Abstract
The ability of water managers to maintain adequate supplies in coming decades depends, in part, on future weather conditions, as climate change has the potential to alter river flows from their current values, possibly rendering them unable to meet demand. Reliable climate projections are therefore critical to predicting the future water supply for the United States, but the resolution of the global climate models (GCMs) often used for climate forecasting is too coarse to resolve the changes that can affect hydrology, and hence water supply, at regional to local scales. We therefore apply a statistical downscaling technique that involves a correction of the cumulative distribution functions of the GCM-derived temperature and precipitation for the 20th century, and the application of the same correction to 21st century GCM projections. This is done for three meteorological stations located within the Coosa River basin in northern Georgia, and is used with a surface hydrology model to calculate future river flow statistics for the upper Coosa River. Results are compared to historical Coosa River flow and to flows calculated with the original, unscaled GCM results to determine the impact of potential changes in meteorology on future flows.
Similar content being viewed by others
References
Al-Mukhtar M, Dunger V, Merkel B (2014) Assessing the impacts of climate change on hydrology of the upper reach of the spree river: Germany. Water Resour Manag. doi:10.1007/s11269-014-0675-2
Bicknell BR, Imhoff JC, Kittle JL, Jr. et al. (2005) Hydrological simulation program—FORTRAN (HSPF). User’s manual for release 12.2 U.S. EPA National Exposure Research Laboratory, Athens, GA, in cooperation with U.S. Geological Survey, WRD, Reston, VA
Chapman D (2009) Allatoona may be next battle in water war. Atlanta Journal-Constitution August 17, 2009
Crosbie RS, Dawes WR, Charles SP, Mpelasoka FS, Aryal S, Barron O, Summerell GK (2011) Differences in future recharge estimates due to GCMs, downscaling methods and hydrological models. Geophys Res Lett 38:L11406. doi:10.1029/2011GL047657
Delworth TL, Coauthors (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19:643–674
Dettinger MD, Cayan D, Meyer M, Jeton A (2004) Simulated hydrologic responses to climate variations and change in the Merced, Carson, and American River basins. Sierra Nevada, California, 1900–2099. Clim Chang 62(no. 1):283–317
Fogel D (2000) Evolutionary Computation. Spie Press, Bellingham
Göncü S, Albek E (2010) Modeling climate change effects on streams and reservoirs with HSPF. Water Resour Manag 24:707–726
Hagemann S, Chen C, Haerter JO, Heinke J, Gerten D, Piani C (2011) Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J Hydrometeorol 12:556–578
Hayhoe K, Cayan D, Field CB, Frumhoff PC, Maurer EP, Miller NL, Moser SC, Schneider SH, Cahill KN, Cleland EE, Dale L, Drapek R, Hanemann RM, Kalkstein LS, Lenihan J, Lunch CK, Neilson RP, Sheridan SC, Verville JH (2004) Emission pathways, climate change, and impacts on California. Proc Natl Acad Sci 101(34):12422–12427
Hayhoe K, Wake C, Anderson B, Liang X-Z, Maurer E, Zhu J, Bradbury J, DeGaetano A, Stoner AM, Wuebbles D (2008) Regional climate change projections for the Northeast USA. Mitig Adapt Strateg Glob Chang 13(5–6):425–436
Hewitson B, Crane R (1996) Climate downscaling: techniques and application. Clim Res 7:85–95
IPCC (2007) In: Core Writing T, Pachauri RK, Reisinger A (eds) Climate Change 2007: Synthesis Report: Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, p 104
Johns TC, Durman C, Banks H, Roberts M, McLaren A, Ridley J, Senior C, Williams K, Jones A, Rickard G, Cusack S, Ingram W, Crucifix M, Sexton D, Joshi M, Dong B, Spencer H, Hill R, Gregory J, Keen A, Pardaens A, Lowe J, Bodas-Salcedo A, Stark S, Searl Y (2006) The new Hadley centre climate model HadGEM1: evaluation of coupled simulations. J Clim 19:1327–1353
Joyner C (2011) Deal’s water plans advance despite concerns. Atlanta J-Constit April 12, 2011
Karl T, Mellio J, Peterson T (2009) Global Climate Change Impacts in the United States. (eds.). Cambridge University Press
Kidson JW, Thompson CS (1998) A comparison of statistical and model-based downscaling techniques for estimating local climate variations. J Clim 11:735–753
Meehl GA, Covey C, Taylor KE, Delworth T, Stouffer RJ, Latif M, McAvaney B, Mitchell JFB (2007) The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394
Michaelsen J (1987) Cross-validation in statistical climate forecast models. J Appl Meteorol 26:1589–1600
