Flood regulation measures (primarily through dams), combined with a range of non-regulative measures, sit at the heart of modern flood management aiming to mitigate flood impacts. However, the undertaken flood protection measure for a particular region is often selected based on how flood risk evolves under historical and future climate scenarios, whereas relatively less attention has been paid to assess the effectiveness and burden of different measures in face of varying flood magnitudes (constrained by hydroclimatic conditions) and protection targets (constrained by human settlements in floodplains). As a result, it remains unclear whether existing dams can realistically meet evolving protection demands, or whether they are already operating under disproportionately increasing pressure.To address this, we introduce a quantitative framework (FRAMES, Zheng & Lin, 2025) to evaluate the applicability and adaptivity of regulative measures. We focus on how systems bear the Operational Load (OL)—defined as the storage demand placed on infrastructure across varying flood magnitudes (Return Periods) and protection targets (Exposure Levels). By analyzing 4,732 global settlements paired with 5,963 dams, we quantify the response patterns of OL across diverse geographic and developmental settings globally. These settlements are further categorized into distinct archetypes based on the marginal effectiveness of their regulative systems. Preliminary findings indicate that 57.5% of global settlement show diminishing returns of applying dams for flood protection. Such results indicate in these regions, management should prioritize land-use controls, zoning, and local resilience measures to alleviate disproportionate infrastructure pressure. Conversely, in regions where regulative potential remains high, emphasis should be placed on maintaining system redundancy and avoiding infrastructure lock-in. This study provides the first global quantitative baseline of flood protection potentials and adaptivity, offering a new foundation for evidence-based decision-making in flood management.

The monitoring and forecasting of flash floods present significant challenges due to their rapid onset. While extreme rainfall events, a primary driver of flooding, are becoming flashier, there is no consensus on whether floods will exhibit similar trends in a warming climate. Here, we assess flash flood risk development over four decades across 741 basins globally using the flashiness index. Our findings reveal a significant shift towards flashier floods in nearly one-third of the basins, predominantly concentrated in cold and arid regions of the northern mid-latitudes. In these regions, the increase in flashiness is largely associated with larger flood peaks rather than changes in timing. We identify basins characterised by rising flood peaks, which are likely to require flood defence infrastructure capable of withstanding more severe flood events, as well as those marked by shorter onset times, which may face challenges in implementing short-term early warning systems. Specifically, the increase in flashiness in most North American basins is mainly associated with rising flood peaks, whereas in most European basins, it is dominated by shorter onset times. These findings underscore the urgency of implementing region-specific strategies to adapt to flashier floods in a warmer future.

Abstract. Accurate simulation of snowmelt runoff (SMR) is critical for water resource management. However, despite the abundance of global hydrological models, little is known about their SMR performance. This study first presents a comprehensive evaluation of SMR across 15 state-of-the-art large-scale models and runoff products by focusing on their biases in first-order indices, i.e., the total volume (Qsum), peak flow (Qmax), and centroid timing (CTQ) of runoff in the snowmelt period. Then by introducing 1,513 snow-dominated basins with increasing basin complexities, we further proposed a novel model robustness metric to quantify how the model performance changes with basin complexity. Our results reveal that (1) most models exhibit underestimated Qsum and Qmax and predict CTQ too early. These biases are particularly pronounced in regions such as the western United States, northern Europe, and northeastern China. (2) Model biases systematically increase with basin complexity, with CTQ exhibiting the strongest sensitivity to increasing mean elevation and topographic variability, while that of Qsum and Qmax is mainly shaped by mean elevation and the diversity of vegetation types in the basin. (3) The robustness assessment further shows that observation-constrained runoff products exhibit the most outstanding performance, followed by the ISIMIP3a and ISIMIP2a models. Overall, global hydrological models exhibit stronger performance in simulating SMR than land surface models. Notably, land surface models perform substantially better for CTQ than for Qsum or Qmax, highlighting their structural advantage in capturing melt timing relative to runoff magnitude. This study provides a benchmark for SMR evaluation and a new framework for assessing model performance under basin complexity, offering crucial insights for future model development and uncertainty reduction.

