90) method is valid for neutral
atmospheric stability. This equation can be applied to either a grass or alfalfa reference
surface, with the aerodynamic and surface resistances treated as functions of vegetation
height. ASCE PM-90 reference ET values are often used as the measure against which to
evaluate the proposed equations.
The ASCE PM-90 form of the combination equation is
ET =
1
c
0.622λρ 1
(es − ea )
P
ra
 r 
∆ + γ 1 + s 
 ra 
∆ ( R n − G ) + k1
(4-14)
where c • ETo is the reference evapotranspiration (mm day -1), c is the conversion factor
used for conversion of MJ m-2 day-1 to mm day-1, Rn is the net radiation (MJ m -2 day -1),
G is the soil heat flux (MJ m -2 day -1), (es-ea) is the vapor pressure deficit of the air (KPa),
ρ is the mean air density at constant pressure (Kg m-3), cp is the specific heat of air (MJ
71
kg -1 oC -1), ∆ is the slope of the saturation vapor pressure temperature relationship (KPa
o
C -1), γ is the psychrometric constant (KPa oC -1), λ is the latent heat of vaporization
(0.0583 KPa oC -1), and rs, ra are the bulk surface and aerodynamic resistances (s m -1).
When using mean daily wind speed in ms-1
k1
0.622 λρ
P
=
(1710
− 6.85T )
(4-15)
The aerodynamic resistance (ra) determines the transfer of heat and water vapor
from the evaporating surface into the air above the canopy.
ra
=
 z − d   zh − d 
ln  m
 ln 

 z om   z oh 
k2 uz
(4-16)
where zm is the height of wind measurements (m), zh is the height of humidity
measurements (m), d is the zero plane displacement height (m), zom is the roughness
length governing momentum transfer (m), zoh is the roughness length governing transfer
of heat and vapor (m), k is the von Karman's constant (0.41), and uz is the wind speed at
height zm (ms -1).
For a wide range of crops the zero plane displacement height, d, and the
roughness length governing momentum transfer, zom, can be estimated from the crop
height, h by the following equations
d = (2 / 3)h
(4-17)
zom = 0.123h
(4-18)
The roughness governing transfer of heat and vapor, zoh, can be approximated by
zoh = 0.1zom
(4-19)
72
The bulk surface resistance describes the resistance of vapor flow through the
transpiring crop and evaporating soil surface. An acceptable approximation to the
complex relation of the surface resistance for dense full cover vegetation is
rs =
r1
LAIactive
(4-20)
where r1 is the bulk stomatal resistance of the well illuminated leaf (s m -1), and LAIactive
is the active leaf area index, (m 2 (leaf area) m -2 (soil surface)).
Only the upper half of the canopy is considered to actively control the transfer of
water vapor and sensible heat. Thus, a general equation for LAIactive is given as
LAI active = 0.5LAI
(4-21)
where LAI is the leaf area index. The LAI is given by LAI = 0.24(h ) where h is the crop
height (cm).
From the original ASCE 90 equation and the equations of aerodynamic and
surface resistances discussed above, the ASCE 90 Penman-Monteith method to estimate
ETo is given by
∆( Rn − G ) + γ (1710 − 6.85T )
c ⋅ ETo =
r
∆ + γ (1 + s )
ra
1
(e s − e a )
ra
(4-22)
4.3.2.4 FAO Penman Monteith method
The FAO56-PM method (Allen et al., 1998) is an hourly or daily grass reference
ET equation derived from the ASCE PM-90 by assigning certain parameter values based
on a specific reference surface. This surface has an assumed height of 0.12 m, a fixed rs
of 70 s m -1, and an albedo of 0.23. The zero plane displacement height and roughness
lengths are estimated as a function of the assumed crop height, so that ra becomes a
73
function of only the measured wind speed. The height for the temperature, humidity, and
wind measurements is assumed to be 2 m. The latent heat of vaporization (λ) is assigned
a constant value of 2.45 MJ kg-1.
