ANN-based parameter regionalization for distributed hydrological models used for low-flow simulation over ungauged French catchments

Understanding and modelling low-flow regimes in ungauged basins remains a key challenge in hydrology and water resources management under a changing climate and anthropogenic pressures. Low-flow rivers, characterized by reduced streamflow over extended periods, play an essential role in ecosystem functioning and water availability. Often overlooked, they represent a substantial portion of the global river network, spanning a wide range of climatic regions, including arid, semi-arid, temperate, humid tropical, boreal, and alpine environments. The variability observed in these watercourses is shaped by regional physiographic and climatic factors, making their behaviour complex to model, especially in ungauged contexts.

To address this limitation, we use SMASH (Spatially-distributed Modelling and ASsimilation for Hydrology), a modular and open-source platform developed at INRAE for distributed hydrological modelling. SMASH supports the combination of process-based conceptual structures and data-driven components such as neural networks, and allows spatially distributed calibration using multi-source hydrometeorological data. It is designed to simulate discharge hydrographs and hydrological states across all grid cells in a catchment, making it particularly suited for representing diverse hydrological responses, including low-flow regimes under varying physiographic and climatic conditions.

This work aims to assess the performance of different hydrological model structures and regionalization strategies through large-scale calibration and validation experiments for simulating low-flow regimes. The assessment relies on a 40-year daily dataset covering 248 French catchments representative of diverse hydrometeorological conditions. Each model–regionalization configuration is assessed based on its ability to simulate low-flow dynamics, capture seasonal variability, and preserve overall water balance, with particular attention to its generalization capacity in ungauged contexts. In this framework, artificial neural networks (ANN) are applied to perform parameter regionalization based on physiographic attributes, as part of a data-driven strategy aimed at improving transferability to ungauged basins.

Results will highlight the most effective combinations of model structure and regionalization method. This work aims to improve hydrological modelling in ungauged basins through hybrid approaches combining conceptual models with data-driven tools.