Rivers impact the well-being of humans and the environment. As they increasingly face planetary-scale stressors, it is critically important to monitor and understand rivers at the global scale. As the only synoptic resource for global primary data on rivers, satellite remote sensing has recently begun to provide unprecedented opportunities for the monitoring, understanding, and prediction of global river behaviour. Despite these advances, the role of satellite remote sensing in global river science has still not been fully explored. New satellite systems and algorithms will enable substantial improvements in river measurements, provide new answers to long-standing or newly emerging scientific questions, and eventually update basic knowledge of rivers to advance global river science. In this Review we explore how remote sensing has been used to study the world’s rivers, examine challenges and opportunities for further advancing our understanding of rivers using existing and upcoming sensors, and identify possible solutions and future research directions. This Review synthesizes the transformative role of satellite remote sensing in reshaping global river science. It offers a strategic roadmap to overcome current challenges and update our fundamental understanding of Earth’s river systems.

Abstract Understanding reservoir regulation of streamflow is critical for hydrological modeling and ecohydrological assessments, yet our knowledge of how reservoirs preferentially modify flow variability remains limited. Here we apply spectral analysis to daily inflow‐outflow data from 205 US reservoirs, decomposing regulation effects into operational mode (how reservoirs modify flow variability) and regulation intensity (magnitude of modification). We identify four distinct operational modes that transcend nominal design purposes, better distinguishing observed regulation behavior than purpose‐based classifications. While the dominant mode strongly attenuates high‐frequency flow variations, reflecting widespread flood control operations, three additional modes (i.e., seasonal amplification, seasonal smoothing, and high‐frequency amplification) are also identified. We also find that larger reservoirs exhibit stronger regulation consistent with their characteristic operational modes. This work reframes our understanding of the complex reservoir regulation behaviors through a novel frequency‐domain perspective. The insights hold potential to inform improved hydrological modeling.

Human perturbations greatly threaten the health of riverine systems, producing ecological and hydrological consequences that challenge progress toward the Sustainable Development Goals. Yet existing quantifications of these perturbations remain coarse and static, limiting the assessment of their fine-scale impacts on streamflow responses. This study proposes a dynamic reach-level framework to track human-perturbed precipitation-streamflow (P-Q) relationships. By mapping 35 years of dam and urban datasets (1985-2019) onto 17,329 river reaches in the Pearl River Basin, we quantify longitudinal and lateral confinements using annually varying Degree of Regulation (DOR) and urbanized floodplain percentage (URB). We then classify reaches by their perturbation levels and evaluate how these confinements alter streamflow response intensity (conversion efficiency) and response variability (ratio of Q to P variability). Our results show that past static assessments substantially underestimated confinement levels, with longitudinal underestimation in the main channel ranging from 4.07 % to 18.80 %, and lateral underestimation in the Pearl River Delta ranging from 3.9 % to 16.96 %. These gaps arise from intensified damming shifting downstream and urbanization expanding inland. Where both confinements intensify, hydrological responses are characterized by reduced conversion efficiency due to damming and increased variability linked to urbanization, demonstrating that variability changes are more sensitive to lateral confinement. These results delineate regions with elevated flood sensitivity in rapidly urbanizing lowlands and emerging ecological fragility in heavily regulated uplands, which can inform adaptive, sustainability-oriented river management strategies.

Floodplains attract disproportionate concentrations of population and economic activity globally, yet the systemic flood risks emerging from this uneven development remain insufficiently characterized. Through a global analysis spanning 2000-2020, we quantify floodplain development patterns and associated flood losses across nations with varying socio-economic statuses and flood protection levels. Our results reveal that floodplains have experienced faster growth than non-floodplains in both population density and GDP density. These trends show significant variation depending on income and flood protection levels, revealing a divergent risk pattern: wealthier, well-protected nations accumulate greater economic assets at risk, whereas poorer, under-protected nations concentrate larger populations at risk. Furthermore, the sensitivity of flood-induced economic damages to floodplain GDP growth is more pronounced in wealthier nations, whereas the sensitivity of flood-induced fatalities to floodplain population growth is higher in poorer nations. This underscores the distinct economic vulnerability of developed nations and the human vulnerability of developing nations in the face of flooding. Based on these findings, we argue for prioritizing economic resilience and the decoupling of economic development from flood-prone asset accumulation in higher-income nations, while simultaneously emphasizing humanitarian protection and equitable infrastructure investment in lower-income nations.

