Suspended sediment concentration (SSC) plays a vital role in riverbank evolution. Although sediment in the Yangtze River has markedly decreased in recent years, whether different mainstream parts exhibit consistent trends and magnitudes lacks systematic assessments, as does the relationship between SSC variations and river channel morphology. To address this, we developed a robust SSC retrieval model (mean relative error = 24.2%) and applied it to Landsat images (1984-2021) to derive long-term SSC records across the Yangtze River's mainstream. Drivers of SSC changes and their relationships to riverbank erosion and collapses were also examined. Results show pronounced spatial heterogeneity in SSC decline magnitude across all mainstream parts, with the greatest reduction occurring between Yibin and the Three Gorges Dam (-17.9 mg L--(1) yr(-)(1)) and the smallest observed in the estuary part, at -1.4 mg L--(1) yr(-)(1). The spatial pattern of long-term average SSC shifted from "high west, low east" to "low west, high east", alongside a notable reduction in seasonal variability, especially in the upstream region. Inter- and intra-annual SSC changes were primarily driven by sediment discharge reduction resulting from the Three Gorges Dam operation. The substantial SSC decline intensified downstream riverbank erosion, especially in the Jingjiang part, where SSC reduction strongly correlated with increased erosion (r = 0.96, p < 0.05). Severe erosion may lead to frequent bank collapses, as the riverbank collapse frequency in Jingjiang part rose by 121.4% after 2002-2006. These findings could provide valuable insights for sediment management, riverbank stability assessment, and future conservation planning for the Yangtze River.

Accurately characterizing river water level dynamics is essential for understanding watershed hydrological processes, assessing flood risk, and supporting refined water resource management. Launched in December 2022, the Surface Water and Ocean Topography (SWOT) satellite carries the Ka-band Radar Interferometer (KaRIn), enabling wide swath, high resolution observations of inland water surface elevation (WSE) globally, marking a new era of high precision hydrologic remote sensing. However, under complex observational geometries and heterogeneous surface conditions, the accuracy of SWOT derived WSE may degrade, manifested as a reduction in both geolocation and elevation accuracy. This degradation arises from both observation geometry and environmental heterogeneity, including cross track distance, layover effects, terrain slope, water area, and water surface brightness. Here we examine the main stem of the Yangtze River using available SWOT WSE observations from 2023 to 2024, with temporally matched daily in situ water levels from six hydrological stations as reference. We use cross track distance as a controlling variable and quantify how layover and environmental heterogeneity modulate both systematic bias and random dispersion, thereby characterizing the dominant controls governing spatial accuracy degradation. Results show that (1) SWOT captures seasonal and longitudinal water level variations along the Yangtze River, with R2 values of 0.86 to 0.97 and RMSE of 0.15 to 0.42 m; (2) WSE error increases approximately linearly with cross track distance at an average rate of (6 to 9) & times; 10_6 m/m, and the increase is amplified under strong layover conditions; and (3) environmental heterogeneity further shapes the error distribution, with steep terrain (greater than 15 degrees) and small water area (less than 20,000 m2) showing larger random fluctuations and more pronounced systematic deviations. Overall, this study provides basin scale quantitative evidence that the spatial degradation of SWOT WSE accuracy is jointly controlled by observation geometry and environmental heterogeneity, and it clarifies their relative roles in shaping error magnitude and spatial structure. These findings support the development of multi factor error correction approaches and improve the reliability of SWOT applications in large basin hydrological modeling, flood monitoring, and data assimilation.

