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Project Number: 44066-012 (RETA 7532)
June 2012
Knowledge Product of RETA 7532: Water and
Adaptation Interventions in Central and West Asia
Climate Change and Sustainable Water Management
in Central Asia
Prepared by FCG Finnish Consulting Group Ltd
This consultant’s report does not necessarily reflect the views of ADB or the Government concerned, and
ADB and the Government cannot be held liable for its contents. (For project preparatory technical
assistance: All the views expressed herein may not be incorporated into the proposed project’s design.
Climate Change and Sustainable Water
Management in Central Asia
FCG International
Introduction
Climate change has well been documented all over the world in vast number of scientific
investigations and global climate model simulations. The field observations in Central Asia
indicate that the climate has been warming several decades and the consequences of this trend
have already been observed in the everyday life of the population. However, there is still
substantial lack of knowledge on the phenomena related to the climate change and its impacts
on environment and human life.
New climate and hydrological models show that river water must be seen partly as a nonrenewable resource in Central Asia. Today about one third of the water in rivers originates from
the mountain glaciers that are quickly losing their volume due to global climate warming. During
the past decades the rivers have received significant amount of excess water from the melting
glaciers, but in the future this source will increasingly be lost as a consequence of vanishing
glaciers.
Climate change will also make the plains hotter and drier. In the future, water shortage will be a
serious problem for national economy and environment. The need of water will increase at the
same time when the river discharges will diminish. This situation may generate water
management disputes and conflicts between people living in the mountains and plains.
Therefore, the decision-makers of the countries should urgently enhance regional co-operation
and launch programs to increase resilience to negative climate change impacts as well as plan
and implement adaptation interventions.
Increasing temperatures in the mountains will also result in thawing permafrost which again may
mobilize massive landslides and mudflows. Every year these disasters destroy settlements,
agricultural lands and infrastructure. In river basins where snow-melt is the main source of
water, spring floods may become more frequent. The Central Asian mountains have unique
landscapes and nature. The overall climate change will degenerate mountain ecosystems which
maintain biodiversity - rare animals and plants.
The Central Asian countries are signatory members of the United Nations Framework
Convention on Climate Change (UNFCCC). The objective of the Convention is to achieve
stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent
dangerous anthropogenic interference with the climate system. Such a level should be achieved
within a time-frame sufficient to allow ecosystems to adapt naturally to climate change, to
ensure that food production is not threatened and to enable economic development to proceed
in a sustainable manner. The countries should promote sustainable development. Policies and
measures to protect the climate system against human-induced change should be appropriate
for the specific conditions of each country and should be integrated with national development
programs, taking into account that economic development is essential for adopting measures to
address climate change. Capacity building is needed to increase understanding of needs for
policy and strategy formulation and consequent investments for adaptation and resilience to
climate change.
It is important that all the countries have strategies to mitigate climate change by e.g. controlling
GHG emissions and protecting vegetation resources. Especially in Central Asia the countries
should also have strategies to develop resilience and adaptation to climate change. Most
existing strategies underestimate the problems caused by climate change and also wrong
conclusions have been presented. For example, some documents predict that water discharges
in rivers will increase, because glaciers will start to melt. However, glaciers have already been
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melting a century, their volume has decreased and therefore discharges will diminish all the
time.
It is self evident that the whole population of Central Asia will suffer from the climate change
impacts in their daily life. Many national strategy papers on climate change ask questions, what
the changes really are, how vulnerable the region is and what can be done to increase
adaptation and resilience against the likely impacts. The purpose of this knowledge product is to
find answers to these questions based on new scientific information and an ADB funded project.
This information is needed when investment and action plans are drafted and when institutional
management capacities are developed.
In this booklet, results of research activities conducted by an ADB funded project in Central Asia
are described. The project "W ater and Adaptation Interventions in Central and W est Asia"
(RETA 7532) combined field observations with sophisticated satellite based data and created
models to demonstrate impacts of climate change on the hydrology of the Aral Sea Basin.
The project collected past temperature and precipitation data into a 3-dimensional map matrix of
the Aral Sea Basin. This baseline served as a reconstruction of past and present climate of the
basin. The climate change models used were based on the extensive international research on
physics of climate systems (Global Circulation Models). The future climate change scenarios
have been drafted by IPCC (Intergovernmental Panel on Climate Change) and several research
institutes have made projections for future climate. In the project, this information was used to
model changes of the climate parameters in the map matrix.
The hydrological model SPHY utilized the past and predicted climate data to analyze rainfall,
snow formation and melt as well as mass balance of glaciers. Outputs from the model included
water discharges in all the rivers in detailed scale. Such hydrographs describe changes of water
quantity over the annual water cycle and future trends can clearly be seen.
Water allocation model analyzes the changing needs for water for different purposes in each
river section were undertaken using the W EAP framework. It demonstrates well that at the same
time when water is more and more needed, the reality is that river discharges will radically go
down.
ASSESSING THE CLIMATE CHANGE
Earth’s climate varies and changes due natural reason but during the most recent decades the
anthropogenic climate change has become more and more evident; the emissions of the
greenhouse gases have kept on increasing and this has led to a very rapid increase of global
temperatures. This rapid change has many impacts on the earth’s natural environment that are
further reflected to human societies. Some of these impacts may be positive but most of them
have so far been found to be negative and in some areas of the globe the future living
conditions may be worsened very seriously.
The foreseen positive impacts are related to better growing conditions at cold and cool climate
regions where the warming makes growing season longer and increases temperature sums. As
well, heating energy demand is predicted to become smaller and there is a possibility that e.g.
hydropower potential will increase. The most seriously negatively affected areas of the globe
are the areas that already now suffering of the climate related hazards. These areas are
typically experiencing shortage of water leading to drought or their climate may be characterized
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by extreme weather events like floods. The sea level rise is one of the future risks the coastal
areas must be prepared for (IPCC, 2012).
Climate change is linked with the increase of extreme weather events, i.e. increase of extreme
winds, heat waves and torrential precipitation. These extreme events can lead to disastrous
impacts at different sectors of society. In Central Asia there has been a likely increase in the
number of warm days and decrease in the number of cold days. The change in the number of
heavy precipitation cases is not clear (Alexander et al., 2006). According to the climate
projections the maximum temperatures experienced roughly once in 20 years in the past climate
will be met once in 2-5 years at the middle of current century and almost annually at the end of
this century (IPCC, 2012).
