Drought is a complex natural hazard that may have destructive damages on societal properties and even lives. Generally, socioeconomic drought occurs when water resources systems cannot meet water demand, mainly due to a weather-related shortfall in water supply. This study aims to propose a new method, a heuristic method, and a new index, the socioeconomic drought index (SEDI), for identifying and evaluating socioeconomic drought events on different severity levels (i.e., slight, moderate, severe, and extreme) in the context of climate change. First, the minimum in-stream water requirement (MWR) is determined through synthetically evaluating the requirements of water quality, ecology, navigation, and water supply. Second, according to the monthly water deficit calculated as the monthly streamflow data minus the MWR, the drought month can be identified. Third, according to the cumulative water deficit calculated from the monthly water deficit, drought duration (i.e., the number of continuous drought months) and water shortage (i.e., the largest cumulative water deficit during the drought period) can be detected. Fourth, the SEDI value of each socioeconomic drought event can be calculated through integrating the impacts of water shortage and drought duration. To evaluate the applicability of the new method and new index, this study examines the drought events in the East River basin in South China, and the impact of a multi-year reservoir (i.e., the Xinfengjiang Reservoir) in this basin on drought analysis is also investigated. The historical and future streamflow of this basin is simulated using a hydrologic model, Variable Infiltration Capacity (VIC) model. For historical and future drought analysis, the proposed new method and index are feasible to identify socioeconomic drought events. The results show that a number of socioeconomic drought events (including some extreme ones) may occur in future, and the appropriate reservoir operation can significantly ease such situation.

Hydrological models play an important role in water resource management, but they always suffer from various sources of uncertainties. Therefore, it is necessary to implement uncertainty analysis to gain more confidence in numerical modeling. The study employed three methods (i.e., Parameter Solution (ParaSol), Sequential Uncertainty Fitting (SUFI2), and Generalized Likelihood Uncertainty Estimation (GLUE)) to quantify the parameter sensitivity and uncertainty of the SWAT (Soil and Water Assessment Tool) model in a mountain-loess transitional watershed—Jingchuan River Basin (JCRB) on the Loess Plateau, China. The model was calibrated and validated using monthly observed streamflow at the Jingchuan gaging station and the modeling results showed that SWAT performed well in the study period in the JCRB. The parameter sensitivity results demonstrated that any of the three methods were capable for the parameter sensitivity analysis in this area. Among the parameters, CN2, SOL_K, and ALPHA_BF were more sensitive to the simulation of peak flow, average flow, and low flow, respectively, compared to others (e.g., ESCO, CH_K2, and SOL_AWC) in this basin. Although the ParaSol method was more efficient in capturing the most optimal parameter set, it showed limited ability in uncertainty analysis due to the narrower 95CI and poor P-factor and R-factor in this area. In contrast, the 95CIs in SUFI2 and GLUE were wider than ParaSol, indicating that these two methods can be promising in analyzing the model parameter uncertainty. However, for the model prediction uncertainty within the same parameter range, SUFI2 was proven to be slightly more superior to GLUE. Overall, through the comparisons of the proposed evaluation criteria for uncertainty analysis (e.g., P-factor, R-factor, NSE, and R²) and the computational efficiencies, SUFI2 can be a potentially efficient tool for the parameter optimization and uncertainty analysis. This study provides an insight into selecting uncertainty analysis method in the modeling field, especially for the hydrological modeling community.

Understanding the role of reservoirs in the terrestrial water cycle is critical to support the sustainable management of water resources especially for China where reservoirs have been extensively built nationwide. However, this has been a scientific challenge due to the limited availability of continuous, long‐term reservoir operation records at large scales, and a process‐based modeling tool to accurately depict reservoirs as part of the terrestrial water cycle is still lacking. Here, we develop a continental‐scale land surface‐hydrologic model over the mainland China by explicitly representing 3,547 reservoirs in the model with a calibration‐free conceptual operation scheme for ungauged reservoirs and a hydrodynamically based two‐way coupled scheme. The model is spatially calibrated and then extensively validated against streamflow observations, reservoir storage observations and GRACE‐based terrestrial water storage anomalies. A 30‐year simulation is then performed to quantify the seasonal dynamics of China’s reservoir water storage (RWS) and its role in China's terrestrial water storage (TWS) over recent decades. We estimate that, over a seasonal cycle, China's RWS variation is 15%, 16%, and 25% of TWS variation during 1981–1990, 1991–2000, and 2001–2010, respectively, and one‐fifth of China’s reservoir capacity are effectively used annually. In most regions, reservoirs play a growing role in modulating the water cycle over time. Despite that, an estimated 80 million people have faced increasing water resources challenges in the past decades due to the significantly weakened reservoir regulation of the water cycle. Our approaches and findings could help the government better address the water security challenges under environmental changes.