Developing a pan-European high-resolution groundwater recharge map – Combining satellite data and national survey data using machine learning

Developing a pan-European high-resolution groundwater recharge map – Combining satellite data and national survey data using machine learning

Martinsen, Grith (Geological Survey of Denmark and Greenland)
Bessiere, Helene (Bureau de Recherches Géologiques et Minières (BRGM))
Caballero, Yvan (Université Montpellier II; Bureau de Recherches Géologiques et Minières (BRGM))
Koch, Julian (Geological Survey of Denmark and Greenland)
Collados-Lara, Antonio Juan (Instituto Geológico y Minero de España)
Mansour, Majdi (British Geological Survey)
Sallasmaa, Olli (Geological Survey of Finland)
Pulido-Velazquez, David (Instituto Geológico y Minero de España)
Williams, Natalya Hunter (Geological Survey Ireland)
Zaadnoordijk, Willem (TU Delft Water Resources; TNO)
Stisen, Simon (Geological Survey of Denmark and Greenland)

2022

Groundwater recharge quantification is essential for sustainable groundwater resources management, but typically limited to local and regional scale estimates. A high-resolution (1 km × 1 km) dataset consisting of long-term average actual evapotranspiration, effective precipitation, a groundwater recharge coefficient, and the resulting groundwater recharge map has been created for all of Europe using a variety of pan-European and seven national gridded datasets. As an initial step, the approach developed for continental scale mapping consists of a merged estimate of actual evapotranspiration originating from satellite data and the vegetation controlled Budyko approach to subsequently estimate effective precipitation. Secondly, a machine learning model based on the Random Forest regressor was developed for mapping groundwater recharge coefficients, using a range of covariates related to geology, soil, topography and climate. A common feature of the approach is the validation and training against effective precipitation, recharge coefficients and groundwater recharge from seven national gridded datasets covering the UK, Ireland, Finland, Denmark, the Netherlands, France and Spain, representing a wide range of climatic and hydrogeological conditions across Europe. The groundwater recharge map provides harmonised high-resolution estimates across Europe and locally relevant estimates for areas where this information is otherwise not available, while being consistent with the existing national gridded datasets. The Pan-European groundwater recharge pattern compares well with results from the global hydrological model PCR-GLOBWB 2. At country scale, the results were compared to a German recharge map showing great similarity. The full dataset of long-term average actual evapotranspiration, effective precipitation, recharge coefficients and groundwater recharge is available through the EuroGeoSurveys' open access European Geological Data Infrastructure (EGDI).

Effective precipitation
Groundwater recharge
Machine learning
Pan-European
Recharge coefficient
Satellite data

http://resolver.tudelft.nl/uuid:d69f154a-a187-4fd1-9fb2-ab640c80189f

https://doi.org/10.1016/j.scitotenv.2022.153464

0048-9697

Science of the Total Environment, 822, 1-15

Institutional Repository

journal article

© 2022 Grith Martinsen, Helene Bessiere, Yvan Caballero, Julian Koch, Antonio Juan Collados-Lara, Majdi Mansour, Olli Sallasmaa, David Pulido-Velazquez, Natalya Hunter Williams, Willem Zaadnoordijk, Simon Stisen