Modeling the drivers of eutrophication in Finland with a machine learning approach
Abstract Anthropogenic eutrophication is one of the most common threats to inland water quality, often causing toxic algal blooms and loss of aquatic biodiversity. Mitigating the harmful impacts of eutrophication requires managing nutrient inputs from the catchment focusing on the major local driver...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-05-01
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| Series: | Ecosphere |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ecs2.4522 |
| _version_ | 1797817059271245824 |
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| author | Sara Heikonen Maria Yli‐Heikkilä Matias Heino |
| author_facet | Sara Heikonen Maria Yli‐Heikkilä Matias Heino |
| author_sort | Sara Heikonen |
| collection | DOAJ |
| description | Abstract Anthropogenic eutrophication is one of the most common threats to inland water quality, often causing toxic algal blooms and loss of aquatic biodiversity. Mitigating the harmful impacts of eutrophication requires managing nutrient inputs from the catchment focusing on the major local drivers of eutrophication. These drivers can be identified using models that predict lake trophic state based on characteristics of the lake and its catchment. In this study, we aimed to extend the spatial scope of these models by identifying drivers of eutrophication in a large sample of lakes (1547) distributed across Finland. Moreover, we used satellite‐observed chlorophyll a (chl a) concentration as trophic state indicator, instead of site‐sampled data, which is commonly used in existing research. We identified major drivers of eutrophication on river basin district to country scale based on 11 catchment and lake characteristics, applying the random forest algorithm. On country scale, the catchment and lake characteristics explained 41% of the variation in lake chl a concentrations, and on river basin district scale, 20%–44%. Catchment and lake hydromorphology were the most important explanatory characteristics. Especially, high natural eutrophication level, shallow mean depth of lake, and small share of lake area in the catchment were related to increased lake chl a concentration. Moreover, depending on the dominant land use type in the model area, share of agricultural area and share of peatland area in the catchment were ranked among the most important drivers of increased lake chl a concentration. The results suggest that trophic state predictive models utilizing satellite‐observed chl a concentration could provide an additional, cost‐effective tool for addressing lake eutrophication, especially in areas without and extensive on‐site monitoring network. |
| first_indexed | 2024-03-13T08:46:55Z |
| format | Article |
| id | doaj.art-4f70503af34d4ac685bfe0c3687936d0 |
| institution | Directory Open Access Journal |
| issn | 2150-8925 |
| language | English |
| last_indexed | 2024-03-13T08:46:55Z |
| publishDate | 2023-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecosphere |
| spelling | doaj.art-4f70503af34d4ac685bfe0c3687936d02023-05-30T00:04:33ZengWileyEcosphere2150-89252023-05-01145n/an/a10.1002/ecs2.4522Modeling the drivers of eutrophication in Finland with a machine learning approachSara Heikonen0Maria Yli‐Heikkilä1Matias Heino2Water and Development Research Group Aalto University Espoo FinlandStatistical Services Unit Natural Resources Institute Finland Jokioinen FinlandWater and Development Research Group Aalto University Espoo FinlandAbstract Anthropogenic eutrophication is one of the most common threats to inland water quality, often causing toxic algal blooms and loss of aquatic biodiversity. Mitigating the harmful impacts of eutrophication requires managing nutrient inputs from the catchment focusing on the major local drivers of eutrophication. These drivers can be identified using models that predict lake trophic state based on characteristics of the lake and its catchment. In this study, we aimed to extend the spatial scope of these models by identifying drivers of eutrophication in a large sample of lakes (1547) distributed across Finland. Moreover, we used satellite‐observed chlorophyll a (chl a) concentration as trophic state indicator, instead of site‐sampled data, which is commonly used in existing research. We identified major drivers of eutrophication on river basin district to country scale based on 11 catchment and lake characteristics, applying the random forest algorithm. On country scale, the catchment and lake characteristics explained 41% of the variation in lake chl a concentrations, and on river basin district scale, 20%–44%. Catchment and lake hydromorphology were the most important explanatory characteristics. Especially, high natural eutrophication level, shallow mean depth of lake, and small share of lake area in the catchment were related to increased lake chl a concentration. Moreover, depending on the dominant land use type in the model area, share of agricultural area and share of peatland area in the catchment were ranked among the most important drivers of increased lake chl a concentration. The results suggest that trophic state predictive models utilizing satellite‐observed chl a concentration could provide an additional, cost‐effective tool for addressing lake eutrophication, especially in areas without and extensive on‐site monitoring network.https://doi.org/10.1002/ecs2.4522catchment characteristicschlorophyll alake characteristicsrandom forestsatellite observationtrophic state modeling |
| spellingShingle | Sara Heikonen Maria Yli‐Heikkilä Matias Heino Modeling the drivers of eutrophication in Finland with a machine learning approach Ecosphere catchment characteristics chlorophyll a lake characteristics random forest satellite observation trophic state modeling |
| title | Modeling the drivers of eutrophication in Finland with a machine learning approach |
| title_full | Modeling the drivers of eutrophication in Finland with a machine learning approach |
| title_fullStr | Modeling the drivers of eutrophication in Finland with a machine learning approach |
| title_full_unstemmed | Modeling the drivers of eutrophication in Finland with a machine learning approach |
| title_short | Modeling the drivers of eutrophication in Finland with a machine learning approach |
| title_sort | modeling the drivers of eutrophication in finland with a machine learning approach |
| topic | catchment characteristics chlorophyll a lake characteristics random forest satellite observation trophic state modeling |
| url | https://doi.org/10.1002/ecs2.4522 |
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