Skip to main content

Advertisement

SpringerLink
Log in

Global climate change and its impacts on water resources planning and management: assessment and challenges

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Population explosion and its many associated effects (e.g. urbanization, water pollution, deforestation) have already caused enormous stress on the world’s fresh water resources and, in turn, environment, health, and economy. According to latest World Health Organization estimates, about 900 million people still lack access to safe drinking water, about 2.5 billion people lack access to proper sanitation, millions of people die every year from water-related disasters and diseases, and economic losses in the order of billions of dollars occur due to water-related disasters. With the global climate change anticipated to have threatening consequences on our water resources and environment both at the global level and at local/regional levels (e.g. increases in the number and magnitude of floods and droughts, increases in sea levels), a general assessment is that the future state of our water resources will be a lot worse than it is now. The facts that over 300 rivers around the world are being shared by two or more nation states and that there are already numerous conflicts in the planning, development, and management of water resources in these basins further complicate matters for future water resources planning. In view of these, any sincere effort towards proper management of our future water resources and resolving potential future water-related conflicts will need to overcome many challenges. These challenges are both biophysical science-related and human science-related. The biophysical science challenges include: identification of the actual causes of climate change, development of global climate models (GCMs) that can adequately incorporate these causes to generate dependable future climate projections at larger scales, formulation of appropriate techniques to downscale the GCM outputs to local conditions for hydrologic predictions, and reliable estimation of the associated uncertainties in all these. The human science challenges have social, political, economic, and environmental facets that often act in an interconnected manner; proper ‘communication’ of (or lack thereof) our climate-water ‘scientific’ research activities to fellow scientists and engineers, policy makers, economists, industrialists, farmers, and the public at large crucially contributes to these challenges. The present study is intended to review the current state of our water resources and the climate change problem and to detail the challenges in dealing with the potential impacts of climate change on our water resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Abarbanel HDI, Lall U (1996) Nonlinear dynamics of the Great Salt Lake: system identification and prediction. Clim Dyn 12:287–297

    Article  Google Scholar 

  • Arnell NW, Hudson DA, Jones RG (2003) Climate change scenarios from a regional climate model: estimating change in runoff in southern Africa. J Geophys Res 108(D16):4519–4528

    Article  Google Scholar 

  • Bardossy A, Bogardi I, Matyasovszky I (2005) Fuzzy rule-based downscaling of precipitation. Theor Appl Climatol 82:119–129

    Article  Google Scholar 

  • Barnett T, Hasselmann K, Chelliah M, Delworth T, Hegerl G, Jones P, Rasmusson E, Roeckner E, Ropelewski C, Santer BD, Tett S (1999) Detection and attribution of recent climate change: a status report. Bull Am Meteorol Soc 80:2631–2659

    Article  Google Scholar 

  • Barnett T, Malone T, Pennell W, Stammer D, Semtner B, Washington W (2004) The effects of climate change on water resources in the west: introduction and overview. Clim Change 62:1–11

    Article  Google Scholar 

  • Benestad RE (2001) A comparison between two empirical downscaling strategies. Int J Climatol 21:1645–1668

    Article  Google Scholar 

  • Bergstrom S, Carlsson B, Gardelin M, Lindstrom G, Pettersson A, Rummukainen M (2001) Climate change impacts on run-off in Sweden—assessments by global climate models, dynamical downscaling and hydrological modeling. Clim Res 16:101–112

    Article  Google Scholar 

  • Beven KJ (1993) Prophecy, reality and uncertainty in distributed hydrological modelling. Adv Water Resour 16:41–51

    Article  Google Scholar 

  • Beven KJ (2002) Uncertainty and the detection of structural change in models of environmental systems. In: Beck MB (ed) Environmental foresight and models: a manifesto. Elsevier, The Netherlands, pp 227–250

    Chapter  Google Scholar 

  • Beven KJ (2006) On undermining the science? Hydrol Processes 20:3141–3146

    Article  Google Scholar 

  • Beven KJ, Binley AM (1992) The future of distributed models: model calibration and uncertainty prediction. Hydrol Processes 6:279–298

    Article  Google Scholar 

  • Beven KJ, Young P (2003) Comment on ‘Bayesian recursive parameter estimation for hydrologic models’ by M. Thiemann, M. Trosset, H. Gupta, and S. Sorooshian. Water Resour Res 39(5). doi:10.1029/2001WR001183