Study region: The study region is the upper Hanjiang River Basin in China. Study focus: Understanding how spatially concurrent hydrological droughts (HD) respond to meteorological droughts (MD) is crucial for early drought warning and mitigation, yet such assessments remain limited under human-altered conditions. This study examines two sub-regions (S1 and S2) in the upper Hanjiang River Basin using monthly precipitation and streamflow data (1961-2020) to characterize the inter-regional MD-HD linkages through elementary and Copula functions. New hydrological insights for the region: Results indicate that during the natural pre-change period, both S1 and S2 exhibited strong elementary functional relationships between MD and HD magnitudes (R2 > 0.8). When the MD magnitude of S1 reached a threshold of 1.16, HD onset occurred, and the HD magnitude of S2 fell within a 70 % probability interval of [0.58, 3.16] with a most likely value of 0.70. In the post-change period, human interventions-particularly reservoir regulation and irrigation-disrupted these elementary linkages, yielding more complex nonlinear propagation patterns. The Copula framework successfully quantified the probabilistic linkage of concurrent HDs across regions, enabling estimation of HD severity in one basin based on MD information from another. These findings provide quantitative insight into cross-regional drought propagation and offer practical implications for hydrological drought monitoring and management in data-scarce or regulated basins.

Future hydrological droughts in reservoir-regulated regions remain uncertain due to the complex interactions between climate change and reservoir operation. Existing studies usually rely on simplified empirical representations of historical reservoir operations and rarely consider the role of optimal reservoir operation policies. Here, we used the upper Hanjiang River basin (UHRB) in China as a case study to project its future hydrological drought evolution using standard streamflow indices (i.e., SSI-1, SSI-3, and SSI-12) and to quantify the roles of climate change and reservoir operation. A long short-term memory (LSTM)-based hydrological model, coupled with a physics-informed LSTM reservoir model, was developed and driven by bias-corrected climate outputs from five global climate models to project future drought conditions under three scenarios (SSP126, SSP370, and SSP585). The results indicate that future climate change over the UHRB is projected to reduce natural streamflow and exacerbate hydrological droughts, with the most severe impacts projected in the far-future period (2071-2100) under SSP585. The traditional Ankang Reservoir operation reduces the frequency, duration and severity of short-term hydrological droughts (SSI-1 and SSI-3) under all scenarios, but shows limited effectiveness for long-term droughts (SSI-12). Importantly, optimal reservoir operating policies that aim to maximize hydropower generation and power generation guarantee rate reveal clear trade-offs between hydrological drought risk and hydropower benefits, thereby underscoring the importance of enhancing reservoir operation strategies for future drought management in reservoir-regulated basins.

Study region The Middle and Lower Yangtze River, China. Study focus This study integrates multi-source remote sensing data (Landsat and Google Earth) and field-measured topographic data to establish a quantitative framework for identifying bank collapse, which progresses from large-scale screening to localized analysis. Using Google Earth Engine (GEE), we automated the extraction of banklines from 2004 to 2024 and employed the Digital Shoreline Analysis System (DSAS) to quantify the erosion rate. After an initial large-scale screening, six high-risk river segments (e.g., Xiangjiazhou, Qigongling) were selected for detailed analysis, where five key bank collapse indicators were quantified: bank slope, toe erosion slope, Bank-Groove Elevation Difference, Main Stream Proximity Distance, and bankline change rate. New hydrological insights for the region Spatially varying thresholds were identified: bank slope (0.1-0.5), toe erosion slope (0.1-0.25), Bank-Groove Elevation Difference (>15 m), and Main Stream Proximity Distance (0.3-0.5 times the channel width). Following the implementation of systematic bank protection after 2015, erosion rates were reduced by 20-30 %. Unprotected banks, however, saw an acceleration in collapse rates. The channel incision induced by the Three Gorges Dam increased instability in unprotected areas, while protected segments showed stable morphodynamics. This study provides a quantitative analysis of bank collapse risk indicators for the Middle and Lower Yangtze River, offering scientific methods and evidence for intelligent bank collapse screening.

