Best practice in high-frequency water quality monitoring for improved management and assessment; a novel decision workflow.

The use of high-frequency water quality monitoring has increased over several decades. This has mostly been motivated by curiosity-driven research and has significantly improved our understanding of hydrochemical processes. Despite these scientific successes and the growth in sensor technology, the large-scale uptake of high-frequency water quality monitoring by water managers is hampered by a lack of comprehensive practical guidelines.

Value and limitations of machine learning in high-frequency nutrient data for gap-filling, forecasting, and transport process interpretation.

High-frequency monitoring of water quality in catchments brings along the challenge of post-processing large amounts of data. Moreover, monitoring stations are often remote and technical issues resulting in data gaps are common. Machine learning algorithms can be applied to fill these gaps, and to a certain extent, for predictions and interpretation.

Climate variability effects on eutrophication of groundwater, lakes, rivers, and coastal waters in the Netherlands.

Many aquatic ecosystems in densely populated delta areas worldwide are under stress from overexploitation and pollution. Global population growth will lead to further increasing pressures in the coming decades, while climate change may amplify the consequences for chemical and ecological water quality. In this study, we explored the effects of climatic variability on eutrophication of groundwater, streams, rivers, lakes, estuaries, and marine waters in the Netherlands.