Climate change and socioeconomic development are projected to increase global fluvial flood risk. Most studies have focused on large rivers or aggregated regional assessments, leaving limited understanding of how flood risk varies across river sizes. By integrating high-resolution hydrodynamic simulations, demographic projections and current flood defence levels across the contiguous USA, our projections suggest that smaller rivers tend to exhibit greater sensitivity to climate change-induced increases in flood intensity by 2050. Meanwhile, flood defences along small rivers are currently the weakest, potentially placing nearby populations at disproportionately higher risk. Restoring future flood risk along small rivers to historical levels would require the highest flood defence investments among all river sizes-up to similar to US$4 million (2005) per kilometre. The elevated flood risk for residents along small rivers appears to be primarily driven by increasing climate-related hazard severity and low defence levels, whereas flood risk along larger rivers is dominated by rising exposure. Together, these results indicate a potential mismatch between future flood defence needs and existing defence levels across river sizes, highlighting the urgent need to prioritize investments for communities along small rivers.

Quantifying the evolution and recovery of groundwater drawdown funnels in the North China Plain (NCP) remains a grand challenge due to limited spatial resolution, vertical ambiguity, and attribution complexity in current satellite-based monitoring. Here, we present an integrative framework that addresses these bottlenecks by combining data-driven downscaling of GRACE gravimetric data, stratified signal separation using multi-depth well observations, and causality analysis driven by climate and groundwater abstraction indicators. This framework enables ~5 km resolution groundwater storage reconstruction, delineates 3D funnel morphology via Gaussian fitting, and attributes dynamic changes through wavelet-informed causality inference. Our key findings include: (1) The NCP groundwater system experienced a three-stage trajectory—initial slow decline (–0.05 cm/year), accelerated depletion (-0.19 cm/year), and partial recovery (+0.16 cm/year)—with funnel radius expanding from 209 km in 2002 to 340 km in 2022. (2) Funnel morphology transformed substantially, with the lowest groundwater level deepening from 20.93 m to 52.95 m and lateral half-width expanding from 0.56 m to 4.01 m over two decades. (3) The combined analysis of the transfer function noise (TFN) model and causality analysis based on information flow further revealed that early funnel changes were dominated by climate (~75% contribution), later shifts by intensive pumping (~90%), and recent recovery partially driven by precipitation (~16%), with aquifer response time increasing from 20 to 60 months, indicating a weakened response and delayed recovery. The lagged responses revealed by the causality analysis redefines our understanding of anthropogenic impacts on aquifer systems and offers a transferable path toward sustainable groundwater governance under changing climate and socioeconomic pressures.

Rapid hydropower expansion and irrigation growth heighten water competition, further intensified by climate variability in transboundary basins. Existing models overlook spatial heterogeneity in irrigation demand, limiting their ability to capture water reallocations across systems. Here, we develop a hydrological modeling framework that integrates reservoir operations, irrigation withdrawals, and future climate projections to quantify Water-Energy trade-offs. A key innovation is the inclusion of a hydraulic infrastructure topology module that uses intelligent remote sensing canal detection technique to detect irrigation canals and establish river-reservoir-field connectivity. Historical simulations reveal that prioritizing hydropower generation can reduce downstream irrigation water availability by up to 14%, with dry-season impacts up to five times greater than those in the wet season. Under future scenarios (2021-2040), irrigation demand is projected to increase by 63-68%, largely driven by the expansion of irrigated areas. However, projected increases in dry-season precipitation under future climate change could mitigate these trade-offs, reducing average irrigation shortfalls to 7%. Our findings highlight how interdependencies between irrigation water and hydropower are reshaped by climate and infrastructure development, offering a new framework for evaluating adaptive resource management in transboundary river systems.

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.

Floods are destructive, yet quantifying 3D dynamics remains challenging for traditional satellites. The SWOT mission provides Water Surface Elevation (WSE) measurements for flood monitoring. We assessed the catastrophic Miyun flood (July 2025) where rainfall was extreme. We developed the SWOT-FVE framework to reconstruct hydrographs and volumes, revealing level rises up to 3.2 m and volume increases of 1.54 billion m³. These findings demonstrate SWOT’s unique capability to resolve 3D flood responses, advancing flood assessment and resilience planning.