Study region The Dongli River Basin (DRB), a representative basin within the Three Gorges Reservoir Area (TGRA). Study focus Landscape pattern optimization is critical for advancing ecological sustainability and water resource conservation. However, the mechanisms by which landscape patterns should be optimized based on hydrological responses remain poorly understood. Our study combined Path-generating Land Use Simulation (PLUS) model and Soil and Water Assessment Tool (SWAT) to evaluate runoff responses to landscape pattern changes under both natural and optimized development scenarios across different time scales. Furthermore, landscape optimization strategies across different topographic slopes (steep, moderate, gentle) were provided to identify key thresholds of landscape indices. New hydrological insights for the region Modeling analyses reveal that natural development in landscape pattern offers limited hydrological benefits, whereas optimized development induces distinct runoff responses across slope categories. Moderate slopes were identified as core zones for hydrological regulation, exhibiting the most substantial alterations in runoff (-51.85–224.72%) and serving as critical areas for the runoff infiltration balance. Based on the identified optimization thresholds, maintaining specific forest (32.54–51.93%) and grassland (32.54–33.19%) proportions, while increasing landscape connectivity (Contagion index, CONTAG>63.86) and reducing patch fragmentation (Patch density, PD<1.34), can effectively increases surface resistance and delay hillslope runoff convergence. These findings provide quantitative spatial decision making support for ecological restoration and flood-drought mitigation strategies in TGRA.

As warmer temperatures enhance atmospheric moisture, hydrological droughts tend to intensify in most regions of the globe. Consequently, younger generations are expected to face a more severe risk of hydrological drought during their lifetimes, emphasizing the critical issue of intergenerational inequity due to climate change. To quantify exposure to hydrological drought across generations, we constructed a cascade model chain for drought simulation using hybrid terrestrial models, based on 5 GCM outputs under SSP5-85, five hydrological models and a deep learning model. We then projected future univariate and bivariate hydrological drought evolution in 4091 river basins, and quantified lifetime exposure to drought for the age groups born in 2020 and 1960. Drought severity and duration are projected to increase substantially in the Eastern America, Southern Brazil and Western Europe, over 79 % of basins. Extreme droughts far beyond historical records are expected to become more frequent and impact Western Europe in particular. Of note, the exposure of the different age groups to hydrological drought shows a notable disequilibrium. Exposure of people born in 2020 to hydrological drought hazards is projected to increase by 12 % over the late 21st century compared to those born in 1960, indicating that the acceleration of climate change is expected to increase the lifetime risk of future generations. The exposure factor of the newborns is 1.4 times higher than that of 80 years of age under warming condition. Our findings underscore that future drought conditions under extreme warming pose a significant threat to the living conditions of younger generations.

Understanding extreme precipitation (EP) evolution is crucial for global climate adaptation and hazardous disasters prevention. However, spatial non-stationarity of urbanization relationships with EP variations has been rarely discussed in a complex topographic context. Taking the city Liuzhou in China as the example, this study separately quantified the evolution of EP intensity, magnitude, duration, and frequency on different temporal scales with Innovative Trend Analysis (ITA). Based on a finer spatial (5 km grid) scale and multiple temporal (daily, daytime, nighttime, and 14 h) scale analyses, it innovatively identified spatially varying urbanization effects on EP with more details in different elevations. Our results indicate that: (1) from 2009 to 2023, EP events became more intense, persistent, and frequent, particularly for higher-grade EPs and in the steeper north of Liuzhou; (2) despite the globally negative correlations, spatial correlations between comprehensive urbanization (CUB) and each EP index on individual temporal scales were still explicitly categorized into four types using LISA maps-high-high, high-low, low-low, and low-high; (3) Geographically Weighted Regression (GWR) was demonstrated to precisely explain the response of most EP characteristics to multiple manifestation of urbanization with respect to population (POP), economy (GDP), and urban area (URP) expansion (adjusted R2: 0.5-0.8). The predictive accuracy of GWR on urbanization and EPs was spatially non-stationary and variable with temporal scales. The local influential strength and direction varied significantly with elevations. The most significant and positive influences of three urbanization predictors on EPs occurred at different elevation grades, respectively. Compared with POP and GDP, urban area percent (URP) was indicated to positively relate to EP changes in more areas of Liuzhou. The spatial and quantitative relationships between urbanization and EPs can help to guide effective urban planning and location-specific management of flood risks.

