Surface water storage and fluxes in rivers, lakes, reservoirs and wetlands are currently poorly observed at the global scale, even though they represent major components of the water cycle and deeply impact human societies. In situ networks are heterogeneously distributed in space, and many river basins and most lakesespecially in the developing world and in sparsely populated regionsremain unmonitored. Satellite remote sensing has provided useful complementary observations, but no past or current satellite mission has yet been specifically designed to observe, at the global scale, surface water storage change and fluxes. This is the purpose of the planned Surface Water and Ocean Topography (SWOT) satellite mission. SWOT is a collaboration among the (U.S.) National Aeronautics and Space Administration (NASA), Centre National d"Études Spatiales (CNES, the French Spatial Agency), the Canadian Space Agency (CSA), and the United-Kingdom Space Agency (UKSA), with launch planned in late 2020. SWOT is both a continental hydrology and oceanography mission. However, only the hydrology capabilities of SWOT are discussed here.After a description of the SWOT mission requirements and measurement capabilities, we review the SWOT-related studies concerning land hydrology published to date. Beginning in 2007, studies demonstrated the benefits of SWOT data for river hydrology, both through discharge estimation directly from SWOT measurements and through assimilation of SWOT data into hydrodynamic and hydrology models. A smaller number of studies have also addressed methods for computation of lake and reservoir storage change or have quantified improvements expected from SWOT compared to current knowledge of lake water storage variability. We also briefly review other land hydrology capabilities of SWOT, including those related to transboundary river basins, human water withdrawals, and wetland environments. Finally, we discuss additional studies needed before and after the launch of the mission, along with perspectives on a potential successor to SWOT.

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With the upcoming SWOT satellite mission, which should provide spatially dense river surface elevation, width and slope observations globally, comes the opportunity to assimilate such data into hydrodynamic models, from the reach scale to the hydrographic network scale. Based on the HiVDI (Hierarchical Variational Discharge Inversion) modeling strategy (Larnier et al. [1]), this study tackles the forward and inverse modeling capabilities of distributed channel parameters and multiple inflows (in the 1D Saint-Venant model) from multisatellite observations of river surface. It is shown on synthetic cases that the estimation of both inflows and distributed channel parameters (bathymetry-friction) is achievable with a minimum spatial observability between inflows as long as their hydraulic signature is sampled. Next, a real case is studied: 871 km of the Negro river (Amazon basin) including complex multichannel reaches, 21 tributaries and backwater controls from major confluences. An effective modeling approach is proposed using (i) WS elevations from ENVISAT data and dense in situ GPS flow lines (Moreira [2]), (ii) average river top widths from optical imagery (Pekel et al. [3]), (iii) upstream and lateral flows from the MGB large-scale hydrological model (Paiva et al. [4]). The calibrated effective hydraulic model closely fits satellite altimetry observations and presents real like spatial variabilities; flood wave propagation and water surface observation frequential features are analyzed with identifiability maps following Brisset et al. [5]. Synthetic SWOT observations are generated from the simulated flowlines and allow to infer model parameters (436 effective bathymetry points, 17 friction

Hydrological extremes, in particular floods and droughts, impact all regions across planet Earth. They are mainly controlled by the temporal evolution of key hydrological variables like precipitation, evaporation, soil moisture, groundwater storage, surface water storage and discharge. Precise knowledge of the spatial and temporal evolution of these variables at the scale of river basins is essential to better understand and forecast floods and droughts. In this article, we present recent advances on the capability of Earth observation (EO) satellites to provide global monitoring of floods and droughts. The local scale monitoring of these events which is traditionally done using high-resolution optical or SAR (synthetic aperture radar) EO and in situ data will not be addressed. We discuss the applications of moderate-to low-spatial-resolution space-based observations, e.g., satellite gravimetry (GRACE and GRACE-FO), passive microwaves (i.e. SMOS) and satellite altimetry (i.e. the JASON series and the Copernicus Sentinel missions), with supporting examples. We examine the benefits and drawbacks of integrating these EO datasets to better monitor and understand the processes at work and eventually to help in early warning and management of flood and drought events. Their main advantage is their large monitoring scale that provides a "big picture" or synoptic view of the event that cannot be achieved with often sparse in situ measurements. Finally, we present upcoming and future EO missions related to this topic including the SWOT mission. Keywords Floods • Droughts • Large scale • Terrestrial water storage • GRACE • SMOS • Satellite altimetry • SWOT Water Storage on the Continents: General RemarksFreshwater represents less than 3% of the total amount of water on Earth. On land, freshwater is stored in various reservoirs such as ice caps, snow, glaciers, groundwater, soil moisture (in the unsaturated soil and root zone, i.e. in the upper few metres of the soil (e.g., Hillel 1998)) and surface water bodies (rivers, lakes, man-made reservoirs, wetlands and inundated areas) (Fig. 1). These different storage compartments are in direct interactions with the atmosphere. For example, in the tropical Pacific, long-term droughts and floods are under the influence of the El Niño-Southern Oscillation (ENSO) events (e.g., Ward et al. 2014; Fok et al. 2018 and references therein).