Soil moisture is essential for precision agriculture, flood prediction, and irrigation management. Current satellite-based soil moisture datasets generally suffer from coarse spatial resolution, and substantial efforts have been devoted to generating high-spatial-resolution soil moisture products. Machine learning methods lack physical constraints, while high-resolution land surface process simulations are limited by inaccurate surface parameterization. This study proposes a novel framework integrating machine learning and a land data assimilation system (LDAS) to downscale coarse-resolution satellite soil moisture products. The framework combines the advantages of statistical and physical models and improves the spatial resolution and temporal variability of soil moisture active passive (SMAP) soil moisture by incorporating multisource high-resolution surface variables. First, the random forest (RF) model is adopted to characterize the complex nonlinear relationship between coarse-resolution SMAP data and land surface variables. The trained RF model subsequently predicts soil moisture at a 0.05 degrees & times; 0.05 degrees resolution using fine-resolution inputs. Second, the RF-based downscaled soil moisture results are assimilated into the LDAS to optimize system parameters, yielding high-accuracy soil moisture datasets with improved spatiotemporal resolution. The integrated downscaling framework is validated against in situ measurements from three soil moisture monitoring networks across the Tibetan Plateau. The proposed framework outperforms traditional in situ-based machine learning downscaling methods, with a ubRMSE lower than 0.04 m(3) m(-3), an R higher than 0.7, and an MBE lower than 0.03 m(3) m(-3). This study provides an effective integration strategy of machine learning and LDAS, which offers great potential for generating global high-resolution soil moisture products.

Study region 532 minimally disturbed catchments in the contiguous United States (CONUS). Study focus The Budyko framework provides a representation of long-term water-energy partitioning. However, its practical application depends on the reliable estimation of the parameter ω, whose dominant controls remain inconsistently identified across regions. Here, we investigate whether a minimal set of climatic and physiographic variables can explain the spatial variability of ω across hydroclimatically diverse catchments. We apply a machine-learning-based variable selection framework to identify indispensable controls and evaluate the influence of hydroclimatic heterogeneity on predictor selection. New hydrological insights Results demonstrate that only four variables—the fraction of precipitation falling as snow, aridity index, precipitation seasonality index, and compound topographic index—are sufficient to capture the majority of the spatial variability of ω across CONUS, while vegetation indices play a relatively minor role. We further show that hydroclimatic heterogeneity can obscure the identification of dominant controls on ω when predictor selection relies solely on correlation-based approaches. Using the four selected controls, we developed both a random forest (RF) model and a multivariate linear regression (MLR) model. Both achieved good performance on independent test basins (RF: R² = 0.80, RMSE = 0.227; MLR: R² = 0.616, RMSE = 0.315), and both substantially outperformed two existing ω parameterizations (Li model: R² = −0.784, RMSE = 0.679; Bai model: R² = −0.099, RMSE = 0.533). When incorporated into the Fu–Budyko equation, the estimated ω reproduces annual runoff variability with high accuracy (R² = 0.94, RMSE = 98.52 mm). These findings demonstrate that much of the large-scale variability of ω can be explained by a small set of hydroclimatic and physiographic controls, providing a physically interpretable and parsimonious basis for Budyko applications in ungauged and data-scarce regions.

Identifying the flux partition regimes between soil moisture (SM) and evaporative fraction (EF, ratio of evapotranspiration to available energy) is important for understanding the hydrometeorological processes as well as the development of Land Surface Models (LSMs). However, evaluating the SM-EF regimes (i.e., whether EF is controlled by energy or water availability) in LSMs remains challenging since concurrent large-scale observations of the diagnostic variables are often scarce. Recent advancements in satellite techniques provide unique advantages for evaluating the models' performance. Here, we use long-term satellite SM data sets NNSM and observation-based meteorological forcing data sets to evaluate the SM-EF regime in six major reanalyses (i.e., GLDAS-Noah, GLDAS-CLSM, MERRA2, NCEP-FNL, ERA5, and JRA5). The analyses are conducted over China and North America. The results show that at large scale, all data sets consistently overestimate the degree of water limitation, though the bias varies across models and largely depends on how each parameterizes soil and vegetation controls on surface water-energy partitioning. The models' land-atmosphere coupling configurations only show moderate influence on EF regimes, and the effect of surface soil layer thickness is minimal. The comparison of model behaviors between the two study regions further reveals that inter-model discrepancies are much more pronounced in China than in North America. This reflects differences in data quality and model calibration density between the two regions. Our study therefore advances our understanding in land surface models' hydrometeorological behaviors and provides valuable reference to further improve the model parameterization on surface energy partitioning processes.

