Ben-Gurion University of the Negev Jacob Blaustein Institutes for Desert Research

Albert Katz International School for Desert Studies

Sustainable development and protection

of water resources in arid lands

Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science"

By: Irina Aidarov

November 2006

Ben-Gurion University of the Negev Jacob Blaustein Institutes for Desert Research

Albert Katz International School for Desert Studies

Sustainable development and protection

of water resources in arid lands

Thesis submitted in partial fulfillment of the requirements for the degree of "Master of Science"

By: Irina Aidarov

Under the supervision of Dr. Alexander Yakirevich and Prof. Eilon Adar

Department of Environmental Hydrology & Microbiology

Zuckerberg Institute for Water Research

Author's Signature …………….……………………… Date …………….

Approved by the Supervisor…………….……………. Date …………….

Approved by the Supervisor…………….……………. Date …………….

Approved by the Director of the School …………… Date ……………

i

ABSTRACT

An analysis of the management of limited water resources in arid lands shows

that, to date, attention has been paid mostly to economic and technological problems,

while environmental damage has been considered as a “progress cost” or has been

neglected altogether. This approach to water management is based on a “cost-

efficiency” model. However, it has been found that the “progress cost” can be

considerable, and comparable to production costs. In particular, using this approach for

the development of agricultural land in the Ily River-Lake Balkhash basin in Kazakhstan

led to construction of the rice-irrigation system, characterized by low technical

performance, over the sand dune terrain. Heavy water application and high infiltration

losses had negative environmental effects: a decrease in the fertility of the irrigated

lands, pollution of groundwater and surface water, desiccation of the Ily River delta and

violation of the Lake Balkhash ecosystems.

The major aims of this research were to assess the negative impacts of irrigation

and to develop and apply a model to assess sustainable management of water and land

resources in the Akdalinsky irrigated lands of the Ily River basin.

A net present value (NPV) criterion of efficiency was used to compare different

scenarios of agricultural development in the study area. The NPV criterion accounts for

the benefits, in monetary terms, from agricultural production and the costs due to

changing soil fertility, salinization and contamination of soil and water resources.

Relatively simple models were used to assess the NPV components.

Four alternative scenarios of use of water, land and material resources were

considered: 1) Exploitation of the existing rice irrigation system with rice fields

occupying 62.5% of irrigated land (Soviet era policy); 2) Reconstruction of the existing

rice irrigation system and changing the structure of the irrigated land by decreasing rice

fields to 37.5% of irrigated land; 3) Development of furrow irrigation aimed at

ii

production of forage crops for cattle breeding; and 4) Development of highly efficient

irrigation based on sprinkling, also aimed at producing forage crops for cattle breeding.

The restrictions in each scenario were maximum available total water consumption,

available investments and maintaining water content and salinity of the root zone within

admissible limits.

Estimation of the economic benefits and ecological costs was based on long-

term forecasting of water and salt regimes in irrigated lands as well as on pollution of

water resources, using mathematical models of water flow and solute transport

(WASTR3-A) and hydrological and pesticide balance (GLEAMS).

Results of the simulations and the comparison of NPVs for the alternative

scenarios led to the following conclusions:

Ecological damage to the irrigated land and water resources depends on the

structure of agricultural development, techniques and technology of irrigation. In

scenarios 1 and 2, soil fertility decreases and intensive pollution of water resources

occurs due to low technical performance of the irrigation systems (water-use and land-

use efficiencies of 0.5-0.75 and 0.64-0.85, respectively). The cost of the ecological

damage in scenarios 1 and 2 is 1.4 and 1.36 times higher than the value of the benefits

from selling agricultural produce. In scenarios 3 and 4, improvement of the irrigation

system (by increasing water-use and land-use efficiencies to 0.85-0.95 and 0.90-0.98,

respectively) and changing agricultural crop patterns lead to a decrease in water

consumption per unit yield, a decrease in pollution and an increase in irrigated soil

fertility. In all scenarios, development of irrigated land leads to an increase in natural

pasture fertility (especially in scenarios 3 and 4), since forage production on irrigated

land decreases pasture load. Scenario 4 was found to be the most efficient and to

provide the maximum NPV.

iii

ACKNOWLEDGEMENTS

I would like to express my gratitude to all those who enabled me to complete this

thesis.

The first thanks must go to my supervisors. I thank my supervisor Dr. Alex

Yakirevich for his many astute comments, constant willingness to share his broad

knowledge, and for his wise yet flexible approach, encouraging my diversions into wider

topics of water management. I thank my other supervisor Prof. Eilon Adar, especially for

introducing me to arid land hydrogeology and hydrology modeling. Eilon kept an eye on

the progress of my work and was always available when I needed his advice. I am deeply

indebted to both my supervisors whose help, stimulating suggestions and encouragement

helped me throughout the research and writing of this thesis.

I would like to express my thanks to Prof. Ivan Aidarov (Moscow State University

of Environmental Engineering, Russia) whose expertise, understanding, and patience added

considerably to my graduate experience. I appreciate his vast knowledge and skill in many

areas: land improvement and water industry, regulation of water and salt regimes of

irrigated lands.

Special thanks are due to Prof. Vasiliy Veselov and Dr. Vladimir Panichkin

(Institute of Hydrogeology and Hydrophysics, Kazakhstan) who helped me to understand

the ecological and land reclamation problems of water resources in Lake Balkhash.

The US Agency for International Development partly supported my work within the

framework of the project CA21-021 “Sustainable development and protection of water

resources in the irrigated land of the Ily River delta, Kazakhstan”.

I am grateful to Dr. Leah Orlovsky (Department of Solar Energy and Environmental

Physics, Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the

iv

Negev) for her support, helpful suggestions and comments during my study. Her expertise

in desert studies and remote sensing analysis improved my research skills and prepared me

for future challenges.

I am also greatly indebted to Dr. Tal Svoray (Department of Geography and

Environmental Development, Ben-Gurion University of the Negev) for getting me

interested in Geographical Information Systems, for introducing me to GIS technology and

concepts, and for teaching me geographical skills.

Special thanks go to Zoe Groner for her reading and editing my thesis.

I wish to thank the Association of Holocaust Survivors from the Former USSR for

awarding me the 2004 Prize for Excellence in Water Research.

I thank my parents Leonid and Taisia Shesterov for giving me life in the first place

and for educating me; and my mother-in-law Ninel Aidarov for unconditional support and

encouragement to pursue my interests.

Finally, this thesis could not have been accomplished without Peter Aidarov, my

husband, and Jasmin, my daughter, who were always with me no matter how dubious my

decisions. They always give me warm encouragement and love in every situation.

vTABLE OF CONTENTS CHAPTER 1. INTRODUCTION 1

1.1. Development of water resources and irrigation in arid lands 1

1.2. Systems approach to water-resource management 6

1.3. Aims of water management 9

1.4 Water management in arid lands and environmental protection 11

1.5. Research aims 17

CHAPTER 2. NATURAL AND ECONOMIC CONDITIONS IN THE ILY RIVER-LAKE BALKHASH BASIN

19

2.1. General information 19

2.2. Natural conditions 21

2.2.1. Climate 21 2.2.2. Topographical and geological structure 23 2.2.3. Surface water and groundwater 26 2.2.4 Vegetation 29 2.2.5. Topsoil 30

2.3. Economic activity 31

2.4. Akdalinsky irrigation system 33

CHAPTER 3. IMPACT OF ANTHROPOGENIC ACTIVITIES ON THE ENVIRONMENT IN THE ILY RIVER-LAKE BALKHASH BASIN

38

3.1 Assessing environmental impacts in the Ily River-Lake Balkhash basin

38

3.2 Heat balance 49

3.3 Hydrological and hydrochemical conditions 51

3.3.1 The Kapchagay water-storage reservoir 52 3.3.2. Akdalinsky irrigation system 53 CHAPTER 4. SUSTAINABLE MANAGEMENT OF WATER RESOURCES IN THE ILY RIVER BASIN

64

4.1. A model for water-resource management 64

4.1.1. Aims and objectives 64 4.1.2. Available water resources 65 4.1.3. Quantitative criterion 66

4.2. Alternative use scenarios for water, land and economic resources

70

vi 4.3. Forecast of water flow, salt transport and pesticide pollution in soil and water resources

75

4.3.1. Simulations with the WASTR3-A and GLEAMS models 75 4.3.2. Impact of water-management scenarios on environmental conditions

82

4.4. Calculating the NPV criterion 87

CONCLUSION

93

REFERENCES

96

APPENDICES

APPENDIX 1: Long-term forecasting of water-salt regimes of irrigated lands, calculation of irrigated area and pollution of the environment

under the various scenarios

103

APPENDIX 2: NPV calculation 107

vii LIST OF FIGURES

Figure 2.1. Ily-Balkhash region general map

20

Figure 2.2. Areal distribution of annual precipitation in the Ily-Balkhash region

22

Figure 2.3. Topographical structure of the Ily-Balkhash basin

24

Figure 2.4. Geological cross section of the southern Balkhash zone from Malaisary range to Lake Balkhash

25

Figure 2.5. Schematic map of Akdalinsky's irrigated land

34

Figure 3.1. LANDSAT images of the study area

39

Figure 3.2. Results of supervised classification in the Bakanass part of the Akdalinsky area (26 May, 1990).

41

Figure 3.3. Results of supervised classification in the Bakanass part of the Akdalinsky area (13 May, 2000).

42

Figure 3.4. Results of supervised classification in the Bakhbakhty part of the Akdalinsky area (26 May, 1990)

43

Figure 3.5. Results of supervised classification in the Bakhbakhty part of the Akdalinsky area (13 May, 2000)

44

Figure 3.6. Verification of classification results for the year 2000

45

Figure 3.7. Relative area (%) of major classes

46

Figure 3.8. Temporal variation in rice productivity and rice growing area

47

Figure 3.9. Areas of agricultural fields of alfalfa and other crops

48

Figure 3.10. Irrigated lands occupied by major agricultural crops in 2000

48

Figure 4.1. Variation of mean TDS content in the upper 0-0.7 m soil layer

78

Figure 4.2. Modeled temporal variations in mean groundwater level

78

Figure 4.3. Modeled temporal variations of TDS concentration in groundwater

79

Figure 4.4. Simulated salt concentration of drainage water flux 79

Figure 4.5. Pesticide concentrations in the runoff, 1987-88 (scenario 1) 82

Figure 4.6. Pesticide concentrations in the sediment, 1987-88 (scenario 2)

82

viiiFigure 4.7. Comparison of NPV values for different scenarios 89

LIST OF TABLES

Table 1.1. Development of global irrigation and water consumption for irrigation

2

Table 1.2. Water reservoirs used for irrigation

3

Table 1.3. Irrigation technique and drainage of irrigated lands

5

Table 2.1 Annual distribution of precipitation for different areas in the Ily-Balkhash region

22

Table 2.2. Chemical composition of the Ily River

26

Table 2.3. Chemistry of groundwater in the Upper Quaternary deposits

27

Table 2.4. Total water balance in the central part of the basin under natural conditions

28

Table 2.5. Salt balance in the central part of the basin under natural conditions

28

Table 2.6. Salt content in serozem and brown soils in the upper 0-100 cm layer

31

Table 2.7. Water requirements for the national economy sector in the basin

32

Table 2.8 Chemical composition of wastewater and drainage water

33

Table 2.9 Influence of the Akdalinsky irrigation systems on the Ily River

37

Table 3.1. Temporal changes in spatial land structure of the Ily-Balkhash basin

38

Table 3.2. Parameters of the LANDSAT images

39

Table 3.3. Components of heat balance and hydrothermal index under natural and anthropogenic conditions

50

Table 3.4 Salinity and chemical composition of water in the Kapchagay water- storage reservoir (1985)

53

Table 3.5 Concentration of biogens in the Kapchagay water-storage reservoir

53

Table 3.6 Crop allocation in the Akdalinsky irrigated lands, %

54

Table 3.7 Rice-irrigation characteristics depending on permeability and soil salinization

55

Table 3.8 Drainage water discharge and related factors

59

ixTable 3.9 Salinity and concentration of major ions in return flow

59

Table 3.10 Crop yields in the study area

61

Table 3.11 Water balance of the Akdalinsky irrigated lands

62

Table 3.12 Salt balance of the Akdalinsky irrigated lands

62

Table 4.1. Values of the parameter β

68

Table 4.2. Comparison between results of simulations with WASTR3-A code and observations

77

Table 4.3. Pesticide application in 1987-1988

80

Table 4.4. Comparison between simulated and observed pesticide contents

81

Table 4.5. Averaged (over 8 years) components of water balance for different scenarios

84

Table 4.6. Average soil water salinity in the root zone

84

Table 4.7. Calculations of irrigation area for different scenarios

85

Table 4.8 Groundwater pollution (average over 3 m depth) by biogens and Bolero over the total irrigation area for different scenarios

86

Table 4.9. Calculated mass (ton) of salts and pollutants being discharged into the Ily River under the different scenarios

86

Table 4.10 Normative ecological and economic characteristics

87

Table 4.11. Calculated NPV components

88

Table 4.12. Specific characteristics of economic and ecological benefits and damages for different scenarios

92

1

1. INTRODUCTION 1.1. Development of water resources and irrigation in arid lands Arid lands occupy about 48,000,000 km2 or 33% of the Earth’s surface and are

characterized by the following basic features (Hansen et al., 1979; Soil Conservation

Service, USA, 1993; FAO UNESCO, 1997; Klon and Wolter, 1998; Seckler et al., 1998):

• High and stable solar radiation, high air temperatures and evaporation: net

radiation flux ; the total sum of “active” air

temperatures (air temperature more than ) is about 3,000 to 11,000°C;

evaporation mm/year.

/year2kJ/cm250170R ÷=

C010

500,18000 −=E

• Low and unstable precipitation: 25090 −=P mm/year; moistening coefficient

(ratio of precipitation to potential evaporation) 3.005.0 −=mK .

• Low natural crop productivity (0.3-1.0 ton/ha).

• Desert and serozem soils with low natural and high potential fertility.

• Soil and water resources subjected to salinization processes.

• High bio-climatic potential, effective use of which is possible only under

irrigation and appropriately regulated water regimes.

Worldwide development of irrigation agriculture occurred most intensively from

1900 to 1985. During the last two decades, the intensity of irrigation development has

decreased because of water-quality deterioration and exhaustion of water resources (Table

1.1) (Nikolskiy-Gavrilov, 1999).

Data from Table 1.1 show that in the last century, specific water consumption for

irrigation has decreased by only 3%. Irrigation constitutes the major use of water in arid

lands, consuming from 70 to 90% of all developed water resources. If these proportions are

maintained, then total water-resource depletion may occur in arid lands by 2025. At present,

2

a water-supply deficit can be observed in many arid countries. The most acute water-supply

problems exist in Central Asia, Africa, Mexico and Argentina, i.e. in countries where water-

use efficiency is low (Seckler et al., 1998; Nikolskiy-Gavrilov, 1999).

Table 1.1. Development of global irrigation and water consumption for irrigation (Nikolskiy-Gavrilov, 1999).

Year Characteristics 1880 1900 1950 1960 1970 1985 1995 2000

Area of irrigated lands, millions ha 8 48 94 140 198 250 270 280

Total water consumption for irrigation, km3 75 450 864 1,288 1,822 2,392 2,484 2,500

Water consumption for irrigation, m3/ha 9,400 9,400 9,200 9,200 9,200 9,200 9,200 9,100

Exhaustion of water resources is not the only negative consequence of irrigation in

arid lands. Surface-water and groundwater pollution, soil salinization, and desertification

processes usually occur simultaneously (Aidarov et al., 1991; Denecke, 1997; Galder, 1998;

Amarasinghe et al., 1999; Brown, 1999; Droogers et al., 1999; Ximing, 2004). It is

therefore very important to study the experiences gained from water-resource management

in arid lands and to analyze the factors that induce negative effects.

Water resources in arid zones originate mainly from rivers, with two sources of

recharge: glaciers and rainfall. For those fed by glaciers, head rivers originate in zones of

high humidity (mountain areas). The middle and delta parts of these rivers are usually

associated with arid lands, where dissipation of river discharge occurs (e.g., Amu-Daria,

Sir-Daria, Ganges, Euphrates, Ily and Tiger, among others). These rivers are characterized

by relatively small variations in their long-term discharge: the coefficient of variation

usually does not exceed 0.3. Annual discharge distribution is favorable for irrigation

(because of summer floods). Using most of the discharge (up to 90%) for irrigation

development can be achieved via regulation with seasonal storage reservoirs that

3

redistribute annual discharge in accordance with water requirements. Long-term runoff

regulation under these conditions is usually ineffective.

Rivers recharged by rainfall, on the other hand, usually have an unevenly

distributed discharge. Coefficients of annual runoff variation may exceed 1 (e.g., Limpopo

River in Africa). In practice, such an annual discharge distribution does not fit irrigation

requirements. Regular irrigation in this case can be on the basis of runoff regulation only

(Zaytcev, 1968; Burt and Plusquellec, 1990).

Regulating river discharge by constructing water-storage reservoirs in arid lands

allows an increase in the area of the irrigated lands because of the increase in available

water resources. At the same time, this leads to an increase in water loss via evaporation

from the reservoir surface (Table 1.2) and deterioration of water quality in the reservoir as a

result of a decrease in water circulation and an increase in pollutant accumulation.

Table 1.2. Water reservoirs used for irrigation (Avakian et al., 1987; De la Lanza and Garcia, 1995).

Reservoir volume, km3Continent Total Effective 1

Water surface area, km2

Water losses by evaporation from effective volume,

% Asia 817 334 30,657 11 North America 204 147 10,210 5 South America 97 45 4,860 11 Australia, Oceania* 78 28 5,100 25 Africa 812 530 104,000 20 Europe 14 10 480 3 Total 1,950 1,070 150,200 14.5 (average)

*Australia, New Zealand, New Caledonia, Papua New Guinea, Fiji.

Sedimentation processes at the floor of the reservoirs lead to the accumulation of

heavy metals, pesticides and other contaminants, which are sources of secondary water

pollution. Unsustainable redistribution of discharge can also lead to deterioration of water

1 Volume of water that can be safely used

4

resources. Decreasing discharge during the winter and summer can lead to a decrease in

delta watering, with subsequent desiccation and desertification. For example, the use for

irrigation (a total area of about 6 million ha) of most of the discharge of the Amu-Daria,

Sir-Daria, Chu, and Talas rivers in Central Asia during both winter and summer seasons has

led to land degradation, salinization and desertification of these areas. Similar processes

have been observed in the Ily River delta after construction of the Kapchagay water-storage

reservoir, where about 100,000 ha of land were desiccated (Ratkovich, 1993; Veselov et al.,

1996). Regulation of river discharge and water use for irrigation strongly affects the hydro-

ecological conditions of inland reservoirs: water inflow to the Aral Sea decreased from 62

to 3.8 km3/year and sea level decreased by more than 20 m. Inflow to Lake Balkhash

decreased from 15 to 12.9 km3/year, and to Lake Issik-Kul from 3.86 to 2.56 km3/year,

while water level in these lakes decreased by about 2 to 3 m (Ratkovich, 1993; Veselov et

al., 1996).

Extensive development of irrigation in arid lands causes deterioration and pollution

of soil and water resources. The main reasons for this are bad planning and unsustainable

use of water resources, on the one hand, and imperfect irrigation technology on the other.

Until recently, planning of water-resource management and land-use allocation was carried

out without taking into account the ecological consequences of economic activity.

Moreover, irrigation technology in most arid countries is characterized by a low technical

level of irrigation systems (efficiency factor of about 0.6-0.8) and primitive irrigation

techniques (mainly furrow and/or flood irrigation, Table 1.3), leading to enormous water

losses of 30 to 40% of total water intake.

Due to excessive irrigation and the consequent rise in groundwater levels, the

installation of expensive drainage systems is required, increasing the cost of crop

production. This also introduces the problem of disposing of the drainage effluent, which is

5

usually loaded with salts and, when discharged into the river, contaminates it downstream.

An analysis of existing data shows that the volume of drainage water may reach up to 30%

of total water consumed for irrigation. About 60% of the drainage water is dumped into

surface canals and rivers, 30% into closed surface depressions, 7% directly into lakes and

the sea, and about 3% is re-used for irrigation. Salinity of the drainage water is usually 3 to

5 g/l, including 0.3 μ g/l biogens and 0.3 μ g/l pesticides (Aidarov et al., 1991; Denecke,

1997; Seckler et al., 1998).

Table 1.3. Irrigation technique and drainage of irrigated lands (Aidarov et al., 1991; Soil

Conservation Service, USA, 1993; Denecke, 1997; Klon and Wolter, 1998; Seckler et al., 1998).