  • Bürger G, Chen Y (2005) Regression-based downscaling of spatial variability for hydrologic applications. J Hydrol 311:299–317

    Article  Google Scholar 

  • Burke EJ, Brown SJ, Christidis N (2006) Modelling the recent evolution of global drought and projections for the 21st century with the Hadley Centre climate model. J Hydrometeorol 7:1113–1125

    Article  Google Scholar 

  • Busuioc A, Chen D, Hellström C (2001) Performance of statistical downscaling models in GCM validation and regional climate change estimates: application for Swedish precipitation. Int J Climatol 21:557–578

    Article  Google Scholar 

  • Charles SP, Bates BC, Whetton PH, Hughes JP (1999) Validation of a downscaling model for changed climate conditions in southwestern Australia. Clim Res 12:1–14

    Article  Google Scholar 

  • Chiew FHS, McMahon TA (2002) Modeling the impacts of climate change on Australian streamflow. Hydrol Processes 16:1235–1245

    Article  Google Scholar 

  • Christensen NS, Wood AW, Voisin N, Lettenmaier DP, Palmer RN (2004) Effects of climate change on the hydrology and water resources of the Colorado river basin. Clim Change 62:337–363

    Article  Google Scholar 

  • Commission on Sustainable Development (CSD) (1997) Comprehensive assessment of the freshwater resources of the world. Report of the Secretary-General, United Nations Economic and Social Council, New York

    Google Scholar 

  • Coulibaly P, Dibike YB, Anctil F (2005) Downscaling precipitation and temperature with temporal neural networks. J Hydrometeor 6:483–496

    Article  Google Scholar 

  • de Villiers M (1999) Water wars: is the world’s water running out? Weidenfeld & Nicolson Ltd, UK

  • Diaz-Nieto J, Wilby RL (2005) A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom. Clim Change 69:245–268

    Article  Google Scholar 

  • Dibike YB, Coulibaly P (2006) Temporal neural networks for downscaling climate variability and extremes. Neural Networks 19(2):135–144

    Article  Google Scholar 

  • Douville H, Chauvin F, Planton S, Royer J-F, Salas-Mélia D, Tyteca S (2002) Sensitivity of the hydrological cycle to increasing amounts of greenhouse gases and aerosol. Clim Dyn 20:45–68

    Article  Google Scholar 

  • Duan Q, Sorooshian S, Gupta VK (1992) Effective and efficient global optimization for conceptual rainfall-run-off. Water Resour Res 28:1015–1031

    Article  Google Scholar 

  • Duan Q, Gupta HV, Sorooshian S, Rousseau AN, Turcotte R (2002) Calibration of watershed models. Water science and application series, vol 6. American Geophysical Union, Washington, pp 1–346

  • Elhance AP (1999) Hydropolitics in the third world: conflict and co-operation in international river basins. United States of America Institute of Peace Press, Washington, DC

    Google Scholar 

  • Elsner JB, Tsonis AA (1993) Nonlinear dynamics established in the ENSO. Geophys Res Lett 20:213–216

    Article  Google Scholar 

  • Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modeling to impacts studies: recent advances in downscaling techniques for hydrological modeling. Int J Climatol 27(12):1547–1578

    Article  Google Scholar 

  • Frei C, Christensen JH, Déqué M, Jacob D, Jones RG, Vidale PL (2003) Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. J Geophys Res 108(D3):4124. doi:10.1029/2002JD002287

    Article  Google Scholar 

  • Frei C, Schöll R, Fukutome S, Schmidli J, Vidale PL (2006) Future change of precipitation extremes in Europe: an intercomparison of scenarios from regional climate models. J Geophys Res-Atmos 111:D06105. doi:10.1029/2005JD005965

    Article  Google Scholar 

  • Fuentes U, Heimann D (2000) An improved statistical-dynamical downscaling scheme and its application to the Alpine precipitation climatology. Theor Appl Climatol 65:119–135

    Article  Google Scholar 

  • Gangopadhyay S, Clark M, Rajagopalan B (2005) Statistical downscaling using K-nearest neighbors. Water Resour Res 41:W02024. doi:10.1029/2004WR003444