The impact of extreme events on society is not dependent only on the frequency and magnitude
of extreme weather events but also on the vulnerability of society to extreme events and also
the exposure of the society. Vulnerability and exposure depend on the country’s level
infrastructure including the capacity to operate efficient early warning systems. For example,
countries having habitation in areas exposed frequently to the severe weather events suffer
more of the negative impacts of climate change. The increasing population may also lead to
non-resilient development of social structure of a society (IPCC, 2012).
Climate change requires adaptation to the future conditions. This need to adaptation has been
recognized almost with one accord by the various stakeholders of the globe. The adaptation to
climate change is not only adaptation to something that will realize after decades. Climate
change is already existing phenomena influencing the conditions. As well, being prepared to
future extreme conditions contributes to the preparedness of current extreme weather events.
These strategies offer economic benefits almost immediately and same time reduces
vulnerability of society on long run (IPCC, 2012).
Efficient adaptation to climate change requires wide and multidisciplinary information; starting
from the basic meteorological observations ending to very sophisticated socioeconomic
analyses. The National Meteorological and Hydrological Institutes (NMHI) have an important
role as the provider of the climate and hydrological observation data series and climate statistics
and analyses. Typically NMHIs also are receiving and processing remote sensed environmental
earth observations, i.e., satellite measurements. As well, NMHIs possess typically a
comprehensive knowledge on the modeling of future climate. NMHIs role is also becoming
wider as the role of climate change communication is growing larger and larger.
When the future climate conditions are been estimated the most important scientifically based
tool for that purpose are the climate models (Fig. 1). These very sophisticated models rely on
the basic laws of physics. Present models have modules for atmospheric, ocean and earth
surface processes. The models include the interaction between the different modules like the
exchange of heat between surface and atmosphere, evaporation, surface friction, flow of water
from the continental areas to the ocean etc. The model calculations are done in a grid covering
the whole globe at several levels from the deep ocean up to top of the atmosphere. Running the
models require very large computer resources typically called as super computers. Global
climate model simulations are done in several research center having needed computer and
human resources. These centers have agreed on sharing the climate projections based on their
model enabling also countries with no needed modeling resources to get the needed climate
information (Meehl et al., 2000). The Coupled Model Intercomparison Project (CMIP) was
established under the World Climate Research Programme (W CRP), the W orking Group on
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Coupled Modelling (W GCM) as a standard experimental protocol for studying the output of
coupled atmosphere-ocean general circulation models (AOGCMs). CMIP provides infrastructure
in support of climate model diagnosis, validation, intercomparison, documentation and data
access. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) has archived
much of the CMIP data and provides other support for CMIP. Phase three of CMIP (CMIP3)
included "realistic" scenarios for both past and present climate forcing. The research based on
this dataset provided much of the material used in the Intergovernmental Panel on Climate
Change (IPCC) Fourth Assessment Report (AR4) and dataset provided basis for numerous
scientific research articles and projects. The same way Phase five of the CMIP (CMIP5) is the
key source of climate model simulations used in the Fifth Assessment Report (AR5). Altogether
more than 20 Modelling Centers are making climate simulations and providing data for the use
of climate research community (see CLIVAR Exchanges Newsletter, 2011).
There are some differences when CMIP5 archived simulations are compared with the previous
CMIP3 simulations. CMIP5 models can be regarded more comprehensive, there is a broader
set of experiments and wider variety of scientific questions, the models have higher spatial
resolution as 50% models have latitudinal resolution finer that 1.3 ° (only one CMIP3 model is
as detailed), there is a richer set of output fields and the documentation is more detailed (Taylor
et al., 2012).
Construction of climate change projections; Required steps to estimate
future climate
External factors affecting
climate (e.g., greenhouse
gas concentrations) as a
function of time
Temperature
Temperature
Atmos. Land Ocean
Ice
Climate model: laws of
nature as a computer
program, as well as the
current knowledge allows
“Long weather forecast”
Time
Climate = statistical
properties of weather
Time
Figure 1. Construction of Climate Change Projections
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Climate projections stored in the CMIP database are simulations made using models having
global coverage (GCM). Though the spatial resolution of these models has improved it is still
relatively coarse for the small scale studies. The spatial variation of e.g. terrain elevation has
large influence on the spatial variation of temperature and precipitation. To be able to assess
the local features of future climate the prediction made to the GCM grid must first be
downscaled to a denser grid or event to point values (Fig. 2). This downscaling can be done by
applying either dynamical or statistical methods. In case of dynamical downscaling a Regional
Climate Model (RCM) with dense grid is run for the study area. RCM needs as input the large
scale features from the global model. In case of statistical downscaling first the relationship
between large scale features of climate and local observations is defined and this statistically
defined dependence is applied for the projected future climate available from the global models
(e.g. Benestad et al., 2008).
Dynamical downscaling is a physically justified approach and e.g. includes many of the
feedbacks of the climate system. However, this method is very dependent on the boundary
conditions defined by the GCM and e.g. the magnitude of the change is roughly the same as in
the GCM. The output is also sensitive to parameterizations used in the RCM and method
requires a lot of computing resources and also human know how. This method can seldom be
applied to assess the climate change as predicted by a large number of GCMs and/or various
emission scenarios. Statistical downscaling requires less computational effort than dynamical
downscaling and it offers the opportunity for testing scenarios for many decades or even
centuries. It is possible to use large number Global model results and different emission
scenarios. The drawback is that the method requires long time series of observations needed.
One can also question can present day climate depict future conditions?
The principle of downscaling with a statistical model
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a) search for statistical
relationships between
the observed climate
and broad-scale
circulation features
b) b) Use the broad-scale
circulation features as
projected in GCMs to
develop projections of
local climate
Climate of the
global model
-20
-10
statistical relationships ->
-5
-10
-15
Observations
of climate
-15
-10
-10
-10
-5
-5
Climate of the
statistical model
-15
-10
-5
-10
Figure 2: The principle of Downscaling with a Statistical Model.
Source: Finnish Meteorological Institute.
-15
-5
-10
-15
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Processing and analyzing the large meteorological and hydrological dataset require advanced
analyzing tools. More and more the R environment (http://www.r-project.org/) has been applied
in this kind of applications1.