Owing to the substantial spatiotemporal variability and intricacy in the hydrodynamic and water quality responses to water diversion, the determination of impact on the aquatic environment has been predominantly qualitative. The quantitative assessment of the impact of anthropogenic activities (water diversion) on the aquatic environment has the potential to enhance the accuracy of a comprehensive benefit evaluation. The present study utilised hydrological data, encompassing flow rate (Q) and water quality index (WQI) in the lakes Taohu and Gehu basin (TGHs), from two typical water resource allocations in the Taihu basin. The study revealed how hydrodynamics and water quality in response to water diversion. Furthermore, it introduced the concept of "water diversion impact value" to quantify the impacts of water diversion on the aquatic environment. The results indicated when the overall water diversion effect showed enhancement in water quality, the WQI impact value was positive alongside a negative Q impact value (E-WQI=1.417,E-Q=-14.2m(3)/s); Conversely, when water diversion improved hydrodynamic conditions, the WQI impact value was negative accompanied by a larger positive Q impact value (E-WQI=-3.17,E-Q=7.215m(3)/s ). Principal component analysis (PCA) further validated these results, demonstrating that prolonged and stable diversion was positively correlated with WQI, while short and fluctuating diversion was positively correlated with Q, thereby supporting the method of evaluating the effects of water diversion using impact values. In addition, the TGHs was divided into three distinct components: namely mainstream areas, tributary areas, and lake areas, in accordance with the respective hydrological compartment. The present study set out to explore the response mechanisms of three distinct hydrological compartments to water diversion. The findings revealed that the impact extremes predominantly occurred in the mainstream areas, exhibited minimal occurrence in lake areas, and manifested within the range between the two in tributary areas. The diversion impact value has been shown to be a reliable metric for evaluating the effects of water diversion, thereby providing substantial theoretical underpinnings for the enhancement of aquatic environments.

As warmer temperatures enhance atmospheric moisture, hydrological droughts tend to intensify in most regions of the globe. Consequently, younger generations are expected to face a more severe risk of hydrological drought during their lifetimes, emphasizing the critical issue of intergenerational inequity due to climate change. To quantify exposure to hydrological drought across generations, we constructed a cascade model chain for drought simulation using hybrid terrestrial models, based on 5 GCM outputs under SSP5-85, five hydrological models and a deep learning model. We then projected future univariate and bivariate hydrological drought evolution in 4091 river basins, and quantified lifetime exposure to drought for the age groups born in 2020 and 1960. Drought severity and duration are projected to increase substantially in the Eastern America, Southern Brazil and Western Europe, over 79 % of basins. Extreme droughts far beyond historical records are expected to become more frequent and impact Western Europe in particular. Of note, the exposure of the different age groups to hydrological drought shows a notable disequilibrium. Exposure of people born in 2020 to hydrological drought hazards is projected to increase by 12 % over the late 21st century compared to those born in 1960, indicating that the acceleration of climate change is expected to increase the lifetime risk of future generations. The exposure factor of the newborns is 1.4 times higher than that of 80 years of age under warming condition. Our findings underscore that future drought conditions under extreme warming pose a significant threat to the living conditions of younger generations.

Global warming has intensified extreme precipitation events, posing challenges to economic and ecological systems. While the SWOT satellite offers high-resolution water elevation monitoring via Ka-band radar, it faces accuracy issues from noise and cross-orbit error. Therefore, we used SWOT data to evaluate the potential flood risk during the 7/2023 Beijing-Tianjin-Hebei flooding event in China, focusing on the Gangnan Reservoir. High-frequency noise in SWOT water surface elevation was identified using the Fourier transform and denoised with product quality-control parameters. Elevation accuracy was validated against ICESat-2. A monthly flood estimation method was developed based on SWOT features and traditional hydrological models, using probability distribution functions and time series reconstruction to identify flood characteristics such as water level, volume, and peak timing. The denoising process demonstrated centimeter-level accuracy for 100/250 m resolution SWOT products (0.19 +/- 0.11 m and 0.09 +/- 0.05 m). During the flooding event, the Gangnan Reservoir experienced an 8.46-m rise in water level, leading to a 52.17-125.49% increase in flooded area. The flood peak was observed within 5-15 January 2024. This research holds scientific importance for regional flood disaster monitoring, optimal allocation of water resources, and decision-making for disaster prevention and mitigation.