    Article  Google Scholar 

  • Gleick PH (1993) Water in the 21st century. In: Gleick PH (ed) Water in crisis: a guide to the world’s fresh water resources. Oxford University Press, Oxford, pp 105–113

    Google Scholar 

  • Grayson RB, Blöschl G (2000) Spatial patterns in catchment hydrology: observations and modelling. Cambridge University Press, Cambridge

    Google Scholar 

  • Gupta HV, Sorooshian S, Yapo PO (1998) Towards improved calibration of hydrologic models: multiple and incommensurable measures of information. Water Resour Res 34:751–763

    Article  Google Scholar 

  • Gupta HV, Thiemann M, Trosset M, Sorooshian S (2003) Reply to comment by K. Beven and P. Young on ‘Bayesian recursive parameter estimation for hydrologic models.’ Water Resour Res 39(5). doi:10.1029/2002WR001405

  • Hagemann S, Machenhauer B, Jones R, Christensen OB, Déqué M, Jacob D, Vidale PL (2004) Evaluation of water and energy budgets in regional climate models applied over Europe. Clim Dyn 23:547–567

    Article  Google Scholar 

  • Hanssen-Bauer I, Achberger C, Benestad RE, Chen D, Forland EJ (2005) Statistical downscaling of climate scenarios over Scandinavia. Clim Res 29:255–268

    Article  Google Scholar 

  • Hasselmann K (1997) Multi-pattern fingerprint method for detection and attribution of climate change. Clim Dyn 13:601–612

    Article  Google Scholar 

  • Hegerl GC, von Storch H, Hasselmann K, Santer BD, Cubasch U, Jones PD (1996) Detecting greenhouse-gas-induced climate change with an optimal fingerprint method. J Clim 9:2281–2306

    Article  Google Scholar 

  • Hegerl GC, Hasselmann K, Cubasch U, Mitchell JFB, Roeckner E, Voss R, Waszkewitz J (1997) Multi-fingerprint detection and attribution analysis of greenhouse gas, greenhouse gas-plus aerosol and solar forced climate change. Clim Dyn 13:613–634

    Article  Google Scholar 

  • Hewitson BC, Crane RG (1996) Climate downscaling: techniques and application. Clim Res 7:85–95

    Article  Google Scholar 

  • Huth R (1999) Statistical downscaling in central Europe: evaluation of methods and potential predictors. Clim Res 13:91–101

    Article  Google Scholar 

  • IPCC (1990) Climate change: the IPCC scientific assessment. In: Houghton JT, Jenkins GJ, Ephraums JJ (eds) Cambridge University Press, Cambridge

  • IPCC (2000) Emissions scenarios. In: Nakicenovic N, Swart R (eds) Special Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, 570 pp

  • IPCC (2001) Climate change 2001: the scientific basis. In: Houghton JT et al (eds) Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • IPCC (2007a) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt, KB, Tignor M, Miller HL (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • IPCC (2007b) Climate change 2007: impacts, adaptation and vulnerability. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • IPCC (2007c) Climate change 2007: mitigation of climate change. In: Metz B, Davidson OR, Bosch PR, Dave R, Meyer LA (eds) Contribution of working group III to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge

  • Jakeman AJ, Hornberger GM (1993) How much complexity is warranted in a rainfall-runoff model? Water Resour Res 29(8):2637–2650

    Article  Google Scholar 

  • Kavetski D, Franks SW, Kuczera G (2002) Confronting input uncertainty in environmental modelling. In: Duan Q, Gupta HV, Sorooshian S, Rousseau AN, Turcotte R (eds) Calibration of watershed models. AGU Books, Washington, pp 49–68

    Google Scholar 

  • Keller CF (2008) Global warming: a review of this mostly settled issue. Stoch Environ Res Risk Assess 23:643–676. doi:10.1007/s00477-088-0253-3

    Article  Google Scholar 

  • Kilsby CG, Tellier SS, Fowler HJ, Howels TR (2007) Hydrological impacts of climate change on the Tejo and Guadiana rivers. Hydrol Earth Syst Sci 11(3):1175–1189

    Article  Google Scholar 

  • Kirchner JW (2006) Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology. Water Resour Res 42:W03S04. doi:10.1029/2005WR004362

