Integration of clustering and principal component analyses in MCDA to identify feasible regions for managed aquifer recharge
Journal
Journal of Hydrology
ISSN
0022-1694
Date Issued
June 2026
Abstract
Managed aquifer recharge (MAR) provides a nature-based solution to water scarcity issues, contributing to the design of effective water management policies. The novelty of this study lies in the integration of multivariate statistical methods within the framework of multicriteria decision analysis to provide nonsubjective, semi-automatic estimations of MAR feasibility based on three thematic layers, particularly intrinsic (hydrogeological, topographical, meteorological) features, water availability and demand for MAR. The concept of MAR typology is used to define the MAR problem and select a set of criteria for each thematic layer that are relevant for the Sado River Basin (southern Portugal). The hierarchical clustering algorithm (HCA) is used to partition the study area into distinct subregions based on selected criteria. For each subregion and thematic layer, a set of weight coefficients for the criteria is generated via products of the cumulative variance of the principal scores with the corresponding eigenvector matrix, which are then used to generate multiple realizations of the thematic maps. The results reveal that the majority of the study area (60%) has mean feasibility scores less than 0.54, whereas the remaining 40% (highest values) vary within a smaller interval (0.54–0.67). The present results are consistent with the previous study, with both identifying the same high-suitability regions. Additionally, it allows exploration of the variability in criteria weights across multiple realizations, providing a preliminary indication of how this variability can influence the results. This approach can easily be implemented in software and ultimately automated, supporting the unsupervised streamlining of the decision-making process.
Affiliation
Technical University of Dresden
Funding
This research is in the framework of the AGREEMAR project which is funded by National Funding Agencies from Germany (Bundesministerium für Bildung und Forschung - BMBF), Cyprus (Research & Innovation Foundation - RIF), Portugal (Fundaçao para a Ciencia e a Tecnologia - FCT), Spain (Ministerio de Ciencia e Innovacion - MCI) and Tunisia (Ministere de l’Enseignement Superieur et de la Recherche Scientifique - MESRS) under the Partnership for Research and Innovation in the Mediterranean Area (PRIMA). The PRIMA programme is supported under Horizon 2020 by the European Union’s Framework for Research and Innovation. The authors also acknowledge the “EXCELSIOR”: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment H2020 Widespread Teaming project (https://www.excelsior2020.eu, accessed on 13 March 2023). The “EXCELSIOR” project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 857510, from the Government of the Republic of Cyprus through the Directorate General for the European Programmes, Coordination and Development and the Cyprus University of Technology.
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