The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California
1Civil Engineering Dept., Santa Clara University, Santa Clara, CA, USA
2Escuela de Física, Universidad de Costa Rica, San José, Costa Rica
3Scripps Institution of Oceanography, La Jolla, CA, USA
4US Geological Survey, Water Resources Division, La Jolla, CA, USA
Abstract. Three statistical downscaling methods were applied to NCEP/NCAR reanalysis (used as a surrogate for the best possible general circulation model), and the downscaled meteorology was used to drive a hydrologic model over California. The historic record was divided into an "observed" period of 1950–1976 to provide the basis for downscaling, and a "projected" period of 1977–1999 for assessing skill. The downscaling methods included a bias-correction/spatial downscaling method (BCSD), which relies solely on monthly large scale meteorology and resamples the historical record to obtain daily sequences, a constructed analogues approach (CA), which uses daily large-scale anomalies, and a hybrid method (BCCA) using a quantile-mapping bias correction on the large-scale data prior to the CA approach. At 11 sites we compared three simulated daily flow statistics: streamflow timing, 3-day peak flow, and 7-day low flow. While all downscaling methods produced reasonable streamflow statistics at most locations, the BCCA method consistently outperformed the other methods, capturing the daily large-scale skill and translating it to simulated streamflows that more skillfully reproduced observationally-driven streamflows.