GISAREG—A GIS based irrigation scheduling simulation model ... en papel... · (GIS), was...

GISAREG—A GIS based irrigation scheduling

simulation model to support improved

water use

P.S. Fortes a, A.E. Platonov b, L.S. Pereira a,*

a Agricultural Engineering Research Center, Institute of Agronomy, Technical University of Lisbon,

Tapada da Ajuda, 1349-017 Lisbon, Portugalb Scientific Information Center of the Interstate Commission for Water Coordination (SIC-ICWC),

Karasu-4, 700187 Tashkent, Uzbekistan

Accepted 1 September 2004

Available online 23 March 2005

Abstract

To support improved irrigation scheduling in the Syr Darya basin, Uzbekistan, the ISAREG model

was selected due to previous applications in similar climates. It is a conceptual non-distributed water

balance model for simulating crop irrigation schedules at field level and to compute irrigation

requirements under optimal and/or water stressed conditions. To provide for the use of the model at

the project scale, a new version of the model, integrated with a Geographical Information System

(GIS), was developed. This GIS based application is aimed at supporting the implementation of

improved farm irrigation management and, in a later phase, to also help project management. The

integration concerns the creation of spatial and weather GIS databases usable by ISAREG, the

models operation for different water management scenarios, and the production of crop irrigation

maps and time dependent irrigation depths at selected aggregation modes, including the farm scale.

The resulting information on alternative irrigation schedules is, therefore, spatially distributed and

shall be used both to support irrigation scheduling advising and to help in the identification of

practices that may lead to water saving and provide for salinity control. The paper includes brief

descriptions of the models, the databases and the integration of the models, as well as main features of

the GISAREG application. Results relative to the comparative analysis of irrigation scheduling

scenarios show the capabilities of the model to support the selection of water saving alternatives. The

appropriateness of implementing 15–20-day time intervals between irrigations is shown. In addition,

www.elsevier.com/locate/agwat

Agricultural Water Management 77 (2005) 159–179

* Corresponding author. Tel.: +351 21 3653480; fax: +351 21 3621575.

E-mail addresses: [email protected] (P.S. Fortes), [email protected] (A.E. Platonov),

[email protected] (L.S. Pereira).

0378-3774/$ – see front matter # 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.agwat.2004.09.042

results relative to compare net irrigation requirements for wet and dry years show the usefulness of

the model in handling time series of weather data.

# 2005 Elsevier B.V. All rights reserved.

Keywords: Water balance; Irrigation requirements; Irrigation water savings; Aral Sea basin; Modeling and

simulation

1. Introduction

Irrigation scheduling is the farmers decision process relative to ‘‘when’’ to irrigate and

‘‘how much’’ water to apply at each irrigation event. It requires knowledge of crop water

requirements and yield responses to water, the constraints specific to the irrigation method

and respective on-farm delivery systems, the limitations of the water supply system relative

to the delivery schedules applied, and the financial and economic implications of the

irrigation practice (Smith et al., 1996). When adequately applied, irrigation scheduling

favors improved water use, i.e. reduced irrigation demand and higher water and land

productivity (Pereira et al., 2002).

A large number of tools are available to support field irrigation scheduling, from sensors

to simulation models (Martın Santa Olalla et al., 1999; Tarjuelo and de Juan, 1999).

Irrigation scheduling models are particularly useful to support individual farmers and

irrigation advisory services (Ortega et al., 2005). A main interest in using models results

from their capabilities to simulate alternative irrigation schedules relative to different

levels of allowed crop water stress and to various constraints in water availability (Pereira

et al., 1995).

The irrigation scheduling simulation model ISAREG is after long in use in several parts

of the World for evaluating current irrigation schedules, selecting the most appropriate

irrigation scheduling for several crops, and for computing crop irrigation requirements

using weather data time series (Teixeira and Pereira, 1992; Liu et al., 1998). The model is

able to generate irrigation scheduling alternatives that are evaluated from the relative yield

loss produced when crop evapotranspiration is below its potential level. Examples of those

applications to winter and summer irrigated crops are presented by Oweis et al. (2003) and

Zairi et al. (2003) for surface irrigation in the Mediterranean region, and Liu et al. (2000)

and Campos et al. (2003) for surface irrigated crops in North China.