In the project ADB TA-7532 “W ater and Adaptation Interventions in Central and W est Asia”
detailed climate projections were needed for the future decades. Projections of future climate
were based on GCM simulations reported in the 4th assessment report of the IPCC (Randall et
al. 2007). Model simulations of five different models (Table 1) in the intermediate emission
scenario were used. The criteria for the selection of these models was that their spatial
resolution was 1.9° or higher and the models originated from different countries. This ensured
that the models are genuinely separate models and this way the results obtained by employing
these models depicted the scale of variation different climate projections possess. As well, in a
comparison made among 19 different GCMs the ability of these four models to simulate the past
climate was as good as the accuracy of any other model.
Model
Table 1. Global climate models used in the study.
Institute
Country
Resolution
Canada
1.9˚ x 1.9˚
CNRM-CM3
Canadian Centre for Climate Modeling and
Analysis
Météo-France
France
1.9˚ x 1.9˚
ECHAM5/MPI-OM
Max Planck Institute for Meteorology
Germany
1.5˚ x 1.5˚
MIROC3.2(HIRES)
Centre for Climate System Research
(University of Tok yo)
Japan
1.1˚ x 1.1˚
NCAR-CCSM3
National Center for Atmospheric Research
USA
1.4˚ x 1.4˚
CGCM3(T63)
Source: Randall et al. (2007)
The climate scenarios produced by the selected five GCMs were generated to daily temperature
and precipitation data sets for the period 2011-2050 by a method known as the delta change
method (e.g., Arnell, 1996). The use of delta change method for the estimation of the change in
near future is well justified (Räisänen and Räty 2012). First the differences between the
simulated current and future climates were computed and then these changes were added to
the high resolution (0.2° x 0.2°) daily temperature and precipitation data sets for the period
2001-2010. The gridded daily mean temperature data to be used as a base of generating
scenarios for the future, were produced by kriging interpolation (e.g. Krige, D. G., 1951) from
daily temperature observations during the period 2001-2010. The gridded daily precipitation
data for the period 2001-2010 originates from satellite based data of the Tropical Rainfall
Measuring Mission (TRMM; Huffman et al. 2007; Huffman et al. 2012) and Precipitation
Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN;
Sorooshian et al. 2000). The processing of climate data; interpolation and downscaling in the
project was done using the R software environment.
During the next 40 years, the mean temperatures were projected to rise in the Central Asian
region everywhere around the year with an annual mean temperature rise of about three
1
R is a free software environment for statistical computing and graphics. The use of R has expanded recently and is
has become almost a standard in statistical computing and data processing. R is available as Free Software under
the terms of the Free Software Foundation's GNU General Public License in source code form. This means that
there are no installation or maintenance costs related to R.
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degrees. The warming was projected to be strongest in the mountains and in the northern parts
of the area (Fig. 3).
Figure 3. Average change of annual mean temperature vs. simulations for 2045-2065.
Average change of annual mean temperature (°C) between the control simulations (simulation period:
1971-2000) and the simulations into the future (five simulation period: 2045-2065).
3.4
5
3.4
3.2
4
3
3
°C
Uzbekistan
Kyrgyzstan
2.8
40
2
3.
Turkmenistan
2
Tajikistan
3
3.4
35
LATITUDES
45
Kazakhstan
3.4
3.2
60
65
70
LONGITUDES
75
1
0
8
9
Figure 4. Average change of annual precipitation vs. simulations for 2045-2065.
Average change of annual precipitation (%) between the control simulations (simulation period:
1971-2000) and the simulations into the future (five simulation period: 2045-2065).
5
5
10
5
10
Uzbekistan
0
Kyrgyzstan
40
5
0
35
Turkmenistan
Tajikistan
5
−5
60
0
65
70
LONGITUDES
75
−10
%
LATITUDES
45
Kazakhstan
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The projected changes in annual precipitation were relatively small during the coming 40 years
and varied from model to model. The already dry south-western parts were projected to become
even drier especially during summer time. In contrast to this, in the northern parts and in the
mountains the annual total precipitation was projected to increase by 5-10% (Fig. 4).
The predicted climate changes in Central Asia can lead to more favorable conditions in the parts
of the area in Kazakhstan (Lioubimtseva and Hennebry, 2012). The growing season is projected
to become longer and availability of water may improve. However, in the regions suffering
already now on water shortage, the conditions will become even more difficult as temperature
and evaporation will raise and precipitation decrease. Parts of the region are predicted to
become more and more arid like the western parts of Turkmenistan, Uzbekistan and
Kazakhstan. For example in Uzbekistan, the economy is very dependent on irrigated agriculture
which is using substantial amount of water resources of Amu Darya and thus the foreseen
decrease of these resources can have severe impact on the economy of the country (Schlüter
et al., 2010).
In Kazakhstan, the drought of 1997-1998 destroyed nearly half of the harvest in most grainproducing provinces and led to a deterioration of the financial security of farms. In Tajikistan,
drought causes the largest amount of economic damage, estimated on the average of 1.7
million US dollars during a 10 year period. The country faced severe droughts in 2000-2001 and
3 million people were at risk of famine. Hot and dry weather prevailed and the country lost a
considerable part of its cereal crop, with the livestock sector being severely affected. In the
same years also Uzbekistan and Turkmenistan faced serious problems in food production. In
February 2005, Tajikistan was hit by heavy snowfall in the Rasht Valley where two meters of
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snow had fallen in two days (ADRC 2006). Exceptional heat wave events and torrential rains
have been dangerous in mountainous areas where sudden snow melt has generated
destructive avalanches, floods and mudflows. Such calamities require several casualties every
year and economic losses are substantial.
Understanding of climate change and its impacts is essentially important in Central Asia where
climate risks are high. The adaptation to the predicted large risks requires coordination among
the Central Asian countries, like in case of Aral Sea water system water management involves
Kyrgyz Republic, Tajikistan, Uzbekistan, Kazakhstan and Turkmenistan (Ibatullin, et al. 2009).
IMPACTS OF CLIMATE CHANGE
Scientists and policy makers allied in the United Nations Intergovernmental Panel on Climate
Change (IPCC) state that “it is very likely that most of the observed increase in global average
temperatures is due to increase in anthropogenic greenhouse gasses concentrations”. The
models applied by the IPCC members strongly indicate that those changes will intensify over
the coming century. Moreover these models provide abundant evidence that freshwater
resources are vulnerable and have the potential to be strongly impacted by climate change, with
wide-ranging consequences for human societies and ecosystems. Observed warming over the
last decades has altered the hydrological cycle already. Typical examples include changing
precipitation patterns, intensity and extremes, changes in snow and ice cover and changes in
runoff.
