TY - JOUR
T1 - Developing a pan-European high-resolution groundwater recharge map – Combining satellite data and national survey data using machine learning
AU - Martinsen, Grith
AU - Bessiere, Helene
AU - Caballero, Yvan
AU - Koch, Julian
AU - Collados-Lara, Antonio Juan
AU - Mansour, Majdi
AU - Sallasmaa, Olli
AU - Pulido-Velazquez, David
AU - Williams, Natalya Hunter
AU - Zaadnoordijk, Willem Jan
AU - Stisen, Simon
N1 - Funding Information:
The research has been carried out within the TACTIC project, part of the GeoERA programme, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 731166 , the participating geological survey organisations and Innovation Fund Denmark under agreement no 8055-00073B. In addition, some French partners were also supported by the INDECIS project financed by the European ERA4CS Joint Call for Transnational Collaborative Research Projects. Mansour publishes with the permission of the Executive Director of the British Geological Survey (UKRI).
Funding Information:
The research has been carried out within the TACTIC project, part of the GeoERA programme, which received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no 731166, the participating geological survey organisations and Innovation Fund Denmark under agreement no 8055-00073B. In addition, some French partners were also supported by the INDECIS project financed by the European ERA4CS Joint Call for Transnational Collaborative Research Projects. Mansour publishes with the permission of the Executive Director of the British Geological Survey (UKRI). Timo Kroon of Deltares is thanked for providing the Dutch model results and Edwin Sutanudjaja and Niko Wanders from Utrecht University for providing Pan-European PCR-GLOBWB 2 groundwater recharge results. Sandra Lanini and St?phanie Pinson from BRGM are acknowledged for their respective participation in the French case for potential groundwater recharge and European IDPR computation. Access to the dataset will be provided through the European Geological Data Infrastructure (EGDI) via URL: http://www.europe-geology.eu/groundwater/.
Publisher Copyright:
© 2022 The Authors
PY - 2022/5/20
Y1 - 2022/5/20
N2 - 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).
AB - 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).
KW - Effective precipitation
KW - Groundwater recharge
KW - Machine learning
KW - Pan-European
KW - Recharge coefficient
KW - Satellite data
KW - DK-model
UR - http://www.scopus.com/inward/record.url?scp=85124161700&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.153464
DO - 10.1016/j.scitotenv.2022.153464
M3 - Article
C2 - 35093341
AN - SCOPUS:85124161700
SN - 0048-9697
VL - 822
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 153464
ER -