This study proposes a new hybrid model for monthly streamflow predictions by coupling a physically based distributed hydrological model with a deep learning (DL) model. Specifically, a simplified hydrological model is first developed by optimally selecting grid cells from a distributed hydrological model according to their soil moisture characteristics. It is then driven by bias corrected general circulation model (GCM) predictions to generate soil moistures for the forecasting months. Finally, model‐simulated soil moisture along with other predictors from multiple sources are used as inputs of the DL model to predict future monthly streamflows. The proposed hybrid model, using the simplified Variable Infiltration Capacity (VIC) as the hydrological model and the combination of Convolutional Neural Network and Gated Recurrent Unit (CNN‐GRU) as the DL model, is applied to predict 1‐, 3‐, and 6‐month ahead reservoir inflows for the Danjiangkou Reservoir in China. The results show that the hybrid model consistently performs better than VIC and CNN‐GRU models with great improvement in Kling‐Gupta efficiency (KGE) values for lead times up to 6 months. Additional tests indicate that hybrid models based on CNN‐GRU outperform those based on LASSO, XGBoost, CNN, and GRU models. Moreover, compared with the distributed hydrological model, the hybrid model greatly reduces the computation burden of rolling prediction. It also saves decision‐makers the time and effort of trying different combinations of predictors, which is indispensable when building DL models. Overall, the new hybrid model demonstrates great potential for monthly streamflow prediction where training data are limited.

Climate and land use/cover (LUC) are the two most significant factors that directly affect the runoff process. However, most research on runoff response has focused mainly on projected climate variation, while future LUC variability has been neglected. Therefore, the objective of this study is to examine the impacts of projected climate and LUC changes on runoff. Future climate scenarios are projected using the Quantile Mapping (QM) method, and future LUC scenarios are predicted with the Cellular Automaton-Markov (CA-Markov) model. Three different scenarios are simulated and compared to evaluate their impacts: Scenario 1 (LUC of 2010 and climate during the 2011–2050 period, abbreviated S1), Scenario 2 (LUC of 2010, 2020, 2030, 2040 and 2050 and climate of the historical wet year, normal year and dry year, abbreviated S2) and Scenario 3 (LUC of 2010, 2020, 2030, 2040 and 2050 and corresponding climate projections of 2011–2020, 2021–2030, 2031–2040 and 2041–2050 period, abbreviated S3). These three scenarios are then input into the Soil and Water Assessment Tool (SWAT) model to assess runoff responses. Beijiang River Basin, located in southern China, is used in this case study. The results obtained from S1, S2 and S3 show that runoff change in this basin is mainly caused by climate change; warmer temperatures and greater precipitation increase runoff. LUC change has little influence on runoff at the whole-basin scale, but changes in runoff components are more notable in the urban area than in the natural region at the sub-watershed level. The impact of LUC change in urbanized region on runoff components differ obviously among the wet, normal and dry years, and surface runoff and groundwater are found to be more sensitive to urbanization. Runoff depth is predicted to increase in this basin under the impacts of both climate and LUC changes in the future. Climate change brings greater increase in water yield and surface runoff, whereas LUC change leads to changes in allocation between surface runoff and groundwater in the urban region.

Sustainable management of the water–energy–food (WEF) nexus remains an urgent challenge, as interactions between WEF and human sensitivity and reservoir operation in the water system are typically neglected. This study proposes a new approach for modeling the WEF nexus by incorporating human sensitivity and reservoir operation into the system. The co-evolution behaviors of the nexus across water, energy, food, and society (WEFS) were simulated using the system dynamic model. Reservoir operation was simulated to determine the water supply for energy and food systems by the Interactive River–Aquifer Simulation water resources allocation model. Shortage rates for water, energy, and food resulting from the simulations were used to qualify their impacts on the WEFS nexus through environmental awareness in society. Human sensitivity indicated by environmental awareness can then adjust the co-evolution behaviors of the WEFS nexus through feedback loops. The proposed approach was applied to the mid–lower reaches of the Hanjiang River basin in China as a case study. Results indicate that environmental awareness shows the potential to capture human sensitivity to shortages from water, energy, and food systems. Parameters related to boundary conditions and critical values can dominate environmental awareness feedback to regulate socioeconomic expansion to maintain the integrated system from constant resource shortages. The annual average energy shortage rate thereby decreased from 17.16 % to 5.80 % by taking environmental awareness feedback, which contributes to the sustainability of the WEFS nexus. Rational water resources allocation can ensure water supply through reservoir operation. The annual average water shortage rate decreased from 15.89 % to 7.20 % as water resources allocation was considered. Threats from water shortage on the concordant development of the WEFS nexus are significantly alleviated, particularly for the area with a limited regulating capacity for the water project. Therefore, this study contributes to the understanding of interactions across the WEFS systems and helps in improving the efficiency of resource management.

