Constraining the water budget of a small agricultural headwater catchment using flux tower and soil data, satellite-based evapotranspiration, and semi-distributed hydrological modelling

On a global scale, the impacts of climate change on the hydrological cycle are clearly visible and documented. Some damages to ecosystems and humanity tend to be irreversible, particularly extreme events such as floodings and droughts, which are expected to occur more frequently (IPCC 2023). Quantifying the processes at work in the hydrological cycle at the catchment scale is very complex due to landscape heterogeneity and non-linear interactions. However, it can be addressed by constraining the problem using different sources of data and modelling tools. In this study, conducted at the ORE AgrHyS agro-hydrological observatory (Brittany, NW France), we compared ground-based measurements of actual evapotranspiration (AET) and soil water content (SWC) with satellite-based AET estimates and the outputs of a semi-distributed hydrological model. AET fluxes were measured by eddy-covariance at the FR-Nzn flux tower site (part of the EUROFLUX network), located on grazed grassland at the catchment head. SWC measurements were performed with TDR (Time-Domain Reflectometry) sensors at different locations and measurement depths in the watershed (5cm depth at most sites, but also down to 50 cm at the flux tower). Satellite-based AET estimates were computed using Landsat 8 OLI-TIRS products as well as local and meteorological model data (AROME model, by Météo France). The hydrological model used multi-site daily rainfall measurements and potential evapotranspiration to simulate daily specific discharge at the outlet and further constrain soil water balance, water table depth, flow rates and AET fluxes at the catchment scale. Additionally, the SWC TDR measurements performed over the catchment were compared to estimates obtained from the Copernicus Sentinel-1 radar product. Modelled and measured AET fluxes were compared over the period 2016-2024 to explore their inter-annual variability, extreme weather events (heat waves and droughts), and to discuss discrepancies between models and measurements. The potential of spatialized AET combining local measurements and modelled outputs is also studied. Such a combination of data sources is expected to improve modelling tools and to reduce uncertainties in AET and water balance at the catchment-scale.