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The allocation of points in a river network to pixels of a coarse-resolution hydrological modelling grid is a well-known issue, especially for hydrologists who use measurements at gauging stations to calibrate and validate distributed hydrological models. To address this issue, the traditional approach involves examining grid cells surrounding the considered river point and selecting the best candidate, based on distance and upstream drainage area as decision criteria. However, recent studies have suggested that focusing on basin boundaries rather than basin areas could prevent many allocation errors, even though the performance gain is rarely assessed. This paper compares different allocation methods and examines their relative performances. Three methods representing various families of methods have been designed: area-based, topology-based and contour-based methods. These methods are implemented to allocate 2580 river points to a 1 km hydrological modelling grid. These points are distributed along the entire hydrographic network of the French southeastern Mediterranean region, covering upstream drainage areas ranging from 5 to 3000 km 2. The results indicate that the differences between the methods can be significant, especially for small upstream catchment areas. [ABSTRACT FROM AUTHOR]

To date, long short-term memory (LSTM) networks have been successfully applied to a key problem in hydrology: the prediction of runoff. Unlike traditional conceptual models, LSTM models are built on concepts that avoid the need for our knowledge of hydrology to be formally encoded into the model. The question, then, is how we can still make use of our domain knowledge and traditional practices, not to build the LSTM models themselves, as we do for conceptual models, but to use them more effectively. In the present paper, we adopt this approach, investigating how we can use information concerning the hydrologic characteristics of catchments for LSTM runoff models. In this first application of LSTM in a French context, we use 361 gauged catchments with very diverse hydrologic conditions from across France. The catchments have long time series of at least 30 years. Our main directions for investigation include (a) the relationship between LSTM performance and the length of the LSTM input sequence within different hydrologic regimes, (b) the importance of the hydrologic homogeneity of catchments when training LSTMs on a group of catchments, and (c) the interconnected influence of the local tuning of the two important LSTM hyperparameters, namely the length of the input sequence and the hidden unit size, on the performance of group-trained LSTMs. We present a classification built on three indices taken from the runoff, precipitation, and temperature regimes. We use this classification as our measure of homogeneity: catchments within the same regime are assumed to be hydrologically homogeneous. We train LSTMs on individual catchments (local-level training), on catchments within the same regime (regime-level training), and on the entire sample (national-level training). We benchmark local LSTMs using the GR4J conceptual model, which is able to represent the water gains/losses in a catchment. We show that LSTM performance has the highest sensitivity to the length of the input sequence in the Uniform and Nival regimes, where the dominant hydrologic process of the regime has clear long-term dynamics; thus, long input sequences should be chosen in these cases. In other regimes, this level of sensitivity is not found. Moreover, in some regimes, almost no sensitivity is observed. Therefore, the size of the input sequence in these regimes does not need to be large. Overall, our homogeneous regime-level training slightly outperforms our heterogeneous national-level training. This shows that the same level of data adequacy with respect to the complexity of representation(s) to be learned is achieved in both levels of training. We do not, however, exclude a potential role of the regime-informed property of our national LSTMs, which use previous classification variables as static attributes. Last but not least, we demonstrate that the local selection of the two important LSTM hyperparameters (the length of the input sequence and the hidden unit size) combined with national-level training can lead to the best runoff prediction performance. [ABSTRACT FROM AUTHOR]

Reducing uncertainty and improving robustness and spatio-temporal extrapolation capabilities remain key challenges in hydrological modeling especially for flood forecasting over large areas. Parsimonious model structures and effective optimization strategies are crucially needed to tackle the difficult issue of distributed hydrological model calibration from sparse integrative discharge data, that is in general high dimensional inverse problems. This contribution presents the first evaluation of Variational Data Assimilation (VDA), very well suited to this context but still rarely employed in hydrology because of high technicality, and successfully applied here to the spatially distributed calibration of a newly taylored grid-based parsimonious model structure and corresponding adjoint, over a large sample. It is based on the Variational Data Assimilation (VDA) framework of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, underlying the French national flash flood forecasting system Vigicrues Flash. It proposes an upgraded distributed hourly rainfall-runoff model structure employing GR-based operators, including a non-conservative flux, and its adjoint obtained by automatic differentiation for VDA. The performances of the approach are assessed over annual, seasonal and floods timescales via standard performance metrics and in spatio-temporal validation. The gain of using the proposed non-conservative 6-parameters model structure is highlighted in terms of performance and robustness, compared to a simpler 3-parameters structure. Spatially distributed calibrations lead to a significant gain in terms of reaching high performances in calibration and temporal validation on the catchments sample, with median efficiencies respectively of NSE = 0.88 (resp. 0.85) and NSE = 0.8 (resp. 0.79) over the total time window on period p 2 (resp. p 1). Simulated signatures in temporal validation over 1443 (resp. 1522) flood events on period p 2 (resp. p 1) are quite good with median flood (NSE ; KGE) of (0.63; 0.59) (resp. (0.55; 0.53)). Spatio-temporal validations, i.e. on pseudo ungauged cases, lead to encouraging performances also. Moreover, the influence of certain catchment characteristics on model performance and parametric sensitivity is analyzed. Best performances are obtained for Oceanic and Mediterranean basins whereas it performs less well over Uniform basins with significant influence of multi-frequency hydrogeological processes. Interestingly, regional sensitivity analysis revealed that the non conservative water exchange parameter and the production parameter, impacting the simulated runoff amount, are the most sensitive parameters along with the routing parameter especially for faster responding catchments. This study is a first step in the construction of a flexibe and versatile multi-model and optimization framework with hydbrid methods for regional hydrological modeling with multi-source data assimilation. [ABSTRACT FROM AUTHOR]