The Penman-Monteith form of the combination equation is
ETo =
1
λ
∆ (Rn − G ) + ρ a c p
(es − ea )
ra
 r 
∆ + γ 1 + s 
 ra 
(4-23)
where Rn is the net radiation (MJ m -2 day -1), G is the soil heat flux (MJ m -2 day -1), ρ is
the mean air density at constant pressure (Kg m -3), cp is the specific heat of air (MJ kg -1
o
C -1), es is the saturation vapor pressure (KPa), ea is the actual vapor pressure (KPa), es-
ea is the saturation vapor pressure deficit (KPa), ∆ is the slope of the saturation vapor
pressure temperature relationship (KPa oC -1), γ is the psychrometric constant (KPa oC -1),
and rs, ra are the bulk surface and aerodynamic resistances (s m -1).
The FAO56-PM aerodynamic resistance equation is identical to the ASCE PM-90
formulation. The FAO56-PM aerodynamic resistance equation for a grass reference
surface is calculated for reference conditions. Assuming a constant crop height of 0.12 m
and a standardized height for wind speed, temperature and humidity at 2 m, the
aerodynamic resistance for the grass reference surface is only a function of wind speed at
2 m. The bulk surface resistance that describes the resistance of vapor flow through the
transpiring crop and evaporating soil surface also follows the ASCE PM-90 formulation.
The working FAO 1998 Penman-Monteith method to estimate ETo is given by
900
u 2 (es − ea )
T + 273
∆ + γ (1 + 0.34 u 2 )
0.408 ∆ (R n − G) + γ
ETo
=
(4-24)
74
4.3.3 Soil Water Holding Capacity
The amount of water that can be used by a crop depends on the water holding
characteristics of the soil and the rooting depth of the crop. The soil’s water holding
capacity (WHC) governs the amount of rainfall or irrigation that can be retained in the
soil. Specty and Isberie (1996) identified WHC as one of the major bottlenecks in
estimating irrigation. The total available WHC is the amount of water held in the rootzone between field capacity and permanent wilting point. Field capacity (FC) is the
amount of water held in soil after excess water has drained away. Permanent wilting
point (PWP) is the soil water content at which the plants have extracted all the water that
can be extracted from a soil. Theoretically, crops can use all the water available between
FC and PWP (Izuno and Haman, 1995). But some water is strongly adhered to the soil
particles (hygroscopic water) and is difficult for the plants to extract. The available WHC
represents the amount of water that the plant can withdraw from the soil without
undergoing stress.
Soil WHC is controlled primarily by the soil texture. Soil texture is a reflection of
the particle size distribution of a soil. In general, the higher the percentage of silt and clay
sized particles, the higher the water holding capacity. The small particles (clay and silt)
have a much larger surface area than the larger sand particles. This large surface area
allows the soil to hold a greater quantity of water. Soils with higher WHC can provide
water to plants longer than soils with low WHC, such as fine sands.
4.3.4 Crop Root Zone Depth
The crop root zone depth is very important for the process of water transfer from
soil to the plant. The crop root zone depth defines the depth of the soil profile from which
the plant can extract soil water. The crop root zone also determines the extent (depth) to
75
which the water budget simulation for the crop be simulated. The extent and depth of a
root system determines how much water can be extracted by the vegetation from the soil
and recycled back into the atmosphere. With deeper roots, the soil volume is expanded
and more soil water is accessible for evapotranspiration during dry periods. The crop
rooting depth varies with crop species, type, and stage of growth.
4.4 Analysis Results and Discussion
This section presents the results of the GWRAPPS simulations estimating the
irrigation requirements using twenty different combinations of ET, soil WHC and crop
root zone depth. Significant differences in the irrigation requirements were identified.
The results are analyzed and the sensitivity of the model to each of the critical variable
under consideration is discussed.
4.4.1 Evapotranspiration Analysis Results
Eight GWRAPPS simulation runs were performed using the four ET methods
discussed above. The WHC and the crop root zone depth are kept constant through all the
simulation runs. The only component varied during the simulation runs is the reference
ET data.
Figures 4-1 and 4-2 show the normal irrigation requirements estimated for ferns
and potatoes respectively using the four ET methods. Table 4-1 provides the summary of
the annual irrigation requirements for ferns and potatoes. The normal irrigation
requirements ranged from 58.33 cm to 64.75 cm for ferns and 12.66 cm to 14.79 cm for
potatoes. The 1-in-10 irrigation requirements ranged from 69.72 cm to 74.49 cm for ferns
and 19.60 cm to 21.02 cm for potatoes. The FAO PM method underestimated the
irrigation requirements throughout the entire season by approximately 3% for ferns and
by 5% for potatoes as compared to the irrigation requirements estimated using ASCE 90
76
PM method. These differences are equivalent to approximately 0.81 million cubic meters
for ferns and 0.44 million cubic meters of water for potatoes. Hargreaves and IFAS
Penman methods exhibited seasonal variation in the estimated irrigation requirements.