Different irrigation techniques and the area they cover

Country Irrigated area,

million ha

Water application

(total) m3/ha

Drainage area,

million ha

Flood irrigation, million ha

Sprinkling irrigation, million ha

Drip irrigation, million ha

India 59.02 9090 5.80 58.10 0.66 0.26 China 52.60 8.80 20.0 51.13 1.20 0.27 USA 21.40 9.40 47.50 8.26 11.45 1.69 Iran 7.60 8.40 0.04 7.35 0.20 0.05 Mexico 6.50 10.00 5.20 5.90 0.60 - Uzbekistan 4.30 12.60 4.00 4.30 - - Turkey 4.20 5.90 3.14 4.07 0.12 0.01 Spain 3.60 5.50 - 1.78 0.91 0.91 Egypt 3.30 13.70 3.00 2.75 0.45 0.10

Dumping drainage water into surface-water reservoirs leads to contamination and

deterioration in the quality of water resources. In Mexico, about 73% of surface water and

60% of groundwater has been polluted to date (concentrations of some pollutants exceed

sanitary standards); in the USA about 95% of surface water and 21% of groundwater are

polluted (FAOSTAT Internet Database: http://apps.fao.org/lim500/nphwrap.pl?irrigation&Domain=LUI&servlet=1).

The average salinity of surface water in the Aral Sea basin is 1.7 to 2 g/l (Aidarov et al.,

1991; Soil Conservation Service, USA, 1993; Denecke, 1997; FAO UNESCO, 1997).

6

Pollution of surface-water resources may cause political problems with transboundary

rivers. For example, the USA is obligated to transport 1.85 km3 water per year via the

Colorado River to Mexico. However, only 1.677 km3/year of poor-quality water (salinity 1-

1.25 g/l) is actually delivered. Many countries in Central Asia and the Middle East face the

same problems.

Deterioration of surface-water quality leads to the development of negative

processes in irrigated lands, namely, soil and groundwater contamination and salinization.

Due to increases in irrigation-water salinity, more water is required to leach accumulated

salts below the root zone and prevent secondary salinization (Averianov, 1978; Aidarov et

al., 1991; Loucks, 2000).

Summarizing the above environmental effects of water-management malpractice in

arid lands, it is clear that methods are needed to assess a strategy for sustainable

exploitation of natural resources (soil and water), including: improvement of irrigation

technology, decrease in water consumption and more effective allocation of agricultural

lands, while preventing soil and water salinization and contamination.

1.2. Systems approach to water-resource management

The problems of water development in arid regions need to be considered using a

systems approach (Biswas, 1974), which takes into account all elements of ecological and

technogenic systems. The decision-making must be based on not only criteria of economic

efficiency, which take into consideration profits obtained from agricultural activity, but also

rational use of natural resources and possible damage to the environment. Thus, the systems

approach constitutes a methodical basis for assessing sustainable development of natural

resources. This approach allows us to examine the natural environment as a whole,

organized system (landscape), consisting of mutually interconnected components (a

7

boundary atmospheric layer, plants, soils, and groundwater). The most important steps are

as follows (Hamilton et al., 1969; Biswas, 1972, 1973a, b, 1974, 1976):

• Studying the structure and basic properties of the system under consideration, selecting

its major components and examining the interactions between them. At this stage, the

available data are collected and analyzed.

• Selecting the integral parameters and describing some basic properties of major system

components (atmosphere, plants, soils, surface water and groundwater). The selection

of these integral parameters is not formal. A principle of simplicity must be applied and

a minimal number of parameters chosen, i.e. only those that are essential to

characterizing feedback and system functioning.

• Studying the current state of the system and its components, and analyzing the changes

and major reasons for their occurrence as a result of anthropogenic activity. This also

includes examining the conditions of soil and water resources, sources of pollution and

reasons for any decrease in soil fertility.

• Describing major processes observed in the investigated system. Selecting conceptual

and mathematical models. Determining the hydrogeological and hydrochemical

parameters by solving inverse problems and using experimental data. Estimating the

model's accuracy and ability to describe the dynamics of the processes in a quantitative

manner.

• Investigating system dynamics (groundwater levels, soil salinity, water quality and

productivity of irrigated lands) in order to reveal specific features and system behaviors

as a result of introduced scenarios of anthropogenic activity. At this stage,

mathematical models are used to simulate processes of water flow and solute transport

in soils.

8

• Estimating ecological damage to the natural environment according to different

scenarios of reclamation and agricultural activity. This damage should be estimated for

all components of the natural environment (atmosphere, crops, soils, surface water and

groundwater) depending on the particular irrigation strategy used, and using available

legislative documents.

• Selecting the most efficient scenario for water-resource development which also

protects the environment. This is based on the introduction of economic criteria,

taking into account investments, profits and damages.

Taking into consideration that water is closely linked with other environmental

components such as air, vegetation and soil, the use of water resources for economic

purposes inevitably changes the environmental system as a whole, as well as the socio-

economic conditions of the population. Therefore, to develop a strategy for water-resource

management, it is necessary to define directions of economic activity and main water

consumers. Usually, the major economic activity in arid regions is agriculture based on

irrigation. Therefore, development and use of water resources for irrigation is accompanied

by the following changes in an environmental system (Biswas, 1972; Averianov, 1978,

1956; Ratkovich, 1993; Veselov et al., 1996):

- Changing hydrological conditions as a result of water storage and

consequent decrease in river discharge.

- Deterioration of conditions in inland water reservoirs.

- Changes in thermal, water and salt balances in irrigated areas and nearby

lands.

- Changes in soil fertility and agricultural crop production as a result of

salinization.

- Changing socio-economic conditions.

9

- Changing groundwater-flow patterns and quality.

- Decrease in irrevocable water consumption.

- Changing hydrochemical regimes and deterioration of surface-water quality due to

dumping of saline and polluted water.

Thus, when solving the water-management problem, it is necessary to consider a

common environmental system (river and surrounding lands), including its major

interconnected and interdependent components—air, vegetation, soils, surface water and

groundwater (Hamilton et al., 1969; Biswas, 1976).

It is well known that the systems approach defines the research object as a system.

In this investigation, we consider a system that consists of the middle and lower parts of a

river basin located in an arid zone (specifically, the Ily River basin located in South

Kazakhstan). Note that for such a complex system (including water reservoirs, irrigated

lands, and other water consumers), using soil and water resources will affect ecological and

socio-economic conditions in both the river delta and inland water reservoirs that constitute

closed elements of the river system (Ratkovich, 1993).

1.3. Aims of water management Man's history has always been closely tied to water as a basis for existence. For

centuries, water was considered an inexhaustible environmental resource, to be used

without restriction, and the main problem was moving the water from a source to

consumers. The problem of storing polluted wastewater emerged with industrial

development. However, at that time, the major focus was on economic and technical

development, and prevention of environmental pollution was essentially overlooked

(Biswas, 1976).

10

In the last few decades however, drastic deterioration in water quality and

ecological conditions has led to important changes in water-management planning. When

assessing a policy of water-resource development, indices and criteria accounting for the

environmental conditions are now considered together with economic requirements to

ensure sustainable development. At the same time, experience has shown that the choice of

an optimal scenario for the management of water and other resources depends on pre-

determined goals. In defining the economic goals of water-resource management, the most

efficient variant is one that ensures the maximum difference between benefits and costs. In

this case, environmental damage is considered a “progress cost” and is usually neglected

(Biswas, 1976). If we include environmental quality as one of the objectives of water-

resource management, then the most effective scenario will be one that achieves economic

goals while minimizing “outside effects” (such as environmental pollution and ecosystem

deterioration), which are difficult to express in monetary terms.

In arid conditions, the main task in planning and managing water resources lies in

further developing economic activity (e.g., irrigated agriculture), providing rational use and

protection of natural resources, improving socio-economic conditions of the local

population, and preserving the hydrological and hydrochemical balance in river deltas and

inland reservoirs.

Realization of this approach should be based on the following specific objectives:

- Preventing environmental deterioration (soils, groundwater and surface

water) as a result of agricultural development, by improving agricultural

technology and varying areas and structure (crops) of irrigated lands.

- Increasing the yield of agricultural crops while minimizing water expenses

for unit crop production.

11

- Lowering anthropogenic load on the environment as a result of technological

progress (increase in efficiency factor, use of modern irrigation techniques

etc.), and achieving substantial decreases in water intake, return water and

irrevocable water consumption.

- Providing guaranteed water discharge to deltas and inland reservoirs.

1.4. Water management in arid lands and environmental protection

The development of water-management models originated in the analysis and

description of runoff variation mechanisms and other components of water balance.

Observations of temporal series of discharge, precipitation and evaporation formed the

basis for such models. This assumed that the processes were uniform and the experimental

data representative. Thus, the first “precipitation-runoff” models (Meyer, 1915; Russell,

1989) were based on analyses of surface-water balance, which consisted of precipitation,

evaporation and runoff.

Increasing water requirements and a conflict between available river discharge and

consumer needs led to the need for discharge regulation, and as a result, assessment of not

only the mean annual volume of discharge, but also its maximal values, required to design

dams and other hydraulic-engineering constructions. Therefore, unit hydrograph models

were developed based on water balance over relatively short time intervals (hours)

(McCarthy, 1938; Snyder, 1938; Clark, 1945). One of these models' shortcomings was the

requirement for very detailed data on precipitation and evaporation.

Water infiltration into the soil was not considered in the early models. Horton

(1933) proposed taking into account the infiltration of precipitation and calculating

balances for both surface water and groundwater. This type of modeling required

12

knowledge of the soil's hydraulic properties, which made the models difficult to apply to

large basins characterized by heterogeneous geological and hydrogeological conditions.

The next step was the development and application, in the early 1950s, of stochastic

models based on continuous probability distributions of runoff fluctuations (Bower et al.,

1962; Dorfman et al., 1962; Dorfman, 1965; Hufschmidt et al., 1966; Kritskiy and Menkel,

1968, 1981; Jacoby and Loucks, 1972; Ratkovich, 1993).

Management of groundwater resources is an important problem, especially when

this water is used for irrigation. In many cases, combined models of groundwater and

surface-water management must be considered. In arid lands, irrigation affects the

groundwater level and hydrochemistry, while dumping drainage water influences surface

water and groundwater. Over the last decades, numerous physical-mathematical models

have been developed to describe water flow in the vadose zone and groundwater (e.g.

Bochever et al., 1969; Bear, 1972; de Marsily, 1986; Bear and Verruijt, 1987).

The storage of water is aimed at increasing available water resources to enable

regional economic development, and it requires an assessment of the efficiency of water

and land use. As a result, economic “cost-efficiency” models were developed (Klein and

Goldberger, 1955; Dorfman, 1962; Moore and Hedges, 1963; Howe and Easter, 1971;

Wollman and Bohem, 1971; Heady, 1972; Silk et al., 1972; Biswas, 1976; Kou, 1976).

These models were used at a time when economic and technological developments were

accepted as major aims. The models established a relationship between exploitation of

limited water and other natural resources. The main water consumer in arid zones is

agriculture; therefore, estimation of the economic consequences of developing natural

resources via irrigation technology and methodology is a very important task. Such

economic models were widely used during the technological revolution. However,

development of land and water resources based on these models led to severe

13

environmental damage. Thus, water management needs to be multi-objective, providing

benefits “to all”, because many natural and material resources are involved in economic

activity (Kazanowski, 1968, 1972; Monarchi et al., 1973; David and Duckstein, 1974). The

complexity of this multi-objective planning can be overcome by introducing relatively

simple models accounting for the general efficiency criterion, to be used in the initial stage

of planning devoted to the development of a general scheme.

In the USA, this problem was acknowledged much earlier and checks were

implemented by the National Environmental Policy Act of 1969 (NEPA, 1969). The Water

Resources Council prepared a document “The Law and the Standards”, in which the

following two aims concerning water and land resources were formulated (Simon, 1957;

Policies, Standards, and Procedures in the Formulation, Evaluation, and Review of Plans

for Use and Development of Water and Related Land Resources, 1962; White, 1969; Water

Resources Council, 1973): accelerating national economic development, and improving

environmental quality.

The latter aim needs additional clarification. Analyses of the current ecological

crisis emphasize three major aspects:

- ecological-economic: concerned with exhaustion and degradation of

renewable natural resources (water, biota, soil);

- ecological-biological: concerned with destabilization of our species as a

result of anthropogenic impact and alteration of major environmental

parameters;

- socio-political: concerned with contradictions between global (regional)

problems of environmental pollution/degradation and specific approaches to

solving these problems.

14

Thus, planning and management of water and other natural resources cannot be

limited to considering only economic problems. Any strategy of natural-resource

management must take into account economic, ecological, social and political factors.

However, only economic and some ecological factors can be expressed quantitatively, i.e.

in monetary terms. It is difficult to quantitatively assess the social and political factors that

must be taken into account during the decision-making process, these being particularly

important for Central Asian countries. McKinney and Cai (1996) and McKinney et al.

(1997) developed hydrology-inferred policy analysis tools to be used for water allocation

decision-making on a river-basin scale. This work involved the development of

optimization models for the Amu-Daria and Sir-Daria basins in the Aral Sea basin of

Central Asia using GAMS and ArcView GIS software. This hydrology-inferred approach

has been recently extended to an economic-optimization approach that considers cropping

decisions and irrigation- and drainage-system improvements. Lee and Howitt (1996)

modeled water and salt balances in the Colorado River basin to determine salinity levels

that maximize net returns to agriculture and to municipal-industrial (MI) users at select

locations in the basin. Nonlinear crop-production functions and MI costs per unit of salinity

were derived for inclusion in the objective function, which was solved using

GAMS/MINOS software. Three scenarios were considered: (1) economic optimality; (2) no

change in cropping patterns with subsidies for salinity-control measures; and (3) cropping

changes with subsidies to maintain agricultural profits. The first-best, economically optimal

scenario indicated major declines in cropped area with significant returns to MI uses. Of the

two scenarios with subsidies, the cropping changes subsidized to maintain profits indicated

marginally lower total subsidies with a minor, but significant reduction in salinity. The

authors noted that optimal solutions were modeled without consideration of transaction

costs or equity criteria.

15

Important economic concepts that need to be examined through integrated

economic-hydrologic river basin modeling include transaction costs, agricultural

productivity effects of allocation mechanisms, inter-sectoral water allocations,

environmental impacts of allocations, and property rights in water for different allocation

mechanisms (McKinney et al., 1999). Water/crop production functions for the irrigated

water uses—evapotranspiration models, simulation models, estimated models, and hybrid

models—are a necessary component of economic approaches in river-basin management.

The main approaches that form the methodological basis for strategic economic appraisal

are cost-benefit analysis and cost-effectiveness analysis. Cost-benefit analysis is carried out

in order to compare the economic-efficiency implications of alternative actions. The

benefits from an action are contrasted with the associated costs (including opportunity

costs) within a common analytical framework. The benefits and costs are usually measured

physically in widely differing units. The benefits and costs of each option are determined

relative to the common scenario that would prevail if no action were taken. The net benefit

of each option is given by the difference between the costs and benefits. The most

economically efficient option is that with the highest present value of net benefit, i.e. net

present value (NPV); economic efficiency requires selection of the option with maximum

NPV. Options are economically viable only where the NPV that they generate is positive

(Lingkubi and Leitch, 1996; Zwarts et al., 2006). Cost-effectiveness analysis (also known

as least-cost analysis) is used to identify the most cost-effective option for achieving a pre-

set objective or criterion. The relevant objective is set, options for achieving it are

identified, and the most cost-effective option is identified as that with the lowest present

value of costs. It is implicitly assumed that the benefits of meeting the goal outweigh the

cost and that the action is therefore economically viable (Turner et al., 2004).

16

The interdisciplinary nature of water problems requires new methods to integrate the

technical, economic, environmental, social, and legal aspects into a coherent framework.

Water-resource development and management should incorporate environmental, economic

and social considerations based on the principles of sustainability. They should include the

requirements of all users as well as those relating to the prevention and mitigation of water-

related hazards, and should constitute an integral part of the socio-economic development

planning process (Young et al., 1994).

The objective function is an essential instrument designed to reflect the host of

rules, principles, and constraints in water-resource management in a modeling framework.

In many cases, several objectives (economic efficiency, social well-being, environmental

sustainability, etc.) have to be dealt with simultaneously. Some of these criteria have been

applied in multiple-objective decision analysis methods, a traditional approach to solving

water-resource management problems (Chankong and Haimes, 1983). However, economic

objective functions can be combined more easily with hydrologic models than

environmental or social well-being criteria that are often difficult to express in quantitative

terms.

In our research, we used the general efficiency criterion expressed in terms of NPV,

which considers benefits and costs in monetary terms (Simon, 1957; Buras and Hall, 1961;

Policies, Standards, and Procedures in the Formulation, Evaluation, and Review of Plans

for Use and Development of Water and Related Land Resources, 1962; Buras, 1963; Burt,

1964; White, 1969; Water Resources Council, 1973; Lilian Bernhardi et al., 2000; Karin,

2004):

∑=

− −+−=T

ti

tNtt CDInRNPV

1

)1)(( (1.1)

17

NPV is the net present value for a given time interval (T, years), $; Rt is the sale proceeds at

time t, including cost of production and effects of improving the environment, $; Int is

expenses and environmental damage, $; DN is the rate of discounting (the value that an

investor can get in a dependable place, for example, a dependable bank deposit. This value

increases when risk from a specific project is taken into account); Ci is the capital

investment, $.

Despite the introduction of the general efficiency criterion, considerable difficulties

remain in quantitatively assessing ecological and social damage and other factors. Existing

legal and normative documents define environmental damage in terms of notions and

categories (Vershkov, 1999; Pererva, 2000) which can be estimated as economic losses

expressed by the cost of environmental deterioration as a result of anthropogenic activity.

1.5. Research aims

The aim of this research was to quantitatively evaluate a complex of hydrological

features in order to decrease negative anthropogenic effects on the environment in an arid

agricultural area under intensive agriculture development. This was based on:

• Identification of potential sources of natural and artificially enhanced

recharge, and assessment of the distribution of pollution and salinization of

surface and underground water.

• Adaptation and implementation of models for predicting the path and rates

of contaminant migration, and simulation of scenarios for various types of

water-resource exploitation.

• Analysis of the results of hydrological simulations to determine measures for

decreasing the risks of environmental pollution.

18

• Assessment of rational scenarios for sustainable development of water

resources in the study area.

The study area was the Ily River basin of Kazakhstan. This research makes use of

the results of long-term experimental observations carried out by Kazakhstani scientists in

the Ily-Balkhash area (Ily and Karatal river basins) (Veselov et al., 1996).

19

2. NATURAL AND ECONOMIC CONDITIONS IN THE ILY RIVER- LAKE BALKHASH BASIN

2.1. General information

The study area is located in the southeastern part of Kazakhstan, between latitudes

440 and 480 N, and longitudes 720 and 840 E (Figure 2.1). The total watershed area of the

basin is 413,000 km2, about 15% of which is located within Chinese borders. Lake

Balkhash is located at the western end of the depression, Lakes Sasykol and Alakol are in

its eastern part, the sands of Sary-Ishikotrau, Taukum and Moinkum occupy the southern

part, and the Bektashit sands occupy the north (Ahmedsafin et al., 1980; Sidikov and

Chuande, 1993; Veselov et al., 1996). The largest rivers of the basin are Ily, Karatal, Aksu,

and Lepsy. Mean annual renewable surface-water resources of the basin amount to 24.7

km3/year. Lake Balkhash occupies an area of 18,210 km2, and its estimated total water

volume is 105 km3. The distribution of salinity and chemical composition in the lake water

is very heterogeneous because of the non-uniform supply of fresh water from inflowing

rivers: average inflow is about 75% at the western part of the lake and 25% at the eastern

part. The western Balkhash is mainly fresh water, while the water in the eastern Balkhash is

brackish. Renewable groundwater resources amount to 68.4 m3/s (yearly volume of 2.16

km3/year). Today, water intake for the national supply is 6.7 to 7.1 km3/year, of which the

proportion of pumping groundwater does not exceed 8% (Ahmedsafin et al., 1980; Sidikov

and Chuande, 1993; Veselov et al., 1996). A big industrial-agricultural complex has been

established in the region. Major industries that consume water resources are metal,

electricity, food and agricultural manufacturers. A total area of about 660,000 ha is irrigated

for growing rice, wheat, corn, tobacco, sugar beet, vegetables and fruits. The basin

population is about 3,000,000 (Ahmedsafin et al., 1980; Sidikov and Chuande, 1993;

Veselov et al., 1996).

20

Balkhash Lake

Kapchagai Reservoir

Ily River Delta

Akdalinsky Land area

Almaty

Figure 2.1 Ily-Balkhash region general map

21

2.2. Natural conditions

2.2.1. Climate

The climate in the Lake Balkhash basin is continental, characterized by large daily

and annual variations in air temperature, and high levels of solar radiation. Climatic

variability in the high-mountain areas depends on the altitude and relief. Mean annual

temperature is about 2 to 5°С in the plains and -5 to -10°С in the foothills. Mean air

temperature during the coldest month (January) is -16°С in the northern part and -5°С in the

southern part of the plain's territory. Mean air temperature during the hottest month (July) is

about 20 to 25°С. Distribution of mean air temperature during the warm period is as

follows: May: 16.7 °С, June: 22.1°С, July: 24°С, August: 21.7°С, and September: 15.9°С.

