THE ASPECTS OF WATER BALANCE IN THE IRRIGATEED AREA ...

THE ASPECTS OF WATER BALANCE IN THE IRRIGATEED AREA

(Southwest part of Urumieh Lake)

Orang Behmanesh January 2003

THE ASPECTS OF WATER BALANCE IN THE IRRIGATED AREA (SOUTHWEST PART OF URUMIEH LAKE)

II

THE ASPECTS OF WATER BALANCE IN THE IRRIGATED AREA

(Southwest part of Urumieh Lake)

by

Orang Behmanesh Thesis submitted to the International Institute for Geo-information Science and Earth Observation in partial fulfilment of the requirements for the degree of Master of Science in Rangeland and Agricul-ture Management Degree Assessment Board Dr.J.de.Leuw (Chairman � Supervisor) NRM Department, ITC Prof.Dr.F.D.Dmeer (External Examiner) Free University, Amsterdam Ir.m.c.Bronsveld (member) NRM Department, ITC Prof. A.M.J. Meijerink, (member) WRS Department, ITC Dr. J.Ghoddosi (member) SCWMRC � Kalak, Iran

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION

ENSCHEDE, THE NETHERLANDS


III

Disclaimer This document describes work undertaken as part of a programme of study at the International Institute for Geo-information Science and Earth Observation. All views and opinions expressed therein remain the sole responsibility of the author, and do not necessarily represent those of the institute.


IV

Abstract The future availability of water for human use depends on how water resources are managed. Espe-

cially in the arid and Semi arid regions, e g. study area where management of water resources is

important and vital. Therefore for management of available water resources and its preservation in

terms of quantity and quality, it is necessary to study the source of water and appropriate method of

utilisation.

This research has applied remote sensing and GIS technique to determined the aspects of wa-

ter balance in irrigated area in the Southwest part of Urumieh Lake. In order to undertake the

study the dynamics of irrigated area determined and the volume of crop water requirement

and consumed groundwater for irrigation have calculated from 1991 to 2001using GIS and

CROPWAT model. Then the environmental impacts of water use on the discharge to the lake,

dynamics of shore zone and impacts of groundwater use on saline area extension and changes

of groundwater salinity have been investigated.

The hydrograph of groundwater fluctuations in last ten years illustrate decrease of the water table. The drought of recent years has resulted a sharp rise in demand for water in these regions and more and more groundwater is used. It increased from 20-30% to 70-80 during last 10 years. This has caused a lowering of water table about 1.5 m in this period. There is a correlation between dynamics of shore zone and precipitation, discharge of rivers in to the lake and consumed water for irrigation. Depends on these factors the shore zone is very dynamic. The most transgression of the lake has occurred in 1994 and the most retro-gression is related to 2002. Frequently fluctuation of Lake Boundary has caused the saliniza-tion of surrounding area. Extent of this area has been increased about 4650 hectare during last 35 years, which about 60% of this is related to last 9 years. Soil salinization through lake water incursion has occurred in the areas where are close to the coast of the lake. Excessive pumping has allowed the landward penetration of brine. It has accelerated in recent years that drought and extraction of water for irrigation have reduced flow of the rivers to the lake. It is more critical in the areas where the only resource for irrigation is groundwater and there is no suffi-cient fresh water to leach salt away. It can be observed especially in the eastern south part of the study area.


V

Acknowledgement First of all, I am grateful to god who provided this academic opportunity, strength and knowledge to complete this research. Many people made valuable contributions both in their personal and /or official capacities to this re-search work, but space will not permit me to name them all. I am particularly indebted Mr. K. Bronsveld for his innumerable suggestions. He supervised this re-search. I would like also to acknowledgement the following individuals who made positive contributions. Prof.Dr Mijerink. my second supervisor Dr. Gholami. my Iranian supervisor JIk members in Iran Dr. A. Abkar and his colleagues It is my pleasures to extend a word of appreciation to all my colleagues specially M.J.Poorpaighambar and J. Yarahmadi. Finally, I would remember my family, particularly to thanks my wife, for taking up all the responsibilities during absence from home, and my son Idin.

CONTENTS


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1. Introduction ................................................................................................................... 1 1.1. Statement of the problem........................................................................................................1 1.2. Research objectives: ...............................................................................................................1 1.3. Methods:.................................................................................................................................2

1.3.1. Data pre-processing ........................................................................................................2 1.3.2. Determining of dynamics of irrigated area using RS techniques and GIS Procedures ..2 1.3.3. Crop type classification ..................................................................................................2 1.3.4. Irrigation water requirements calculation.......................................................................2 1.3.5. Estimation of available surface water in last 10 years....................................................3 1.3.6. Estimation of ground water use:.....................................................................................3 1.3.7. Determining of different aspects of water balance .........................................................4 1.3.8. Determining of environmental impacts of groundwater use ..........................................4

1.4. Materials.................................................................................................................................5 2. Area discription ............................................................................................................. 6

2.1. Location..................................................................................................................................6 2.2. Climate ...................................................................................................................................6

2.2.1. Temperature....................................................................................................................6 2.2.2. Relative humidity ...........................................................................................................6 2.2.3. Precipitation....................................................................................................................7

2.3. Geology ..................................................................................................................................8 2.4. Soil .......................................................................................................................................10

3. Data preprocessing ...................................................................................................... 13 3.1. Climate data..........................................................................................................................13

3.1.1. Rainfall .........................................................................................................................13 3.1.2. Temperature..................................................................................................................15 3.1.3. Relative humidity .........................................................................................................15 3.1.4. Sun shin duration& Wind speed...................................................................................16

3.2. Surface water........................................................................................................................16 3.2.1. River discharge:............................................................................................................16 3.2.2. Available surface water (for irrigation) ........................................................................17

3.3. Drought period: ....................................................................................................................18 4. Applied GIS and RS to crop classification.................................................................. 19

4.1. Image pre-processing ...........................................................................................................19 4.2. Statistical Image classification .............................................................................................19 4.3. Confusion matrix..................................................................................................................20 4.4. Final crop map......................................................................................................................22 4.5. Total irrigated area ...............................................................................................................22

5. CROPWAT model....................................................................................................... 24 5.1. General .................................................................................................................................24 5.2. Input data..............................................................................................................................24 5.3. Calculation methods .............................................................................................................24 5.4. Output...................................................................................................................................25 5.5. Irrigation water requirements calculation.............................................................................26 5.6. Obtained results for research area ........................................................................................27 5.7. Errors of model.....................................................................................................................29


VII

6. Aspects of water balance ............................................................................................. 30 6.1. Concept of a water balance...................................................................................................30

6.1.1. Spatial boundaries ........................................................................................................30 6.1.2. Temporal boundaries ....................................................................................................30

6.2. Method used to compute the water balance .........................................................................30 6.3. Groundwater level fluctuations ............................................................................................34 6.4. Errors of computation...........................................................................................................36

7. Environmental impacts of water use ........................................................................... 37 7.1. Impact of irrigation water use on reduction of discharge to the lake ...................................37 7.2. Impact of irrigation water use (groundwater) on soil salinization........................................39

7.2.1. Causes of salinization ...................................................................................................39 7.2.2. Dynamics of lakeshore in wet and dry periods ............................................................39 7.2.3. Irrigation water quality .................................................................................................40 7.2.4. Relation between groundwater use and ground water quality changes........................41 7.2.5. Saline areas...................................................................................................................43

7.3. Conclusion............................................................................................................................46 8. Conclusions and Discussion ........................................................................................ 48

8.1. Satellite images.....................................................................................................................48 8.2. Dynamics of irrigated area ...................................................................................................48 8.3. CROPWAT model ...............................................................................................................48 8.4. Water resources ....................................................................................................................48 8.5. Aspects of water balance ......................................................................................................49 8.6. Dynamics of shore zone .......................................................................................................49 8.7. Groundwater quality and soil salinity changes.....................................................................49

9. Refrences ..................................................................................................................... 50 10. ppendix ........................................................................................................................ 52

List of Table


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Table 2.1: Description of geology map ..................................................................................................9 Table 2.2: Soil classification and different soil series in the study area...............................................10 Table 2.3: Brief description of soil series (main characteristics) in the study area ..............................11 Table 3.1:Meteorological stations list (recording length & available data) .........................................13 Table 3.2: Weighting coefficients of the stations in the study area based on Thissen polygon ........................15 Table 3.3:Temperature in the synoptic station of Urumieh..........................................................................15 Table 3.4: Average Sun shin duration (hr) in the Urumieh synoptic station (analyzed using Urumieh synoptic

data)...............................................................................................................................................16 Table 3.5: Wind speed (Km/hr) in the Urumieh synoptic station (analysed using Urumieh synoptic data).......16 Table 3.6:Mean discharges in the gauge stations (in the last 30 years) .........................................................17 Table 3.7: Irrigation efficiency for different water resources in the study area (Report of jam-ab-Iran, 1998)..18 Table 4.1: Confusion matrix for the classification with ten categories for 2001 ............................................21 Table 4.2: Confusion matrix for the classification with ten categories for 2000 ............................................21 Table 4.3: Area per class derived from different images for 2001 and 2000 .................................................23 Table 4.4: Area separated into new categories for comparison with satellite image .......................................23 Where Si is the area cultivated with the crop, i. ...................................................................................26 Table 5.1: Crop water requirement and irrigation water requirement for different cultivated crops in the study

area in 2001 (according to CROPWAT results) .................................................................................27 Table 5.2: Crop water requirement and irrigation water requirement for different cultivated crops in the study

area in 2000 (according to CROPWAT model results) .......................................................................27 Table 5.3: Crop water requirement and Net irrigation water requirement for main crops, which are cultivated in

the study area for last 10 years in mm/period for 1 hectare (according to CROPWAT model results) .....28 Table 5.4: Net Irrigation Water Requirement according to classification map categories in m.c.m (Calculation

has been done based on classification map for 2000&2001 and collected data from Western Azerbaijan Organization for 1991-1999)............................................................................................................29

Table 6. 1 Spatial boundary of various areas..............................................................................................32 Table 6.2: Determined groundwater use and pumped groundwater (GWIn) based on equation No.3 in m.m.c

during last 10 years .........................................................................................................................32 Table 6.3: computed components of water balance in m.c.m (during last 10 years).......................................33 Table 7.1: Correlation between groundwater electrical conductivity changes with determined components of

water balance (correlation matrix) ....................................................................................................43 Table 7.2: saline area extent according to available soil survey reports ........................................................44

List of Figures Figure 2.1: Mean monthly temperature (°C) in the study area (analyzed using Urumieh synoptic station

in last 50 years) ..............................................................................................................................6


IX

Figure 2.2: Monthly relative humidity in Urumieh station (analyzed using Urumieh synoptic station in last 50 years)...................................................................................................................................7

Figure 2.3: Annual Precipitation in Urumieh synoptic station (analyzed using Urumieh synoptic station in last 50 years) ..............................................................................................................................7

Figure 2.4: Monthly mean precipitation in Urumieh synoptic station (analyzed using Urumieh synoptic station in last 50 years)...................................................................................................................8

Figure 2.5: geology map of study area (according to geology map of Urumieh prepared by geology organization of Iran) units of maps explained in table 2.1 .............................................................9

Figure 2.6: soil map of study area according to soil survey report of 1999 (A.Ghaemian, 1999) main characteristics of units is explained in table 2.3. ..........................................................................12

Figure 3.1:Mass curves of rain gauge stations Figure 3.2: Average monthly rainfall ........14 Figure 3.3: Thiessen polygon map of the study area of rain gauge stations ........................................14 Figure 3.4: Ombrothermic Curve of the study area (analyzed using Urumieh synoptic data) .............16 Figure 3.5: Location of the gauge Figure 3.6: Mean monthly discharge of rivers..................17 Stations .................................................................................................................................................17 Figure 3.7: Drought periods in the study area for last 25 years ...........................................................18 Figure 3.8: Yearly and moving average rainfall in the study area .......................................................18 Figure 4.1: Typical crop calendar, Urumieh plain; prepared base on interview with farmers and reports

of Agriculture organization of W.A province; (Planning date Harvesting date)20 Figure 4.2: Land cover and crop type map for 2001 ............................................................................22 Figure 4.3: Land cover and crop type map for 2000 ............................................................................22 Figure 6.1: A Water balance requires the definition of 3-D and temporal boundaries, and all inflows

and outflows across those boundaries as well as the change in storage within those to boundaries.......................................................................................................................................................30

Figure 6.2: Changes of groundwater consumption in last 10years according to estimated volume. ...32 Figure 6.3: Correlation between groundwater use and surface water inflow in the study area............34 Figure 6.4:Groundwater levels (in meter) in October 1991, created by interpolating the point map of

water levels in the pysometric wells) ...........................................................................................34 Figure 6.5:Groundwater levels (in meter) in October 1991, created by interpolating the point map of

water levels in the pysometric wells.............................................................................................35 Figure 6.6: Mean monthly level of groundwater in the study area according to determined Thiessen

polygons (based on existing pysometric wells) in last 10 years...................................................35 Figure 6.7: Groundwater fall (in meter) in last 10 years, it shows difference between groundwater

levels in October 1991 with September 2001...............................................................................36 Figure 7.1: Irrigation water use and discharge of rivers in c.m.c; based on determined aspects of water

balance (table 6.3) ........................................................................................................................38 Figure 7.2:Water discharges to the lake in last 10 years ......................................................................38 Figure 7.3: Scatter plot of water use against discharge of rivers in last years (based on obtained results

in previous chapters) ....................................................................................................................38 Figure 7.4: Annual fluctuation of Urumieh Lake in last 40 years (analyzed based on collected data

from western Azerbaijan power organization) .............................................................................40 Figure 7.5: Shore zone of the lake in 1987, 1994, 2001 and salinity zone in 2001 .............................40 Figure 7.6: Electrical conductivity map of groundwater (Agriculture wells) in micromohs/cm, created

according to collected data from Western Azerbaijan Water Organization and field measurements during the fieldwork. This map is related to 2002........................................................................41


X

Figure 7.7: Salinity hazard map of groundwater in the study area for agriculture purpose, created using relative electrical conductivity map (figure 7.6) based on Wilcox classification method............42

Figure 7.8: scatter plot of groundwater use and electrical conductivity of wells in last 6 years (analyzed based on collected data from Western Azerbaijan power organization and estimated ground water use). ..............................................................................................................................................42

Figure 7.9: saline area changes (extent of saline area) according to classified maps of 1987 and 2001 (saline area expansion as a result of retrogression and transgression of the lake in last 15 years)45

Figure 7.10: Soil electrical conductivity map of research area (in ds/m) created based on field measurements in 2002 ..................................................................................................................45

Figure 7.11: Saline and Non-saline area distribution, in the research area, according to electrical conductivity map (see figure 7.10) and Wilcox classification method ........................................46


1

1. Introduction

Coastal environments are very dynamic with many cyclic and random processes owing to a variety of causes one of the aspects of coastal zone dynamics is the use of the water. The demand for water is growing rapidly as population, industrial activities expand and irrigated agriculture continues to in-crease. World resource institutes report that from 1940 to 1990, withdrawals of freshwater from rivers, lake and aquifers have increased (A.Alizadeh, 1999). Often water withdrawals are clearly unsustain-able, such as pumping from subsoil aquifers at rates greater than they are recharged. Therefore, the future availability of water for humans will depend on how water resources are managed. Urumieh Lake is the largest water body entirely within Iran, at an elevation of 1275 m. It covers an area of 5800 Km2, whit a maximum depth of 16 m and volume of 45 * 109 m3 (Korzun et al., 1978). It is a saline lake whit a very salt content of more than 310000 mg L-1 TDS (Hammer, 1986). The envi-ronmental stress on coastal zones of Urumieh Lake is rapidly growing and there is a need to protect the coastal environment and ensure its sustainable production and development (Santez, 2000). In order to achieve this goal it is necessary to understand the processes that influence the coastal environments and the way in which they interact. In particular groundwater use that has caused the extension of soil salinity on the surrounding area of this lake.

1.1. Statement of the problem The study area is located in a Semi arid zone in the southwestern part of the Urumieh Lake where it is one of the most important agriculture areas in the Western Azerbaijan province of Iran. Management of water resources is important and vital in this area. In recent years droughts, the building new dams in the catchments of the Urumieh Lake and develop-ment of irrigated area has caused the reduction of river discharge to the lake. This caused many nega-tive environmental impacts (such as soil salinization and reduction of lake level) both on surrounding area of the lake and into the lake as well (Report of soil survey, 1999). In order to find workable reso-lution it seems that study on the different aspects of water balance, variations of shore zone and exten-sion of salinity in surrounding area is imperative. This appears to lead providing guideline for devel-opment of the area in regard to proper land and water management.