The seasonal variations observed in this analysis are similar to the variations observed by
Jacobs and Satti (2001). During summer, Hargreaves overestimated the irrigation
requirements. For ferns, the overestimation ranged from approximately 5% to 21%. For
potatoes, the overestimation was approximately 6%. During the same period, the IFAS
Penman method overestimated the irrigation requirements for ferns by approximately 2%
to 15%. Both these methods underestimated the irrigation requirements during winter.
The differences ranged from 15% for ferns using IFAS Penman to 35% for potatoes using
Hargreaves method. Among the three methods compared with the ASCE 90 PM method,
irrigation requirements using the FAO PM ET method provided the best agreement. This
agreement is seen for both ferns and potatoes.
The differences in the methodologies used for calculating ET were the primary
source for the variations in the irrigation requirements. Hargreaves is a temperature-based
method i.e., the main component in calculating ET is temperature. In a humid climate
with strong convective systems, the Hargreaves method overestimates ET during summer
and underestimates during winter (Jacobs and Satti, 2001). As explained previously,
stomatal resistance describes the resistance of vapor flow through the transpiring crop
and evaporating soil surface. Thus, stomatal resistance is a vital component in calculating
crop evapotranspiration (McCabe et al., 1992; Singh et al., 1993). Neither Hargreaves nor
IFAS Penman methods consider stomatal resistance in calculating ET while ASCE 90
PM and FAO PM methods do consider stomatal resistance in estimating ET. The
77
magnitude of the variation in irrigation requirements between different ET methods is
very high. Also, this variation is not constant throughout the growing season. Therefore,
the choice of the ET method is critical in efficient crop water requirements estimation.
4.4.2 Soil Water Holding Capacity Analysis Results
Six GWRAPPS simulation runs were performed to determine the sensitivity of
the AFSIRS model to the soil WHC. The net irrigation requirements for ferns and
potatoes was estimated for three different WHC scenarios: the minimum, average, and
maximum WHC. All the simulations were run using the same climate information and
the GWRAPPS multiple soil scenario.
Figures 4-3 and 4-4 show the normal irrigation requirements estimated for ferns
and potatoes respectively. Table 4-1 provides the annual normal, 1-in-5 drought and 1-in10 drought irrigation requirements. The irrigation requirements were negatively
correlated to the soil WHC. Again, soils with higher WHC can provide water longer to
plants than soils with lower WHC. Correspondingly, from figures 4-3 and 4-4 it is
observed that the higher WHC resulted in lower irrigation needs and lower WHC resulted
in higher irrigation needs. The differences in the monthly normal irrigation requirements
ranged from 0 cm to 0.39 cm for ferns and 0.15 cm to 0.56 cm for potatoes. The
differences in annual irrigation requirements were approximately 2.7 cm for ferns and
1.64 cm for potatoes. In figure 4-3, it is observed that the differences in the irrigation
requirements for ferns were relatively low during the drier months (May, June and July).
During summer, most of the water required by the plant is supplied through irrigation due
to high evapotranspiration rates and low rainfall. For ferns, the annual irrigation
requirements were overestimated by about 4% using minimum WHC and underestimated
by 4% using maximum WHC. The mean absolute error ranged from 0.9 cm for maximum
78
WHC to 1.07 cm for minimum WHC. The irrigation requirements for potatoes were more
sensitive to the WHC than ferns. The annual normal irrigation requirements were
overestimated by approximately 11% using minimum WHC and underestimated by about
9% using maximum WHC. The mean absolute error ranged from 0.52 cm for maximum
WHC to 0.64 cm for minimum WHC. The variation in the differences in the 1-in-10
irrigation requirements is similar to that of normal irrigation requirements. The 1-in-10
irrigation requirements were overestimated by approximately 4% for ferns and 9% for
potatoes when using minimum WHC. When using maximum WHC, the 1-in-10 irrigation
requirements were underestimated by approximately 2% for ferns and 5% for potatoes.
