Accounting for interannual variability: A comparison of options for water resources climate change impact assessments

[1] Empirical scaling approaches for constructing rainfall scenarios from general circulation model (GCM) simulations are commonly used in water resources climate change impact assessments. However, these approaches have a number of limitations, not the least of which is that they cannot account for changes in variability or persistence at annual and longer time scales. Bias correction of GCM rainfall projections offers an attractive alternative to scaling methods as it has similar advantages to scaling in that it is computationally simple, can consider multiple GCM outputs, and can be easily applied to different regions or climatic regimes. In addition, it also allows for interannual variability to evolve according to the GCM simulations, which provides additional scenarios for risk assessments. This paper compares two scaling and four bias correction approaches for estimating changes in future rainfall over Australia and for a case study for water supply from the Warragamba catchment, located near Sydney, Australia. A validation of the various rainfall estimation procedures is conducted on the basis of the latter half of the observational rainfall record. It was found that the method leading to the lowest prediction errors varies depending on the rainfall statistic of interest. The flexibility of bias correction approaches in matching rainfall parameters at different frequencies is demonstrated. The results also indicate that for Australia, the scaling approaches lead to smaller estimates of uncertainty associated with changes to interannual variability for the period 2070–2099 compared to the bias correction approaches. These changes are also highlighted using the case study for the Warragamba Dam catchment.

[1]  K. Trenberth,et al.  The changing character of precipitation , 2003 .

[2]  Demetris Koutsoyiannis,et al.  Coupling stochastic models of different timescales , 2001 .

[3]  Francis H. S. Chiew,et al.  Modelling the impacts of climate change on Australian streamflow , 2002 .

[4]  Reto Knutti,et al.  Should we believe model predictions of future climate change? , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[5]  Hayley J. Fowler,et al.  Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling , 2007 .

[6]  I. Seierstad,et al.  Impacts of climate change on Blue Nile flows using bias-corrected GCM scenarios , 2008 .

[7]  R. Mehrotra,et al.  Development and Application of a Multisite Rainfall Stochastic Downscaling Framework for Climate Change Impact Assessment , 2010 .

[8]  F. Chiew,et al.  Influence of Rainfall Scenario Construction Methods on Runoff Projections , 2009 .

[9]  Ashish Sharma,et al.  A Comparison of Australian Open Water Body Evaporation Trends for Current and Future Climates Estimated from Class A Evaporation Pans and General Circulation Models , 2010 .

[10]  Renu Joseph,et al.  ENSO Evolution and Teleconnections in IPCC’s Twentieth-Century Climate Simulations: Realistic Representation? , 2006 .

[11]  M. Babel,et al.  Spatial disaggregation of bias-corrected GCM precipitation for improved hydrologic simulation: Ping River Basin, Thailand , 2007 .

[12]  H. Fowler,et al.  Using regional climate model data to simulate historical and future river flows in northwest England , 2007 .

[13]  A. Pitman,et al.  Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions , 2007 .

[14]  E. Maurer,et al.  Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods , 2007 .

[15]  T. Huntington.  Evidence for intensification of the global water cycle: Review and synthesis , 2006 .

[16]  T. Wigley,et al.  Statistical downscaling of general circulation model output: A comparison of methods , 1998 .

[17]  J. Hansen,et al.  Bias correction of daily GCM rainfall for crop simulation studies , 2006 .

[18]  Assessing future droughts in Australia - a nesting model to correct for long-term persistence in general circulation model precipitation simulations , 2009 .

[19]  J. Christensen,et al.  On the need for bias correction of regional climate change projections of temperature and precipitation , 2008 .

[20]  H. Künsch,et al.  Bayesian multi-model projection of climate: bias assumptions and interannual variability , 2009 .

[21]  M. P. Wand,et al.  Generalised linear mixed model analysis via sequential Monte Carlo sampling , 2008, 0810.1163.

[22]  Quan J. Wang,et al.  A method for coupling daily and monthly time scales in stochastic generation of rainfall series , 2007 .

[23]  N. Reynard,et al.  The effects of climate change due to global warming on river flows in Great Britain , 1996 .

[24]  R. Knutti.  Hotter or not? Should we believe model predictions of future climate change? , 2008 .

[25]  Kenneth R. Sperber,et al.  ENSO simulation in coupled ocean-atmosphere models: are the current models better? , 2006 .

[26]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[27]  D. Lettenmaier,et al.  Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs , 2004 .

[28]  Geoffrey G. S. Pegram,et al.  A nested multisite daily rainfall stochastic generation model , 2009 .

[29]  F. Sun,et al.  Contributions of Indian Ocean and Monsoon Biases to the Excessive Biennial ENSO in CCSM3 , 2009 .

[30]  R. Wilby,et al.  A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom , 2005 .