The main limitation of this type of simulation models is that model computations are

performed at the crop field scale for specific soil, crop, and climate conditions, which

characterize that crop field and the respective cropping and irrigation practices. When the

computation procedure is applied at the region scale it becomes heavy and slow due to the

need to consider a large number of combinations of field and crop characteristics to be

aggregated at sector or project scales (e.g. Teixeira et al., 1996), or adopting geostatistical

tools such as krigging (Sousa and Pereira, 1999). However, the spatially distributed

characteristics of the input data required by ISAREG makes their integration with a

Geographical Information System (GIS) particularly attractive and useful as proved with

other water balance models (Burrough and McDonell, 1998). The interest of this

approach is increased when remote sensed crop data is available as for this application.

P.S. Fortes et al. / Agricultural Water Management 77 (2005) 159–179160

The GIS treat the input water balance data as a set of layers (Tim, 1996) and is able to

identify for each cropped field the respective set of predominant characteristics. Former

GIS applications include regional evapotranspiration (Hashmi et al., 1995), crop

evapotranspiration (Ray and Dadhwal, 2001), crop yields estimation (Qing and Linvill,

2002), delivery scheduling analysis (Rowshon et al., 2003), or the assessment of recharge

from irrigated fields (Chowdary et al., 2003). Many other applications to irrigation are

reported but most of them do not refer to an integration of the models with the GIS, as it is

the case of previous ISAREG applications with GIS (San-Payo and Teixeira, 1996; Pereira

et al., 1999).

Assuming that each field is homogeneous, it is possible to automatically call the model

for each cropped field represented in the GIS crop fields theme and then up-scaling the

results produced using a variety of attributes. This is the basic procedure adopted in

GISAREG, which is an effective integration of the ISAREG model with a GIS. This

application is developed in the framework of a research project aimed at water saving and

salinity control in selected irrigation areas of the Syr Darya River Basin, in Uzbekistan,

Kyrgyzstan and Tajikistan, Aral Sea Basin.

A general view on the problems, possible solutions and prospects to the Aral Sea Basin

is presented by Dukhovny (2003). Issues for water saving at farm level in the area are

discussed in other papers in this issue: Horst et al. (2005) discuss improvements relative to

surface irrigation systems and the need to combine them with improved irrigation

scheduling aimed at water saving; Stulina et al. (2005) use the RZWQM model to simulate

alternative crop management practices, including a combination with ISAREG results,

aimed at water saving and improved water productivity.

The main interest in using GISAREG results from the capability of the model to

simulate alternative irrigation schedules relative to different levels of allowed crop water

stress as well as to various constraints in water availability. The irrigation scheduling

alternatives are evaluated from the relative yield loss produced when crop evapotranspira-

tion is below its potential level. The specific objectives of this paper include the

presentation of the model development and examples of its application to the computation

of spatially distributed crop irrigation requirements and to simulate the irrigation demand

aggregated at main nodes of the distribution system. The application herein refers to the

farming area Gafura Gulyama, Syr Darya Province, Uzbekistan.

2. The ISAREG model

The ISAREG model is an irrigation scheduling simulation model that performs the soil

water balance at the field scale. The model is described with detail by Teixeira and Pereira

(1992), Liu et al. (1998) and Pereira et al. (2003). The water balance is performed for a

multilayered soil and follows the classical approach referred by Doorenbos and Pruitt

(1977). Various time step computations are adopted, from daily up to monthly, depending

on weather data availability. Inputs are precipitation, reference evapotranspiration (ETo),

total and readily available soil water, soil water content at planting, and crop factors

relative to crop growth stages, crop coefficients, root depths and water-yield response

factors (Fig. 1).

P.S. Fortes et al. / Agricultural Water Management 77 (2005) 159–179 161

ISAREG has two auxiliary programs, EVAP56 to compute ETo, and KCISA to compute

the crop factors (Fig. 1). EVAP56 performs the calculation of ETo with any of the

alternative methods proposed in the guidelines FAO 56 (Allen et al., 1998), depending on

the availability of weather data. KCISA uses the FAO methodology (Allen et al., 1998) to

compute the time averaged crop coefficients for the initial, mid and end season

(Kc,ini, Kc,mid and Kc,end), the soil moisture depletion fraction for no stress ( p), and the

effective rooting depths (Zr) for each crop development stage (Rodrigues et al., 2000). Four

crop development periods are considered: initial, crop development, mid season and end

season.