1.2. Research objectives: The main objective of this research is the determination of aspects of water balance in irrigated area in the west part of Urumieh Lake in last 10 years. Focusing on the following objectives:

Analysing the water balance Analysing dynamics of the irrigated area Estimation of demand water for cropping Estimation of groundwater consumption and study relation with salinity changes in the coastal area

The flowing questions will be answered,

What is the amount of difference aspects of the water balance? How is the variation of shore zone and extension of salinity in the surrounding area?


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What is the amount of consumed ground water for irrigation? What is the surface area of different crops in the irrigated area? Is there any relation between groundwater consumption and salinity changes?

1.3. Methods:

Mentioned objectives have been investigated as following steps:

1.3.1. Data pre-processing

• Collecting of required data on meteorological, rain gauges, and discharge stations. • Converting of data to the same calendar • Testing data for consistency by using Double mass curve method • Missing data were filled trough the weighted average and station- year methods. • GIS plotted meteorological data (using ILWIS software).

1.3.2. Determining of dynamics of irrigated area using RS techniques and GIS Proce-dures

It was carried out using available images and collected data from Western Azerbaijan Agriculture Or-ganisation as following steps.

Preparation of cropping calendar for study area (Using results of interview with farmers and collected data from Western Azerbaijan Agriculture organisation). Determining year to year variation of irrigated area using image classification techniques and collected data. Preparation of historic trend of irrigated land considering type of crops (Using image classifi-cation techniques and collected data from offices of Western Azerbaijan Agriculture organisa-tion, see chapter 4).

1.3.3. Crop type classification

The Analyses of the available meteorological data are described in chapter 3. Outcome of these analy-ses are used to specify which years are wet or dry. RS techniques were used for determining the varia-tion of cultivated area in wet and dry years, some of these years were selected as representative for research period (last 10 years) and relative images were classified. This has been carried out using Aster and Landsat images for determining the extent of cultivated different crops and variation of shore zone in 1987,2000 and 2001(available images). Both of supervised and unsupervised classification has been used. Interview with farmers and field observation played an important role in this procedure. Detailed methods used are described in chapter 4.

1.3.4. Irrigation water requirements calculation

Water requirement (CWR) was calculated on the basis of the monthly effective rainfall (P eff) and ref-erence evapotranspiration (Eto) that was calculated based on FAO method (FAO Irrigation and Drain-age Paper 24. 1977) using CROPWAT model. For a given crop, i, and a given cropping period:


3

Unit: mm (1) Where: kcit is the crop coefficient of the given crop, i, during the crop growth stage, t, and where T is the last growth stage. Each crop has its own water requirements. Net irrigation water requirements (NIWR) in a specific scheme for a given year are thus the sums of individual crop water requirements (CWRi) calculated for each irrigated crop, i. Multiple cropping (several cropping periods per year) is thus automatically taken into account by separately computing crop water requirements for each cropping period.

Unit: m3

(2) Si = Cultivated area (crop, i,)

Unit: m3

/ha (3) S = Total cultivated area Detailed methods are described in chapter 5.

1.3.5. Estimation of available surface water in last 10 years

The available surface water for irrigation was estimated based on rain gauge station data and volume of transferred water into traditional irrigation canals, which collected from Western Azerbaijan Water Organization. The irrigation efficiency was also used in this estimation (see chapter 3).

1.3.6. Estimation of ground water use:

Ground water consumption was calculated as following steps: NIWR - SWU= GWU (1)

Where: NIWR is net irrigation water requirement for whole area for a given year. SWU is the surface water use for irrigation GWU is the ground water use for irrigation NIWR was calculated based on following equation: NIWR = ∑ NIWRi * CAi (2)

NIWR � (1/E*SWU) = 1/E*GWU (3)


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Where ES is the irrigation efficiency for surface water resources and EG is the irrigation efficiency for ground water resources.

1.3.7. Determining of different aspects of water balance

The most basic equation for water budgets is based on the hydrologic cycle where water moves from the atmosphere to the Earth�s surface to various destinations, and finally to the atmosphere:

P=I+ET+R Where �p� represents precipitation of all forms, �I�, represents that portion of water into the ground as that provides groundwater recharge (infiltration),�R� represents runoff. As noted above, this equation can be applied to long-term averages, to specific years, to hydrological data, etc. The analysis for re-gional purpose will be difficult. An annual water balance for irrigated area can be such:

P+SWin +GWin – (ETa +SWout + GWout+ Psp) =∆S

Where: P: the total precipitation (see chapter 3 & 6) SWIn: the inflow of surface water (according to gauge station data) GWIn: the amounts of withdrawal groundwater (see paragraph 1.3.6) ETa: the actual evapotranspiration (see chapter 5) SWout: the surface water that leaves the system (according to gauge station data) GWout: the groundwater that leaves the system (chapter 3 & 5) Psp: the surplus precipitations (Ptota - Peff) ∆S: the water storage variation It should be mentioned that because lack of soil infiltrations data, GWout was neglected in this re-search.

1.3.8. Determining of environmental impacts of groundwater use

1.3.8.1Estimation of reduction of water discharge to the Lake in last 10 years It was carried out based on following equation

Q into the lake = Q gauging station – Q irrigation

Where: Q into the lake = discharge to the lake Q gauging station= recorded discharge in the gauge station data that area located in the beginning of the study area (see chapter3) Q irrigation = irrigation water use (has been calculated in chapter 6 according obtained results in chapter 5) 1.3.8.2 Determination of dynamics of shore zone in last 10 years It was carried out using available images, lake level fluctuation data and Digital Elevation model of the study area. (See chapter 7) 1.3.8.3Determination of changes of soil and groundwater salinity in the coastal area


5

This has carried out according to fallowing stages:

• Determining the saline areas using classification maps of 1987, 2000,2001 • Determining the area where affected by fluctuation of lake boundary directly (maxi-

mum boundary of lake has specified using lake level fluctuation data and Digital ele-vation Model of the study area)

• Determining the spatial situation and extent of saline area in 1964 and 1999according to available soil survey results

• Investigation of ground water quality changes in last 10 years according to collected data from Water Organization of Western Azerbaijan and field measurements

• Investigation relation between ground water use and electrical conductivity changes in last 10 years

• Preparation soil electrical conductivity map according to field measurements • Preparation soil electrical conductivity and groundwater salinity hazard maps for study

area • Comparing extent of saline areas in 19964, 1999 and 2002 • Comparing spatial situation of saline areas in 19964, 1999 and 2002 • Determining salinity extension based on mentioned stages

Detail methods are described in chapter 7.

1.4. Materials

• Satellite Images(TM 1987, TM2000, ETM2001, Aster2001) • Long-term meteorological stations data are shown in table Long-term hydrometric sta-

tion data • Selected irrigation and Pysometic wells data (levels and EC) • Fieldwork results (sample set, control set and interview results for crop type and crop

calendar) are shown in tables No. 1.5 & 1.6 • Soil survey reports and maps (1963, 1974 and 1999) prepared by soil and water divi-

sion of Agriculture research center of western Azerbaijan. • Field measurements (soil and groundwater electrical conductivity) • Geology maps (1/25000) prepared by geology organization of Iran • Topographic maps (1/50000) prepared by cartography organization of Iran

Some of the collected data are shown in appendix.


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2. Area discription

2.1. Location The study area, the irrigated area in the southwest part of Urumieh Lake is located in the West Azer-baijan province, west north of Iran, between 37°, 10′ to 37°, 35′ north latitude and 44°, 50′ to 45°, 21′ east longitude. The water resources for irrigation include deep and semi deep wells, the Shaharchay River and the Barandozchay River, which are flowing with a west east direction into the Lake.

2.2. Climate Northwest of Iran, including the study area, have cold and moist winters and mild temperature sum-mers. The Siberian cold and moist anticyclone affects this area from the north. When pressure differ-ences, between the high pressures Siberian anticyclone and low pressure southern air above the Per-sian gulf, is high, northern air comes to Azerbaijan and make it cold and cause snowfall in winter. Rainfalls when the west wind coming from the Mediterranean Sea are dominant.

2.2.1. Temperature

Temperature of the study area are vary between an average maximum 31 °C in July and a minimum of -6 °C in Jeanery with average of 11 °C per month. Mean monthly temperature in Urumieh synoptic station, according to last 50 years data, is shown in figure 2.1.

Average monthly Temperature in Urumieh sinoptic station

-30-25-20-15-10-505

1015202530354045

OCT. NOV. DEC. JAN. FEB. MAR. APR. MAY JUNE JULY AUG. SEP.Month

Mon

thly

tem

pera

ture

(c)

mean_daily mean_min mean_max Low est hiest

Figure 2.1: Mean monthly temperature (°C) in the study area (analyzed using Urumieh synoptic station in last 50 years)

2.2.2. Relative humidity

Considering the data of Urumieh synoptic station, generally the relative humidity per year ranges be-tween 76% in Juan and 48% in July. The mean monthly relative humidity is shown for Urumieh syn-optic station in figure 2.2.


7

Relative humidity in Urumieh synoptic station

0102030405060708090

100

OCT. NOV. DEC. JAN. FEB. MAR. APR. MAY JUNE JULY AUG. SEP.

Month

Rel

ativ

e hu

mid

ity %

AV AV.MAX AV.MIN MAX MIN

Figure 2.2: Monthly relative humidity in Urumieh station (analyzed using Urumieh synoptic station in last 50 years)

2.2.3. Precipitation

Precipitation is the main input of water to the earth surface, and it is the primary input vector of the hydrologic cycle. In Iran, the precipitation originates from humid flows with low-pressure centers, which moves from west to east within seven moth of year (from October to may). The majority of these flows (more than 60%) enter from west of Iran, this flow is known as Mediterranean low pres-sure. Other portion of precipitation, which is originated in the northern Atlantic Ocean, enters to Iran from southwest. In high part of Shaharchay and Brandoz basins, the form of precipitation especially in winter is snow but in low part (plain) and the study area, in most of time, its form is rain, and in January and February snowfall can be observed. The monthly mean rainfall and the annual rain fall in Urumieh synoptic sta-tion for 50 years data are shown in figure No. 2.3, 2.4.

Annual Precipitation in urumieh synoptic station

0

10 0

2 0 0

3 0 0

4 0 0

5 0 0

6 0 0

7 0 0

19 6 0 19 6 5 19 7 0 19 7 5 19 8 0 19 8 5 19 9 0 19 9 5 2 0 0 0 2 0 0 5

Year

prec

ipita

tion

(mm

)

Figure 2.3: Annual Precipitation in Urumieh synoptic station (analyzed using Urumieh synoptic station in last 50 years)


8

Monthly mean precipitation in Urumieh synoptic station

0

10

20

30

40

50

60

70

JAN. FEB. MAR. APR. MAY JUNE JULY AUG. SEP. OCT. NOV. DEC.

Month

Prec

ipita

tion

(mm

)

Figure 2.4: Monthly mean precipitation in Urumieh synoptic station (analyzed using Urumieh synoptic station in last 50 years)

2.3. Geology

Based on the geologic map (at scale 1:250,000) prepared by geology organization of Iran, geology of the study area belongs to different geological era from Pre-Cambrian to Quaternary (fig.2.3). Descriptions of these formations are as follows: Pre-Cambrian: The oldest recognized geology formation; in the study area belongs to Pre-Cambrian, which include metamorphosed rhyolit, acid tuff and porphyry quartzite. These formations can be observed in the west part of the study area near the Kokia, Eisalo and Babarod villages. Permian: The Permian consists of Dorud and Rutheh formations. Dorud formation includes sandstone, quartzite (red and pink) locally some shale and conglomerate, and Rutheh formation, includes limestone, dolo-mitic limestone and some dolomites. These formations occur on the southern part of study area. Tertiary: Western part of the region mainly consists of rocks OF tertiary age such as sandstone, conglomerate, some shale and marl. These sedimentary rocks belong to Oligo-Miocen. Fine-grained sandstone is the major tock type. There are also, coarse-grained sandstone together with micro-conglomerate and sandy marls and limestone in some parts. Quaternary: Quaternary includes fans and terraces deposits. The main agriculture areas are located in this part of the region, due to gentle slope and its productive soils. Table 2.1 shows geology and lithology of the study area as the legend of the figure 2.5.


9

Figure 2.5: geology map of study area (according to geology map of Urumieh prepared by geology organi-zation of Iran) units of maps explained in table 2.1 Table 2.1: Description of geology map

Geological area Symbol Rock type QT2 Young terraces and alluvial fan

Qal Land slid and slump

Quaternary

Qs Salt swamps Ml Limestone, white grey Mpl Tuff, sandstone, silt stone, conglomerate Ms Sandstone, conglomerate, marl, some red limestone

Tertiary

Na Andesite and associated intermediate rocks, volcanics breccia

Pd Sandstone, quartz tic sandstone, red locally with some shale and conglomerate

Permian

Pr Limestone, dolomitic limestone, some dolomite, laterite lenses

Precambrian PEV Metamorphosed rhyolite, acid tuff, quartz porphyry


10

2.4. Soil

The soils, distinguished in the study area, are Inceptisol that have ochric, cambic and calcic horizons. Description of the series of these soils and their characteristics are explained in tables 2.2 & 2.3 and relative map is shown in figure 2.6 (N. Ghaemian, 2000, Soil and water research center of western Azarbaijan). Table 2.2: Soil classification and different soil series in the study area

U.S.D.A soil taxonomy Land unit and Physio Graphy

Soil series Soil No. Family Subgroup Order

F.A.O/UNESCO 1988

Plateaux Rashakan 1 Fine, Mixed, Mesic Typic calcixerepts Incep-tisol

Haplic calcisols

Didan 2 Fine loamy, Mixed, Mesic Fluventic Haplox-erepts

Inceptisol Calcaric cambisols

Kokia 3 Fine loamy, Mixed, Mesic Typic calcixerepts Inceptisol Haplic calcisols Dash Agher 4 Fine, Mixed, Mesic Typic calcixerepts Inceptisol Haplic calcisols

Piedmont Plains

Balanej 5 Fine, carbonatic, Mesic Typic calcixerepts Inceptisol Haplic calcisols River Baranduzx 6 Loamy over sandy skeletal,

Mixed, Mesic Fluventic Haplox-erepts


Agh-cheziveh

7 Fine loamy, Mixed, Mesic Fluventic Haplox-erepts


Hqaftpestan 8 Fine, Mixed, Mesic Fluventic Haplox-erepts


Chubtarash 9 Coarse loamy, Mixed, Mesic

Typic En-doaquepts

Inceptisol Eutric Gleysols

Sarajogh 10 Fine loamy, Mixed, Mesic Typic En-doaquepts


Drbrood 11 Fine, Mixed, Mesic Fluaquentic En-doaquepts


Gurttapeh 12 Fine, Mixed, Mesic Vertic En-doaquepts

Inceptisol Calcic Gleysols

Alluvial Plains

Arablu 13 Coarse loamy, mixed, (sodic phase)

Typic Halaquepts Inceptisol Haplic Solonchaks

Jabal kandi 14 Fine, Mixed, Mesic (sodic phase)

Typic Halaquepts Inceptisol Haplic Solonchaks Low Lands

Ordoshahi 15 Fine, Mixed, Mesic Typic Halaquepts Inceptisol Haplic Solonchaks


11

Table 2.3: Brief description of soil series (main characteristics) in the study area No.

