环境科学
水文学(农业)
水资源管理
地质学
岩土工程
作者
Qiang Liu,Liqiao Liang,Tao Sun,Xuan Wang,Denghua Yan,Chunhui Li
标识
DOI:10.1016/j.scitotenv.2024.172912
摘要
Drought will inevitably affect linkages between different water components, which have previously been investigated across different spatiotemporal scales. Elucidating drought-induced precipitation (P) partition effects remain uncertain because they involve drought propagation, even inducing streamflow (Q) non-stationarity. This study collected data on 1069 catchments worldwide to investigate Q and evapotranspiration (ET) impacts from P deficit-derived reductions in drought propagation. Results show that P deficits trigger soil moisture drought, subsequently inducing negative Q and ET anomalies that vary under different climate regimes. Generally, drought-induced hydrological legacies indicate that breaks in hydrological linkages cause a relatively rapid Q response (i.e., negative Q anomaly), amplified by drought strength and duration. Compared with the Q response, the ET response to drought stress involves a more complex, associative vegetation response and an associative evaporative state controlled by water and energy, which lags behind the Q response and can also intensify with increasing drought severity and duration. This is confirmed by the ET response under different climate regimes. Namely, in drier climates, a positive ET anomaly can be detected in its early stages, this is unusual in wetter climate. Additionally, Q and ET sensitivity to drought strength can be mechanistically explained by the water and energy status. This implies that ET is mainly controlled by water and energy, resulting in higher and lower drought sensitivity within water- and energy-limited regions, respectively. Understanding the impacts of drought on Q and ET response is essential for identifying key linkages in drought propagation across different climate regimes. Our findings will also be useful for developing early warning and adaptation systems that support both human and ecosystem requirements.
科研通智能强力驱动
Strongly Powered by AbleSci AI