Jida Wang

The 2024 Amazonian drought presented an unprecedented hydroclimatic anomaly. While prior research focused on atmospheric drivers and river impacts, the response of Amazon fluvial lake system (AFLs) has lacked large‐scale quantification. This study investigates the hydrological response of 838 fluvial lakes to the 2024 drought by integrating high‐resolution water surface elevation (WSE) data from the Surface Water and Ocean Topography (SWOT) mission with Sentinel‐1 water area observations. Lake‐specific dynamics were contextualized using basin‐wide terrestrial water storage anomalies (TWSA) from the GRACE‐FO mission. Results reveal that 95% of AFLs experienced persistent water level declines, averaging a −2.41 m anomaly relative to the 2018–2023 baseline. The response was spatially heterogeneous, with central sub‐basins exhibiting the most severe declines (average −3.53 m; maximum −13.82 m). Total AFLs storage loss peaked at ∼149 Gt during the 2024 drought, with 23% of the largest, most hydrologically‐connected lakes contributing over 86% of total deficit. The drought signal propagated systematically from west to east (upstream to downstream) over 75 days, confirmed by a strong positive correlation ( r = 0.67) between the timing of minimum WSE and lakes' longitudinal distance downstream. Furthermore, basin‐wide AFL storage changes correlated most strongly with TWSA at a 1‐month lag ( r = 0.94), although the lag corresponding to the strongest correlation varied among sub‐basins. Our findings provide the first basin‐scale, high‐resolution characterization of AFLs dynamics during an extreme drought by quantifying event's severity, spatial heterogeneity, and temporal propagation. These findings clarify how extreme droughts propagate through and deplete surface‐water storage across fluvial‐lake systems.

The seasonal pulse of rivers migrates across the planet, shaped by atmospheric circulation, basin memory and human control, with river discharge carrying this movement along valley corridors globally. Three years of the Surface Water and Ocean Topography (SWOT) mission global discharge product reveal these dynamics, their reorganization by climate teleconnections, and disruption by regulation. Seasonal activation emerges and evolves through the year: tropical activation reaches +64% above the annual mean in the northern Amazon and monsoon activation +52% in the Ganges in 2024. These patterns are undersampled by sparse and transboundary in-situ networks; SWOT's dense sampling reveals a misrepresentation of basin dynamics of up to 27%. The SWOT period spans the transition from the strong 2023 El Niño to neutral and La Niña conditions in 2025, when seasonal discharge shifts by roughly twice its typical year-to-year variation in the northern Amazon-Orinoco system, while the strengthening 2025 La Niña drives the Congo down by about four-fifths of a typical year's variation. SWOT further exposes where regulation breaks seasonal regimes: the Grand Ethiopian Renaissance Dam delays the Blue Nile by 2.8 months and dampens it by 46%, while the Aswan High Dam shifts the downstream Nile by 6 months and dampens it by 59%. SWOT thus opens a new view of the global river network, following water movement as it emerges, shifts, and is interrupted across continents.

Poyang Lake, China's largest floodplain lake, plays a crucial role in regional hydrology by seasonally regulating water to mitigate floods and sustain extensive wetland ecosystems. However, complex hydrodynamics and significant spatial hydraulic gradients often invalidate the traditional “flat‐water” assumption, making reliable estimation of the lake's true seasonal buffering capacity a persistent challenge. To address this, we developed a SWOT‐based Floodplain Lake wAter volume and Storage cHange (SWOT‐FLASH) framework. By integrating two‐dimensional water surface elevations from the SWOT mission with reconstructed full‐lake bathymetry, this method explicitly resolves spatial surface gradients to estimate a massive seasonal buffering capacity of 22.71 ± 1.96 km ³ for Poyang Lake, accounting for 95.10% of the maximum lake water storage recorded from July 2023 to July 2025. Beyond revealing the lake's critical regional flood‐mitigation role, this framework introduces a physically realistic tool for quantifying lake water storage dynamics in complex river‐floodplain lake systems worldwide.

Hydropower is pivotal to the global transition toward carbon‐neutral energy, yet its actual potential remains constrained by uncertainties. Global assessments have traditionally relied on static metrics, such as dam height, which obscure the dynamic water fluctuations that govern operational reliability. Here, we leverage primary observations from the novel Surface Water and Ocean Topography (SWOT) satellite to construct the first global, observation‐based inventory of energy storage potential for 4,025 major reservoirs. We find that the global average hydraulic head was 32.13 ± 0.64 m in 2024, but with significant spatial heterogeneity shaped by topography and substantial intra‐annual variability. Fluctuations mostly exceed 10 m in monsoonal and arid regions, indicating greater variability in the head‐related component of hydropower storage potential. By generalizing these observations to the world's reservoirs (totally 26,388, >1 km ² ), we quantify the global energy storage potential at 638.14 ± 218.52 TWh. Crucially, we identify a latent opportunity in non‐powered dams: retrofitting existing structures could unlock an additional gross theoretical energy storage potential of 84.88 ± 81.90 TWh, equivalent to 15.34% of current storage in powered dams, without considering engineering and operational constraints. By accumulating global reservoir energy storage potential based on commissioning dates and present‐day attributes, our analysis further reveals that this potential has expanded eightfold since 1960, with development shifting decisively from industrialized nations to developing regions. This satellite‐based assessment provides quantitative information on reservoir energy storage dynamics that can inform global energy‐system modeling, water‐energy planning, and evaluations of hydropower's potential contribution to decarbonization.

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.

The SWOT Level 2 Lake Single-Pass Vector Product, LakeSP, provides a standardized data set for tracking global lake dynamics. However, spurious measurements remain in LakeSP time series, making robust filtering for scientific applications a persistent challenge. While native LakeSP quality flags encode multiple error sources, fixed combinations of these flags can be overly stringent, creating temporal gaps, or overly permissive, retaining errors that distort seasonal signals. We introduce a Heuristic Adaptive Lake Filtering (HALF) framework, which balances error removal with preservation of hydrological variability using lake-specific rules. HALF filters LakeSP water surface elevation (WSE) time series in three steps: (1) calibrating lake-specific heuristic thresholds for key diagnostic variables to derive a physically constrained baseline; (2) iteratively removing outliers through low-pass filtering while enforcing temporal-coverage criteria; and (3) harmonizing intra-cycle WSE inconsistency through cross-pass bias correction. We evaluated HALF for both LakeSP Versions C and D using gauge observations for approximately 1,000 lakes worldwide and benchmarked its performance against native quality flags. Across validated lakes, HALF reduced WSE mean absolute errors to median values of 0.11–0.12 m and 68th-percentile (P68) values of 0.17–0.18 m, while retaining a median of at least 80% of raw observations. The retained time series yielded median errors in normalized seasonal WSE variability of 0.09–0.14 and P68 errors of 0.17–0.28, along with median LakeSP–gauge correlations of 0.92–0.93. Performance remained robust across lake sizes, regions, ice conditions, and product versions, suggesting that HALF improves the accuracy-coverage tradeoff and supports broad-scale LakeSP applications.

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.