Attribution of streamflow and its seasonal variation to dual nature-society drivers using CMIP6 data and hydrological models

Abstract

Attributing changes in streamflow processes is crucial for water resource management as well as for understanding and mitigation of flood and drought risks. However, most existing attribution methods lack a unified approach to handle various causal variables, making them unsuitable for comprehensive attribution assessments. Therefore, this study proposed a framework to quantitatively attribute the impacts of natural and anthropogenic climate change, land use and cover change (LUCC), and human water withdrawal on streamflow and its seasonality. The framework consists of three steps: (1) bias correction of Coupled Model Intercomparison Project Phase 6 (CMIP6) data and construction of a dualistic nature-society water cycle model; (2) simulation of streamflow processes and identification of streamflow seasonality under different climate forcing and LUCC scenarios; and (3) quantitative attribution of streamflow evolution characteristics. The Weihe River Basin (WRB) in China has been selected as a case study area for the proposed attribution framework. The quantitative analysis indicates that natural and anthropogenic climate change, LUCC, and human water withdrawal account for 20.8%, 27.9%, 4.6%, and 46.7% of the decreasing trend in streamflow volume and –42.4%, –28.1%, –5.1%, and 175.6% of the weakening trend in streamflow seasonality in the WRB, respectively. These results suggest that human water withdrawal reduces streamflow and weakens its seasonality, while the other three factors contribute to streamflow reduction but enhance its seasonality. Overall, this study effectively distinguishes the impacts of anthropogenic and natural climate change on streamflow processes, thus providing a deep understanding of the influences of human-induced hydro-climate change.