Attribution Analysis of Streamflow Changes Based on Large-scale Hydrological Modeling with Uncertainties

Attribution analysis is widely used to assess the impacts of environmental change on water resources. However, the chain of uncertainty involved is often not given sufficient attention, which can lead to inaccurate assessments and poor responses. This study aims to build a framework for attribution analysis of streamflow changes considering uncertainties. Under this framework, a large-scale Soil and Water Assessment Tool (SWAT) model is estab-lished and calibrated using streamflow data collected from key stations, with model param-eter posterior distributions obtained from the Differential Evolution Adaptive Metropolis (DREAM) algorithm. A multi-route attribution analysis to attribute streamflow change to the influence of driving factors is performed. The developed methodology is applied to a case study of the Upper Yangtze River Basin (UYRB) in China. Results reveal that: (1) Streamflow decreases significantly in the UYRB with varying characteristics at small scale. (2) Precipitation plays the most dominate role in driving streamflow changes with the larg-est uncertainty, while other driving factors behave differently in various river basins. (3) Changes in precipitation, maximum temperature, wind speed and land use/ cover change (LUCC) tend to decrease streamflow, while changes in minimum temperature and relative humidity tend to increase streamflow in the UYRB. These findings can help enhance the understanding of the influence of climate change and human activities on streamflow, and provide further insights into the adaptive water resources management.