Soil series

Depth

Top soil Texture

O.S %

T.S %

Erosion

Drainage

Top soil Infiltration

Top soil Ec

Top soil PH

1 Rshakan Very deep

Silty clay Loam

2-5

0-2

M - high

Well

Low

0.63

7.9

2 Didan Very deep

Silty clay Loam

2-5

0-2

Slight

Well

Moderately Low

0.63

7.4

3

Kokia

Very deep

Loam with 3-15% gravel in top soil

0-2

0-1

Slight

Well

Moderate

0.59

7.9

4 Dash agher Very deep

Clay loam 0-2

0-1

Slight

Well

Low

0.95

7.8

5 Balanej Very deep

Silty clay loam

0-2

0-1

Slight

Moderate

Low

0.83

7.7

6

Barandoz

Semi deep

Loam 3-15% gravel in top soil

0-2

0-1

Slight

Well

High

0.83

7.6

7 Aghjeh ziveh

Very deep

Loam 0-2

0-1

Slight

Well

Moderately low

0.46

7.9

8 Haftpestan Very deep

Silty loam 0-2

0-1

Slight

Moderate

Low

2.11

7.6

9 Chubtarash Very deep

Clay loam 0-2

0-1

Slight

Moderate

Moderately low

1.05

8.5

10 Sarajogh Very deep

Loam 0-2

0-1

-

M- Bad

Moderately Low

0.95

8.2

11 Darbrooa Very deep

Silty Loam 0-2

0-1

-

Bad

Very Low

2.11

7.6

12 Gurt tapeh Very deep

Clay 0-2

0-1

-

M- Bad

Very Low

1.39

8.2

13 Arablu Semi deep

Loam 0-2

0-1

-

Bad

High

33.2

7.5

14 Jabal kandi Very deep

Silty clay loam

0-2

0-1

-

Bad

Low

2.61

8.8

15 Ordoshahi Very deep

Silty clay 0-2

0-1

-

Bad

Low

28.1

8


12

Figure 2.6: soil map of study area according to soil survey report of 1999 (A.Ghaemian, 1999) main char-acteristics of units is explained in table 2.3.


13

3. Data preprocessing

3.1. Climate data

3.1.1. Rainfall

3.1.1.1 Rainfall data: Precipitation of the study area has been investigated for a 25 years period, using data of the Urumieh synoptic station and four rain gauge stations of which two are located inside and two are outside of the study area (the location of the stations, distribution and recording periods of stations are shown in fig-ure 3.3 and table 3.1). The reliability of the data has been checked using double mass curve method and missing data were completed through the weighted average and station- year methods. The accu-mulated rainfall data has been plotted against the time (years), and the result is shown in figure 3.2. There is no discontinuity the stations, and the data has been used in the further analysis. Table 3.1:Meteorological stations list (recording length & available data)

Station

Location

Altitude

Measured Parameters

Recording

length

Available data for analysing

Synoptic of Urumieh

45° 05′ E 37° 32′N

1312.5

Rainfall, Evaporation, Wind speed, Sunshine duration, Relative humidity, �

1972 -2002

Daily, Monthly, Yearly

Dizaj 45° 04′E 37° 10′N

1320

Rainfall 1974 -2002 Daily, Monthly, Yearly

Babarood 45°14′E 37° 24′N

1285

Rainfall, Temperature 1974 -2002 Daily, Monthly, Yearly

Golmankhaneh 45° 14′E 37° 36′N

1280

Rainfall, Temperature 1974 -2002 Daily, Monthly, Yearly

Ghasaemlo 45° 09′E 37° 21′N

1330

Rainfall 1974 -2002 Daily, Monthly, Yearly

3.1.1.2 Monthly course of rainfall: The average monthly rainfalls of 5 meteorological stations have been plotted in figure 3.3. There is a relatively wet period from October till January and January to May is the wettest period, with snow-fall. July, August and September are dry. 3.1.1.3 Areal rainfall To estimate the areal rainfall for any area, point rainfall needs to be converted to area depth of rainfall. There are various methods to do this; in the present study Thiessen method has been used. Figure 3.2 shows the Thiessen polygons of the study area and location of the meteorological stations. The relative areal weight for 5 polygons enclosing the corresponding stations is presented in table 3.2.


14

Monthly rainfall for 5 stations

0

10

20

30

40

50

60

OCT DEC FEB APR JUN AUG

Month

Rai

nfal

l (m

m)

Babarood Gholmankhaneh Dijaj

Synoptic Ghasemlo

Figure 3.1:Mass curves of rain gauge stations Figure 3.2: Average monthly rainfall

Figure 3.3: Thiessen polygon map of the study area of rain gauge stations

Mass curve for meteorological stations (in the study area)

0

2000

4000

6000

8000

10000

12000

73-74 76-77 79-8082-83 85-86 88-89 91-9294-95 97-98 00-01years

Cum

ulat

ive

annu

al ra

infa

l (m

m)

Babarood Dizaj Gholmankhaneh

Ghasemlo synoptic


15

Table 3.2: Weighting coefficients of the stations in the study area based on Thissen polygon Station name Relative weight of polygon Area of polygon (Km2)

Synoptic (Urumieh) 0.18 70.079 Babarood 0.47 179.340 Dizaj 0.09 34.789 Ghasemlo 0.07 26.174 Gholmankhaneh 0.19 72.831

The monthly mean areal rainfall in the study area has been calculated by Thiessen method according to relative weight of polygons using meteorological satiations data for last 25 years. These data will be used in future steps of research.

3.1.2. Temperature

For calculating Reference Crop Evapotranspiration (ETo), which will be used to determining of crop water requirement (CWR), monthly climatic data (temperature, humidity, wind speed and sun shine duration) are necessary. Investigation of these parameters has been done using Urumieh synoptic sta-tion data, which is located in the west-north part of the study area. Mean annual temperature, average of minimum and maximum temperature of the study area are shown in table 3.3. The warmest month is July; average mean daily temperature in this month is 24 °C, whereas January has the lowest aver-age mean daily temperature (-2.1°C). Dry season of the study area has been determined using Om-brothermic curve where rainfall and temperature were plotted against months. Figure 3.4 shows the Ombrothermic curve of the study area. Based on this curve, there is a dry period, from middle of May to the middle of October. Table 3.3:Temperature in the synoptic station of Urumieh Month Temperature JAN. FEB.

MAR. APR. MAYJUNE

JULY AUG.SEP. OCT. NOV.DEC.

AN-NUAL

Mean -2.1 -0.3 5 10.9 15.6 20.1 23.9 23.4 19.4 13.3 6.8 1.1 11.4 Average. Min -7.4 -5.3 -0.9 4.5 8.2 12.0 15.7 14.9 10.5 5.6 0.5 -4.0 4.6 Average. Max 2.3 4.4 10.1 16.7 22.1 27.4 31.2 30.8 27.1 20 12.1 5.4 17.5

3.1.3. Relative humidity

Relative humidity is one of the most important factors that directly affects evapotranspiration. Based on Urumieh synoptic station�s data the mean annual relative humidity is 61%. And the average mini-mum and average maximum value of this parameter in the study area is 43% and 79%. The monthly average relative humidity for Last 25 years (which are necessary for calculating of reference evapotranspiration and other steps of present research).


16

Ombrothermic Curve

-505

10152025

JAN

.

FEB.

MAR

.

APR

.

MAY

JUN

E

JULY

AUG

.

SEP.

OC

T.

NO

V.

DEC

.

Month

Tem

pera

ture

(c

)

0.010.020.030.040.050.060.0

Prec

ipita

tion

Temperature Precipitation

Figure 3.4: Ombrothermic Curve of the study area (analyzed using Urumieh synoptic data)

3.1.4. Sun shin duration& Wind speed

Sun shin duration and wind speed are the other parameters that are necessary for calculating reference evapotranspiration by Penman-Montith method. For preparing these data, data of Urumieh synoptic station has been used. General aspects of sun shin duration and wind speed in the Urumieh synoptic station are shown in tables 3.4 and 3.5. Table 3.4: Average Sun shin duration (hr) in the Urumieh synoptic station (analyzed using Urumieh syn-optic data)

Month Sun shin Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Average

Mean 7.3 5.4 4.1 4.3 5.5 6.1 7.3 9.0 11.2 11.7 11.0 9.8 7.7

Max. 8.8 7.4 5.6 6.9 7.9 8.4 9.3 11.0 12.8 12.5 12.1 10.9 8.6

Min. 4.6 2.6 2.2 1.5 3.6 4.2 4.2 5.6 9.6 9.9 9.7 8.3 7.1

Table 3.5: Wind speed (Km/hr) in the Urumieh synoptic station (analysed using Urumieh synoptic data)

Month Wind speed Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep AverageMean 3.0 2.5 2.1 1.9 2.7 3.6 5.1 4.3 4.2 4.0 3.7 3.6 3.4 Max 5.8 6.1 4.7 5.2 6.1 7.3 9.5 8.4 8.3 7.6 7.3 6.7 6.0 Min 1.6 0.7 0.6 0.6 0.6 2.0 2.0 2.0 1.9 1.9 1.8 1.8 1.9

3.2. Surface water

3.2.1. River discharge:

Barandoz- chay, Shahar-chay and Balanij-chay Rivers are the surface water resources for irrigation in the study area, which are flowing with west-east direction into the Urumieh Lake. The discharge of


17

these rivers are gauging in four gauge stations, which are located in the beginning and inside of the irrigated area (figure 3.5). Recorded data of these stations are available for last 30 years. The mean discharge of the Shahar-chay and Barandoz-chay rivers is 5.2 and 9.4 m3/s. The mean discharge re-corded in the station and mean monthly discharges of rivers are shown in table 3.6 and figure 3.6.

Mean monthly discharge of the Rivers

0.0

5.0

10.0

15.0

20.0

25.0

Oct Dec Feb Apr Jun

Aug

Dis

char

ge (m

3/s)

shahar-chay balanij-chay barandoz-chay

Figure 3.5: Location of the gauge Figure 3.6: Mean monthly discharge of rivers Stations Table 3.6:Mean discharges in the gauge stations (in the last 30 years)

Station River Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Annual

Band Shahar-chay 0.9 1.7 1.8 1.5 1.5 2.7 9.3 18.3 16.2 6.2 1.9 0.8 5.2

Ghasemlo Balanij-chay 0.6 1.0 1.2 1.2 1.2 1.8 4.2 3.7 1.4 0.6 0.3 0.3 1.4

Babarod Barandoz-chay 1.5 5.1 6.5 6.0 6.3 8.6 18.3 26.9 16.3

16.

3 1.3 0.2 9.4

Dizaj Barandoz-chay 2.6 5.4 5.9 5.3 5.1 6.8 14.9 23.4 17.6 8.6 2.9 1.6 8.3

3.2.2. Available surface water (for irrigation)

The main use of Barandoz-chay and Balanij-chay rivers is irrigation and the amount of the water that are using for other purpose is less than 1%. About 30% of Urumieh city water consumption is pro-vided by Shahar-chay River. It, depends on different season, varies between 0.3 to 0.5 m3/s (Report of jam-ab-Iran, 1998). Available surface water has been calculated for each month using discharge gauge stations data and collected data about volume of surface water use from Western Azerbaijan Power Organization based on irrigation efficiencies (Report of agriculture research center of western Azerbaijan, 1996), which are shown in table 3.7.


18

Table 3.7: Irrigation efficiency for different water resources in the study area (Report of jam-ab-Iran, 1998)

Irrigation efficiency

Water resource

Transition % Distribution % Applied % Total % Traditional irrigation system 83 85 47 33.2 Well 94 95 50 44.7

3.3. Drought period: Drought is a stochastic and unpredictable phenomenon, but its effects would be permanent when it occurred. There are various methods to determining the quantitative aspect of drought. One of these methods is runs approach method, which was used in present research. Drought period in the study area (for last 25 years), according runs approach and moving average methods are shown in figure 3.7 and 3.8 It shows that there is a drought period from 1996 to 2000 in recent years.

Drought periods in the study area according to runs approach method

73-74 75-76 77-78 79-80 81-82 83-84 85-86 87-88 89-90 91-92 93-94 95-96 97-98 99-00

X-Xo

Figure 3.7: Drought periods in the study area for last 25 years

Yearly and moving average rainfall (Babarod raingauge station)

0100200300400500600

73-74 76-77 79-80 82-83 85-86 88-89 91-92 94-95 97-98 00-01

Year

Prec

ipita

tion

(mm

)

Precipitation Average

3 per. Mov. Avg. (Precipitation) 5 per. Mov. Avg. (Precipitation)

Figure 3.8: Yearly and moving average rainfall in the study area


19

4. Applied GIS and RS to crop classification

Classification in general is grouping of similar objects and separation of dissimilar ones. It can be per-formed by quantitative interpretation of the image based on the statistical analysis of the spectral vari-ables. During this process each pixel is classified into a limited number of discrete classes. The use of statistical algorithms for this purpose is called a �statistical Pattern Recognition.� Other techniques are based on structure measurements including the study of shape, texture, and orientation and this is called �Structural Pattern recognition.� These procedures can be combined in the classification proc-ess(Molina, 1992). In this research image processing involves crop classification using Aster and Landsat satellite im-ages. It was done for both identifying crop types and crop planted area to estimate water demands.

4.1. Image pre-processing Successful identification of crops required multi-temporal images. While the Aster image for date 2 July 2001, Landsat image for 18 March 2001 and TM images for 3 June 2000, July 1990 and 1987 just were selected and (considering figures 3.7 and 3.8 which indicate that year 2000 is a dry and 2001 is a wet year) the classification has been done for 2000 and 2001 years using available images. For doing this, various layers such as principal component (Pc), Normalized Difference Vegetation Index (NDVI) and different bands of the images have been used. An unsupervised classification map and different color composites were used during fieldwork where the training samples of the existing crops were specified by both interviewing with farmers and field observation. Cropping patterns and cropping calendars also were determined using available data at the offices of the Agriculture Organization of Western Azerbaijan and interviews with farmers. Typi-cal crop calendar for the study area is shown in figure 4.1.

4.2. Statistical Image classification

There are two approaches to extract spectral information that adequately represents the spectral charac-teristics of the image to be classified. These are supervised and unsupervised classification (Richards 1986). One should also consider a third approach, which is a �hybrid� supervised/unsupervised strat-egy that aspires to extract the attributes of both methods. Unsupervised classification: By this method, image pixels are assigned to spectral classes without the user having previous knowledge about the study area. Applying clustering methods, i.e. procedures to determine the spectral class of each pixel, usually performs it. Supervised classification: This method involves selection of areas in the image, which statistically characterize the categories of interest. The supervised approach requires prior knowledge about the area, which can be derived from work or from reference data on the area. Information about the area has to be supplied by the user through training samples (Salem 1995).


20

Figure 4.1: Typical crop calendar, Urumieh plain; prepared base on interview with farmers and reports of Agriculture organization of W.A province; (Planning date Harvesting date) In the present research both mentioned methods were used, as unsupervised for primary map (used for fieldwork) and supervised for final classification. Considering that ILWIS software has some different algorithms to classification, after some tries it was found that the maximum likelihood classifier per-formed the best. It carried out by comparing different results with specified terrain samples. Maxi-mum likelihood classifier was used to make 5 different maps. These maps were created using different combination of images and bands. These selection bands were based on visual assessment of different band combinations (see appendix). These maps and bands were the following: Alfalfa map; ETM march 2001 and Tm July 2000 (FCC 7,4,2 and 4,3,2) and Aster July (FCC 3,2,1) Orchard map; ETM march 2001 and Tm July 2000 (Pc 1,2,3) Rangeland map; ETM march 2001 (FCC4, 3,2) and Tm July 2000 (FCC 4,3,2) Settlement map; Aster July 2001 (FCC3, 2,1) it separated visually Final classes map (final classification map): Aster July 2001 (FCC3, 2,1) was used for 2001 and Tm July 2000 (FCC 4,3,2) was used for 2000. Finally, using the metioned method nine classes were determined. The legend as shown in fig.4.2 and 4.3 were adopted for the image classification. The final map were created according to the following ILLWIS formula: Rangeland=Rangeland map =”Rangeland” Final class_R=iff (Rangeland, Rangeland map, Final class) It is noticeable that the Final class (map) for 2000 and 2001 are different. Alfalfa =Alfalfa map =” Alfalfa” Final class_RA= iff (Alfalfa, Alfalfa map, Final class_R) Orchard= Orchard map=” Orchard” Final class_RAO=iff (Grape, Grape map, Final class_RA) Settlement =Settlement map=” Settlement” Final class_RAOS=iff (Settlement, Settlement map, Final class_RAO)

4.3. Confusion matrix To get an idea of the overall accuracy of the classification, user has to be made a test set, which con-tains additional ground truth data. Crossing the test set with the classified image and creation of a so-

Oct Nov Des Jan Feb Mar Apr May Jun Jul Aug Sep Winter Wheat Barley Summer Sugar beet Cucumber Corn Sunflower All year Alfalfa Grape Apple


21

called confusion matrix is an established method to assess the accuracy of a classification(A.Gieske 2002). A part of ground truth samples was employed to generate a confusion matrix for both of classified maps (table 4-1 and 4-2). Table 4.1: Confusion matrix for the classification with ten categories for 2001

