Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning
Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand...
| Autores principales: | , , , , , |
|---|---|
| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/173072 |
| _version_ | 1855528243365412864 |
|---|---|
| author | Steinbach, S. Bartels, A. Rienow, A. Kuria, B. T. Zwart, Sander J. Nelson, A. |
| author_browse | Bartels, A. Kuria, B. T. Nelson, A. Rienow, A. Steinbach, S. Zwart, Sander J. |
| author_facet | Steinbach, S. Bartels, A. Rienow, A. Kuria, B. T. Zwart, Sander J. Nelson, A. |
| author_sort | Steinbach, S. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). Our results suggest that shortand longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management. |
| format | Journal Article |
| id | CGSpace173072 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1730722025-10-26T13:00:44Z Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning Steinbach, S. Bartels, A. Rienow, A. Kuria, B. T. Zwart, Sander J. Nelson, A. turbidity prediction water reservoirs remote sensing machine learning modelling water quality agricultural water management satellite observation Small reservoirs are increasingly common across Africa. They provide decentralised access to water and support farmer-led irrigation, in addition to contributing towards mitigating the impacts of climate change. Water quality monitoring is essential to ensure the safe use of water and to understand the impact of the environment and land use on water quality. However, water quality in small reservoirs is often not monitored continuously, with the interlinkages between weather, land, and water remaining unknown. Turbidity is a prime indicator of water quality that can be assessed with remote sensing techniques. Here we modelled turbidity in 34 small reservoirs in central Kenya with Sentinel-2 data from 2017 to 2023 and predicted turbidity outcomes using primary and secondary Earth observation data, and machine learning. We found distinct monthly turbidity patterns. Random forest and gradient boosting models showed that annual turbidity outcomes depend on meteorological variables, topography, and land cover (R2 = 0.46 and 0.43 respectively), while longer-term turbidity was influenced more strongly by land management and land cover (R2 = 0.88 and 0.72 respectively). Our results suggest that shortand longer-term turbidity prediction can inform reservoir siting and management. However, inter-annual variability prediction could benefit from more knowledge of additional factors that may not be fully captured in commonly available geospatial data. This study contributes to the relatively small body of remote sensing-based research on water quality in small reservoirs and supports improved small-scale water management. 2025-02 2025-02-16T10:10:28Z 2025-02-16T10:10:28Z Journal Article https://hdl.handle.net/10568/173072 en Open Access Elsevier Steinbach, S.; Bartels, A.; Rienow, A.; Kuria, B. T.; Zwart, Sander Jaap; Nelson, A. 2025. Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning. International Journal of Applied Earth Observation and Geoinformation, 136:104390. [doi: https://doi.org/10.1016/j.jag.2025.104390] |
| spellingShingle | turbidity prediction water reservoirs remote sensing machine learning modelling water quality agricultural water management satellite observation Steinbach, S. Bartels, A. Rienow, A. Kuria, B. T. Zwart, Sander J. Nelson, A. Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning |
| title | Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning |
| title_full | Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning |
| title_fullStr | Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning |
| title_full_unstemmed | Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning |
| title_short | Predicting turbidity dynamics in small reservoirs in Central Kenya using remote sensing and machine learning |
| title_sort | predicting turbidity dynamics in small reservoirs in central kenya using remote sensing and machine learning |
| topic | turbidity prediction water reservoirs remote sensing machine learning modelling water quality agricultural water management satellite observation |
| url | https://hdl.handle.net/10568/173072 |
| work_keys_str_mv | AT steinbachs predictingturbiditydynamicsinsmallreservoirsincentralkenyausingremotesensingandmachinelearning AT bartelsa predictingturbiditydynamicsinsmallreservoirsincentralkenyausingremotesensingandmachinelearning AT rienowa predictingturbiditydynamicsinsmallreservoirsincentralkenyausingremotesensingandmachinelearning AT kuriabt predictingturbiditydynamicsinsmallreservoirsincentralkenyausingremotesensingandmachinelearning AT zwartsanderj predictingturbiditydynamicsinsmallreservoirsincentralkenyausingremotesensingandmachinelearning AT nelsona predictingturbiditydynamicsinsmallreservoirsincentralkenyausingremotesensingandmachinelearning |