Integrated Analysis of Spatial Water-Quality Gradients, Hotspots, and Inferred Hydrological Resilience Using Bioindicators and Machine Learning in a Semi-Arid River Basin (Ecuador)

Water-quality degradation in semi-arid basins is strongly influenced by spatial heterogeneity and cumulative anthropogenic pressure. This study characterises spatial gradients, identifies contamination hotspots, and evaluates system behaviour in the Jipijapa River micro-basin, Ecuador, through an integrated analytical framework. A multi-year dataset (2023–2025; n = 27) from nine monitoring sites was analysed using non-parametric statistics, regulatory exceedance-based hotspot detection, the BMWP/Col index, Spearman correlations adjusted by false discovery rate, and exploratory machine-learning models (Random Forest and ε-SVR) with leave-one-out cross-validation. Results showed a significant longitudinal gradient, with dissolved oxygen decreasing from 6.1 to 2.1 mg L−1 and BOD5 increasing from 6.1 to 111.0 mg L−1 downstream. Five hotspots were identified, mainly in the lower reach, while BMWP/Col values declined from 118.3 to 37.0, indicating ecological degradation. Correlation analysis revealed strong coupling between BOD5 and dissolved oxygen (ρ = −0.916), modulated by altitude and vegetation cover. Machine-learning models showed high internal consistency, although their use was restricted to diagnostic pattern detection rather than operational prediction. Overall, the convergence of physicochemical, ecological, hotspot, and modelling evidence supports an inferred spatial resilience gradient and provides a locally adaptable framework for prioritising watershed interventions in data-limited semi-arid basins.