Long-duration energy storage is essential for stabilizing electricity systems dominated by wind and solar power, with pumped hydroelectric storage (PHS) as a mature long-duration storage solution. However, the global potential of PHS under realistic hydrological, environmental, and social constraints has not been systematically quantified. Here, we present a global bottom-up assessment integrating hydrology, topography, environment, and transmission accessibility. Screening 2.89 million river and 50 million mountain valleys, we identify 18.82 +/- 2.66 PWh of economically and environmentally viable PHS resources that are highly concentrated in mountainous regions. Even under projected cost reductions of competing storage technologies through 2050, PHS remains the most cost-effective option for storage durations exceeding four hours and could deliver over 100 hours of storage for over half of global electricity demand. This study defines a physically constrained benchmark for the role of PHS in future power systems and provides a framework for integrating complementary storage technologies to support resilient, low-carbon electricity systems.

The water surface elevation (WSE) of rivers serves as fundamental data for various hydrological research and applications. The recently launched Surface Water and Ocean Topography (SWOT) satellite offers a revolutionary altimetry approach by providing wide-swath elevation mapping using a SAR Interferometer (InSAR) operating at Ka-band. While SWOT provides unprecedented spatio-temporal coverage of WSE, it has not been systematically compared with reference water stage databases. Currently, due to difficulties in accessing recent and globally homogenous gauge station records, established WSE derived from radar altimetry (RA) missions is the most suitable dataset to perform global validation of WSE. This study presents the first global-scale intercomparison of the two altimetry systems, the wide-swath InSAR technique used for the first time by SWOT and the classical along-track RA using the SAR technique, and identifies several representative factors influencing their consistency. SWOT WSE are compared with virtual stations derived from Sentinel-3 and Sentinel-6 missions, across five different node quality categories ("good", "suspect", "degraded", "bad" and a combined "all" group without "bad" data). The analysis further examines the potential influences from river width, river ice, backscattering coefficients (sigma0), and dark water fraction in modulating data consistency. The root mean square error (and correlation coefficient) between WSE from SWOT and RA in "good" and "suspect" data are 0.80 m (0.85) and 1.62 m (0.78), respectively, while those for "degraded" and "bad" data rise significantly to 8.80 m (0.60) and 16.91 m (0.50). The combined "all" category yields an overall RMSE (CC) of 5.15 m (0.65). For rivers wider than 160 m, SWOT measurements with "good" and "suspect" quality demonstrate notably improved consistency with RA compared to narrower rivers. Under frozen conditions, the reduced consistency between SWOT and RA is most evident in the "degraded" and "bad" quality data, with average reductions in CC of 0.17 and 0.21, respectively. In addition, radar backscatter strongly impacts the quality of SWOT-based WSE, as both extremely low values (dark water) and very high values (specular ringing) can lead to unrealistic estimates. Overall, this study offers important insights into the global performance of SWOT-based WSE estimation and informs the future refinement and application of SWOT data in hydrological research.

