EGU22-4158
https://doi.org/10.5194/egusphere-egu22-4158
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Pan-European high-resolution groundwater recharge mapping – combining satellite data and national survey data using machine learning

Simon Stisen1, Grith Martinsen1, Helene Bessiere2, Yvan Caballero2, Julian Koch1, Antonio Juan Collados-Lara3, Majdi Mansour4, Olli Sallasmaa5, David Pulido Velázquez3, Natalya Hunter Williams6, and Willem Jan Zaadnoordijk7,8
Simon Stisen et al.
  • 1Geological Survey of Denmark & Greenland, GEUS, Oester Voldgade 10, Copenhagen K, Denmark (sst@geus.dk)
  • 2French Geological Survey, BGRM - 3 avenue Claude-Guillemin, BP 36009 45060 Orléans Cedex 02, France
  • 3Spanish Geological Survey, IGME-CSIC – Urb. Alcázar del Genil, 4-Edif. Zulema, Bajo. 18006 – Granada, Spain
  • 4British Geological Survey, BGS – Keyworth, Nottingham, NG12 5GG, United Kingdom
  • 5Finnish Geological Survey, GTK – Geological Survey of Finland, Vuorimiehentie 5, P.O. Box 96, FI-02151 Espoo, Finland
  • 6Geological Survey Ireland, GSI - Haddington Road, Dublin D04 K7X4, Ireland
  • 7Geological Survey of the Netherlands, TNO – Princetonlaan 6, 3584 CB Utrecht, Netherlands
  • 8Delft University of Technology, Faculty of Civil Engineering and Geosciences, Water Resources Section, Stevinweg 1, 2628 CN Delft, Netherlands

Groundwater recharge quantification is essential for sustainable groundwater resources management, but typically limited to local and regional scale estimates. A high-resolution (1 km x 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 datasets and seven national gridded recharge estimates. 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 estimates. 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).

How to cite: Stisen, S., Martinsen, G., Bessiere, H., Caballero, Y., Koch, J., Juan Collados-Lara, A., Mansour, M., Sallasmaa, O., Pulido Velázquez, D., Hunter Williams, N., and Jan Zaadnoordijk, W.: Pan-European high-resolution groundwater recharge mapping – combining satellite data and national survey data using machine learning, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4158, https://doi.org/10.5194/egusphere-egu22-4158, 2022.