Assessing Hydrologic Impact of Climate Change with Uncertainty Estimates: Bayesian Neural Network Approach

A major challenge in assessing the hydrologic effect of climate change remains the estimation of uncertainties associated with different sources, such as the global climate models, emission scenarios, downscaling methods, and hydrologic models. There is a demand for an efficient and easy-to-use rainfall–runoff modeling tool that can capture the different sources of uncertainties to generate future flow simulations that can be used for decision making. To manage the large range of uncertainties in the climate change impact study on water resources, a neural network–based rainfall–runoff model—namely, Bayesian neural network (BNN)—is proposed. The BNN model is used with Canadian Centre for Climate Modelling and Analysis Coupled GCM, versions 1 and 2 (CGCM1 and CGCM2, respectively) with two emission scenarios, Intergovernmental Panel on Climate Change (IPCC) IS92a and Special Report on Emissions Scenarios (SRES) B2. One widely used statistical downscaling model (SDSM) is used in the analysis. The st...