Kazakhstan: Overall Approaches and Preliminary Results from Country Study

Sergei Kavalerchik

Asya Fisher

Main Administration for Hydrometeorology at the Cabinet of Ministry of the Republic of Kazakhstan

Vsevolod Golubtsov

Edward Monocrovich

Olga Pilifosova

Irene Yeserkepova

Paishan Kozhahmetov

Lubov Lebed

Olga Glumova

Ivan Skotselyas

Valery Lee

Svetlana Dolgih

Zoja Korneeva

Svetlana Mizina

Dmitriy Danchuk

Kazakh Scientific-Research Hydrometeorological Institute

Ervin Gossen

Alexei Startsev

Academy of Agricultural Sciences

Maria Amirhanova

State Statistical Committee

Nina Inosemtseva

Georgy Papafanasopulo

Ministry of Energy and Fuel Resources

Boris Akimov

Valentin Matveev

Ministry of Industry

Vladimir Medvedev

Alshin Ahmedzhanov

Ministry of the Environment

SUMMARY: This report provides the overall approach and some results of the work of the "Kazakhstan Climate Change Study" project in three main areas: inventory of greenhouse gas (GHG) emissions and sinks, mitigation assessment, and vulnerability assessment. In the first area, the information on greenhouse gas sources and some sinks, estimates of emissions and removals for Republic of Kazakhstan for 1990, as well as a brief description of the methodology used to evaluate these estimates and the associated uncertainties are given. An estimation of future CO2 emissions from 1990 through 2000 is also presented. For the mitigation assessment, the principal methods and approaches for evaluating mitigation options for six sectors and some possible results in the energy sector are provided. The vulnerability assessment is addressing the following sectors: agriculture, forestry, and water resources. The methodology for this assessment, which includes both empirical-statistical and simulation approaches, is described. Also, this article discusses some preliminary results and uncertainties of the vulnerability assessment on the basis of GCM-based climate change scenarios associated with increasing CO2 concentrations in the atmosphere.


Unfavorable consequences of climate change in connection with anthropogenic increase of the concentrations of CO2 and other greenhouse gases in the atmosphere generate concern throughout the world and in Kazakhstan, too. The Republic of Kazakhstan is one of the 150 countries that signed the United Nations Framework Convention on Climate Change (UNFCCC). The Kazakhstan Government supports international cooperation on climate change issues.

The Republic of Kazakhstan covers 2,717,000 sq km with a population of over 17 million. The Republic consists of 19 regions, 220 districts, and over 80 cities and towns.

The Republic consumes about 10 million tons of coal, and its coal reserves are estimated at 39 billion tons. Oil production amounts to 26.6 million tons with the reserves estimated at about 2,357 million tons. There are also deposits of natural gas. The production of electric power exceeds 90 billion kWh including over 82 billion kWh produced by thermal stations. Per capita electric power consumption is 6,100 kWh annually.

The sowing areas of Kazakhstan exceed 35 million hectares including about three million hectares of irrigated land. The gross yield of grain is 25 to 30 million tons per year. Part of the grain is exported. The leading branch of animal husbandry is sheep breeding based on desert and semidesert pastures which occupy vast expanses. The number of cattle livestock is 9 million. Pigs, camels, and horses are also bred.

Forests in Kazakhstan occupy a small area, only 3.6 percent of the total territory (9,648,000 ha). Out of these, 1,800,000 ha are covered with coniferous forests, and the rest with leaf-bearing woods and shrubs. The largest portion, 4.7 million ha, is covered with saksaul.

The U.S.-Kazakhstan Project, "Kazakhstan Climate Change Study," was initiated on October 1, 1993. The main objectives of this project are to carry out the following:

Two committees have been organized for the fulfillment of these activities. These are the governmental and working committees. The representatives of nine Ministries and Departments of Kazakhstan take part in the implementation of the project. The Laboratory of Climate Change Study was established in the Kazakh Scientific Research Hydrometeorological Institute (KazNIGMI) to coordinate the work on the project.


Inventory of GHG Emission and Sinks

The Intergovernmental Panel on Climate Change (IPCC), together with other international scientific organizations (United Nations Environmental Program, Organization for Economic Cooperation and Development, Global Environment Facility, International Energy Agency, etc.), has prepared the methodology that was used for this work (IPCC Draft Guidelines for National Greenhouse Gas Inventories, 1994). These guidelines provide for comparison and estimation of the authenticity of work obtained in different countries.