The ISAREG model computes the actual evapotranspiration from the potential crop

evapotranspiration (ETc = KcETo) depending upon the soil water availability in the root

zone. The model starts the soil water simulations with an initial soil water content provided

by the user or simulated from an antecedent period of fallow, where simulation starts at end

of summer, when most of soil water is consumed, or by the winter, when replenishment of

soil water may be assumed. Irrigation depths and dates are set in various ways depending


Fig. 1. Diagram of ISAREG model and links with programs KCISA and EVAP56.

on the available data and the user options relative to the application depths and the soil

water thresholds. The impacts of water stress on yields are assessed using the approach

proposed by Stewart et al. (1977), where the relative yield losses are a function of the

relative evapotranspiration deficit through the water-yield response factor, Ky. The model

includes an algorithm for improved computation of the groundwater contribution (GW)

and the percolation (Fernando et al., 2001; Liu et al., 2001), where GW is a function of the

groundwater table depth, soil water storage, soil characteristics influencing capillarity and

ETc. An algorithm to take into consideration the salinity impacts on ETc and yields is also

added (Campos et al., 2003).

The ISAREG model performs the irrigation scheduling simulations according to user-

defined options such as:

� to define an irrigation scheduling to maximize crop yields, i.e. without crop water stress;

� to define an irrigation scheduling using selected irrigation thresholds, including for

allowed water stress and responding to water restrictions imposed at given time periods;

� to evaluate yield and water use impacts of a given irrigation schedule;

� to test the model performance against observed soil water data and using actual

irrigation dates and depths;

� to execute the water balance without irrigation;

� to compute the net crop irrigation requirements. Including to perform the frequencial

analysis of irrigation requirements when a weather data series is considered.

The model input data can be provided at run-time by keyboard or trough pre-defined

ASCII Files. The input data required for ISAREG and the auxiliary program KCISA are

summarized in Table 1. Each item concerns a specific input file.

The ISAREG model has been validated in numerous applications (e.g. Liu et al., 1998;

Oweis et al., 2003; Zairi et al., 2003). For the Syr Darya basin it was validated using

appropriate meteorological and soil water data sets relative to observations performed with

cotton in Gafura Gulyama, and with cotton and wheat in Fergana Valley, Uzbekistan

(Cholpankulov et al., 2004).

3. GISAREG, the model integration with a GIS

3.1. GISAREG feature

The integration of ISAREG and KCISA with GIS follows a close coupling strategy

developed in a commercial GIS (ArcView 3.2) using Avenue script language. ISAREG and

KCISA where converted into dynamic link libraries (DLL), i.e. compiled collections of

procedures or functions, that can be called from the new application and linked to it at run-

time. This allows a smooth integration between GIS and the simulation models. Differently

to the ISAREG Windows version, where the programs KCISA and EVAP56 are integrated

with the model (Fig. 1), in the GISAREG distinct links with the GIS are adopted, so the

EVAP56 computations are performed out of the GIS and the ETo data are input to

GISAREG.



Table 1

Model input data

Input data for ISAREG

Meteorological data

Effective precipitation, Pe (mm)

Reference evapotranspiration, ETo (mm)

Crop data (when KCISA not used)

Dates of the crop development stages

Crop coefficients, Kc

Root depths, Zr (m)

Soil water depletion fraction for no stress, p

Seasonal water-yield response factor, Ky

Soil data (multi-layered soil)

Layer depths

Soil water at field capacity, uFC (mm mm�1)

Soil water at wilting point, uWP (mm mm�1)

Initial soil water content, ui (mm mm�1)

Ground water contribution

Potential groundwater contribution, G (mm day�1), or groundwater table and soil data

Irrigation data

Irrigation thresholds

Options to define application depths

Restrictions on water availability (time periods and water volumes)

Input data for KCISA (Kc’s computation)

Meteorological data

Precipitation, Pe (mm)

Reference evapotranspiration, ETo (mm)

Number of rainfall events, n (�)

Wind speed, U2 (m s�1)

Minimum relative humidity, RH (%), or maximum and minimum temperature, Tmax and Tmin (8C)

