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.

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.

The Global Climate Observing System (GCOS) identifies river discharge as an Essential Climate Variable (ECV), critical for understanding climate dynamics and managing water resources (GCOS, 2022). However, no satellite instrument currently exists to directly measure river discharge, which must instead be estimated indirectly. The ESA River Discharge Climate Change Initiative (CCI) precursor project (https://climate.esa.int/en/projects/river-discharge/) addresses this challenge by developing innovative methodologies based on satellite remote sensing data.Four complementary approaches are being explored: (1) the use of long-term satellite radar altimeter time series of water surface elevations, combined with rating curves to estimate discharge; (2) The use of satellite imagery data to obtain river width, combined with rating curves to estimate discharge; (3) multispectral sensor data in the near-infrared (NIR) band, used to analyze river flow variability through the reflectance ratio between wet and dry pixels; and (4) a hybrid approach combining these two techniques. Radar altimeters offer the advantage of weather-independent measurements, while multispectral sensors provide higher temporal resolution but are limited by cloud cover.This proof-of-concept study focuses on 54 locations across 18 river basins, spanning 2002–2022. The sites represent a variety of climatic zones, drainage areas (from 50,000 km² to the Amazon basin), levels of human activity, and availability of in situ data. The project showcases the potential for satellite-based global river discharge estimation, validated through comparisons with on-the-ground measurements.This presentation will outline the methodologies employed, the computed discharge time series along with their validation during the first Phase of this precursor project (2023-2024), the objectives for the second phase, which has just started, and the progress achieved.

Study region: Oueme floodplain, southern Benin, West Africa. Study focus: The Oueme floodplain is a vast hydrological system feeding the Nokoue lagoon via the So and Oueme rivers, crossing an extensive wetland region. Driven by the African monsoon, this area is marked by pronounced seasonal and interannual hydrological variability, leading to recurrent flooding that significantly threatens local communities' safety and livelihoods. This study aims to assess the spatio-temporal variability of flooded areas in the Oueme floodplain from 2015 to 2023, using both in-situ measurements and satellite remote sensing. New hydrological insights for the region: The analysis reveals a clear seasonal cycle of flooding, modulated by interannual variability. Sentinel-1A data show that flood extent fluctuates widely, ranging from 20 to 160 km2, and identify the So region as the most flood-prone area. Importantly, the study detects a significant upward trend in flood extent over the study period. Flood dynamics are primarily governed by cumulative January-September rainfall, with a critical threshold of 1070 mm beyond which flooding expands rapidly. Altimetry data further indicate that a 6 m rise in water surface elevation north of the Oueme river results in increased flood expansion. Temporary hydrological connections appear during flood events, adding complexity to floodplain hydrodynamics and enhancing connectivity across the basin. Overall, this study provides new insights into the mechanisms driving floods in the Oueme floodplain using remote sensing.

Water level and water level changes of lentic water bodies such as lakes, wetlands, and reservoirs are rarely available, despite the relevance of these to maintain ecosystems (e.g., by providing drinking water, food, and cultural activities; and by supporting biodiversity). One alternative is to measure water surface elevation with radar nadir altimeters, whose use has increased as the capabilities of measuring inland water bodies have improved. However, their effectiveness has primarily focused on lakes larger than similar to 100 km(2), and the analysis of contributing factors to obtaining high accuracies is typically performed for a few lakes with special cases. We evaluated water surface elevation change from Sentinel-3 and Jason-3 nadir altimeter missions in lakes and reservoirs using in-situ observations from national governmental institutions and the citizen science network from the project Lake Observations by Citizen Scientists and Satellites. Utilizing the accuracy metrics of Pearson correlation coefficient, R, and the unbiased Root Mean Square Error, ubRMSE, over 27 waterbodies, we found a median ubRMSE of 0.15 m and R of 0.88. We combined the results from 61 additional lakes in a Random Forest algorithm with a permutation of importance to evaluate the impact of 7 factors on the accuracy metrics. Although water surface variability was the most contributing factor to the accuracy, we found differences in the order of variables depending on the evaluation metric used. Our results contribute to show the potential of the nadir altimeters to estimate water surface elevation changes in small lakes, present the advantages of using citizen science monitoring, and the relevance of water surface variability in relation to other factors contributing to high altimeter accuracy. Our implementation of the level of importance algorithm shows potential to systematically evaluate the nadir altimeter validation work available in the literature.

