Robust Inference and Management Prioritisation of Sub-Basin Water-Quality Heterogeneity in the Pra River Basin, Ghana

Aims: River-basin averages may conceal management-relevant differences among tributary systems. This study quantified the magnitude, precision, and management relevance of differences in Water Quality Index (WQI) among the Birim, Offin, and Pra sub-basins of the Pra River Basin, Ghana. Study Design: Secondary quantitative analysis of an existing cross-sectional, station-level WQI dataset. Place and Duration of Study: The study covered 150 georeferenced monitoring stations distributed across the Birim, Offin, and Pra sub-basins of the Pra River Basin, Ghana. The secondary statistical analysis was conducted in 2026 using previously developed station-level WQI data. Methodology: The study did not re-derive the WQI or repeat spatial interpolation. Descriptive statistics and 95% confidence intervals were used to summarise sub-basin WQI patterns. Distributional characteristics were assessed using the Shapiro-Wilk test. Overall differences among sub-basins were evaluated using Welch's one-way analysis of variance, followed by Games-Howell post hoc pairwise comparisons. Effect-size measures were estimated to assess the practical magnitude of observed differences. Results: Mean WQI was highest in Offin \((120.57 \pm 15.67)\), followed by Birim \((93.33 \pm 10.82)\) and Pra \((74.17 \pm 7.22)\). Welch's test showed significant differences among the sub-basins, \(F(2,89.96)=198.36, p<0.001\). Variance-partition effect sizes were very large \(\left(\eta^2=0.728 ; \omega^2=0.723\right)\), while Games-Howell comparisons showed very large to extremely large standardised differences across all pairwise contrasts. Conclusion: WQI differed substantially among the three sub-basins, indicating that basin-wide averages may obscure spatial differences relevant to water-quality management. Offin emerged as the highest priority for surveillance and source investigation, Birim as a corrective-monitoring priority, and Pra as a preventive-protection priority. The study provides decision-oriented evidence by quantifying uncertainty and practical effect magnitude while avoiding unsupported attribution of pollution to specific sources.