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

Abstract

Murari Lal Gaur and Urvashiben Parmar

River water quality monitoring systems worldwide generate extensive long-term datasets, yet their use in policy and sustainability reporting remains largely limited to compliance-based assessments. Under the Sustainable Development Goals (SDGs), particularly SDG-6, there is a growing need to demonstrate spatially coherent, trend-based improvement in water quality and protection of aquatic ecosystems. This study presents an integrative framework that transforms routine river water quality monitoring data into SDG-aligned policy intelligence through systematic integration with satellite reflectance information. Using the Mahi River Basin as a representative medium-scale river system, long-term in-situ observations are combined with Earth observation data to reinterpret water quality in terms of signals, trajectories, and river-reach behavior rather than isolated parameters and stations. The analysis emphasizes temporal consistency, spatial coherence, and persistence of stress or recovery patterns, enabling differentiation between chronic degradation, transitional recovery, and emerging risk zones. Building on this synthesis, a policy translation framework links monitoring intelligence to interpretive diagnostics and action prioritization. The approach demonstrates how existing monitoring systems can be repurposed to support SDG-6 reporting, ecosystem-oriented governance, and targeted river basin management without new monitoring infrastructures, and is transferable to data-constrained river basins globally.

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