4.4.3 Crop Root Zone Depth Analysis Results
Ten GWRAPPS simulation runs were performed to determine the sensitivity of
AFSIRS model to the crop root zone depth. For each crop, the crop water requirements
were estimated with five different crop root zone depths. These root zone depths include
the -20%, -10%, 0%, +10%, and +20% variations to the actual root zone depths provided
in the AFSIRS crop database. In the AFSIRS crop database, the actual root zone depth of
ferns is 25.4 cm and the minimum and maximum root zone depths of potatoes are 30.48
and 45.72 cm. All the simulations were run using the same climate information, average
WHC and the GWRAPPS multiple soil scenario. Thus, crop root zone depth is the only
variable that is subjected to change during different GWRAPPS simulation runs.
Figures 4-5 and 4-6 show the normal irrigation requirements for ferns and
potatoes using five different root zone depths. The irrigation requirements are negatively
correlated with the crop root zone depth. For ferns, a perennial crop, AFSIRS uses a
constant root zone depth for the entire irrigation period. From figure 4-5, there are no
large variations in the monthly normal irrigation requirements among the different root
79
zone depths except for 30.48 cm. In ferns, except for the crop root zone depth of 30.48
cm, the normal irrigation requirements ranged from 60.62 to 61.24 cm. The crop root
zone depth of 30.48 cm resulted in the least irrigation requirements of 56.44 cm. The
monthly and annual irrigation requirements for root zone depth of 30.48 cm were
significantly different from those using other depths. This is due to an extended root zone
and a modified soil profile with a higher WHC. The overall irrigation requirements are
overestimated by approximately 0.7% equivalent to approximately 0.11 million cubic
meters, when using shallow root zone depths. Depths greater than 25.4 cm resulted in
underestimation by approximately 0.3%. The 1-in-10 drought irrigation requirements also
followed a similar trend and overestimated by 0.8% when using shallow depths and
underestimated by 0.6% when using depths greater than 25.4 cm. The worst-case
scenario of using a root zone depth of 30.48 cm resulted in underestimation of normal
irrigation requirements by 8.1% and 1-in-10 drought irrigation requirements by 5.6%.
In potatoes, the minimum root zone depth is kept constant as 30.48 cm and the
maximum crop root zone depth is varied during for the GWRAPPS simulation runs.
Potatoes exhibited higher differences in irrigation requirements in the later growth stages
of the crop than those in the earlier growth stages. Potato is an annual crop and as
described earlier, in AFSIRS, the crop root zone development for annual crops has four
growth stages. The average lengths of the growth stages differ by crop and are given as
fractions of the crop-growing season. For potatoes, the fractions are 0.23, 0.29, 0.29 and
0.19. The minimum root zone depth of potatoes is 30.48 cm. Figure 4-7 shows the crop
root zone development of potatoes of the five maximum root zone depths considered in
the sensitivity analysis. As it can be observed from figure 4-7, the root zone depths are
80
the same during growth stage 1 and increase linearly to the maximum root zone depth
during growth stage 2. The maximum root zone depth is attained by the end of growth
stage 2 and is maintained throughout growth stages 3 and 4.
The irrigation requirements estimated also followed a similar pattern. The
irrigation requirements estimated were the same during growth stage 1 for all the five
maximum root zone depth scenarios. The irrigation requirements differed between the
different crop root zone depths during growth stages 2 through 4. This variability is
directly related to the variability in the crop root zone depth. Again, deeper root zones
allow more soil volume to be explored and thus require less water to be supplied. This
explains the higher differences in the irrigation requirements during the later growth
stages of the crop. The overall irrigation requirements are overestimated by
approximately 4% when using shallow maximum root zone depths and underestimated
by approximately 4% when using deeper maximum root zone depths. The 4% difference
represents approximately 0.59 million cubic meters of water. Similarly, the 1-in-10
irrigation requirements were overestimated by 4% when using depths less than the actual
root zone depth and underestimated by 2% when using depths greater than the actual crop
root zone depth. The annual crops exhibited more sensitivity to the root zone depths than
the perennial crops during later growth stages. The normal irrigation requirements during
growth stage 4 for potatoes were overestimated by 10% while the 1-in-10 irrigation
requirements were overestimated by 24% when using shallower depths.
4.4.4 Discussion
The AFSIRS model showed varying levels of sensitivity to the critical variables.
