Skip to main content

Will climate change exacerbate water stress in Central Asia?


Millions of people in the geopolitically important region of Central Asia depend on water from snow- and glacier-melt driven international rivers, most of all the Syr Darya and Amu Darya. The riparian countries of these rivers have experienced recurring water allocation conflicts ever since the Soviet Union collapsed. Will climate change exacerbate water stress and thus conflicts? We have developed a coupled climate, land-ice and rainfall-runoff model for the Syr Darya to quantify impacts and show that climatic changes are likely to have consequences on runoff seasonality due to earlier snow-melt. This will increase water stress in unregulated catchments because less water will be available for irrigation in the summer months. Threats from geohazards, above all glacier lake outbursts, are likely to increase as well. The area at highest risk is the densely populated, agriculturally productive, and politically unstable Fergana Valley. Targeted infrastructural developments will be required in the region. If the current mismanagement of water and energy resources can be replaced with more effective resource allocation mechanisms through the strengthening of transboundary institutions, Central Asia will be able to successfully address these future climate-related challenges.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. 1.

    We are grateful to an anonymous reviewer for details on the climate and precipitation characteristics in the different Central Asian regions.

  2. 2.

    We have also analyzed the 0.5-degree gridded temperature data from the CRU TS2p1 dataset (available at: which exhibits broadly similar trends to those seen at the six stations.

  3. 3.

    A recent Oxfam report on Central Asia, for instance, argues that “retreating glaciers and more extreme weather could dangerously erode food security, livelihoods and even regional stability in 2050” (Swarup 2010).

  4. 4.

    This additional contribution to runoff will however only be available over a strictly limited time until land-ice and snow storage are depleted.

  5. 5.

    In view of future GHG emissions projected by the IPPC, the IEA, and other institutions, the A2 temperature trend may well become reality over the next decades.

  6. 6.

    The development and calibration of the rainfall-runoff model for the Syr Darya is described in detail in Pereira-Cardenal et al. (2011).

  7. 7.

    A number of challenges in extracting regional information and trends from IPCC AR4 GCMs have been noted and are discussed elsewhere (Sellars et al., Simulating climate variability and change in Central Asia using a coupled NHMM-AR1 model, in preparation).

  8. 8.

    With 19.5 km3 total capacity, the Toktogul reservoir is the largest storage facility in the Syr Darya basin. Together with four smaller downstream reservoirs, the facilities have a combined hydropower generation capacity of 2,870 MW (The World Bank 2004).

  9. 9.

    To estimate population growth at the subcatchment level we used a logistic growth model to extrapolate gridded population figures in 2010 (CIESIN 2010) and national growth rates as reported by the United Nations (2007).

  10. 10.

    This hazards map can guide initial monitoring efforts towards the subcatchments which show the largest climate sensitivity of land ice towards climatic changes. However, we would like to emphasize that for this assessment, a linear dynamic model was used. Whereas the linearity assumption of land ice sensitivity towards climate is certainly a good first-order approximation for small-scale volumetric fluctuations, it is problematic in the case of large-scale changes. Hence, solely relying on such model-based output is certainly not the most advisable strategy for the identification of best mitigation strategies in relation to these distributed hazards. Rather, a detailed analysis should utilize remotely sensed data, combined with in-situ observations, to ultimately produce more reliable and detailed hazard maps.


  1. Aizen VB, Aizen EM, Melack JM, Kreutz KJ, Cecil LDW (2004) Association between atmospheric circulation patterns and firn-ice core records from the Inilchek glacierized area, central Tien Shan, Asia. J Geophys Res 109(10.1029):D08304

    Article  Google Scholar 

  2. Aizen VB, Aizen EM, Kuzmichonok VA (2007) Glaciers and hydrological changes in the Tien Shan: simulation and prediction. Environ. Res. Lett. 2:045019

    Article  Google Scholar 

  3. Allen RG (2000) Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. J Hydrol 229(1–2):27–41

    Article  Google Scholar 

  4. Armstrong R, Raup B, Khalsa SJS, Barry R, Kargel J, Helm C, Kieffer H (2005) GLIMS glacier database. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media

  5. Bagla P (2009) No sign yet of Himalayan meltdown, Indian report finds. Science 326(5955):924

    Article  Google Scholar 

  6. Bagla P (2010) Climate science leader Rajendra Pachauri confronts the critics. Science 327(5965):510

    Article  Google Scholar 

  7. Barnett TP, Adam JC, Lettenmaier DP (2005) Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438(7066):303–309

    Article  Google Scholar 

  8. Bernauer T, Siegfried T (2011) Climate change and international water conflict in central asia. J Peace Res (forthcoming)

