An integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis method for quantifying climate change impact on runoff of the Amu Darya River basin

Abstract As one of the most pressing issues in the world, climate change has already caused evident impacts on natural and human systems (e.g., hydrological cycle, eco‐environment and socio‐economy) in recent decades. In this study, an integrated multi‐GCMs Bayesian‐neural‐network hydrological analysis (MBHA) method is developed for quantifying climate change impacts on runoff. MBHA incorporates multiple global climate models (multi‐GCMs), …