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).

Areas where there is a scarcity of water frequently experience significant drought periods, which may become exacerbated in the future due to climate change. In this paper we propose a novel and integrated method for a semi-distributed analysis of the impact on potential future meteorological, hydrological, agronomical and operational droughts within a basin. We analyse the propagation and correlation of the different types of droughts, and then this analysis can be used to plan sustainable adaptation strategies. The proposed method is based on sequential applications of different statistical techniques and mathematical models. We have applied several statistical downscaling techniques to generate consistent local future climate scenarios considering both basic and drought statistics. This allows us to analyse the sensitivity of the results to the applied technique and the spatial distribution. A chain of models has been used to propagate climate scenarios to analyse the hydrological, agricultural, and operational impact. We have applied a clustering analysis to historical data to identify homogeneous hydro-climate areas used to analyse the spatial distribution of the impact. The approach has been applied in the Segura basin (in south-eastern Spain). The simulations of the impact in the 3 generated ensemble scenarios on the whole Segura Basin system for the horizon 2071-2100 under the RCP8.5 emission scenario show a significant mean reduction (40.9-59.1%) of the available resources, an increase in pumping rates in aquifers (36.4-42.7%) and lower guarantees (96.3% in the historical period and 75.0-77.6% in the future scenarios) for demand supply. The spatial distribution of the impact is heterogeneous, with the hydro-climate areas near to the coast for agricultural and operational droughts being more affected. An analysis of correlation between the meteorological and operational droughts shows the maximum correlation for a time delay of around 4 months. This information could help to identify when measures to reduce the operational impact should start to be applied when a meteorological drought starts.

The mean precipitation measurements in a Mediterranean Alpine catchment in Sierra Nevada show an inversion of the gradient with the altitude beyond a certain threshold. Is it due to a real pattern or it can be explained by systematic error of solid precipitation measurement in gauges? Can we assess climatic fields in an alpine catchment from gauge measurement? This article describes a research developed to answer both questions in the Alto Genil Basin. As commonly happens in most of the basins, the spatio‐temporal information from climate gauges is limited; therefore to reduce uncertainty in estimates of climatic fields, some secondary information should be introduced. Since orographic conditions clearly influence precipitation, the relationship between this climatic variable and elevation is usually included as secondary information into the estimates. However, while there is a clear relationship between temperature and elevation, the relationship between precipitation and elevation is not so simple. In this article the analysis of the data performed allow us to demonstrate that there is a real inversion of the gradient within this Mediterranean Alpine area as other authors previously pointed in some tropical and subtropical zones. The intensity of this phenomenon and the altitude threshold from which it appears can be altered as a consequence of the undercath of the solid precipitation. To estimate precipitation fields, we have used different hypotheses about the intensity of the undercatch taking into account empirical corrections obtained for nearby mountain ranges. An analysis of the sensitivity of the results to the assumed undercatch hypothesis shows that it is not possible to estimate properly precipitation fields (the sensitivity of the results to the adopted hypothesis is high) in these alpine areas if we only have information about the precipitation measurements at the stations.