Multidimensional Analysis and Typology of the Waters of the Sidi Chahed Reservoir and Its Tributaries (Morocco): A Principal Component Analysis and Clustering Approach

Abstract

The watershed of the Sidi Chahed reservoir, including the tributaries Oued Mikkès and Oued Mellah, faces significant challenges in water quality. The Oued Mellah is characterized by high salinity, while the Oued Mikkès is subject to intense anthropogenic pressure from agricultural activities and domestic discharges, threatening its aquatic ecosystems. These issues are exacerbated by the semiarid climatic conditions of the region, which worsen water quality degradation. This study evaluates the typology of Sidi Chahed reservoir and tributaries’ waters (Oued Mellah and Oued Mikkès) according to a multidimensional approach of physicochemical parameters. ANOVA tests, Spearman, correlation tests, and principal component analysis (PCA) were conducted on 17 parameters which have been measured at six stations throughout a full hydrological cycle (December 2022–November 2023). The ANOVA tests indicate that the physicochemical parameters are basically influenced by seasonal variations, with less marked spatial variations. The two initial PCA axes explain 47.52% of the overall variance, representing gradients of mineralization and organic pollution. Station and period classification into spatiotemporal groups was carried out by using the K-means clustering method, and seven different groups were established. The result shows that summer months are characterized by high mineralization and high temperatures, while winter months experience more severe organic pollution. This typology delineates critical periods and areas for water quality and provides a solid foundation for targeted water resource management. The implications for preserving aquatic ecosystems and reducing anthropogenic influences are discussed. This integrated methodology serves as an instructional model for analyzing anthropogenic-geological-climatic interactions through multivariate statistics, enabling evidence-based water management in semiarid regions (see Case Study Questions).