The science community is starting to understand better how the climate system works and
hydrologist are able to evaluate the impact of changes in climate on the water resources.
Obviously not only the change in climate is affecting our water resources, but climate change is
expected to exacerbate current stresses on water resources from population growth and
economic and land-use change. For Central Asia, mountain snow pack, glaciers and small ice
caps play a crucial role in freshwater availability. Retreating glaciers and reductions in snow
cover as observed over recent decades are projected to accelerate throughout the 21st century.
Consequences will be a reduction in overall water availability, lower hydropower potential, and
changing seasonality of flows in regions supplied by melt water from Tien Shan and Pamir
mountains.
Besides the projected changes in precipitation, the increase in temperature is an important
factor for Central Asia. The large scale irrigation systems are nowadays already suffering from
water shortage, and higher temperatures will increase the water required by the irrigated crops.
Moreover higher temperatures will also have an impact on the natural vegetation and
evaporation from these areas will increase so that less water becomes available to flow into the
streams and rivers.
Glaciers
Glaciers cover 18,128 km2 from the Aral Sea Basin and they have an important role in hydrology
as they release melt water especially during the dry summer months. Mountain glaciers include:
1) Small cirque glaciers resting on rather steep mountain slopes; 2) Large ice caps covering
mountain tops associated with valley glaciers, narrow and long ice tongues flowing down in Ushaped valleys.
The percentage of glaciated area of the two catchments differs significantly. In the Amu Darya
Basin glaciers cover 15,500 km2 (2% from the area) and in the Syr Darya Basin 1,800 km2
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(0.15% from the area). The biggest glaciers are located in the Pamir Mountains in Tajikistan
(Fig. 5).
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Figure 5. Occurrence of glaciers in the upper watersheds of the Aral Sea Basin in 2010 and
2050.
It is a well known fact that after the ice-age (Holocene) the glaciers reached their maximum
latest in 1850s. In most glaciers the terminal moraine representing the maximum extent can
clearly be seen in the valleys especially in satellite images. Glaciers respond relatively quickly to
changes in climate (temperature, precipitation, humidity and cloudiness). If the snowfall
(accumulation) sustaining a glacier declines, or if there is an increase in ice loss (ablation),
these will result in recession of the glacier or an overall thinning of the ice mass (or both).
Melting of glaciers has accelerated since the Little Ice Age (c. 1650 – 1850) due to the gradual
climate warming (Oerlemans 2005; Seversky 2006; Kutuzov & Shahgedanova 2009; Kelly
2013).
All the glaciers have their own dynamics and it is common that sometimes their margins are
retreating and some other times advancing. Some glaciers have periods of very rapid
advancement called surges. These glaciers exhibit normal movement until suddenly they
accelerate, then return to their previous state. During these surges, the glacier may reach
velocities far greater than normal speed and the margin may advance. For example, the
Medvejiy glacier in Tajikistan moves periodically downhill every 12-15 years.
As temperatures have risen almost everywhere, the retreat of glaciers from mountain valleys is
one of the most visible symbols of global warming. The overall picture of widespread recession
is unequivocal and reflects the well-known record of global warming. The situation in Asia is
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more complex. Although there seems to be a general loss of ice in this region, some regions
with higher altitudes such as the Karakorum show an increase, and uncertainties about rates of
change are considerable (Immerzeel et al. 2010). Some glaciers located in higher altitudes may
grow as they are well above the ablation (snowline) altitude. Increase of precipitation may result
in growth of glaciers. However, the general trend in Asia is that the glaciers are melting in an
accelerating mode.
Vanishing Glaciers
The Fedchenko glacier is a large glacier covering over 649 km2 in the Pamir mountains in
central Tajikistan at altitudes of 2900 - 6300 m (Fig. 6). The associated valley glacier is 77 km
long and narrow ice tongue. It is the biggest glacier in the world outside of the polar regions.
The maximum thickness of ice is 1000 m, and the volume of the glacier including its tributaries
is estimated at 144 km3. The edge of this glacier has retreated 1 km in 70 years. During the
recent decades, the valley glacier has thinned 1 m/yr while its surface area decreased by 11
km2 and it lost about 2 km3 of ice (Aizen & Aizen 2010). As a consequence of climate warming,
more than 40 million m3 of melt water has annually been fed to the Naryn River from the
contraction of Fedchenko glacier only. If we consider all the glaciers of the basin, the total
volume of such non-renewable water resources is immense.
The glaciers of Tajikistan have lost more than 20 km3 of ice in the 20th century. During the last
decades, the glaciers in the mountains of south-east Kazakhstan was annually reduced by
0.85% as regards glacier area, and 1.0% in ice volume (Alamanov et al. 2006).
The glaciers in the Tien Shan and Pamir are retreating and the rates of retreat vary between
regions and time periods. The largest retreat rates have been observed in the northern Tien
Shan where glaciated area has declined by 30-40% during the second half of the 20th century.
Acceleration of glacier retreat has been noted in the eastern Pamir from 7.8% over 1978–1990
to 11.6% over 1990–2001 periods. Glaciers have lost 12.6% (0.33% /yr) of their 1965 area in
the 1965-2003 period. Small glaciers have diminished more than the average (Kutuzov &
Shahgedanova 2009).
It has been estimated that glaciers in the Syr Darya basin have lost 14% of their total volume
over the last 60 years and that 15-40% of the volume will be lost in the coming 40 years
(Siegfried et al., 2010).
The glacial area in the Amudarya basin has shrunk 13.1% from 1957 to 1980, i.e., from 7144 to
6205 km² (Agaltseva 2005).
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Figure 6. The Fedchenko glacier in the Pamir mountains of central Tajikistan is the largest
mountain glacier in the World. The width of the glacier is c. 2 km. (3D visualization, Google
Earth).
