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

• Published in: International journal of climatology Vol. 41; no. 5; pp. 3411 - 3424
• Main Authors: Su, Yuanyuan, Li, Yongping, Liu, Yuanrui, Huang, Guohe, Jia, Qimeng, Li, Yanfeng
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.04.2021; Wiley Subscription Services, Inc
• Subjects: Amu Darya River basin; Atmospheric precipitations; Bayesian analysis; Bayesian neural network; Catchment area; Catchments; Climate change; Climate models; Climatic analysis; Climatic data; Environmental impact; Glacial runoff; Glaciers; Glaciohydrology; Global climate; Global climate models; HBV‐light; hybrid hydrological model; Hydrologic analysis; Hydrologic cycle; Hydrologic models; Hydrologic networks; Hydrological analysis; Hydrological cycle; Hydrology; Mathematical models; Monthly runoff; Neural networks; Precipitation; Probability theory; Rainfall; River basins; Rivers; Runoff; runoff ensemble prediction; Temperature; Temperature rise; Water availability
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.7026

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), hydrological model (HBV‐light), and Bayesian neural network (BNN) within a general framework. MBHA can provide the reliable prediction for runoff as well as reflect the impact of climate change on data scarcity catchments. MBHA is applied to the Amu Darya River basin in Central Asia. Climate data are derived from multiple GCMs (i.e., GFDL‐ESM2G, HadGEM2‐AO and NorESM1‐M) under RCP4.5 and RCP8.5. Several findings can be summarized: (1) during 2021–2100, both precipitation and temperature would increase, with more precipitation falling as rain instead of snow; (2) by 2100, glacier areas are predicted to reduce by 62.3% (RCP4.5) and 71.9% (RCP8.5); (3) under RCP8.5, monthly runoff would increase by 11.2% in 2021–2060 and reduce by 5.0% in 2061–2100; this is because the glaciers would rapidly disappear with the rising temperature after 2060. The findings suggest that the shrinked glacier and the reduced runoff threaten the water availability especially in summer seasons as well as affect the agricultural irrigation in the downstream of the Amu Darya River. 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), hydrological model (HBV‐light), and Bayesian neural network (BNN) within a general framework. MBHA is applied to the Amu Darya River basin in Central Asia. The findings suggest that the shrinked glacier and the reduced runoff would threaten the water availability especially in summer seasons for the Amu Darya River basin.