Geodetic altimeters provide unique observations of the river surface longitudinal profile due to their long repeat periods and densely spaced ground tracks. This information is valuable for calibrating hydraulic model parameters, and thus for producing reliable simulations of water level for flood forecasting and river management, particularly in poorly instrumented catchments. In this study, we present an efficient calibration approach for hydraulic models based on a steady-state hydraulic solver and CryoSat-2 observations. In order to ensure that only coherent forcing/observation pairs are considered in the calibration, we first propose an outlier filtering approach for CryoSat-2 observations in data-scarce regions using simulated runoff produced by a hydrologic model. In the hydraulic calibration, a steady-state solver computes the WSE profile along the river for selected discharges corresponding to the days of CryoSat-2 overpass. In synthetic calibration experiments, the global search algorithm generally recovers the true parameter values in portions of the river where observations are available, illustrating the benefit of dense spatial sampling from geodetic altimetry. The most sensitive parameters are the bed elevations. In calibration experiments with real CryoSat-2 data, validation performance against both Sentinel-3 WSE and in-situ records is similar to previous studies, with RMSD ranging from 0.43 to 1.14 m against Sentinel-3 and 0.60 to 0.73 against in-situ WSE observations. Performance remains similar when transferring parameters to a one-dimensional hydrodynamic model. Because the approach is computationally efficient, model parameters can be inverted at high spatial resolution to fully exploit the information contained in geodetic CryoSat-2 altimetry.

Perfect foresight hydroeconomic optimization models are tools to evaluate impacts of water infrastructure investments and policies considering complex system interlinkages. However, when assuming perfect foresight, management decisions are found assuming perfect knowledge of climate and runoff, which might bias the economic evaluation of investments and policies. We investigate the impacts of assuming perfect foresight by using Model Predictive Control (MPC) as an alternative. We apply MPC in WHAT-IF, a hydroeconomic optimization model, for two study cases: a synthetic setup inspired by the Nile River, and a large-scale investment problem on the Zambezi River Basin considering the water-energy-food nexus. We validate the MPC framework against Stochastic Dynamic Programming and observe more realistic modelled reservoir operation compared to perfect foresight, especially regarding anticipation of spills and droughts. We find that the impact of perfect foresight on total system benefits remains small (<2%). However, when evaluating investments and policies using with-without analysis, perfect foresight is found to overestimate or underestimate values of investments by more than 20% in some scenarios. As the importance of different effects varies between scenarios, it is difficult to find general, case-independent guidelines predicting whether perfect foresight is a reasonable assumption. However, we find that the uncertainty linked to climate change generally has more significant impacts than the assumption of perfect foresight. Hence, we recommend MPC to perform the economic evaluation of investments and policies, however, under high uncertainty of future climate, increased computational costs of MPC must be traded off against computational costs of exhaustive scenario exploration.

Satellite altimetry has become an important data source for discharge estimation. Stage-fall-discharge (SFQ) rating curves are necessary for discharge estimation at backwater-affected river reaches because of non-uniqueness of stage-discharge (SQ) relationships. We used a hydrodynamic model to simulate stage, water surface fall, and discharge at six backwater-affected reaches and generated SQ/SFQ rating curves everywhere along the river. The simulated SQ rating curves showed that the uncertainties of the estimated discharge were on the order of 150%. The uncertainties were reduced to less than 35% when using SFQ rating curves in rivers with significant falls. We used ICESat-2 laser altimetry, which synchronously measures stage and fall, to estimate discharge with the simulated SFQ rating curves in the Missouri River. The study highlights the importance of backwater effects for discharge estimation, particularly for VS located upstream of major tributary junctions, and showcases the possibilities of ICESat-2 laser altimetry for EO-based discharge estimation.

Introduction HYDROCOASTAL is a two year project funded by ESA, with the objective to maximise exploitation of SAR and SARin altimeter measurements in the coastal zone and inland waters, by evaluating and implementing new approaches to process SAR and SARin data from CryoSat-2, and SAR altimeter data from Sentinel-3A and Sentinel-3B. Optical data from Sentinel-2 MSI and Sentinel-3 OLCI instruments will also be used in generating River Discharge products. New SAR and SARin processing algorithms for the coastal zone and inland waters will be developed and implemented and evaluated through an initial Test Data Set for selected regions. From the results of this evaluation a processing scheme will be implemented to generate global coastal zone and river discharge data sets. A series of case studies will assess these products in terms of their scientific impacts. All the produced data sets will be available on request to external researchers, and full descriptions of the processing algorithms will be provided Objectives The scientific objectives of HYDROCOASTAL are to enhance our understanding of interactions between the inland water and coastal zone, between the coastal zone and the open ocean, and the small scale processes that govern these interactions. Also the project aims to improve our capability to characterize the variation at different time scales of inland water storage, exchanges with the ocean and the impact on regional sea-level changes The technical objectives are to develop and evaluate new SAR and SARin altimetry processing techniques in support of the scientific objectives, including stack processing, and filtering, and retracking. Also an improved Wet Troposphere Correction will be developed and evaluated. Presentation The presentation will describe the different SAR altimeter processing algorithms that are being evaluated in the first phase of the project, and present results from the evaluation of the initial test data set. It will focus particularly on the performance of the new algorithms over inland water.

Image cross-correlation techniques, such as Particle Image Velocimetry (PIV), can estimate water surface velocity (vsurf) of streams. However, discharge estimation requires water depth and the depth-averaged vertical velocity (Um). The variability of the ratio Um/vsurf introduces large errors in discharge estimates. We demonstrate a method to estimate vsurf from Unmanned Aerial Systems (UASs) with PIV technique. This method does not require any Ground Control Point (GCP): the conversion of velocities from pixels per frame into meters per time is performed by informing a camera pinhole model; the range from the pinhole to the water surface is measured by the drone-board radar. For approximately uniform flow, Um is a function of the Gauckler-Manning-Strickler coefficient (Ks) and vsurf. We implement an approach that can be used to jointly estimate Ks and discharge by informing a system of 2 unknowns (Ks and discharge) and 2 non-linear equations: i) Manning’s equation ii) mean-section method for computing discharge from Um. This approach relies on bathymetry, acquired in-situ a-priori, and on UAS-borne vsurf and water surface slope measurements. Our joint (discharge and Ks) estimation approach is an alternative to the widely used approach than relies on estimating Um as 0.85vsurf. It was extensively investigated in 27 case studies, in different streams with different hydraulic conditions. Discharge estimated with the joint estimation approach showed a mean absolute error in discharge of 19.1% compared to in-situ discharge measurements. Ks estimates showed a mean absolute error of 3.2 m^{1/3} /s compared to in-situ measurements.