Abstract
Catchments are complex systems, which require regular monitoring of hydro-chemical parameters in space and time to provide comprehensive datasets. These are needed to characterize catchment behavior on a local level, make future projections based on models, implement mitigation measures and meet policy targets. However, many developing countries lack a good infrastructure for hydrological monitoring since its establishment is costly and the required resources are often not available. To overcome such challenges in data scarce regions like Kenya, a participatory citizen science approach can be a promising strategy for monitoring water resources. This study evaluates the potential of using a contributory citizen science approach to explore spatiotemporal turbidity and suspended sediment dynamics in the Sondu-Miriu river basin, western Kenya. A group of 19 citizen scientists was trained to monitor turbidity using turbidity tubes and water levels with water level gauges in six nested subcatchments of the Sondu-Miriu river basin. Over the course of the project, a total of 37 citizen scientists participated and contributed to the overall dataset of turbidity. The sampling effort and data contribution varied from year to year and among participants with the majority of the data (72%) originating from 8 (22%) citizen scientists. Comparison between citizen-scientist collected suspended sediment data and measurements from automated stations showed high correlation (R2 > 0.9) which demonstrates that data collected by citizen scientists can be comparable to data collected using expensive monitoring equipment. However, there was reduced precision of the measurements of suspended sediment concentrations at low and high levels attributed largely to the detection limitations of the turbidity tubes and citizen scientists not capturing major sediment export events. Suspended sediment concentrations were significantly higher downstream (109 ± 94 mg L−1), a subcatchment dominated by agriculture and rangeland with low forest vegetation cover, as compared to a subcatchment with high forest cover (50 ± 24.7 mg L−1). This finding indicates that forest cover is a key landscape feature to control suspended sediment concentrations in the region. Future citizen science projects should focus on motivation and engagement strategies and the application of robust methods with improved detection limits and resolution to advance hydrological monitoring.
Highlights
In the tropics, natural ecosystems and water resources are increasingly threatened by several factors including growing human population, climate change, deforestation, and increased cropping and grazing intensities (Smith et al, 2016; Shupe, 2017; Berihun et al, 2019)
This study evaluates the potential of using a contributory citizen science approach to explore spatiotemporal turbidity and suspended sediment dynamics in the Sondu-Miriu river basin, western Kenya
This study evaluated the potential of citizen science to monitor suspended sediment concentrations in a remote tropical river basin using turbidity measured with turbidity tubes as a proxy
Summary
Natural ecosystems and water resources are increasingly threatened by several factors including growing human population, climate change, deforestation, and increased cropping and grazing intensities (Smith et al, 2016; Shupe, 2017; Berihun et al, 2019). Much of the recent focus in hydrogeochemical research has been on the use of models, remote sensing and high resolution automated monitoring systems to further improve our understanding of ecological systems (Baldyga et al, 2008; Jacobs et al, 2018; Esteves et al, 2019). These approaches have been widely used to inform decision making for environmental management, they are expensive and their application within most developing countries is still hampered by poor infrastructure and technical capabilities (Olang and Fürst, 2011; Nardi et al, 2020). As the acquisition of field data with high spatial and temporal resolution is very important for sustainable water resource management and governance, this clearly raises the need to explore alternative cost-effective approaches for data collection (Njue et al, 2019; Malthus et al, 2020)
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