Class Alfalfa

Orchard apple

Orchard grape

Summer crop

Winter crop

Water body

Other

Settlement Rangeland

Saline area

UNCLASSIFIED

ACCURACY

Alfalfa 45 15 0 0 0 0 0 0 0 0 0 0.75 Orchard Apple 19 84 3 0 0 0 0 0 0 0 0 0.79 Orchard Grape 1 1 80 7 0 0 3 0 8 1 0 0.79 Summer Crop 0 6 17 73 3 0 5 0 1 0 0 0.7 Winter Crop 0 1 1 2 59 0 5 0 6 1 0 0.79 Water Body 0 0 0 1 0 40 2 0 0 0 0 0.93 Other 1 0 2 1 4 1 37 3 0 0 0 0.76 Settlement 0 0 0 0 0 0 0 24 0 0 0 1 Rangeland 0 0 3 0 4 1 0 1 52 5 0 0.79 Saline Area 0 0 0 0 0 2 0 0 0 32 0 0.94 RELIABILITY 0.68 0.79 0.75 0.87 0.84 0.91 0.71 0.86 0.78 0.82 Average Accuracy = 82.31 %, Average Reliability = 80.08 %, Overall Accuracy = 79.34 % Table 4.2: Confusion matrix for the classification with ten categories for 2000

Class Alfalfa Orchard apple

Orchard grape

Summer crop

Winter crop

Water body Other Settlement Rangeland

Saline area UNCLASSIFIED ACCURACY

Alfalfa 45 15 0 0 0 0 0 0 0 0 0 0.75 Orchard apple 19 84 3 1 0 0 0 0 0 0 0 0.79 Orchard grape 1 1 80 5 0 5 3 0 8 1 0 0.77 Summer crop 0 4 5 30 0 0 5 0 2 1 0 0.64 Winter crop 0 2 7 0 47 1 7 0 3 0 0 0.7 Water body 0 0 0 0 0 16 0 0 0 2 0 0.89 Other 1 0 2 0 4 1 36 3 0 0 0 0.77 Settlement 0 0 0 0 0 0 0 24 0 0 0 1 Rangeland 0 0 3 1 2 0 0 1 52 4 0 0.83 Saline area 0 0 0 0 0 1 1 0 0 20 0 0.91 RELIABILITY 0.68 0.79 0.8 0.81 0.89 0.67 0.69 0.86 0.8 0.71 Average Accuracy = 80.33 %, Average Reliability = 77.02 %, Overall Accuracy = 77.64 % According to confusions matrix some obvious points should be noted.

• Unlike the perennial crops such as alfalfa or orchards, some classes e.g. summer crop, winter crop and rangeland were difficult to classify. Because according to date of images, some of annual crops were harvested and other were in the growing period, and also more part of rangelands was dry for this reason there is some overlap in the separated of these classes.

• Except of orchards, other field�s size were very small, due to this problem and lack of multi-temporal image, the classification was difficult.


22

4.4. Final crop map

Classified maps show some noise in the large homogeneous area, for reducing isolated pixels within homogenous areas the majority filter was applied. Final crop maps for both 2000 and 2001 years were given after adding roads and residential area on classified maps (figure 4-1and 4-2)

Figure 4.2: Land cover and crop type map for 2001

Figure 4.3: Land cover and crop type map for 2000

4.5. Total irrigated area The areas for each class or crop type can be found through attribute table of classified maps. Table 4-3 shows the derived area separately for each class. It should be noted that there is some classes such as Rangeland, which are not part of the irrigated area (cropped area) as viewed by the satellite image then it was therefore taken out of the sum for the cropped area. These classification results were compared crop data that provided by the Provincial Offices of the Ministry of agriculture (see appendix), in the


23

Western Azerbaijan, It was necessary that some classes of crop data from this office is aggregated the classes produced in the classification map (e.g. sugar beet, sunflower, cucumber and tomato) are put in the class� summer crop�. The irrigated area estimates of table 4-3 have been compared with estima-tion that it was made by the Provincial Offices of the Ministry of agriculture. All data have been com-piled in the table 4.4. Table 4.3: Area per class derived from different images for 2001 and 2000

Year 2001 Year 2000 Class N. Pix N. Pixpct Pctnotund Area (ha) N. Pix N. Pixpct Pctnotund Area (ha) Alfalfa 8184 0.71 1.9 730 8168 0.71 1.91 728 Orchard apple 100532 8.7 23.33 8963 100319 8.69 23.45 8944 Orchard grape 91936 7.96 21.33 8196 91732 7.94 21.44 8178 Other 17643 1.53 4.09 1573 17167 1.49 4.01 1530 Rangeland 15804 1.37 3.67 1409 15530 1.34 3.63 1385 Saline area 89199 7.72 20.7 7952 68012 5.89 15.9 6063 Settlement 4536 0.39 1.05 404 4372 0.38 1.02 390 Summer crop 61324 5.31 14.23 5467 62181 5.38 14.53 5543 Water body 799 0.07 0.19 71 20769 1.8 4.85 1852 Winter crop 40923 3.54 9.5 3648 39628 3.43 9.26 3533 Min 799 0.07 0.19 71 0 0 0 390 Max 100532 8.7 23.33 8963 100319 8.69 23.45 8944 Average 43088 3.73 10 3841 38898 3.37 9.09 3815 Std 39489 3.42 9.16 3520 35987 3.12 8.41 3157 Sum 430880 37.3 99.99 38413 427878 37.05 100 38146 The results should be interpreted with some care, because the results show some difference between results of the two methods. But the results are very similar and the image classification shows the spa-tial distribution of crops. Table 4.4: Area separated into new categories for comparison with satellite image

2001 2000 Derived from image Ground troth Derived from image Ground troth

Class Area (ha) % Area (ha) % Area (ha) % Area (ha) % Alfalfa 730 2.7 820 3.2 728 2.7 840 3.2 Orchard apple 8963 33.2 8650 33.3 8944 33.2 8650 33.1 Orchard grape 8196 30.4 7750 29.9 8178 30.4 7750 29.6 Summer crop 5467 20.2 4830 18.6 5543 20.6 4730 18.1 Winter crop 3648 13.5 3890 15.0 3533 13.1 4200 16.0 27004 100 25940 100 26926 100 26170 100


24

5. CROPWAT model

5.1. General CROPWAT is a decision support system developed by the Land and Water development division of FAO for planning and management of irrigation. CROPWAT is meant as a practical tool to carry out standard calculations for reference evapotranspiration, crop water requirements and crop irrigation re-quirements, and more specifically the design and management of irrigation schemes. It allows the de-velopment of recommendations for improved irrigation practices, the planning of irrigation schedules under varying water supply conditions, and the assessment of production under rain fed conditions or deficit irrigation. (Clarke et al, 1998). Procedures for calculation of the crop water requirements and irrigation requirements are based on methodologies presented in FAO Irrigation and Drainage Papers No. 24 "Crop water requirements" and No. 33 "Yield response to water�. The model uses the FAO (1992) Penman-Montieth method for calculating the reference crop evapotranspiration. These estimates are used in crop water requirements and irrigation scheduling calculations.

5.2. Input data Calculations of the crop water requirements and irrigation requirements are carried out with inputs of climatic, crop and soil data. For the estimation crop water requirements (CWR) the model requires: a) Reference Crop Evapotranspiration (Eto) values measured or calculated using the FAO Penman-Montieth equation based on decade/monthly climatic data: minimum and maximum air temperature, relative humidity, sunshine duration and wind speed; b) Rainfall data (daily/decade/monthly data); monthly rainfall is divided into a number of rain storm each month; c) A Cropping Pattern consisting of the planting date, crop coefficient data files (including Kc values, stage days, root depth, depletion fraction) and the area planted (0-100% of the total area); a set of typi-cal crop coefficient data files are provided in the program. In addition, for Irrigation Scheduling the model requires information on: d) Soil type: total available soil moisture, maximum rooting depth and initial soil moisture depletion (% of total available moisture). e) Scheduling Criteria; several options can be selected regarding the calculation of application timing and application depth (e.g. 80 mm every 14 days, or irrigate to return the soil back to field capac-ity when all the easily available moisture has been used).

5.3. Calculation methods The values of decade or monthly Reference Crop Evapotranspiration (Eto) are converted into daily values using four distribution models (the default is a polynomial curve fitting). The model calculates the Crop Water Requirements using the equation: CWR=Eto*Kc*area planted. This means that the peak CWR in mm/day can be less than the peak Eto value when less than 100% of the area is planted in the cropping pattern.


25

The average values of crop coefficient for each time step are estimated by linear interpolation between the Kc values for each crop development stage. The �Crop Kc� values are calculated as Kc*Crop Area, so if the crop covers only 50% of the area, the �Crop Kc� values will be half of the Kc values in the crop coefficient data file. For crop water requirements and scheduling purposes, the monthly total rainfall has to be distributed into equivalent daily values. CROPWAT for Windows does this in two steps. First the rainfall from month to month is smoothed into a continuous curve (the default curve is a polynomial curve, but can be selected other smoothing methods available in the program e.g. linear interpolation between monthly values). Next the model assumes that the monthly rain falls in 6 separate rainstorms, one every 5 days (the number of the rainstorms can be changed). The model has available four Effective Rainfall methods (the USDA SCS method is the default). For the scheduling calculations can be se-lected two options: Irrigation Scheduling and /or Daily Soil Moisture Balance. The Irrigation Schedul-ing option shows the status of the soil moisture every time new water enters the soil, either by rainfall or a calculated irrigation application. Daily Soil Moisture Balance option shows the status of the soil every day throughout the cropping pattern, how the soil moisture changes in the growing season. User defined irrigation events and other adjustments to the daily soil moisture balance can be made when the Scheduling Criteria are set to �user-defined�. Total Available Moisture (TAM) in the soil for the crop during the growing season is calculated as Field Capacity minus the Wilting Point times the current rooting depth of the crop. Readily Available Moisture (RAM) is calculated as TAM * P, where P is the depletion fraction as defined in the crop coefficient (Kc) file. To avoid crop stress, the calculated soil moisture deficit should not fall bellow the readily available moisture.

5.4. Output After entering all the data, CROPWAT for Windows automatically calculates the results as tables or

plotted in graphs. The time step of the results can be any convenient time step: daily, weekly, decade

or monthly. The output parameters for each crop in the cropping pattern are:

-Reference crop evapotranspiration � Eto (mm/period);

-Crop Kc - average values of crop coefficient for each time step;

-Effective rain (mm/period) - the amount of water that enters the soil;

-Crop water requirements � CWR or Etm (mm/period);

-Irrigation requirements �IWR (mm/period);

-Total available moisture �TAM (mm);

-Readily available moisture � RAM (mm);

-Actual crop evapotranspiration � Etc (mm);

-Ratio of actual crop evapotranspiration to the maximum crop evapotranspiration -Etc/

Etm (%);

-Daily soil moisture deficit (mm);

-Irrigation interval (days) & irrigation depth applied (mm).

-Lost irrigation (mm)� irrigation water that is not stored in the soil (i.e. either surface


26

Runoff or percolation);

-Estimated yields reduction due to crop stress (when Etc/Etm falls below 100%).

5.5. Irrigation water requirements calculation Crop water requirements (CWR) are calculated on the basis of monthly effective rainfall (Peff) and ref-

erence evapotranspiration (ETo), the first being calculated from average rainfall following the USDA

Soil Conservation Service method and the latter being calculated following the Penman-Monteith ap-

proach (FAO, 1992). For a given crop, i, and a given cropping period:

∑=

−=T

tPeffEToKciCWR

0).( Unit: mm (1)

Where kcit is the crop coefficient of the given crop, i, during the growth stage, t, and T is the last

growth stage.

Each crop has it�s own water requirements. Net irrigation water requirements (NIWR) in a specific

scheme for a given year is thus the sum of individual crop in a specific scheme for a given year are

thus the sum of individual crop water requirements (CWRi) calculated for each irrigated crop, i.

SiiCWRNIWRN

i.

1∑

=

= Unit: m3 (2)

Where Si is the area cultivated with the crop, i. Dividing by the area of the scheme (S, in ha), a value of irrigation water requirements is obtained, ex-pressed in m3/ha or in mm (1 mm = 10 m3/ha).

S

SiCWRiNIWR

n

i∑

== 1.

Unit: m3/ha (3)

The cropping intensity of the scheme can be defined as:

S

Sin

i∑

=1 (4)

To account for losses of water incurred during conveyance and application to the field, an efficiency factor should be included when calculating the irrigation water requirements for a scheme. The effi-ciency (E) of water distribution covers the efficiency of water conveyance, the field canal efficiency and the field application efficiency. It results in the gross irrigation water requirement (GIWR) per unit of area.

NIWREGIWR ./1= Unit: mm (5) For computation the first three stages of these units the CROPWAT software has been used and the latter being have been calculated using obtained results from chapter4 (crop type classification) and table 3.7(see chapter 3).


27

5.6. Obtained results for research area

The volume of water requirement for each crop, which had been cultivated in 2000 and 2001, has been

determined based on using prepared data in the chapters 3, 4 (crop type maps derived from classifica-

tion procedure) and CROPWAT software. The results are shown in tables5.1 and 5.2.

Mentioned parameters for 1991 to 1999 were calculated using collected data from Azerbaijan Agricul-

ture Organization, because there was not available any satellite images for these dates. It is noticeable

that, according the collected data the summer crop group includes cash crop 45%, sunflower 25%,

sugar beet 15% and maize 15%. And the crop water and other parameters have been calculated based

on these percentages and summarized in table 5.3.

Table 5.1: Crop water requirement and irrigation water requirement for different cultivated crops in the study area in 2001 (according to CROPWAT results)

Crop type

CWR

(mm/period) Effect. Rain (mm/period)

NIWRi

(mm/period) NIWRi

(m3/ha)

GIWRi (m3/ha) For ground water re-

sources

GIWRi (m3/ha) For surface water

resources Alfalfa 965 158 807 8070 18054 24307 Maize 548 22 527 5268 11784 15866 Sugar beet 693 54 640 6402 14321 19282 Sunflower 526 22 505 5045 11287 15197 Tomato 650 38 612 6119 13689 18430 Wheat 641 275 497 4968 11115 14965 Apple 722 126 596 5960 13333 17952 Grape 614 97 517 5170 11566 15572 Cucumber 410 21 379 3790 8479 11416 Table 5.2: Crop water requirement and irrigation water requirement for different cultivated crops in the study area in 2000 (according to CROPWAT model results)

Crop type

CWR (mm/period)

Effect. Rain (mm/period)

NIWRi

(mm/period) NIWRi

(m3/ha)

GIWR (m3/ha) For ground water

resources

GIWR (m3/ha) For surface water re-

sources Alfalfa 970 116 854 8540 19105 25723 Maize 555 23 532 5323 11907 16032 Sugar beet 703 56 647 6466 14466 19476 Sunflower 533 23 510 5098 11404 15355 Tomato 659 42 617 6167 13796 18575 Wheat 654 130 563 5631 12598 16961 Apple 730 87 653 6530 14609 19669 Cucumber 420 20 400 4000 8949 12048 Grape 620 80 540 5400 12080 16265


28

Table 5.3: Crop water requirement and Net irrigation water requirement for main crops, which are culti-vated in the study area for last 10 years in mm/period for 1 hectare (according to CROPWAT model re-sults)

91-92

92-93

93-94

94-95

95-96

96-97

97-98

98-99

99-2000

2000-2001

ETm 871 920 925 926 927 908 923 955 970 965 Alfalfa Peff 296 349 342 288 269 198 156 93 116 168 NIWR 575 571 583 638 658 710 767 862 854 807 ETm 611 660 665 666 667 648 663 695 730 722 Apple Peff 236 279 274 230 215 159 124 74 87 126 NIWR 374 380 391 435 452 490 538 621 653 596 ETm 506 555 560 561 562 543 558 590 620 614 Grape Peff 203 213 230 224 173 120 93 66 80 97 NIWR 303 342 330 337 389 424 465 524 540 517 ETm 512 551 560 543 559 532 545 555 555 548 Maize Peff 135 124 139 113 92 64 48 38 23 22 NIWR 427 486 464 457 509 469 502 520 532 527 ETm 621 668 679 668 669 647 667 682 703 693 Sugar beet Peff 186 197 198 141 161 111 91 68 56 54 NIWR 461 524 515 530 557 553 587 617 647 640 ETm 487 524 533 518 531 506 519 529 533 526 Sunflower Peff 135 124 137 113 92 64 48 38 23 22 NIWR 394 453 432 426 475 441 473 493 510 505 ETm 591 636 646 632 639 615 632 646 659 650 Tomato Peff 163 164 171 135 130 86 70 60 42 38 NIWR 441 506 488 497 534 529 562 586 617 612 ETm 574 613 615 625 610 604 620 646 654 641 Wheat Peff 346 398 427 359 325 223 198 141 130 275 NIWR 330 369 361 447 397 447 479 566 563 497

Regarding to applied category for classes in classification map (see table 4.1 and 4.2), net irrigation requirement for each class in m.c.m determined and the results are summarized in table 5.4.


