Department of Physiology & Cell Biology Ben-Gurion University of the Negev, Israel
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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,
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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
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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):
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