  Classical calibration methods in hydrology typically rely on a single cost
function computed on long-term streamflow series. Even when hydrological models
achieve acceptable scores in NSE and KGE, imbalances can still arise between
overall model performance and its ability to simulate flood events,
particularly flash floods. In this study, the potential of using multi-scale
signatures is explored to enhance multi-criteria calibration methods for
spatially distributed flood modeling, which remains considerable challenges. We
present a novel signatures and sensitivity-based calibration approach
implemented into a variational data assimilation algorithm capable to deal with
high dimensional spatially distributed hydrological optimization problems. It
is tested on 141 flash flood prone catchments mostly located in the French
Mediterranean region. Our approach involves computing several signatures,
including flood event signatures, using an automated flood segmentation
algorithm. We select suitable signatures for constraining the model based on
their global sensitivity with the input parameters through global
signature-based sensitivity analysis (GSSA). We then perform two multi-criteria
calibration strategies using the selected signatures, including a
single-objective optimization approach, which transforms the multi-criteria
problem into a single-objective function, and a multi-objective optimization
approach, which uses a simple additive weighting method to select an optimal
solution from the Pareto set. Our results show significant improvements in both
calibration and temporal validation metrics, especially for flood signatures,
demonstrating the robustness and delicacy of our signatures-based calibration
framework for enhancing flash flood forecasting systems.
