Study of Climate Change Impact on Flood Frequencies: A Combined Weather Generator and Hydrological Modeling Approach

Abstract

limate change is expected to lead to more frequent and intensive flooding problems for watersheds in the south part of China. This study presented a coupled Long Ashton Research Station Weather Generator (LARS-WG) and Semidistributed Land Use?Based Runoff Processes (SLURP) approach for flood frequency analysis and applied it to the Heshui watershed, China. LARS-WG, as a weather generator, was used to offer 46 sets of climate data from seven general circulation models (GCMs) under various emission scenarios (i.e., A1B, B1, and A2) over near-term and future periods (i.e., T1, 2011?30; T2, 2046?65; and T3, 2080?99). SLURP is a continuous, spatially distributed hydrological model that uses parameters from physiographic data to simulate the hydrological cycle from precipitation to runoff. Flood frequency analysis based on Pearson type III distributions was followed to analyze statistics of annual peaks. The final results (from ensembles of multimodels and multiscenarios) indicated that the magnitudes of a 200-yr return flood for T1, T2, and T3 would increase by 5.23%, 4.08%, and 12.92%, respectively, in comparison to the baseline level; those under the most extreme condition (i.e., worst scenario) would be 25.18%, 31.00%, and 44.46%, respectively. Various GCMs and emission scenarios suggested different results. But the ECHAM5/Max Planck Institute Ocean Model was found to give a more worrying intensification of flood risks and the Commonwealth Scientific and Industrial Research Organisation Mark, version 3.0, and the Community Climate System Model, version 3, were relatively conservative. The study results were useful in helping gain insight into the flood risks and its uncertainty under future climate change conditions for the Heshui watershed, and the proposed methodology is also applicable to many other watersheds in Southeast Asia with similar climatic conditions.