Information about yearly fuel consumption of all fuels for 1990 is the basis for the calculation of GHG (greenhouse gases) emission from HPSs (heat power station) and large boiler-houses. Also, the year 1990 conforms to the IPCC recommendation. This information was obtained from the documents of the State Statistical Accounts. The yearly fuel consumption is recorded in tons for every HPS and for their sources of supply (e.g., deposit, oil refineries, etc.). Knowing the percentage content of carbon dioxide in every fuel, we can determine the quantity of carbon dioxide burned by simple multiplication of this percent by the volume of consumed fuel.

However, the methodology of IPCC can be difficult to apply. This is possibly connected with the fact that some countries do not have as good an initial data base as Kazakhstan does. For comparison of our results with data of other countries and to use the IPCC software, we made our calculations with IPCC methods.

Mitigation Analysis

The aim of Kazakhstan's mitigation analysis is to develop recommendations about options to decrease GHG emissions and increase CO2 absorptions by vegetation. All the possibilities of GHG emission decrease in the branches of Kazakhstan economy were evaluated taking into account both their benefits and costs. The scope of these branches includes energy, industrial, transportation, residential and commercial, agriculture, and forestry sectors. The evaluation of the total potential for decrease of GHG emissions in Kazakhstan will be based on two scenarios relating to "pessimistic" and "optimistic" hypotheses of development of the Kazakhstan economy for period through the year 2020.

The methodology will consist of the calculations of the likely decrease of GHG emissions with fuel switching, the introduction of the new technologies, the increased use of renewable energy sources, more active use of CH4, and other options. The official state statistical data will be used. If these data are not available, the methods of balance accounts will apply. The latter are based on data which were obtained by the Scientific Research Institutes of Kazakhstan as well as on calculation of specific coefficients contained in the IPCC methodology.

The creation of scenarios of economic development will take into account the predicted assessments of the Departments of Trade Ministry, Kazakhstan Ministry of Economics (former State Planning Committee of Kazakhstan) and Kazakhstan Institute of Economics of Academy of Science. To carry out the mitigation analysis in the energy sector the ENPEP model‹ developed by Argonne National Laboratory‹ will be used.

Vulnerability Assessment

The climate change vulnerability assessment in Kazakhstan is addressing the following sectors: crops, potatoes, grasslands, sheep-breeding, water resources, and forestry. These sectors have been chosen as being of the widest interest to our country and as having the most susceptibility to climate change.

The first step in the vulnerability assessment was the development of future climate change scenarios. Scenarios of the main climate change for Kazakhstan to asses both long-term (doubling CO2 in 2050‹ 2075) and short-term (2010, 2030) impacts were prepared. The climate change under a doubling of CO2 was obtained on the basis of General Circulation Models (GCM) outputs. We used outputs of three GCMs: the model of Canadian Meteorological Center (CCCM) (Boer et al., 1991), the model of Geophysical Fluid Dynamics Laboratory (GFDL) (Manage and Wetherald, 1987), and a transitional version of the same model (GFDL-Transient).

The near-term climate scenarios were obtained using the Probabilistic Forecast Model (PFM) (Gruza and Rankova, 1991) we adapted to Kazakhstan (Pilifosova, 1991) for the year 2010 and the results of GFDL‹T for 2030. Moreover, to evaluate future climate changes for comparison, a baseline climate scenario was used. This scenario represents the current climate for the base period 1951‹ 1980 without a warming trend in the baseline.

The assessment of vulnerability in some sectors was based on models developed in Kazakhstan. Thus, for the estimation of yield of agricultural crops and grasslands we used a nonstationary dynamic model that had been developed by KazNIGMI several years ago (Lebed and Belenkova, 1991). A similar model was employed for estimating the yield of potatoes (Glumova, 1988). An assessment of the vulnerability of sheep-breeding was carried out on the basis of an unfavorable weather conditions criterion for sheep productivity (Gulinova, 1988). A mathematical runoff model was applied in the water resources sector (Golubtsov et al., 1989).

In general, the main principle for application of these models to the vulnerability assessment was defined as follows. Different climate variables (air temperature, insolation, humidity, rainfall, etc.) were used as model input parameters. The simulation first was run under the current climate conditions in accordance with a baseline climate scenario. Then input climate parameters were changed according to the regional climate change scenarios and used in another simulation. The difference between these simulation outputs represented the changes in the yield of agricultural crops, grasslands, livestock (sheep), water resources, and forests, which occurred due to climate change impacts.