In this study, the future melting rate of glaciers in the Aral Sea Basin was modeled more
precisely (see Box: Modeling Glacier Processes). The modeling work revealed what will happen
to the glaciers and runoff in the coming 40 years (Fig. 7). The present records show that small
ice caps especially from Tien Shan will disappear and also the glacial melt water will
dramatically be reduced (Fig. 8). It is important to realize that climate warming and melting of
glaciers have already been going on especially after 1940. It is evident that there is an
accelerating trend in the melting of glaciers.
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Figure 7. Change in glacier extent in Central Asia according to various GCM projections.
Usually aerosols, small particles or droplets suspended in the atmosphere, have cooling effect
on climate. Such particles originate from regional biomass burning, industrial pollution and dust
storms caused by desertification. However, whenever the black carbon or dust will accumulate
onto the glacier surfaces, melting will be accentuated. Thick gravel (debris) beds on top of a
glacier will slow down its melting. To protect glaciers, it is important that Central Asian countries
will control their own pollution emissions, desertification and wildfires.
Figure 8. Geomorphological evidence, old terminal moraines representing earlier and
more extensive glaciers, show that glaciers have already receded substantially during
the last 150 years. In this case only 1/3 of the maximum extent is left (Kazakh-Kyrgyz
Republic border, Almaty; 3D visualization, Google Earth).
Modeling Glacier Processes
The modeling of processes involving glaciers is based on the so-called Degree Day Factor. The
use of temperature index or degree day models is widespread in cryospheric models to estimate
ice and snow melt. In these models an empirical relationship between melt and air temperature
based on a frequently observed correlation between these two quantities.
“Melt from clean ice glaciers” is defined as the air temperature (if above 0 °C) multiplied by the
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degree day factor for clean ice, multiplied by the clean ice fraction of the glacier cover and the
cell fraction with glacier cover. For the melt from debris covered glaciers the calculation is
similar, although a different degree day factor for debris covered glaciers is specified. Melt rates
for debris covered glaciers are lower, since incoming radiation and other heat flows are blocked
by the (thick) debris cover.
The total glacier melt is then calculated by summing the two components from clean ice glacier
melt and debris covered glacier melt. A part of glacial melt also refreezes in the glacier when it
percolates the ice.
For each cell, the model determines if precipitation falls as snow or rain by comparing the actual
air temperature to a critical temperature. W hen air temperature is below or equal to the critical
temperature, precipitation will fall as snow. When air temperature is above the critical
temperature, precipitation will fall as rain.
The potential snow melt is defined as the air temperature (if above 0 °C) multiplied by a degree
day factor for snow multiplied by the cell fraction covered with snow. The actual snow melt
however, is limited by the thickness of the snow pack. No more snow can be melted than the
amount of snow which is available at the considered time step. The snow storage is then
updated, to be used for the next time step. The updated snow storage is the ‘old’ snow storage
with the fresh snow added and the actual snow melt subtracted.
The water resulting from snow melt will partially refreeze as it infiltrates the underlying snow
pack. The maximum of water that can refreeze is defined by the water storage capacity of the
snow pack which depends on the thickness of the snow pack present and the storage capacity
of snow (e.g. the total millimeters of melt water that can refreeze per millimeter of snow). The
actual amount of water that is stored in the snow pack is defined as the water stored in the snow
pack during the previous time step summed by the actual snow melt. Snow melt will become
actual snow melt when the amount of snow melt exceeds the water storage capacity of the
snow pack.
Glacial Lakes
Because of the rapid melting and retreat of the margin of glaciers, new proglacial lakes may be
generated in places. It is common that older ice-cored moraines create dams in front of fast
receding valley glaciers (Fig. 9). Also glacier itself may act as a dam for waters of an adjacent
valley. Ice dam may be formed as a result of fast advancing, surging glacier. Such lakes may
have extensive amount of water and whenever the dam collapses, a catastrophic flood may
occur.
There are hundreds of proglacial lakes in Central Asia and many of them have been classified
to be dangerous. The recent ice surges, outbursts of glacier-dammed lakes and floods of glacier
rivers have caused major disasters. In the Kyrgyz Republic, there are several lakes with
unstable natural dams and there is a permanent threat of outburst. Of more than 1,000 high
mountain lakes, 199 have been identified as being dangerous. Since 1952 to 2007 about 70
cases of dangerous outbursts with human victims occurred on the territory of the Kyrgyz
Republic. In Tajikistan the situation is even worse (ADRC 2006).
In 1963 and 1973, the surges of the 15 km long Medvezhi glacier in the Pamir mountains,
Tajikistan, have caused lake formation, outburst and subsequent floods into the Vanch River.
The 1-2 km long glacier advance created 100 m high ice dam which dammed a lake of over 20
18
million m3 of water and debris. The outbursts of that lake have generated a series of large flood
waves. Due to early warning and monitoring, there were no victims, although infrastructural
damage was significant (Novikov 2002). Recently 2011, the surge took place again.
Increasing glacial lake outbursts can be related to climate warming as receding glaciers
generate proglacial lakes. Thawing of an ice-core from terminal moraines damming a lake may
breach causing sudden mudflow down to the valley. Central Asian countries have governmental
institutions mapping and managing emergency situations. Flood protection interventions and
early warning systems are needed in areas identified to be vulnerable to glacier instability and
lake outbursts.
Figure 9. When an ice-core of a terminal moraine damming a proglacial lake will melt, a
catastrophic flood and mudflow may occur. The lake is 2 km in diameter. (Petrov Lake, Kyrgyz
Republic. 3D visualization Google Earth).
Water Resources
The main rivers in Central Asia, Syr Darya and Amu Darya, play a prominent role in the region.
People depend on the water for their domestic use, farmers cannot exist without irrigation, the
environment alters if water resources are changing, and hydropower supplies the necessary
energy in the region. Many initiatives have been started to ensure a proper and sustainable use
of the water resources in the region, where difficult decisions regarding sharing benefits have to
be faced. Besides ongoing economic development with associated changes in water
requirements, it is clear that climate change puts an additional challenge to appropriate planning
and managing of the water source.
19
Currently most water in the region is generated in the upstream mountainous areas which will
flow from smaller streams into bigger streams and finally into the two main rivers: Syr Darya and
Amu Darya. The origin of this water in the rivers exits of four components:
 Rainfall can flow overland directly into the streams;
 Rainfall can infiltrate into the soil and will flow through the soil into the streams, or becomes
available as groundwater resource;
 Precipitation can fall as snow and will flow into the streams when it melts;
 Precipitation can feed glaciers and will after longer times melt and flow into the streams.