Abstract Assimilating the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) has been proven a promising approach to improving terrestrial hydrological simulation, but its impact under different climatic regimes and on hydrological responses to important climatic oscillations (i.e., El Niño‐Southern Oscillation (ENSO)) remains unclear. This study constructed a land surface data assimilation (DA) system based on a multi‐physics ensemble constructed by the Noah‐Multiparameterization land surface model (Noah‐MP) and the Data Assimilation Research Testbed over the southern contiguous United States (CONUS). The system features a set of high‐skill and low‐interdependence parameterization configurations as the assimilation ensemble, based on which the daily‐scale GRACE TWS was assimilated using the Ensemble Adjustment Kalman Filter. Results show that DA enhances the performance of TWS anomaly in the southeastern/south‐central CONUS, the Rocky Mountains, the Sierra Nevada, and the Pacific Northwest. For soil moisture, DA offers more skill gains in the deep layer than in the surface layer. For snow water equivalent, DA improves the performance in the northeastern/central CONUS and the western mountains, but offers limited gains in the southern CONUS, where the snow cover is shallow and seasonal. DA improves the runoff simulations in arid and semi‐arid regions where Noah‐MP performs poorly, but slightly degrades the performance in humid regions where Noah‐MP performs well. DA also improves the fidelity of runoff–ENSO linkages in semi‐arid and arid regions by eliminating spurious correlations. Our findings confirm the effectiveness of GRACE DA in reducing hydrological simulation uncertainties and demonstrate its potential for improving water management planning under climatic oscillations.

Abstract The Clumping Index (CI) critically regulates canopy radiation, yet its role in reshaping global heat fluxes under land‐atmosphere (L‐A) interactions remains poorly quantified. We integrated satellite‐derived CI into the Community Earth System Model (CESM) to assess its impacts through L‐A coupled simulations. Results show that CI systematically shifts energy partitioning by increasing ground sensible heat and evaporation while reducing vegetation counterparts, with spatial patterns modulated by L‐A coupling. Rather than acting as a uniform amplifier, L‐A coupling modifies the regional expression of CI‐induced flux changes through atmospheric feedback pathways. Comparisons with FLUXCOM and flux‐tower observations provide complementary large‐scale and site‐level context, and suggest that including CI together with L‐A coupling generally improves simulated sensible and latent heat fluxes across most plant functional types, with needleleaf vegetation as an exception. Our study provides new insights into how vegetation structure regulates global energy balance within the L‐A coupling framework.

The Surface Water and Ocean Topography (SWOT) mission has opened up numerous possibilities for remote sensing of river discharge (hereafter RSQ). As one unique feature, SWOT measures water surface elevation (WSE) and width (W) simultaneously, providing river hypsometry above the lowest SWOT-observed level to constrain channel geometric parameters. However, studies have yet evaluated the potential of utilizing SWOT-constrained channel parameters in improving the RSQ accuracy. In this study, by utilizing 15 sites in northern China as the testbed and over 500 global stations for validation, we developed a framework to improve Landsat-based RSQ by harnessing SWOT. We first proposed a range of filters to remove the noisy data in SWOT, which generally improved the ability of SWOT data in depicting river hypsometry with stronger strengths of the monotonic WSE-W relationship. Further, we combined SWOT data and the prior knowledge of median discharge, developing an innovative “SWOT and A-Priori information (SWAP)” method to derive the channel submerged area needed by RSQ algorithms. Uncertain parameters in SWAP were perturbed to generate probabilistic estimates of discharge. By validating our results in highly critical and ungauged settings, we achieved an RSQ accuracy with 11 out of 15 stations showing positive Kling-Gupta Efficiency (KGE) in northern China, and 66.4% at the global scale (557 stations). This result outperforms mainstream RSQ algorithms (e.g., BAM/geoBAM, W83), while further improves with more reliable SWOT data, prior information, remotely sensed river width (i.e., 75.8% positive KGEs). Our study proves the potential of SWOT in improving RSQ accuracy via constrained channel parameters. Future efforts can extend SWAP to further improve RSQ by additionally introducing spatio-temporally varying Manning’s roughness or other SWOT observables.

Abstract The lack of direct observations for flood regulation capacity largely compromises our objective assessment of a basin's flood mitigation strategies in confronting global change. This study proposes a novel Precipitation‐Inundation Paired Events (PIPE) extraction frame‐work, which employs 3D clustering analyses of precipitation paired up with satellite‐based inundation maps, allowing for the spatiotemporal analyses of event‐based Inundation‐to‐Precipitation (IP) ratio. Results from 142 events in the Yangtze River Basin show that the proposed metric can effectively capture the natural controls on flooding, reflected in inundation responses to key precipitation characteristics such as its spatiotemporal distribution and trajectories. Notably, after the operation of the Three Gorges Dam, the median IP Ratio for moderate and long‐duration events declined significantly, representing a ∼15% reduction in inundation area per unit precipitation volume. This study offers a novel tool for quantifying the spatiotemporal dynamics of a basin's flood regulation capacity and guiding flood management studies.