The total “active” air temperature (∑ Ct в 010> ) during the period of vegetative growth

(April-September) in the eastern part of the basin (Karatal River) is 2,600 to 2,800°С, while

in the western part (Akdalinsky irrigated lands) it is 2,700 to 3,000°С.

The distribution of precipitation in this area is extremely non-uniform as a result of vertical

zonation: in the plains, precipitation is 100 to 250 mm annually, while in the mountains it is

on the order of 800 to 1,000 mm. Distribution of annual precipitation in the Ily-Balkhash

basin depends on land-surface altitude and is shown in Figure 2.2. Temporal distribution of

precipitation for different areas in the basin is presented in Table 2.1 (Ahmedsafin et al.,

1980; Problems of Water Resources Research in Central Asia, 1993; Veselov et al., 1996).

22

Figure 2.2. Areal distribution of annual precipitation (mm) in the Ily-Balkhash region.

Table 2.1. Annual distribution of precipitation for different areas in the Ily-Balkhash region.

Precipitation amount, mm Months Ily River delta Akdalinsky area Almaty Medeo

(mountain) 1 15 20 23 31 2 15 16 27 35 3 8 26 53 83 4 14 31 66 130 5 5 19 71 176 6 8 30 48 119 7 17 19 24 63 8 10 9 20 41 9 19 17 17 48 10 18 36 35 67 11 14 28 40 60 12 15 19 32 37

Amount 158 270 456 890

23

Solar radiation ranges from 419 to 524 KJ/cm2 annually; duration of vegetative

growth is about 180 days. The annual ratio of thermal-balance components is typical for

arid zones: heat consumption for evapotranspiration is 30%, the heat exchange between

land and atmosphere is 60%, and heat transfer in the soil is 10% (data according to

meteorological stations in the Ily-Balkhash region). Large amounts of heat consumed for

heat exchange between land and atmosphere, together with low precipitation, lead to low

air humidity in the basin's plain (especially during the summer). Relative air humidity

values in the Akdalinsky irrigation-system area during the summer are: May 52%, June

48%, July 41%, August 40%, September 44%. Mean annual air humidity is about 59%.

Potential evaporation varies in the range of 900 to 1,000 mm.

The hydrothermal regime can be characterized by the following index, presented as

a ratio between net radiation flux and latent heat of precipitation (Budiko, 1977):

LPRR = (2.1)

where R is the net radiation flux, kJ/cm2.year; P is the precipitation amount, cm/year; and L

is the latent heat of evaporation, kJ/cm3. The estimated values of this index for different

areas within the region are as follows: Ily River delta R = 4.7; Akdalinsky irrigated land

R = 2.5; Almaty R = 2.0; Medeo R = 1.1 (Bazilevich and Rodin, 1971; Volobuev, 1974;

Budiko, 1977).

2.2.2. Topographical and geological structure

The topographical structure of the area is presented in Figure 2.3. It is a flat, closed

depression (about 750 km from north to south, and 900 km from west to east) resulting

from intensive deformation during Alpine times, and filled with alluvial-proluvial

sediments.

24

A

A

Figure 2.3. Topographical structure of the Ily-Balkhash basin (Veselov et al., 1996). Key: 1. mountain ranges, 2. sands, 3. mountain ranges with glaciers, 4. boundaries of the territory and watershed divides; A-A: line of geological cross section (see Fig 2.4).

The geological structure of the southern Balkhash depression is an Alpine syncline

with Mesozoic and Cenozoic formations lying on a Paleozoic basement (Figure 2.4).

25

Figure 2.4. Geological cross section of the southern Balkhash zone from Malaisary range to Lake Balkhash (Veselov et al., 1996).

Paleozoic rocks are composed of metamorphosed and dislocated formations

deposited over long periods. The overlying Cenozoic deposits consist of sedimentary rocks

of the Oligocene, Neogene and Quaternary ages with a thickness of up to 1,000 m.

Oligocene deposits are formed mainly from sandy clay. The Neogene rocks are clay and

sand lenses in their lower part, and sand interbedded with clays in their upper part. The

Neogene rocks are covered by Quaternary deposits (alluvial-lake and alluvial-eolian type,

thickness from 240 to 300 m), which are represented mainly by sands and to a lesser extent

by sandy silt, silt and clay.

26

2.2.3. Surface water and groundwater

The basin's hydrographic system includes about 45,000 rivers, only 5% of which

are longer than 10 km. The biggest rivers are Ily, Karatal, Lepsy and Aksu.

Two zones can be distinguished based on the hydrological conditions: a zone in

which surface flow is generated (mostly the mountain part of the basin with glaciers and

high levels of precipitation) and a zone in which surface flow is dissipated (mainly the

plain) by evaporation and infiltration. The Ily River drains groundwater in the mountains,

while recharging groundwater in the plains. The total volume of surface-water resources in

the basin is 24.7 km3/year and consists of the the Djungarsky and Alatay rivers (26.5%),

located west of the Ily River (6.5%), the Zailiyskiy and Alatau rivers (12.3%), rivers of the

Shu-Iliyskiy mountains (0.2%), and the Ily River (52.1%) (Ahmedsafin et al., 1980;

Sidikov and Chuande, 1993; Veselov et al., 1996).

The total dissolved solids (TDS) content in water from the Ily River varies from 240

to 600 mg/l in the mountain part, upon groundwater drainage, with much smaller variations

in the plain, where river recharges groundwater. Chemical composition (calcium-carbonate

type) is presented in Table 2.2 (Ahmedsafin et al., 1980; Sidikov and Chuande, 1993;

Veselov et al., 1996).

Table 2.2. Chemical composition of the Ily River, mg/l. Sampling station HCO3 Cl SO4 Ca Mg Na TDS Ily River, Bakanas 159 28 89 48 17 28 369

Orographic conditions in the Lake Balkhash basin depend on the altitude of the land

surface. This is expressed by geographical zoning of the climatic and landscape belts, from

desert zones, to glaciers and snowfields where annual precipitation is up to 1,000 mm. This

affects the processes of groundwater formation even more than geological and lithological

27

structure. Main areas of groundwater recharge are mountain regions, foothills and alluvial

cones of mountain rivers. Main sources of groundwater recharge under natural conditions

are surface water (more than 15% of runoff) and infiltration of rainwater (>30% of

precipitation). The plain area of the basin is a zone of groundwater recharge. Rainfall

infiltration is no more than 2 to 5% of its total amount and does not play a significant role

in groundwater recharge.

The aquifers in the basin's plain - alluvial and recent deposits of Middle and Upper

Quaternary age, are composed of sand, loamy sand, and loam. Their thickness is 50 to 70 m

near Lake Balkhash and up to 240 m in the deepest part of the depression near Bakanass.

The depth of the groundwater is 5 to 7 m along the river valleys and between sand ridges,

and 15 to 18 m on the sand ridges. Groundwater chemical composition is predominantly of

the calcium-bicarbonate and sodium-sulfate type; TDS content varies from 0.5 to 1.6 g/l

(Table 2.3) (Ahmedsafin et al., 1980; Sidikov and Chuande, 1993; Veselov et al., 1996).

Aquifers of the upper horizon are interconnected hydraulically; the general direction

of groundwater flow is from the southeast to the northeast towards Lake Balkhash.

Groundwater in sand massifs is recharged by infiltration of surface water (mainly rivers),

surface infiltration during periods of precipitation and underground inflow from mountain

regions. In the foothills of the northern Balkhash zone, to the north of Lake Balkhash,

groundwater is recharged mainly by infiltration of winter and spring precipitation.

Table 2.3. Chemistry of groundwater in the Upper Quaternary deposits, mg/l. HCO3 Cl SO4 Ca Mg Na + К ∑ 232-451 71-370 44-270 70-90 12-50 57-325 500 – 1600

Tables 2.4 and 2.5 present the water and salt balances in the plain (central) part of

the basin under natural conditions (Ahmedsafin et al., 1980).

28

Table 2.4. Total water balance in the central part of the basin under natural conditions (Ahmedsafin et al., 1980; Sidikov and Chuande, 1993; Veselov et al., 1996).

Inflow Outflow ∑ ∑P C Irr Е I RI R

Million m

7,437 540 16,178 570 24,725 14,780 0,03 200 9,745 24,7253/year

% 30 2 65,5 2,5 100 60 - 1 39 100 Note: P is precipitation, C is moisture condensation, I and I are surface-water and groundwater inflow, respectively, R and R are surface-water and groundwater outflow, respectively, Irr is uptake water for irrigation, and Е is evaporation.

The data presented in Table 2.4 indicate that the major sources of water inflow are

surface water (65.5%) and precipitation (30%), while water outflow consists mainly of

surface flow to Lake Balkhash (60%) and evaporation (39%).

Using the water-balance data and information on surface-water and groundwater

salt concentrations, we can calculate salt balance in the central part of the basin (Table 2.5).

Table 2.5. Salt balance in the central part of the basin under natural conditions. Influx Outflux Balance

∑ ∑PG IG IG RG RG Million t/year 0.22 6.15 0.57 6.94 5.91 0.03 5.94 +1.00

PGNote: is salt influx with precipitation (TDS = 0.03 g/l), IG is salt influx with surface water (TDS = 0.38 g/l), is salt influx with groundwater (TDS = 1.00 g/l),IG RG is salt outflux with surface water (TDS = 0.40 g/l), RG is salt outflux with groundwater (TDS = 1.00 g/l).

The data presented in Table 2.5 indicate that the central part of the basin is a zone of

modern salt accumulation. The annual increase in salt content is small: about 0.038 t/ha per

year in a layer of 5 m.

29

2.2.4. Vegetation

The type of vegetation cover in the Lake Balkhash basin depends on the vertical

geographical zoning and the hydrothermal regime in the area. The following vegetation

types exist within the bounds of the lake (Korovin, 1961; Kazakhstan Vegetation Cover,

1966; Veselov et al., 1996):

- Tugai plants make up the typical vegetation cover in the Ily River valley,

occupying about 5% of the basin area. The hydrothermal regime in this area

under natural conditions is influenced by low groundwater depth and surface

flooding during snow melt. The hydrothermal index is 25,1 −≤R .

Vegetation is present as trees and sub-shrubs (turanga, saksaul, etc.),

covering almost 100% of the surface area.

- Saksaul shrubs and grassy cereals make up the typical vegetation in the

sandy areas in the southeast and central parts of the basin (Sary-Ishikotrau

sands, Taukum, Muyunkum). The surface area is about 40% of the total

basin area. The hydrothermal index is 7,45,2 −=R . Cover does not exceed

50 to 65%.

- Wormwood-halophytic plants, which are typical vegetation for the

northwestern part of the basin (Bektault fine sands), occupy about 40% of

the total basin area. Hydrothermal index: 0,35,2 −=R . Cover does not

exceed 50 to 60%.

- Steppe grass cereals and forest plants make up the typical vegetation in the

mountain regions (Djungarsky Alatay, Tarbagatay River), occupying about

10% of the basin area. Vegetation consists of alpine meadows and forests.

Hydrothermal index: 5,11−≤R .

30

2.2.5. Topsoil

Various soils are present in the top cover of the Lake Balkhash basin. Soil

variability and type are defined by altitude zoning, hydrothermal regime and vegetation

type. Plains are covered by desert steppe soils; foothills are covered by soils of subtropical

semi-deserts and low-grass semi-savannas. In mountain regions, the topsoil type varies with

altitude. Several topsoil zones exist within the basin boundaries (Lobova, 1967; Veselov et

al., 1996):

- Alpine and sub-alpine belts (1,800-2,300 m) characterized by mountain-

forest and mountain-meadow chernozem soils.

- Mountain-forest-steppe belt (1,400-1,800 m) covered by mountain-forest,

dark-gray and chernozem soils.

- Mountain-steppe belt (800-1,400 m) characterized by mountain chernozem

and dark-chestnut soils.

- Desert-steppe piedmont belt (600-800 m) containing light-chestnut and serozem

soils.

- Desert-steppe belt (300-600 m) containing serozem, brown and gray-brown

soils, solonchaks, and alluvial floodplain soils that are typical of northern deserts

(Korovin, 1961; Kazakhstan Vegetation Cover, 1966; Babaev et al., 1986;

Volobuev, 1974; Veselov et al., 1996).

Desert soils formed on the ancient alluvial plain and sub-aerial Ily River delta. Most

soils in the middle and upper parts of the Ily River (serozem, gray-brown and brown soils)

are slightly saline in the uppermost 1 m. Salinity is mainly of the chloride-sulfate type, with

gypsum inclusions and increased alkalinity at some locations (Table 2.6).

31

Table 2.6. Salt content in serozem and brown soils in the upper 0-100 cm layer.

Major elements, meq/100 g TDS, pH g/100 g HCO Cl SO Ca Mg Na 3 48-8.5 0.4-0.72 0.11-0.28 0.12-0.60 6-10 5-8 0.4-1.0 0.6-1.3

The underlying horizons at depths of 1.5 to 3 m contain considerable amounts of

water-soluble salts. Thus, there exists the potential for secondary salinization under

irrigation in this area.

2.3. Economic activity

Several industrial plants (non-ferrous metallurgy, metal-working, food industry,

etc.) are situated on the basin's periphery (Almaty, Taldi-Kurgan, etc.). Agriculture is the

main type of economic activity in the central part of the basin (middle and upper Ily River

area).

An economic “cost-efficiency” model was used for water-resource management in

the Ily River basin, Kazakhstan. A clear profit value of agricultural production was used as

the major efficiency criterion. Ecological consequences of land reclamation and water-

related activities were not considered. It was decided that the Ily River water resources

would be used for extensive development of irrigated agriculture in the middle and lower

parts of the river basin. To provide a stable water supply, the Kapchagay water-storage

reservoir was constructed (total water volume 28.1 km3; effective water volume 6.64 km3).

Irrigated land area within the basin boundaries is 662,000 ha, including 312,000 ha

in the Taldi-Kurgan region and 350,000 ha in the Almaty region. Irrigated land in the

middle and upper Ily River basin (downstream of the Kapchagay water reservoir) is about

30,000 ha (Akdalinsky irrigated area, Fig. 2.1). Note that there are also large areas (188,000

ha) of irrigated land upstream of the Kapchagay water-storage reservoir, including land in

32

the Karatal River basin—112,100 ha, the Aksu River basin—32,200 ha, and the Lepsy

River basin—43,700 ha. The development of irrigation in those areas has a strong impact

on the water quality in and downstream of the Kapchagay reservoir.

The major emphasis of irrigated agriculture is on rice-growing.

The total irrevocable water consumption required for different national economic

sectors is about 5 km3/year (Table 2.7) (Ahmedsafin et al., 1980; Sidikov and Chuande,

1993; Veselov et al., 1996).

Table 2.7. Water requirements for the national economy sector in the basin Water consumers Water

intake, Return flow, Irrevocable water

consumption, km3/year km3/year km3/year

Water supply for the urban population 0.33 0.23 0.10 Industrial water supply 0.14 0.07 0.07 Agricultural irrigation 7.15 2.0 5.15 Basin irrigation 0.02 - 0.02 Rural water supply 0.06 0.05 0.01 Pasture watering 0.03 - 0.03 Pond farming 0.06 0.05 0.01 Total: 7.79 2.4 5.39

According to the data presented in Table 2.7, agricultural irrigation is the major

water user, consuming 90% of the total water supply and 96% of irrevocable water.

Irrigation return flow is the main source of soil and surface water pollution. About 72% of

this water is dumped into the Ily River, while 28% is directed to land fields. Only 10 to

20% of total wastewater volume dumped into the Ily River is treated biologically.

Salinity and chemical composition of drainage and wastewater dumped into the Ily

River are presented in Table 2.8 (Veselov et al., 1996). These data indicate that irrigated

land is a significant source of pollution in the Ily River basin. Unsustainable exploitation of

irrigation systems has led to soil degradation, contamination of surface and underground

water, and deterioration of the population's health due to consumption of low-quality water

and food. As a result, the water quality in the Ily River has deteriorated in recent years. In

33

this work, we will concentrate on the Akdalinsky irrigation system, located in the central

part of the Ily River-Lake Balkash basin.

Table 2.8. Chemical composition of wastewater and drainage water. Component Wastewater, mg/l Collector-drainage water,

mg/l TDS 200-1,500 700-1,000 Including: Nitrates 2-9 1.3-2 Ammonia nitrogen 2-13 0.05-0.14 Pesticide Benzex - 0.05-0.16 Biochemical oxygen demand 3-38 1.2-1.6 Chlorides 6-89 48-50 Sulfates 23-566 200-230 Calcium No data 50-60 Magnesium No data 35-50 Sodium No data 80-110 Pesticide Saturn - 0.002-0.60 Copper 0.01 - Zink 0.006 - Detergents 0.4-0.7 - Lead 0.03 - Colloids 6-23 - Oil products 0.003-0.65 -

2.4. Akdalinsky irrigation system

The Akdalinsky province (Figure 2.1) is the second highest agricultural producer in

Kazakhstan (13.6%). The middle and lower parts of the Ily River basin are the most likely

areas for agricultural development. This region is characterized by favorable soil, climatic,

water and labor resources. About 80% of the total investment funds have been allocated for

the development of irrigation in the region (Kritskiy and Menkel, 1981; Veselov et al.,

1996).

The central part of this area is the Akdalinsky irrigation system located on the right

side of the Ily River (Figure 2.5), between the Taukum and Sary-Ishikotrau sands. It

34

includes the irrigated lands of Bakhbakhtinsky, Tasmurunsky and Bakanassky with a total

surface area of 30,000 ha.

Figure 2.5. Schematic map of Akdalinsky's irrigated land.

Akkol

Jenis

Taukum Sands

Ily River

Sary-Ishikotrau Sands

Bakhbakhty

50 years October

BMC

AMC

Main Collector (MCR)

S

N

A A

A

A

Legend:

1 - Irrigated lands (I-Bakhbakhtinsky, II-Tasmurunsky, III-Bakanassky)2 - Main channels (TMC-Tasmuransky, AMC-Akdalinsky, BMC-Bakanassky)3 - Collectors (MCR-Main collector, UCR-United collector)4 - Line of geohydrological cross section5 - observation wells6 - towns and villages

TMC

UCR

Balkhash

Lake

KapchagaiWater Storage

The main directions of agricultural development and crop allocation of agricultural

land were assessed based on an economic model, in which clear profit value was defined by

the following equation (Veselov et al., 1996):

∑ −=n

iif InСYP1

)( (2.1)

P is the net profit value, ruble/ha; Y is the yield of agricultural products, ton/ha; Cf i is the

cost of agricultural products, ruble/ton; Ini is the cost of agricultural production and

exploitation of the irrigation system, including water cost, ruble/ton; n is the number of

agricultural species in crop rotation.

35

The following clear profit values were calculated using equation (2.1) for different

scenarios of agricultural crop production:

Rice (62.5% rice, 25% alfalfa and barley, 12.5% grain crops),

P = 600-650 ruble/ha; f

Grain, P = 350-400 ruble/ha; f

Forage P = 200-250 ruble/ha. f

As a result, the rice-growing scenario was accepted as the major direction for agricultural

development on the Akdalinsky irrigated lands (Veselov et al., 1996). In 1967, construction

of the rice-irrigation systems began. Water from the Ily River with a TDS of 0.2 to 0.7 g/l

was transported through the Tasmuransky and Bakanassky main channels and used for

irrigation. Shallow (0.5-1.0 m deep) open drains connected to the open on-farm collectors

(2-3 m deep) represented the drainage system. Drainage water from the irrigated lands was

dumped into the Ily River through main collectors (Figure 2.5). This water had a high

concentration of sulfate (190-210 mg/l), nitrate (0.5-2.1 mg/l), ammonia (up to 0.25 mg/l)

and nitrite (up to 0.07 mg/l) (Veselov et al., 1996).

By 1975 and into the '80s, the drawbacks of the system (total area of irrigated land

about 30,000 ha) became apparent. The system was characterized by a high specific length

(per unit area) of open irrigation and drainage canals, a low land-use coefficient (0.64), a

low efficiency factor (0.5) [about half of the irrigated water (22,000 m3/ha) was lost by

infiltration from canals because of low technical performance of the rice-irrigation

systems], and high infiltration losses. Actual annual water application was 35,000 to 70,000

m3/ha, water intake from the Ily River was about 1 km3, irrevocable water consumption was

0.3 km3, and volume of drainage water (salinity 0.6-0.7 g/l) was 0.7 km3. The pesticides

Propanid, Saturn, Ordram, HexaChloroCycloHexane (HCCH) and others were used for

weed destruction during the rice-crop rotation. Observations show that pesticide content in

36

the groundwater increased to 0.17 μ g/l, resulting in significant amounts of pesticides and

biogenic contaminants in the drainage water (Veselov et al., 1996). The latter was

recharged back into the Ily and Karatal rivers, which, despite their poor water quality, were

used by the local population for domestic purposes. Pesticide content in the Ily River

increased from 0.045 to 0.32 μ g/l, about 4.6-fold the maximum admissible concentration.

