Cédric Hervé David

The Surface Water and Ocean Topography (SWOT) satellite has the potential to improve knowledge of river discharge globally. However, SWOT discharge algorithms are informed by global hydrological model data, and a portion of the model's systematic bias is passed along to the SWOT discharge time series. To address this issue, we present a new algorithm designed to reduce systematic bias in SWOT discharge. The algorithm analytically integrates basin‐scale information by applying mass conservation to time‐averaged SWOT discharge and estimates of regionalized runoff. We test the algorithm on three river basins with drainage areas on the order of 10 ⁵ km ² . We find that when the time‐averaged SWOT discharge observations are spatially unbiased (i.e., their spatially averaged error is close to zero) the algorithm significantly reduces error in SWOT discharge: the median (interquartile range) absolute value of the normalized error is 15% (10%–19%) for the Willamette River basin, and 12% (7%–48%) for the upper Ohio River basin, significant reduction in SWOT discharge error (median values of 30% and 31%, respectively). However, for the Loire River basin, which has high (∼60%) spatial bias, the algorithm does not reduce error associated with SWOT discharge. These results indicate that understanding and reducing spatial bias at the basin scale is key for reducing time series bias in river discharge. Based on a preliminary assessment of spatial bias globally, we find that SWOT discharge could achieve its target accuracy (30% discharge error, with most of it temporal bias) even in ungaged basins, enabling SWOT discharge to significantly increase accuracies of the global water budget.

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.

Rivers are vital for water security, yet scalable freshwater monitoring and global knowledge of river mean depth and discharge remains limited by sparse in situ data, satellite observations of river surface only and global river flow estimations relying on models with insufficient hydraulic information. Without a robust global product for river bathymetry derived from satellite altimetry, large-scale hydrodynamic assessments remain limited. Here we present a near-global dataset of discharge rating curves, and river mean depths at 26 000+ river locations, integrating satellite water elevation observations with a global discharge monthly reanalysis over 30 years. Climatic anomaly-filtering and Bayesian calibration provide robust river elevation-discharge relationships yielding median KGE between rated and modeled discharge of 0.41. We produce reliable discharge estimates, validated against public discharge databases, and river mean depth derived from multiple satellite altimetry missions across diverse hydroclimates and flow conditions, along with uncertainty. These results offer a scalable, transferable framework for deriving hydraulic geometry relationships from satellite-model synergies. The approach is likely to improve with the emergence of future high-resolution missions, as well as advancements in global hydrology models and datasets.

We synthesize community priorities for advancing flood science and flood decision support over the next decade. We argue that useful information for flood monitoring and forecasting require integrated observations and models that quantify where water is, how much is stored, how fast it moves, how watershed conditions prime flood response, and how river, floodplain, and coastal geometry control inundation. We identify four priority science gaps: topo-bathymetry, surface water dynamics, antecedent land-surface conditions, and high-resolution precipitation forcing for flash floods. Progress will require sustaining trusted baseline observations while investing in targeted innovation, model-observation fusion, and decision-ready products developed with operational partners. Together, these advances will improve early warning, emergency response, infrastructure planning, recovery, insurance, zoning, and long-term flood-risk reduction. This white paper is an outcome of a community-led, community-engaged effort to elevate hydrologic science opportunities which would have significant benefit to support decision-making for drought, floods, water quality, water supply and water demand.

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.

Rivers are Earth’s most renewable and accessible freshwater resource ¹ , yet global estimates of the magnitude and variability in river water storage have remained few and inconsistent ^1–9 . Previous estimates of variability have relied either on sparse and asynchronous remote-sensing observations ¹⁰ or on hydrological models constrained by incomplete understanding of surface-water balance and poorly known river channel characteristics ^2,3 . The insufficient knowledge of temporal variations in river water storage across space hinders effective management of this critical freshwater resource ^11,12 . Here we present near-global-scale observations of active river channel geometry and associated monthly changes in water storage at the reach scale derived from the first water year (October 2023 to September 2024) of the Surface Water and Ocean Topography (SWOT) mission at 126,674 reaches worldwide. Clear patterns of riverbed shape and storage variability expectedly emerge across major basins. SWOT reveals a range of 313.1 ± 129.5 km³ in global annual river storage variability, approximately 28% lower than the lowest previously modelled estimates for the same wide reaches. Although the Amazon’s 2024 record drought, the observational challenges in the Arctic and the revisit frequency of SWOT almost certainly contribute to the discrepancy, the observations point to distinct knowledge limitations in surface-water science. These findings highlight key opportunities to improve the fundamental representation of surface-water dynamics in global models and to better inform water resource management and disaster mitigation at scale.

