,,,- Institute of Environmental Assessment and Water Research, Barcelona, Spain
Understanding the interactions between climate variability and groundwater quality remains a major challenge in arid and semi-arid regions, where aquifers are increasingly affected by altered recharge regimes and more frequent droughts. The aims of these study are to: (i) investigate how climate and groundwater quality linkages and, (ii) propose a data-driven machine learning framework to predict hydrochemical parameters in a pilot catchment of the Upper Guadiana Basin (Spain).
Daily climatic variables (maximum and minimum temperature and precipitation) from the Spanish Meteorological Agency were compiled together with hydrochemical data collected between 2001 and 2021, including electrical conductivity, pH, dissolved oxygen, and major ions (HCO₃⁻, Cl⁻, NO₃⁻, SO₄²⁻, Na⁺, Mg²⁺, Ca²⁺) measured at ten groundwater sampling points from Guadiana River Basin authority.
The proposed methodology analyzes the variability, correlations, and long-term behaviour of the hydrochemical dataset in order to identify which parameters respond most clearly to climate conditions. Subsequently, a climate driven predictive component is constructed using multivariate regression models based on ensemble methods, such as Random Forest. The climate predictors obtained from this step allows the estimation of each hydrochemical variable. This workflow allows both datasets to be integrated in a coherent way despite their different temporal resolutions, while keeping the influence of climate on groundwater quality interpretable.
The resulting data-driven framework will support the prediction of groundwater quality parameters and the assessment of aquifer sensitivity under contrasting climate scenarios. Beyond its local application, the methodology offers a transferable and efficient approach for groundwater management in regions facing increasing climate stress and contributes to the climate change impact assessments and practical decision-support tools.
Keywords: groundwater quality, climate variability, machine learning, upper Guadiana basin.
How to cite: Vizcarra Mondragon, S. L., Jurado Elices, A., Pujades Garnes, E., and Salehi Siavashani, N.: Machine Learning framework for groundwater quality prediction in the upper Guadiana basin under climate variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-522, https://doi.org/10.5194/egusphere-egu26-522, 2026.