As a result of excessive irrigation, groundwater depth decreased from 5-7 m to 1.5-2.5 m

and its salinity increased from 0.75 to 0.9 g/l due to salt flushing from the upper soil layers.

Heavy irrigation of rice led to the leaching of considerable amounts of salt, fertilizer and

pesticide into the groundwater, and the rest was dumped through the drainage system into

the surrounding territory. The Akdalinsky irrigated lands are located in a region of alluvial-

proluvial and lake-alluvial sand deposits (220-250 m thick) that are underlain by an

impermeable Neogene clay formation. Sand is covered on the surface by sandy loam and up

to 5-m thick loam deposits. Groundwater flows to the northwest, towards Lake Balkhash.

Hydraulic conductivity of the upper sand layer, to a depth of 20 m, is on the order of

16 m/day (according to pumping tests), and transmissivity is on the order of 600 to 1,900

m2/day. The hydraulic conductivity of the upper loam deposits is about 0.2 to 2.0 m/day and

the specific yield is 0.07 to 0.14. Such conditions favor the leaching of salts and

contaminants from the soil into the groundwater and their further spread.

Soil desalinization was accompanied by intensification of chemical weathering

processes, leaching of organic matter, dehumification, alkalization and, ultimately, a

decrease in soil fertility. Actual rice yield was 0.68 of the expected 7,000 kg/ha, alfalfa

yield was 0.6 (of an expected 13,000 kg/ha), and grain yield was 0.3 (of 5,000 kg/ha

expected) (Veselov et al., 1996). As a result, a decrease in discharge of the rivers in the

basin was observed, together with a lowering of the water level in Lake Balkhash, which

37

was also associated with the deterioration of water quality in the lake. Desertification

features were observed over the entire area, associated with qualitative and quantitative

changes in groundwater, which is the central component of the ecological system.

Groundwater level rose by 2 to 7 m within and around the irrigated area, as well as in the

adjacent regions. This led to considerable deterioration of soil conditions. Further

development of a rice-oriented irrigation system (up to a planned 250,000 ha) in this area

was therefore terminated.

The impact of irrigation is reflected in the deteriorating water quality downstream of

the Ily River, due to increases in salinity, and in the concentration of biogens and pesticides

(Lobova, 1967; Avakian et al., 1987) (Table 2.9).

Table 2.9. Influence of the Akdalinsky irrigation systems on the Ily River.

Sampling station Ily River water quality Upstream of

irrigated area Downstream of irrigated area

TDS, mg/l 370 430 , mg/l HCO 172 200 3

Cl, mg/l 28 36 , mg/l SO 74 86 4

Ca 51 57 Mg 16 19 Na+K 30 35 Biogen concentration, mg/l NO 0.018 0.022 2NO 2.48 2.50 3NH 0.09 0.08 4Biochemical oxygen demand

4.4 7.2

Pesticide concentration, μ g/l Saturn 0 0.0242 DDT 0 0.0259 DDE 0 0.005 α 0.024 0.024 Benzex

0.007 0.007 β Benzex γ Benzex 0.014 0.016 Note: About 10% of the Ily River discharge is diverted.

38

3. IMPACT OF ANTHROPOGENIC ACTIVITIES ON THE ENVIRONMENT IN THE ILY RIVER-LAKE BALKHASH BASIN

3.1. Assessing environmental impacts in the Ily River-Lake Balkhash basin

Long-term practices of water-resource development and irrigation throughout the

world, including Kazakhstan and other Central Asian countries, have strongly affected

natural processes by changing the hydrological, hydrochemical and ecological conditions,

increasing geochemical fluxes in the system, and changing micro-climatic conditions both

within the irrigated lands and in neighboring areas, and even over large basins. These

changes have resulted in a general trend of environmental deterioration.

To assess the impact of anthropogenic activity on natural conditions in the study

area, we analyzed existing data regarding temporal changes in major natural system

components in the Ily-Balkhash basin (Buras, 1963; Kazakhstan Vegetation Cover, 1966;

Kazanowski, 1972; Karin, 2004). The results are summarized in Table 3.1.

Table 3.1. Temporal changes in spatial land structure of the Ily-Balkhash basin.

Area, % Components of the natural system Natural

conditions Modern state

Mountain forests and alpine meadows 10 7.5Tugai vegetation and bushes in the Ily River delta 5 4.7Water reservoirs 5 5.5Sandy deserts 40 39.4Irrigated lands - 2.5Knolls 40 39.5Settlements, industrial plants and factories - 0.9

Total: 100 100

The data presented in Table 3.1 indicate that new biotic and abiotic elements have

appeared in the basin's structure, including bare mountainsides, irrigated lands characterized

by a hydrological regime that is unusual for the sandy desert and foothills, the Kapchagay

water-storage reservoir, settlements, factories and plants. Changes in the spatial structure of

the basin do not seem to be significant, amounting to only 7 or 8%. However, as will be

39

shown below, these changes have had a strong impact on environmental conditions in the

basin, especially in the middle and delta parts of the Ily River, as well as Lake Balkhash.

Deforestation of mountains and construction of the Kapchagay water-storage reservoir have

changed the natural fluid and solid discharge conditions in the area, affecting river water

quality and hydrogeological and hydrochemical conditions in the basin. Agricultural

development has affected the water and salt balances in the irrigated lands and nearby areas

due to increased groundwater recharge, and dumping of wastewater and drainage water into

the river.

Two LANDSAT images (Figure 3.1) for May 26, 1990 and May 13, 2000 (for

parameters see Table 3.2) were processed to assess the impact of irrigation on the hydro-

ecological conditions of the Akdalinsky irrigation system and neighboring territory.

Table 3.2. Parameters of the LANDSAT images (30 m pixel size). N Date Description 1 05-26-1990 Landsat 5, path 150, row 29, zone 43 2 05-13-2000 Landsat 7, path 150, row 29, zone 43

a) b)

N N

Figure 3.1. LANDSAT images of the study area: a) May 26, 1990; b) May 13, 2000.

Digital maps and photos of the settlements, water bodies, plants, agricultural fields,

irrigated lands and topographic maps (1:200,000) were utilized to decode the satellite

40

images. A pretreatment procedure was carried out to obtain reliable relations between

biophysical surface parameters and values of brightness: radiometric classification,

atmospheric correction, and geometric correction. A number of ground control points

(GCPs) were selected from the topographic maps for geometric correction and transformed

to the Transverse Mercator coordinate system adopted in Kazakhstan. The methodological

device ERDAS IMAGINE for supervised image classification was used to analyze the

satellite images. This classification consists of a grading process of image elements (pixels)

to produce a final number of classes on the basis of attribute values (DN—digital number).

If a pixel satisfies some classification conditions, it defines a class corresponding to these

conditions (ERDAS Field Guide; ERDAS, Inc., Atlanta, GA). Small standard plots

(signatures) and single pixels were chosen in the satellite images corresponding to the

following eight major classes observed at land surface: 1) agricultural fields, 2) clay desert,

3) takyr (soil composed mainly of clay particles; a takyric horizon comprises a crust and a

platy structured lower part), 4) dense natural vegetation, 5) salty crust (solonchaks), 6)

sandy desert with saksaul-type vegetation, 7) vegetated sands, and 8) water bodies and rice

fields.

The results were tested through accuracy assessment by calculating the error matrix

that compares the relationships between ground-truth data (reference data) and classified

results, category by category. The overall accuracy of the final maps was good (75%) in

1990, and very good (89%) in 2000. Maps of supervised classification of the study area are

presented in Figures 3.2-3.5. A visual check of supervised classification veracity was

carried out, particularly in the locations where it was difficult to distinguish between

different classes. Photos from Kazakhstan were used at the locations chosen for verification

(Figure 3.6).

41

Figure 3.2. Results of supervised classification in the Bakanass part of the Akdalinsky area (26 May, 1990).

42

Figure 3.3. Results of supervised classification in the Bakanass part of the Akdalinsky area (13 May, 2000).

43

Figure 3.4. Results of supervised classification in the Bakhbakhty part of the Akdalinsky area (26 May, 1990).

44

Figure 3.5. Results of supervised classification in the Bakhbakhty part of the Akdalinsky area (13 May, 2000).

Figure 3.6. Verification of classification results for the year 2000.

45

46

Agricultural fields have a specific, recognizable vegetation density. This property

served as a basis for additional verification of selected classes using a Normalized

Difference Vegetation Index (NDVI). The vegetation density was ranked from 0—low (red)

to 1—high (green). The results of the verification using the NDVI show almost total

coincidence between selected classes (agricultural fields and water bodies/rice fields) and

ranking of vegetative density.

Digital analysis of supervised classification maps revealed that from 1990 to 2000,

the area covered by the rice fields decreased while groundwater level remained close to the

soil surface (according to data of hydrogeological monitoring). This led to an increase in

soil salinization, a decrease in vegetation-covered area and desertification of the irrigated

area and neighboring lands. During this 10-year period, the area of vegetated sands

decreased from 3,944 to 3,714 km2, dense natural vegetation (bushes) decreased from 545

to 138 km2, salty crust (solonchaks) increased from 7 km2 to 447 km2, sandy desert with

saksauls increased from 66 to 579 km2, and takyrs and clay desert decreased from 80 to 46

km2. The area of the water bodies was smaller in 2000 (83 km2) than in 1990 (265 km2),

mainly because the rice fields were not yet flooded on May 13, 2000. Figure 3.7 shows the

relative areas of the major classes in the study area.

1990 year

77%

11%2%0%1%5%4%Vegetated Sands Dense natutal vegetation (bushes)

Clay desert / Takyr Salty crust (solonchaks)

Sandy desert + Saksaul growth Water bodies / Rice checks

Agricultural fields

2000 year

72%

2% 2% 11%9% 1% 3%

Vegetated Sands Dense natutal vegetation (bushes)

Clay desert / Takyr Salty crust (solonchaks)

Sandy desert + Saksaul growth Water bodies / Rice checks

Agricultural fields

Figure 3.7. Relative area (%) of major classes.

47

Analysis of rice-production data for the Akdalinsky irrigation system is presented in

Figure 3.8. Note that between 1990 and 2000, the area covered by rice fields decreased

dramatically. This was mainly because of the economic difficulties that beset agriculture

after the collapse of the Soviet Union and Kazakhstan’s transition to an independent state.

The rice yield also decreased during this period, since application of pesticides and

fertilizers was practically stopped.

Figure 3.8. Temporal variation in rice productivity and rice growing area

(Veselov et al., 1996; http://www.minagri.kz).

Analysis of production data for alfalfa and other crops (barley, wheat) revealed that

the area occupied by these crops increased from 1990 to 2000 (Figure 3.9). Alfalfa, barley

and wheat were used to partly replace rice in order to dampen the rice fields' negative

impact on the environment.

48

Figure 3.9. Areas of agricultural fields of alfalfa and other crops (Veselov et al., 1996; http://www.minagri.kz).

Figure 3.10 shows a comparative plot of the irrigated areas occupied by major

agricultural crops (rice, alfalfa and barley) growing in 2000. The rice fields occupy around

30% of the total agricultural area.

45.19 km2

45.19 km250.37 km2 alfalfa

otherrice

42.24 km2

55.92 km250.77 km2

alfalfaotherrice

Bakanass region (140.75 km2 total area) Bakhbakhty region (148.93 km2 total area)

Figure 3.10. Irrigated lands occupied by major agricultural crops in 2000.

The main drainage collector also significantly affects the environment. Inspection of

the collector's relief profile revealed that the altitude of its bottom and the water level in the

collector at many locations were higher than the groundwater level of the surrounding area.

Therefore, highly saline drainage water flows from Bakhbakhty to Bakanass and on to the

Ily River. Infiltration of this water along the collector leads to salinization of the

surrounding territory (Figures 3.3 and 3.5).

49

The remote-sensing analysis results indicate that irrigation in the study area has had

a negative environmental impact in the region.

To assess the rate of the changes occurring with anthropogenic activity, the structure

and properties of the investigated landscape were analyzed. The aim of this analysis was to

examine system components, their interrelations and evolution with time using certain

integral parameters. Below, we consider the parameters used to characterize the system.

3.2. Heat balance To estimate the dynamics of heat balance as a result of anthropogenic activity, data

on albedo values and heat-balance components were used (Kazakhstan Vegetation Cover,

1966; Biswas, 1974; Avakian et al., 1987; Brown, 1999). The hydrothermal coefficient ( R ) is

the basic parameter characterizing the relationship between solar radiation and

precipitation:

PrLRR⋅

= (3.1)

where R = LE+H+QT is the net radiation flux (KJ/cm2.year ), H and QT are sensible heat

flux and soil heat flux, respectively (KJ/cm2.year), E is evapotranspiration (cm/year), Pr is

annual precipitation (cm/year), L is the latent heat of evaporation (KJ/сm3). To account for

the changes induced by agricultural activities (irrigation), this parameter is transformed as

follows:

AA

IrPrLRR

−−

+=

11

)(1

1 (3.2)

where А and А1 are the albedo of the land surface under natural and anthropogenic

conditions, respectively, Ir is the irrigation requirement (cm/year). This parameter also

50

accounts indirectly for changes in the remaining components of the system (crops, soils,

and water resources).

Evapotranspiration was calculated according to Biswas (1974):

)1(1)( RshRchR

thRIrPrE +−+= (3.3)

The soil heat flux was approximated at 10% of the net radiation flux (Biswas,

1974), and sensible heat flux (exchange with atmosphere) at 16% (Table 3.3).

Table 3.3. Components of heat balance and hydrothermal index under natural and

anthropogenic conditions. Area Parameter Mountain forests Irrigated lands

1. Surface albedo Natural conditions 0.12 0.22 Anthropogenic conditions 0.22 0.12

2. Net radiation flux, kJ/cm2.year Natural conditions 285 251 Anthropogenic conditions 251 277

3. Heat consumption for evapotranspiration, kJ/cm2.year

Natural conditions 210 (74%) 75 (30%) Anthropogenic conditions 175 (70%) 204 (74%)

4. Heat exchange in soil, kJ/cm2.year Natural conditions 28 (10%) 25 (10%) Anthropogenic conditions 24 (9%) 29 (11%)

5. Sensible heat flux, kJ/cm2.year Natural conditions 47 (16%) 151 (60%) Anthropogenic conditions 52 (21%) 42 (15%)

6. Hydrothermal index ( R ) Natural conditions 1.14 4 Anthropogenic conditions 1 0.5

Data presented in Table 3.3 indicate that mountain deforestation is accompanied by

a decrease in net radiation flux, heat consumption for evaporation and heat exchange in the

soil, and an increase in heat exchange with the atmosphere. Negative consequences of

deforestation result in disruption of atmospheric circulation and an increased risk of mud-

51

flow development. The increase in surface-water discharge as a result of a decrease in

evaporation cannot be considered a positive factor in mud-flow development.

Irrigation development in the basin led to a 1.1-fold increase in the net radiation

flux, a 3-fold increase in heat consumption for evapotranspiration, and a 3.6-fold decrease

in heat exchange with the atmosphere. Heat exchange with the soil was slightly raised.

Thus, irrigation development leads to an increase in irrevocable water consumption due to

increased evapotranspiration.

3.3. Hydrological and hydrochemical conditions

The land in the middle and lower parts of the Ily River (bounded by the Kapchagay

water-storage reservoir in the southeast and the Ily River delta in the northwest) is of great

interest in studying the effects of irrigation on hydrological and hydrochemical conditions

for the following reasons:

- major changes in the Ily River discharge occur in the Kapchagay reservoir

section which accumulates all environmental changes taking place upstream;

- the lands in the middle and lower parts of the Ily River represent a zone of

extensively developing irrigation characterized by large volumes of

irrevocable water consumption, surface-water and groundwater pollution,

and effects on hydrological and hydrochemical conditions of the Ily River

delta and Lake Balkhash;

- the studied part of the basin area represents a zone of intensive geochemical

fluxes and modern salt accumulation. The latter can increase under irrigation

development.

52

3.3.1. The Kapchagay water-storage reservoir The Kapchagay water-storage reservoir (total volume 28.14 km3, effective volume

6.64 km3 and dead storage volume 21.50 km3) was constructed at the end of the 1960s, with

the aim of using the Ily River water for irrigation of a total of 430,000 ha of arid land

downstream, including 250,000 ha of rice-crop rotation. A power station was also

constructed to produce electricity. Reservoir filling started in 1970 but by 1985, its water

volume was only 14 km3, because of compensatory water passes to maintain the

hydrochemical conditions of Lake Balkhash. At that time, early spring discharges (with a

water volume of 1.25 km3) to the Ily River delta were provided instead of the regular

spring-summer floods that usually continue from May to July. By filling the reservoir

completely and increasing evaporation from the water surface (0.9 km3/year), the average

annual discharge downstream decreased from 620-670 m3/s to 480-490 m3/s (Avakian et

al., 1987). This decrease in discharge, together with daily oscillations in discharge in the aft

bay (due to water passes induced by the power station), led to significant deterioration of

the water ecosystem all along the river, from the Kapchagay reservoir to Lake Balkhash.

Cessation of spring-summer flooding caused desiccation and desertification of the river

delta over an area of about 1,000 km2, and development of desert-type vegetation instead of

the water-marsh and meadow vegetation types.

Regulation of water discharge and slowing of the water's circulation in the reservoir

affected water quality. Water salinity increased slightly from the upper river section to the

dam, together with chloride, magnesium and sodium concentrations (Table 3.4). Moreover,

the concentrations of biogens (Table 3.5) exceeded maximum permissible levels.

53

Table 3.4. Salinity and chemical composition of water in the Kapchagay water-storage reservoir (1985).

Chemical content Index TDS −3HCO −2

4SO −Cl +2Ca +2Mg +Na C2 358.1 168.5 68.2 26.2 43.8 17.2 34.2 C1 376.8 168.5 80.1 27.6 42.9 18.7 39.6

Note: C1 is the concentration of near-dam parts of the reservoir; C2 is the concentration in the upper section of the reservoir (inflowing water to the reservoir). Table 3.5. Concentration of biogens in the Kapchagay water-storage reservoir. Biogen Mean concentration,

mg/l Maximum

concentration, mg/l Maximum permissible

concentration, mg/l NO3 4.0 7.1 5.0 NO2 0.05 0.28 0.02 NH4 0.04 0.1 0.05 P2O5 0.11 0.36 0.20 The data presented in Tables 3.4 and 3.5 indicate that water downstream of Kapchagay

water storage has deteriorated.

3.3.2. Akdalinsky irrigation system The central part of the study area is the Akdalinsky irrigation system, constructed

over sand dune terrain and located on the right bank of the Ily River (Figure 2.5), between

the Taukum and Sary-Ishikotrau sands. It includes the irrigated lands of Bakhbakhty,

Tasmuran and Bakanass, with a total surface area of 30,000 ha. Construction of rice-

irrigation systems on Akdalinsky land started in 1967 and continued till 1985. Water from

the Ily River with a TDS content of 0.2 to 0.7 g/l is transported through the partly paved

main Tasmuran and Bakanass canals and used for irrigation. The irrigation network is

composed of open unpaved canals; usually, flood and border irrigation are applied.

Efficiency of the irrigation network varies from 0.3 to 0.72 (average 0.50), including

efficiency of the main and inter-farm canals from 0.68 to 0.99; efficiency of the distribution

canals from 0.6 to 0.9; and efficiency of on-farm canals from 0.60 to 0.80 (Avakian et al.,

54

1987). Low efficiency of the irrigation canals and large irrigation requirements lead to large

losses of water via infiltration.

In the past, most of these lands were used for growing rice (45-54%). Actual water

consumption for irrigation of rice in 2001 reached up to 53,000 m3/ha. Crop rotation also

included alfalfa, corn, barley and wheat. Rotation of crops is necessary to restore soil

fertility after growing rice for two to three years.

It was found that growing rice on the same field over several years leads to a

decrease in yield: after the first and second years, rice yields decreased by 0.4 and 1 ton/ha,

respectively (Avakian et al., 1987; Soil Conservation Service, USA, 1993). Analysis of data

on crop allocation in irrigated lands shows that the proportion of rice fields has significantly

decreased since 1977 (Table 3.6).

Table 3.6. Crop allocation in the Akdalinsky irrigated lands, %. Crop 1977 1987 2000

Rice 81 52 32 Alfalfa 14 36 36 Grain crop 3 9 24 Unused area 2 3 8

Rice is irrigated by the basin-check method, while for other crops, border or furrow

irrigation is used. Note that the study area is the most northern zone for growing rice. The

total sum of “active” air temperatures there is 2,800 to 3,000 °С, while the total sum of

“active” air temperatures required for fast-ripening rice is about 3,000 °С (Avakian et al.,

1987). The application of water for rice growing (irrigation requirements) depends on the

properties of the soils composing the vadose zone. Therefore, the Akdalinsky irrigation

system was constructed over sand dune terrain characterized by soils with high hydraulic

conductivity values (Table 3.7).