29

Table 5.4: Net Irrigation Water Requirement according to classification map categories in m.c.m (Calcu-lation has been done based on classification map for 2000&2001 and collected data from Western Azer-baijan Organization for 1991-1999)

91-92 92-93 93-94 94-95 95-96 96-97 97-98 98-99 99-00 00-01

Area 812 810 780 788 786 760 735 725 728 730 Etm 7.1 7.5 7.2 7.3 7.3 6.9 6.8 6.9 7.0 7 Peff 2.4 2.8 2.7 2.3 2.1 1.5 1.1 0.7 0.8 1.2

Alfalfa

NIWR 4.7 4.6 4.5 5.0 5.2 5.4 5.6 6.3 6.2 5.8 Area 8655 8660 8800 8800 8850 8911 8935 8944 8944 8963 Etm 52.9 57.1 58.5 58.6 59.0 57.8 59.2 62.1 64.7 64.5 Peff 20.5 24.2 24.1 20.3 19.0 14.1 11.1 6.6 8.2 11.9

Apple


Grape


Summer crop


Winter Crop


Total

NIWR 97.9 106.6 105.0 113.5 120.1 127.6 138.7 155.9 159.1 149.9

5.7. Errors of model

The CROPWAT model is very sensitive to climatic and crop growth stages data. Hence, the

input data of this model should have high accuracy. To run CROPWAT model one needs to

calibrate and validate the obtained results with local lysimeter measurements. Due to lack of

these data it was not possible to do calibration and validation it for study area. Although there

is a reference manual that generally calibrated the model for different crops in Iran

(A.A.Farshi et al., 1997), which was used in the results. Despite these issues, since this model

is the best method among 20 main methods in the world (A.Alizadeh, 2000), it was applied to

estimate crops water requirement in this research.


30

6. Aspects of water balance

6.1. Concept of a water balance A "water balance" is an accounting of all water volumes that enter and leave a 3-dimensioned space (Figure 6. 1) over a specified period of time. Changes in internal water storage must also be consid-ered. Both the spatial and temporal boundaries of a water balance must be clearly defined in order to compute and to discuss a water balance. A complete water balance is not limited to only irrigation wa-ter or rainwater or groundwater, etc., but includes all water that enters and leaves the spatial bounda-ries (Charles M. Burt, 1999). Figure 6.1: A Water balance requires the definition of 3-D and temporal boundaries, and all inflows and outflows across those boundaries as well as the change in storage within those to boundaries.

6.1.1. Spatial boundaries

Water balances can be conducted for a field, for a farm, for a water district, for a hydrologic basin, etc. The same concepts apply to all units, but one must be absolutely clear about which boundary one is talking and making computations about. Table 6.1 shows typical spatial boundaries of various areas. The lower boundary for irrigation (water) districts can be quite different, depending upon whether there is or is not groundwater pumping or a high water table (Burt, 1999). Spatial boundary of this re-search is limited to an agricultural area (south part of Urumieh plain).

6.1.2. Temporal boundaries

A water balance has temporal (time) boundaries as well as physical boundaries. All of the values of water balances (rain, irrigation water supply, ET, etc.) change from one year to another. It is unwise to examine the balance for just a single year -- it may be a wet year or a drought year, or perhaps a "nor-mal" year. Some types of data (groundwater inflows, outflows, and change in storage, crop ET) are difficult to evaluate accurately on a single year basis. Therefore, a multi-year evaluation of the data is recommended for the calculations of the water balance components. Data for single years should be determined in most cases, but should be combined into one larger table for a 3 or 4-year "average" computation (Burt, 1999).

6.2. Method used to compute the water balance

Precipitation provides part of the water crops need to satisfy their transpiration requirements. The soil, acting as a buffer, stores part of the precipitation water and returns it to the crops. In humid climates, this mechanism is sufficient to ensure satisfactory growth in rain fed agriculture. In arid and semi arid


31

climates (such as study area) or during extended dry seasons, irrigation is necessary to compensate for the evaporation deficit due to insufficient or erratic precipitation. Net irrigation water requirements in irrigation are therefore defined as the volume of water needed to compensate for the deficit between potential evapotranspiration and effective precipitation over the growing period of the crop. It varies considerably with climatic conditions, seasons, crops and soil types. For a given month, the crop water balance can be expressed as follows:

IWR = Eto. Kc – P - ∆S Where: IWR is the net irrigation water requirement needed to satisfy crop water demand Kc is a coefficient varying with crop type and growth stage Eto is the reference potential evapotranspiration, depending on climatic factors P is the precipitation ∆S is the change in soil moisture from previous month This formula could not be used because lack of data (and time). Since the main goal of the present re-search is investigation of impacts of the groundwater use on saline area extension. Aspects of water balance have been determined in order to estimating of groundwater consumption. The spatial bound-ary of this research is limited to the irrigated area and this calculation was done from 1991 to 2001. An annual water balance for this area can be such as following equitation:

NIWR - SWU= GWU (1)

Where: NIWR is net irrigation water requirement for whole area for a given year. SWU is the surface water use for irrigation GWU is the ground water use for irrigation NIWR was calculated based on following equation:

NIWR = ∑ NIWRi * CAi (2)

NIWRi is the net irrigation water requirement for cropi and CAi is the cultivated area of cropi. These parameters have been obtained in chapter 5 (See table 5.4). Estimation of irrigation water use requires an estimate of the water effectively withdrawal for irriga-tion, i.e. the volume of water extracted from rivers and aquifers for irrigation purposes. Irrigation wa-ter withdrawal normally far exceeds the consumptive use of irrigation because of water lost in its dis-tribution from its source to the crops. The ratio between the estimated irrigation water requirements and the actual irrigation water withdrawal is usually referred to as "irrigation efficiency". The value of this parameter in the study area is 33.2 % for surface water resources and 44.7 % for groundwater re-sources (see table 3.7). Therefore the equation (1) can be changed to equation (3).

NIWR � (1/E*SWU) = 1/E*GWU (3) Where ES is the irrigation efficiency for surface water resources and EG is the irrigation efficiency for ground water resources. Components of this equation have been calculated using prepared data in previous chapters and col-lected data from Azerbaijan Water Organization during fieldwork (such as gauge station data and data of transferred water into traditional channels). The groundwater use for last 10 years was esti-mated. The results are shown in table 6.2. It shows that the volume of groundwater consumption espe-cially in drought years has been increased. The trend of groundwater consumption in last 10 years is illustrated in figure 6.2.


32

Table 6. 1 Spatial boundary of various areas. Space Upper

boundary Lower boundary Horizontal

boundaries Farm Crop canopy Bottom of root zone Farm fields Conveyance system Water surface Canal bottom All diversions, spills,

and discharge points

Water District without groundwater pumping

Crop canopy Bottom of root zone District

Water District with groundwater pumping

Crop canopy Bottom of aquifer District

Water District without groundwater pumping, but with a high water table

Crop canopy

Bottom of aquifer that is tied into the high water table

District

Table 6.2: Determined groundwater use and pumped groundwater (GWIn) based on equation No.3 in m.m.c during last 10 years Year

Annual precipitation (mm)

Cultivated area (Ha)

NIWR (m.c.m)

SWU (m.c.m)

GWU (m.c.m)

Pumped ground water (m.c.m) or GWin

91-92 413.1 26952 97.9 80.4 17.5 39.1 92-93 473.1 26944 106.6 82.3 24.3 54.3 93-94 499.5 26958 105.0 74.7 30.3 67.8 94-95 434.9 26934 113.5 77.2 36.3 81.2 95-96 389.9 26988 120.1 82.9 37.1 83.1 96-97 278.3 26998 127.6 79.8 47.8 106.9 97-98 234.6 26995 138.7 54.0 84.7 189.4 98-99 176.6 26987 155.9 23.6 132.3 295.9 99-00 163.4 26926 159.1 39.8 119.3 266.8 00-01 306.5 27004 149.9 34.7 115.3 257.8

Changes of ground water use in last 10 years

0.050.0

100.0150.0200.0250.0300.0350.0

91-92 92-93 93-94 94-95 95-96 96-97 97-98 98-99 99-00 00-01

Year

Wat

er u

se (m

.c.m

)

GWU(m.c.m) pumped ground w ater(m.c.m)

Figure 6.2: Changes of groundwater consumption in last 10years according to estimated volume. For investigating effect of water use on reduction of discharge to the lake, computation of other com-ponents of water balance was necessary. The aspects of annual water balance for this purpose can be determined using equation No.4.


33

P+SWin +GWin – (ETa +SWout + GWout+ Psp) =∆S (4) Where: P: the total precipitation SWIn: the inflow of surface water GWIn: the amounts of withdrawal groundwater (see table 6.2) ETa: the actual evapotranspiration SWout: the surface water that leaves the system GWout: the groundwater that leaves the system Psp: the surplus precipitations (Ptota - Peff) ∆S: the water storage variation Components of equation No. 4 were determined for last 10 years and results are presented in table6.3. It should be mentioned that because lack of data, GWout was neglected in this research. And only the certain parameters were determined which are necessary in future stages of study. Table 6.3: computed components of water balance in m.c.m (during last 10 years) Year P SWin GWin ETa SWout Psp 91-92 111.3 726.6 39.1 153.8 451.0 55.4 92-93 127.5 914.6 54.3 166.2 561.0 67.8 93-94 134.7 701.3 67.8 167.8 481.0 71.8 94-95 117.1 628.2 81.2 167.5 427.7 63.2 95-96 105.2 475.1 83.1 167.9 285.5 57.4 96-97 75.1 523.8 106.9 163.0 286.7 39.7 97-98 63.3 363.5 189.4 167.0 202.4 35.1 98-99 47.7 139.1 295.9 174.3 68.2 29.3 99-00 44.0 174.3 266.8 179.8 54.7 23.3 00-01 82.8 166.4 257.8 178.9 62.0 53.7 This table reverse that there is a linear correlation between SWin and GWin (see figure 6.3). The contri-bution of withdrawal groundwater (GWin) for irrigation has increased due to reduction of surface water inflow (SWin) in last 10 years. The variation range of crop water (ETa) requirement (ETa) had been limit during this period. It is noticeable that, in this research water balance was calculated an On-farm irrigation scale and it was calculated for the study area with focus on calculation of groundwater use and it�s variation in last 10 years.


34

scatter plot of surface water inflow and(SWin) and groundwater use (GWin)

y = -2.5577x + 850.19R2 = 0.8841

50150250350450550650750850950

30 80 130 180 230 280 330

Groundwater use (m.c.m)

Surf

ace

wat

er in

flow

(m.c

.m)

SWinLinear (SWin)

Figure 6.3: Correlation between groundwater use and surface water inflow in the study area

6.3. Groundwater level fluctuations The groundwater level is varies depending on the topographic conditions, amount of discharge and recharge i.e. in the study area, it�s level decrease from foot of the mountains (plain entrance) toward Urumieh lake coast, and also in the wet periods for surplus of recharge amounts to discharge, ground-water level increase. While, in the dry periods inverse of this condition occurs. Figure 6.4 and 6.4 show the variation of groundwater levels in the study area. For investigating of groundwater fluctuations and preparing relative hydrograph, gauged values of wa-ter level in existing pysometric wells (30 wells) were used. It has been carried out after determining Thiessen polygons for the study area. The mean monthly groundwater levels in last 10 years were de-termined and relative hydrograph was drowned (see figure 6.6). It shows that the annual minimum level is related to September and annual maximum of that is in April. The maximum value of ground-water is 1287.5 m above sea level in April 1995 and minimum is 1283.5m above sea level in Septem-ber 2000. And also it indicates that the level of groundwater has been decreased about 1.5 m in last 5 years.

Figure 6.4:Groundwater levels (in meter) in October 1991, created by interpolating the point map of wa-ter levels in the pysometric wells)


35

Figure 6.5:Groundwater levels (in meter) in October 1991, created by interpolating the point map of water levels in the pysometric wells

Grounwater level fluctuations in last 10 years

1281

1282

1283

1284

1285

1286

1287

1288

91 92 93 9 4 95 96 97 9 8 99 2 00 0 2 00 1

Year

Wat

er L

evel

(m)

Grounwater level

Figure 6.6: Mean monthly level of groundwater in the study area according to determined Thiessen poly-gons (based on existing pysometric wells) in last 10 years For getting better idea about groundwater level changes in last 10 years, the difference between groundwater levels in September 2001 (last of water year, which is shown in figure 6.5) with ground-water level in October 1991 (beginning of water year, which is shown in figure 6.4) has been deter-mined. Figure 6.7 indicates the decrease of water level during this period. It shows that the maximum fall of the water level was occurred in the middle part of the study area. In this case the probability of replacing of Lake Water into groundwater and changes of groundwater quality in the coastal area is high.


36

Figure 6.7: Groundwater fall (in meter) in last 10 years, it shows difference between groundwater levels in October 1991 with September 2001 The volume of groundwater loss in this period, according to effective porosity of the study area can be estimate based on following equation.

Groundwater loss = groundwater fall *effective porosity* Area But because of interaction effects of groundwater and lake it would not be accurate. According to mentioned equation and figure 6.6 the estimated volume is 72 m.c.m. (Effective porosity of study area according to jam-ab-Iran report is 18%).

6.4. Errors of computation

Because lack of data some aspects of water balance (such as the groundwater that leaves the system GWout, groundwater consumption for human use, etc) could not be calculated and obtained results are accurate only for agriculture purpose. It cannot be proper for regional purpose.


37

7. Environmental impacts of water use Recent years drought together with building new water reservoirs in the catchment of Uru-mieh lake have caused the reduction of water discharge to the lake. The lake surface has de-clined severely in last 4 years (figure 7.4). And human activity has accelerated this. Conse-quently many environmental impacts can now be observed both on surrounding area of the lake and into the lake as well. The most important human activities, which may effects the lake and surrounding areas of the lake, is ground water use for irrigation. In this chapter the impact of irrigation water use on reduction of discharge to the lake and the impact of groundwater use on soil salinization were investigated.

7.1. Impact of irrigation water use on reduction of discharge to the lake

Reduction of water discharge to the lake was estimated based on determined aspects of water balance that were calculated in chapter 6 (see table 6.3) using following equation.

Q Into the lake = Q Gauging station- Q Irrigation Where: Q into the lake = Discharge to the lake Q gauging station= Recorded discharge in the gauge station, which are located in the beginning of the study area (see chapter3) Q irrigation = Irrigation water use includes groundwater and surface water use for irrigation (has been calculated in chapter 6 according to obtained results in chapter 5) The variation of total surface flow (total gauged volume) and total irrigation water use (groundwater and surface water) is shown in figure 7.1,which indicates that the consumed wa-ter for irrigation is more than discharge of rivers from 1997 up to now. The components of mentioned equation computed and the results are shown as figure 7.2, which indicates the de-scending trend of discharge to the lake in last 10 years. This implies a water flow from lake to the groundwater. For getting better idea about water use effect on discharge to the lake in recent years, the water use and discharge of rivers were plotted with each other and is shown in figure 7.3. Which indicates that there is a negative and significant correlation between them. The reduc-tion of discharge is accelerated by water use especially ground water use (according to figure 6.2 which shows that the ground water use has increased in this period). The amount of re-duction had been more than 200 m.c.m in 1999.