The magnitude of the variation in annual normal irrigation requirements from different
ET methods was not significantly high when compared to the variation in the monthly
81
normal irrigation requirements. The monthly irrigation requirements varied seasonally
among the different ET methods. The Hargreaves and the Penman 84 methods
underestimated during colder months and overestimated during hotter months. The
overestimation and underestimation of the irrigation requirements by the same ET
method mitigated the magnitude of the differences in the annual irrigation requirements.
For example for ferns, the Hargreaves method overestimated the irrigation requirements
by 21% in the month of August and underestimated by 15% in the month of January.
However, the annual requirements were overestimated by approximately 6% only. The
worst-case scenario of using IFAS Penman method resulted in underestimation of normal
irrigation requirements by 35% for ferns and 64% for potatoes during wetter months.
Therefore, the choice of the ET method is critical when the permits are issued on a
monthly basis.
The irrigation requirements from different WHCs did not exhibit seasonal
variation. Throughout the growing season, the monthly irrigation requirements were
overestimated when using minimum WHC and underestimated when using maximum
WHC. The available soil water content affects the variation in the monthly irrigation
requirements. The magnitude of the variation in the irrigation requirements is directly
correlated to the soil water content. The variation is less during drier months and high
during wetter months. During the worst-case scenario, the minimum WHC overestimated
by approximately 12% for ferns and 48% for potatoes during drier months. The
maximum WHC underestimated by approximately 7% for ferns and 31% for potatoes
during wetter months. The high magnitude of variation for potatoes was observed during
the early growth stages of the crop.
82
The crop root zone depth does not affect the variation of the irrigation
requirements significantly. The model exhibits a high sensitivity to the crop root zone
depth only when the soil profile has distinctly different soil properties with depth. This
variation is clearly observed in Figure 4-5 where the irrigation requirements using a crop
root zone depth of 30.48 cm for ferns were significantly lower than the irrigation
requirements from different crop root zone depths. For ferns, the variation ranged
between 1% and 3% for depths below 30.48 cm. When using a depth of 30.48 cm for
ferns, the magnitude of variation ranged from approximately 3% to 17%. The magnitude
of variation is also dependent on the crop being a perennial or an annual crop. Perennial
crops exhibited relatively less sensitivity to the change in crop root zone depth than the
annual crops. Annual crops were more sensitive to the crop root zone depths during the
later growth stages. The differences in the crop root zone development estimations by the
AFSIRS model for perennial and annual crops were the primary source for the
differences in sensitivity to the crop root zone depth.
4.5 Conclusion
GWRAPPS was used to study the sensitivity of the AFSIRS model to its critical
variables. The sensitivity was examined based on the changes in the monthly and annual
irrigation demands. Out of the three variables considered for this analysis, the irrigation
requirements were more sensitive to ET than to soil WHC and the crop root zone depth.
The crop root zone depth was the least sensitive among the three under similar soil
profile conditions.
The analysis studies the possible effects on crop water requirements when the
critical variables involved in the model are varied. From this analysis, it can be concluded
that crop water requirements are sensitive in different ways to changes in each of the
83
environmental, soil and climate variables studied. The irrigation requirements decreased
with increase in WHC and crop root zone depths. This can be observed throughout the
entire season for both ferns and potatoes. The response of irrigation requirements to
different ET methods varied seasonally. The irrigation requirements estimated by FAO
PM method were in reasonable agreement with those from ASCE 90 PM method
throughout the entire season. The crop water requirements estimated using Hargreaves
and IFAS Penman methods exhibited very high seasonal variability.
Table 4-1. Summary of the irrigation requirements estimated using GWRAPPS for two crops, four ET methods, three WHCs and five
crop root zone depths.