55

Table 3.7. Rice-irrigation characteristics depending on permeability and soil salinization. Soil hydraulic conductivity, m/day Index

0.5 – 0.73 0.05 – 0.20 0.27 – 0.43 0.07 – 0.28 0.8 – 0.98 Soil salinity, % <0.3 0.6 0.3 – 0.6 0.3 0.6 Net irrigation requirement, 103 m3/ha

35 – 37.6 28.6 – 31.5 32 – 34.6 22.9 – 32.5 38.6 – 42.5

Rice productivity, centner/ha 35.6 – 49.5 36.6 – 55.4 36.4 – 58.6 39.8 – 57.5 34.2 – 35.8Water consumption per unit yield, m3/centner

852 652 701 569 1160

Average irrigation rate for rice is 28,000 m3/ha; for alfalfa 6,220 to 7,910 m3/ha and for

barley 4,000 m3/ha (Avakian et al., 1987). Analysis of water balance in the Akdalinsky

irrigated lands from 1980 to 2002 shows that average water intake for irrigation from the

Ily River varied from 0.5 to 1.113 km3/year, depending on the area irrigated, whereas actual

water application for irrigation varied from 40,000 to 60,000 m3/ha (average 44,000 m3/ha).

About half of this water (22,000 m3/ha) was lost by infiltration from canals because of low

technical performance of the rice-irrigation systems. Heavy infiltration losses led to a rise in

groundwater levels and deterioration of hydrological and hydrochemical conditions in the

irrigated and adjacent lands. Average groundwater depth of 5 to 7 m under natural

conditions decreased to 1.8-2.5 m after construction of the irrigation system. Actual

groundwater depths varied from 0 to 0.5 m during the irrigation period and decreased to 3-

3.2 m during the post-irrigation period. In the last few years, average groundwater depth

has increased to 2.9-3 m as a result of the decline in rice fields to 30% of the crop-growing

area.

In 2000, the salinity of the Ily River water diverted to the main Tasmuransky canal

was about 370 mg/l, with the following chemical composition (mg/l): HCO3—159-183;

Cl—28; SO4—58-89; Ca—48-54; Mg—13-17; Na+K—28 (Lobova, 1967). To increase

water discharge in the irrigated Bakanass area, this water was mixed with water from the

main drainage collector (volume about 150,000,000 m3, and salinity 1,097-1,187 mg/l), and

56

transported by the main Bakanass canal (Figure 2.5). The resulting water salinity in this

canal was 577 to 678 mg/l.

To assess hydrochemical conditions in the study area, data from previous

investigations (analyses of groundwater samples from 296 wells collected from 1976-2002)

was analyzed. The results indicate that groundwater salinity changed only slightly, while

the dynamics of chemical composition were characterized by increasing concentrations of

HCO3, CO3 and Na, and decreased concentrations of Cl, Ca and SO4. This indicated the

development of soil sodification and alkalization. To complete the existing database with

hydrochemical information concerning environmental tracers and stable isotopes, additional

groundwater samples were collected in May (65 samples) and August (56 samples) of 2003

(Yakirevich et al., 2005). The samples were analyzed for their pH, electrical conductivity

(EC), TDS, and concentrations of the major elements: Cl, SO4, Br, NO3, HCO3, Na, K, Ca,

Mg, PO4, stable isotopes, and trace elements such as: Al, B, Ba, Cd, Co, Cr, Cu, Fe, Mn,

Mo, Ni, Pb, Se, Si, Sn, Sr, Ti, V, Zn, Li, Ag, As, Sb, and Hg (concentrations of the trace

elements were quite small). The TDS in the samples collected in the Bakhbakhty part of the

irrigation system ranged from 218 to 2,028 mg/l, with a mean value of 695 mg/l. The TDS

in the samples collected in the Bakanass part of the irrigation system ranged from 166 to

969 mg/l and the mean value was 505 mg/l. Most of the water samples were characterized

by relatively high concentrations of up to 1,008 and 450 mg/l sulfate, 360 and 545 mg/l

hydrocarbonate, and 34 and 51 mg/l nitrate, in the Bakhbakhty and Bakanass regions,

respectively. Comparing pre-irrigation (May 2003) and post-irrigation (August 2003) water

samples, we noted a general trend of decreasing TDS, Cl, Ca, Mg, PO4 and HCO3

concentrations in the groundwater, probably due to leaching of salts by irrigation and

drainage (as a result of heavy irrigation). Concentrations of SO4, NO3, and K increased due

to fertilizer application. The content of Na and the pH of the groundwater also increased.

57

Hydrogeological and chemical data were used to characterize the effects of

irrigation on hydrogeochemical conditions in the study area using statistical methods

(Yakirevich et al., 2005). SPSS cluster analysis was performed on the spatial distribution of

hydrochemical data to specify the extent of aquifer heterogeneity. This allowed the

delineation of hydrological sub-regions with homogeneous properties (water bodies) in

order to further assess major flow patterns, sources of groundwater contamination and

hydraulic parameters. TDS concentration was the major factor affecting cluster analysis,

indicating a pseudo-linear correlation among all the dissolved minerals. Water samples with

low TDS (200-400 mg/l) and chemical composition close to that of the irrigation water (Ily

River) belong to a cluster located in the irrigated fields and close to the irrigation canals.

Clusters with TDS of 600 to 1,000 mg/l and 1,200 to 1,800 mg/l include water samples

collected in boreholes located at the periphery of the irrigated land, in non-irrigated areas

and close to drainage collectors. We note that in the pre-irrigation period (May 2003), more

clusters were delineated; however, after the end of irrigation, concentration distribution

became more uniform, indicating a heavy irrigation effect on groundwater chemistry.

Analysis of the chemical data indicates that in the study area, groundwater chemistry obeys

quasi-steady-state conditions. In other words, introducing Ily River water into irrigation

channels and starting intensive irrigation in mid-May significantly changes groundwater

levels and chemistry. Massive infiltration from rice fields and unpaved irrigation channels

leaches salts, fertilizers and herbicides into the shallow groundwater, part of which is

drained into collectors flowing back into the Ily River. Elevated groundwater, due to

massive irrigation during the hot summer season, induces evaporation, which increases

topsoil salinization in the surrounding area. After harvesting crops in September-October,

hydrological and hydrochemical conditions return to a state close to that observed before

58

irrigation, producing a so-called seasonal oscillation pattern. However, the general

hydrological conditions prevail all year long in the elevated groundwater.

Propanid, Saturn, Ordram, HCCH and other pesticides have been used in the past

for weed destruction during the rice-crop rotation. Concentrations of pesticides in the

drainage water increase sharply during dumping of water from the rice fields (usually in the

middle of June and the middle of July). The content of the pesticide Saturn in the

groundwater varied from 0 to 0.14 μ g/l, of Benxez from 0.0095 tо 0.14 μ g/l, of DDT from

0.0003 to 0.0167 μ g/l, and of Ordram from 0 to 0.008 μ g/l (Avakian et al., 1987; Lobova,

1967).

The drainage network is aimed at regulating water and salt regimes in the vadose

zone and groundwater. The drainage system consists of shallow open drains (0.5-1.0 m

deep, distance between drains 50-75 m) connected to open on-farm collectors (2-3 m deep).

Drainage water from the irrigated lands is dumped into the Ily River through the main

drainage collector (Figure 3.12). The volumes of dumped surface water and drainage water

over a long period of time vary from 10,000 to 24,400 m3/ha. Temporal variation in

drainage-water discharge is presented in the Table 3.8. Note that it was not possible to

accurately distinguish between volumes of dumped surface water and drainage water,

because discharge goes to the same collection network.

Data presented in Table 3.8 demonstrate low water-use efficiency in the Akdalinsky

irrigation system. Return flow of surface and drainage water is 21-67% of total water

consumption. The ratio of evapotranspiration to return flow varies in the range of 0.26 to

1.13 and depends on the number of rice fields in the crop rotation. For rice fields equal to

81% and 32% of the total crop area, this ratio is 0.26 and 0.50, respectively.

59

Table 3.8. Drainage water discharge and related factors. Years Discharge of drainage

water, 106 m3/year Proportion of total

water consumption, % Evapotranspiration/

drainage water discharge 1970 39 21 0.56 1980 241 48 0.41 1981 312 59 0.37 1982 259 44 0.38 1983 309 57 0.31 1984 357 61 0.26 1985 516 65 0.30 1986 513 49 0.51 1987 482 47 0.65 1988 595 67 0.50 1989 733 66 0.36 1990 733 66 0.36 2000 300 50 0.78 2001 249 39 0.89 2002 221 35 1.13

For example, for an irrigation system with good performance (no rice growing), the return

flow should not exceed 10 to 15% of total water consumption, while the ratio of

evapotranspiration to return flow, which characterizes efficiency of irrigation, can be 3.5 to

5, or even higher. The dynamics of salinity and the concentration of the major ions in the

return flow are presented in Table 3.9.

Table 3.9. Salinity and concentration of major ions in return flow.

Chemical composition, mg/l Years TDS, mg/l HCO3 Cl SO4 Ca Mg Na+K

1990 700 268 52 204 66 44 66 1991 650 269 50 166 49 46 70 1992 664 271 48 172 54 40 79 1993 652 283 44 160 54 44 67 2000 645 268 44 160 56 44 73 2003 1025 165 73 414 115 58 102

Note that the salinity of the drainage water changed very little from 1990 to 2000.

TDS of the drainage water were even lower than the salinity of the groundwater, which

means that return flow includes a large portion of dumped surface water. Salinity of return

flow had drastically increased by 2003 as a result of the decrease in rice growing. This may

60

indicate the initiation of a secondary salinization process. Simultaneously, ratios of

4SOCl , ( )MgCaHCO +3 and ( )MgCaNa + decreased.

The content of biogens and pesticide concentrations in the drainage water are quite

high (Lobova, 1967; Avakian et al., 1987): NO2—0.03 mg/l, NO3—1.15 mg/l, NH4—0.05

mg/l, biochemical oxygen demand (BOD)—1.22 mg/l, Saturn—8 μ g/l, Benzex—0.3 μ g/l,

and DDT—0.02 μ g/l. Input of these chemicals with respect to total contamination is

relatively low, and today, the risk of directly poisoning the population through the drinking

water is low. However, some species of fish and other low-level organisms (planktonic

sources of fish food) can die as a result of the high toxicity of these chemicals to aquatic

fauna.

The upper soil layer of the Akdalinsky irrigated land is represented by serozem.

Under natural conditions, about 14.5% of the area is not salinized; 17.7% exhibits weak

salinity, 20% intermediate salinity, 21.1% strong salinization, and 25.7% of the area is

either soil that is unsuitable for agriculture or represented by solonchaks (Lobova, 1967;

Avakian et al., 1987).

A high proportion of rice fields (about 80% at the initial stage) and heavy irrigation

led to intensive soil washing and rapidly decreasing salinity in the unsaturated zone. These

processes slowed down with time; however, they were accompanied by an increase in

chemical weathering, leaching of organic and inorganic matter, dehumification and

alkalization that finally led to a decrease in soil fertility (Avakian et al., 1987).

One of most important factors characterizing economic efficiency of irrigation

agriculture is the dynamics of crop production. Table 3.10 gives the dynamics of the yields

of different crops in the study area.

61

Table 3.10. Crop yields in the study area.

Crop production, centner/ha

Crop production, centner/ha

Crop production, centner/ha

Yea

r

Ric

e

Alfa

lfa

Whe

at,

barle

y

Yea

r

Ric

e

Alfa

lfa

Whe

at,

barle

y

Yea

r

Ric

e

Alfa

lfa

Whe

at,

barle

y

1974 31.2 - - 1982 48.1 - - 1989 67.2 - - 1975 42.8 - - 1983 44.6 - - 1990 58.2 - - 1976 41.2 - - 1984 42.3 - - 1991 68.4 - - 1977 59.1 - - 1985 43.6 - - 1992 48.4 - - 1978 43.2 - - 1986 53.6 - - 1993 55.8 - - 1980 42.2 130 15 1987 55.6 - - 2000 32 31.6 16.5 1981 46.3 - - 1988 49.4 - - 2001 33 41.0 16.7

The data presented in Table 3.10 indicate that crop production (rice 31.2 to 67.2

centner/ha; alfalfa 31.6 to 130 centner/ha, barley and wheat 15 to 17 centner/ha) is much

smaller than the yield afforded by climatic conditions, namely, for rice about 70 centner/ha;

alfalfa, 130 centner/ha; wheat and barley, 50 centner/ha. In other words, actual production

of rice varies from 0.45 to 0.98 of the climatically affordable yield; alfalfa production

ranges from 0.24 to 1.0, and wheat and barley production is about 0.3.

To confirm the conclusion of low efficiency of the Akdalinsky irrigation system, an

analysis of water and salt balances (Tables 3.11 and 3.12) was conducted based on data

from Avakian (1987) and Lobova (1967).

62

Table 3.11. Water balance of the Akdalinsky irrigated lands, 106 m3, from 1970 to 2002. Inflow Outflow Year

W P ∑ in D Е G ∑ out

cWΔ aWΔ %,σ ∑ in

E

, % ∑+

out

GD

, % 1970 181 3 184 39 22 35 96 +88 +49 +21 12 77 1980 496 4 500 241 100 70 411 +89 +82 +1.4 20 76 1981 525 6 531 312 115 9 436 +95 +67 +5.3 22 74 1982 585 8 593 259 98 69 426 +167 +85 +13.8 17 77 1983 532 14 546 309 97 9 415 +131 +31 +18.3 18 77 1984 580 9 589 357 93 9 459 +130 +36 +16 16 80 1985 782 15 797 516 155 15 686 +111 +61 +16.3 19 77 1986 1015 23 1038 513 264 27 804 +234 +72 +15.6 25 67 1987 992 32 1024 482 314 36 832 +192 +115 +7.5 31 62 1988 852 33 885 595 299 41 935 -50 +50 -11.3 34 68 1989 1053 60 1113 733 267 39 1039 +74 +42 +2.9 24 74 1990 1053 60 1113 733 267 39 1039 +74 +42 +2.9 24 74 2000 525 72 597 300 234 28 562 +35 +43 +1.3 39 58 2001 467 172 639 249 221 35 505 +134 +39 +14.9 35 56 2002 467 172 639 221 249 39 509 +130 +35 +14.9 39 51 W is water intake for irrigation, P is precipitation, D is return flow of surface and drainage water, Е is evapotranspiration, G is groundwater flow balance (calculated using data of groundwater level monitoring), ∑∑ −=Δ outincW is the calculated water balance, aWΔ is actual water balance (calculated using monitoring data of groundwater level and water

content in the unsaturated zone), 100⋅Δ−Δ

=∑ in

ac WWσ is the error.

Table 3.12. Salt balance of the Akdalinsky irrigated lands, 103 tons.

Influx Outflux Year

WG PG ∑G DG GG ∑G cGΔ

1970 68.8 0.1 68.9 30 38 68 0.9 1980 188.5 0.1 188.6 168.7 56 224.7 -36.1 1981 199.5 0.2 199.7 202.8 7.2 210 -10.3 1982 222.3 0.2 222.5 168.4 55.2 223.6 -1.1 1983 202.3 0.4 202.6 200.9 7.2 208.1 -5.5 1984 220.4 0.2 220.6 232.1 7.2 239.3 -18.7 1985 297.2 0.4 297.6 335.4 12 347 -49.4 1986 385.7 0.7 386.4 333.5 21.6 355.1 +31.3 1987 377 0.9 378 313.3 28.8 342.1 +35.9 1988 323.8 1 234.8 386.8 32.8 419.6 -184.8 1989 400.1 1.8 402.1 476.4 31.2 507.6 -105.5 1990 400.1 2 402.1 476.4 31.2 507.6 -105.5 2000 199.5 2 201.5 195 22.4 217.4 -15.9 2001 177.5 5 182.5 162 28 190 -7.5 2002 177.5 5 182.5 144 31.2 175.2 +7.3

Gw is salt influx with irrigation, Gp is salt influx with precipitation, GD is salt outflux with return flow of surface and drainage water, GG is salt outflux with groundwater flow, cGΔ is calculated salt balance.

63

The data presented in Tables 3.11 and 3.12 indicate that, despite the relatively large

errors in water-balance calculations, conclusions regarding low ecological and economic

efficiency of the existing irrigation system in the Akdalinsky area and its negative impact

on soil and water resources are justified. The evapotranspiration value assesses productivity

of irrigated lands. The efficiency of the existing irrigation system is defined as 100∑ in

E

and ranges from 12 to 39%. High values correspond to less rice growing. The impact of

existing rice systems on the environment can be estimated using the index 100∑+

out

GD ,

which ranges from 51 to 77%. Here lower values correspond to smaller discharges of return

water. Total solute outflux ( GD GG + ) ranges from 68,000 to 507,600 ton/year, which

essentially influences surface-water quality.

Summarizing the above data, we can conclude that construction of the irrigation

systems in the Ily River-Lake Balkhash basin had a definite negative impact on the

environment because of their low technical performance and efficiency. Increasing the area

of irrigated land up to 2 million ha (as initially planned) would have catastrophic

consequences, similar to those observed in the Aral Sea basin. This is because the Ily River

is the major source of water for Lake Balkhash. Decreasing water discharge to the lake

would lead to lowering its level, shrinking its area and deterioration of the environment.

Under existing conditions, the problem may be partly solved by developing a methodology

for sustainable development of land and water resources.

64

4. SUSTAINABLE MANAGEMENT OF WATER RESOURCES IN THE ILY RIVER BASIN

4.1. A model for water-resource management Based on analyses of the different approaches to water-resource management

(Chapter 1) and the environmental impact of agricultural development, here we formulate a

model to assess sustainable management of water resources in that part of the Ily River

basin that is located downstream of the Kapchagay water-storage reservoir. Construction of

the model includes the following steps:

1. Definition of the global aims and specific objectives of water-resource

management.

2. Assessment of available water, land and economic resources, main water

consumers, ecological restrictions.

3. Elaboration of the quantitative criterion.

4. Consideration of alternative scenarios for the use of water, land and

economic resources.

5. Comparison of alternative scenarios according to the NPV criterion (1.2) and

choice of the one corresponding to the maximum NPV.

Below we consider these model-formulation steps.

4.1.1. Aims and objectives The major aims of water-resource management in the study area are to increase

agricultural production while protecting the environment. Accomplishing these goals

should also enable efficient use of water resources, and protection of regional soil and

climatic resources, as well as of the ecosystems in the Ily River delta and Lake Balkhash.

65

Realization of these aims is based on the following specific

objectives:

- Efficient development of irrigation, taking into account available water resources,

i.e. the water volume, defined as the difference between the guaranteed discharge

from the Kapchagay reservoir and the discharge of the Ily River delta

(downstream of irrigation system), required to ensure ecological safety of the

delta and Lake Balkhash.

- Assessment of irrigated land capacity and agricultural structure, taking into

account the efficient use of water, soil and climatic resources.

- Design and construction of the irrigation system, ensuring minimum negative

impact on the environment.

4.1.2. Available water resources To assess available surface-water resources, we used information on guaranteed

discharge from the Kapchagay water-storage reservoir (upstream of the study area) and the

discharge from the Ily River that ensures ecological safety of the delta and Lake Balkhash

(downstream of the study area). The discharge from the Kapchagay reservoir is composed

of power and ecological proportions of a total volume of 12.5 km3/year, which is 100%

guaranteed (Milliman, 1968; Ratkovich, 1993). The Ily River water downstream of the

Kapchagay reservoir has a salinity of 0.4 g/l, and a calcium-carbonate-type chemical

composition. This water can be used for irrigation and agricultural water supply (with

minimal treatment).

We assume that water supply to the local population has the highest priority, while

water supply for irrigation takes second place. Note that there is no developed industry in

the area and no plans for any. The local population currently amounts to about 40,000 and it

66

is expected to increase to 50,000 inhabitants by the year 2020. The mean daily water

consumption (also accounting for watering of homestead lands) is about 150 l/ person; thus,

total water consumption is currently , and is

expected to increase to in 2020. These values do

not exceed 0.02% of the Ily River discharge downstream of the Kapchagay reservoir.

yearm /102.236515.0000,40 36×=××

yearm /107.236515.0000,50 36×=××

Environmental requirements for water resources downstream of the study area

involve the water discharge necessary to ensure ecological safety of the Ily River delta and

Lake Balkhash, which amounts to 11.5 km3 (Ratkovich, 1993; Veselov et al., 1996). Thus,

the volume of irrevocable water consumption that can be used for irrigation development is

. Climatic features in the study area

provide conditions for practically constant water consumption for irrigation with time.

yearkm /997.0107.2105.11105.12 3699 =×−×−×

The return flow (surface and drainage water) from irrigated lands is dumped into the

Ily River. Dumping of domestic wastewater into the Ily River is not accounted for because

of the absence of a centralized sewage system. Note that TDS concentration in the Ily River

water should not exceed 0.6 g/l in order to protect fishery and other environmental

resources (Veselov et al., 1996).