    Article  Google Scholar 

  • Kleinen T, Petschel-Held G (2007) Integrated assessment of changes in flooding probabilities due to climate change. Clim Change 81(3-4):283–312. doi:10.1007/s10584-006-9159-6

    Article  Google Scholar 

  • Konikow LF, Bredehoeft JD (1992) Groundwater models cannot be validated? Adv Water Resour 15:75–83

    Article  Google Scholar 

  • Leung LR, Qian Y, Bian X, Hunt A (2003) Hydroclimate of the western United States based on observations and regional climate simulation of 1981–2000. Part II: mesoscale ENSO anomalies. J Clim 16:1912–1928

    Article  Google Scholar 

  • Leung LR, Qian Y, Bian X, Washington WM, Han J, Roads JO (2004) Mid-century ensemble regional climate change scenarios for the Western United States. Clim Change 62:75–113

    Article  Google Scholar 

  • Levitus S, Antonov JI, Wang J, Delworth TL, Dixon KW, Broccoli AJ (2001) Anthropogenic warming of Earth’s climate system. Science 292:267–270

    Article  CAS  Google Scholar 

  • Lorenz EN (1963) Deterministic nonperiodic flow. J Atmos Sci 20:130–141

    Article  Google Scholar 

  • Maurer EP, Hidalgo HG (2008) Utility of daily vs monthly large-scale climate data: an intercomparison of two statistical downscaling approaches. Hydrol Earth Syst Sci 12:551–563

    Article  Google Scholar 

  • McDonnell JJ, Woods RA (2004) On the need for catchment classification. J Hydrol 299:2–3

    Google Scholar 

  • Milly PCD, Dunne KA, Vecchia AV (2005) Global pattern of trends in streamflow and water availability in a changing climate. Nature 438:347–350

    Article  CAS  Google Scholar 

  • Mirza MMQ (2003) Climate change and extreme weather events: can developing countries adapt? Clim Policy 3(3):233–248

    Article  Google Scholar 

  • Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12:2256–2284

    Article  Google Scholar 

  • Murphy J (2000) Predictions of climate change over Europe using statistical and dynamical downscaling techniques. Int J Climatol 20:489–501

    Article  Google Scholar 

  • Ohlsson L (1995) Hydropolitics: conflicts over water as a development constraint. Zed Books, London

    Google Scholar 

  • Palaniappan M, Gleick PH (2008) Peak water. In: Gleick PH (ed) The world’s water 2008–2009. Island Press, Washington, DC, pp 1–16

    Google Scholar 

  • Perrin C, Michel C, Andréassian V (2001) Does a large number of parameters enhance model performance? Comparative assessment of common catchment model structures on 429 catchments. J Hydrol 242:275–301

    Article  Google Scholar 

  • Popper KR (1934) The logic of scientific discovery, English edition. Routledge, London

  • Prudhomme C, Reynard N, Crooks S (2002) Downscaling of global climate models for flood frequency analysis: where are we now? Hydrol Processes 16:1137–1150

    Article  Google Scholar 

  • Refsgaard JC, Henriksen HJ (2004) Modelling guidelines—terminology and guiding principles. Adv Water Resour 27:71–82

    Article  Google Scholar 

  • Salathé EP (2003) Comparison of various precipitation downscaling methods for the simulation of streamflow in a rainshadow river basin. Int J Climatol 23:887–901

    Article  Google Scholar 

  • Salathé EP (2005) Downscaling simulations of future global climate with application to hydrologic modelling. Int J Climatol 25:419–436

    Article  Google Scholar 

  • Santer BD, Taylor KE, Wigley TML, Penner JE, Jones PD, Cubasch U (1995) Towards the detection and attribution of an anthropogenic effect on climate. Clim Dyn 12:77–100

    Article  Google Scholar 

  • Santer BD, Wigley TML, Simmons AJ, Kallberg PW, Kelly GA, Uppala SM, Ammann C, Boyle JS, Bruggemann W, Doutriaux C, Fiorino M, Mears C, Meehl GA, Sausen R, Taylor KE, Washington WM, Wehner MF, Wentz FJ (2004) Identification of anthropogenic climate change using a second-generation analysis. J Geophys Res. doi:109:10.1029/2003JD005075