Ferns
Potatoes
1-in-10
1-in-5
Normal
1-in-10
1-in-5
Normal
Sensitivity ET method
WHC
Root zone
Variation Irrigation Irrigation Irrigation Irrigation Irrigation Irrigation
(cm)
(cm)
(cm)
(cm)
(cm)
(cm)
(%)
Baseline
ASCE 90 PM
Average
0%
61.03
68.38
71.48
14.79
18.80
20.84
Climate
Root zone
Average
0%
64.75
71.65
74.49
13.94
17.70
19.60
IFAS Penman
Average
0%
58.33
67.23
71.12
12.66
16.66
19.60
FAO PM
Average
0%
59.07
66.55
69.72
14.08
18.59
21.02
ASCE 90 PM
Minimum
0%
63.73
70.70
73.59
16.43
20.65
22.75
ASCE 90 PM
Maximum
0%
58.80
66.73
70.13
13.46
17.58
19.77
ASCE 90 PM
Average
-20%
60.62
67.87
70.91
15.33
19.51
21.64
ASCE 90 PM
Average
-10%
60.72
68.12
71.23
14.97
18.96
20.99
ASCE 90 PM
Average
+10%
61.24
68.74
71.91
14.59
18.68
20.79
ASCE 90 PM
Average
+20%
56.44
64.18
67.49
14.27
18.33
20.45
84
Soil
Hargreaves
85
10
Hargreaves
IFAS Penman
Normal Irrigation (cm)
8
ASCE 90 PM
FAO PM
6
4
2
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 4-1. Comparison of normal irrigation requirements of ferns for four different ET
methods.
10
Hargreaves
IFAS Penman
Normal Irrigation (cm)
8
ASCE 90 PM
FAO PM
6
4
2
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 4-2. Comparison of normal irrigation requirements of potatoes for four different
ET methods.
86
10
Minimum WHC
Average WHC
Normal Irrigation (cm)
8
Maximum WHC
6
4
2
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 4-3. Comparison of normal irrigation requirements of ferns for three different
WHC scenarios.
10
Minimum WHC
Average WHC
Maximum WHC
Normal Irrigation (cm)
8
6
4
2
0
Jan
Feb
Mar
Apr
May June July
Aug
Sep
Oct
Nov
Dec
Month
Figure 4-4. Comparison of normal irrigation requirements of potatoes for three different
WHC scenarios.
87
10
20.32 cm
22.68 cm
8
25.4 cm
Normal Irrigation (cm)
27.94 cm
30.48 cm
6
4
2
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 4-5. Comparison of normal irrigation requirements of ferns for five different root
zone depths.
10
40.64 cm
43.18 cm
Normal Irrigation (cm)
8
45.72 cm
48.26 cm
50.80 cm
6
4
2
0
Jan
Feb
Mar
Apr
May
June
July
Aug
Sep
Oct
Nov
Dec
Month
Figure 4-6. Comparison of normal irrigation requirements for potatoes for five different
root zone depths
88
60
Root Depth (cm)
50
40
40.64 cm
43.18 cm
30
45.72 cm
48.26 cm
20
Growth
Stage 1
10
Growth
Stage 2
Growth
Stage 3
50.80 cm
Growth
Stage 4
0
0
0.23
0.52
0.81
1
Relative Crop Growing Season
Figure 4-7. Potatoes crop root zone development for five root zone depths considered in
St. Johns County.
CHAPTER 5
SUMMARY AND DISCUSSION
5.1 Summary
The main objective of this research was to develop a consistent modeling tool for
providing a uniform method for modeling consumptive use water requirements in both
permitting and planning applications in Florida. To achieve this objective, GWRAPPS, a
GIS-based decision support system was developed. GWRAPPS runs in a Windows
environment and uses object oriented technology to tightly couple ArcGIS8.1 with the
AFSIRS model. The GWRAPPS’ user interface handles the selection of crop and
location specific information. It also handles data exchange between the spatial data and
the AFSIRS model.
GWRAPPS can be applied to determine the consumptive water use requirements
at different scales ranging from single field to local municipalities to County to Water
Management District scales. The GWRAPPS considers the spatial variability of climate
and soil through an easy to use interface. These unique features of GWRAPPS were the
limitations of the AFSIRS model to be used at different scales. Case studies on a single
farm using AFSIRS and GWRAPPS simulations showed the importance of the spatial
variability of climate and soil on the crop water use requirements. The GWRAPPS, with
its ability to consider the soil variability, provides a comprehensive picture of the total
89
90
water demand that is not readily apparent due to the complex interactions of soil
characteristics and their relative contribution to the area of interest.
The ability of the GWRAPPS to model crop water use requirements at different
scales allowed assessment of sensitivity of the AFSIRS model to the environmental, crop
and soil factors. GWRAPPS simulations were performed on a regional scale on two
different crops with twenty different combinations of evapotranspiration (ET), soil water
holding capacity and crop root zone depth. The analysis showed that the AFSIRS model
is most sensitive to evapotranspiration and least sensitive to crop root zone depth. The
choice of the ET method was critical as different ET methods exhibited seasonal
variation in irrigation requirements.