38

Variation of Irrigation water use and total dischargev of rivers in last 10 years

100

300

500

700

900

1100

91-92 92-93 93-94 94-95 95-96 96-97 97-98 98-99 99-00 00-01Year

Wat

er u

se a

nd D

isch

arge

m

.c.m

water use discharge

Figure 7.1: Irrigation water use and discharge of rivers in c.m.c; based on determined aspects of water balance (table 6.3)

Figure 7.2:Water discharges to the lake in last 10 years

Figure 7.3: Scatter plot of water use against discharge of rivers in last years (based on obtained results in previous chapters)

Discharg water to the lake

-400

-200

0

200

400

600

800

1991-1992

1992-1993

1993-1994

1994-1995

1995-1996

1996-1997

1997-1998

1998-1999

1999-2000

2000-2001

Year

disc

harg

e (m

.c.m

)

Discharg

Scatter plot of water use - discharge

y = -6.9105x + 2787.5R2 = 0.8261

0

200

400

600

800

1000

150 250 350 450

water use (m.c.m)

disc

harg

e of

rive

rs

(m.c

.m)

Linear(discharge)


39

Therefore the groundwater uses will probably an effect on the declining of the lake surface and salini-zation of groundwater recourses (because of interaction effects of ground water and lake water). It has caused the replacement of lake saline water with ground water. According to table 3.7 (see chapter3) the efficiency of irrigation is very low .So this impact can be ad-justed by improvement of irrigation system and increasing irrigation efficiency.

7.2. Impact of irrigation water use (groundwater) on soil salinization

7.2.1. Causes of salinization

Nearly all-soluble salts in the soil originate from the weathering of rocks and minerals in the earth�s crust. Ultimately, salts released during weathering reach the sea through the leaching and trough the river systems. Soluble salts are also exchanged between the land and the sea trough erosion following the uplift of marine sediments and exposure at the earth�s surface. Salt also permeates the soil directly through seawater intrusion and wind transport of salt spray (Abort and Gupta, 1992). According to Thomas and Middleton (1993), the cause of salinization and akalinization can be categorised and briefly summarised in five groups: poor cultivated techniques, indirect ef-fects of irrigation schemes, vegetation changes, seawater incursion and disposal of saline wa-ters. Frequently irrigation causes salinization. The quality as well as quantity of irrigation water is effective in soil salinization. There are four main reasons for irrigation to cause salinization: water leakage from supply canals, over application of water, poor drainage and insufficient water application to leach salts away (Barow, 1991). Soil salinization through the seawater incursion is a well-known problem in areas where ex-cessive groundwater pumping has allowed the land ward penetration of brine (Speece and Wilkinson, 1982). This research has investigated the direct impact of lake level fluctuation and quality of with-drawn water for irrigation on salinity extension in the south part of Urumieh plain (two most important reasons of saline area expansion). It was carried out as following steps. Soil salinization through lake water incursion is a problem in the areas e g. study area where excessive water pumping has allowed the landward penetration of brine. A further problem is created in this area when drought and extraction of water for irrigation have reduced the flow of the rivers to the lake (re-cent years). Soil salinization in the study area can be resulted of two factors, direct effect of lake boundary fluctua-tion and salinity of irrigation water (groundwater quality). So separating the saline area according to reasons of salinization was important. These factors were investigated according fallowing stages.

7.2.2. Dynamics of lakeshore in wet and dry periods

The direct effect of the lake boundary fluctuation on the surrounding area can be significant for salini-zation. Regarding to figure 7.4, which shows the fluctuation of Lake Levels in last 40 years, the high-est level is related to 1994(1279.9 m) and the lowest level is in 2001. To specify the shore zone in this period, the area, which was covered by the lake, was determined using Digital Elevation Model of the study area. Available satellite images were used for specifying shore zone in 1987 and 2001. The shore zone of the lake in 1987,1994 and 2001 is shown in figure 7.5. It indicates that the main part of


40

the coastal saline areas (which are classified as saline area in classification maps, figure 4.1 and 4.2) had been affected by fluctuation of the lake boundaries.

Anuual fluctuation of lake level in last 40 years

1273.01274.01275.01276.01277.01278.01279.0

1962

1964

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

Year

lake

leve

l (m

)

min max mean

Figure 7.4: Annual fluctuation of Urumieh Lake in last 40 years (analyzed based on collected data from western Azerbaijan power organization)

Figure 7.5: Shore zone of the lake in 1987, 1994, 2001 and salinity zone in 2001

7.2.3. Irrigation water quality

7.2.3.1 Surface water quality According to Western Azerbaijan Water Organisation report (Jabal & Dolama study report, 1999), the quality of the surface water resources in the research area, based on the Wilcox classification method, is C2S1. It has a good quality for agriculture purpose. Its salinity is less than 500 micromohs/cm and alkalinity (according to SAR) is less than 0.9. Field observations and measurements confirm the re-corded data. 7.2.3.2 subsurface (ground) water quality Groundwater quality has been investigated using collected data (related to 41wells) from Western Azerbaijan Power Organisation and field measurements. In this investigation Ec and SAR parameters

Shore zone

Shore zone

Shore zone 1987

Salinity boundary 2001


41

have been considered. Recorded maximum value of Ec is 18000 micromohs/cm in the Hesar village and the minimum is 485 in the Mirshekarlo village. The chemical composition of ground water in the research area varies depending upon the depth and proximity of the aquifers to the mountain or to the Urumieh Lake. Generally, groundwater quality for agriculture purpose in the western margins of the plain is more suitable than in the eastern areas; where salt contents are higher. The electrical conduc-tivity of water increases in the direction of ground water flow. It increases sharply in wells that are close to the coastal areas. The variation of groundwater�s electrical conductivity and salinity hazard (based on Wilcox classification method) is shown in figure 7.6 and 7.7.

7.2.4. Relation between groundwater use and ground water quality changes

The volume of groundwater use and mean electrical conductivity of groundwater in last 6years (re-garding to available electrical conductivity data) are plotted to each other in figure 7.8. It indicates there is a linear correlation between groundwater consumption and the electrical conductivity. It can be mentioned that the groundwater use is not the only factors which effects on the ground water salin-ity changes. The groundwater use is also affected by other factors such as crop water requirement (or ETa), surface water inflow and precipitation. To specify correlation between these factors the relative matrix prepared (correlation matrix) and is indicated in table 7.1. According this matrix there is a significant correlation between groundwater electrical conductivity changes and crop water requirement and also a negative and significant correlation with surface in-flow. Or on the other hand can be said groundwater use is depend on water requirement and surface water in-flow.

Figure 7.6: Electrical conductivity map of groundwater (Agriculture wells) in micromohs/cm, created ac-cording to collected data from Western Azerbaijan Water Organization and field measurements during the fieldwork. This map is related to 2002.


42

Figure 7.7: Salinity hazard map of groundwater in the study area for agriculture purpose, created using relative electrical conductivity map (figure 7.6) based on Wilcox classification method.

Figure 7.8: scatter plot of groundwater use and electrical conductivity of wells in last 6 years (analyzed based on collected data from Western Azerbaijan power organization and estimated ground water use).

scater plot of groundwater use - Ec (wells)

R2 = 0.8125

950

1000

1050

1100

1150

1200

70 120 170 220 270 320

groundwater use (m.c.m)

Ec (m

icro

moh

s/cm

)

Linear (Series1)


43

Table 7.1: Correlation between groundwater electrical conductivity changes with determined components of water balance (correlation matrix) Ec P ETa Qin IWU GWU Ec (groundwater electrical conductivity)

1.000 .

-0.509 0.007

0.930**

0.007-0.939**

0.0050.795

0.059 0.901*

0.014 P (precipitation) -0.509

0.303 1.000

. -0.408 0.617

0.192-0.845*

0.034 -0.749

0.086 ETa (actual evapotranspira-tion)

0.930** 0.005

-0.408 0.422

1.000 .

-0.911

0.0120.792

0.061 0.822

0.045 Qin (surface water inflow) 0.939**

0.005 0.617 0.192

-0.911*

0.0121.000

. -0.817

0.047 -0.976**

0.001 IWU (irrigation water use) 0.795

0.509 -0.845*

0.034 0.792

0.061-0.817

0.0471.000

. 0.857*

0.001 GWU (ground water use) 0.901*

0.14 -0.749 0.086

0.822*

0.045-0.976**

0.0010.857

0.029 1.000

.

**. Correlation is significant at the, 0.01, level (2-tailed) *. Correlation is significant at the, 0.01, level (2-tailed)

7.2.5. Saline areas

7.2.5.1 the saline areas as a results of both retrogression and transgression of lake (fluctuation of lake level) Urumieh Lake is the largest water body entirely within Iran, at an elevation of 1275 m. It covers an area of 5800 Km2, with a maximum depth of 16 m and volume of 45 * 109 m3 (Korzun et al., 1978). It is a saline lake with salt content of more than 310000 mg L-1 TDS (Hammer, 1986). Frequently retro-gression and transgression of the lake has caused the salinization of the surrounding area. Distinguish-ing of this area is easy in Landsat and Aster images with a false colour composite of 4,3,2 and 3,2,1 bound combination. Salinised areas have a white or grey appearance in these images. Comparing of separated saline areas in Landsat images of 1987 and 2001 (available images) shows that the extent of the saline area has increased in this period. Saline area extension was taken place not only towards the lake but also towards the irrigated area. Figure 7.9 shows the changes of the saline area in this period. 7.2.5.2 the saline areas as results of ground water use From the agriculture point of view, saline soils are those that contain sufficient natural soluble salts in the root zone to adversely affect the growth of most crops. Soluble salts commonly present are chlorides and sulphates of sodium, calcium and magnesium (FAO, 1988). For the purpose of definition, saline soils have an electrical conductivity of the saturation extract of more than 4 ds/m at 25 °C (Richards, 1954). This boundary between saline and nonsaline soils is worldwide used, although the terminology committee of the Soil Science Society of America has lowered it to 2ds/m.


44

Figure 7.10 shows the soil electrical conductivity map, which was created using results of field measurements in July 2002 (by interpolation of point data by moving average). It indi-cates that the salinity is increasing from western margin of plain (foot of mountain) towards the lake. Especially it is accelerated in areas where are close to the coast of lake. It is notice-able that the mentioned trend is close to water quality variation trend (see figure 7.6). For determining the saline area, the soil electrical conductivity map (figure 7.10) was reclassi-fied (according to the Wilcox classification method) and is shown in figure 7.11. Comparing the soil surveys results of 1960 and 1998 with created electrical conductivity and saline area maps (figure 7.10 and 7.11) show that the saline areas have been extended. The extension of saline areas (caused by different factors) for 3 supposed periods are given in table 7.2. Saline area extension that have separated based on cause of generation is presented in table 7.1.It was prepared using soil survey reports of 1960 and 1999 prepared by Soil and Water division of Agricultural research Centre of Western Azerbaijan and soil salinity map (figure 7.11) Table 7.2: saline area extent according to available soil survey reports Year Saline area affected by transgres-

sion and retrogression of lake Saline area caused by poor drainage condi-tion

Saline area caused by other reasons

1960 4100 Hectare 5000 Hectare 2200 Hectare 1998 7200 Hectare 2200 Hectare 4800 Hectare 2002 7747 Hectare 2050 Hectare 7500 Hectare

The following points about table 7.2 are noticeable: Saline area affected by transgression and retrogression of lake is the area, which are explained in 7.4.1 section. Saline areas are also caused by the topographic condition where the main limitation is poor drainage. In these area the land cover is mostly rangeland (according to field observation and classification map figure 4.2 and 4.3). Saline areas caused by other reasons are almost covers by irrigated area. The main reason of saliniza-tion is quality of withdrawal water. It is critical when the only water resource for irrigation is an aqui-fer. Distribution of this is mainly in the eastern and especially in the northeast part of the research area. Comparing of figure 7.10 and 7.7 shows that the groundwater salinity hazard is very high in this area.overling of these maps is shown in figure 7.12. Overlying of this map shows that in the 85% of saline area the salinity hazard of groundwater is high and very high.


45

Figure 7.9: saline area changes (extent of saline area) according to classified maps of 1987 and 2001 (sa-line area expansion as a result of retrogression and transgression of the lake in last 15 years)

Figure 7.10: Soil electrical conductivity map of research area (in ds/m) created based on field measure-ments in 2002


46

Figure 7.11: Saline and Non-saline area distribution, in the research area, according to electrical conduc-tivity map (see figure 7.10) and Wilcox classification method

7.3. Conclusion

• Extractions of water for irrigation have reduced the flow of the rivers to the lake. It has accel-erated the impact of drought on lake surface declining.

• The coastal areas are affected directly by the lake boundary fluctuation. Transgression of the lake has degraded 3100 hectare of Rangelands and farms in 1994.

• The groundwater electrical conductivity change has an ascension trend from east margin of the plain towards the lake. It is increased sharply in the wells that are close to the coast of the lake. According to Salinity hazard map of ground water (figure 7.7), salinity hazard in 9067 hectare is medium, in 18183 hectare is high and in 11070 hectare is very high.

• Excessive water pumping has allowed the landward penetration of brine in the farms. It has caused the salinization of the soil and saline areas extension in last 10 years. It has occurred in the east and especially northern east of the study area, where the areas are proximate of the lake coast. The main water resource is groundwater in these areas and there is no sufficient water application to leach salts away.

• Conforming of saline areas in soil salinity map (figure7.11) with areas where have high and very high salinity hazard in salinity hazard map of groundwater (figure 7.7) indicates that the main reason of soil salinity in these areas is ground water use.

• The salinity hazard map of groundwater can be used as a workable map for forecasting of re-sults of excessive ground water use in the study are.

• Preparing salinity risk map based on salinity hazard map and soil characteristics for getting proper workable results is necessary. It could not possible in this research because lack of data and time.


47

• It seems that the best way for adjustment of saline areas extension hazard in the study area is improvement of applied irrigation system and increasing of irrigation efficiency.


48

8. Conclusions and Discussion

8.1. Satellite images Applying satellite images to map agricultural crop is easy and gives acceptable results (accuracy be-tween 80-85%). Due to applying images the perennial crops such as alfalfa and orchards was classified with reasonable accuracy. Successful identifications of agricultural crops required multi-temporal im-ages. To identify most agricultural crops, at least two satellite images were needed (middle and late growing season) to help differentiate between crops of similar appearance (According this issue, to get more accurate results, it is recommended to use multi-temporal images to agricultural crop classifica-tion). However the time of collection of ground truth is important. For most agricultural crops, espe-cially short season crops and areas with multiple crops grown per year, it is suggested that mapping should be done as closely as possible to the time of the satellite images acquisition. Knowledge of lo-cal farming information is essential to planning fieldwork. Also, Some times, the obtained information of farmers during the fieldwork was not completely reliable. Consulting with local county agricultural agents is suggested. Some errors may have occurred because these factors were not met.

8.2. Dynamics of irrigated area

According to obtained results irrigated (Agriculture) area includes orchards 63%, summer crop 18%, winter crop 16% and alfalfa 3%. These percentages are almost the same in wet and dry years (And there is no difference between extent of them in different years).

8.3. CROPWAT model The CROPWAT model is very sensitive to climatic and crop growth stages data. Hence, the input data of this model should have high accuracy. To run CROPWAT model one needs to calibrate and vali-date the obtained results with local lysimeter measurements. Due to lack of these data it was not possi-ble to do calibration and validation it for study area. Although there is a reference manual that gener-ally calibrated the model for different crops in Iran (Farshi et al. 1997), which was used to validate the results. Despite these issues, since this model is the best method for the same region as Iran (A.Alizadeh, 1999) it was applied to estimate crops water requirement in this research. More research should be done in this area.

8.4. Water resources Irrigation of the study area is done by traditional methods, for this reason on-farm water application is high and the irrigation practice has a low efficiency of about 33.2 for surface water recourses and 44.7% for ground water resources. Although some parts of water uses for agricultural purposes sup-plied from surface water, but shortage and unevenly distribution of annual rainfall caused the rivers of these regions offer low discharge. This resource supplied just a part of agricultural demands. Lack of cemented irrigation canals coupled with low irrigation efficiency caused the existing water is not be used properly. In normal climatic condition, 60% of irrigation water requirement provides from sur-face water it has decreased to 20 � 25% in recent drought years.


49

The hydrograph of the ground water fluctuations in last ten years illustrated a decrease of the water table. The drought of recent years has resulted a sharp rise in demand for water in these regions and more and more groundwater is used. It increased from 20-30% to 70-80 during last 10 years. This has caused a lowering of water table about 1.5 m in this period. People, who depend for their water supply on water from sallow wells, are now faced with a lower water yield or even experience a complete dry-ing up of the wells.

8.5. Aspects of water balance Determined aspects of water balance shows that input of water to the plain is less than irrigation water requirement in last 4 years. So water deficit has provided from ground water (ground water consump-tion has increased sharply in this period). It has caused reduction of groundwater table and discharge to the lake as well.