Crop data

Crop planting or season initiation date

Dates or lengths of the crop development periods

Root depths, Zr (m)

Soil water depletion fraction for no stress, p; seasonal water-yield response factor, Ky

Soil data (evaporation layer)

Layer depth, Ze (m)

Initial soil water content, uie (mm mm�1)

Soil textural percentages

Soil water content at field capacity, uFC (mm mm�1)

Soil water at wilting point, uWP (mm mm�1)

Soil and water salinity data, ECe (dS m�1)

Irrigation data (initial period, for computing, Kc,ini),

Fraction of soil surface wetted by the irrigation, fwForeseen number of irrigation events

Foreseen depth of irrigation events, I (mm)

GISAREG do not implement all the ISAREG simulation options but only required to

satisfy the objectives of the study:

1. to schedule irrigation aiming at maximum yields;

2. to simulate an irrigation schedule with allowed water stress depending upon the users

selected irrigation thresholds and water restrictions;

3. to execute the water balance without irrigation;

4. to compute the net crop irrigation requirements, including to perform the frequencial

analysis of irrigation requirements when a weather data series is considered.

The GIS component of the application allows the simulation of different user defined

simulation scenarios and the disposal of specific tools for their creation and management.

A simulation scenario concerns a given spatial distribution of crops, irrigation methods,

irrigation scheduling options, and water restrictions. The simulation units correspond to the

cropped fields.

The GISAREG application is performed adopting the following sequential procedures:

1. Loading the spatial and non-spatial database;

2. GIS overlay procedures to identify the main soil and climate characteristics of each

cropped field, which produces the ‘‘Default’’ simulation table;

3. Creation of ISAREG and KCISA input files relative to items listed in Table 1;

4. Calling of KCISA and ISAREG for each cropped field to compute its crop irrigation

requirements and irrigation scheduling relative to a user selected crop scenario;

5. GIS reading of ISAREG outputs for mapping and integrating the results.

The essential feature of GISAREG referring to data and models is presented in Fig. 2:

each field is characterized by a combination of crop, soil and weather characteristics;

KCISA computes then the respective crop and soil input files for ISAREG; these files

together with the ET and rainfall data files are used for ISAREG simulations following a


Fig. 2. GISAREG structure: GIS overlay operation to identify the simulation units, creation by KCISA of the crop

and soil files, and ISAREG simulation relative to a crop scenario producing respective irrigation depths and

timings per a field or aggregated to a selected area.

selected crop scenario; finally, simulation results relative to a given date or summed up for

a selected period of time are mapped for the selected areas.

3.2. Database

The GIS database is constituted by point and polygon themes data, the first relative to

weather data and the second to soil, crops and fields data. Weather data refers to one or

more years. When weather data series are used, multiple simulations are performed to

determine the frequency of crop water and irrigation requirements, or to perform an

irrigation planning analysis relative to selected years such as dry, average and wet years.

The meteorological stations are identified and the respective weather data are stored in

ASCII files formatted according to ISAREG and KCISA requirements (Table 1).

A MS Access database is used to store the non-spatial data that will be coupled with the

respective polygon GIS themes (Fig. 3). This database is constituted by the following

tables:

� The crop successions table, which defines the annual crops succession (two crops that

successively occupy the same field) and concerns data on these crops and on the

respective irrigation method used. It includes the codes for identification of the crop

succession and respective designation, the winter crop and the respective irrigation

method, and the summer crop and irrigation method.

� The crop table, including: the crop identification code and its designation; the crop type

code (1 = ‘‘bare soil’’, 2 = ‘‘annual crop’’ and 3 = ‘‘annual crop with a soil frozen

period’’), which identifies the time period to be used by the model to estimate the initial

soil moisture; and the crop input data referred in Table 1 required for computing the crop

evapotranspiration using the recent FAO methodology (Allen et al., 1998).

� The irrigation methods table, containing the code and designation of the irrigation

method, and the respective fraction of soil surface wetted by irrigation.

� The soils table, including the soil identification code and designation and the soil data

described in Table 1 required for performing the water balance of the evaporative layer

(Allen et al., 1998; Rodrigues et al., 2000) and the soil water balance of the cropped soil

(Pereira et al., 2003).

