Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opportunity to expand the FID. However, whether the FID expanded by remote sensing can improve the accuracy of flood susceptibility modeling needs further study. In this study, a framework was proposed for improving the accuracy of flood susceptibility prediction (FSP) by combining machine learning models and the expanded FID using Sentinel-1A radar images. Five widely used machine learning models were employed to verify the accuracy of the proposed method by taking Wuhan City as a case study, including the random forest (RF), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) models. Sentinel-1A images from time points before, during, and after flood events were used to expand the FID for training the machine learning models. The results showed that the performance of the machine learning models for predicting flood susceptibility was improved greatly by considering the expanded FID, being improved by approximately 1.14–19.74% based on the area under the receiver operating characteristic curve (AUC). Among the used machine learning models, taking into account all the statistical indicators, the ANN showed the best performance, while the SVM showed the best generalization performance in Wuhan City. According to the results of the ANN model, approximately 19% of the area in Wuhan City, mainly distributed near rivers and lakes, is at a high flood susceptibility level. This study provides an essential reference for flood susceptibility analyses in regions with limited flood sampling data.

A good water budget involving four variables, including precipitation (P), evapotranspiration (ET), streamflow (R), and terrestrial water storage change (TWSC), is reflected in two aspects: a high accuracy against observations for each budget component and the low water budget closure residual error (ΔRes). Due to the lack of consideration of observations of budget components in existing water budget closure assessment methods (BCMs), when the ΔRes of budget components is low, their error against respective observations may still be high. In this study, we assess the water budget closure accuracy of satellite/reanalysis-based hydrological data products over mainland China based on six popular P products and multiple datasets of additional budget components (ET, R, and TWSC). The results indicated that the ΔRes changes between ±15 mm over mainland China. Satellite P products such as GPM IMERG showed better performance by comparing them with rain gauge-based observations. However, reanalysis P products such as GLDAS and FLDAS showed a better water budget closure since the selected datasets of additional budget components (ET and R) are also derived from reanalysis datasets. This indicates that these same data sources for budget components make it easier to close the water budget. The further development of satellite P products should consider the closure of the water budget with other water cycle variables.

Affected by global climate change and rapid socio-economic development, China faces serious water security issues, especially in terms of water shortages, flood disasters, and water-related ecological and environmental problems

Affected by global climate change and rapid socio-economic development, China, along with many other countries worldwide, faces a series of water security issues, such as water shortages, flood disasters, and water-related ecological and environmental problems

GRACE卫星反演的水储量信息为研究水循环过程提供了高精度数据，但如何把GRACE卫星水储量数据同化到现有分布式水文模型的径流模拟过程，已成为限制GRACE数据应用于水文模型、提高径流模拟精度的关键科学问题。论文在综述国内外研究进展的基础上，归纳总结GRACE水储量数据同化的常用方法、现有数据同化方式的原理及优缺点，以及不同数据同化方式提高水文模型精度的程度。进而，总结GRACE水储量数据同化存在的难点问题，并展望可能的破解途径。梳理结果表明，GRACE水储量数据同化面临的难点问题包括水文模型精细的建模需求与GRACE数据粗糙时空分辨率的矛盾问题，以及水文模型单独模拟水储量分量与GRACE数据表示陆表总的水储量变化的矛盾问题；综合前人在不同流域的研究结果表明，通过GRACE水储量数据同化，水文模型水储量模拟值的均方根误差减少了约5%~40%，相关性系数提高了约10%~50%，但径流模拟精度的提高幅度比较有限，相关性系数提高了约2%~16%。研究结果将为推动GRACE卫星水储量数据同化的水文模型开发及应用研究提供理论和方法参考。

【目的】为了客观理解中国不同城市群之间的人水和谐发展现状与差异，弥补当前人水和谐关系研究缺乏跨城市群对比研究的不足，本文对比分析中国主要城市群之间的人水和谐发展关系。【方法】以中国3个地理气候差异显著的珠江三角洲、长江中游和京津冀城市群为研究对象，构建人水和谐度评估指标体系，利用单指标量化-多指标综合-多准则集成的人水和谐度评估方法，对2000—2022年各城市群的人水和谐度进行量化评估，并分析城市群之间的人水和谐度发展差异及影响因素。【结果】①2000—2022年，中国三大城市群人水和谐度均呈上升趋势，但健康度、协调度和发展度的提升并不均衡，三者的协同发展仍是当前的主要矛盾之一。②珠江三角洲城市群人水和谐水平相对较高，其次是京津冀城市群，二者的人水和谐度均达到“较为和谐”水平。相较之下，长江中游城市群由于发展度和协调度较低，其人水关系仍处于“较不和谐”水平。③当前我国主要城市群人水和谐度主要受水资源总量、废水排放量及污水处理率等因素的制约。【结论】基于当前人水和谐度的区域差异及演变特征，建议各城市群加强水资源和水生态保护力度，合理优化水资源配置，促进城市群协同发展和水资源可持续管理。同时，应重点关注健康度、发展度和协调度的均衡提升，推动人水关系和谐发展。