At global scale, there is still considerable uncertainty about the spatial and temporal variability of water storage and fluxes at the surface of continents. This is even more critical in the context of global climate change and the increasing human pressure on water resources. Despite this context, the following scientific questions remain difficult to answer, due to the coarse spatio-temporal resolution of current data: what is the global distribution of the heterogeneous change undergone by continental surface waters? What is the impact of anthropogenic pressure on water flows and stocks? What is the impact of these changes on the frequency and intensity of hydrological extremes (high and low waters)? To answer these questions, the Global Climate Observing System (GCOS) has identified river levels/discharges and lake/reservoir levels/volumes as essential climate variables, and recommends daily sampling (GCOS, 2022). Besides, extreme events, such as floods or droughts, cover a wide range of spatio-temporal scales. At present, water volume variations can only be observed by satellite at the coarsest scales (and are therefore of interest only for floods on the scale of the world's largest watersheds). The lack of observation of these events in basins with little or no in situ instrumentation is a major issue to understand, simulate and forecast these events. Observing these events globally, at least on a daily scale, would make it possible to quantify local flooding, thus greatly improving our knowledge of these events.One of the main issue to tackle these questions is the still rather coarse temporal sampling of current satellite missions, particularly altimetry missions. To overcome it, we are proposing the SMall Altimetry Satellites for Hydrology (SMASH) mission. This is a constellation of around 10 compact nadir radar altimeters optimized to provide daily observations of water levels in rivers, lakes and reservoirs along the constellation tracks. The specifications of the SMASH mission are the following: daily temporal sampling, observe water bodies larger than 100 m x 100 m and rivers as narrow as 50 m, with an accuracy on water elevation ~10 cm, and should provide products in near-real time and over the long term (10 years) in open access (open science and FAIR principles).Combining "high temporal frequency/low spatial frequency" measurements from the SMASH mission with "high spatial frequency/low temporal frequency" measurements from swath altimetry missions (current SWOT or futur Sentinel-3 Next Generation Topography missions) would cover unprecedented time and space scales and should open new fields of research.

The implementation of action plans for sustainable water resources management requires daily river discharge time series at gauging stations, which are already decreasing in number worldwide. Although the development of remote sensing-based methods for river discharge estimation has proven its effectiveness worldwide, the temporal frequency especially at the daily scale for river discharge estimation is the most important research question to be explored. In this context, the study proposed a methodological framework to establish a virtual station (VS) where the information retrieved from the multi-mission satellites was merged using the non-parametric copula function for river discharge estimation. Here, in the first step, both passive (C/M) and active (altimeter) remote sensing signals can be integrated by deriving the joint probability distribution using the copula functions of the Archimedean family. Subsequently, the Frank copula was evaluated as the best-fit copula function as measured by the goodness-of-fit-test and subsequently selected for establishing the VS by merging the information. The proposed framework was tested on more than 10 rivers around the world. Here, MODIS from Aqua and Terra, Landsat series, and MSI from Sentinel-2 images were used for the C/M approach, whereas SARAL AltiKa, Sentinel-3 A and B, and Cryosat-2 mission altimeters were considered for water level retrieval. The established VSs along the river can be able to derive long near daily discharge time series while evaluating against the in situ discharge with reasonable accuracy measured by Nash-Sutcliffe efficiency, Root Mean Square Error, and Kling-Gupta efficiency. Conclusively, the establishment of this kind of VSs along the river can be able to derive missing discharge data records and long near-daily discharge time series along any world river which is one of the key variables for hydro climatological studies. Keywords: Remote Sensing, Virtual Station, Copula, Satellite merging, River Discharge, Altimeters