The dominant characteristics of each simulation unit (cropped field) are identified by the

GIS through spatial data relative to crops, soils and meteorological stations concerning the

following input themes (Fig. 3):

� A polygon theme with the delimitation of the crop fields, which is associated with a table

relative to field attributes, including the identification code of each crop field, the

identification of the annual crop succession, and other relevant information as desired by

the user;

� A polygon theme with the delimitation of soil types having associated a table of soil

attributes, and where soils are identified by an appropriate code;

� A point theme with the location of the meteorological stations, which is also associated

with a table of attributes where the meteorological stations are identified by a code.


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Fig. 3. Schematic representation of the spatial and non-spatial databases and respective inter-relations.

The spatial databases relate with the non-spatial MS Access database and meteor-

ological ASCII files through the above-mentioned attributes tables as described in

Fig. 3.

3.3. Operation with spatial data

GISAREG operation initiates by loading the spatial and non-spatial database followed

by GIS overlay procedures which allow the identification of the soil, climate and cropping

characteristics of each cropped field (Fig. 2). The GIS generates a Thiessen polygons theme

that defines the geographical influence of each meteorological station (Fig. 4). Thus,

overlaying the meteorological Thiessen polygons theme with the field polygons theme, it

results assigning the climate data to each field (Fig. 4). In the following, a selection of the

fields to be considered for the simulation is performed. Non-cropped fields are excluded.

The last overlay procedures consist of the intersection between the soils themes and the

cropped fields, which is followed by the identification of the dominant soil type in each

field polygon, so assigning only one soil data set to each field (Fig. 4).

The weather data utilized in this application refer to the meteorological stations of Syr

Darya and Djizak and to the period 1970–1999, consisting of 10-day data on precipitation,

maximum and minimum temperature, average relative humidity, sunshine duration, and

wind speed. Data were provided by the Central Asian Scientific Research Institute of


Fig. 4. Above: intersecting cropped fields with the climatic Thiessen polygons and identification of the climatic

data dominant in each field; below: intersecting soils with cropped fields and identification of the soil dominant in

each field. For both operations, also included the identification of fields to be simulated.

Irrigation (SANIIRI), Tashkent. The crop fields’ polygons were obtained from

digitalization and interpretation of satellite images at SIC-ICWC, Tashkent. The crops

assigned to each field were obtained from comparing the NDVI relative to two images

taken in different dates (April and August) using a automated computer classification. The

soil maps are originated from the SIC-ICWC database and soil attributes were provided by

SANIIRI (G. Stulina, personal communication). Because fields are large, generally of

several hectares each, corresponding to those of former state and cooperative farms, the

accuracy of remote sensed data was adequate. The soil data were obtained from past soil

surveys in the area, which were complemented with laboratory analysis, also having

adequate quality for the aimed application.

Once crop, climatic and soil characteristics are assigned to each cropped field, a

simulation table (ST) is built. This ST is a *.dbf table relative to each ‘‘Simulation

Scenario’’, which stores the data required to perform the corresponding simulation for all

crop fields in the selected area (Table 2). In the ST, every row corresponds to a cropped field

but fields having a crops succession (a winter and a summer crop) are represented by two

rows.

The ST columns are filed by default with the values stored in the non-spatial database

referred above, but columns relative to the dominant soil and meteorological station are

filled through the GIS overlay procedures described above. New simulation scenarios are

built by editing the cells of the ST and saving that them with a user defined descriptive

name. Each created simulation scenario is stored in the hard disk in a form *.dbf file, which

may be easily imported to the ST for consulting and modification, or to execute a

simulation with the selected scenario.


Table 2

Description of the simulation table

Codes

Field identification

Crop

Irrigation method,

Dominant soil in the field,

Meteorological station

Irrigation scheduling option

Water restrictions

Data for building a simulation scenario

Crop seeding or planting date

Lengths of the crop stages

Initial period, Lini (days)

Development, Ldev (days)

Mid stage, Lmid (days)

End stage, Lend (days)

Harvest date, Harv-date = Seed-date + Lini + Ldev + Lmid + Lend

Initial soil moisture in the top and deeper layers

Crop field area

3.4. Application interface

The main window for the GISAREG application consists of three areas (Fig. 5): one for

mapping, including the legend; another for the simulation table described above, or for

presentation of results in table format; and the third for scenarios management. In addition

to the ArcView’s default commands, there are several contextual menus, buttons and tools

that where purposefully built to ease data input, building scenarios, and commanding the

simulation operations and chart outputs. The example of Fig. 5 includes on the top left the

contextual menus ‘‘Scenario’’, ‘‘Water Restrictions’’ and ‘‘Irrigation Scheme’’, which

command the ISAREG options referred before, and on the top right the buttons and tools

for scenario editing and chart presentation.