8.6. Dynamics of shore zone

There is a correlation between dynamics of shore zone and precipitation, discharge of rivers in to the lake and consumed water for irrigation. Depending on these factors the shore zone is very dynamic. It is noticeable that the study area is a part of Urumieh lake catchment and the situation of other parts effect on the lake as well. The most transgression of the lake has oc-curred in 1994 and the most retrogression is related to 2002. Frequently fluctuation of Lake Boundary has caused the salinization of surrounding area. Extent of this area has been in-creased about 4650 hectare during last 35 years, which about 60% of this is area related to last 9 years.

8.7. Groundwater quality and soil salinity changes Soil salinization through lake water incursion has occurred in the areas where are close to the coast of the lake. Excessive pumping has allowed the landward penetration of brine. It has accelerated in recent years that drought and extraction of water for irrigation have reduced flow of the rivers to the lake. It is more critical in the areas where the only resource for irrigation is groundwater and there is no suffi-cient fresh water to leach salt away. It can be observed especially in the northeast part of study area where the direct impact of lake boundary fluctuation is less than other part of coastal area (because of topographic condition). In figure 7.7 salinity hazard of ground water use has been specified. According to this map salinization risk in the farms that are close to the costal area is very high. Salinity in the study area will be extended by continuation of excessive withdrawal of ground water. Recommendation The best way for adjustment of saline areas extension hazard in the study area is improvement of ap-plied irrigation system and increasing of irrigation efficiency Preparing salinity risk map based on groundwater salinity hazard map and soil characteristics is neces-sary for determining the hazard zone for exploitation from the wells.


50

9. Refrences Alizadeh, A., (1999). Principal of applied hydrology. Razavi cultural publication. Iran Alizadeh, A., (1998).Soil, Watr and Plant Relationship. Razavi cultural publication. Iran Baybordi.M, (1990), Soil and Water Relationship. Tehran University publication. Iran Farshi, A .A et al., (1997), An estimation of water requirement of main field crops and orchards in Iran. Soil and Water Institute. Iran Hummer, U.T., (1986). Saline lake ecosystems of the world. Dodercht; Drw. Junk publisher. 616 pp. Cleark et al., (1998). CROPWAT 4 windows user guide. FAO Richards,J. (1986). Remote sensing. Digital image analysis. An introduction. Berlin, springer � velag. Salem, A.S.(1995). The application of remote sensing and GIS in detecting citrus crop land cover variation. Enchede, The Netherlands, ITC. Molina, I, (1992). Multistage pattern recognition utilising high-resolution remote sensing data. En-schede. The Netherlands, ITC. A.Gieske, N.T.M.Akbari, (2002). Crop and land cover classification by Landsat 7 (July 2000) for the Zayandeh Rud basin. Research reports No.11. Esfahan Agriculture Organisation. Iran Barrow, C.J.,(1991). Land degradation. Cambridge university press, Cambridge, England. Abrol,I.P. and R.K. Gupta.,(1992). Managing salt affected soils and poor-quality waters for sustain-able crop productivity. In; Evaluation for sustainable land management in the developing world. IBSRAM proceedings No.12, volume 2 Thomas D.S.Gand N.J.MIDDLTON.1993. Salinization new perspective on a major desertification is-sue. Journal of arid environments, 24. Naseri, M.Y., (1998). Characteriztication of salt-affected. Soils for modelling sustainable land man-agement in Semi arid environment; A case study in the gorgan region, Northeast Iran. Phd thesis. Uni-versity of GHENT. Food and Agriculture Organisation of United Nations (FAO)., (1977). Irrigation and drainage paper 24. Food and Agriculture Organisation of United Nations (FAO).,(1997). Irrigation potential in Africa.


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Unesco press, (1974). Methods for water balance computation. An International guide for research and practise. Unesco press place de fontenoy, 75700 Paris. Wolters,-W;et al., (1989). Managing the water balance of the fayonum Depression, Egypt. Irrigation and drainage systems. Hoogeveen, J. (1999). A regional water balance of the Aral Sea through GIS. Land and Water Devel-opment Division. Food and Agriculture Organisation of United Nations (FAO). Study of Comprehensive project of agriculture development in western Azerbaijan province. (1997), Agriculture ministry. Iran. , Report No.3, Volume 3.(santez) Study of Urumieh lake catchments, (1997). Jam-ab-Iran, power ministry of Iran. Jabal & Dolama case study report.(1998). Western Azerbaijan Agriculture Organisation Farshad.A., (2000). Soil degradation. Enschede. The Netherlands. ITC Abbott.CL., Hasnip. N.J.,(1997). The safe Use of Marginal Quality in Agriculture.Report No.OD 140. Department for International Development (DFID) Bui.E.,(1997). Assessing the regional risk of salinization over the Dalrymple shire. Technical Report N0.26/97.CSIRO LAND and WATER.USTRALIA Beekma.J et al.,(1998). Soil salinization, water table fluctuations and water quality in the Bosque del Apache.


52

10. Appendix A. Data A1. Extent of different crops cultivated in last 10 years (according to collected Data from Agriculture Organization). A2. Meteorological stations list (recording length & available data) A3. Electrical conductivity data of selected wells (collected from Western Azerbaijan water organiza-tion) A4. Crop type samples (field observation and interview with farmers) A5. Orchard type samples (field observation) A6. Well samples (field measurements & interview) Figure Calculated CWR and IWR for sugar beet Calculated CWR and IWR for maize C. Text C.1 Appearance of different band combination, which were used in classification

A1. Extent of different crops cultivated in last 10 years(according to collected Data from Agri-culture Organization).


53

Year Alfalfa Apple Grape Summer crop Winter crop Sum

1987 659 8552 8154 5324 4222 26911

1988 655 8555 8150 5336 4215 26911

1989 658 8560 8150 5196 4351 26915

1990 802 8560 8155 5193 4217 26927

1991 815 8655 8140 5358 3987 26955

1992 812 8655 8135 5329 4021 26952

1993 810 8660 8136 5464 3874 26944

1994 780 8800 8158 5205 4015 26958

1995 788 8800 8156 5222 3968 26934

1996 786 8850 8155 5445 3752 26988

1997 760 8911 8174 5532 3621 26998

1998 735 8935 8177 5671 3477 26995

1999 725 8944 8175 5488 3655 26987

2000 728 8944 8178 5543 3533 26926

2001 730 8963 8196 5467 3648 27004

A2. Meteorological stations list (recording length & available data)

Name of station Kind of mete-orological sta-tion

Measured Parameters

Recording length

Available data for analysing

Urumieh Synoptic Rainfall, evaporation, wind speed, Sunshine duration, Rela-tive humidity, �

1972 -2002

Daily, Monthly, Yearly

Ghasemlo Rain gauge Rainfall 1974 -2002 Monthly, Yearly Aghbolagh Rain gauge Rainfall 1974 -2002 Monthly, Yearly Hashem abad Rain gauge Rainfall 1974 -2002 Monthly, Yearly Dizaj urumieh Rain gauge Rainfall 1974 -2002 Monthly, Yearly Zar abad Rain gauge Rainfall 1974 -2002 Monthly, Yearly Babarood Rain gauge Rainfall, temperature 1974 -2002 Monthly, Yearly Sagargan Rain gauge Rainfall 1974 -2002 Monthly, Yearly Mir abad Rain gauge Rainfall 1974 -2002 Monthly, Yearly Band Rain gauge Rainfall, temperature 1974 -2002 Monthly, Yearly Golmankhaneh Rain gauge Rainfall 1974 -2002 Monthly, Yearly Gharalar Rain gauge Rainfall 1974 -2002 Monthly, Yearly Mosh abad Rain gauge Rainfall 1974 -2002 Monthly, Yearly Tipik Rain gauge Rainfall 1974 -2002 Monthly, Yearly Abajaloieh sofla Rain gauge Rainfall 1974 -2002 Monthly, Yearly Kalhor Rain gauge Rainfall 1974 -2002 Monthly, Yearly Kamp Rain gauge Rainfall, temperature 1974 -2002 Monthly, Yearly

A3. Electrical conductivity data of selected wells (collected from Western Azerbaijan water or-

ganization)


54

Well No. Location Ec 1997 Ec 1998 Ec 1999 Ec 2000 Ec 2001 Ec 2002

1 505800, 4158380 1655 1750 1664 1700 1745 1620 2 510150, 4159300 710 565 690 560 740 775 3 514000, 4159150 750 900 1210 960 1140 1430 4 518250, 4158300 947 957 880 920 920 935 5 520400, 4157400 823 712 950 790 810 870 6 521250, 4155850 720 506 640 580 585 600 7 517400, 4155700 796 1315 1570 1560 1390 1220 8 513750, 4155650 642 544 630 660 640 700 9 509800, 4155500 1010 740 910 730 800 790 10 506750, 4151000 991 871 1190 1000 1050 1080 11 511750, 4152200 717 610 570 610 645 675 12 515850, 4151300 520 512 650 560 565 550 13 518850, 4151700 707 469 530 530 528 520 14 519100, 4148000 510 424 530 540 540 535 15 521000, 4144500 480 424 496 514 535 525 16 522100, 4140600 700 412 469 490 780 880 17 523000, 4138100 580 614 632 670 695 680 18 520800, 4134250 580 600 641 655 680 675 19 519250, 4138250 620 600 670 735 780 840 20 517500, 4141000 440 420 480 460 480 485 21 516650, 4144900 480 710 570 600 620 615 22 516050, 4148350 610 707 750 878 895 936 23 512250, 4148650 1601 1510 1570 1640 1630 1660 24 513500, 4144250 961 930 930 950 970 990 25 510200, 4143050 902 910 940 970 972 983 26 513150, 4141800 498 420 533 720 730 725 27 513000, 4135800 620 530 649 680 690 710 28 523500, 4156100 705 705 810 870 942 960 29 52280, 4145550 1878 1900 2030 2342 2555 2570 30 524450, 4137750 7205 7070 8020 8750 8980 9010 31 521000, 4147490 1100 1050 1345 1468 1587 1600 32 521020, 4150280 787 775 895 947 998 1080 33 524360, 4140070 550 545 580 693 762 770 A4. Crop type samples (field observation and interview with farmers)


55

No. Date Sa. No.

Coordinate E

Coordinate N Crop 2002 Crop 2001 Condition

Planting Date

Flowering Date

Harvesting Date

1 19-Jul 18 450736 373107 Wheat Fallow Harvestable 12 Oct 5-15 May 1-15 Jul

2 19-Jul 22 450856 372721 Fallow or rain fed Wheat Harvested

3 19-Jul 26 450610 372431 Summer crop Summer crop Before flowering 15 May 25 Jul After 10 Agu

4 19-Jul 28 451115 372321 Sunflower Sugar beet 80% flowering 10 Apr 10 Jul 5-25 Sep

5 19-Jul 29 451109 372322 Sugar beet Marrow Under growing 5 Apr 20 Sep-30 Oct

6 20-Jul 7 451501 372634 Summer crop Summer crop 80% flowering 10 Apr 6 Jul After 20 Jul

7 20-Jul 8 451458 372638 Sunflower Sunflower 20-30% flowering 24 Apr 15 Jul 5-25 Sep

8 20-Jul 17 451513 372657 Sunflower Sunflower or bare Before flowering

10 Apr 25 Jul 5-25 Sep

9 20-Jul 017 451503 372303 Grape + summer crop Wheat Under growing 20 May 30 Jul After 10 Agu

10 20-Jul 015 451502 372257 Summer crop Wheat Before flowering 10 May 24 Jul After 10 Agu

11 20-Jul 019 451508 372258 Sugar beet Wheat Under growing 30 Mar 20 Sep-30 Oct


13 23-Jul 12 451442 373352 Wheat Wheat Harvested 19 Oct 5-15 May 1-15 Jul





18 23-Jul 26 451401 373030 Sunflower Wheat Before flowering 12 May 30 Jul 5-25 Sep

19 23-Jul 27 451359 373029 Sunflower Sunflower 10% flowering 5 May 25 Jul 5-25 Sep

20 23-Jul 29 451358 373033 Sunflower Sugar beet Before flowering 1 May 30 Jul 5-25 Sep

21 23-Jul 32 451235 373010 Alfalfa Alfalfa or wheat Under growing

22 23-Jul 34 451227 372956 Alfalfa Alfalfa Harvested

23 27-Jul 1 451021 372331 Marrow Ploughed 20-30% flowering 13 Apr 20 Jul 10 Sep

24 27-Jul 3 451108 372302 Sugar beet ? Under growing 28 Mar 20 Sep-30 Oct

25 27-Jul 4 451259 371259 Sunflower Sunflower 20-30% flowering 19 Apr 20 Jul 15 Sep

26 27-Jul 7 451432 372127 Alfalfa Alfalfa Under growing


28 27-Jul 12 451157 372846 Bare Wheat Bare

30 27-Jul 16 451235 372830 Alfalfa Alfalfa Harvested

31 27-Jul 17 451245 372814 Alfalfa Alfalfa (planted in last summer) Under growing


33 27-Jul 20 451254 372815 Summer crop Summer crop Before flowering 11 May 31 Jul 15 Agu

34 27-Jul 21 451336 372824 Sunflower Ploughed 20-30% flowering 24 Apr 24 Jul 5-25 Sep

35 27-Jul 22 451338 372818 Sugar beet Corn Under growing 30 Mar 20 Sep-30 Oct


A4. Crop type samples (field observation and interview with farmers)

No. Date Sa. Coordinate Coordinate Crop 2002 Crop 2001 Condition


56

No. E N Planting Date

Flowering Date

Harvesting Date

37 27-Jul 25 451350 372810 Corn Corn 20-30 cm, Canopy=10-20%

30 May

------

----

38 27-Jul 26 451330 372821 Summer crop Summer crop Before flowering 10 May 2 Agu

39 27-Jul 27 451331 372818 Sunflower Summer crop Before flowering 10 May 31 Jul 5-25 Sep

40 27-Jul 29 451317 372809 Summer crop Summer crop Before flowering 20 May 30 Jul

41 27-Jul 30 451322 372807 Sunflower Corn 20-30% flowering 22 Apr 24 Jul 5-25 Sep

42 28-Jul 2 451155 372722 Summer crop Summer crop Before flowering 13 May 2 Agu


44 28-Jul 2 451508 372253 Sugar beet Wheat Under growing 30 Mar 20 Sep-30 Oct

45 28-Jul 7 451622 372359 Grape + Summer crop

Grape + Sum-mer crop Under growing

5 May

5 Agu

46 28-Jul 18 451253 372641 Sugar beet Ploughed Under growing 28 Mar

20 Sep-30 Oct

47 30-Jul 6-Im1 451145.34 372733.6 Sunflower Sunflower 20-30% flowering

22 Apr

24 Jul

5-25 Sep

48 30-Jul 7-Im1 451149.59 372735.2 Summer crop Before flowering

11 May

31 Jul

After 15 Agu

49 30-Jul 9-Im1 451037.01 372809.8 Wheat Sugar beet Harvestable

12 Oct

5-15 May

1-15 Jul

50 30-Jul 6-Im2 451107.82 372323.3 Sugar beet Marrow Under growing

30 Mar

20 Sep-30 Oct

51 30-Jul 7-Im2 451118.38 372319.84 Sunflower Sugar beet 80% flowering

10 Apr

10 Jul

5-25 Sep

52 30-Jul 9-Im2 451109.72 372310.08 Colza Barley Harvestable

12 Oct

5-15 May

1-15 Jul

53 31-Jul 11-Im2 451346.36 372054.68 Wheat Wheat Harvestable

12 Oct

5-15 May

1-15 Jul

54 31-Jul 15-Im2 451424.79 372054.68 Sunflower Wheat Before flowering

1 May

30 Jul

5-25 Sep

55 31-Jul 6-Im4 450943.74 372846.7 Wheat Wheat Harvestable

12 Oct

5-15 May

1-15 Jul

56 31-Jul 14-Im4 451045.21 372907.6 Sugar beet Wheat Under growing

5 Apr

20 Sep-30 Oct

57 31-Jul 15-Im4 451007.63 372950.8 Alfalfa Alfalfa Under growing

A5. Orchard type samples (field observation) No. Date Sample

No. Coordinate E Coordinate N Orchard type Canopy

% Internal cultivation Age (year)