    Google Scholar 

  • Sivakumar B (2000) Chaos theory in hydrology: important issues and interpretations. J Hydrol 227(1–4):1–20

    Article  Google Scholar 

  • Sivakumar B (2004a) Chaos theory in geophysics: past, present and future. Chaos Solitons Fractals 19(2):441–462

    Article  Google Scholar 

  • Sivakumar B (2004b) Dominant processes concept in hydrology: moving forward. Hydrol Processes 18(12):2349–2353

    Article  Google Scholar 

  • Sivakumar B (2008a) Dominant processes concept, model simplification and classification framework in catchment hydrology. Stoch Environ Res Risk Assess 22((6):737–748. doi:10.1007/s00477-007-0183-5

    Article  Google Scholar 

  • Sivakumar B (2008b) Undermining the science or undermining Nature? Hydrol Processes 22(6):893–897

    Article  Google Scholar 

  • Sivakumar B (2008c) The more things change, the more they stay the same: the state of hydrologic modeling. Hydrol Processes 22(21):4333–4337

    Article  Google Scholar 

  • Sivakumar B (2009) Nonlinear dynamics and chaos in hydrologic systems: latest developments and a look forward. Stoch Environ Res Risk Assess 23:1027–1036. doi:10.1007/s00477-008-0265-z

    Article  Google Scholar 

  • Sivakumar B, Sorooshian S, Gupta HV, Gao X (2001) A chaotic approach to rainfall disaggregation. Water Resour Res 37(1):61–72

    Article  Google Scholar 

  • Sivakumar B, Jayawardena AW, Li WK (2007) Hydrologic complexity and classification: a simple data reconstruction approach. Hydrol Processes 21(20):2713–2728

    Article  Google Scholar 

  • Sivapalan M, Takeuchi K, Franks SW, Gupta VK, Karambiri H, Lakshmi V, Liang X, McDonnell JJ, Mendiondo EM, O’Connell PE, Oki T, Pomeroy JW, Schertzer D, Uhlenbrook S, Zehe E (2003) IAHS decade on predictions in ungauged basins (PUB), 2003–2012: shaping an exciting future for the hydrological sciences. Hydrol Sci J 48(6):857–880

    Article  Google Scholar 

  • Sorooshian S, Gupta VK (1983) Automatic calibration of conceptual rainfall-runoff models: the question of parameter observability and uniqueness. Water Resour Res 19(1):251–259

    Article  Google Scholar 

  • Stott PA, Tett SFB, Jones GS, Allen MR, Mitchell JFB, Genkins G (2000) External control of 20th century temperature by natural and anthropogenic forcings. Science 290:2133–2137

    Article  CAS  Google Scholar 

  • Stott PA, Tett SFB, Jones GS, Allen MR, Ingram WJ, Mitchell JFB (2001) Attribution of twentieth century temperature change to natural and anthropogenic causes. Clim Dyn 17:1–21

    Article  CAS  Google Scholar 

  • Sullivan CA, Meigh JR (2005) Targeting attention on local vulnerabilities using an integrated indicator approach: the example of the climate vulnerability index. Water Sci Technol 51(5):69–78

    CAS  Google Scholar 

  • Sullivan CA, Meigh JR, Giacomello AM, Fediw T, Lawrence P, Samad M, Mlote S, Hutton C, Allan JA, Schulze RE, Dlamini DJM, Cosgrove W, Priscoli DJ, Gleick PH, Smout I, Cobbing J, Calow R, Hunt C, Hussain A, Acreman MC, King J, Malomo S, Tate EL, O’Regan D, Milner S, Steyl I (2003) The water poverty index: development and application at the community scale. Natural Resources 27:189–199

    Article  Google Scholar 

  • Tett SFB, Stott PA, Allen MR, Ingram WJ, Mitchell JFB (1999) Causes of twentieth century temperature change near the Earth’s surface. Nature 399:569–572

    Article  CAS  Google Scholar 

  • Tsonis AA, Elsner JB (1988) The weather attractor over very short timescales. Nature 333:545–547

    Article  Google Scholar 

  • Turton A, Henwood R (2002) Hydropolitics in the developing world: a southern African perspective. African Water Issues Research Unit (AWIRU), University of Pretoria, Pretoria

