The DATA-REACH module, a data-driven based approach within the REACH tool, focuses on analyzing past climate data and predicting future groundwater conditions using advanced statistical and machine learning techniques. DATA-REACH employs several key methodologies:

• Retrospective Climate Analysis: This involves conducting comparative analyses of climate indices like the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to identify historical wet and dry periods. It also assesses the potential deterioration of groundwater chemical and quantitative status associated with these climate trends.
  
  The analysis successfully identified dry and wet periods using SPI and SPEI. Correlations were found between climate indices and various physicochemical parameters (e.g., chlorides, electrical conductivity, sodium, temperature, nitrates), indicating how these parameters respond to climatic variations. However, limitations due to data scarcity and inconsistent sampling periodicity were noted.
• Predictive Models: Machine learning models are trained and validated to provide monthly probabilistic predictions for target variables such as piezometric levels, electrical conductivity, and reservoir volume. This process includes data collection, preprocessing, model learning, evaluation, and prediction, with SHAP (SHapley Additive exPlanations) used to interpret feature importance.
  
  The models demonstrated high consistency and accuracy in reproducing hydrogeological dynamics, showing low uncertainty and high similarity to real behavior for piezometric levels, electrical conductivity, and reservoir volume. They effectively captured seasonal fluctuations and trends.
• Climate Projections: Global climate projections are downscaled to regional levels for demo sites, characterizing future trends in precipitation and temperature. Projections consistently showed descending trends in precipitation and ascending trends in minimum and maximum temperatures across demo sites, with varying magnitudes depending on the specific climate change scenario (SSP2 and SSP5) and geographical location.
  
  DATA-REACH offers a highly replicable and transferable solution for understanding and predicting groundwater behavior under climate change. Its retrospective analysis is valuable for identifying groundwater areas vulnerable to climate impacts, making them suitable candidates for MAR. The predictive models provide crucial forecasts for water quality and quantity variables, supporting proactive decision-making. Overall, the module generates essential insights into climate change impacts on groundwater resources, which are vital for developing effective MAR strategies and risk management plans.