Calibration of a conceptual distributed model is challenging due to a number of reasons, which include fundamental (model adequacy and identifiability) and algorithmic (e.g., local search vs. global search) issues. The aim of the presented study is to investigate the potential of the variational approach for calibrating a simple continuous hydrological model (GRD; Génie Rural distributed involved in several flash flood modeling applications. This model is defined on a rectangular 1 km2 resolution grid, with three parameters being associated with each cell. The Gardon d'Anduze watershed (543 km2) is chosen as the study benchmark. For this watershed, the discharge observations at five gauging stations, gridded rainfall and potential-evapotranspiration estimates are continuously available for the 2007–2018 period at an hourly time step. In the variational approach one looks for the optimal solution by minimizing the standard quadratic cost function, which penalizes the misfit between the observed and predicted values, under some additional a priori constraints. The cost function gradient is efficiently computed using the adjoint model. In numerical experiments, the benefits of using the distributed against the uniform calibration are measured in terms of the model predictive performance, in temporal, spatial and spatiotemporal validation, both globally and for particular flood events. Overall, distributed calibration shows encouraging results, providing better model predictions and relevant spatial distribution of some parameters. The numerical stability analysis has been performed to understand the impact of different factors on the calibration quality. This analysis indicates the possible directions for future developments, which may include considering a non-Gaussian likelihood and upgrading the model structure. [ABSTRACT FROM AUTHOR]

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Summary: A method to estimate antecedent soil moisture conditions is proposed to improve the accuracy of flash flood forecasts at ungauged locations. The method combines two indexes: a ‘climatic’ temporal index calculated in each cell using an uncalibrated soil moisture accounting (SMA) scheme and a spatial ‘statistical’ index giving the “average saturation state” usually encountered before a flood. The proposed method was carried out on two different models: an event-based distributed model, and a REGRESSION model. Simulation results were analysed for 562 individual events, issued from 160 catchments located in the South of France. The presented method improved the efficiency of both models. This indicates that the both proposed indexes give relevant information for estimating antecedent soil moisture conditions at ungauged locations. Furthermore, a contingency statistical analysis based on a 2-year flood threshold showed that alerts were better forecasted by the distributed model. These encouraging results suggest that further efforts should be made for the development of the method and its operational application. [ABSTRACT FROM AUTHOR]

The French national service in charge of flood forecasting (SCHAPI) has implemented, in collaboration with Irstea and Météo-France, a national flash flood warning system, Vigicrues Flash, to provide timely warnings for small-to-medium ungauged basins. Operational since March 2017 for about 10,000 municipalities, this system complements flood warnings produced by the Vigicrues procedure for French monitored rivers. Vigicrues Flash is based on a discharge-threshold flood warning method called AIGA. In the current operational version, a simplified hourly distributed rainfall-runoff model ingests radar-gauge Quantitative Precipitation Estimate (QPE) grids from Météo-France at a 1-km² resolution to produce real-time peak discharge estimates on any river cell. This hourly event-based distributed model is coupled to a continuous daily rainfall-runoff model, which provides baseflow and a soil moisture index (for each 1-km² pixel) at the beginning of the hourly simulation. Every 15 minutes, the discharges are compared to regionalized flood frequency estimates, which were derived from long-term streamflow simulations. The automated warning system determines rivers exceeding the high flood and very high flood thresholds (associated to years of return periods), as well as the associated municipalities that might be impacted. Flood hazard maps are published on a web platform and warning messages are automatically sent to registered users to help them better mitigate flood risk impacts.To better anticipate flash flood events and extend the coverage of this automated service, the warning system is being enhanced to include a single fully distributed hydrologic model, run at sub-hourly time step. Several calibration and regionalization methods are being tested to better account for basins spatial heterogeneities while maintaining consistency across spatial scales. Evaluation is carried out for about 2000 French basins on the 2008-2018 period to show improvements in terms of flash flood event detection and effective warning lead time. Other enhancements include integrating high-resolution precipitation nowcasts available on a 6-hour forecast horizon, accounting for and reducing hydrometeorological uncertainties via ensemble forecasting and data assimilation, and incorporating a vulnerability assessment component to provide risk-based decision-relevant warnings. [ABSTRACT FROM AUTHOR]