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi-observation local ensemble-Kalman-filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in-situ hydroclimatic records and when assuming uncertainties of large-scale hydrologic-hydrodynamic models. Assimilating WSE outperforms daily discharge and water-level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state-of-the-art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large-scale hydrologic-hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research. The use of satellites to collect information from far away helps us to understand how water behaves on the continents. But combining all this data with uncertain computer models is complicated. This study introduces a new method called multi-observation local ensemble-Kalman-filter (MoLEnKF) to combine many different kinds of data at once. We tested MoLEnKF in the Amazon River basin, using satellite data on water levels, terrestrial water storage, flood extent and soil moisture. MoLEnKF by using all these observations at the same time obtained better results holistically than the individual experiments, improving our ability to predict aspects such as the amount of discharge, water level and evapotranspiration. This study is a step forward and could be really useful for understanding and predicting water-related phenomena worldwide, especially in a context of scarce or no availability of in-situ observations. Multi-observation local ensemble-Kalman-filter (MoLEnKF) advances multi-observation and multi-scale assimilation, overcoming holistically univariate experiments MoLEnKF improves the simulation of large-scale hydrologic-hydrodynamic uncertain models using only remotely sensed data MoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission

Water resources play a crucial role in the global water cycle and are affected by human activities and climate change. However, the impacts of hydropower infrastructures on the surface water extent and volume cycle are not well known. We used a multi-satellite approach to quantify the surface water storage variations over the 2000-2020 period and relate these variations to climate-induced and anthropogenic factors over the whole basin. Our results highlight that dam operations have strongly modified the water regime of the Mekong River, exhibiting a 55 % decrease in the seasonal cycle amplitude of inundation extent (from 3178 km2 to 1414 km2) and a 70 % decrease in surface water volume (from 1109 km3 to 327 km3) over 2000-2020. In the floodplains of the Lower Mekong Basin, where rice is cultivated, there has been a decline in water residence time by 30 to 50 days. The recent commissioning of big dams (2010 and 2014) has allowed us to choose 2015 as a turning point year. Results show a trend inversion in rice production, from a rise of 40 % between 2000 and 2014 to a decline of 10 % between 2015 and 2020, and a strong reduction in aquaculture growth, from +730 % between 2000 and 2014, to +53 % between 2015 and 2020. All these results show the negative impact of dams on the Mekong basin, causing a 70 % decline in surface water volumes, with major repercussions for agriculture and fisheries over the period 2000-2020. Therefore, new future projects such as the Funan Techo canal in Cambodia, scheduled to start construction at the end of 2024, will particularly affect 1300 km2 of floodplains in the lower Mekong basin, with a reduction in the amount of water received, and other areas will be subjected to flooding. The human, material and economic damage could be catastrophic.

Abstract The impact of an episodic river flood is intimately linked to its duration. Yet it is still unclear how often should a river be observed to accurately determine the occurrence and duration of extreme events. Here we assess flow statistics along with peak flow event detection and duration as a function of the discharge sampling period for large tributaries of the Mississippi basin using hourly gages over 2010–2022. Median event durations above high quantiles spatially vary from around 2 days upstream to 30 days downstream. Discharge mean, standard deviation, and quantiles can all be estimated within 2.5% error for sampling periods up to 8 days. A minimum temporal sampling 4× (2×) finer than peak flow event median duration is required to detect 95 ± 3% (85 ± 5%) of events and to estimate their duration within 90 ± 5% (75 ± 10%) median accuracy. Our findings have direct implications for future satellite missions concerned with capturing flood events.

The SWOT satellite is a near-nadir Ka-band interferometric radar, capable of monitoring water bodies larger than 6 ha. Launched in December 2022, the satellite was in a calibration/validation orbit until July 2023, where it acquired measurements every day over certain regions. Canadian lakes were ice-covered at the start of the calibration period, offering the opportunity to study the Ka-band backscatter in the presence of ice and snow. The SWOT signal is also affected when the water surface is very smooth (e.g. in the absence of wind), or attenuated by heavy precipitation. These preliminary results demonstrate for the first time the impact of ice, snow, wind, and rain on the detection of water bodies by the SWOT satellite signal.