The definition of the irrigation options and water availability restrictions to be

considered when a simulation is performed are made through auxiliary windows. Irrigation

scheduling options refer to the selection of application depths and the definition of soil

water thresholds, equal or below the optimal one, which is defined for the depletion fraction

for no stress p (Allen et al., 1998). The application depths may be defined in several ways

such as fixed irrigation depths (D) depending on the irrigation method used, or the amount

of water required to refill the root zone storage up to the total available soil water (TAW) or

to a percentage of TAW. The water restrictions apply to selected time periods and refer to a

minimum interval between two successive irrigations, or to the total water depth that may

be used for irrigation during a given time interval. Different water restrictions and


Fig. 5. General view of the GISAREG application interface including the crops map and respective legend, the

simulation table (ST) and the scenarios management window (below on right). On top left, ST contextual menus

and, on top right, buttons and tools for scenario editing and chart outputs.

irrigation scheduling options may be assigned to selected fields, crop systems or cropped

areas through the simulation table.

GISAREG allows to simulate a scenario using a crop systems distribution pattern

different from that observed in the field by editing the ‘‘crop’’ column of the ST, or through

a specific window aimed at randomly assigning the crop systems within the project area

according to some user-defined coverage percentages. That spatial distribution of the crop

systems may be submitted to restrictions on the use of some soil type by a given a crop, e.g.

not allowing to assign a rice crop system to sandy soils. A map may be created showing the

spatial distribution of the crop systems that satisfy the user-defined criteria (Fig. 6), which

may be later imported to the ST and be used for a simulation scenario.

The simulation of multiple scenarios created as referred above allows visualizing the

impacts of alternative crop systems and irrigation management options on water use and

productivity, thus selecting the best alternatives for further implementation.

3.5. Outputs

GISAREG outputs can be presented in tabular, graphical or mapping formats and may

concern a single field, all fields inside a selected area, or the total area under analysis. In

addition, results may refer to a single date, e.g. crop water deficits at a selected day, or the

total simulation period, e.g. the crop irrigation requirements relative to a selected scenario.

Annual results are stored in the results table (RT), described in Table 3, which is a *.dbf

table named after the respective simulation scenario and simulation year. For any


Fig. 6. Mapping a simulation scenario where crop systems are randomly assigned to each field but subject to a

selected crop distribution coverage and to restrictions on soil type.

simulation scenario, there will be as many results tables (RT) as many years the user

decided to simulate.

Using ArcView mapping capabilities, any of the outputs included in RT may be mapped

for the entire region or for a selected area. In addition, results of the water balance for any

crop field may be visualized in a chart format (Fig. 7) clicking over that crop field record in


Table 3

Data produced in the results table (RT)

Codes

Field

Crop

Simulation results

Annual crop irrigation requirements (mm)

Readily available water (RAW) at the beginning and end of the irrigation period (mm)

Total available water (TAW) in deep soil layers at the beginning of the irrigation (mm)

Percolation due to excess of irrigation (mm)

Precipitation during the irrigation period (mm) and non-used precipitation (mm)

Cumulated actual and maximum evapotranspiration (mm), ETa and ETm, respectively

ETa/ETm ratio

Relative yield loss, Qy (�)

Critical unit flow rate Qc (l s�1 ha�1) corresponding to the largest application depth

Irrigation date when Qc was computed

Critical period length (days) when Qc is required

Monthly irrigation requirements (mm) given in 12 columns

Fig. 7. Simulation results presented as a map of crop irrigation requirements, and water balance charts for two

fields where different irrigation schedules were applied, for no stress on left, and for an allowed water deficit on

right.

the RT and then pressing the auxiliary water balance button. This allows appropriate

analysis of water balance results for any simulation unit, similarly with that provided with

ISAREG. The example in Fig. 7 shows comparative results for two fields where different

irrigation management options were applied, one aiming at maximizing yields without

allowing water stress, the other relative to deficit irrigation, thus adopting a lower threshold

for timing irrigations.