The Surface Water and Ocean Topography (SWOT) mission, to be launched in 2021, will provide water surface elevations, slopes, and river width measurements for rivers wider than 100 m. In this study, synthetic SWOT data are assimilated in a regional hydrometeorological model in order to improve the dynamics of continental waters over the Garonne catchment, one of the major French catchments. The aim of this paper is to demonstrate that the sequential assimilation of SWOT-like river depths allows the correction of river bed roughness coefficients and thus simulated river depths. An extended Kalman Filter is implemented and the data assimilation strategy was applied to four experiments of gradually increasing complexity regarding observation and model error over the 1995-2000 period. With respect to a "true" river state, assimilating river depths allows the proper retrieval of constant and spatially distributed roughness coefficients with a root mean square error of 1 m 1/3 s -1 , and the estimation of associated river depths. It was also shown that river depth differences can be assimilated, resulting in a higher root mean square error for roughness coefficients with respect to the true river state. The last study shows how one can take into account more realistic sources of SWOT error measurements, in particular the importance of the estimation of the tropospheric water content in the process.Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted:ferometry. This was called Water and Terrestrial Elevation Recovery (WATER), providing water elevation maps for two 50 km swaths. In 2007, the National Research Council recommended this new satellite mission to NASA, under the name Surface and Ocean Topography (SWOT), so as to measure both the ocean and land water surface topography.This new mission, conjointly developed by NASA, CNES (Centre National d'Etudes Spatiales), CSA/ASC (Canadian Space Agency/Agence Spatiale Canadienne) and UKSA (United-Kindom Space Agency), is planned for launch in 2021 and will observe the whole continental water-estuaries-ocean continuum. SWOT is designed to observe a large fraction of rivers and lakes globally and will provide observations of their seasonal cycles. SWOT will be the first altimetry mission to observe intermediate (or regional)-scale basins with a relatively high frequency, i.d. for temperate regions such as western Europe: 50 000 -200 000 km 2 , providing a quasi-global coverage of between 78°S and 78°N in 21 days, the duration of a full orbital cycle (Pavelsky et al. 2014). Water level measurement errors are expected to be 10cm aggregating pixels over a 1 km 2 water area (e.g., a 10-km reach length for a 100-m-wide river) (Rodríguez 2016). This offers a new opportunity for the linking of open water surface elevations, land surface processes, and meteorology more closely on this scale. Thanks to the SWOT mission, the evolution in terms of the time of the surface water storage will be revealed. This information will allow a better understanding of the term Q both spatially...

Abstract. Land surface models combined with river routing models are widely used to study the continental part of the water cycle. They give global estimates of water flows and storages but not without non-negligible uncertainties; among which inexact input parameters have a significant part. The incoming Surface Water and Ocean Topography (SWOT) satellite mission, with a launch schedule for 2021, will be dedicated to measure water surface elevations, widths and surface slopes of rivers larger than 100 meters at global scale. SWOT will provide a significant amount of new data for river hydrology and they could be combined, through data assimilation, to global-scale models in order to correct their input parameters and reduce their associated uncertainty. The objective of this study is to present a data assimilation platform based on the asynchronous ensemble Kalman filter (AEnKF) that assimilates synthetical SWOT observations of water elevations to correct the input parameters of a large scale hydrologic model over a 21-day time window. The study is applied on the ISBA-CTRIP model over the Amazon basin and focuses on correcting the spatial distribution of the river Manning coefficients. The data assimilation algorithm, tested through a set of Observing System Simulation Experiments (OSSE), is able to retrieve the true value of the Manning coefficients within one assimilation cycle most of the time and shows perspectives in tracking the Manning coefficient temporal variations. Ultimately, in order to deal with potential bias between the observed and the model bathymetry, the assimilation of water elevation anomalies was also tested and showed promising results.

