Climate change is altering global temperature and precipitation patterns, with particularly strong effects expected in Mediterranean regions, where reduced groundwater recharge and increased evapotranspiration may affect groundwater-dependent ecosystems. This study provides a preliminary, indicator-based assessment of the potential sensitivity of the Cabecera del Bornova Groundwater Body (Guadalajara, Spain), which sustains the Somolinos karst wetland, under natural conditions and protected as a Natural Groundwater Reserve and Natural Lacustrine Reserve. Empirical correlations were established between accumulated deviations of historical precipitation and observed piezometric levels in two monitoring piezometers using second-degree polynomial functions. The most informative relationships were obtained for piezometer ZE01, particularly at the daily scale, whereas the second piezometer showed weaker relationships. These functions were applied to regionalized climate projections generated with the FICLIMA methodology from ten CMIP6 models under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios from the IPCC Sixth Assessment Report. The results indicate a general decreasing tendency in empirical piezometric-level indicators throughout the 21st century, although the magnitude of the response is highly sensitive to the selected rainfall station, temporal resolution, climate model and scenario. Extreme projected declines are interpreted as extrapolation-sensitive outputs rather than deterministic predictions of aquifer drawdown or groundwater-reserve depletion. Direct impacts on lagoon level, spring discharge or wetland extent cannot be quantified with the dataset. The results highlight the need to expand piezometric monitoring, instrument the Manadero del Bornova spring, monitor lagoon water levels and develop physically based recharge and groundwater-flow models.

The most used methods to evaluate the vulnerability to contamination of aquifers are based on overlay index maps, such us DRASTIC, GOD and AVI. These methods assign weighting and rating values to hydrogeological characteristics, introducing subjectivity in the evaluation. In this research, a new methodology is proposed to eliminate some of that subjectivity. The methodology evaluates the vulnerability to contamination of a detrital aquifer using K-means cluster analysis with a new set of parameters. The set is composed of some parameters extracted from these methods, as well as other new ones that have a significant influence on the movement of contaminants. Application of the Principal Components Analysis (PCA) technique before using K-means cluster allowed the selection of the relevant parameters. In order to validate the methodology, this was applied to a detrital aquifer located at central Spain (the so-called “Aluviales Jarama-Tajuña” aquifer) with a significant agricultural development. To compare the traditional methods of vulnerability assessment with the K-means cluster, nitrate concentration was used as a pollution indicator. Thus, 23 groundwater quality samples were used to correlate (Spearman´s correlation coefficient) the vulnerability values with nitrate concentration to validate the most suitable method. The results showed that GOD and AVI were not appropriate methods to evaluate the vulnerability of the aquifer, because they use few variables which do not represent relevant features for the vulnerability assessment. Alternatively, DRASTIC and K-means cluster analysis obtained higher Spearman´s correlation coefficients. The relevant features selected by PCA analysis were depth of groundwater (D), net recharge (R), and land use (L). The new proposed method grouped data in three clusters (low, moderate and high vulnerability), increasing the Spearman´s correlation from 34% of DRASTIC method to 48%. This result confirmed the advantage of applying cluster analysis in the assessment of groundwater vulnerability in detrital aquifers.

It is possible to assess the harm that society suffers from an anthropogenic event based on the impact of groundwater pollution on society. Indexing methodologies are commonly applied to assess the social vulnerability of aquifers. However, they assign weighting and rating values for the different factors involved, make them very subjective. This research proposes to assess the social vulnerability to groundwater pollution by considering three factors: the uses of groundwater resources, the exposed population, and the socio-economic losses. In order to eliminate the subjectivity of indexing methodologies, a K-means cluster analysis was used to assess the social vulnerability. Using this method, a social vulnerability map can be produced with greater objectivity. The proposed methodology was applied to an aquifer located in central Spain, an area with significant agricultural development. Low population density and unproductive zones result in low social vulnerability in most of the area. Nevertheless, high social vulnerability is observed in the southern regions due to agricultural development which leads to higher socio-economic variables and demand for groundwater resources for agricultural activities. Similarly, high social vulnerability is observed in the northeast, mainly influenced by groundwater use and exposed population. These results show that social vulnerability in most of the study area is not very significant for assessing the risk of groundwater contamination, because the damage to the social, environmental or economic sector is low. However, in the south and northeast in the study area, pesticides and fertilisers should be used with caution, as they significantly increase the risk of groundwater contamination and thus the impact on society. The K-means clustering method proved to be an objective and effective option for assessing social vulnerability to groundwater pollution in aquifers.