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Premier risque naturel en France, les inondations peuvent entraîner des pertes humaines et provoquer d’importants dégâts matériels. Parmi elles, les crues soudaines causées par des pluies très fortes enregistrées en très peu de temps restent encore difficiles à anticiper et à gérer. La méthode AIGA présentée dans ce article est un outil destiné à mieux identifier ces pluies intenses et anticiper ces crues rapides, notamment quand elles se manifestent au sein de petits bassins versants non équipés de station de mesure. Après une présentation des principes de la méthode, les auteurs s'intéressent à sa mise en oeuvre dans des dispositifs opérationnels de surveillance et d'anticipation des aléas hydrométéorologiques.

Estimating spatially distributed hydrological parameters in ungauged catchments poses a challenging regionalization problem and requires imposing spatial constraints given the sparsity of discharge data. A possible approach is to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters. This paper introduces a Hybrid Data Assimilation and Parameter Regionalization (HDA‐PR) approach incorporating learnable regionalization mappings, based on either multi‐linear regression or artificial neural networks (ANNs), into a differentiable hydrological model. This approach demonstrates how two differentiable codes can be linked and their gradients chained, enabling the exploitation of heterogeneous data sets across extensive spatio‐temporal computational domains within a high‐dimensional regionalization context, using accurate adjoint‐based gradients. The inverse problem is tackled with a multi‐gauge calibration cost function accounting for information from multiple observation sites. HDA‐PR was tested on high‐resolution, hourly and kilometric regional modeling of 126 flash‐flood‐prone catchments in the French Mediterranean region. The results highlight a strong regionalization performance of HDA‐PR especially in the most challenging upstream‐to‐downstream extrapolation scenario with ANN, achieving median Nash‐Sutcliffe efficiency (NSE) scores from 0.6 to 0.71 for spatial, temporal, spatio‐temporal validations, and improving NSE by up to 30% on average compared to the baseline model calibrated with lumped parameters. Multiple evaluation metrics based on flood‐oriented hydrological signatures also indicate that the use of an ANN leads to better performances than a multi‐linear regression in a validation context. ANN enables to learn a non‐linear descriptors‐to‐parameters mapping which provides better model controllability than a linear mapping for complex calibration cases. Key Points: Novel approach for regional calibration of a distributed hydrologic model using learnable and non‐linear descriptors‐to‐parameters mappingsOriginal combination of numerical adjoint model and neural network Jacobian: accurate gradients enable high‐dimensional optimizationExtensive case study in flash‐flood‐prone Mediterranean region shows effective regionalization of high‐resolution model with neural network [ABSTRACT FROM AUTHOR]

Occurring at small temporal and spatial scales, flash floods (FF) can cause severe economic damages and human losses. To better anticipate such events and mitigate their impacts, the French Ministry in charge of Ecology has decided to set up a national FF warning system over the French territory. This automated system will be run by the SCHAPI, the French national service in charge of flood forecasting, providing warnings for fast-responding ungauged catchments (area ranging from ~10 to ~1000 km2). It will therefore be complementary to the SCHAPI’s national “vigilance” system which concerns only gauged catchments. The FF warning system to be implemented in 2017 will be based on a discharge-threshold flood warning method called AIGA (Javelle et al. 2014). This method has been experimented in real time in the south of France in the RHYTMME project (http://rhytmme.irstea.fr). It consists in comparing discharges generated by a simple conceptual hourly hydrologic model run at a 1-km2 resolution to reference flood quantiles of different (e.g., 2-, 10- and 50-year) return periods. Therefore the system characterizes in real time the severity of ongoing events by the range of the return period estimated by AIGA at any point along the river network. The hydrologic model ingests operational rainfall radar-gauge products from Météo-France and takes into account the baseflow and the initial soil humidity conditions to better estimate the basin response to rainfall inputs. To meet the requirements of the future FF warning system, the AIGA method has been extended to the whole French territory (except Corsica and overseas French territories). The calibration, regionalization and validation procedures of the hydrologic model were carried out using data for ~700 hydrometric stations from the 2002-2015 period. Performance of the warning system was evaluated with various contingency criteria (e.g., probability of detection and success rate). Furthermore, specific flood events were analysed in more details, by comparing warnings issued for exceeding different critical flood quantiles and their associated timing with field observations. The performance results show that the proposed FF warning system is useful, especially for ungauged sites. The analysis also points out the need to account for the uncertainties in the precipitation inputs and the hydrological modelling, as well as include precipitation forecasts to improve the effective warning lead time.

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]