Obviously, in a number of cases the application of these models required the transition of climate parameters from one space-time averaging scale into another. For example, monthly means of air temperature and precipitation were obtained from the GCMs. But models of the yield of agricultural crops require the 10-day mean data. After the data adaptation and fitting of model parameters, analysis of the model sensitivity to different changes (e.g. incremental scenarios) of input climate parameters was conducted. For example, the estimation of vulnerability of crops and sheep when air temperature is increased by 0.5°C through 4.0°C was conducted. Input data included observed climatic data from numerous reference books, data on crops productivity of The State Committee on Statistics and some experimental data from agricultural fields.

In addition to models developed in Kazakhstan, we used the DSSAT (Decision Support System for Agrotechnology Transfer) model. DSSAT integrates the models which generally describe the development, growth and yield of crops on homogenous area of soil exposed to certain weather conditions. This system was useful for running and validating the models, for conducting sensitivity analysis, and for evaluating the variability and risks of different management strategies for a range of locations specified by soil and weather data. The CERES-Wheat model (Ritchie, Otter, 1985) from DSSAT developed by IBSNAT (International Benchmark Sites Agrotechnology Transfer) was used for spring and winter wheat productivity vulnerability assessment in Kazakhstan, which was based on the GFDL and CCCM scenarios.

The Holdridge Model was used for the assessment of vulnerability of forestry. We prepared the distribution of Holdridge life zones (Holdridge, 1967) under the current climate conditions as well as the maps of these zones for four climate change scenarios on the basis of GCM outputs for a doubling of CO2 levels in the atmosphere: GISS (Hansen et al., 1983), UKMO (Wilson and Mitchell, 1987), OSU (Shlesinger and Zhao, 1989), and GFDL. These GCM outputs were built into the Holdridge model.

The Holdridge model relates the current spatial distribution of vegetation to features of the climate system. The Holdridge classification is suitable for examining the broad-scale patterns of vegetation as they relate to climate and how changes in climate patterns may influence the suitability of a region to support different vegetation/forest types. However, this approach does not address vegetation processes per se and as such cannot be used to predict the temporal dynamics of species composition and stand productivity, features that are important in evaluating the potential impacts of environmental change on forest resources and conservation. In order to make up the maps of the territorial distribution of forest we chose the additional scheme, which connected the forests distribution with a precipitation and evapotranspiration (PET) model.


The GHG emission inventory for the Kazakhstan territory for 1990 was completed following the IPCC methodology. The largest stationary sources include HPSs and district boiler houses, 23 enterprises of the ferrous and nonferrous metal industry, 11 enterprises of the oil and gas industry, 8 enterprises of the chemical industry, 5 of the largest machine- building plants, and 10 cement and asbestos plants (all together 105 enterprises). The emissions from other fuel consuming enterprises (i.e., food and light industries, municipal economy, agriculture and cattle-breeding) were also taken into account. The nonstationary sources such as internal-combustion engines on automobiles, locomotives, air-crafts and river-boats, on agricultural and constructional engineering were defined as separate sources.

The results of calculations of annual CO2, CH4, CO, N2O, NOx and nonmethane volatile compounds emissions are divided into 13 groups:

Estimates of emissions of nitrous oxide, carbon monoxide and nonmethane volatile compounds were obtained from the records at the State Statistical Accounts. Emissions of carbon dioxide, methane, and nitrous oxide have been determined by balance calculations taking into account real fuel consumption, quantity of cattle, rice area, and other data. The emission factors recommended by IPCC and regional institutes were used in the calculations. Values of specific GHG emissions of internal combustion engines were obtained from the Kazakh Scientific Research Institute of Motor Transport.

As a result, both the summary emissions of all six GHGs for 1990 and the contribution of separate sources (or branches of industry) were defined. The calculation results are presented in Table 1. The results are expressed in gigagrams (Gg) in accordance with the IPCC. More than 90 percent of all GHG emissions is, as expected, from CO2 (198,729 Gg or nearly 200 million ton/yr). Thus the per capita CO2 emission is over 11 tons/yr.

CO2 absorption from the atmosphere by forests of Kazakhstan was estimated. The calculations have shown that forests absorb up to 1,530 Gg/yr or less than 2.55 of the total emissions.