The Surface Water and Ocean Topography (SWOT) space mission will map surface area and water level changes in lakes at the global scale. Such new data are of great interest to better understand and model lake dynamics as well as to improve water management. In this study, we used the large-scale SWOT simulator developed at the French Space National Center (CNES) to estimate the expected measurement errors of the water level of different water bodies in France. These water bodies include five large reservoirs of the Seine River and numerous small gravel pits located in the Seine alluvial plain of La Bassée upstream of the city of Paris. The results show that the SWOT mission will allow to observe water levels with a precision of a few tens of centimeters (10 cm for the largest water reservoir (Orient), 23 km2), even for the small gravel pits of size of a few hectares (standard deviation error lower than 0.25 m for water bodies larger than 6 ha). The benefit of the temporal sampling for water level monitoring is also highlighted on time series of pseudo-observations based on real measurements perturbed with the simulated noise errors. Then, the added value of these future data for the simulation of lake energy budgets is shown using the FLake lake model through sensitivity experiments. Results show that the SWOT data will help to model the surface temperature of the studied water bodies with a precision better than 0.5 K and the evaporation with an accuracy better than 0.2 mm/day. These large improvements compared to the errors obtained when a constant water level is prescribed (1.2 K and 0.6 mm/day) demonstrate the potential of SWOT for monitoring the lake energy budgets at global scale in addition to the other foreseen applications in operational reservoir management.

<p>With the upcoming SWOT satellite mission, which should provide spatially dense river surface elevations, widths and slopes observations globally, comes the need to pertinently use such data into hydrodynamic models, from the reach to hydrographic network scales. Based on the HiVDI (Hierarchical Variational Discharge Inversion) modeling strategy ([1,2], DassFlow software<sup>1</sup>), this work tackles the forward and inverse modeling capabilities of distributed channel parameters and inflows (in the 1D Saint-Venant model) from multisatellite observations of river surface. Several synthetic cases are designed to study fluvial and torrential flows signatures and assess the inference capabilities of model parameters (inflows, bathymetry, friction) given different observation patterns. Accurate inferences of both inflows and distributed channel parameters (bathymetry-friction) is achievable even with a minimum spatial observability between inflows. A sensitivity analysis of the inferences to prior hydraulic parameter values and to regularization parameters is performed. Next a real case is studied: 871km of the Negro river (Amazon basin) including complex multichannel reaches, 21 tributaries and backwater controls from major confluences. An effective modeling approach is proposed using (i) WS elevations from ENVISAT observations and dense in situ GPS flow lines, (ii) average river top widths from optical imagery, (iii) upstream and lateral flows from the MGB large-scale hydrological model [3]. The calibrated effective hydraulic model closely fits satellite altimetry observations of WS signatures and contains real-like spatial variabilities and flood wave propagations (frequential features analyzed with identifiability maps [2]). Synthetic SWOT observations are generated from the simulated flowlines and the identifiability of model parameters (579 bathymetry points, 17 friction patches and 22 upstream and lateral hydrographs) is tested using the HiVDI computational inverse method and given hydraulically coherent prior guesses and regularization parameter values. Inferences of channel parameters carried out on this fine hydraulic model applied at large scale give satisfying results considering the challenging inverse problems solved globally in space and time, even with noisy SWOT data. Inferences of spatially distributed temporal parameters (lateral inflows) give satisfying results as well, with even small scale hydrograph variations being infered accurately.</p><div> <p>This study brings insights in:</p> </div><ol><li> <p>the hydraulic visibility of multiple inflows hydrographs signature at large scale with SWOT;</p> </li> <li> <p>the simultaneous identifiability of spatially distributed channel parameters and inflows by assimilation of satellite altimetry data;</p> </li> <li> <p>the need to further taylor and scale hydrodynamic models and assimilation methods to improve potential information feedbacks to hydrological modules in integrated chains.</p> </li> </ol><div> <p><strong>References:</strong></p> </div><p>[1] Larnier, Monnier, Garambois, Verley. (2019) River discharge and bathymetry estimations from SWOT altimetry measurements.</p><p>[2] Brisset, Monnier, Garambois, Roux. (2018) On the assimilation of altimetric data in 1d Saint-Venant river flow models. AWR, doi: 10.1016/j.advwatres.2018.06.004.</p><p>[3] Paiva, Buarque, Collischonn, et al. Large-scale hydrologic and hydrodynamic modeling of the amazon river basin. WRR, doi: 10.1002/wrcr.20067.</p><p> </p><p> </p>