Quantifying uncertainty in catchment-scale runoff modeling under climate change (case of the Huaihe River, China)

Climate change impact assessment on runoff plays a key role for catchment-scale water resources planning and adaptation. Quantification of impacts on water resources modeling at the catchment-scale is characterized by uncertainties of hydrological model parameters, emission scenarios, and global climate model outputs (including the downscaling techniques). The hydrological parameter uncertainty may show the different magnitude with that of Global Climate Models (GCMs). Much less systematic work has been done to estimate climatic uncertainty and to assess the climatic and hydrologic model outputs within an uncertainty perspective. In this study, based on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology, a systematic uncertainty analysis framework is proposed in terms of a comparable variable. Climatic uncertainty was involved in an ensemble manner through combination of the results from emission scenarios A1B, A2 and B1 and global climate models (CSIRO, NCAR, MPI). The biases of downscaled temperature and precipitation simulations from GCMs were processed by a delta change approach method. Addressing into TOPMODEL, it was analyzed for the future runoff variation and uncertainties impact by climate change. The results indicate uncertainties introduced by different sources lie in different magnitudes, which highlights the new challenges to cope with the multiscales uncertainty in decision-making process. (C) 2012 Elsevier Ltd and INQUA. All rights reserved.