  9. Bucknall J, Klytchnikova I, Lampietti J, Lundell M, Scatasta M, Thurman M (2003) Irrigation in Central Asia—social, economic and environmental considerations. Tech. rep., The World Bank

  10. Center for International Earth Science Information Network (CIESIN) (2010) Columbia University; United Nations Food and Agriculture Programme (FAO); and Centro Internacional de Agricultura Tropical (CIAT). Gridded Population of the World: Future Estimates (GPWFE)

  11. Cogley JG, Kargel JS, Kaser G, van der Veen CJ (2010) Tracking the source of glacier misinformation. Science 327(5965):522

    Article  Google Scholar 

  12. Dyurgerov M, Meier MF, Bahr DB (2009) A new index of glacier area change: a tool for glacier monitoring. J Glaciol 55(192):710–716

    Article  Google Scholar 

  13. ECMF (2009) Operational surface analysis dataset

  14. Giorgi F, Christensen J, Hulme M, von Storch H, Whetton P, Jones R, Mearns L, Fu C, Arritt R, Bates B, Benestad R, Boer G, Buishand A, Castro M, Chen D, Cramer W, Crane R, Crossly J, Dehn M, Dethloff K, Dippner J, Emori S, Francisco R, Fyfe J, Gerstengarbe F, Gutowski W, Gyalistras D, Hanssen-Bauer I, Hantel M, Hassell D, Heimann D, Jack C, Jacobeit J, Kato H, Katz R, Kauker F, Knutson T, Lal M, Landsea C, Laprise R, Leung L, Lynch A, May W, McGregor J, Miller N, Murphy J, Ribalaygua J, Rinke A, Rummukainen M, Semazzi F, Walsh K, Werner P, Widmann M, Wilby R, Wild M, Xue Y (2001) Climate change 2001: the scientific basis. Contribution of working group to the third assessment report of the intergouvernmental panel on climate change, chap. Regional Climate Information- Evaluation and Projections. Cambridge University Press, Cambridge, United Kingdom and New York, USA

    Google Scholar 

  15. Gleditsch NP, Nordås R (2007) Climate change and conflict. Polit Geogr 26(6):627–638 (special issue)

    Article  Google Scholar 

  16. Greene AM, Robertson AW, Smyth P, Triglia S (2011) Downscaling forecasts of Indian monsoon rainfall using a nonhomogeneous hidden Markov model. Q. J. R. Meteorol. Soc. doi:10.1002/qj.788

  17. Greuell W, Smeets P (2001) Variations with elevation in the surface energy balance on the Pasterze (Austria). J Geophys Res 106(D23):31717

    Article  Google Scholar 

  18. Haeberli W, Beniston M (1998) Climate change and its impacts on glaciers and permafrost in the Alps. Ambio 27(4):258–265

    Google Scholar 

  19. Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8(1):38–55

    Article  Google Scholar 

  20. Immerzeel WW, van Beek LPH, Bierkens MFP (2010) Climate change will affect the asian water towers. Science 328:1382–1385

    Article  Google Scholar 

  21. Kirshner S (2005) Modeling of multivariate time series using hidden Markov models. Ph.D. thesis, University of California, Irvine

  22. Malone EL (2010) Changing glaciers and hydrology in Asia—addressing vulnerabilities to glacier melt impacts. Tech. rep., USAID

  23. Mearns R, Norton A (2010) Social dimensions of climate change: equity and vulnerability in a warming world. World Bank Publications

  24. Merton RK (1995) The Thomas theorem and the Matthew effect. Soc Forces 74(2):379–422

    Google Scholar 

  25. MetzCanziani O, Palutikof J, Van Der Linden P, Hanson C B (2007) Climate change 2007: mitigation of climate change: contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change, illustrated edn. Cambridge University Press, Cambridge

    Google Scholar 

  26. Micklin P (2007) The aral sea disaster. Annu Rev Earth Planet Sci 35:47–72

    Article  Google Scholar 

  27. NAM Technical Reference and Model Documentation (2000) DHI - Water & Environment, Denmark

  28. Nayar A (2009) When ice melts. Nature 461:1042–1046

    Article  Google Scholar 

  29. Oerlemans J (2001) Glaciers and climate change. Taylor & Francis

  30. Oerlemans J (2005) Extracting a climate signal from 169 glacier records. Science 308(5722):675

    Article  Google Scholar 

  31. Parry ML, Canziani OF, Palutikof JP, Van Der Linden PJ, Hanson CE (2007) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press

