CawSAR: an open-source framework for preprocessing hydroclimatic data in physically based hydrological modelling

Water resource management is a major challenge for the coming decades. Its effective application across diverse territories therefore relies on an accurate representation of hydrological processes, generally achieved through physically based distributed hydrological models which in turn depend on spatially consistent and representative hydroclimatic forcing. At regional scales, capturing local variability in hydroclimatic drivers (precipitation, temperature, evapotranspiration) often requires combining datasets with different spatial resolutions and methodological assumptions.

Within the Eau-SPRA project (ADEME, France 2030 Programme), the CaWaQS model (Flipo et al., 2022; Flipo et al., 2023) is applied to the Loire River basin to support socio-hydrological modelling from regional to local scales. CaWaQS is a coupled distributed surface–subsurface hydrological model simulating both river discharge and groundwater dynamics. It currently lacks an explicit snow representation, which can significantly affect hydrological dynamics across scales, particularly in large river basins such as the Loire and under climate change conditions (Valéry et al., 2014).

To address these challenges, we developed CawSAR (CaWaQS Snow Accounting Routine), an open-source Python-based preprocessing framework designed to harmonize multi-source climate data (e.g. reanalysis products, radar observations) over a target study area. Based on a 3D matrix representation (time, x, y) of climate fields, it integrates multiple functionalities within a single, reproducible workflow. Climate data are harmonized through systematic downscaling, upscaling and regridding performed on a grid-cell basis using physical external-drift adjustments (altimetric gradient). CawSAR also enables cross-comparison of climate data sources across different spatio-temporal scales and implements a degree-day snow model to compute snow accumulation and melt. Finally, it generates liquid input time series (sum of liquid rainfall and snowmelt) fully compatible with the CaWaQS core model, ensuring direct integration into hydrological simulations.

Applied to the Loire basin, CawSAR illustrates how physically based preprocessing and multi-source harmonization enhance hydroclimatic forcing consistency for regional-scale hydrological modelling.