The knowledge of river discharge is crucial for both water resources management activities and for flood risk mitigation. In situ river gauge stations are normally used to monitor river discharge but they suffer from many limitations such as low station density, incomplete temporal coverage as well as delays in data access. Therefore, the development of methods to estimate river discharge from satellite data is strategic especially over data scarce regions where the decline of availability in situ observation data seems inexorable.

In the last decade, ESA has funded different initiatives in the field of discharge estimation, such as the SaTellite based Runoff Evaluation And Mapping and River Discharge Estimation (STREAMRIDE) project, which proposes the combination of two innovative and complementary approaches, STREAM and RIDESAT, for estimating river discharge.
The innovative aspect of the two approaches is an almost exclusive use of satellite data. In particular, precipitation, soil moisture and terrestrial water storage observations are used within a simple and conceptual parsimonious approach (STREAM) to estimate runoff, whereas altimeter and Near InfraRed (NIR) sensors are jointly exploited to derive river discharge within RIDESAT. By modelling different processes that acts at the basin or at local scale, the combination of STREAM and RIDESAT are able to provide less than 3-day temporal resolution river discharge estimates in many large rivers of the world (e.g., Mississippi, Amazon, Danube, Po), where the single approach fails. Indeed, even if both the approaches demonstrated high capability to estimate accurate river discharge at multiple cross sections they are not optimal under certain conditions such as in presence of densely vegetated and mountainous areas or in non-natural basins with high anthropogenic impact (i.e., in basin where the flow is regulated by the presence of dams, reservoirs or floodplains along the river; or in highly irrigated areas).

Here, we present some new advancements of both STREAM and RIDESAT approaches which help to overcome the limitations encountered. In particular, specific modules (e.g., reservoir or irrigation modules for STREAM approach) as well as algorithm retrieval improvements (e.g., to take into account the sediment and the vegetation for RIDESAT algorithm) were implemented. Furthermore, in order to exploit the complementarity of the two approaches, the two river discharge estimates were also integrated within a simple data integration framework and evaluated over sites located on the Amazon and Mississippi river basins. Results demonstrated the added-value of a complementary river discharge estimate with respect to a stand-alone estimate.

Over the last few years, satellite radar altimetry measurements have become more and more numerous over inland waters, thanks to improvements in the tracking function as well as enhanced measurement modes (from pulse-limited low resolution mode to high resolution synthetic aperture radar). This higher quality and abundance of measurements also raises the question of their processing: from historical retracking algorithms (e.g. the Offset Centre Of Gravity - OCOG retracking) to most recent innovative ones (e.g. Fully-Focused SAR). In particular, it is of common knowledge in the hydrology community that current ground segments using the OCOG algorithm provide, on frequent occasions, biased water surface height estimates and is not reliable at global scale. The need for more reliable and robust processing methods is therefore one of the biggest challenges for radar altimetry over land.
The main challenge is that contrary to ocean altimetry, the radar signal over inland waters is highly variable both in space and time, as it depends on the nature and size of the water body observed (lakes, rivers, flood plains, etc.) as well as on surface conditions.
In this study, we focus specifically on rivers where various types of waveforms can be acquired: sinc²-like peak (the most frequent ones), asymmetric peak, multiple peaks, distorted Brown-like waveform. We address the representativeness of the signal’s specularity, as it has been documented in the literature that the radar backscattered signal over rivers is often highly specular and can be modeled as a squared cardinal sine function. We use two parallel approaches: simulation of radar signals over a specular surface, and analysis of real data acquired by current Sentinel-3 and Sentinel-6 Ku-Band missions over rivers.
In the theoretical approach, we use simulations of various specular surfaces and analyze both the amplitude and phase behaviors. It is interesting to analyze the relative impact of the observing system configuration (e.g. radar bandwidth, altitude, sampling) and the nature of the surface (e.g. geometry of the observed scene, backscatter coefficient) on the simulated radar signal.
In the data analysis approach, we use a unique dataset of one hundred rivers worldwide to perform a statistical analysis of the specular nature of the signal and other characteristics in function of the scene configuration. River width is one but not the only parameter impacting the measured signal.
With this study, we aim at understanding the most significant factors controlling the measured radar signal over rivers in order to build a robust and universal processing algorithm capable of providing reliable water surface height estimates to inland waters users.
In the perspective of the Surface Water and Ocean Topography (SWOT) mission, nadir altimetry more than ever stands as an important asset for calibration and validation of this mission as well as for the design of future altimetry missions such as the Copernicus Sentinel-3 Next Generation: Topography mission, which will necessarily address the question of processing performance over inland waters.

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 regardless of the observing system, data assimilation generally improves discharge estimation on the basin. We, therefore, discuss limits and perspectives for application in the framework of Observing System Experiments (using real observations).

Satellite altimetry products provide dense observations of water surface elevation (WSE) that can inform hydrodynamic modeling and stage-discharge rating curves estimation. In the present study, we used multi-mission satellite altimetry, e.g., ICESat-1/2, Jason-2/3, and Sentinel-3A/B, to calibrate and validate hydrodynamic models for a 1000-km reach of the Yellow River, the second-longest river in China. We first calibrated spatially distributed Manning-Strickler coefficients by using a steady-state hydraulic solver and ICESat-1/2 altimetry. Considering that river ice jams in the cold season increase channel wetted perimeter, reduce channel hydraulic radius, and increase effective channel roughness, resulting in increased river stages in some river segments, two different scenarios were assumed: river roughness parameters were stable in time (S1); river roughness was varying between the cold and warm seasons, and thus two sets of roughness parameters were calibrated separately (S2). The calibrated sets of roughness parameters were further applied to configure a detailed one-dimensional hydrodynamic model, i.e., MIKE HYDRO River, to simulate time-continuous WSE along the river course. The simulated WSE was validated by satellite altimetry datasets monitored at 24 virtual stations by Jason-2/3 and Sentinel-3 A/B. The calibrated hydrodynamic model was used to determine stage-discharge rating curves for the virtual stations. The discharge of the virtual stations was estimated and validated by comparing with gauging data. The quality and robustness of the rating curves were analyzed. Several factors determine the quality and uniqueness of rating curves (proximity to tributaries, hydraulic hysteresis), and hydraulics at VS locations should be investigated in detail before rating curves are combined with satellite altimetry to estimate discharge.