Abstract Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.

Abstract. Reconstructing streamflow across river networks is increasingly challenging in the context of heavily modified land surface conditions. Here we present a Data Integration model with Satellite Embeddings (DISE), a reach-scale residual-learning framework that integrates Google Satellite Embeddings (SE; compact learned vector representations of satellite imagery) from the AlphaEarth Foundation Model with a recently developed discharge simulation (GRADES-hydroDL) by learning corrections toward gauge observations. We evaluate DISE at 41 gauging stations in the Yangtze River Basin using leave-one-station-out cross-validation, with embeddings aggregated over each reach’s contributing subcatchment. Simulations incorporating SE consistently outperform the GRADES-hydroDL baseline, with mean aggregation emerging as the most balanced strategy. Improvements are most pronounced for magnitude and bias: compared to GRADES-hydroDL, median KGE increases from 0.485 to 0.594 and median NSE from 0.301 to 0.533, while correlation gains remain modest, suggesting SE primarily help the model capture streamflow volume and variability rather than timing. Control experiments further show that SE enhance spatial generalization beyond both meteorological forcings and traditional hydro-environmental reach attributes (RiverATLAS): compared to the base configuration without spatial context, adding SE alone increases median KGE from 0.473 to 0.594; when SE are further added on top of RiverATLAS, median KGE increases from 0.497 to 0.567. Once SE are included, adding RiverATLAS can even slightly reduce performance. Embedding-driven gains weaken where streamflow is governed by processes not directly visible from surface imagery, particularly complex reservoir operations. Nevertheless, SE can still provide useful information when forcing-based corrections are limited. These results demonstrate that SE provide analysis-ready, information-rich representations of land surface heterogeneity that measurably strengthen streamflow reconstruction across river networks. DISE offers a scalable pathway to inject high-resolution Earth observation context into river-network modeling, improving predictions in basins where conventional forcings and hydro-environmental descriptors are often insufficient.

Rivers are profoundly shaped by human activity along their water-land interfaces (riverfronts), yet the global distribution and drivers of these imprints remain poorly understood. Here we present a high-resolution global map of 7.52 million kilometers of riverfronts using satellite imagery and deep learning. We find that nearly 20% of global riverfronts are anthropogenically modified, primarily by agriculture (13.43%) and built-up areas (6.29%). A distinct modification belt spans parts of Africa and Eurasia, accounting for ~60% of global alterations, with imprint densities six times higher than elsewhere. This lateral fragmentation represents a pervasive human pressure that differs fundamentally from dam-induced longitudinal fragmentation, and is closely linked to deteriorating water quality and biodiversity threats. Environmental constraints and region-specific socio-economic dependencies further shape these spatial patterns. Our findings inform riverfront management and underscore the urgent need to reconcile development with the ecological integrity of these transitional zones.

Cryosphere degradation and vegetation dynamics due to climate change on the Tibetan Plateau profoundly impact runoff and water security downstream, yet their contributions remain largely unquantified. Here, we quantify the contributions of these cryospheric factors to runoff dynamics in southeastern Tibetan Plateau basins using state-of-the-art attribution methods. Results show runoff increased by 14 %-83 % from the period 1982-1995 to the period 1996-2010s in the upper Brahmaputra, Salween, and Yangtze basins, respectively. Precipitation increase dominates the runoff increase in the upper Salween basin. In contrast, the accelerating melt of glaciers and ice-rich permafrost plays a prominent role in the runoff increases in the upper Brahmaputra and Yangtze basins. Vegetation greening enhances evapotranspiration, partly counteracting the runoff increases in these basins. Despite the increase in the runoff of these large rivers, the downstream water availability per capita decreases after 1995 due to population growth. This study provides new insights into the interactions of climate, cryosphere, and vegetation on water availability in the Tibetan Plateau and supports forward-looking local water management.