Lake Tanganyika in East Africa contains 17% of the free freshwater on the Earth's surface and provides important ecosystem services to similar to 13 million people in the region. It is one of the great lakes in East Africa for which a significant rise in water level between 2019 and 2020 led to flooding, with major environmental consequences and social impacts. This study focused on the Lake Tanganyika basin water balance between 2003 and 2021 to assess the influence of recent climate variability on lake water level variations (due in particular to the floods of 2020 and 2021) and to explore early warnings of flooding in the lake's surrounding lowlands. This process is performed using remote sensing data. For the computation of the basin's water balance, we compared variations in the watershed total water storage (TWS) with the basin water flux calculated using rainfall, evaporation (E), evapotranspiration (ET) and discharges data. The space-time variations in rainfall, E and ET were analyzed by decomposing their time series into trend and seasonal signals and applying (only for rainfall) multivariate statistical analysis to the decomposed signals. For flood mapping, we calculated the MNDWI spectral water index from Sentinel-2 images acquired between 2017 and 2022. Our study showed that the basin water balance is closed when rainfall from Era5 is combined with E and ET from GLEV and MOD16A2, respectively. During the 2003-2021 period, over the entire watershed, water losses of similar to 70 km3 due to lake E were offset by an increase in water inflows of similar to 100 km3 in the rest of the watershed. During the period from 2003 to 2021, the E rate from the lake was stable overall, while the ET and rainfall mainly in the Malagarasi basin increased significantly. The surface water storage (SWS), which represents the variation in lake water volume derived from altimetry measurements, corresponds to 41.8% of the TWS, groundwater storage corresponds to 57.7% of the TWS, and the soil moisture is less than 0.5%. The TWS strongly correlated with the SWS (similar to 91%), with a one-month lag in the SWS variations in response to the TWS fluctuations. Therefore, the SWS in May, when the flood risk is the highest, was estimated using TWS in February, March and April with accuracies of 85%, 94% and 95%, respectively. This valuable information could be integrated into flood management tools, particularly for areas such as Gatumba city and the Ruzizi Delta Nature Reserve, which were heavily affected by the May 2021 floods.

Abstract. Large scale hydrological models like CTRIP and MGB are essential for simulating river dynamics and supporting large-scale climate studies. Their accuracy can be significantly improved through satellite data assimilation. This study leverages 20 years of high-resolution discharge data (2000–2020) from the ESA Climate Change Initiative (CCI) to enhance CTRIP and MGB models via ensemble Kalman Filter frameworks (HyDAS and HYFAA). Applied to the Niger and Congo basins, the models assimilate discharge data derived from altimetry and multispectral imagery, alongside water surface elevation (WSE) anomaly data, to evaluate their impact on model performance. Discharge assimilation was more effective than WSE anomaly assimilation, as it provided a more direct input for improving model accuracy. Temporal data density was the key factor in reducing bias and enhancing the simulation of seasonal flow patterns, with spatial coverage and data quality also playing important roles. In the Niger Basin, the assimilation of denser discharge data resulted in a significant bias reduction, which should improve the representation of long-term climate trends. Furthermore, the higher temporal resolution allowed for better capture of flow variability, which is crucial for both seasonal climate studies and short-term predictions, such as extreme hydrological events. The study also emphasizes the trade-offs between data resolution and quality, particularly in the Congo Basin. Future advancements include merging altimetry and multispectral discharge data, improving the discharge retrieval algorithms using SWOT data, and refining data assimilation techniques to improve climate studies and river system modeling in complex, climate-impacted basins.

