STARS4Water partner INRAE presented a poster on ‘Better mapping of groundwater-surface water exchanges over the Seine River catchment in a surface hydrological model’ at the EGU2024 Session HS8.2.12. The Seine catchment is characterised by a complex, multi-layered aquifer system where the river loses water in some places and gains water in others. This research proposes a multi-objective model calibration strategy in order to improve the physical realism of the simulated Inter-catchment Groundwater Flows (IGFs). The hydrological model used is the GRSD semi-distributed model. The model simulation is optimised on two fluxes: river discharge and actual evapotranspiration using MODIS satellite estimates. It was hypothesized that better IGFs could be estimated if the water balance is more constrained by evaporation. Different objective functions are explored to identify the most efficient way to use satellite data by looking at the model robustness in time and space.

Reference: Hsu, S.-C., de Lavenne, A., Andréassian, V., Rabah, A., and Ramos, M.-H.: Better mapping of groundwater-surface water exchanges over the Seine River catchment in a surface hydrological model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024.

STARS4Water partners UCM and IGME gave an oral presentation on ‘Nitrate spatial predictions by means of machine learning to improve groundwater monitoring networks’ at the EGU2024 Session HS8.2.12. This study introduces a method to support the design of groundwater quality monitoring networks through machine learning spatial predictions. Several supervised classification algorithms were trained to identify spatially distributed variables explaining the presence of nitrates in the groundwater of various aquifers in central Spain, including the Madrid Tertiary Detrital Aquifer. The dataset comprised over 240 nitrate concentration measurements and 20 explanatory variables related to geology, climatic factors, and pressures such as agricultural land, urban areas or intensive farming location. Subsequently, the algorithms with the best predictive capability were used to map nitrate contamination in order to locate unmonitored sites where contamination is likely to occur. The map-based output of this approach facilitates identifying new areas of interest requiring observation points. This method provides an alternative to expert-based criteria for locating new groundwater monitoring stations and is easily transferable to other environments.

Reference: Gómez-Escalonilla, V., Martínez-Santos, P., Pacios, D., Ruíz-Álvarez, L., Díaz-Alcaide, S., Montero-González, E., Martín-Loeches, M., and De la Hera-Portillo, Á.: Nitrate spatial predictions by means of machine learning to improve groundwater monitoring networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10066