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

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).

Projected impacts of climate change on farmers' extraction of groundwater from crystalline aquifers in South India.

Local groundwater levels in South India are falling alarmingly. In the semi-arid crystalline Deccan plateau area, agricultural production relies on groundwater resources. Downscaled Global Climate Model (GCM) data are used to force a spatially distributed agro-hydrological model in order to evaluate Climate Change (CC) effects on local groundwater extraction (GWE). The slight increase of precipitation may alleviate current groundwater depletion on average, despite the increased evaporation due to warming. Nevertheless, projected climatic extremes create worse GWE shortages than for present climate. Local conditions may lead to opposing impacts on GWE, from increases to decreases (+/-20 mm/year), for a given spatially homogeneous CC forcing. Areas vulnerable to CC in terms of irrigation apportionment are thus identified. Our results emphasize the importance of accounting for local characteristics (water harvesting systems and maximal aquifer capacity versus GWE) in developing measures to cope with CC impacts in the South Indian region.