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Patterns, Risks, and Forecasting of Irrigation Water Quality Under Drought Conditions in Mediterranean Regions

2025-06Artigo científico Acesso aberto
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dc.contributor.authorTomaz, Alexandra
dc.contributor.authorCatarino, Adriana
dc.contributor.authorTomaz, Pedro
dc.contributor.authorFabião, Marta
dc.contributor.authorPalma, Patrícia
dc.contributor.institutionGeoBioTec - Geobiociências, Geoengenharias e Geotecnologias
dc.contributor.institutionFaculdade de Ciências e Tecnologia (FCT)
dc.contributor.institutionDCT - Departamento de Ciências da Terra
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2025-08-26T21:45:20Z
dc.date.available2025-08-26T21:45:20Z
dc.date.issued2025-06
dc.descriptionThis 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.
dc.description.abstractThe 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.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent2240838
dc.identifier.doi10.3390/w17121783
dc.identifier.issn2073-4441
dc.identifier.otherPURE: 127781351
dc.identifier.otherPURE UUID: f6181738-718d-4ddf-bbe8-65b8836d5125
dc.identifier.otherScopus: 105009070074
dc.identifier.urihttp://hdl.handle.net/10362/186921
dc.identifier.urlhttps://www.scopus.com/pages/publications/105009070074
dc.language.isoeng
dc.peerreviewedyes
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT//2023.04004.BD/PT
dc.subjectclimate variability
dc.subjecthydro-agricultural areas
dc.subjectmachine learning
dc.subjectmultivariate statistics
dc.subjectsalinity
dc.subjectsodicity
dc.subjectBiochemistry
dc.subjectGeography, Planning and Development
dc.subjectAquatic Science
dc.subjectWater Science and Technology
dc.subjectSDG 13 - Climate Action
dc.titlePatterns, Risks, and Forecasting of Irrigation Water Quality Under Drought Conditions in Mediterranean Regionsen
dc.typejournal article
degois.publication.issue12
degois.publication.titleWater (Switzerland)
degois.publication.volume17
dspace.entity.typePublication
rcaap.rightsopenAccess

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