Search

Study region: The Laohahe basin, north of China Study focus: In a changing environment, some river’s hydrological regimes have significantly changed and may threatened river ecosystem. Therefore, it is urgent to improve our understanding for the driving mechanism of the evolution of the hydrological regimes. In this study, a comprehensive comparison was carried out to assess the application of the Long Short-Term Memory (LSTM) and the Variable Infiltration Capacity (VIC) model in river ecohydrological regimes alteration attribution. Firstly, the ecohydrological indicators changes were evaluated, including streamflow, ecosurplus, ecodeficit, and hydrological health. Then, the VIC and LSTM models were applied to quantify the climatic and anthropic effects on river ecohydrological indicators, respectively. Finally, the attribution results were compared. New hydrological insights: The research results revealed that (1) the streamflow decreased since 1980 and dramatically decreased after 2000. The ecohydrological indicators also changed significantly (α < 0.05). (2) The alterations were mainly driven by human activities, contributed to more than 80 %. (3) The LSTM and VIC showed little performance difference in quantitative attribution at the monthly and yearly scale, while the LSTM performed a little bad in streamflow simulation at the daily scale. The convenient feature and better simulation capabilities of LSTM provides a new choice for the attribution of ecohydrological regimes evolution, providing a better service in managing local water resources and safeguarding river ecosystems.

Study region: Global aridity zones. Study focus: Evaluating drought under the impact of climate change is crucial for ensuring sustainable development and ecological security, especially in global arid zones where the environment is vulnerable to global warming. However, there are large uncertainties in evaluating the future drought risk of arid zones due to uncertainties within climate projections and the use drought-related indices that may not be appropriate for all assessments. Therefore, under the precondition of bias correction applied to CMIP6 data, the drought detection capability of drought indices calculated from multimodel ensemble was evaluated. Based on the drought index with the strongest detection capability and the Reliability-Resilience-Vulnerability (RRV) framework, the future drought risk in global arid zones were predicted. New hydrological insights for the region: Among the drought indices calculated from CMIP6 historical simulations, SPEI offered the best representation of drought patterns. According to the CMIP6 future projections, SSP1–2.6 can effectively mitigate the impact of future drought risk compared with the historical period. In scenarios with higher emissions, the equatorial to northern latitude 30° region will experience more severe drought. For SSP5–8.5, the areas in the arid zones where drought risk deteriorates significantly are several times larger than in other scenarios. To mitigate drought risks, most nations should support a greener, low-emission approach in line with sustainable development goals, reducing the risk of drought-related damage.

Study region: Catchment area above the Huaxian station along the Wei River Basin, China. Study focus: This study attempts to construct a new Non-stationary Standardized Streamflow Index (NSSI) applicable to the variable streamflow sequence of the Wei River Basin based on the climate index and the optimal anthropogenic index, and analyse the drought characteristics of the basin. The climate index is used to quantify climate change factors and three anthropogenic indices are used to quantify the factor of human activities, including the reservoir index, the human-induced index calculated based on the Variable Infiltration Capacity (VIC) hydrological model and the Long Short-Term Memory (LSTM) model machine learning approach, respectively. New hydrological insights for the region: The human-induced index based on the LSTM model is more suitable for quantifying anthropogenic factors in the Wei River Basin. The NSSI performs better than the SSI in drought identification. The NSSI based on the LSTM model can capture more frequent severe drought and extreme drought events. The frequency of severe drought and extreme drought is higher in summer and autumn than in the others. The NSSI can better characterize the hydrological drought processes under a non-stationary condition, thus it can provide a more effective reference for regional drought assessment and related policy-making from the perspective of a changing environment.