Tidal flat topography is a fundamental attribute affecting inundation dynamics, sediment transport, and ecosystem functioning, yet accurate and spatially consistent large-scale monitoring remains challenging. Here, we leveraged satellite altimetry from the Surface Water and Ocean Topography (SWOT) mission to develop a novel, large-scale framework for deriving tidal flat topography from SWOT data, and demonstrated its capability by generating a high-accuracy, national-scale elevation dataset for China. By combining a percentile-based aggregation of multi-temporal water-surface elevation observations with a tide-constrained, adaptive best-quantile (best-q) reconstruction strategy, followed by linear interpolation for gap filling, we improved both vertical accuracy and spatial completeness. Validation against airborne LiDAR, GNSS-RTK surveys, and ICESat-2 photon data demonstrates robust performance across diverse coastal settings, achieving RMSE = 0.34-0.47 m and R2 = 0.81-0.88 at a horizontal resolution of 100 m. Compared with existing large-scale digital elevation models (DEMs), the SWOT-derived topography not only improves vertical accuracy by over 80% but also providing substantially more complete spatial coverage of tidal flat elevations. Spatial analyses reveal pronounced latitudinal gradients, with higher tidal flats concentrated in low-latitude regions and extensive low-lying flats dominating northern estuarine and deltaic systems. This study establishes a scalable framework for tidal-flat elevation retrieval and provides a foundational dataset to support coastal monitoring and sustainable management.

This study uses multiple data sources, including remote sensing products (ESA CCI and SMAP), reanalysis data (ERA5), and ground-based observations from the International Soil Moisture Network (ISMN), to systematically analyses the change in global surface soil moisture from 1983 to 2023. Contrary to recent findings suggesting a decline in soil moisture has contributed to sea-level rise via groundwater release in the 21st century, our multi-source analysis does not indicate a significant, widespread drying trend on a global scale. Results reveal substantial discrepancies among datasets: from 2000 to 2023, the CCI product indicates moistening over approximately 89.22% of global land area, particularly in eastern North America, eastern South America, southern Africa, and eastern Asia, whereas ERA5 shows drying over 68.39% of land area. The SMAP product exhibits an intermediate trend during 2015–2023. Analysis based on global station data further confirms a strong positive correlation between surface and subsurface soil moisture (especially within 0–40 cm), supporting the use of remotely sensed surface information to infer deeper soil water conditions. We argue that the drying signal in ERA5 likely arises from model artifacts, including overestimated canopy interception evaporation in its land surface model and systematic underestimation of precipitation in its forcing data. Thus, the global drying trend reported in some model-based studies may not reflect actual terrestrial water storage changes. This study underscores the importance of using multi-source observations to critically evaluate hydrological change and cautions against drawing large-scale conclusions about soil moisture decline and its implications for sea-level rise based on single-model outputs. In addition, we highlight the need to improve retrieval algorithms, and refine model physics to enhance the monitoring and understanding of soil moisture changes in climate and hydrological studies.

The leaf-onset date is sensitive to climate warming. It is widely reported that the temperature sensitivity of the leaf-onset date (ST) of deciduous broadleaf forest (DBF) may decrease under dormancy-period warming. However, evidence of how boreal-DBF ST may generally change under dormancy-period warming is still lacking. Here, by analysing climate and satellite data, we find that, between 1982-1996 and 1998-2012, 74% of all 0.5 degrees x 0.5 degrees boreal-DBF-containing grid cells with a rise in boreal-DBF dormancy-period temperature exhibited an increase in boreal-DBF ST. We demonstrate that the observed general increase in boreal-DBF ST is largely attributable to a warming-related enhancement in dormancy-period chilling accumulation. Furthermore, we show that phenology models systematically underestimated the magnitude of the observed change in the mean boreal-DBF ST across all boreal-DBF-containing grid cells by a mean of 85%. This study has implications for improving phenology models and understanding the carbon cycle in boreal regions.

With the intensification of climate change and environmental pollution, reducing carbon monoxide (CO) emissions has become a focal point of global environmental governance. Existing research often relies on site-specific observational data, limiting continuous spatiotemporal analysis while failing to quantify the impacts of complex factors, such as vegetation structure. This study integrated passive and active satellite remote sensing sensors with aerodynamic models to analyze the spatiotemporal distribution of vegetation contributions to CO reduction across China from 2013 to 2022. This study extends traditional site-scale research to a continuous spatial scale, enhancing model accuracy through high-resolution satellite data. Our findings indicate that vegetation in regions east of the Hu-line primarily contributes to CO dry deposition, with a cumulative deposition of 5.2854 million tons, improving air quality by 0.00216% and yielding economic benefits of 7.436 billion USD. Forest-type vegetation contributes nearly ten times more to CO dry deposition than that of non-forest types, with significant spatiotemporal differences. Evergreen broadleaf trees contribute the most, with a cumulative reduction of 2.7826 million tons. Wavelet transform coherence analysis identifies the leaf area index (LAI) as the parameter with the highest coherence with CO dry deposition across all time-frequency scales, averaging a wavelet coherence of 0.79. The SHapley Additive exPlanations (SHAP) analysis indicates that temperature is the main factor influencing temporal fluctuations. These results provide a deeper understanding of vegetation's role in the global carbon cycle, offering significant implications for carbon reduction policies, vegetation management, and ecosystem service enhancement in China and globally.