Reservoirs and dams are essential infrastructure in water management; thus, information of their surface water area (SWA), water surface elevation (WSE), and reservoir water storage change (RWSC) is crucial for understanding their properties and interactions in hydrological and biogeochemical cycles. However, knowledge of these reservoir characteristics is scarce or inconsistent at the national scale. Here, we introduce comprehensive reservoir datasets of 338 reservoirs in China, with a total of 470.6 km³ storage capacity (50 % Chinese reservoir storage capacity). Given the scarcity of publicly available gauged observations and operational applications of satellites for hydrological cycles, we utilize multiple satellite altimetry missions (SARAL/AltiKa, Sentinel-3A and Sentinel-3B, CroySat-2, Jason-3, and ICESat-2) and imagery data from Landsat and Sentinel-2 to produce a comprehensive reservoir dataset on the WSE, SWA, and RWSC during 2010–2021. Validation against gauged measurements of 93 reservoirs demonstrates the relatively high accuracy and reliability of our remotely sensed datasets. (1) Across gauge comparisons of RWSC, the median statistics of the Pearson correlation coefficient (CC), normalized root mean square error (NRMSE), and root mean square error (RMSE) are 0.89, 11 %, and 0.021 km³, with a total of 91 % validated reservoirs (83 of 91) having good RMSE from 0.002 to 0.31 km³ and NRMSE values smaller than 20 %. (2) Comparisons of WSE retracked by six satellite altimeters and gauges show good agreement. Specifically, the percentages of reservoirs having good and moderate RMSE values smaller than 1.0 m for CryoSat-2 (validated in 30 reservoirs), SARAL/AltiKa (9), Sentinel-3A (34), Sentinel-3B (25), Jason-3 (11), and ICESat-2 (26) are 77 %, 75 %, 79 %, 87 %, 81 %, and 82 %, respectively. By taking advantages of six satellite altimeters, we are able to densify WSE observations across spatiotemporal scales. Statistically, around 96 % of validated reservoirs (71 of 74) have RMSE values below 1.0 m, while 57 % of reservoirs (42 of 74) have good data quality with RMSE values below 0.6 m. Overall, our study fills such a data gap with regard to comprehensive reservoir information in China and provides strong support for many aspects such as hydrological processes, water resources, and other studies. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.7251283 (Shen et al., 2021).

A large number of reservoirs have been built in the Yangtze River basin in the last few decades, the operation of which inevitably impacts on natural runoff. It is important to quantify the impacts of a cascade reservoir group operation on the hydrological regime in the downstream Yangtze River. The indicators of hydrologic alteration (IHA), range of variability approach (RVA), and Dundee hydrological regime assessment (DHRA) methods, which are based on five essential characteristics, i.e., magnitude, time, frequency, retardation, and rates of change, have been widely used to quantitatively analyze variation in the hydrological regime before and after the dam construction. The observed flow series at Yichang and Datong hydrological stations, located in the middle and lower reaches of the Yangtze River, respectively, are divided into pre- and post-dam construction periods by the Mann-Kendall (MK) rank correlation method. The results of the MK test reveal that the annual minimum discharge at Yichang Station has significantly changed since 2000. The analysis results of the IHA, RVA, and DHAR methods show that the hydrologic regime changed moderately after 1999 and is close to severe change after 2008 at Yichang station; and changed slightly after 1999 (the post-dam construction period) and moderately after 2008 at Datong station. A new typical year assessment method that can avoid the requirement for a long data series was proposed and compared with the RVA and DHAR methods. The results indicate that the hydrologic regime severely changed at Yichang station and moderately changed at Datong in 2014, which is consistent with the results of the above methods. This study further demonstrates that the changes are mainly reflected in the factors related to low flow such as mean discharge from January to March, minimum discharge, frequency and duration of low pulse, and so on. It is expected that further hydrological alterations will occur as the number of large cascade reservoirs constructed and operated in the Yangtze River basin increases.

Drought–flood abrupt alternation (DFAA), which is defined as the rapid transition between drought and flood in a short period, amplifies the negative impacts of individual drought or flood. DFAA is divided into drought to flood (DTF) and flood to drought (FTD) according to the sequences of drought and flood. Previous studies of identifying DFAA events have mostly been conducted over a long timescale (e.g., over a month), leading to inaccurate identification or omissions of DFAA events. In addition, frequency analysis of DTF and FTD events, which is vital for the design of hydraulic structures and water supply systems, has rarely been studied. This study establishes an identification method for DFAA events based on a daily-scale standardized weighted average of the precipitation index (SWAP) and conducts frequency analysis of DTF and FTD events based on copula theory. The Han River Basin (HRB), China, a crucial area for water resource management, was selected as the study area. Our findings disclose that DFAA events occurred less frequently with larger intensities and durations between 1961 and 2020. The temporal trends of DTF and FTD events varied similarly, while the number of DTF events were less than that of FTD events, indicating that FTD is the main performance of DFAA in HRB. In addition, the identification processes of DFAA events at the Wuhan station were analyzed in detail and proved that SWAP is an effective index for capturing the change of precipitation and accurately depicting the occurrences of droughts and floods. Furthermore, drought intensity (DI) and flood intensity (FI) were selected for bivariate frequency analysis. An OR case was defined as DI ≥ di or FI ≥ fi, while an AND case was defined as DI ≥ di and FI ≥ fi. The results of frequency analysis showed that joint return periods (JRPs) of DFAA events under the OR case are basically equal in three sub-basins, meaning a similar occurrence of probability of drought–flood disaster. JRPs under the AND case increased from the upper and middle basins to the lower basin as a whole, indicating the decreasing risk of abrupt drought–flood transitions. Overall, this study may have potential value in the early warning and mitigation of DFAA disaster.