The largest sources of CO2 emission are heat power stations and district boiler-houses (48.5 percent), residential boiler-houses and stoves (17.2 percent), internal combustion engines (ICE) (12.9 percent), and enterprises of ferrous and nonferrous metallurgy (5.2 percent). The largest sources of NOx emission are ICEs (53.7 percent) and heat power stations (36.4 percent). The largest sources of CH4 emission are from solid waste open dumps and wastewater treatment (49.5 percent) and from agriculture (27 percent). The largest sources of CO emissions are ICEs (67.8 percent), metallurgy (18.3 percent), residential boiler-houses and stoves (3.2 percent). Data are presented in percentages of the total emissions of the respective gas.

In accordance with the IPCC guidelines, the estimation of the initial data reliability (uncertainty) was made. The most reliable are the data on heat power stations, which give 49,5 percent of the total emission of CO2, 39 percent of the total emission of NOx, and 19 percent of the total emission of CO. The data were obtained by the analyses of CO and NOx contained in waste gases, and CO2 was obtained by the balance calculations. The probabilistic errors here do not exceed 5 percent. In other branches of industry the power registration data are not highly accurate so that possible errors are within the limits of 20 percent. The most unreliable calculation results are those connected with ICE (13 percent of CO2 emission, 53.7 percent of NOx emission and 67 percent of CO emission). As for the estimation of the authenticity of the data, the comparison of our indices with the data reported by the State Statistical Committee showed that variations on separate gases were within the limits of 5ѳ 20 percent. For example, the total NOx emission from stationary sources in 1990 was 330 Gg in the State Statistical Committee data but it was 314.7 Gg in our calculations. The emission indices for residential boiler-houses and stoves are the most unreliable, but these emissions are not high.

In Table 2 the predicted data of CO2 emission for the period from 1991 to 2000 taking into account expert assessments and fuel consumption of main branches of economy are shown. These data show that the total CO2 emission volume for the decade from Kazakhstan territory will be 1,582,000 Gg. At the same time CO2 absorption by forests is estimated at 40,000 Gg. Thus, the difference (without consideration of CO2 absorption by other reservoirs) will be 1,542,000 Gg.

<h3Mitigation The results of an evaluation of options to mitigate net emissions of greenhouse gases in the energy sector by the reconstruction and modernization of old HPSs, and the use of steam-gas cycles is presented. The decrease of specific fuel consumption from 350 g c.f./kWh (grammies conventional fuel/kWh)(on Rankine cycle) to 190 or 160 g c.f./kWh is achieved with electric power output by central heating. In addition the specific CO2 emissions decrease by 480 g c.f./kWh. When additional electric power is produced by central heating cycle the decrease of CO2 emission is estimated to be 25,872 Gg for 1996- 2020.

The Aktubinsk HPS building that will use a steam-gas cycle and produce 954 NW of power and 6 billion kWh annual electric power production will be completed in 2000. Similar powerplants with less capacity are expected to be put into operation in Uralsk and Atyrau (in 2000- 2005). The problem of replacing traditional steam turbine engines with gas turbines in Uralsk HPS‹ 1, Atyrau HPS, Shimkent HPS‹ 1, HPS‹ 2, Jambyl HPS‹ 3, Jambyl state district electric power station (SDEPS) is being studied. When energy is produced by steam-gas HPSs the CO2 emission will decrease by 11,988 Gg. The total decrease of CO2 emission from energy sources is estimated at 37,860 Gg.

Concerning the use of renewable sources, the most promising project is the development of a wind-electrical station "Jungarskie vorota" with 300 megawatts power and 900 billion kWh annual power production. In addition the wind-electric engines in the Chilick corridor, Kurday passage, Jengiz-To, Derjavinka, and Mugojary are projected to be put into operation. Also wind- electric engines with small capacity are planned for remote locations for water pumping, heating and electricity generation. The decrease of CO2 emission associated with renewable energy options will be 4,627.2 Gg for 1996‹ 2020.

Vulnerability Assessment

As a result of the above described approaches, "optimistic" (GFDL) and "pessimistic" (CCCM) scenarios under 2 x CO2 conditions, were defined. GFDL- Transient outputs give an "intermediate" scenario. According to the GFDL model, a minimum increase of temperature is expected in summer, when most of territory will be 4‹ 5° C warmer. The maximum (about 8°C) is expected to be in the winter. The mean annual temperature increase is about 5°C. According to CCCM, the mean annual temperature increase is 7°C and the maximum is of 12°C in the spring. In most cases, the relative changes of precipitation will be in the range of 0.8‹ 1.2 or 80‹ 120 percent (i.e., within the normal limits).