Selecting the appropriate area on the map, or the respective crop fields’ records in the

simulation table, selected results aggregated to that area may be computed and displayed.

The example in Fig. 8 refers to the computation of the demand hydrograph relative to a

main node of the distribution system. The fact that the base remote sensing image is used in

the GIS makes easier to identify the irrigation distribution network that concerns the area

under analysis. This procedure is relevant to the management of irrigation projects when

both the demand and the delivery are simulated.

Further developments concern the on-going application to the Fergana Valley, also in

the Syr Darya basin, Uzbekistan. For this area, the GIS database includes a groundwater

table depth raster GIS layer having a time scale of 10 days, which allow for better

considering groundwater contribution to the crop water requirements. A soil salinity raster

GIS layer is also included for considering impacts of salinity on evapotranspiration and

crop yields. For the Fergana Valley, also a decision support tool for demand and delivery is

being developed by adapting the SEDAM model (Goncalves et al., 2003) to operate with

GIS. In this application, GISAREG is used interactively with a surface irrigation model and

a variety of scenarios may be analyzed; the respective results are used to create attributes

relative to irrigation performances, water and land productivity, and economic indicators.

4. Application

GISAREG was applied to assess the crop irrigation requirements at Gafura-Gulyama

comparing different irrigation scenarios aiming at water savings. The area has an arid

climate, with cold winter and hot summer, where only storm rains occur from May to


Fig. 8. Seasonal irrigation demand hydrograph relative to a selected node of the distribution system.

September. The soils are generally heavy and deep, with high water holding capacity,

similar to those described by Stulina et al. (2005). The main summer crop is cotton and

winter crop is wheat, generally furrow irrigated. Despite land privatization is underway,

farms and fields are very large as commonly adopted in state and cooperative farms.

Simulations refer to the crop data observed in the year 1999 and scenarios are the

following:

� OY, aiming at attaining the optimal yield, i.e. adopting the p depletion fraction for no

stress as irrigation threshold;

� RES15, using the same irrigation threshold but imposing a 15 days time interval between

successive irrigations;

� RES20, as above but with a time interval of 20 days;

� STRESS, adopting a threshold 50% below that for OY.

Data in Table 4 refers to the crop distribution pattern observed in 1999 and the

corresponding net irrigation requirements. The highest demand was from the grain maize

(558 mm) followed by cotton (511 mm).

Simulation results are presented in Table 5 relative to the four scenarios. Results

indicate that a water saving representing 9% of the net irrigation demand corresponding to


Table 4

Observed crop pattern and respective crop irrigation requirements for 1999

Summer crop Area Crop irrigation requirements

(ha) (%) (mm) (m3)

Bare soil 772 25 0 0

Cotton 2,195 71 511 11,205,690

Maize (grain) 32 1 558 180,950

Maize (silage) 87 3 309 269,170

Vegetables 11 0 213 22,989

Total 3,097 100 11,678,799

Table 5

Comparing net irrigation requirements NIR (m3) for summer crops and relative yield losses Qy (%) referring to

several irrigation scheduling scenarios aiming at water saving

Summer crops Simulation scenarios

OY RES15 RES20 STRESS

NIR (m3) Qy

(%)

NIR (m3) Qy

(%)

NIR (m3) Qy

(%)

NIR (m3) Qy

(%)

Cotton 10,596,166 2.0 9,696,851 7.3 9,192,325 11.5 7,362,381 21.8

Maize (corn) 180,205 0.0 154,813 17.9 120,861 34.4 106,625 44.2

Maize (silage) 279,549 0.0 248,062 8.3 203,927 20.5 163,281 34.9

Vegetables 22,406 0.1 21,456 2.7 18,380 8.1 9,929 35.0

Total 11,078,326 – 10,121,182 9,535,493 7,642,216

Water saving

relative to OY

957,144 (8.6%) 1,542,833 (13.9%) 3,436,110 (31%)

the OY scenario is attainable when adopting RES15, which leads to relative yield losses of

7.3% for the cotton crop but >17% for grain maize. The RES20 scenario produces 14%

water savings in relation to the OY net demand but the relative yield losses increase to

11.5% for cotton crop and >30% for grain maize. The STRESS scenario leads to a very

high demand reduction (3.4 million m3, i.e. 31% relative to OY) but to quite large yield

losses (Table 5). Consequently, the scenario STRESS could only be considered if severe

restrictions on water supply are enforced because not only yield losses are high but also it

does not allow appropriate control of salinity, which is required in this area.