  32. Pereira-Cardenal S, Riegels N, Berry PA, Smith R, Yakovlev A, Siegfried T, Bauer-Gottwein P (2011) Real-time remote sensing driven river basin modeling using radar altimetry. Hydrol Earth Syst Sci 15:241–254

    Article  Google Scholar 

  33. Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission–a new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogramm Remote Sens 57(4):241–262

    Article  Google Scholar 

  34. Raskin P, Hansen E, Zhu Z, Stavsky D (1992) Simulation of water-supply and demand in the aral sea region. Water Int 17(2):55–67

    Article  Google Scholar 

  35. Robertson AW, Kirshner S, Smyth P (2004) Downscaling of daily rainfall occurrence over Northeast Brazil using a hidden Markov model. J Clim 17(22):4407–4424

    Article  Google Scholar 

  36. Robertson AW, Kirshner S, Smyth P, Charles SP, Bates BC (2006) Subseasonal-to-interdecadal variability of the Australian monsoon over North Queensland. Q J Royal Meteorol Soc 132(615):519–542

    Article  Google Scholar 

  37. Robertson AW, Moron V, Swarinoto Y (2009) Seasonal predictability of daily rainfall statistics over Indramayu district, Indonesia. Int J Climatol 29:1449–1462

    Article  Google Scholar 

  38. Schaefer JM, Denton GH, Barrell DJA, Ivy-Ochs S, Kubik PW, Andersen BG, Phillips FM, Lowell TV, Schluchter C (2006) Near-synchronous interhemispheric termination of the last glacial maximum in mid-latitudes. Science 312(5779):1510

    Article  Google Scholar 

  39. Siegfried T, Bernauer T (2007) Estimating the performance of international regulatory regimes. Water Resour Res 43:W11406. doi:10.1029/2006WR005738

    Article  Google Scholar 

  40. Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller HL (2007) IPCC, 2007: Climate change 2007: the physical science basis. contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. New York, Cambridge University Press

    Google Scholar 

  41. Swarup A (2010) Oxfam. Tech. rep., Oxfam International, Dushanbe, Tajikistan

  42. The MathWorks (2003) MATLAB version R2011a, Natick, Massachusetts: The MathWorks Inc

  43. The World Bank (2004) Water energy nexus in Central Asia: improving regional cooperation in the Syr Darya basin. Tech. rep., The World Bank

  44. Timbal B, Hope P, Charles S (2008) Evaluating the consistency between statistically downscaled and global dynamical model climate change projections. J Clim 21:6052–6059

    Article  Google Scholar 

  45. United Nations Department of Economic and Social Affairs (2007) World population Prospect—the 2006 Revision. Tech. rep., United Nations

  46. United Nations Department of Economic and Social Affairs (2011) Population Division, World Population Prospects: The 2010 Revision, New York

  47. Verbist K, Robertson AW, Cornelis W, Gabriëls D (2010) Seasonal predictability of daily rainfall characteristics in central-northern Chile for dry-land management. J Appl Meteoratol Clim 49(9):1938–1955

    Article  Google Scholar 

  48. Wilby RL, Hay LE, Leavesley GH (1999) A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado. J Hydrol 225(1–2):67–91

    Article  Google Scholar 

  49. Wilson L (1973) Variations in mean annual sediment yield as a function of mean annual precipitation. Am J Sci 273(4):335

    Article  Google Scholar 

  50. Yip S, Ferro CAT, Stephenson DB, Hawkins E (2010) A simple, coherent framework for partitioning uncertainty in climate predicitions

Download references


Support from the CORC-ARCHES program at the Lamont-Doherty Earth Observatory, the Swiss Network for International Studies (SNIS) and the International Research School of Water Resources (FIVA) in Copenhagen is acknowledged. We would specifically like to thank Peter Schlosser for facilitating CORC-ARCHES funding. Andrew W. Robertson’s work was supported by the National Oceanic and Atmospheric Administration through a Cooperative Agreement with Columbia University. The Open Society Institute is acknowledged for providing partial funding of a research trip to Central Asia. We thank DHI and Roar Askær Jensen for providing free access to the MIKE software package. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy.

Author information



Corresponding author

Correspondence to Tobias Siegfried.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

(PDF 147 KB)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Siegfried, T., Bernauer, T., Guiennet, R. et al. Will climate change exacerbate water stress in Central Asia?. Climatic Change 112, 881–899 (2012).

Download citation


  • Climate Sensitivity
  • Shuttle Radar Topography Mission
  • Tropical Rainfall Measuring Mission
  • Global Circulation Model
  • Irrigation Water Demand