The GWRAPPS, with its unique features to consider climate and soil variability at
different scales is an effective tool
5.2 Discussion
GWRAPPS is an efficient system in estimating the consumptive water use
requirements at different scales. GWRAPPS integrates a field scale model AFSIRS to
GIS using the tightly coupling approach. Tightly coupling involves data transfer between
the GIS and the field scale model. Embedding is a more efficient way of integration as it
eliminates data transfer between the components. This could be achieved in GWRAPPS
if the AFSIRS model is rewritten using an object oriented language.
From the sensitivity analysis, it has been observed that the choice of the ET
method. Therefore, additional work should be done to determine the best ET estimation
method for Floridan weather conditions. The GWRAPPS climate interpolation tool is
computationally intensive and time consuming. The limitation of GIS to store large
temporal data necessitated the storage of data generated from the GWRAPPS climate
91
interpolation tool in a relational database management system. This adds some additional
data exchange in the GWRAPPS. Additional research is necessary in improving the data
storage capabilities of GIS. A web-based implementation of GWRAPPS can allow
farmers to estimate the crop water requirements for different crops and help them in
making a decision about what to irrigate.
APPENDIX A
AFSIRS CROP AND IRRIGATION SYSTEMS
Annual Crops
Barley
Beans, Green
Beans, Dry
Beets
Broccoli
Brussel Sprouts
Cabbage
Carrots
Cauliflower
Celery
Clover
Corn, Field
Corn, Sweet
Cotton
Cucumber
Eggplant
Field Crops
Generic Crop
Greens, Herbs
Lettuce
Melons
Millet, Forge
Millet, Grain
Oats
Onion, Dry
Onion, Green
Peanuts
Peas
Peppers, Green
Potatoes
Radish
Rice
Small grains
Small vegets
Sorghum
Soybean
Spinach
Squash
Perennial Crops
Alfalfa
Avocado
Blueberry
Citrus
Ferns
Generic Crop
Grapes
Nursery, Cntr.
Nursery, Fld.
Pasture
Peaches
Pecans
Sod
Sugarcane
Turf, Golf
Turf, Landscape
92
Irrigation Systems
User Specified System
Micro-irrigation, Drip
Micro-irrigation, Spray
Multiple Sprinkler
Sprinkler, Container Nursery
Sprinkler, Large Guns
Seepage, Subirrigation
Crown Flood
Flood
93
Strawberry
Sunflowers
Sweet potato
Tobacco
Tomato
Wheat
APPENDIX B
FORTRAN PROGRAM TO CALCULATE ET USING ASCE 90 PM
c===============================================================
c
This program calculates daily ET values using
c
ASCE 90 Penman-Monteith equation.
c
c
Author: Sudheer Reddy Satti
c===============================================================
program dailyASCE90
c===============================================================
c
zh = height of humidity measurement in metres
c
zc = height of crop canopy in metres
c
zm = height of wind measuremnt in metres
c
elev = elevation in metres
c
rs = solar radiation in MJ/m2
c
tmax = maximum temperature in C
c
tmin = minimum temperature in C
c
rhmin = Minimim Relative Humidity in %
c
uz = wind speed in m/s
c
rain = rain in mm
c===============================================================
c
Declaring the variables
integer deg,min,jday,year
real tmax,tmin,tavg,ktmax,ktmin
real emax,emin,es,ea,ET
real uz,zm,zc,zh,zoh,zom,rain,elev
real albedo,delta,lambda
real ra,rs,rso,rns,rnl
real deltr,phir,omegar
real ft,fed,frs
real pi,sbc,exp,gama,gsc
real rhmin
character*10,fin
character*10,fout
character*200,dummy
data pi /3.141592654/
94
95
data sbc /4.903e-9/
data exp /2.7183/
data gama /0.066/
data gsc /0.082/
c accepting the input file name