Knowledge about those four different components is very relevant for understanding current and
future water resources. Rainfall flowing directly into a stream can result in rivers filling up quickly
and can cause flooding. While rainfall that enters into the soils is buffered and will generate a
much more constant inflow into the streams (this is why fighting land degradation is so
important). Precipitation falling as snow can be seen as a very useful natural buffer, since only
when temperatures are increasing during spring and summer this snow will melt and will
become available as liquid water for downstream users; just at the moment when water is
mostly needed. Finally water stored in glaciers is released very slowly and gradually providing a
stable flow during spring and summers even if rains have been low.
In Figure 10 the relative contribution from glacier melt compared to total flow is presented. It is
clear that in the Amu Darya glacier melt is an important contributor to the entire flow, especially
in the smaller streams at higher elevations. Total water resources generated in the upstream
parts of the Aral Sea Basin, as shown in Table 2, indicate that for the upstream Amu Darya
almost 40% of the total flow is generated by glacier melt, while for the upstream Syr Darya this
figure is just above 10%. It is clear that by receding glaciers caused by climate change this will
have a major impact on total flow as well as timing of flows.
20
Figure 10. The relative contribution of glacier melt to the total flows in streams and rivers. The
upper reaches of the Amu Darya river are mostly fed by meltwaters from the glaciers of the
Pamir Mountains.
Table 2. The relative flow contribution of the four components for the upstream Syr Darya and
upstream Amu Darya over the period 2001-2010.
Direct runoff
Base flow
Snow melt
Glacier melt
Syr Darya
31%
23%
35%
11%
Amu Darya
16%
19%
27%
38%
Future Water Availability in Syr Darya and Amu Darya
To project future water resources in order to support appropriate planning, scientists have
developed advanced so-called assessment tools. These assessment tools have been
developed to mimic the world we are living in and can also be used to explore our future world if
the climate changes. Assessment tools have been used over various scale levels from the
entire world down to individual farmer plots. Using these impact assessment tools the future of
the water resources in Central Asia has been evaluated.
The impact assessment was based on two modeling approaches. Current and future water
resources were assessed using the SPHY model, which is a revised high-resolution version of
the PCR-GLOBWB hydrological model. SPHY is a conceptual, dynamic and distributed model.
SPHY runs on a daily basis on a 6 arc-minute grid and was setup for the period 2000-2050.
Changes in irrigation water requirements are based on: (i) changes in irrigated area and (ii)
changes in crop water demand as a consequence of a changing climate. Projected changes in
domestic and industrial demands are based on the relationships between Gross Domestic
Product (GDP) and Gross Domestic Product per Person (GDPP). A water allocation model was
used to link water supply and water demand and to explore adaptation strategies. This model,
referred to as ARAL-WEAP, was developed using the WEAP package which considers the
following features: streams, reservoirs, groundwater, irrigation demands, domestic demands
and industrial demands. ARAL-WEAP was run on a monthly base for the period 2000-2050.
The cost-effectiveness of various adaptation measures to close the supply-demand gap was
assessed by means of the “water-marginal cost curve”. Such cost curves show the cost and
water saving potential of a range of different strategies - spanning productivity improvements,
demand reduction and supply expansion – to close the water supply-demand gap.
The relative contribution of these four different sources of water in the rivers is most likely going
to change in the future. Rainfall patterns will be shifting and temperatures will increase resulting
in a lower contribution of snow melt and especially of glacier melt. Figure 11 indicates that total
annual runoff and changes in flow contribution can be substantially. Though the figure indicates
that glacier melt will be reduced substantially, still over 50% of the glaciers will remain but at
higher altitudes so that melt will be lower. Recent scientific developments indicate that some
specific glaciers will melt faster in the short run so an increase in inflow might be expected. The
current analysis show that inflow into the downstream areas will decrease by 22-28% for the Syr
Darya and 26-35% for the Amu Darya by the year 2050. This range in projected decreases
reflects the uncertainty in the climate projections. The relatively small range in these projections
21
is caused by the fact that only one climate scenario (A1B) was used; the uncertainty reflects
therefore only the limitation within our current scientific knowledge as reflected in the range of
GCM used. Strongest decreases in stream flow are expected for the late summer months
(August, September, October), where inflow into downstream areas decreases around 45% for
both river basins.
The major user of fresh water in the region is irrigated agriculture. About 97% (93,800 Mm 3 per
year) is consumed by irrigated agriculture, while other sectors such as domestic and industry
consume about 2,700 Mm3 per year. Climate change will not only reduce available water
resources as explained in the previous sections, it will also increase the demand by crops as
higher temperatures will result in elevated evaporation rates. Based on the assessment tools as
developed for the regions it is projected that total water demand in the Syr Darya basin
increases by about 3 to 4% in 2050. Annual water demand in the Amu Darya basin increases by
about 4 to 5% (Fig. 12).
The combined effect of higher demand and lower inflow will amplify the current water shortage
in the two River Basins. For the Syr Darya the assessments show that the total water shortage
will increase to a level of 13,700 Mm3 per year in 2050; this is about 35% of total demand. For
the Amu Darya annual unmet demand increases to 29,400 Mm3 per year in 2050 (about 50% of
total demand). It is clear that such a reduction will put substantial challenges for the region to
cope with (Fig. 13).
Figure 11. Typical examples of changes in total flow and flow composition of two main
reservoirs in the Syr Darya (top) and Amu Darya (bottom). Note that after 2050 about 50 % of
22
the glacier extent has been lost and remaining glaciers are above the 0oC level and they will
hardly contribute to streamflow anymore.
Figure 12. Changes in annual water demand and unmet demand (water shortage) under climate
change for the two main basins in Central Asia.
23
Figure 13. Satellite image from the Isfara River, Fergana Valley (Tajik-Uzbek border). During the
irrigation season, the rivers flowing down from the mountains receive major part of their water
from melting glaciers. All the river water is used for irrigation (3D visualization, Google Earth).
Floods
Floods have caused major disasters in Central Asia destroying roads, bridges, villages
and there have also been big number of human casualties. An important question is, if
in the future floods will become less frequent as the overall volume of water in the rivers
will decrease. The modeling results show that in most rivers Spring time floods will
remain the same as in the recent past. However, there will be major differences in
flooding between the river basins and these should be understood whenever flood
management is developed.