The understanding and prediction of the variability of the hydrological state of watersheds across the planet is growing, as water is a fundamental human need and therefore important for science and society. In the last 20 years, significant advances have been made toward hydrological modeling of large river basins, and also continental to global-scale land areas. Remote sensing has been widely used in hydrology because, in addition to being a clear advantage in regions with a poor monitoring network, it has proven to be suitable for use in global and continental hydrological applications.The estimation of river discharge is of paramount importance, as it is considered an aggregator of all water cycle processes in the basin. On the one hand, estimates of discharge solely from space remain limited because they are not the primary focus of current satellite missions. On the other hand, simulations of hydraulic variables have been performed with large-scale hydrologic and hydrodynamic models, but the accuracy of their estimates can be improved with recent techniques such as data assimilation (DA). DA techniques have been developed to use remotely sensed datasets to obtain the best estimate of the current state of a system by optimally combining observations and large-scale hydrological models. Recent studies have also demonstrated the advantages of assimilating several types of datasets at the same time, which can help to further constrain the model state variables to be more physically representative.Thus, the main objective of this research is to develop a proof-of-concept for estimating hydraulic variables such as discharge and water level by assimilating multiple remotely sensed datasets into a large-scale hydrologic and hydrodynamic model. Experiences on the assimilation of different mission datasets into a large-scale hydrological model are discussed, including radar altimetry-derived water level from JASON, ENVISAT and Sentinel missions, terrestrial water storage from GRACE mission, flooded area extent from SWAMPS database and soil moisture from the SMOS mission.To develop our proof-of-concept, the Amazon as the study area. We used the hydrologic-hydrodynamic MGB model and the Local Ensemble Kalman Filter as the DA method as it has been commonly used in hydrologic models. Different localization and multivariable assimilation techniques were implemented to improve the effectiveness of the DA.The results indicate that the multi-mission assimilation approach is able to smooth/average the improvement of the state variables of the model, such as discharge and water level anomaly, compared to the experiment of assimilating the mission datasets individually. This proof-of-concept allows us to spatialize the improvement of the dynamics of hydrological-hydrodynamic variables based on large-scale hydrologic modeling and DA from global remote sensing sources only, without requiring in-situ data. As our proof-of-concept is based on datasets globally available and a hydrologic-hydrodynamic model that can be applied almost everywhere, it is fully replicable in any region of the world and represents a great potential for regional to continental studies.

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human-water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of socio-hydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023,https://doi.org/10.5880/GFZ.4.4.2023.001).

The main motivation for this study is to evaluate the use of real time observations from different sources for hydrological forecasting. The advent of new satellite missions providing high-resolution observations of continental waters has raised the question of how to use them, especially in conjunction with models. At the same time, the multiplication of extreme events such as flash floods points to the need for tools that can help anticipate such disasters. To do so, it is necessary to set up a forecasting system that is generic enough to be used with different types of data and to be applied to different basins. It is in this perspective that a platform named HYdrological Forecasting system with Altimetry Assimilation (HYFAA) was implemented, which encompasses the MGB large scale hydrological model and an EnKF module that corrects model states and parameters whenever observations are available. As a preliminary study towards operationnability, the platform was tested in offline mode, in the framework of Observing Systems Simulation Experiments (OSSEs). Discharge estimates from three different observing systems were generated, namely in-situ streamflow measurement stations, Hydroweb radar altimetry, and the future SWOT interferometry mission. In this study, we chose to assimilate these data separately in order to analyze the capacity of the system to adapt itself to different orbital characteristics, especially coverage and repetitivity. This also allows us to quantify the contribution of SWOT. The MGB model, developed within the large-scale hydrology research group of the University of Rio Grande do Sul (Brazil), is a physically based and distributed hydrological model, which was coupled to an externalized Ensemble Kalman Filter (EnKF) to give corrected estimates of the model state variables and parameters. HYFAA is run on the Niger river basin over a reanalysis period and its performance against a control ensemble simulation (without data assimilation) is assessed to quantify the impact of assimilating observations from the different observing systems. The results show that data assimilation leads to significant improvements of NRMSE and KGE of the simulated discharge, everywhere on the basin and regardless of the observation system considered. Moreover, it is shown that the correction of the hydrodynamic parameters helps to improve the performance of the assimilation, in particular when observations are dense in space, probably due to the concomitant correction of forcing biases. The assimilation of SWOT data combined with a selection method provides the best correction of the discharge on the river itself as well as on its tributaries, giving promising perspectives for the prediction of flash floods. We therefore discuss limits and prospects for application in the framework of Observing System Experiments (using real observations).