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1 16-Jul 10 451424.6 372115.6 Apple 45-50 Alfalfa + grass >10 2 16-Jul 11 451428.1 372115.6 Apple 45-50 Alfalfa + grass >10 3 16-Jul 15 451304.8 372433.6 Grape 35-40 >5 4 16-Jul 16 451231.2 372521.6 Grape 35-41 >5 5 18-Jul 19 450739 373107 Apple 50-60 Grass 30-50 cm >5 6 18-Jul 23 450905 372718 Apple 40-50 Alfalfa >5 7 18-Jul 27 451003 372530 Grape 40-50 >5 8 20-Jul 2 451356 372540 Apple 40-50 Alfalfa + grass >10 9 20-Jul 18 451512 372703 Apple 35-40 Alfalfa >10 10 20-Jul 20 451224 372538 Grape 30-40 >5 11 20-Jul 21 451228 372838 Grape 30-40 >5 12 20-Jul 22 451253 372539 Grape 30-40 >10 13 20-Jul 10015 451516 372320 Apple 40-45 Alfalfa >5 14 23-Jul 2 451019 373208 Grape 50-60 >10 15 23-Jul 3 451021 373225 Grape 40-45 >10 16 23-Jul 5 451018 373252 Apple 60-70 Alfalfa >10 17 23-Jul 6 451046 373316 Grape 50-60 >10 18 23-Jul 7 451125 373345 Grape 70-80 >10 19 23-Jul 18 451437 373345 Grape 70-80 >10 20 23-Jul 22 451308 373207 Grape 50-60 Other kind of trees

(scattered) >10

21 23-Jul 25 451309 373144 Grape 50-61 >10 22 23-Jul 33 451227 373012 Apple <10 Alfalfa <2 23 27-Jul 8 450838 373153 Walnut 30-40 Alfalfa >10 24 27-Jul 9 450846 373209 Plum 20-30 Alfalfa <5 25 27-Jul 10 450849 373209 Plum + walnut 20-30 Alfalfa <5 26 28-Jul 13 451117 372521 Grape 40-50 >5 27 28-Jul 16 450841 372512 Apple 45-55 >10 28 28-Jul 17 450831 372508 Apple 45-55 Alfalfa + grass >10 29 28-Jul 18 450823 372506 Apple 45-55 Alfalfa + grass >10 30 28-Jul 19 450810 372503 Apple 45-55 Alfalfa + grass >10 31 28-Jul 20 450646 372444 Apple 50-60 Alfalfa + grass >10 32 28-Jul 21 450534 372434 Apple 60-70 Alfalfa + grass >10 33 28-Jul 22 450539 372514 Apple 45-55 Alfalfa + grass >10 34 28-Jul 23 450549 372541 Apple 35-40 Alfalfa + grass >5 35 28-Jul 24 450559 372558 Apple 55-65 Alfalfa + grass >10 36 28-Jul 25 450726 372606 Apple 60-70 Alfalfa + grass >10 37 29-Jul 4 451609 372322 Grape 40-45 >5 38 29-Jul 27 451451 373302 Grape 40-45 >5 39 29-Jul 32 451300 373146 Mulberry 60-70 Grass 0-10 cm >10 40 29-Jul 38 451212 372813 Mulberry 50-60 Alfalfa >10 41 30-Jul 1-Im2 450904.43 372517.6 Apple 55-65 Alfalfa + grass >10 42 30-Jul 2-Im2 450917.58 372429.6 Apple 55-65 Alfalfa + grass >10 43 30-Jul 8-Im2 451107.57 372314.6 Apple 45-55 Grass 30-50 cm >10 44 30-Jul 10-Im2 451255.40 372224.4 Apple 40-50 Grass 30-50 cm >5 45 30-Jul 14-Im2 451427.4 372050.41 Apple 40-50 Grass 30-50 cm >5 46 30-Jul 1-Im3 451020.68 373136.1 Grape 40-45 >10

A5. Orchard type samples (field observation)


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47 30-Jul 2-Im3 451024.31 373136.3 Apple 45-55 Alfalfa + grass >10 48 30-Jul 3-Im3 451029.29 373135.6 Grape 40-45 >5 49 30-Jul 12-Im3 450842.98 373210.3 Grape 40-45 >5 51 31-Jul 1-Im4 450853.35 372826.0 Apple 45-50 Alfalfa + grass >10 52 31-Jul 5-Im4 450917.52 372833.7 Apple 45-55 Grass 30-50 cm >10 53 31-Jul 8-Im4 451000.11 372854.5 Apricot 30 Alfalfa <5 54 31-Jul 9-Im4 451000.42 372856.7 Peach+Apple 45 Alfalfa >5 55 31-Jul 10-Im4 451003.10 372847.6 Apple 40-45 Alfalfa >5 56 31-Jul 11-Im4 451039.36 372858.5 Apple 80-90 Grass >15 57 31-Jul 12-Im4 451036.94 372857.7 Apple 50-60 Alfalfa >10 58 31-Jul 13-Im4 451048.81 372907.9 Peach 30-40 Alfalfa <5 59 31-Jul 16-Im4 451012.74 373029.0 Grape 40-45 >5 A6. Well samples (field measurements & interview) No. Sample Date Coordinate E Coordinate N Depth EC Q " wet Q " dry W.L.F (W.Y) W.L.F (D.Y)


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No. year year m m 1 2 15-Jul 451454.6 372349.2 20+8 0.751 3 6 2 4 15-Jul 451527.6 372421.6 1.147 3 1 15-Jul 451439 372410.8 0.78 4 5 15-Jul 451630.6 372423.4 0.77 5 7 15-Jul 451634.8 372308.4 4+0 9.01 6 8 15-Jul 451628.2 372305.4 21.5 7 9 15-Jul 451519.2 372308.4 0.524 5 5 8 13 15-Jul 451418 372116.8 1.1 5 5 9 16 b 15-Jul 451618 372308.4 8+6 3.49 3 3 6 10 3 20-Jul 451356 372538 9+6 0.74 3 3 8 11 5 20-Jul 451437 372611 8+17 1.09 4 3 8 17 12 9 20-Jul 451501 372635 8+25 0.9 3 3 6 12 13 10 20-Jul 451457 372632 7+0 1.156 4 3 3 6 14 12 20-Jul 451457 372745 7+4 0.93 3 3 3 10 15 15 20-Jul 451554 372717 1.5 3 3 16 18 20-Jul 451512 372703 0.68 3 3 17 23 20-Jul 451252 372540 8+12 1.223 3 3 7 7 18 4 23-Jul 451019 373227 8+12 1.97 3 3 6 6 19 8 23-Jul 451145 373357 8+0 1.5 1 1 20 13 23-Jul 451441 373354 7+0 6.6 3 3 21 14 23-Jul 451440 373352 7+10 6.8 3 3 7 10 22 20 23-Jul 451525 373320 8+0 0.8 3 3 5 5 23 23 23-Jul 451308 373208 7+18 0.88 24 24 23-Jul 451307 373210 8+47 0.68 3 3 6 6 25 27 23-Jul 451359 373029 8+12 0.45 3 3 7 7 26 31 23-Jul 451233 373128 8+0 0.54 3 3 4 4 27 5 28-Jul 451350 372058 0+100 0.56 5 5 28 15 28-Jul 451204 372817 7+25 0.72 3 3 5 10 29 19 28-Jul 451253 372814 7+54 1.17 2.5 2.5 8 8 30 21 28-Jul 451336 372820 0+100 0.43 6 6 31 23 28-Jul 451419 372834 4+0 1.6 2 1 32 31 28-Jul 451319 372811 7+53 0.61 3 3 9 9 33 1 29-Jul 451155 372720 8+20 0.8 3 3 8 8 34 5 29-Jul 451040 372745 0.91 3 3 35 6 29-Jul 451034 372808 0.74 3 3 36 7 29-Jul 451002 372852 8+40 0.94 3 3 8 8 37 8 29-Jul 451030 372922 8+25 0.77 3 3 8 8

38 11 29-Jul 451008 372950 9+18 0.87 3 3 12 12

39 12 29-Jul 451124 372521 8+5 0.72 3 3 8 8

A6. Well samples (field measurements & interview)

No. Sample Date Coordinate E Coordinate N Depth EC Q " wet Q " dry W.L.F (W.Y) W.L.F (D.Y)


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No. year year m m 40 14 29-Jul 451051 372508 0.81 3 3 41 15 29-Jul 450915 372524 0.64 3 3 6 6 42 3 30-Jul 451505 372258 0.84 2.5 3 5 6 43 5 30-Jul 451539 372350 8+0 0.75 2 2 5 5 44 6 30-Jul 451601 372346 0.45 45 7 30-Jul 451622 372359 8+0 1.17 3 3 6 6 46 8 30-Jul 451627 372401 4+0 0.8 3 3 47 9 30-Jul 451503 372343 0.8 3 3 48 10 30-Jul 451543 372516 1.1 1 1 49 11 30-Jul 451503 372510 7+3 0.85 3 2 3 7 50 13 30-Jul 451513 372504 7+0 0.87 3 3 7 7 51 15 30-Jul 451602 372750 >50 52 16 30-Jul 451527 372721 2.56 53 19 31-Jul 451446 373407 7+0 7.38 4 4 5 7 54 20 31-Jul 451445 373405 9+0 6.4 1 1 55 21 31-Jul 451456 373348 3+32 1.87 3 3 5 5 56 22 31-Jul 451510 373350 7+10 10.5 3 2.5 5 5 57 23 31-Jul 451540 373352 3+0 18.3 3 3 3 3 58 25 31-Jul 451539 373342 7+0 6.97 3 3 59 26 31-Jul 451532 373334 7+6 5.78 3 3 7 7 60 28 31-Jul 451453 373305 61 29 31-Jul 451557 373304 6+67 3 3 5 5 62 30 31-Jul 451555 373304 5+0 9 63 31 31-Jul 451538 373257 0+60 1.5 3 3 2.5 2.5 64 32 31-Jul 451300 373126 >25 0.78 3 3 5 5 65 34 31-Jul 451451 373119 6+0 0.65 1 1 66 35 31-Jul 451449 373121 5+66 0.52 3 2.5 67 36 31-Jul 451358 372955 7+12 1.08 3 3 7 7 68 37 31-Jul 451325 372934 6+8 0.77 3 3 6 6 69 39 31-Jul 451359 372750 7+0 0.78 3 3 7 7 1/11/2003 CropWat 4 Windows Ver 4.2 ******************************************************************************


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Crop Water Requirements Report ****************************************************************************** - Crop # 1 : Sugarbeets - Block # : [All blocks] - Planting date : 1/4 - Calculation time step = 10 Day(s) - Irrigation Efficiency = 70% ------------------------------------------------------------------------------ Date ETo Planted Crop CWR Total Effect. Irr. FWS Area Kc (ETm) Rain Rain Req. (mm/period) (%) ---------- (mm/period) ---------- (l/s/ha) ------------------------------------------------------------------------------ 1/4 29.31 100.00 0.35 10.26 12.40 11.63 0.00 0.00 11/4 33.63 100.00 0.35 11.77 11.65 10.96 0.81 0.01 21/4 37.80 100.00 0.39 14.65 10.45 9.90 4.75 0.08 1/5 41.69 100.00 0.61 25.30 8.83 8.45 16.85 0.28 11/5 45.19 100.00 0.85 38.38 6.85 6.66 31.72 0.52 21/5 48.18 100.00 1.09 52.60 4.71 4.65 47.95 0.79 31/5 50.58 100.00 1.20 60.69 1.82 1.80 58.89 0.97 10/6 52.31 100.00 1.20 62.78 0.00 0.00 62.78 1.04 20/6 53.33 100.00 1.20 64.00 0.00 0.00 64.00 1.06 30/6 53.61 100.00 1.20 64.33 0.00 0.00 64.33 1.06 10/7 53.13 100.00 1.20 63.75 0.00 0.00 63.75 1.05 20/7 51.91 100.00 1.14 59.45 0.00 0.00 59.45 0.98 30/7 49.99 100.00 1.05 52.25 0.00 0.00 52.25 0.86 9/8 47.42 100.00 0.94 44.83 0.00 0.00 44.83 0.74 19/8 44.28 100.00 0.84 37.44 0.00 0.00 37.44 0.62 29/8 40.67 100.00 0.75 30.33 0.00 0.00 30.33 0.50 ------------------------------------------------------------------------------ Total 733.01 692.82 56.71 54.04 640.15 [0.66]


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1/11/2003 CropWat 4 Windows Ver 4.2 ****************************************************************************** Crop Water Requirements Report ****************************************************************************** - Crop # 1 : MAIZE (Grain) - Block # : [All blocks] - Planting date : 1/5 - Calculation time step = 10 Day(s) - Irrigation Efficiency = 70% ------------------------------------------------------------------------------ Date ETo Planted Crop CWR Total Effect. Irr. FWS Area Kc (ETm) Rain Rain Req. (mm/period) (%) ---------- (mm/period) ---------- (l/s/ha) ------------------------------------------------------------------------------ 1/5 41.69 100.00 0.30 12.51 8.83 8.45 4.06 0.07 11/5 45.19 100.00 0.30 13.56 6.85 6.66 6.90 0.11 21/5 48.18 100.00 0.33 16.11 4.71 4.65 11.46 0.19 31/5 50.58 100.00 0.54 27.16 1.82 1.80 25.36 0.42 10/6 52.31 100.00 0.76 39.85 0.00 0.00 39.85 0.66 20/6 53.33 100.00 0.99 52.61 0.00 0.00 52.61 0.87 30/6 53.61 100.00 1.18 63.12 0.00 0.00 63.12 1.04 10/7 53.13 100.00 1.20 63.75 0.00 0.00 63.75 1.05 20/7 51.91 100.00 1.20 62.29 0.00 0.00 62.29 1.03 30/7 49.99 100.00 1.20 59.98 0.00 0.00 59.98 0.99 9/8 47.42 100.00 1.16 55.27 0.00 0.00 55.27 0.91 19/8 44.28 100.00 0.96 42.35 0.00 0.00 42.35 0.70 29/8 40.67 100.00 0.72 29.42 0.00 0.00 29.42 0.49 8/9 18.86 100.00 0.55 10.32 0.00 0.00 10.32 0.34 ------------------------------------------------------------------------------ Total 651.13 548.31 22.21 21.55 526.76 [0.65] ------------------------------------------------------------------------------ * ETo data is distributed using polynomial curve fitting. * Rainfall data is distributed using polynomial curve fitting. ****************************************************************************** M:\CLIMAT~1\MAIZ2001.TXT


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C1. Appearance of different band combination, which were used in classification Alfalfa: This crop is normally observed in the fields as dark green. It has a red appearance on the False Colour Composite (3,2,1) of the Aster and (4,3,2) of the ETM images. Based on crop calendar separating of this class from orchards by FCC (7,4,2) ETM images, with a light green appearance, is possible. Orchards_apple: This class can usually be seen with inter cultivated alfalfa in the study area and has a dark red appearance on the FCC. The separating of this class is easy but appearance of the young gardens, which their canopy covers is less than 30%, is near to alfalfa. The last group was separated using different sample set includ-ing principal component layers and different bonds of images for March and July dates. Orchard_grape: Separating of this class from rangeland and cash crop is difficult. It is caused by kind of culti-vated of this plant in the study area. It became detached using different colour composite of principal component layers, NDVI and different bonds of images. Summer crops: This class includes the crops that is cultivated after March and harvested before October such as: Sunflower: Based on date of image, this crop type has bright pink on the FCC 432;this date was the initial of its growing stages. For that reason it didn�t covered completely the planted area. Sugar beet: This crop has bright red appearance on the FCC 432 Aster image. As the acquisition date of image was development time of growing stage of it, for that it covered completely the planted lands and it�s colour was very similar to alfalfa. For this reason, classifying of it was very difficult. Winter crop: According to the crop calendar and fieldwork observation, date of the Aster image was coinciding with harvesting stage of this crop. However, It has a turquoise appearance on the aster image. Pattern of har-vested plots and training samples were very useful to distinguish of this area. Rangeland: this class was distinguished by applying NDVI as an ancillary band into sample set, also some parts of rangeland have very bright red appearance especially in the around of Urumieh Lake in the wet lands. They are adopted with salinity condition such as Salsola. Saline area: This class has a grey and white appearance on the FCC and it can be separated easily. Other: this class includes rods, industrial centres and other no irrigated area Water body: the part of lake in the 2000 which has been appeared as a saline area in the 2001

THE ASPECTS OF WATER BALANCE IN THE IRRIGATEED AREA ...

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