Tomaz, AlexandraCatarino, AdrianaTomaz, PedroFabião, MartaPalma, Patrícia2025-08-262025-08-262025-062073-4441PURE: 127781351PURE UUID: f6181738-718d-4ddf-bbe8-65b8836d5125Scopus: 105009070074http://hdl.handle.net/10362/186921This work is a contribution to the projects CREATE (UIDB/06107/2023) and Geobiotec (UIDB/04035/2023), both funded by FCT—Fundação para a Ciência e a Tecnologia, Portugal, and for the FitoFarmGest Operational Group (PDR2020-101-030926). Publisher Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland.The seasonal and interannual irregularity of temperature and precipitation is a feature of the Mediterranean climate that is intensified by climate change and constitutes a relevant driver of water and soil degradation. This study was developed during three years in a hydro-agricultural area of the Alqueva irrigation system (Portugal) with Mediterranean climate conditions. The sampling campaigns included collecting water samples from eight irrigation hydrants, analyzed four times yearly. The analysis incorporated meteorological data and indices (precipitation, temperature, and drought conditions) alongside chemical parameters, using multivariate statistics (factor analysis and cluster analysis) to identify key water quality drivers. Additionally, machine learning models (Random Forest regression and Gradient Boosting machine) were employed to predict electrical conductivity (ECw), sodium adsorption ratio (SAR), and pH based on chemical and climatic variables. Water quality evaluation showed a prevalence of a slight to moderate soil sodification risk. The factor analysis outcome was a three-factor model related to salinity, sodicity, and climate. The cluster analysis revealed a grouping pattern led by year and followed by stage, pointing to the influence of inter-annual climate irregularity. Variations in water quality from the reservoirs to the distribution network were not substantial. The Random Forest algorithm showed superior predictive accuracy, particularly for ECw and SAR, confirming its potential for the reliable forecasting of irrigation water quality. This research emphasizes the importance of integrating time-sensitive monitoring with data-driven predictions of water quality to support sustainable water resources management in agriculture. This integrated approach offers a promising framework for early warning and informed decision-making in the context of increasing drought vulnerability across Mediterranean agro-environments.2240838engclimate variabilityhydro-agricultural areasmachine learningmultivariate statisticssalinitysodicityBiochemistryGeography, Planning and DevelopmentAquatic ScienceWater Science and TechnologySDG 13 - Climate Actionjournal article10.3390/w17121783https://www.scopus.com/pages/publications/105009070074