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
PY - 2022
Y1 - 2022
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
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
VL - 822
SP - 1
EP - 15
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
M1 - 153464
ER -