In basins which have extensive middle-altitude uplands and lots of snow, have biggest
flood risk in May-June. This peak is the highest in the annual hydrographs and the
situation will remain the same also in the future. A typical example is the Naryn River
Basin (the upper Syr Darya Basin above the Toktogul reservoir) (Fig. 14a). A torrential
rain or a sudden warm period in Spring or Summer time may cause major flooding
disasters. This was the case e.g. 2005 in the Zarafshan and Pyanj rivers, Tajikistan,
where more than 11,000 people had to be evacuated from flooded areas.
24
In the areas of high-altitude uplands, high mountains and major glaciers, the Spring time
flooding is not so common. Glacial melt is a slow process and it cannot generate floods.
Highest discharges are measured in the late Summer and melting of glaciers is a main
contributor of water. In such basins flood risk will decrease as the volume of glaciers will
continue to diminish. There is no any reason to develop flood protection in these basins.
A typical example is the Vakhsh River basin (the upper Amu Darya Basin above the
Nurek reservoir) (Fig. 14b).
In the areas of lower mountains and plains (in central parts of the Aral Sera Basin) thin
snow and small cirque glaciers (ice caps) are typical. Here snow accumulation will
decrease and the small glaciers will disappear during the next few decades. In the
future, water shortage will be severe and flooding will not occur at all. The Dushanbe,
Samarkand, Tashkent, Chimkent, Bishkek and Almaty regions can be mentioned as
example areas.
25
Figure 13 a and b. Annual hydrographs for the Nurek reservoir (Tajikistan, Amu Darya
Basin) and Toktogul reservoir (Kyrgyz Republic, Syr Darya Basin) in the past (field
observations) and in the future modeled using five different climate change projections.
In January-May the discharges (Q) will remain similar as today, but major reduction will
take place in June-November discharges because of diminishing glaciers.
Climate hazards in the Kyrgyz Republic
The regions most frequently affected by climate related natural disasters are the Jalal-Abad and
Osh oblasts, followed by the Chui and Issyk-Kul oblasts. Approximately 80% of all the disasters
occur between May and August. The most destructive natural disasters of the past 10 years
include:
• Torrential rain and an earthquake in Osh and Jalal-Abad in 1992 destroyed 51,440 hectares of
agricultural land and affected 20,000 people; direct economic damage was estimated at US$ 31
million;
• Heavy rainfalls, snowfalls and frosts in spring 1993 caused economic losses estimated at US$
21 million;
• Large-scale landslides and mudflows in 1994 in the Osh and Jalal-Abad oblasts killed 115
people and made 27,000 homeless; economic damage: US$ 36 million;
• A glacial lake outburst flood in 1998 killed over 100 people and caused damage over an areas
stretching to Uzbekistan;
• Severe and widespread floods in Jalal Abad in 1998, caused by torrential rains, damaged or
destroyed an estimated 1,200 houses and public buildings; direct economic damage was
estimated at US$ 240 million.
(UN 2000)
Permafrost and slope instability
As a result of climate warming, the snowline will raise 200 - 300 m in average until 2050.
Extensive parts of hill slopes which have always been covered by snow or have had frozen
ground (permafrost) will melt. Thawing of permafrost in the higher mountains will make the
slopes unstable and this will generate landslides and mudflows. At first, only the surface layers
of the ground will thaw and the underlying icy ground will form a slide surface. The thawed
surface layers may slide down fast to the valley and destroy forest, infrastructure and
settlements on the way (Fig. 15).
Landslides and mudflows have been common in Central Asia and every year settlements,
infrastructure, agricultural lands and natural areas are destroyed. Also human casualties are
common in major disasters. Mass movements are muddying water resources and filling
reservoirs with sediments. In the Kyrgyz Republic, more than 200 settlements and
communication structures are located in landslide-prone zones. About 2,500 landslides have
been registered in the south since the mid-1950s (UN 2000). In average, landslides cause 46
deaths in 10 years. In Tajikistan, in worst years more than one thousand houses have been
destroyed in mudflows. Some 85% of Tajikistan’s area is threatened by mudflows (mountainous
areas, hill slopes and river valleys) and 32% of the area is situated in the high mudflow risk zone
(ADRC 2006).
26
In permafrost areas the depth of frozen ground can be several hundred meters. Consequently
permafrost prevents groundwater flow as it is frozen. Melting of permafrost again will result in
increased groundwater flow which will end up to lakes and rivers. It is difficult to estimate the
volume of water released from the thawing permafrost, but certainly it is significant.
It is possible to map the future areas subject to landslides and mudflows by using similar SPHY
hydrologic model that was used here to estimate river discharges. The model is able to define
altitude zone where thawing of permafrost will take place now and in the future. Such risk
assessments could be important whenever infrastructures and settlements are planned and
emergency preparedness developed.
Figure 15. Thawing of permafrost in the higher mountains will make the slopes unstable
and this will generate landslides and mudflows (Mailuu Suu, Kyrgyz Republic. M.
Punkari).
27
(maybe this does not need caption – figure is related to permafrost chapter – shows how the
rising temperature will thaw permafrost in wide areas)
Drying environment
The lower parts of the Aral Sea Basin is mostly arid area where precipitation is 40-200 mm/yr.
Desertification is one of the most severe problems in Central Asia where huge areas of fertile
land is lost every year. Land degradation from overgrazing, soil erosion, salt damage to irrigated
land, and desertification is directly affecting the livelihood of nearly 20 million rural inhabitants.
Farm yields are reported to have dropped 20 - 30% across the Central Asian region since the
1990s. About 70% of the total area of Turkmenistan has become desert, while salinized irrigated
areas account for 50% in Uzbekistan and 37% in Turkmenistan (CASILM). In the future, the
continuous increase of temperature and evaporation, slight decrease of precipitation in the most
arid lowlands and the radical reduction of river discharges will worsen the situation.
It is likely that wildfires in the plains will increase due to climate change and this again will result
in soil degradation, erosion and desertification (U.S. Forest Service 2013). Human activities as
overgrazing and fuel wood collection will contribute desertification especially in populated areas.
