A Complementary Streamflow Attribution Framework Coupled Climate, Vegetation and Water Withdrawal

Abstract

Quantifying the contributions of climate change (CC) and human activities (HA) to streamflow alteration is significant for effective water resources management. However, numerous studies fail to differentiate the individual impacts of various HA on streamflow. In this study, a comprehensive streamflow attribution framework that incorporates climate, vegetation, and water withdrawal (WW) was proposed. In this framework, traditional streamflow attribution methods such as statistical analysis (Double Mass Curve and Slope Change Ratio of Accumulative Quantity), elasticity (Budyko), and modeling simulation (Variable Infiltration Capacity and Long Short-term Memory) are employed to separate the influence of meteorological factors (MF) on streamflow. Subsequently, the impacts of WW on streamflow are assessed using global WW data. The Residual Analysis method is utilized to quantify the effects of vegetation alteration caused by both CC (Lcc) and HA (Lha) on streamflow alteration. To demonstrate the applicability of our proposed framework, two stations, Xianyang and Huaxian, located within the Weihe River Basin in Northwest China were selected as the case study area. The results demonstrated that compared to the baseline period (1961–1990), the average contributions of MF, Lcc, Lha, and WW to streamflow reduction during the variation periods (1991–2019) were as follows: for the Xianyang station, 26.0%, 13.5%, 30.9%, and 29.6% respectively; and for the Huaxian station, 28.9%, 5.5%, 17.7%, and 47.9% respectively. Additionally, during the variation periods, the contributions of CC and HA to vegetation variation were 30.5% and 69.5% respectively in Xianyang, and 23.7% and 76.3% respectively in Huaxian. The framework developed herein also provides a solution for quantifying the indirect effects of CC on streamflow through vegetation.