  • United Nations Economic & Social Affairs (UN/ESA) (2007) World population prospects: the 2006 revision, highlights, Working paper no. ESA/P/WP.202

  • United Nations Environment Programme (UNEP) (2007) Global environmental outlook 4: environment for development. Progress Press Ltd, Malta

  • United Nations International Strategy for Disaster Reduction (UN/ISDR) (2007) Droughts, desertification and water security. United Nations

  • United Nations World Water Assessment Program (UN/WWAP) (2003) World water development report: Water for people, water for life. http://www.unesco.org/water/wwap/wwdr/index.shtml

  • Vrugt JA, Bouten W, Gupta HV, Sorooshian S (2002) Toward improved identifiability of hydrologic model parameters: the information content of experimental data. Water Resour Res 38(12):1312. doi:10.1029/2001WR001118

    Article  Google Scholar 

  • Vrugt JA, ter Braak CJF, Gupta HV, Robinson BA (2009) Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling. Stoch Environ Res Risk Assess 23(7):1011–1026. doi:10.1007/s00477-008-0274-y

    Article  Google Scholar 

  • Wagener T, Sivapalan M, Troch PA, Woods RA (2007) Catchment classification and hydrologic similarity. Geogr Compass 2:901. doi:10.1111/j.1749-8198.2007.00039.x

    Article  Google Scholar 

  • Wilby RL (1998) Statistical downscaling of daily precipitation using daily airflow and seasonal teleconnection indices. Clim Res 10:163–178

    Article  Google Scholar 

  • Wilby RL, Harris I (2006) A framework for assessing uncertainties in climate change impacts: low flow scenarios for the River Thames, UK. Water Resour Res 42:W02419. doi:10.1029/2005WR004065

    Article  Google Scholar 

  • Wilby RL, Wigley TML (1997) Downscaling general circulation model output: a review of methods and limitations. Prog Phys Geogr 21:530–548

    Article  Google Scholar 

  • Wilby RL, Wigley TML, Conway D, Jones PD, Hewitson BC, Main J, Wilks DS (1998) Statistical downscaling of general circulation model output: a comparison of methods. Water Resour Res 34:2995–3008

    Article  Google Scholar 

  • Wilby RL, Hay LE, Gutowski WJJ, Arritt RW, Takle ES, Pan Z, Leavesley GH, Clark MP (2000) Hydrological responses to dynamically and statistically downscaled climate model output. Geophys Res Lett 27:1199–1202

    Article  Google Scholar 

  • Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Change 62:189–216

    Article  Google Scholar 

  • World Health Organization/United Nations Children’s Fund (WHO/UNICEF) (2008) Progress on drinking water and sanitation. Joint Monitoring Programme (JMP). Geneva, World Health Organization; New York, United Nations Children’s Fund

  • Xu CY (1999) From GCMs to river flow: a review of downscaling methods and hydrologic modelling approaches. Prog Phys Geogr 23(2):229–249

    Google Scholar 

  • Young PC, Parkinson SD (2002) Simplicity out of complexity. In: Beck MB (ed) Environmental foresight and models: a manifesto. Elsevier Science, The Netherlands, pp 251–294

    Chapter  Google Scholar 

  • Young PC, Parkinson SD, Lees M (1996) Simplicity out of complexity in environmental systems: Occam’s Razor revisited. J Appl Stat 23:165–210

    Article  Google Scholar 

  • Zorita E, von Storch H (1999) The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 12:2474–2489

    Article  Google Scholar 

Download references

Acknowledgments

Part of the work leading to this article was carried out while I was visiting Inha University, Korea. Support from the Korea Science and Technology Societies (through the Brainpool fellowship) and Inha University is acknowledged.

Author information

Authors and Affiliations

  1. School of Civil and Environmental Engineering, The University of New South Wales, Sydney, NSW, 2052, Australia

    Bellie Sivakumar

  2. Department of Land, Air and Water Resources, University of California, Davis, CA, 95616, USA

    Bellie Sivakumar

Authors
  1. Bellie Sivakumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bellie Sivakumar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sivakumar, B. Global climate change and its impacts on water resources planning and management: assessment and challenges. Stoch Environ Res Risk Assess 25, 583–600 (2011). https://doi.org/10.1007/s00477-010-0423-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-010-0423-y

Keywords

Search

Navigation