The Aral Sea was once the fourth largest lake in the world, but is now nearing extinction. Longlasting unsustainable water management has caused the Aral Sea to shrink, which will be made
worse by climate change. It has decreased over the last 50 years from 68,000 to about 12,000
km2. W here once 178 species inhabited the Aral region, there are now fewer than 40 (Alamanov
et al. 2006). Salt air pollution from the open sea bottom is dangerous for agriculture and human
and animal health. W arming temperatures are only making it worse - for example, by increasing
evaporation over the widespread irrigated fields and their canals, including the 1300 km man‐
made Karakum canal.
Environmental changes may have several feed-back effects on climate the final results of which
are not yet fully understood. For example, increasing dust storms and wildfires increase
aerosols in the atmosphere which may cool climate. However, these particles deposited onto
snow and glaciers will decrease their albedo and increase melting. Especially the Tajik
environmental experts have raised questions about the protection of glaciers. One of the very
few feasible options is to minimize aerosol emissions by controlling deforestation, erosion and
wildfires as well as by reducing air pollution.
Climate Change in Development Programs
It is clear that the need to adapt to climate change in Central Asia is felt by everybody.
International efforts to limit greenhouse gas emissions will not be sufficient and fast enough to
prevent the harmful effects of changes in precipitation, increase in temperatures and increased
frequency and severity of extreme weather events. Climate change can also create
opportunities, particularly in the agricultural sector. Increased temperatures can lengthen
growing seasons, and higher carbon dioxide concentrations can enhance plant growth.
However, these positive opportunities will not be sufficient to compensate for the negative
effects of climate change as a whole.
The risks of climate change cannot be effectively dealt with, and the opportunities cannot be
effectively exploited, without a clear plan for aligning policies with climate change. Developing
28
such planning involves a combination of high-quality quantitative analysis and consultation of
key stakeholders. It has been well accepted that the most effective plans for adapting to climate
change will involve both human capital and physical capital enhancements. Moreover, it is wellaccepted that the capacity to adapt to changes in climate is in part dependent on financial
resources – in emerging economies, among small-holder farmers with limited financial
resources, adaptive capacity is particularly low. As a result, the donor community will continue
to be a key stakeholder in developing climate change policies and implementation measures
(Ibatullin et al. 2009).
Adaptation to climate change
There is no silver-bullet approach that can be used as the ultimate adaptation strategy. Two
different types of actions are essential to tackle the climate change challenge. First of all,
“development as usual” without consideration of climate risks and opportunities, will not allow us
to face these challenges. Although a range of development activities contribute to reducing
vulnerability to many climate change impacts, in some cases, development initiatives may
increase vulnerability to climatic changes. This integrating of climate change in existing
development planning is sometimes referred to as “mainstreaming” or using a “climate lens” in
existing development planning.
Secondly, separate adaptation planning and implementation is required to overcome the
negative impacts of climate change. It has been advocated that this adaptation planning and
implementation has various dimensions. An important dimension is that some adaptation will
take place autonomous and other adaptation requires actual planning. A typical example of
autonomous adaptation is farmers changing the planting date of their crops as response to
temperature shifts. An example of actual planning is that irrigation water should be delivered
earlier and proper irrigation water requirement monitoring systems should be in place.
A second dimension of adaptation is the timing of response, being the short run or the long run.
The long run adaptation includes issues like building capacity, changing institutions, and large
infrastructural development, amongst others. Typical examples relevant to short run adaptation
in Central Asia are related to water allocation and reservoir operations. Finally the third
dimension to consider in adaptation is the scale-level to consider. In general one should
consider the following scales: farm, community, national, and regional. Each of these scales has
their specific needs and opportunities.
Concrete adaptation options in Central Asia
It is clear that adaptation strategies are very scale dependent and local specific. The
assessment tools as presented in the previous sections have been used to analyze a range of
adaptation actions that might be suitable for the Central Asia region. Based on such a broad
range of adaptation options, actions can be subsequently fine-tuned to national and local
conditions and preference. The options explored can be summarized into three broad
categories: (i) expanding supply of future water availability, (ii) increasing productivity of water,
and (iii) reducing future demand. Within each of these three categories typical options can be
chosen such as: increased reservoir capacity, improved agricultural practice, increased reuse of
water in irrigated agriculture, increased reuse of water for domestic use, reduction of irrigated
areas, reduction of domestic demand, and deficit irrigation.
The ARAL-WEAP models for the Amu Darya and Syr Darya basins were used to evaluate the
impact and effectiveness of these adaptation measures. The WEAP-model was run for five
29
different climate projections, based on five different Global Circulation Models. The impact of
adaptation measures is evaluated for the MIROC GCM, for which the climatic impact for water
availability is closest to the mean of the five outputs. No distinction is made between the Amu
Darya and Syr Darya basin, the effectiveness of the adaptation measures is evaluated for the
total Amu and Syr Darya basin. The effectiveness of the adaptation measures is evaluated for
2041-2050.
For the two main rivers in Central Asia it is expected that total water shortage in 2050 will be
43,000 Mm3 per year. Moreover, changes in monthly flow regimes will change quite
substantially, especially for the Amu Darya resulting from retreat of glaciers and reduction of
snowfall (Fig. 16). By analyzing the various adaptation strategies the effectiveness of each
measure can be assessed. By ranking the adaptation options by their unit costs the so-called
“water-marginal-cost-curve” can be obtained. The water availability cost curve’s use is limited to
comparing measures’ financial costs to close the gap. It is important to note that these might be
different from the economic costs for society as a whole. The cost curve should be therefore
considered as a guide for comparing the financial costs of measures for decision-making (Fig.
17).
It is clear that for Central Asia the most cost-effective adaptation measures are improving
agricultural practice, deficit irrigation, increasing the reuse of water in agriculture and the
reduction of irrigated areas. In general, the measures applied to agriculture are much more
effective compared to the ones related to domestic water use. Applying the most cost-effective
adaptation measures will close the water gap and costs US$ 1,730 million per year in 2050 (net
present value). Closing the water gap caused by climate change only will cost US$ 550 million
per year in 2050.
30
Figure 15. Current and future monthly flows in the two main rivers in Central Asia if no actions
are taken.
Figure 17. Water marginal cost curve Amu and Syr Darya basin. Red arrow indicates total
expected water shortage in 2050; green arrow indicates the hypothetical water shortage in case
no climate change would occur. Note: Cost-axis has been cut off at US$ 0.30. Cost for
decreasing domestic demand is 2.00 $/m3.
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