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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000378 |
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| author | Stefanie Steinbach Anna Bartels Andreas Rienow Bartholomew Thiong’o Kuria Sander Jaap Zwart Andrew Nelson |
| author_facet | Stefanie Steinbach Anna Bartels Andreas Rienow Bartholomew Thiong’o Kuria Sander Jaap Zwart Andrew Nelson |
| author_sort | Stefanie Steinbach |
| collection | DOAJ |
| 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 short- and 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 | Article |
| id | doaj-art-c74879a576c8482598d33c09f0762943 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-c74879a576c8482598d33c09f07629432025-02-08T05:00:00ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104390Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learningStefanie Steinbach0Anna Bartels1Andreas Rienow2Bartholomew Thiong’o Kuria3Sander Jaap Zwart4Andrew Nelson5Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands; Institute of Geography, Ruhr University Bochum, Bochum, Germany; Corresponding author.Institute of Geography, Ruhr University Bochum, Bochum, GermanyInstitute of Geography, Ruhr University Bochum, Bochum, GermanyInstitute of Geomatics, GIS and Remote Sensing (IGGReS), Dedan Kimathi University of Technology, Nyeri, KenyaInternational Water Management Institute (IWMI), Accra, GhanaFaculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The NetherlandsSmall 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 short- and 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.http://www.sciencedirect.com/science/article/pii/S1569843225000378Sentinel-2Water qualityTurbidityAgricultural water management |
| spellingShingle | Stefanie Steinbach Anna Bartels Andreas Rienow Bartholomew Thiong’o Kuria Sander Jaap Zwart Andrew Nelson Predicting turbidity dynamics in small reservoirs in central Kenya using remote sensing and machine learning International Journal of Applied Earth Observations and Geoinformation Sentinel-2 Water quality Turbidity Agricultural water management |
| 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 | Sentinel-2 Water quality Turbidity Agricultural water management |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000378 |
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