4.1.3. Quantitative criterion

The net present value (NPV), expressed by equation (1.2), was used as a general

efficiency criterion to assess sustainable management of water resources in the study area.

Efficiency of development of economic and natural resources is estimated as the economic

benefit resulting from agricultural production (volume and cost of agricultural production)

and the total amount of ecological benefits/damages to the environment, including changing

fertility of irrigated lands, natural pastures, groundwater and surface-water conditions.

67

We extend equation (1.1) and rewrite it in the form:

it

N

T

WIAWA CDInInInRRRRNPV −−×−−−−±±= −∑ )1()(1

21 (4.1)

where NPV is the net present value, $; Т is the development period, years; RA is the cost of

agricultural production, $; 1R± is the cost of ecological benefit (increase in soil fertility)

(+R1) or ecological damage (decrease in soil fertility) (-R1) of irrigated land; 2R± is the

cost of ecological benefit/damage to natural pastures due to increased forage production

(+R2)/overgrazing (-R2); RW is the cost of ecological damage due to groundwater and

surface-water pollution as a consequence of dumping irrigation and drainage water into the

Ily River, $; InA is the annual expense for agricultural production, $; InI is the annual

expense for irrigation-system exploitation, including land-improvement expenses, $; InW is

the cost of water for irrigation, $; DN is the rate of discounting ( 08.006.0 ÷=ND )

characterizing minimal-level requirements for investment profits; and Ci is the capital

investment, $.

The cost of agricultural production for any crop rotation is defined as:

∑ ××=n

AiiA CYR1

αω (4.2)

where ω is the irrigated area, ha; Y i is the yield of an agricultural crop, ton/ha; iα is the

proportion of fields of the crop in a crop rotation; CA is the specific price of the crop, $/ton;

and n is the number of agricultural crops in the crop rotation.

Quantitative estimation of the ecological benefit/damage as a result of changing soil

fertility is quite complicated and not a well-developed procedure. Analysis of existing

approaches (Dokuchaev, 1949; Bazilevich and Rodin, 1971; Volobuev, 1974; Budiko,

1977; Pegov and Homiakov, 1991; Veselov et al., 1996; Vershkov, 1999; Pererva, 2000)

68

reveals that the method developed by Volobuev (1974) is relatively simple, while

accounting for the major factors affecting soil fertility. This method is based on analysis of

energy consumption for soil formation, which depends on relative soil moisture. Soil

moisture strongly influences biological productivity, processes of chemical weathering,

humus accumulation, alkalization, etc. Solar energy consumption for soil formation is

defined by the following equation (Volobuev, 1974):

)exp( RRQ β−×= (4.3)

where Q is the solar energy consumption for soil formation, kJ/cm2.year; R is the net

radiation flux, kJ/cm2.year; LPRR = , where P is the precipitation and irrigation amount,

cm/year, and L is latent heat of evaporation, kJ/cm3; β is the parameter characterizing the

intensity of biological and soil processes, which depends on the R value (Table 4.1).

Table 4.1. Values of the parameter β (Volobuev, 1974).

R 3≥ 2.5 2.0 1.5 1.0 0.5 0.3 β 0.5 0.65 0.9 1.10 1.9 4.2 6.5

Soil fertility is proportional to solar energy consumption for soil formation, while a

relative change in soil fertility is defined by the following equation (Volobuev, 1974):

QQQS −

=Δ 11 (4.4)

1SΔ is the change in relative soil fertility; Q1 and Q are the solar energy consumption for

soil formation on irrigated and virgin land, respectively, kJ/cm2.year.

The costs of the ecological benefit (+R1) or ecological damage (-R1) of irrigated

land are defined by the following equation (Pegov and Homiakov, 1991):

vs ECSR ×××Δ= ω11 (4.5)

69

where R1 is the ecological benefit (due to an increase in soil fertility ) or ecological

damage (due to a decrease in soil fertility

0>ΔS

0<ΔS ), $; ω is the irrigated area, ha; Cs is the

specific cost of the soil as a natural resource defined as the cost of financial losses (gains)

due to a decrease (increase) in soil fertility, $/ha; Ev is the parameter characterizing the

ecological significance of the soil, and is equal to 2.2 (Vershkov, 1999).

The cost of ecological damage for natural pastures suffering from overgrazing (R2)

is also defined by equation (4.5). However, the value characterizing a change in soil fertility

( ) is calculated differently from that for irrigated land. Accounting for the change in

biomass as a result of overgrazing, the change in soil fertility for natural pastures (

2SΔ

2SΔ ) is

defined by the following equation (Volobuev, 1974):

QQ

PS p×Δ=Δ 2 (4.6)

where PΔ is the relative change in pasture productivity compared to natural productivity;

Qp is the solar energy required for biomass production (Qp for the study area is 0.5-0.6 of

the value of Q).

The cost of the ecological damage due to groundwater and surface-water pollution

as a consequence of irrigation and dumping of drainage water into the Ily River (RW) is

defined by (Vershkov, 1999):

E

n

iiSW KМDR ×⎟⎠

⎞⎜⎝

⎛×= ∑

=1 (4.7)

where Ds is the specific cost of ecological damage from water pollution, $/ton; Mi is the

equivalent mass of a pollutant, ton; KE is the parameter characterizing the ecological

significance of water resources, defined as a parameter accounting for the difference in

material and financial losses as a result of deteriorating water quality and a decrease in bio-

70

production in the water system (KE = 2 for the Ily River basin); and n is number of

pollutants. Specific cost of ecological damage is a complex parameter defined as a decrease

in natural resource cost due to a decrease in soil fertility, water-quality deterioration, etc.

leading to financial losses.

The Mi value is calculated according to:

rii KimM ×= (4.8)

where mi is the actual mass of the ith pollutant, ton; Кri is the coefficient of a relative

ecological risk for the ith pollutant. The following pollutants are considered in this model:

Biogens (nitrate Кr = 0.2; ammonium Кr = 1.0)

Water-soluble salts (chloride, sulfate, carbonate, calcium, magnesium and

sodium, Кr = 0.05)

Pesticides (Bolero [Thiobencarb], Benzex [BHC], Propanil, Ordram, DDT

Кr = 2000) (Vershkov, 1999)

The GLEAMS model “Groundwater Loading Effects on Agricultural Management

Systems” (ARS Version 3.0, NRCS version 3.0.1. USDA-ARS AND USDA-NRCS,

Leonard et al., 1987) and the WASTR3-A model “One-dimensional model of water flow

and solute transport in the unsaturated-saturated zone” (Yakirevich and Rex, 1993) were

used to estimate groundwater and surface-water pollution by biogens, pesticides and

salinization.

4.2. Alternative use scenarios for water, land and economic resources

Agriculture is the basis of all economic activity in the region; therefore, further

development requires that land irrigation increase the production of food and fodder crops.

Justification of the area and structure of irrigated lands must be based on improving

environmental conditions and irrigation techniques aimed at: decreasing water consumption

71

and the dumping of wastewater and drainage water into the Ily River; preventing water-

resource pollution; increasing the fertility of irrigated lands and natural pastures; and

increasing fodder-crop production in order to decrease existing pasture load.

Irrigation development in the region depends on financial limitations and

irrevocable water consumption. According to the Agriculture Development Strategy of the

Kazakhstan Republic till year 2010 (1997), planned capital investments for agriculture

development amount to between $2 and 2.5 billion. These investments are distributed

among the provinces based on their role in agricultural production.

Thus, capital investments for irrigation development in the study area are limited

by . The specific cost of designing and constructing an

irrigation system, depending on its engineering standards, is 2,500 to 4,000 $/ha. The limit

of irrevocable water consumption for irrigation is around 1 km

69 10272$8.0136.0105.2 ⋅=××⋅

3/year (see section 4.1.2).

Four scenarios for the exploitation of water and land resources are considered in this

investigation:

Scenario 1. Exploitation of the existing rice-irrigation system. The irrigation system

is characterized by the following parameters:

Efficiency factor = 0.5

Land-use factor = 0.64

Structure of irrigated land use during Soviet period: rice—62.5%, alfalfa—

25%, barley—12.5%

Characteristics of existing drainage system (Chapter 3)

Irrigation technique—flood and furrow

Specific cost of design and construction = 2500 $/ha

72

Scenario 2. Reconstruction of the existing rice-irrigation system and alteration in

structure of the irrigated land. The irrigation system is characterized by the following

parameters:

Efficiency factor = 0.75 (due to decreasing water infiltration and improved

efficiency of irrigation canals by introducing film screens)

Land-use factor = 0.85 (due to reconstruction of checks and canals)

Structure of irrigated land use: rice—37.5%, alfalfa—50%, barley—12.5%

Drainage system characteristics as in scenario 1

Irrigation technique—flood and furrow

Specific cost of design and construction = 3,000 $/ha.

Scenario 3. Development of irrigation for the production of forage crops for cattle

breeding. The irrigation system is characterized by the following parameters:

Efficiency factor = 0.85

Land-use factor = 0.90

Structure of irrigated land use: alfalfa—25%, beet—12.5%, barley—25%,

forage corn—25%, corn—12.5%

Reconstruction of the drainage system: closed-type horizontal drains

(distance between drains 350 m, drain depth 3.5 m) and open collectors

(depth 4 m)

Irrigation technique—furrow

Specific cost of design and construction = 3,500 $/ha.

Scenario 4. Development of irrigation for the production of cattle-forage crops. The

irrigation system is characterized by the following parameters:

Efficiency factor = 0.95

73

Land use factor = 0.98

Structure of irrigated land use: alfalfa—25%, beet—12.5%, barley—25%,

forage corn—25%, corn—12.5%

Reconstruction of the drainage system: closed-type horizontal drains

(distance between drains 400 m, drain depth 3.5 m) and open collectors

(depth 4 m)

Irrigation technique—sprinkling

Specific cost of design and construction = 4,000 $/ha.

The net irrigation rates for rice and concomitant crops in scenarios 1 and 2 were as

recommended by Kazakhstan scientific institutions (Government Program of Agricultural

Area Development in the Kazakhstan Republic during 2004-2010, 2003). The net irrigation

rates for crops in scenarios 3 and 4 were calculated based on forecasting of soil water and

salt regimes by the WASTR3-A model (Yakirevich and Rex, 1993). This model was used

to assess water and solute balance components for the considered scenarios. The model was

based on simultaneously solving the one-dimensional Richards equation for simulating

water flow in unsaturated/saturated zones and the advection-dispersion equation for

simulating salt transport. Inputs for this model were: hydraulic and physico-chemical

parameters of uniform or multi-layer soil profiles, initial distribution of water and solute

content, rotation and characteristics of agricultural crops, parameters of irrigation

techniques and drainage system, temporal climatic data (rainfall, evapotranspiration), and

irrigation regime. As a result of these simulations, we obtained temporal and spatial (with

depth) variations of water and content, groundwater level and recharge, salinity of

groundwater and drainage water, drainage water discharge, full water and solute balances.

74

The Groundwater Loading Effects of Agricultural Management System (GLEAMS)

(Leonard et al., 1987; Knisel et al., 1993) is a functional model used to simulate processes

affecting water-quality events in an agricultural field in order to assess surface-water and

groundwater pollution by pesticides and to estimate changes in irrigated-land fertility. It is a

continuous simulation model that provides detailed predictions of water, sediment, nutrient,

and pesticide movement within and through the roots. To simulate the many processes

occurring in a field, the model is divided into three separate sub-models: hydrology,

erosion/sediment yield, and chemical transport. The chemical transport sub-model is further

subdivided into nutrient and pesticide components so that one or both may be simulated.

The pesticide component of the GLEAMS model is designed to allow simulation of

interactions among pesticide properties, soils, climate, and to enable management of the

effects of pesticide losses in surface runoff, attached to transported sediment, and in

percolate below the root zone or at any other specified depth. To trace the fate of surface-

applied or incorporated pesticides, GLEAMS considers degradation, adsorption, and

convective processes in each of the computational soil layers in the root zone. Upward

movement of pesticides due to evaporation and plant uptake is also included. Inputs for this

model are: climatic data (average monthly maximum and minimum air temperatures, solar

radiation, wind speed, temperature of dew point, amount of precipitation); soil and

hydrological data (porosity, field capacity, wilting point, organic-matter content, grain-size

composition, calcium-carbonate content, pH, surface elevation); agricultural-practice

parameters (types of agricultural crops, root depth, irrigated area, drainage, irrigation

regime, date and rate of pesticide application, main pesticide characteristics). As a result of

the simulations, the GLEAMS model provides estimates of the impact of management

systems, such as planting dates, cropping systems, irrigation scheduling, and tillage

75

operations, on the potential for chemical movement, including chemical content of surface

water, the root zone, groundwater and drainage water.

The results from simulations with the WASTR3-A and GLEAMS models were used

to assess the effect of irrigation on the environment in each scenario.

4.3. Forecast of water flow, salt transport and pesticide pollution in soil and

water resources 4.3.1. Simulations with the WASTR3-A and GLEAMS models Long-term forecasting of the effect of water-management scenarios on the

environment plays a central role in assessing alternatives. Model application must begin

with its calibration and verification. We verified the WASTR3-A and GLEAMS models by

comparing the results of simulations with observations that had been carried out in the

experimental plots of the Akdalinsky irrigated land (Loucks, 2000; Government Program of

Agricultural Area Development in the Kazakhstan Republic during 2004-2010, 2003;

Water Balance of Akdalinsky Irrigated Area from 1970 to 2002). The following data were

used for verification of the models:

1. Volumes, salinity and pesticide content in drainage water and dumped surface

water; mass of salts dumped by the drainage system.

2. Groundwater level, salinity, and pesticide concentration.

3. Concentration of water-soluble salts and pesticides in the root zone.

The WASTR3-A model simulations were carried out for an 8-year period (1980-

1987). The following data were introduced into the model: a two-layer lithological profile,

hydrological and hydrochemical parameters of the layers (parameters were estimated using

experimental data on particle-size distribution), crop rotations, transpiration, evaporation,

precipitation, irrigation requirements and water application, initial distribution of water

76

content, solute (TDS) concentration, groundwater levels, parameters of irrigation and

drainage systems, TDS concentration in rainfall and irrigation water. The upper layer, with

a mean thickness of 2.5 m, is represented by serozem soil with a porosity of 0.46, a

saturated hydraulic conductivity of 0.3 m/day, a bulk density of 1.4 g/cm3, and a

dispersivity of about 0.2 m. The underlying sandy layer has a porosity of 0.38, saturated

hydraulic conductivity of 6.2 m/day, bulk density of 1.7 g/cm3, and dispersivity of about

0.05 m. The alteration-with-time boundary conditions (Dirichlet type for periods of

irrigation, and Neuman type for periods between irrigations) were prescribed at the soil

surface. At the lower boundary, the Cauchy-type boundary condition was assigned as water

flux calculated depending on the groundwater level and drainage-system parameters

(distance between drains 265 m, and drain depth 3.5 m).

The following rotation of agricultural crops was input: 1980 and 1981—rice,

1982—barley, 1983, 1984 and 1985—rice, 1986 and 1987—alfalfa. The net water volumes

applied for irrigation were: rice—28,000 m3/ha, barley—4,000 m3/ha, alfalfa—7,100 m3/ha.

Model performance was estimated by averaging (over the simulation period) the

results of the simulations and comparing them to the available observation data for fields

with similar crop rotations (Loucks, 2000; Government Program of Agricultural Area

Development in the Kazakhstan Republic during 2004-2010, 2003; Water Balance of

Akdalinsky Irrigated Area from 1970 to 2002) (Table 4.2). Consistent agreement was

obtained.

77

Table 4.2. Comparison between results of simulations with WASTR3-A code and observations.

Characteristics Simulated Measured (*)

Evapotranspiration, m3/ha/year 11,000 10,500 Drainage and dumped surface water flow, m3/ha/year 13,600 14,000 Salinity of drainage and dumped surface water, g/l 0.58 0.62 Groundwater depth, m 2.80 2.90 Groundwater salinity, g/l 0.63 0.72 Mass of salt dumping with drainage and surface water, ton/ha 7,900 8,700 Mean salt content in the soil layer 0 – 100 cm, g/100g 0.013 0.014 Mean salt content in the soil layer 0 – 400 cm, g/100g 0.016 0.020

Analysis of the simulation results showed the development of soil-salinity patterns

that are typical for similar hydrogeological conditions. The concentration of water-soluble

salts in the soil drastically decreased after the first year of rice growing because of heavy

water application. From then on, salt concentration changed only very slightly. The salt

content in the soil layer (0-400 cm) after 8 years of irrigation decreased from 0.147 to 0.016

g/100 g, while in the root zone (0-100 cm), it decreased from 0.228 to 0.013 g/100 g.

Based on the results of the simulations and experimental data, one can estimate the

parameter of water-use efficiency (ratio between transpiration and water application) for

different crops: rice—0.17, barley—0.77, alfalfa—0.66. Low values of this parameter

(especially for rice) indicate that water application was too high.

The results of the WASTR3-A simulation indicated that after only 1 year of rice-

field irrigation, the salt content in the root zone decreases drastically, and changes little

thereafter. TDS concentration in the root zone (0-0.7 m) after 8 years of irrigation decreased

from 0.228 to 0.013-0.016 g/100 g in all scenarios (Figure 4. 1).

78

0

0,05

0,1

0,15

0,2

1 2 3 4 5 6 7 8 9 10

Years

TDS

of r

oot z

one,

g/10

0g

Scenario 1Scenario 2Scenario 3Scenario 4

Figure 4.1. Variation of mean TDS content in the upper 0-0.7 m soil layer.

Mean annual calculated groundwater level for scenario 1 varied in the range of 2.7

to 3.2 m, which is close to observed levels (2.53-2.98 m). Calculated mean (over 8 years)

groundwater level was 2.8, 3.2, 3.4 and 3.4 m for scenarios 1, 2, 3 and 4, respectively

(Figure 4.2).

2,5

2,7

2,9

3,1

3,3

3,5

3,7

1 2 3 4 5 6 7 8 9

Years

GW

Lev

el, m Scenario 1

Scenario 2Scenario 3Scenario 4

Figure 4.2. Modeled temporal variations in mean groundwater level.

Simulated TDS concentration in the groundwater was 0.49 to 0.97 g/l for scenario 1,

0.49 to 1.28 g/l for scenario 2, 4.17 to 6.62 g/l for scenario 3, and 4.14 to 6.62 g/l for

scenario 4 (Figure 4.3). The observed salt concentrations in the groundwater varied from

0.57 to 1.15 g/l, which is close to scenario 1. The increase in groundwater salinity in

scenarios 3 and 4 is due to the lowering groundwater level, and a consequent decrease in

79

groundwater discharge to the drainage system. As a result, there is no effective salt washing

from the groundwater, which later on would lead to an increase in soil salinity in the root

zone during vegetation. During the fall-spring rainy season, salts from the vadose zone are

washed back into the groundwater.

0

1

2

3

4

5

6

7

8

0 1 2 3 4 5 6 7 8 9 10

Years

GW

Con

cent

ratio

nl,g

/l

Scenario 1Scenario 2Scenario 3Scenario 4

Figure 4.3. Modeled temporal variations of TDS concentration in groundwater.

After the second year of irrigation, simulated TDS concentration in the drainage

water decreased to 0.9 and 1.3 g/l for scenarios 1 and 2, respectively (Figure 4.4). For

scenarios 3 and 4, the salinity of the drainage water increased. The observed TDS in the

drainage water varied in the range of 0.8 to 2.7 g/l.

0,0

1,0

2,0

3,0

4,0

5,0

6,0

7,0

1 2 3 4 5 6 7 8

Years

Dra

inag

e co

ncen

tratio

n,g/

l

Scenario 1Scenario 2Scenario 3Scenario 4

Figure 4.4. Simulated salt concentration of drainage water flux.

Verification of the GLEAMS model was achieved using available measurement data

for the years 1988-1989. The following pesticides were applied on the Akdalinsky irrigated

80

land: Ordram, Bolero, Propanil, Benzex and DDT. Dates and rates of pesticide application

are presented in Table 4.3 (Loucks, 2000; Government Program of Agricultural Area

Development in the Kazakhstan Republic during 2004-2010, 2003; Water Balance of

Akdalinsky Irrigated Area from 1970 to 2002).