Our calculations based on the observed data in Kazakhstan show that the rise of annual average air temperature is 1°C/100 years and this is approximately twice as much as the mean global rise of temperature. The analysis of prediction curves of temperature and precipitation with the use of the PFM model has allowed us to conclude that the rise of CO2 concentration in the atmosphere will cause an average rise of aridness (the increase of temperature and decrease of precipitation) all over the region. The highest rise of temperature will be 6°C in comparison with the mean temperature for 1951‹ 80. It is expected to occur in the cooler half of the year in the North of Kazakhstan. For the rest of the territory of the Republic, an increase in the temperature of 1‹ 3°C in the summer and 3‹ 4°C in the winter is expected by 2010.

There is a significant probability that the increase of CO2 concentration may cause some increase in atmospheric precipitation in the south and southwest, and an increase of aridness in the west and in the northeast Kazakhstan in the winter. In the summer a decrease of precipitation of 20‹ 50 percent is expected for all of Kazakhstan, except for the western regions.


To estimate the possible impacts of climate change on wheat production in the main wheat producing regions of Kazakhstan, the DSSAT model was used. The DSSAT model combines the CERES-wheat crop growth model under GFDL and CCCM scenarios. The GFDL scenario shows the spring and winter wheat yield increasing in Western and Central Kazakhstan by approximately 10 percent and 5 percent, respectively. However, in Northern Kazakhstan the yield decreases approximately by 12 percent. According to the CCCM scenario, the spring wheat yield would decrease by 35 percent and the winter yield would not decrease significantly. Note that the yields changes under the baseline scenario are about 2‹ 4 percent.

The results of the preliminary analysis on the basis of our models of crops vulnerability made for Western Kazakhstan show that the increase of air temperature for the period of shoot ripening of spring wheat causes significant deterioration of the thermal regime by 20‹ 50 percent relative to optimal conditions. In this case the forecast crops yield is expected not to be above 0.22‹ 0.44 ton/ha. In comparison the spring wheat yield was 0,82 ton/ha in 1991.


For the region located north of the Aral Sea the possible increase of air temperature in the vegetative season of 2°C is accompanied by some increase in grassland productivity (6‹ 20 percent). These increased temperatures may allow for a change in precipitation in the cool season of 30° 40 mm resulting in changes in feed productivity ranging from -18 to + 12 percent. A considerable decrease of productivity up to 40‹ 50 percent from existing level is estimated with temperature increases of 2 to 3°C. The possible climate changes due to a CO2 doubling scenario (e.g., GFDL model) may cause a 2‹ 3 times decrease in feed productivity on Priaralie grasslands in the summer-autumn period with some increase in its reserves in the spring.


The preliminary results on potato productivity were obtained for five North Kazakhstan regions. The calculations of dynamics of dry potato biomass during the vegetation season show the potential for considerable decrease of water storage in soil level. A 5mm decline in water levels would decrease productivity by 5‹ 8 percent and a decrease of water storage of 20 mm causes productivity losses of 20‹ 26 percent. Increasing the air temperature by 0.5°C decreases potato productivity by 2‹ 3 percent. An increase of air temperature by 2°C causes a productivity decline by 6‹ 10 percent.


In estimating vulnerability of sheep-breeding the data from observations of sheep pasture conditions for 1959‹ 1990 and biometeorological parameters defined earlier by other researchers were used. If the number of days in a ten day period with stable hot weather (SHW) equal 6 or more, a decrease of sheep weight is observed. Such unfavorable hot periods which repeat one after another and form a whole period with SHW are being currently observed in the South and East-South of Kazakhstan. An increase of air temperature of 1°C in May and June causes an increase of the average SHW duration of 3 to 6 days. The SHW duration increases slightly less (by 2 to 4 days) with rising air temperature in August and September. If temperatures in both periods increase at the same time by 2°C, the average duration of SHW periods will increase by almost two weeks. Changes in atmospheric precipitation in the summer months do not significantly change the duration of the SHW period.

The preliminary results of the probable influence of climate change on the basis of the three GCM scenarios on water resources of managed river basins in Kazakhstan were obtained. Water resources runoff in the highlands of Kazakhstan increased by 6‹ 12 percent in year 2030 under the CFDL‹ T scenario. However, the water resources runoff is reduced by 20‹ 30 percent under 2xCO2 conditions occurring later in the century according to the CCCM and GFDL scenarios, respectively.