The scenarios RES15 and RES20 are generally feasible and allow for appropriate

application of controlled leaching fractions with every irrigation event. However, RES20 is

questionable for maize. Adopting RES15 produces an appreciable reduction of the net

demand volumes during the peak months of July and August. For July, the demand could

reduce from 4 to 3.45 million m3 (13.8%), and for August it could decrease from 4 to 3.3

million m3 (17.8%). These results are compatible with those presented by Horst et al.

(2005) relative to improving irrigation performances, for another location but the same

crop and similar soils. They clearly state that higher efficiencies are only attainable when

the irrigation intervals be increased.

GISAREG was also applied to assess the inter-annual variation of crop irrigation

requirements. With this purpose, the model was run for all the years of the climatic data set,

and the wet, dry and average demand years were identified. Simulations with the scenarios

OY, RES20 and STRESS were performed for the crop distribution pattern given in Table 4.

The results for the average demand year are presented in Fig. 9. When visualizing such

results, comparing Fig. 9a and b relative to OY and RES20, small differences are apparent


Fig. 9. Mapping crop irrigation requirements for the average demand year adopting three irrigation scheduling

scenarios: (a) OY, without water restrictions, (b) RES20, for 20 days minimum interval between irrigations, and (c)

STRESS, adopting a low irrigation threshold.

but for maize, that has a higher demand. On the contrary, the STRESS scenario (Fig. 9c)

shows evident differences with the other scenarios indicating that respective impacts are

large for most of the field crops.

The season net irrigation requirements (NIR) for the wet and the dry year are compared

for the scenario OY through the GIS maps in Fig. 10. Results show that NIR differences

generally do not exceed 100 mm and are mainly apparent for the cotton crop because, for

such arid climate, with negligible rains during late spring and summer, differences in

irrigation demand from a wet to a dry year mainly reflect differences in early spring

rainfall. This behavior indicates the possibility for establishing generic irrigation calendars

for farmers’ advice that apply in most of the years.

5. Conclusions

The model GISAREG, resulting from the integration of ISAREG with the GIS ArcView

3.2, allows the easy handling of an irrigation scheduling simulation model over a large area

or irrigation project, as well as the visualization of the spatial distribution of the water

demand inside that region or project. Its capabilities for performing a large number of

irrigation scheduling simulations, including for comparing different scenarios aiming at

irrigation water saving, were tested for an irrigated area in the Syr Darya basin, Uzbekistan.

Among results analyzed, the advantage for adopting 15–20-day time intervals between

irrigation was identified when focusing water saving.

The possibility for easily creating and changing scenarios, including the adoption of

crop, field or aerial specific irrigation management options, allows tailoring irrigation

management according to identified requirements. Using the visualization capabilities of

GISAREG, it becomes easy to localize where discrepancies in water management may

occur, how large they are, and which technical interventions may be required. It also allows


Fig. 10. Mapping irrigation water requirements for the dry (a) and the wet (b) years.

an easy analysis of crop water and irrigation requirements, their spatial distribution, and to

compare target with actual values. In addition, it allows computing the demand hydrograph

at main nodes of an irrigation network, or for the entire project area.

Further on-going developments concern the application to the Fergana Valley, where the

GIS database includes groundwater table depth and soil salinity raster GIS layers which

allow for better considering the groundwater contribution and impacts of salinity on

evapotranspiration and crop yields. Other on-going improvements concern the use of

GISAREG with other models constituting a decision support tool applicable at both the

farm and the irrigation system scales.

Acknowledgments

This research is funded by the research contract ICA2-CT-2000-10039 with the

European Union. Thanks are due to Dr. G. Stulina and Dr. M. Horst for providing data, to

Prof. Dr. J. Matos for his advice on the development of the GIS application, and to Prof. Dr.

J.L. Teixeira, Ms. M.J. Calejo and Ms. P. Rodrigues for the support provided relative to the

DLL of the ISAREG and KCISA models.

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