From Monitoring to Meaning: Translating Long-Term River Water Quality Data and Earth Observation into SDG-Aligned Policy Intelligence

River water quality assessments in developing regions are often constrained by limited monitoring infrastructure, yet long-term field-based datasets remain critical for understanding basin-scale dynamics. This study presents a decade-long (2006–2016) spatio-temporal analysis of river water quality in the Mahi River Basin, western India, using routine monitoring data collected across four representative sub-basins. Twelve key physicochemical, nutrient, and sediment-related parameters were analysed to characterise seasonal behaviour, intra-annual variability, and episodic extremes under a monsoon-dominated hydrological regime. Results reveal a well-defined seasonal structure, with wet-season conditions exhibiting pronounced increases in turbidity, suspended solids, organic load indicators, and nutrients, driven by runoff-induced mobilisation of catchment materials. In contrast, dissolved constituents and pH displayed comparatively stable behaviour, reflecting buffering by basin geology and baseflow contributions. Box–whisker analysis further highlights substantial intra-annual variability and short-duration pollution pulses during monsoon months that are not evident from seasonal or annual averages alone. Spatial patterns indicate gradual downstream accumulation of dissolved and organic constituents, suggesting basin-scale rather than point-source dominance. Despite reliance on legacy, field-operable monitoring methods, the consistency of trends across seasons and locations underscores the robustness of the dataset. The study demonstrates the enduring value of long-term observational records for river water quality assessment in data-poor tropical basins and provides a practical baseline to inform monitoring design, irrigation planning, & water quality management under swelling hydro-climatic variability.