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Estimating discharges Q ( x ,  t ) from altimetric measurements only, for ungauged rivers (in particular, those with unknown bathymetry b(x)), is an ill-posed inverse problem. We develop here an algorithm to estimate Q ( x ,  t ) without prior flow information other than global open datasets. Additionally, the ill-posedness feature of this inverse problem is re-investigated. Inversions based on a Variational Data Assimilation (VDA) approach enable accurate estimation of spatio-temporal variations of the discharge, but with a bias scaling the overall estimate. This key issue, which was already highlighted in our previous studies, is partly solved by considering additional hydrological information (the drainage area, A ( k m 2 ) ) combined with a Machine Learning (ML) technique. Purely data-driven estimations obtained from an Artificial Neural Network (ANN) provide a reasonably good estimation at a large scale ( ≈ 10 3 m). This first estimation is then employed to define the first guess of an iterative VDA algorithm. The latter relies on the Saint-Venant flow model and aims to compute the complete unknowns (discharge Q ( x ,  t ), bathymetry b ( x ), friction coefficient K ( x ,  t )) at a fine scale (approximately 10 2 m). The resulting complete inversion algorithm is called the H2iVDI algorithm for \"Hybrid Hierarchical Variational Discharge Inference\". Numerical experiments have been analyzed for 29 heterogeneous worldwide river portions. The obtained estimations present an overall bias (less than 30% for rivers with similar characteristics than those used for calibration) smaller than previous results, with accurate spatio-temporal variations of the flow. After a learning period of the observed rivers (e.g. one year), the algorithm provides two complementary estimators: a dynamic flow model enabling estimations at a fine scale and spatio-temporal extrapolations, and a low complexity estimator (based on a dedicated algebraic low Froude flow model). This last estimator provides reasonably accurate estimations (less than 30% for considered rivers) at a large scale from newly acquired WS measurements in real-time, therefore making it a potentially operational algorithm.

The unprecedented hydraulic visibility of rivers surfaces deformation with SWOT satellite offers tremendous information for improving hydrological‐hydraulic models and discharge estimations for rivers worldwide. However, estimating the uncertain or unknown parameters of hydraulic models, such as inflow discharges, bathymetry, and friction parameters, poses a high‐dimensional inverse problem, which is ill‐posed if based solely on altimetry observations. To address this, we couple the hydraulic model with a semi‐distributed hydrological model, to constrain the ill‐posed inverse problem with sufficiently accurate initial estimates of inflows at the network upstreams. A robust variational data assimilation of water surface elevation (WSE) data into a 1D Saint‐Venant river network model, enables the inference of inflow hydrographs, effective bathymetry, and spatially distributed friction at network scale. The method is demonstrated on the large, complex, and poorly gauged Maroni basin in French Guiana. The pre‐processing chain enables (a) building an effective hydraulic model geometry from drifting ICESat‐2 WSE altimetry and Sentinel‐1 width; (b) filtering noisy SWOT Level 2 WSE data before assimilation. A systematic improvement is achieved in fitting the assimilated WSE (85% cost improvement), and in validating discharge at 5 gauges within the network. For assimilation of SWOT data alone, 70% of data‐model fit is in [−0.25;0.25m] $[-0.25;\\,0.25\\,\\mathrm{m}]$ and the discharge NRMSE ranges between 0.05 and 0.18 (18%–71% improvement from prior). The high density of SWOT WSE enables the inferrence of detailed spatial variability in channel bottom elevation and friction, and inflows timeseries. The approach is transferable to other rivers networks worldwide.

This paper presents a detailed analysis of 10 flash flood events in the Mediterranean region using the distributed hydrological model MARINE. Characterizing catchment response during flash flood events may provide new and valuable insight into the dynamics involved for extreme catchment response and their dependency on physiographic properties and flood severity. The main objective of this study is to analyze flash-flood-dedicated hydrologic model sensitivity with a new approach in hydrology, allowing model outputs variance decomposition for temporal patterns of parameter sensitivity analysis. Such approaches enable ranking of uncertainty sources for nonlinear and nonmonotonic mappings with a low computational cost. Hydrologic model and sensitivity analysis are used as learning tools on a large flash flood dataset. With Nash performances above 0.73 on average for this extended set of 10 validation events, the five sensitive parameters of MARINE process-oriented distributed model are analyzed. This contribution shows that soil depth explains more than 80% of model output variance when most hydrographs are peaking. Moreover, the lateral subsurface transfer is responsible for 80% of model variance for some catchment-flood events' hydrographs during slow-declining limbs. The unexplained variance of model output representing interactions between parameters reveals to be very low during modeled flood peaks and informs that model-parsimonious parameterization is appropriate to tackle the problem of flash floods. Interactions observed after model initialization or rainfall intensity peaks incite to improve water partition representation between flow components and initialization itself. This paper gives a practical framework for application of this method to other models, landscapes and climatic conditions, potentially helping to improve processes understanding and representation.

Flash floods are natural phenomena with environmental, social and economic impacts. To date, few numerical models are able to simulate hydrological processes at catchment scale at a reasonable time scale to describe flash events with accurate details. Considering a ~810 km2 Mediterranean river coastal basin (southwestern France) as a study case, the objective of the present study was to assess the ability of the sub-daily module of the lumped Soil and Water Assessment Tool (SWAT) to simulate discharge (1) time-continuously, by testing two sub-basin delineation schemes, two catchment sizes, and two output time-steps; and (2) at flood time-scale, by comparing the performances of SWAT to the performances of the event-based fully distributed MARINE model when simulating flash flood events. We showed that there was no benefit of decreasing the size of the minimum drainage area (e.g., from ~15 km2 down to ~1 km2) when delineating sub-basins in SWAT. We also showed that both the MARINE and SWAT models were equally able to reproduce peak discharge, flood timing and volume, and that they were both limited by rainfall and soil data. Hence, the SWAT model appears to be a reliable modelling tool to predict discharge over long periods of time in large flash-flood-prone basins.