Extracting river information from high-resolution satellite remote sensing images is of great significance for monitoring and warning of river bank collapses. This article takes the example of the bank collapse with a length and depth of about 100m in Xiaopan of Jiayu County, Hubei Province in December 2021, and establishes a water extraction model based on DeepLabV3+image semantic segmentation technology. The application ability of domestic GF No.1 and No.2 satellites in identifying river bank collapses is discussed. The research results indicate that the DeepLabv3+model can extract water edge information well and the extraction results are reliable. By comparing long-term image images, it can reflect the situation of bank collapse on both sides of the river. The resolution of domestically produced Gaofen-1 and Gaofen-2 satellites is sufficient for accurate identification of larger scale landslides, and further exploration is necessary to enhance their ability to identify smaller scale landslides.

The change in water level in the upper reaches of the Yangtze River is of great significance to flood control and navigation. As the first water control station for the mainstream of the Yangtze River after the Minjiang, Hengjiang, Tuojiang River, and other important tributaries flow into the Yangtze River, it is imperative to forecast the water level of Zhutuo Station accurately. The present study utilizes Microsoft Azure's automated machine learning platform (AutoML) and recurrent neural network (RNN) model to predict water levels at Zhutuo Station. The AutoML approach demonstrates certain advantages over RNN methodologies in terms of operability, resource utilization, computational efficiency, and hardware configuration requirements when predicting the water level of Zhutuo. The results show that the future 1-h forecast performance is similar, the mean absolute error (MAE) and root mean square error (RMSE) of the AutoML platform are 0.0098 and 0.012, respectively, and the MAE and RMSE of the RNN model are 0.0088 and 0.011, respectively. The prediction performance of the RNN model is better in the next 8 h and the next 24 h. The current study's outcomes contribute valuable insights for the real-time monitoring and predictive analytics of water levels, thereby enabling waterway managers to obtain the balance between model complexity and modeling convenience.

Most watersheds around the world have been changing their natural flow patterns because of the coupled effects of climate change and human activities. Understanding the quantitative impacts of projected climate and human factors on future runoff variations is essential for adaptability assessment of water resource management, especially at the seasonal scale where their intra-annual changes are detected. In this study, an extended Budyko framework that combines traditional elasticity and decomposition methods is developed to analyze future sea-sonal runoff variations. The upper reach of the Hanjiang River basin (UHRB) in China is used as a case study. The case results are quantified and compared with the monthly ABCD model using historical hydrometeorological observations over 1961-2020 and near-future climate projections over 2031-2060 from the multi-model ensemble of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). We find that: (1) human ac-tivities including the operation of the Han-to-Wei inter-basin water transfer (IBWT) and reforestation projects have a substantial negative impact on runoff reduction in this region, accounting for a proportion of-70% in energy-limited seasons and-65% in water-limited seasons; and (2) the role of climate change projected by ISIMIP3b will intensify in energy-limited seasons, especially under the high-emission scenario with the increasing trend of effective precipitation,. Additionally, the performance of our extended Budyko framework is sufficiently robust to parameter disturbance experiments, indicating its applicability as an alternative for exploring future seasonal runoff variations.