Table 4.3. Pesticide application in 1987-1988. 1987 1988 Pesticide

Application days (from beginning of year)

Rate, kg/ha

Application days (from beginning of year)

Rate, kg/ha

165 0.85 165 1.7 Ordram 195 0.85 195 1.7 165 0.45 165 0.89 Bolero (Saturn) 195 0.45 195 0.89 165 3.3 165 6.65 Propanil 195 3.3 195 6.65 165 0.03 165 0.06 Benzex 195 0.03 195 0.06 165 0.07 165 0.07 DDT 195 0.07 195 0.07

The model takes into account the dynamics of climatic factors, soil and hydrological

parameters, as well as agricultural aspects. Characteristic parameters of these pesticides

were taken from the GLEAMS database. Analysis of the simulation results demonstrated

very good agreement between modeled and observed water balance: e.g., in 1988,

simulated and observed groundwater recharge was 1,255 and 1,370 mm, respectively;

simulated and observed evapotranspiration was 1,018 and 1,020 mm, respectively;

simulated and observed surface-water discharge was 1,491 and 1,621 mm, respectively.

Pesticide contents were compared using available data from observations of water in

rice checks, groundwater and drainage wastewater (Table 4.4).

Taking into account that systematic observation data were only available for

comparison at some time intervals, the data presented in Table 4.3 suggests that the

GLEAMS model can relatively accurately predict the content of pesticides in groundwater,

81

drainage water and dumped surface water. Note that according to simulations, the

concentrations of Benzex and DDT in the groundwater are zero, whereas small amounts of

these pollutants were observed. This is due to the fact that groundwater level was raised to

the soil surface.

Table 4.4. Comparison between simulated and observed pesticide contents Pesticide concentration

Pesticide

Data Soil, µg/100 g

Groundwater, µg/l

Drainage and dumped surface water, µg/l

simulated 0.062 0.00012 11.0 Ordam measured No data trace 8.0 simulated 0.03 0.0017 7.9 Bolero measured No data 0.0008 8.0 simulated 0.13 0.005 32.5 Propanil measured No data 0.005 10.4 simulated 0.16 0 0.38 Benzex measured No data 0.016 0.30 simulated 0.38 0 0.062 DDT measured No data 0.0018 0.020

The simulations with the GLEAMS model indicated that the Bolero and Ordram

pesticides are the most acceptable in terms of environmental pollution. These pesticides

accumulate less in the soil, groundwater and drainage wastewater than others because of

their short half-lives. Propanil accumulates in large quantities and is therefore highly

polluting. The application of Benzex and DDT has been banned. In the following

simulations, we consider application of Bolero only to estimate environmental pollution in

the study area for different water-management scenarios.

Figures 4.5 and 4.6 demonstrate simulated pesticide losses in the runoff and

sediments for scenario 1 (results for scenario 2 are very similar). This simulation is

computed for 2 years of rice-crop rotation. Pesticides were applied in the middle of June

and in the middle of July. The results show that a considerable amount of the pesticide is

washed away with the runoff water through open drainage channels. The pesticides

82

Propanil, Ordram and Bolero accumulate in the runoff, while pesticides Benzex and DDT

stay mainly in the soil. Today, the use of Benzex and DDT has been banned worldwide.

The application of pesticides in scenarios 3 and 4 is not planned, therefore,

simulations for these cases were not carried out.

0 5 10 15 20 25Time (Months)

0

200

400

600

800

Ord

ram

, Pro

pani

l, B

oler

o C

once

ntra

tion

(g/H

a )

0

2

4

6

8

Ben

zex,

DD

TC

once

ntra

tion

(g/H

a)

OrdramBoleroPropanilBenzexDDT

Pesticides Concentration on Runoff, 1987-88.

Figure 4.5. Pesticide concentrations in the runoff, 1987-88 (scenario 1).

0 5 10 15 20 25Time (Months)

0

2

4

6

Ord

ram

, Pro

pani

l, B

oler

o C

once

ntra

tion

(g/H

a)

0

10

20

30

Ben

zex,

DD

TC

once

ntra

tion

(g/H

a)

Pesticides Concentration on Sediment, 1987-88.

Figure 4.6. Pesticide concentrations in the sediment, 1987-88 (scenario 2).

4.3.2. Impact of water-management scenarios on environmental conditions To assess the effects of different policies of agricultural development and water

management on the environment, long-term simulations of water flow and solute transport

83

in the unsaturated-saturated zone were carried out. The results of these simulations allowed

an estimation of the main components of water, solute and pesticide balances in each

considered scenario. Modeling was conducted for an 8-year crop-rotation period.

Requirements of the simulations included keeping the water content of the root zone within

0.6 and 0.9 of soil field capacity, and keeping the TDS concentration below 5 g/l. These

restrictions define soil conditions that are favorable for plant growth. As mentioned in

section 4.2, the net irrigation rates for rice and concomitant crops in scenarios 1 and 2 were

input according to recommendations by scientific institutions in Kazakhstan (Agriculture

Development Strategy of the Kazakhstan Republic till 2010, 1997). The net irrigation rates

for crops in scenarios 3 and 4 were calculated based on forecasting of water and salt

regimes in soils by the WASTR3-A model to satisfy the above restrictions for water content

and TDS concentration in the root zone during crop vegetation. Simulations were

conducted by introducing actual climatic data for the years 1980-1987. Some of the

simulation results are presented in Tables 4.5-4.6.

The simulation results indicated that when growing rice (scenarios 1 and 2), the net

irrigation rate of concomitant crops (alfalfa and barley) may be reduced by %5.3227 ÷

because of water uptake by roots from the capillary fringe due to a relatively high

groundwater level. This is also made possible due to the intensive soil washing during the

rice-growing years. Note that elimination of rice without increasing water application for

alfalfa and barley leads to the development of soil salinization. This can be clearly seen

when analyzing the simulation results for scenarios 3 and 4 (forage-crop rotation). A

cessation in rice-growing and consequent decrease in water application require additional

soil washing to prevent salinization of the root zone by the capillary rise of solutes from the

groundwater. As a result, the application of irrigation water for alfalfa and barley must be

84

increased by of total evapotranspiration (see Appendix 1, Table 1.1A). Some

components of water balance (irrigation rates, drainage and surface-water discharge,

irrevocable water consumption) calculated for different scenarios are presented in Table

4.5. These water-application rates provide a concentration of solutes in the root zone that is

lower than the admissible maximum (5 g/l) (see Table 4.6).

%87 ÷

Table 4.5. Averaged (over 8 years) components of water balance for different scenarios.

Scenario Water-balance component 1 2 3 4

Net irrigation water rate 18,560 12,370 5,190 5,190Total irrigation water rate 37,120 16,490 6,100 5,460Drainage and surface-water discharge 9,280 6,430 710 640Irrevocable water consumption 27,840 10,060 5,390 4,820 Table 4.6. Average soil water salinity in the root zone.

Variants 1 2 3 4

Yea

r

Time period Crop Salinity g/l

Crop Salinity g/l

Crop Salinityg/l

Crop Salinity g/l

Before irrigation Rice 14.95 Rice 14.95 Alfalfa 14.95 Alfalfa 14.95 1 After irrigation Rice 1.0 Rice 1.05 Alfalfa 3.50 Alfalfa 3.48 Before irrigation Rice 0.83 Alfalfa 0.86 Alfalfa 3.07 Alfalfa 3.08 2 After irrigation Rice 0.77 Alfalfa 2.77 Alfalfa 1.24 Alfalfa 1.24 Before irrigation Alfalfa 0.83 Alfalfa 1.92 Beet 1.37 Beet 1.37 3 After irrigation Barley 1.72 Alfalfa 2.08 Beet 1.78 Beet 1.76 Before irrigation Rice 1.63 Rice 2.12 Barley 1.56 Barley 1.57 4 After irrigation Rice 0.77 Rice 0.97 Barley 2.20 Barley 2.17 Before irrigation Rice 0.85 Barley 0.85 Barley 1.96 Barley 1.97 5 After irrigation Rice 0.80 Barley 1.54 Barley 4.83 Barley 4.75 Before irrigation Rice 0.85 Rice 1.48 F.corn 4.43 F.corn 4.82 6 After irrigation Rice 0.81 Rice 1.18 F.corn 4.72 F.corn 4.68 Before irrigation Alfalfa 0.88 Alfalfa 1.02 F.corn 3.96 F.corn 4.01 7 After irrigation Alfalfa 2.46 Alfalfa 2.99 F.corn 4.25 F.corn 4.27 Before irrigation Alfalfa 2.33 Alfalfa 2.07 Corn 2.14 Corn 3.34 8 After irrigation Alfalfa 3.58 Alfalfa 2.17 Corn 2.52 Corn 2.52

Note: F.corn—forage corn.

Before assessing the agricultural impact on the environment of fertilizer and

pesticide applications, we estimated possible areas of irrigated lands for each scenario

85

based on (1) limitations on irrevocable water consumption and (2) limitations on

investments for irrigation development (Table 4.7). The minimum of these two values was

accepted for each considered scenario. In scenario 1, the calculated area of irrigation was

35,800 ha (including an existing 30,000 ha and 5,800 ha for new irrigation). This area was

limited by the available volume of water resources. In other scenarios, irrigation area was

limited by investments for irrigation development. In scenario 2, the calculated area of

irrigation was 90,670 ha, in scenario 3—77,700 ha and in scenario 4—68,000 ha.

Table 4.7. Calculations of irrigation area for different scenarios.

Scenario Water-balance component 1 2 3 4

Water-resource volume, 106 m3 997.0 997.0 997.0 997.0 Irrevocable water consumption, m3/ha 27,840 10,060 5,390 4,820 Volume of investments for irrigation, 106 $

272.0 272.0 272.0 272.0

Specific cost of irrigation systems, $/ha 2,500 3,000 3,500 4,000 Possible irrigation area (ha)

By water-resource limitations 35,800 99,110 184,970 206,850 By investment limitations 108,800 90,670 77,700 68,000

Pollution of irrigated lands by the pesticide Bolero was estimated using the

GLEAMS model. Application of this pesticide for 15 to 20 years leads to its accumulation

in the soil, to about 2.99 g/ha by the end of irrigation. For total irrigated land area, the

accumulated mass of Bolero is 0.107 ton for scenario 1 and 0.270 ton for scenario 2. For the

forage-crop rotations (scenarios 3 and 4), it was assumed that pesticides were not applied.

Pesticides and biogens (NO2, NO3, NH4) were taken into account to estimate groundwater

pollution for the rice-crop rotation (scenarios 1 and 2), while for the forage-crop rotations

(scenarios 3 and 4), only contamination by biogens was considered. The content of Bolero

in the groundwater was determined from simulations using the GLEAMS model, and the

biogen content was estimated based on experimental observations (Table 4.8 and Appendix

1, Tables 1.2A-1.3A).

86

Table 4.8. Groundwater pollution (average over 3 m depth) by biogens and Bolero over the total irrigation area for different scenarios.

Scenario Pollutant 1 2 3 4

Biogen content in groundwater, ton (*) 609 1,541 1,321 1,156 Pesticide content in groundwater, ton 0.00084 0.002 - -

Pollution of the Ily River was determined using the results of the simulations with

the WASTR3-A and GLEAMS models. Predicted volumes and concentrations of drainage

and surface-water discharge for different scenarios are presented in Table 4.9 and in

Appendix 1, Tables 1.2A-1.3A.

Table 4.9. Calculated mass (ton) of salts and pollutants being discharged into the Ily River under the different scenarios.

Scenario Pollutant 1 2 3 4

Water-soluble salts 103,460 170,080 222,840 99,688 Biogens 408.0 716.2 69.9 53.5 Bolero 2.63 6.65 - -

The impact of irrigation on soil fertility was estimated for each scenario using

calculated water-application rates, climatic data (net radiation, precipitation,

evapotranspiration) and equations (4.5) and (4.6). The calculated relative change in soil

fertility of the irrigated land was: -10.4% for scenario 1, -7.4% for scenario 2, +20% for

scenario 3, and +20% for scenario 4. The calculated relative change in soil fertility of the

natural pastures was: +10.4% for scenario 1, +25.3% for scenario 2, +33.0% for scenario 3,

and +33.6% for scenario 4.

87

4.4. Calculating the NPV criterion The NPV was calculated for each scenario based on the results of simulations

presented in section 4.3 and on the following data (Kritskiy and Menkel, 1981; Avakian et

al., 1987; Veselov et al., 1996; Klon and Wolter, 1998; Brown, 1999):

Volume and cost of agricultural production

Annual production costs (agricultural and land improvement)

Specific cost of soil

Specific cost of soil and water-resource deterioration due to pollution

Cost of irrigation water as a resource

Value of investments

Table 4.10. Normative ecological and economic characteristics (Agriculture

Development Strategy of the Kazakhstan Republic till 2010, 1997; Kazakhstan Governmental Regulation, 2002; Government Program of Agricultural Area Development in the Kazakhstan Republic during 2004-2010, 2003; Bekbolotov and Djaylobaev, 2004).

Scenario Characteristics Units 1 2 3 4

Purchase price of rice $/ton 550 550 - - Specific yield of rice ton/ha 5.0 5.5 - - Purchase price of alfalfa $/ton 50 50 50 50 Specific yield of alfalfa ton/ha 5.0 5.0 10.0 13.0 Purchase price of barley $/ton 110 110 110 110 Specific yield of barley ton/ha 2.5 2.5 4.0 5.0 Purchase price of forage corn $/ton - - 50 50 Specific yield of forage corn ton/ha - - 50.0 60.0 Purchase price of corn $/ton - - 176 176 Specific yield of corn ton/ha - - 8.0 9.0 Purchase price of beet $/ton - - 30 30 Specific yield of beet ton/ha - - 40.0 50.0 Specific cost of soil $/ha 1,000 1,000 1,000 1,000 Specific water-resource damage $/ton* 500 500 500 500 Specific cost of irrigation water $/m3 0.02 0.02 0.02 0.02 * $ per ton of pollutant

88

The costs of economic and ecological damages and benefits were estimated using

equations (4.7) and (4.8). Results are presented in Tables 4.5-4.10 (see Appendix 2, Tables

2.2A-2.6A). The calculated NPV components are presented in Table 4.11.

Table 4.11. Calculated NPV components (million $).

Scenario Value 1 2 3 4

Cost of agricultural production (RA) 41.6 99.6 82.9 95.7 Annual expenses for agricultural production (InA+ InI)

30.1 74.7 65.0 73.0

Cost of irrigation water (InW) 26.6 29.9 9.5 7.4 Damage/benefit due to change in irrigated soil fertility (R1)

-9.8 -18.8 +34.2 +29.9

Change in natural pasture fertility (R2) +31.2 +75.9 +99 +100.8 Water-resource damage (RW) 48.7 116.3 18.8 9.3 Investments in building irrigation systems (Ci) 14.5* 272 272 272 (*) In scenario 1, investments for building irrigation systems are 2,500 $/ha * 5,800 ha = $14,500,000.

Analysis of data from Table 4.11 shows that the cost of ecological damage of

irrigated land and water resources in scenarios 1 and 2 is 36.141.1 ÷ times higher than the

agricultural production cost.

Finally, NPV was calculated using equation (4.1) and data in Table 4.11 (see

Appendix 2, Tables 2.7A-2.10A). Temporal variations in the NPV for different scenarios

are presented in Figure 4.7.

89

-600-500-400-300-200-100

0100200300400

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Years

Net

Pre

sent

Val

ue, m

ill

Scenario 1Scenario 2Scenario 3Scenario 4.

.

Figure 4.7. Comparison of NPV for different scenarios. The data presented in Figure 4.7 suggests scenario 4 to be the most efficient when

taking into account the high economic benefits and absence of ecological damage. This

scenario provides the maximum NPV. Note that the NPV is an integral criterion that only

allows comparison among the alternative scenarios. It is interesting to analyze the structure

of economic and ecological damages and benefits. Considering that the NPV were

calculated for scenarios characterized by different areas of irrigated land, we made

additional calculations of those specific values (per 1 ha of irrigated lands). As can be seen

(Table 4.12), replacing the rice-crop rotation with a forage-crop rotation and improving

irrigation techniques and technology lead to a reduction in the cost of irrigation water, a

decrease in ecological damage from pollution of water resources, and a change in soil

fertility.

In scenarios 1 and 2 (rice-crop rotations), soil fertility decreases as a result of the

intensive washing regime. The irrigation of forage crops leads to an increase in soil fertility

90

in scenarios 3 and 4. The pasture's fertility rises in scenarios 3 and 4 due to increased forage

production for cattle breeding and a consequent decrease in pasture load. As a whole,

ecological benefit is expected in all scenarios (from 600 to 1,922 $/ha).

Efficiency of water-resource use (ratio of cost of agricultural crop yield to total

water intake from the Ily River) is as follows: scenario 1—0.031 $/m3, scenario 2—0.066

$/m3, scenario 3—0.175 $/m3, and scenario 4—0.268 $/m3.

The efficiency of investments is determined as a ratio of NPV ( KNPV∑ ):

scenario 1 = -4.96 $/$, scenario 2 = -2.20 $/$, scenario 3 = +1.19 $/$, and scenario

4 = +1.45 $/$.

Analysis of the NPV indicates that the components most influencing it are:

efficiency of the constructed irrigation system; structure of irrigation lands used (percent of

rice-crop rotations), which defines efficiency of water-resource utilization; and ecological

damages and benefits. Cost of agricultural crops, capital investments and annual expenses

have smaller effects on the NPV. In scenarios 1 and 2 (efficiency factor 0.5-0.75, rice

rotation 62.5-37.5%), ecological damage to water resources (763-672 $/ha) leads to the

decrease in NPV. In scenarios 3 and 4 (efficiency factor 0.85-0.95, no rice rotation),

ecological benefits due to the increases in irrigated land and pasture fertility (+1,472 to

+1,762 %/ha) positively affect NPV. An improvement in technical performance of the

irrigation system leads to a reduction in drainage flow and water-resource pollution.

To conclude, this analysis shows that implementation of scenario 4, aimed at

developing irrigation for forage-crop production, is expected to be the most efficient among

the considered scenarios. This scenario involves building a technically perfect sprinkling

irrigation system characterized by an efficiency factor of 0.95 and land-use efficiency of

91

0.98, irrigation, and use of irrigated lands for forage production. The scenario is

characterized by minimal impact on water resources, and increased soil fertility.

92

Table 4.12. Specific characteristics of economic and ecological benefits and damages for different scenarios.

Benefit/damage from change in soil fertility, $/ha

Damage due to pollution of water resources, $/ha

Scenario Cost of irrigation

water, $/ha

Cost of agricultural production,

$/ha

NPV, $/ha

Cost of agricultural production per 1 m

KNPV

Irrigated land

Pasture Total Ground-water

Ily River

Total $/$ 3 of water, $/m3

1 743 -274 +871 +597 25 1,335 1,360 1,162 -12,397 0.031 -4.96 2 330 -207 +837 +630 25 1,282 1,307 1,098 -6,593 0.066 -2.20 3 122 +440 +1,274 +1,714 25 216 242 1,067 +4,176 0.175 +1.19 4 109 +440 +1,482 +1,922 25 137 162 1,407 +5,812 0.258 +1.45

93

CONCLUSION

An analysis of the management of limited water resources in arid lands shows that

too often, major attention is paid to economic and technological problems, while

environmental damage is considered a “progress cost” or is neglected entirely. Water

management is based on a “cost-efficiency” model. However, it has been found that this

“progress cost” is quite large, and can be comparable to production costs. In particular,

using this approach to develop agricultural land in the Ily River-Lake Balkhash basin in

Kazakhstan led to the construction of a rice-irrigation system over sand dune terrain, which

was characterized by low technical performance. Enormous water application and high

infiltration losses negatively impacted environmental conditions, causing a decrease in

irrigated-land fertility, pollution of groundwater and surface water, and desiccation of the

Ily River.

A sustainable management strategy for land and water resources should take into

consideration quantified economic, ecological, social and political factors. Here, a net

present value (NPV) criterion of efficiency was used to compare different scenarios of

agricultural development in the study area. The NPV criterion accounts, in monetary terms,

for the benefits from agricultural production and damage due to changing soil fertility,

salinization and contamination of soil and water resources.

Four alternative use scenarios for water, land and material resources were

considered: 1) exploitation of the existing rice-irrigation system with rice fields occupying

62.5% of irrigated land (Soviet era policy); 2) reconstruction of the existing rice-irrigation

system and changing the structure of the irrigated land by decreasing rice fields to 37.5% of

irrigated land; 3) development of furrow irrigation aimed at the production of forage crops

for cattle breeding, and 4) development of highly efficient sprinkling irrigation aimed at the

94

production of forage crops. The restrictions were the same for each scenario: maximum

available total water consumption, available amount of investments and ability to keep

water content and salinity of the root zone within admissible limits.

Purchase prices for agricultural production, annual agricultural and land-

reclamation expenses, specific costs of water and soil, and ecological-economic factors

(such as specific costs of damage to water resources due to salinization and contamination),

were taken from the Kazakhstan Ministry of Agriculture's data and related documents.