Risk management has reduced vulnerability to floods and droughts globally 1 , 2 , yet their impacts are still increasing 3 . An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data 4 , 5 . On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change 3 .

Surface water storage is an essential component of the hydrological cycle. Remote sensing offers valuable tools for monitoring both surface water extent from satellite images and water levels from radar altimetry. Combining both information, we were able to estimate the variations of surface water extent and storage in the Lower Mekong Basin from 2000 to 2020. Signatures of the extreme climatic events - floods from 2000 to 2002, of 2011, drought of 2015 clearly appear on both extent and storage. The mean amplitude of these variables shows a strong decrease when comparing the periods of 2000–2010 and 2011–2020. Between these two periods, a large reduction of the annual average number of days with the presence of floods can be observed in most of the Lower Mekong Basin, except around the Tonle Sap (Cambodia) and in some parts of the delta.

Land Surface Models are key tools to study the continental water cycle and can be used to better understand the main hydrological processes and their sensitivity to climate change. Yet, they are subject to potentially large errors, especially over ungauged basins where they cannot be calibrated or validated. The Surface Water and Ocean Topography (SWOT) satellite mission will provide unprecedented measurements of water elevation for all rivers wider than 100 m worldwide. Many recent studies focused on the assimilation of such observations into global hydrologic models, including ISBA-CTRIP developed at Météo-France, and they have demonstrated its added value. The SWOT mission will also provide discharge estimations derived from observed water elevation, river width and slope. The algorithms require ancillary data, such as the roughness coefficient, which needs to be estimated empirically at the global scale, potentially resulting in large errors in the discharge estimation. Yet, it is still unclear whether assimilating discharge instead of water level (or water level anomalies) would lead to better performances in terms of simulated discharge along the river network. In this study, we extended the assimilation of water elevation to river discharge into the CTRIP river routing model. We used the new version of the model at a 1/12 degree spatial resolution, which is more compatible to the resolution of the SWOT discharge product (reach length of about 10 km). The Congo river basin is chosen as a test case. SWOT-like river elevations and discharges are constructed by adding realistic errors to elevations and discharge provided by an independent river routing model, MGB. Also, a realistic satellite orbit is used to provide times and locations of available SWOT observations. The impact of observation errors on the assimilation is analysed, as well as the propagation of discharge corrections through the river network. Finally, model performances using discharge assimilation are compared to those using water level assimilation.

The future surface water and ocean topography (SWOT) satellite mission will provide images of surface water topography for inland water bodies and oceans. Over land, water surface elevation (WSE) will be retrieved at 10 cm accuracy for water bodies with areas > 250 m × 250 m and rivers with widths > 100 m, when averaging over 1 km 2 . Studies have shown that the Ka-band used by SWOT's main payload can be affected by aquatic and emergent riparian vegetation, which in turn could influence SWOT capacity to correctly observe water extent. The current study investigates effects of aquatic and emergent riparian vegetation on SWOT water extent and WSE detection capabilities through the use of NASA/JPL's SWOT simulator (HR). Data from the AirSWOT airborne campaign over Mamawi Lake (163 km 2 ) in the Peace-Athabasca Delta (PAD, Alberta, Canada), are used to establish a land cover classification and backscattering values for simulation inputs. Simulation results have shown that aquatic vegetation has a negligible effect on the SWOT signal. Yet, simulations showed that water extent misclassification can occur for water with emergent riparian vegetation in the specific case of wetlands surrounding lakes (i.e., small differences in backscattering values between surrounding land and water with emergent riparian vegetation). Simulations featuring the smallest difference between emergent riparian vegetation and land (1.3 dB) showed a 32–35% lake extent reduction from true extent. As expected, this study reveals that estimating water extent from SWOT in very wet environments with emergent vegetation can be challenging.