Shallow cumulus (ShCu) influences regional atmospheric circulation and suppresses summer precipitation by modulating surface radiation and energy budgets, yet its macrophysical characteristics and environmental controls remain unclear over the Tibetan Plateau (TP). This study defines ShCu as cumulus with depth ≤2 km and analyzes its properties and environmental drivers using 15 years (2006–2020) of CloudSat and CALIPSO satellite observations, complemented by ERA5 reanalysis and high‐resolution topography. ShCu occurs most frequently in summer and during the daytime, with the highest cloud fractions over the western and southeastern TP. It dominates cumulus across all seasons, accounting for approximately three‐quarters of the cumulus over the TP annually. Among the environmental drivers, near‐surface relative humidity (RH2m) emerges as the primary control, regulating both ShCu occurrence and dominance. Specifically, moderate RH2m levels tend to favor frequent and dominant ShCu by promoting condensation while limiting deep convection, as seen in the western TP year‐round. In contrast, highly moist conditions combined with strong updrafts at 500 hPa in the southeastern TP during summer promote cloud deepening into cumulus congestus, yielding lower ShCu cloud fraction (∼8.8%) and dominance (∼0.62) than in the west (∼14.4%, ∼0.80). Furthermore, ShCu over the TP exhibits a lower average cloud base (∼0.83 km) than in surrounding regions, with the lowest values observed over the western TP during winter and nighttime, closely linked to reduced lifting condensation levels. These findings highlight a distinct ShCu regime over the TP and offer insights for improving its parameterization by incorporating terrain complexity and realistic near‐surface humidity.

The TECIS (Terrestrial Ecosystem Carbon Inventory Satellite), launched in August 2022, achieves high-resolution stereo monitoring of atmospheric clouds and aerosols through multibeamLiDAR and multispectral cameras. Traditional algorithms struggle with accuracy and robustness in vertical layer retrieval under complex conditions. Here, we propose TECIS-CASNet, a universal framework for atmospheric layer identification, integrating TECIS and CALIOP with deep learning transformer theories. To demonstrate its advantage, we applied it to analyze the spatiotemporal distribution of cloud, aerosol, and dust in the Beijing-Tianjin-Hebei (BTH) region of China, where cloud-aerosol variability is high due to topography, climate, and human activity. The TECIS-CASNet improved classification by approximately 15% compared with traditional algorithms, especially under low signal-to-noise ratio and other complex situations. Furthermore, it achieved 95%accuracy with absolute accuracy of 0.01 in optical depth inversion. The framework identified dust transport from central Inner Mongolia (linked to potential deforestation), significantly affecting near-ground air quality. The TECIS-CASNet has great potential for satellite LiDAR data processing and climate change.

Soil moisture (SM) is a key state variable in the climate system through its control on evapotranspiration (ET) and ET-regulated lower atmospheric processes. The SM-ET coupling strength (SECS) is thus closely linked with land-atmosphere interactions and its reliability is crucial for Earth system modeling. However, acquiring global maps of unbiased SECS remains challenging given significant levels of error present in globally available SM and ET products. Triple collocation (TC) provides a possible solution; however, it is difficult to apply globally since it requires access to three independent SM-ET data pairs-a requirement that is difficult to meet in practice. Here, we generate a global SECS map based on a new two-system approach that requires only two independent SM-ET data pairs. This two-system approach is first validated over local ground sites versus a ground-inclusive benchmark SECS. Subsequently, it is applied to generate a global map of SECS with input from various independent globally available SM-ET data pairs (identified using the benchmark SECS). Results suggest that previous TC-based SECS estimates are generally negatively biased due to cross-correlated error present between RS products. Instead, our generated new SECS map is shown to provide more robust mapping of global SECS-thus offering an important reference for improving Earth system models.