5 write(*,10)
10 format(' Enter the input data file ')
read(*,15)fin
15 format(A10)
c
opening the input file name
open(6,file=fin,status='old',ERR=20)
goto 60
20 write(*,21)
21 format('Illegal file name',/)
goto 5
c accepting the output file name
60 write(*,55)
55 format(' Enter the output file name ')
read(*,15)fout
c
opening the output file name
open(7,file=fout,status='new',ERR=65)
go to 25
65 write(*,70)
70 format(//,14x,' File already exists or illegal file name',//, 21 x,
$ ' Enter 1 to overwrite :',/,13x,
$ ' Enter 0 to specify new output file name:')
read(*,*,ERR=65)IFILE
if(IFILE.EQ.0)go to 60
if(IFILE.EQ.1)go to 66
go to 65
66 open(7,file=fout)
25 write(*,69)
69 format(' Enter the height of crop canopy in CENTIMETERS')
read(*,*)zc
c
reading the imput from the input file
read(6,*)
read(6,30)dummy,deg,dummy,min
30 format(A8,I3,A1,I2)
read(6,*)
96
read(6,35)dummy,elev
35 format(A9,f5.1)
write(7,71)
71 format(9x,' ET calculated using ASCE 90 method ',//)
write(7,72)
72 format(9x,'Daily Climatological Data and ET')
write(7,73)deg,min,elev
73 format(9x,'Latitude: ',I3,'-',I2,10x,' Elevation:',f5.1, ' m')
read(6,37)dummy
write(7,37)dummy
read(6,37)dummy
write(7,37)dummy
37 format(A200)
read(6,*)
write(7,*)
phi = (deg/1.0)+(min/60.0)
albedo =0.23
c
Preliminary calculations
zom=0.123*zc
zoh=0.1*zom
d=(2.0/3.0)*zc
c
start of the iterations
do while(.NOT.EOF(6))
c
44
reading from the data file
read(6,*)year,jday,rs,tmax,tmin,rhmin,uz,rain,zm,zh
c
conversion from meters to centimeters
zm=zm*100
zh=zh*100
c
performing calculations
tavg=(tmax+tmin)/2.0
lambda= 2.501 - 0.002361*tavg
delta = (4098.0*0.6108*(exp**(17.27*tavg/(tavg+237.3))))/
*(tavg+237.3)**2
emin = 0.6108*(exp**(17.27*tmin/(tmin+237.3)))
emax = 0.6108*(exp**(17.27*tmax/(tmax+237.3)))
97
ea = emin
es = (emin+emax)/2
phir = phi*pi/180
deltr = 0.409*sin((2*pi*jday/365)-1.39)
X = 1 - (tan(deltr)*tan(phir))**2
omegar = (pi/2.0) - atan((-1.0*tan(deltr)*tan(phir))/X**0.5)
omega = omegar*180/pi
dr = 1 + 0.033*cos(2*pi*jday/365)
ra = (1/pi)*24.0*60.0*gsc*dr*( sin(phir)*sin(deltr)*omegar +
*cos(phir)*cos(deltr)*sin(omegar))
rso = (0.75 + 2e-5*elev) * ra
ktmax = tmax + 273
ktmin = tmin + 273
ft= (sbc/2)*(ktmax**4 + ktmin**4)
fed=(0.34-0.14*sqrt(ea))
frs=(1.34*(rs/rso)-0.35)
rnl=ft*fed*frs
rns=(1-albedo)*rs
rn= rns - rnl
raa=(log((zm-d)/zom)*log((zh-d)/zoh))/((0.41**2)*uz)
rss=200/(0.24*zc)
c1 = delta*rn
c2 = gama*(1710-6.85*tavg)*(es-ea)/raa
c3 = delta+gama*(1+(rss/raa))
ET=((c1 + c2)/c3)/lambda
write(7,50)year,jday,rs,tmax,tmin,rhmin,uz,rain,zm,zh,ET
format(I4,3x,I3,3x,9(f6.2,3x))
50
end do
end
c===============================================================
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BIOGRAPHICAL SKETCH
Sudheer R. Satti was born in Hyderabad, India, on August 17, 1979. He received
his high school diploma from Bhoopathy Public School, Hyderabad, in 1994. He spent
two years in Shoba Rani Junior College, Hyderabad, and completed his intermediate
education with distinction. He enrolled in Vasavi College of Engineering in 1996 after
competing in a state-wide entrance examination conducted by the state government. He
graduated with distinction in 2000 with a Bachelor of Engineering degree in civil
engineering. Soon after, he moved to the United States of America to continue his
education. He enrolled in the Master of Science program at the University of Florida. His
major was in civil engineering with a special concentration in eomatics.
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