Resource Library

This review highlights the limitations of widely used OLS methods in water-quality research and provides practical guidance on modern statistical approaches, such as GLS, mixed-effects models, and Bayesian analyses, that better handle the temporal, spatial, and unbalanced nature of environmental datasets.

This systematic review synthesises decades of research on surface water quality profiling, outlining major pollution sources, widely used Water Quality and Pollution Index models, key parameters, and emerging needs for more technology-driven approaches to improve monitoring and assessment.

This review synthesizes insights from 142 studies showing how satellite remote sensing offers a cost-effective way to monitor key water-quality parameters globally, while highlighting major research gaps in non-optically active indicators and small inland water bodies that call for expanded methods and machine-learning integration.

The Global Freshwater Quality Database GEMStat provides scientifically-sound data and information on the state and trend of global inland water quality. As operational part of the GEMS/Water Programme of the United Nations Environment Programme (UNEP), GEMStat is hosted by the GEMS/Water Data Centre (GWDC) within the International Centre for Water Resources and Global Change (ICWRGC) in Koblenz, Germany.