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

Abstract 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 …