Accurate flood modelling is crucial for disaster prevention. Fine-resolution global routing models can offer more detailed flood information, but balancing model efficiency with accuracy remains challenging. This study examines the conditions under which a fine-resolution model outperforms a coarser one, using the CaMa-Flood model at 0.05 degrees, 0.083 degrees, 0.1 degrees, and 0.25 degrees resolutions across the contiguous United States. The results indicate finer resolution does not improve the simulation of flood timing, but better simulates the daily river discharge and flood peak flow due to better representation of the river network in small rivers. Notably, the improvement in daily discharge simulation is greater than that in peak flow. Nevertheless, uncertainties in channel parameters mean that a more detailed river network does not necessarily yield better flood simulations. For rivers with upstream drainage areas greater than 500 km2, a 0.25 degrees model is sufficient if high-precision channel parameters are unavailable.

Transboundary river basins (TRBs) are at risk of water scarcity-induced conflicts, especially given the rising water demand and impacts of climate change. Despite extensive efforts and some progress, the mechanisms linking water scarcity to conflicts in TRBs remain insufficiently understood, and identifying effective mitigation and adaptation strategies remains a challenge. In this study, we introduce a framework for predicting TRBs vulnerable to water scarcity-induced conflicts, based on the concept of water dependency, defined by monthly water scarcity. This framework successfully explains over 80% of the TRBs experiencing water scarcity-induced conflicts during 2005-2014. Our projections indicate that, without mitigation and adaptation measures, nearly 40% of global TRBs could face potential conflicts driven by water scarcity in 2041-2050, with hotspots in Africa, southern and central Asia, the Middle East, and North America. However, proactive measures such as intra-basin cooperation could reduce this proportion to less than 10%. This study underscores the urgency of increased investment and active stakeholder engagement to foster intra-basin cooperation and avert potential conflicts.

Understanding the dynamic changes in soil moisture (SM) is crucial for studying land-atmosphere interactions in hydrometeorology. While numerous SM datasets have been developed for passive microwave remote sensing systems operating at various frequencies, the consistency of SM dry-down patterns observed by different sensors remains uncertain. Additionally, the use of distinct algorithms across SM products complicates direct comparisons. This study addresses these issues by producing two new enhanced-resolution (approximately 10-km) SM datasets using the same multi-channel collaborative algorithm (MCCA). These datasets are retrieved from the L-band brightness temperature (Tb) from Soil Moisture Active Passive (SMAP) and the C/X/Ku-band Tb from Advanced Microwave Scanning Radiometer 2 (AMSR2), referred to as MCCA SMAP and MCCA AMSR2. The satellite-derived SM data are evaluated and compared using 40 globally distributed SM observation networks at both regional (dense network) and grid scales. The results indicate that the Pearson correlation coefficients (R) of MCCA SMAP and MCCA AMSR2 SM in 23 dense networks are 0.795 and 0.664, respectively, with unbiased root-mean-square deviation (ubRMSD) of 0.041 m3/m3 and 0.048 m3/m3, respectively. Both datasets perform better at the regional scale than at the grid scale. The analysis shows that SMAP outperforms AMSR2 overall, although the sensing capabilities of both payloads decline with increasing vegetation water content (VWC). Further analysis of global SM dry-down patterns reveals minimal differences in the magnitude of SM dry-down between the two payloads, but slightly larger differences in the effective wilting point. A notable disparity was observed in SM memory, with SMAP exhibiting a significantly longer memory than AMSR2. Analysis of SM loss rates shows that SMAP has a lower loss rate compared to AMSR2, consistent with the theoretical expectation that lower-frequency observations, such as those from SMAP, observes deeper soil layers. These results highlight the importance of considering differences in payload configurations when using remote sensing SM products for studies of land-atmosphere interactions in hydrometeorology and for improving land surface models (LSMs).

The Surface Water and Ocean Topography (SWOT) satellite, launched in December 2022, marks a significant breakthrough in hydrological monitoring. Equipped with a Ka-band radar interferometer, nadir altimeter, and radiometers, SWOT enables simultaneous measurements of water levels, widths, and slopes with unprecedented spatiotemporal resolution. This manuscript systematically reviews SWOT's satellite parameters, scientific missions, algorithm processes, and application products to provide a more comprehensive understanding of its capabilities. Validation with ICESat-2 and other reference datasets shows that SWOT’s WSE products achieve an accuracy of up to 0.18 m for inland water surface elevation measurements, thereby significantly improving the precision of water body monitoring. Additionally, SWOT demonstrates substantial potential in flood monitoring by capturing flood inundation dynamics with high spatiotemporal resolution, thereby enhancing rapid response and early warning capabilities. These advancements address critical limitations in existing satellite missions, improving global hydrological observation and supporting water resource management. SWOT’s data products offer essential insights into surface water dynamics, contributing to disaster mitigation and a better understanding of climate change impacts on water resources.