Estimations of economic benefits and ecological damage were based on long-term

forecasting of water and salt regimes in irrigated lands and pollution of water resources,

using mathematical models of water flow and solute transport (WASTR3-A) and

hydrological and pesticide balance (GLEAMS).

Results of the simulations and a comparison of the NPV for the alternative

scenarios led to the following conclusions:

1. In scenario 1, irrigated area is limited by the volume of irrevocable water supply; for

scenarios 2, 3 and 4, irrigation development is limited by the volume of investments.

2. Ecological damage to the irrigated land and water resources depends on the structure

of agricultural development, and the technique and technology of irrigation. In

scenarios 1 and 2, soil fertility decreases and there is intensive pollution of water

resources due to low technical performance of the irrigation system (water-use and

land-use efficiency are 0.5-0.75 and 0.64-0.85, respectively). The cost of the

ecological damage in scenarios 1 and 2 is 1.4 and 1.36 times higher, respectively, than

the value of the benefits from selling agricultural production. In scenarios 3 and 4,

improvement of irrigation techniques (by increasing water- and land-use efficiency to

0.85-0.95 and 0.90-0.98, respectively) and changing agricultural crop patterns lead to

95

a decrease in water consumption per unit yield, a decrease in pollution and an

increase in irrigated soil fertility.

3. In all scenarios 3 and 4, development of the irrigated land leads to an increase in

natural pasture fertility (especially in scenarios), since the pasture load is reduced by

having forage production on irrigated land.

4. Scenario 4 was found to be the most efficient and to provide the maximum NPV.

Thus, investments in infrastructural improvements and crop-pattern changes are

necessary to sustain the irrigated agriculture and the associated environment in the region.

The developed model does not address the treatment of inherent uncertainties

resulting from hydrologic variability, derivation of the abatement cost function that

describes the cost of reducing the generated pollution from each source, and the distribution

of costs between responsible parties, i.e. equity. Accounting for these factors is very

important (e.g., Khadam et al. 2006) and should be a topic for future research.

96

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APPENDIX 1: Long-term forecasting of water-salt regimes of irrigated lands, calculation of irrigated area and pollution of the environment under the various scenarios. Table 1.1A. Net and total water application calculated using the WASTR3-A code, m3/ha.

Scenario Agricultural crop 1 2 3 4

Rice 28,000 28,000 - - Net water application for barley 2,020 2,020 2,020 2,020 Net water application for alfalfa 1st year

2,710 2,710 6,530 6,530

Net water application for alfalfa 2nd year

3,750 3,750 6,530 6,530

Net water application for corn - - 6,350 6,350 Net water application for forage corn - - 6,280 6,280 Net water application for beet - - 5,500 5,500 Average net water application 18,560 12,370 5,190 5,190 Efficiency factor, % 50 75 85 95 Average total water application 37,120 16,490 6,100 5,460

Table 1.2A. Groundwater pollution by biogens and the pesticide Bolero.* Scenario Characteristics

1 2 3 4 Biogen mass in groundwater (in 3-m layer), kg/ha

17 17 17 17

Total biogen content over the whole area, ton

609 1,541 1,321 1,156

Biogen mass in groundwater (in 3-m layer), µg/ha

23.5 23.5 - -

Total pesticide content over the whole area, ton

0.00084 0.002 - -

*Total mass of pollution was calculated as M= Smhc ⋅⋅⋅ , where c is average concentration, h is average thickness of polluted layer, m is porosity, and S is total irrigated land area. Biogen content in groundwater is 1.23 mg/l (Veselov et al., 1996). Average concentration of Bolero in groundwater according to GLEAMS simulations is 0.0017 µg/l. Average depth of polluted groundwater is 3 m, porosity m = 0.48.

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Table 1.3A. Pollution of the Ily River by dumping of surface and drainage water.*

Scenarios Characteristics 1 2 3 4

Dumping of water-soluble salts, ton/ha 2.89 1.942 2.818 1.466 Total dumping of water-soluble salts over the whole area, ton

103,462 176,080 222,844 99,688

Biogen dumping, kg/ha 11.4 7.9 0.9 0.8 Total dumping of biogen over the whole area, ton

408 716.2 69.9 53.5

Dumping of Bolero, g/ha 73.3 73.3 - - Total dumping of Bolero over the whole area, ton

2.63 6.65 - -

*Dumping of salts, the pesticide Bolero and biogens into the Ily River was calculated using the WASTR3-A code, the GLEAMS code and experimental data, respectively.

Table 1.4A. Changes in irrigated land fertility.

Scenarios Characteristics 1 2 3 4

Solar radiation, kJ/cm2.year 60 60 60 60 Precipitation, cm 25 25 25 25 Net irrigation rate (average), cm 185.6 129.7 51.87 51.87

( ) ( 256.0/60/ ×=×= PLRR ) 4.0 4.0 4.0 4.0

)(1 IrrPLRR += 0.47 0.67 1.30 1.30 β 0.5 0.5 0.5 0.5

1β 4.5 3.1 1.4 1.4 Q (equation 4.3), kJ/cm2.year 9.9 9.9 9.9 9.9

1Q (equation 4.3), kJ/cm2.year 7.26 7.50 9.72 9.72 Change in soil fertility (equation 4.4), % -10.4 -7.4 +20.0 +20.0

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Table 1.5A. Changes in natural pasture fertility.

Scenarios Characteristics 1 2 3 4

Crop yield: Alfalfa, ton/ha 5.0 5.0 10.0 13.0 Barley, ton/ha 2.5 2.5 4.0 5.0 Forage corn, ton/ha - - 50.0 60.0 Corn, ton/ha - - 8.0 9.0 Beet, ton/ha - - 40.0 50.0 Mass of forage production under existing conditions (ω = 300,000 ha), c.f.u.*

130,176 **,***

130,176 130,176 130,176

Predicted volume of forage production, c.f.u.

203,344 1,127,141 4,650,345 5,577,768

Increase in forage production, c.f.u. 73,216 997,013 4,520,217 5,447,640 Present load to pasture ( ) 0PΔ 0.66 0.66 0.66 0.66

Predicted load to pasture ( ) 1PΔ 0.47 0.20 0.06 0.05

10 PPP Δ−Δ=Δ 0.19 0.46 0.60 0.61

10055.02 ××Δ=Δ PS , % +10.4 +25.3 +33.0 +33.6 * c.f.u.—center of fodder unit. **Government Program of Agricultural Area Development in the Kazakhstan Republic

during 2004-2010 (2003). ***Mass of forage production defined by the equation

... ufcefficiencyuseLandyieldCrop ×××× αω (whereω is area of natural pastures) α is percent of structure of irrigated land use), i.e. for existing conditions -

176,13064.0000,30)1125.04.1746.025.040( =××××+×× .

Changes in natural pasture fertility were defined using values of produced forage

and decreased pasture load. Calculations of produced forage (in c.f.u.) took into account the

specific value of an agricultural crop (barley—1.0, alfalfa and forage corn—0.46, beet—

0.20) by multiplying this parameter by actual crop mass.

The was calculated as follows. Taking into account that there are about 33,000

head of cattle, each requiring around 30 c.f.u/year, the total forage requirement is

Currently, forage supply in the study area is about 50% of the

required amount, i.e.

PΔ

...000,99030000,33 ufc=×

...000,4955.0000,990 ufc=× . Of this, 60% or 297,000 c.f.u. is pasture

forage, 130,176 c.f.u. is forage production on the existing irrigated lands, and 67,824 c.f.u

is produced on natural holdings (pastures in the Ily River floodlands). Thus, the current

106

pasture load is 66.05.1000,300

000,297000,297=

×=

× nPω (where ω is area of natural pastures, = 1.5

c.f.u. (

nP

http://www.minagri.kz)—the productivity of natural pastures).

The increase in forage production in irrigated lands in all scenarios will lead to a

decrease in pasture load. Total forage production in scenario 1 is

Therefore, the pasture load will drop 1.40 times

(=

...344,698000,495344,203 ufc=+

000,495344,698 ) and will be ...143,212

40.1000,297 ufc= or 47,0

000,450143,212

= ( nP×= ω000,450 ). In that

case . For scenarios 2, 3 and 4 19.047.066.0 =−=ΔP PΔ is +0.46, +0.60 and +0.61,

respectively.

Hence the soil fertility changes for natural pastures ( 2SΔ ) as defined by equation

(4.6) for scenarios 1, 2, 3 and 4 are 0.104, 0.253, 0.33 and 0.336, respectively.

107

APPENDIX 2: NPV calculation Table 2.1A. Calculated cost of agricultural production.

Scenario Crop 1 2 3 4 Crop yield, ton/ha

Rice 5.0 5.5 - - Alfalfa 5.0 5.0 10.0 13.0 Barley 2.5 2.5 40 50 Forage corn - - 50.0 60.0 Corn - - 8.0 9.0 Beet - - 40.0 50.0

Purchase price, $/ton Rice 550 550 - - Alfalfa 50 50 50 50 Barley 110 110 110 110 Forage corn - - 50 50 Corn - - 176 176 Beet - - 30 30

Cost of agricultural production, million $ Rice 39.4 87.4 - - Alfalfa 1.40 9.6 8.7 10.8 Barley 0.8 2.6 7.7 9.2 Forage corn - - 43.7 50.0 Corn - - 12.3 13.2 Beet - - 10.5 12.5 TOTAL, million $ 41.6 99.6 82.9 95.7 Table 2.2A. Cost of ecological damages/benefits due to changing soil fertility and pollution of irrigated lands.

Scenario Parameter 1 2 3 4

Changing soil fertility, % -10.4 -7.4 +20.0 +20.0 Specific cost of soil, $/ha 1,000 1,000 1,000 1,000 Irrigation area, ha 35,800 90,670 77,700 68,000 Damage/benefit, million $ -8.2 -14.8 +34.2 +29.9 Damage due to soil pollution by pesticides, million $

-1.6 -4.0 - -

Total benefit/damage, million $ -9.8 -18.8 +34.2 +29.9

108

Table 2.3A. Cost benefits due to increase in natural pasture fertility.

Scenario Parameter 1 2 3 4

Increase in fertility, as a fraction of 1

+0.104 +0.253 +0.330 +0.336

Specific cost of soil, $/ha 1,000 1,000 1,000 1,000 Pasture area, ha 300,000 300,000 300,000 300,000 Ecological benefit, million $ +31.2 +75.9 +99.0 +100.8 Table 2.4A. Cost of ecological damage due to water-resource pollution.

Scenarios Characteristics 1 2 3 4

Groundwater Biogen content, ton 609 1,541 1,321 1,156 Content of Bolero pesticide, ton 0.00084 0.002 - -

Equivalent mass of pollutants ( rKimnM ×∑= )

Biogen ( ), standard ton 0.1=rK 609 1,541 1,321 1,156 Pesticide ( ), standard ton 10000=rK 8.4 20.0 - - Total, standard ton 617.4 1,561 1,321 1,156

Ecological damage to groundwater (equation 4.7)

375011

××=×⎟⎠

⎞⎜⎝

⎛×= ∑∑

n

nE

n

nSW МKМDR ,

million $*

0.90 2.3 2.0 1.7

Ecological damage to the Ily River Dumping of water-soluble salt, ton 103,462 176,080 222,844 99,688 Biogen dumping, ton 408 716.3 67.9 53.5 Dumping of Bolero pesticide, ton 2.63 6.65 - -

Equivalent mass of pollutants ( rKimnM ×∑= )

Biogen ( ), standard ton 0.1=rK 408 716.0 68.0 54.0 Pesticide ( ), standard ton 10000=rK 26,300 66,500 - - Water-soluble salt (Кr = 0.05), standard ton 5,173 8,804 1,142 4,984 Total, standard ton 31,881 76,020 11,210 5,038

Ecological damage to surface water (equation 1.15)

375011

××=×⎟⎠

⎞⎜⎝

⎛×= ∑∑

n

nE

n

nSW МKМDR ,

million $*

47.8 114.0 16.8 7.6

Total damage (groundwater + surface water), million $

48.7 116.3 18.8 9.3

* =750 $/ton is the specific cost of ecological damage from water pollution of the Ily River basin, = 3 is the parameter characterizing ecological significance of water resources of the Ily River basin (Methods of Assessment of Averted Ecological Damage, 1999).

SD

EK

109

Table 2.5A. Cost of irrigation water.

Scenarios Characteristics 1 2 3 4

Total water application, m3/ha 37,120 16,491 6,100 5,460 Calculated area of irrigation, ha 35,800 90,670 77,700 68,000 Volume of water intake, thousand m3 1,328,896 1,495,239 473,970 371,280 Cost of water as a resource ( $02.0× ), million $

26.60 29.9 9.5 7.4

Table 2.6A. Annual expenses.

Scenarios Expenses 1 2 3 4

Agricultural expenses (55% of production cost), million $

22.9 54.8 45.6 52.6

Land-improvement expenses ( ), $/ha

IIn 200 220 250 300

Land-improvement expenses over the whole area, million $

7.2 19.9 19.4 20.4

Total expenses, million $ 30.1 74.7 65.0 73.0

110

NPV calculation NPV value was calculated using data from Appendices 1 and 2 and equation (4.2). Calculations were performed taking into account the increase in irrigated land area according to planned investments.

Table 2.7A. NPV for scenario 1.

Years ω , ha

RA, million

$

R1, million

$

R2, million

$

RW, million

$

InA + InI, million $

InW, million

$

tND −+ )1( Ci,

million $

NPV, million

$

∑NPV , million $

1 30,000 +34.9 -8.2 0 -40.8 -25.2 -22.3 0.93 7.25 -64.5 -64.5 2 32,900 +34.9 -8.2 0 -40.8 -25.2 -22.3 0.87 7.25 -60.8 -125.3 3 35,800 +38.2 -9.0 +15.6 -44.8 -27.7 -24.4 0.82 0 -42.7 -168.0 4 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.76 0 -32.4 -200.4 5 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.71 0 -30.2 -230.6 6 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.67 0 -28.5 -259.1 7 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.62 0 -26.4 -285.5 8 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.58 0 -24.7 -310.2 9 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.54 0 -23.0 -333.2 10 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.51 0 -21.7 -354.9 11 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.48 0 -20.4 -375.3 12 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.44 0 -18.7 -394.0 13 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.42 0 -17.9 -411.9 14 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.39 0 -16.6 -428.5 15 35,800 +41.6 -9.8 +31.2 -48.9 -30.1 -26.6 0.36 0 -15.3 -443.8

111

Table 2.8A. NPV for scenario 2.

Years ω , ha

RA, million

$

R1, million

$

R2, million

$

RW, million

$

InA + InI, million $

InW, million

$

tND −+ )1( Ci,

million $

NPV, million

$

∑NPV , million $

1 9,000 0 0 0 0 0 0 0.93 -27.0 -27.0 -27.0 2 19,000 +9.9 -1.9 +7.5 -11.5 -7.4 -3.0 0.87 -30.0 -35.6 -62.6 3 30,000 +20.9 -3.9 +15.9 -24.4 -15.7 -6.3 0.82 -33.0 -41.1 -103.7 4 40,000 +33.0 -6.2 +25.1 -38.5 -24.7 -9.9 0.76 -30.0 -46.1 -149.8 5 50,000 +43.9 -8.3 +33.5 -51.3 -33.0 -13.2 0.71 -30.0 -50.2 -200.0 6 60,000 +54.9 -10.4 +41.9 -64.1 -41.2 -16.5 0.67 -30.0 -53.7 -253.7 7 70,000 +65.9 -12.4 +50.2 -77.0 -49.4 -19.8 0.62 -30.0 -56.4 -310.1 8 80,000 +76.9 -14.5 +58.6 -89.8 -57.7 -23.1 0.58 -30.0 -58.8 -368.9 9 90,000 +87.9 -16.6 +67.0 -102.6 -65.9 -26.4 0.54 -30.0 -60.6 -429.5 10 90,670 +98.9 -18.7 +75.3 -115.4 -74.1 -29.6 0.51 2.0 -34.4 -463.9 11 90,670 +99.6 -18.8 +75.9 -116.3 -74.7 -29.9 0.48 0 -30.8 -494.7 12 90,670 +99.6 -18.8 +75.9 -116.3 -74.7 -29.9 0.44 0 -28.0 -522.7 13 90,670 +99.6 -18.8 +75.9 -116.3 -74.7 -29.9 0.42 0 -27.0 -549.7 14 90,670 +99.6 -18.8 +75.9 -116.3 -74.7 -29.9 0.39 0 -25.0 -574.7 15 90,670 +99.6 -18.8 +75.9 -116.3 -74.7 -29.9 0.36 0 -23.1 -597.8

112

Table 2.9A. NPV for scenario 3

Years ω , ha

RA, million

$

R1, million

$

R2, million

$

RW, million

$

InA + InI, million $

InW, million

$

tND −+ )1( Ci,

million $

NPV, million

$

∑NPV , million $

1 7,800 0 0 0 0 0 0 0.93 -27.0 -27.0 -27.0 2 15,600 +8.3 +3.4 +9.9 -1.9 -6.5 -1.0 0.87 -30.0 -19.4 -46.4 3 23,400 +16.6 +6.9 +19.9 -3.8 -13.0 -1.9 0.82 -33.0 -12.7 -59.1 4 31,200 +25.0 +10.3 +29.8 -5.7 -19.6 -2.9 0.76 -30.0 -2.0 -61.1 5 39,000 +33.3 +13.7 +39.8 -7.5 -26.1 -3.8 0.71 -30.0 +5.1 -56.0 6 46,800 +41.6 +17.2 +49.7 -9.4 -32.6 -4.8 0.67 -30.0 +11.4 -44.6 7 54,600 +49.9 +20.6 +59.6 -11.3 -39.2 -5.7 0.62 -30.0 +15.8 -28.8 8 62,400 +56.2 +24.0 +69.6 -13.2 -45.7 -6.7 0.58 -30.0 +18.8 -10.0 9 70,200 +66.6 +27.5 +79.5 -15.1 -52.2 -7.6 0.54 -30.0 +23.3 +13.3 10 77,700 +74.9 +30.9 +89.4 -17.0 -58.7 -8.6 0.51 -2.0 +54.6 +67.9 11 77,700 +82.9 +34.2 +99.0 -18.8 -65.0 -9.5 0.48 0 +58.9 +126.8 12 77,700 +82.9 +34.2 +99.0 -18.8 -65.0 -9.5 0.44 0 +54.0 +180.8 13 77,700 +82.9 +34.2 +99.0 -18.8 -65.0 -9.5 0.42 0 +51.6 +232.4 14 77,700 +82.9 +34.2 +99.0 -18.8 -65.0 -9.5 0.39 0 +47.9 +280.3 15 77,700 +82.9 +34.2 +99.0 -18.8 -65.0 -9.5 0.36 0 +44.2 +324.5

113

Table 2.10A. NPV for scenario 4.

Years ω , ha

RA, million

$

R1, million

$

R2, million

$

RW, million

$

InA + InI, million $

InW, million

$

tND −+ )1( Ci,

million $

NPV, million

$

∑NPV , million $

1 6,800 0 0 0 0 0 0 0.93 -27.0 -27.0 -27.0 2 13,600 +9.6 +3.0 +10.1 -0.9 -7.3 -0.7 0.87 -30.0 -18.0 -45.0 3 20,400 +19.1 +6.0 +20.2 -1.9 -14.6 -1.4 0.82 -33.0 -10.5 -55.5 4 27,200 +31.9 +9.0 +30.3 -2.8 -21.9 -2.2 0.76 -30.0 +3.7 -51.8 5 34,000 +38.3 +12.0 +40.4 -3.7 -29.2 -3.0 0.71 -30.0 +8.9 -42.9 6 40,800 +47.8 +15.0 +50.5 -4.6 -36.5 -3.7 0.67 -30.0 +15.9 -27.0 7 47,600 +57.4 +18.0 +60.6 -5.6 -43.8 -4.4 0.62 -30.0 +21.0 -6.0 8 54,400 +67.0 +21.0 +70.7 -6.5 -51.1 -5.2 0.58 -30.0 +25.6 +19.6 9 61,200 +76.6 +24.0 +80.8 -7.4 -58.4 -5.9 0.54 -30.0 +29.2 +48.8 10 68,000 +86.1 +27.0 +90.9 -8.4 -65.7 -6.7 0.51 -2.0 +60.8 +109.6 11 68,000 +95.7 +29.9 +100.8 -9.3 -73.0 -7.4 0.48 0 +65.6 +175.2 12 68,000 +95.7 +29.9 +100.8 -9.3 -73.0 -7.4 0.44 0 +60.1 +235.3 13 68,000 +95.7 +29.9 +100.8 -9.3 -73.0 -7.4 0.42 0 +57.4 +292.7 14 68,000 +95.7 +29.9 +100.8 -9.3 -73.0 -7.4 0.39 0 +53.3 +348.0 15 68,000 +95.7 +29.9 +100.8 -9.3 -73.0 -7.4 0.36 0 +49.2 +395.2