Michelangeli PA, Vrac M, Loukos H (2009) Probabilistic downscaling approaches: Application to wind cumulative distribution functions. Geophys Res Lett 36:L11708. doi:10.1029/2009GL038401
Murphy J (1999) An evaluation of statistical and dynamical techniques for downscaling local climate. J Clim 12:2256–2284
Prudhomme C, Reynard N, Crooks S (2002) Downscaling of global climate models for flood frequency analysis: where are we now? Hydrol Process 16:1137–1150
Rajagopalan B, Nowak K, Prairie J, Hoerling M, Harding B, Barsugli J, Ray A, Udall B (2009) Water supply risk on the Colorado River: can management mitigate? Water Resour Res 45:W08201. doi:10.1029/2008WR007652
Risbey J, Stone P (1996) A case study of the adequacy of GCM simulations for input to regional climate change assessments. J Clim 9:1441–1467
Salathé E (2005) Downscaling simulations of future global climate with application to hydrologic modelling. Int J Climatol 25:419–436
Samadi S, Carbone G, Mahdavi M, Sharifi F, Bihamta MR (2012) Statistical downscaling of river runoff in a semi arid catchment. Water Resour Manag. doi:10.0007/s11269-012-0170-6
Schmidt GA, Ruedy R, Hansen JE, Aleinov I, Bell N, Bauer M, Bauer S, Cairns B, Canuto V, Cheng Y, Del Genio A, Faluvegi G, Friend AD, Hall TM, Hu Y, Kelley M, Kiang NY, Koch D, Lacis AA, Lerner J, Lo KK, Miller RL, Nazarenko L, Oinas V, Perlwitz JP, Perlwitz J, Rind D, Romanou A, Russell GL, Sato M, Shindell DT, Stone PH, Sun S, Tausnev N, Thresher D, Yao M-S (2006) Present day atmospheric simulations using GISS ModelE: comparison to in-situ, satellite and reanalysis data. J Clim 19:153–192. doi:10.1175/JCLI3612.1
Scinocca JF, McFarlane NA, Lazare M, Li J, Plummer D (2008) Technical note: the CCCma third generation AGCM and its extension into the middle atmosphere. Atmos Chem Phys 8:7883–7930
Shukla J, Hagedorn R, Hoskins B, Kinter J, Marotzke J, Miller M, Palmer TN, Slingo J (2009) Strategies: revolution in climate prediction is both necessary and possible: a declaration at the world modelling summit for climate prediction. Bull Am Meteorol Soc 90:175–178
Stephens GL, L’Ecuyer T, Forbes R, Gettlemen A, Golaz J-C, Bodas-Salcedo A, Suzuki K, Gabriel P, Haynes J (2010) Dreary state of precipitation in global models. J Geophys Res 115:D24211. doi:10.1029/2010JD014532
Stoner A, Hayhoe K, Yang X, Wuebbles D (2012) An asynchronous regional regression model for statistical downscaling of daily climate variables. Int J Clim. doi:10.1002/joc.3603
Strzepek K, Yohe G, Neumann J, Boehlert B (2010) Characterizing changes in drought risk for the United States from climate change. Environ Res Lett 5 doi:10.1088/1748-9326/5/4/0440212
Sun F, Roderick ML, Lim WH, Farquhar GD (2011) Hydroclimatic projections for the Murray-Darling Basin based on an ensemble derived from Intergovernmental panel on climate change AR4 climate models. Water Resour Res 47:W00G02. doi:10.1029/2010WR009829
Tian Y, Xu Y-P, Zhang X-J (2013) Assessment of climate change impacts on river high flows through comparative use of GR4J, HBV and Xinanjiang models. Water Resour Manag 27:2871–2888
U.S. EPA (Environmental Protection Agency) (2007) BASINS 4.0. U.S. Environmental protection agency, Washington, DC. EPA/823/C-07/001
U.S. EPA (Environmental Protection Agency) (2013) Watershed modeling to assess the sensitivity of streamflow, nutrient, and sediment loads to potential climate change and urban development in 20 U.S. Watersheds (Final Report). EPA/600/R-12/058
Vrac M, Stein ML, Hayhoe K, Liang X-Z (2007) A general method for validating statistical downscaling methods under future climate change. Geophys Res Lett 34:L18701. doi:10.1029/2007GL030295
Wood AW, Maurer EP, Kumar A, Lettenmaier DP (2002) Long range experimental hydrologic forecasting for the eastern United States. J Geophys Res 107(D20):4429. doi:10.1029/2001JD000659
Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62(1–3):189–216
Yukimoto S, Noda A, Kitoh A, Hosaka M, Yoshimura H, Uchiyama T, Shibata K, Arakawa O, Kusunoki S (2006) Present-day climate and climate sensitivity in the meteorological research Institute coupled GCM Version 2.3 (MRI-CGCM2.3). J Meteor Soc Jpn 84:333–363
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Werth, D., Chen, KF. The Application of a Statistical Downscaling Process to Derive 21st Century River Flow Predictions Using a Global Climate Simulation. Water Resour Manage 29, 849–861 (2015). https://doi.org/10.1007/s11269-014-0847-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-014-0847-0