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Classical calibration methods in hydrology typically rely on a single costfunction computed on long-term streamflow series. Even when hydrological modelsachieve acceptable scores in NSE and KGE, imbalances can still arise betweenoverall model performance and its ability to simulate flood events,particularly flash floods. In this study, the potential of using multi-scalesignatures is explored to enhance multi-criteria calibration methods forspatially distributed flood modeling, which remains considerable challenges. Wepresent a novel signatures and sensitivity-based calibration approachimplemented into a variational data assimilation algorithm capable to deal withhigh dimensional spatially distributed hydrological optimization problems. Itis tested on 141 flash flood prone catchments mostly located in the FrenchMediterranean region. Our approach involves computing several signatures,including flood event signatures, using an automated flood segmentationalgorithm. We select suitable signatures for constraining the model based ontheir global sensitivity with the input parameters through globalsignature-based sensitivity analysis (GSSA). We then perform two multi-criteriacalibration strategies using the selected signatures, including asingle-objective optimization approach, which transforms the multi-criteriaproblem into a single-objective function, and a multi-objective optimizationapproach, which uses a simple additive weighting method to select an optimalsolution from the Pareto set. Our results show significant improvements in bothcalibration and temporal validation metrics, especially for flood signatures,demonstrating the robustness and delicacy of our signatures-based calibrationframework for enhancing flash flood forecasting systems.

Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods