Reservoirs, as critical nodes in regional water management, play an increasingly important role in drought mitigation. This study aims to optimize the drought-limited water level in the reservoir group and propose an evaluation method for selecting the optimal regulation scheme during drought periods. The reservoir water supply module within the Water Allocation and Simulation (WAS) model was enhanced to optimize the drought-limited water level of the reservoir group. A comprehensive adaptation index (CAI) was developed to quantitatively evaluate the effectiveness of water resource regulation under various drought scenarios. This methodology was applied to large and medium-sized reservoirs in the central Yunnan Province, China. The results show that the optimized drought-limited water level significantly improved the water supply performance of the reservoir group during drought years. Specifically, the optimized drought-limited water level notably reduced severe water shortage for water users in the long series and typical drought years, effectively mitigating the impacts of drought. Additionally, the most suitable water resource regulation strategies for different drought scenarios were identified. These research findings can provide technical references for reservoir management departments and drought operations authorities to formulate drought-limited level for the reservoir group and implement regional drought early warning and defense decision-making.

Accurate and reliable discharge estimation plays an important role in water resource management as well as downstream applications such as ecosystem conservation and flood control. Recently, data-driven machine learning (ML) techniques showed seemingly insurmountable performance in runoff forecasting and other geophysical domains, but they still need to be improved in terms of reliability and interpretability. In this study, focusing on discharge estimation and management, we developed an ML-based framework and applied it to the Huitanggou sluice hydrological station in Anhui Province, China. The framework contains two ML algorithms, the ensemble learning random forest (ELRF) and the ensemble learning gradient boosting decision tree (ELGBDT). The SHapley Additive exPlanation (SHAP) was introduced into our framework to interpret the impact of the model features. In our framework, the correlation analysis of the dataset can provide feature information for modeling, and the quartile method was utilized to solve the outlier problem of the dataset. The Bayesian optimization algorithm was adopted to optimize the hyperparameters of the ensemble ML models. The ensemble ML models are further compared with the traditional stage–discharge rating curve (SDRC) method and the single ML model. The results show that the estimation performance of the ensemble ML models is superior to that of the SDRC and the single ML model. In addition, an analysis of the discharge estimation without considering the flow state was performed. This analysis reveals that the ensemble ML models have strong adaptability. The ensemble ML models accurately estimate the discharge, with a coefficient of determination of 0.963, a root mean squared error of 31.268, and a coefficient of correlation of 0.984. Our framework can prove helpful to improve the efficiency of short-term hydrological estimation and simultaneously provide the interpretation of the impact of the hydrological features on estimation results.

Monitoring and understanding the development of agricultural management requires fine information on multiple agricultural land use classes. According to the main data bulletin of China’s third national land survey released in 2021, the proportion of arable land in China accounts for 16% of the total land area. After excluding areas that are not suitable for development (slope greater than 25 degrees), the remaining area represents arable land affected by human activities, constituting 96.69% of the total arable land area in the country. Artificially irrigated areas are products of the intertwined interactions between the natural environment and human society. However, the original remote sensing image (China’s land use/cover datasets, CLUDs), which is designed to depict the land use and cover patterns in mainland China, categorizes arable land into two main types: paddy fields and dryland. It lacks a subdivision of artificially irrigated areas. We also found a data discrepancy of more than 10% between the statistical data of rice and the paddy field data in CLUDs. To refine land use data, and then improve the simulation accuracy of the hydrological model, this article proposes a dual-source datasets fusion algorithm, learning based on big data, namely the LUCC statistical data fusion (LUSF) algorithm for integrating the remote sensing-based cropland area dataset and statistics dataset. The runoff simulation results show that, in the Yangtze River Basin, using LUSF datasets, the mean absolute percentage error value of monthly simulated runoff decreased by 0.74%, and the root mean square error value decreased by 0.22 million m${}^3$. At the basin scale, the absolute error of the simulated runoff is reduced by an average of 433 million m${}^3$ per year, and 36 million m${}^3$ every month. The LUSF datasets corrected the canopy interception coefficient effectively; the runoff simulation error was reduced by 2.96 billion m${}^3$/a. In the past 40 years, the runoff variation in Dongting Lake and Hanjiang River has been most strongly impacted by changes in the underlying surface. These results reveal that the new data fusion method has some significant improvement over the original method, applicable to the runoff simulation disturbed by strong human activities.