Having analyzed the discrepancies obtained by the Holdridge (PET) model we have calculated the forest and forest-steppe zones in correspondence with the 2° CO2 climate conditions predicted by 4 models (GISS, UKMO, GFDL, OSU). The most pessimistic results were obtained using the UKMO model. According to this model only the northern part of the Republic (the stripe with the width of 70‹ 150 km located along the Northern boundary) remains a forest- steppe zone. The area suitable for forest growth according to UKMO model is expected to be 15 percent of that for current climate.

According to 2xCO2 OSU scenario, the forest area remains in its present-day boundaries. The area of forest-steppe zone according to the IET model is decreased along the Southern and Western boundary (50‹ 70 km). The most optimistic scenario of the forestry is obtained according to the climate change scenario based on the GISS model. It is the only one of four models which predicts the increase of suitable areas for the growth of the forests due to a probable climate warming. The boundary of steppe- forest-steppe is moved by 120‹ 180 km towards the south and the west. According to this scenario the areas suitable for forests growth are increased 1.6‹ 1.8 times.

The impacts of the scenarios of GFDL and the OSU scenarios are midway between the impacts of the GISS and the UKMO scenarios.


Conducting our work on the Kazakhstan Climate Change Study Project, we came across a number of uncertainties and problems. One of them is related to the assessment of future GHG emission in our country (Table). At the present time it is difficult to predict reliably the volume of GHG emissions for the period from 1991 to 2000. The state authorities in Kazakhstan have changed after the USSR was split up, specifically authorities such as the State Planning Committee have been dismantled. For 60 years from 1927, the planning and development of the national economy (in which the share of the private sector was minimal) was led by the government of the USSR in accordance with confirmed five- or seven-year plans. There are no such plans at the Departments and the role of private sector productive forces increases while that of state-owned sector decreases. As a result of that, it is impossible to receive any information from government offices. That is why we had to rely on expert evaluations.

The central problem with the mitigation analysis is the cost assessment of the mitigation options in view of the economical declines, especially production declines and inflation. It is a difficult challenge to predict the development of these processes now.

There are two principal sources of uncertainties in the vulnerability assessment. The first is associated with uncertainty of the climate change scenarios particularly at a regional level. It is known that increased greenhouse gas emissions will likely raise global temperatures and precipitation; however, no reliable suggestions can be made about their regional effect.

The second source of uncertainties arises from the imperfection of the models used in assessment of local conditions. The use of the DSSAT model demands input parameters which do not correspond to our data. For example, information about tillage, chemical composition of fertilization are not available. We often are limited in availability of current meteorological information for the input parameters of models. In this case we have to use the Weather Generators, which do not take into account the local climatic diversity of our regions. The DSSAT model is also oriented for local fields, while we need to obtain estimates for the whole Kazakhstan region.

Similar problems are connected with the use of the Holdridge model. In mountain regions the vertical zonality is formed, which is simulated poorly where the resolution of the database set (0.5*0.5 degrees) is small. Furthermore, such territories contain areas (especially hollows and canyons) which exist due to additional water- flow from the surrounding slopes. The result is that if the evapotranspiration exceeds the precipitation, the vegetation is still formed. Although this forest vegetation is of fragmentary character, the total area of these territories may be considerable. Neither the Holdridge model nor the PET model consider these specific conditions which cause errors in vegetation classification.

Both models fail to predict the pine forests propagation due to the fact that the ordinary pine is a drought-resistant species under the current conditions of Kazakhstan. It forms forests when the deficit of precipitation is 250mm or more. The ordinary pine grows under such conditions only on the sands with good aeration, developing powerful root systems, which can reach soil waters. The soil types are not considered in these models.

Therefore we tried to use the models worked out in KazNIGMI for the vulnerability assessment in agriculture and water resources sectors. But of course, the models we used have their own advantages and disadvantages. Advantages of these models are their good fitting for geographic, climate, and other peculiarities of the region of Kazakhstan and the use of observed data as inputs of model. The major disadvantage is that they first were made for near- term projections (month, season, timeframe) and then were modified for this vulnerability assessment. So we have to transform data from one time-scale averaging to another. This introduced additional uncertainties.


The main conclusions of this work are as follows:


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March 1995

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