A comprehensive review of catchment water quality monitoring using a tiered framework of integrated sensing technologies

Due to the growing threat of climate change, new advances in water quality monitoring strategies are needed now more than ever. Reliable and robust monitoring practices can be used to improve and better understand catchment processes affecting the water quality. In recent years the deployment of long term in-situ sensors has increased the temporal and spatial data being obtained. Furthermore, the development and research into remote sensing using satellite and aerial imagery has been incrementally integrated into catchments for monitoring areas that previously might have been impossible to monitor, producing high-resolution data that has become imperative to catchment monitoring. The use of modelling in catchments has become relevant as it enables the prediction of events before they occur so that strategic plans can be put in place to deal with or prevent certain threats. This review highlights the monitoring approaches employed in catchments currently and examines the potential for integration of these methods. A framework might incorporate all monitoring strategies to obtain more information about a catchment and its water quality. The future of monitoring will involve satellite, in-situ and air borne devices with data analytics playing a key role in providing decision support tools. The review provides examples of successful use of individual technologies, some combined approaches and identifies gaps that should be filled to achieve an ideal catchment observation system. (C) 2021 The Authors. Published by Elsevier B.V.

Automated summary

With the growing threat of climate change, new advances in water quality monitoring strategies are necessary. Reliable monitoring practices can improve understanding of catchment processes affecting water quality. Deployment of long term in-situ sensors and remote sensing using satellite and aerial imagery have become essential for monitoring high-resolution data in previously inaccessible areas.