The advent of global river network data products and river routing models (RRMs) has resulted in substantial improvements to our understanding of the quantity and movement of water in Earth's rivers. However, the majority of vector‐based global river networks are derived from digital elevation models (DEMs) that do not allow representation of river network complexities such as bifurcations and divergences. Resulting discharge estimates may therefore not accurately reflect the movement of water. Satellite remote sensing has enabled the development of river networks that represent increased river complexities—albeit with limited topological accuracy—a necessary ingredient for river routing. Here, we describe an algorithm that generates globally consistent topology for a river network with optically‐derived centerlines—producing new and updated attributes—and adapt an existing vector‐based RRM to handle the added complexity in connectivity (i.e., divergences) via discharge partitioning. We apply the adapted RRM to runoff from an ensemble of land surface models for two case study Pfafstetter level‐2 basins (the Amazon and Mackenzie Rivers) to estimate 3‐hourly discharge between 2015 and 2024. We show that the adapted RRM is able to partition discharge at divergences, which results in a mean increase of 0.75 in Kling‐Gupta Efficiency and 0.31 in correlation coefficient at 6 gauges located on a divergence when compared to routing on a traditional DEM‐derived river network (i.e., contains no divergences). By comparing the resulting hydrographs from the optically‐ and DEM‐derived river networks, we find that flow waves travel faster through portions of river networks where divergences are accurately represented.

The Surface Water and Ocean Topography (SWOT, launched in December 2022) satellite has the potential to improve knowledge of river discharge globally, but preliminary SWOT discharge estimates are prone to timeseries bias, mainly induced by globally available prior information. To address this issue, we present a new algorithm designed to improve SWOT discharge. SFOI analytically integrates basin-scale information by applying mass conservation to time averaged SWOT discharge and estimates of regionalized runoff. We test the algorithm on three river basins with drainage areas on the order of 105 km2. We find that when the time averaged SWOT discharge observations are spatially unbiased (i.e. their spatially averaged error is close to zero) the algorithm significantly improves SWOT discharge: the median (interquartile range) absolute value of the normalized SFOI error is 15% (10-19%) for the Willamette River basin, and 12% (7-48%) for the upper Ohio River basin, significant improvements over SWOT discharge error (median values of 30% and 31%, respectively). However, for the Loire River basin, which has high (~60%) spatial bias, the algorithm does not improve SWOT discharge. These results imply that understanding and reducing spatial bias at the basin scale is key for reducing temporal bias in river discharge timeseries. Assessing spatial bias globally, we find that SWOT discharge should achieve its target accuracy (30% discharge error, with most of it temporal bias) even in ungaged basins, enabling SWOT discharge to provide significant improvements to the global water budget.

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.

The SWOT satellite mission is the first to conduct a global survey of the Earth's surface waters, measuring water surface height, river width, and water surface slope, based on which river discharge is estimated. At mid‐latitudes, the repeat orbit design of SWOT only allows a sampling of twice per repeat cycle, which is considered too low for most hydrological applications. To address the spatiotemporal limitations of SWOT, we develop a method that assimilates SWOT observations across continuous reaches within a single‐branch river network to obtain daily discharge estimates. Our model‐free assimilation method provides a linear dynamic system that includes a process model based on a physically based spatiotemporal discharge correlation model and observation equations utilizing SWOT products. We solve this dynamic system using a simple Kalman filter in the time domain, assimilating SWOT observations and incorporating the physically based prior to estimate daily discharge. Since SWOT discharge products were not yet available during the period of this research, we used synthetic SWOT data sets, introducing random and systematic errors through Monte Carlo simulation. The validation of the estimated discharge against true discharge over all test cases leads to a median correlation as high as 0.95, a median NSE for residuals (mean‐removed discharge) as high as 0.81, and a median relative bias as low as 5%, respectively. These promising results suggest that daily discharge for continuous reaches in a river network can be obtained through our data assimilation framework.