The hydrological cycle, affected by climate change and rapid urbanization in recent decades, has been altered to some extent and further poses great challenges to three key factors of water resources allocation (i.e., efficiency, equity and sustainability). However, previous studies usually focused on one or two aspects without considering their underlying interconnections, which are insufficient for interaction cognition between hydrology and social systems. This study aims at reinforcing water management by considering all factors simultaneously. The efficiency represents the total economic interests of domesticity, industry and agriculture sectors, and the Gini coefficient is introduced to measure the allocation equity. A multi-objective water resources allocation model was developed for efficiency and equity optimization, with sustainability (the river ecological flow) as a constraint. The Non-dominated sorting genetic algorithm II (NSGA-II) was employed to derive the Pareto front of such a water resources allocation system, which enabled decision-makers to make a scientific and practical policy in water resources planning and management. The proposed model was demonstrated in the middle and lower Han River basin, China. The results indicate that the Pareto front can reflect the conflicting relationship of efficiency and equity in water resources allocation, and the best alternative chosen by cost performance method may provide rich information as references in integrated water resources planning and management.

As the water source for the middle route of the South-to-North Water Transfer Project, the Han River in China plays a role of the world’s largest inter-basin water transfer project. However, this human-interfered area has suffered from over-standard pollution emission and water blooms in recent years, which necessitates urgent awareness at both national and provincial scales. To perform a comprehensive analysis of the water quality condition of this study area, we apply both the water quality index (WQI) and minimal WQI (WQI min ) methods to investigate the spatiotemporal variation characteristics of water quality. The results show that 8 parameters consisting of permanganate index (PI), chemical oxygen demand (COD), total phosphorus (TP), fluoride (F-), arsenic (As), plumbum (Pb), copper (Cu), and zinc (Zn) have significant discrepancy in spatial scales, and the study basin also has a seasonal variation pattern with the lowest WQI values in summer and autumn. Moreover, compared to the traditional WQI, the WQI min model, with the assistance of stepwise linear regression analysis, could exhibit more accurate explanation with the coefficient of determination (R 2 ) and percentage error (PE) values being 0.895 and 5.515%, respectively. The proposed framework is of great importance to improve the spatiotemporal recognition of water quality patterns and further helps develop efficient water management strategies at a reduced cost.

长江上游水库蓄水集中在8～10月,此期正值雨季后期,若水库不能在降水集中的雨季结束前蓄至一定水位,将严重影响水库兴利目标的实现.为优化现有蓄水调度方案,该文选用德州农工大学Rainy And Dry Season(RADS)数据集,长系列的雨季划分资料,分析了长江上游雨季结束时间特征.结果表明,RADS与现有全国、长江上游雨季特征的研究结果较为一致,数据集在研究区域适用性较好.乌江流域的雨季结束时间波动范围明显较大,需重视乌江流域的中长期水文气象预报,以提前预警流域雨季较早结束的情况.而对于乌江、岷江大渡河流域水库联合调度方案所规定的9月1日与10月1日的起蓄时间,1961～2007年中曾出现过雨季更早结束的情况,即对于这两个流域的水库而言,统筹考虑防洪任务与预报水情,蓄水时间可考虑进一步提前.

以长江上游30座水库巨型水库群为研究对象,建立提前蓄水多目标联合优化调度模型,采用分区策略、大系统聚合分解、参数模拟优化方法和并行逐次逼近寻优算法求解.研究结果表明:所提模型框架可较好地解决巨型水库群联合蓄水优化调度问题;智能算法对于复杂约束的多目标优化问题可产生大量非劣解;Pareto前沿分布范围均匀且广泛,可供决策者灵活调度.与原设计方案相比,在防洪风险得到控制的前提下,通过水库群提前蓄水联合优化调度,水库总蓄满率由90.40％增加到94.42％,年均增发电量76.5亿kW·h(+3.76％),经济社会效益显著.

欧洲中期天气预报中心近年发布了季节性的GloFAS Seasonal径流和SEAS5降水集合预报产品.选取长江上游6个控制站的径流预报及4个分区的面雨量预报为研究对象,通过计算分析AUC、ROCSS和可靠性等指标,评估了这2种产品对于长江上游水库群蓄水期的枯水情景的预报能力.结果 表明:2种产品提前一个月判断枯水雨情的效果较好,但产品倾向过度预测枯水事件发生的可能性,在实际生产运用中需得到重视.研究成果可为基于中长期预报的长江上游水库群提前蓄水调度提供科学依据与技术支撑.