Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake
Rapid global urbanization has led to water eutrophication, threatening the stability of aquatic ecosystems stability. Chlorophyll-a (Chla), a key indicator of algal biomass, is a widely recognized as a metric for eutrophication. However, existing remote sensing retrieval methods face limitations in...
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Elsevier
2025-09-01
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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/S1569843225004558 |
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| author | Wei Si Zhixiong Chen Chi Yung Jim Ngai Weng Chan Mou Leong Tan Bingbing Liu Dong Liu Lifei Wei Shaoyong Wang Fei Zhang |
| author_facet | Wei Si Zhixiong Chen Chi Yung Jim Ngai Weng Chan Mou Leong Tan Bingbing Liu Dong Liu Lifei Wei Shaoyong Wang Fei Zhang |
| author_sort | Wei Si |
| collection | DOAJ |
| description | Rapid global urbanization has led to water eutrophication, threatening the stability of aquatic ecosystems stability. Chlorophyll-a (Chla), a key indicator of algal biomass, is a widely recognized as a metric for eutrophication. However, existing remote sensing retrieval methods face limitations in addressing complex environmental variations. This study developed an innovative Dynamic Model Pool (DMP) framework to optimize water quality prediction performance dynamically. Using Sentinel-2 satellite imagery and monthly in-situ Chla measurement data from Erhai located in Southwest China spanning 2018 to 2020, this study tested the effectiveness of the DMP framework. The results demonstrated that: (1) The DMP framework dynamically selected the optimal model based on data-specific characteristics. In 2018, the CBR model achieved the highest accuracy, while in 2019, GBR and XGBR were the most accurate. In 2020, GBR outperformed other models. (2) Spatiotemporal Chla distribution maps recorded consistently higher concentrations in the south part of lake, while the central part showed minimal level and variation. (3) Seasonal precipitation and temperature variations and policy implementation were key drivers of Chla concentration changes. Seasonal variations in precipitation and temperature collectively influenced the nutrient input and dilution dynamics in Erhai. Meanwhile, policy interventions implemented between 2018 and 2022, such as pollution interception and wastewater treatment, substantially decreased nutrient inflows during flood seasons and effectively limited nutrient accumulation. |
| format | Article |
| id | doaj-art-1ef9e1ba13ed45b8b0237e11bf1c257c |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-1ef9e1ba13ed45b8b0237e11bf1c257c2025-08-24T05:11:40ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-09-0114310480810.1016/j.jag.2025.104808Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai LakeWei Si0Zhixiong Chen1Chi Yung Jim2Ngai Weng Chan3Mou Leong Tan4Bingbing Liu5Dong Liu6Lifei Wei7Shaoyong Wang8Fei Zhang9College of Geography and Environmental Sciences, Zhejiang Normal University, Jinghua 321004, China; Zhejiang Key Laboratory of Digital Intelligence Monitoring and Restoration of Watershed Environment, Zhejiang Normal University, Jinhua, Zhejiang 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinghua 321004, China; Zhejiang Key Laboratory of Digital Intelligence Monitoring and Restoration of Watershed Environment, Zhejiang Normal University, Jinhua, Zhejiang 321004, ChinaDepartment of Social Sciences, Education University of Hong Kong, Lo Ping Road, Tai Po, Hong Kong, ChinaGeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, USM, Penang, MalaysiaGeoInformatic Unit, Geography Section, School of Humanities, Universiti Sains Malaysia, 11800, USM, Penang, Malaysia; Environmental and Atmospheric Sciences Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Nasiriyah 64001, IraqCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinghua 321004, China; Zhejiang Key Laboratory of Digital Intelligence Monitoring and Restoration of Watershed Environment, Zhejiang Normal University, Jinhua, Zhejiang 321004, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaFaculty of Resources and Environmental Science, Hubei University, Wuhan 430062, ChinaInstitute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610000, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinghua 321004, China; Zhejiang Key Laboratory of Digital Intelligence Monitoring and Restoration of Watershed Environment, Zhejiang Normal University, Jinhua, Zhejiang 321004, China; Corresponding author at: College of Geography and Environmental Sciences, Zhejiang Normal University, Jinghua 321004, China.Rapid global urbanization has led to water eutrophication, threatening the stability of aquatic ecosystems stability. Chlorophyll-a (Chla), a key indicator of algal biomass, is a widely recognized as a metric for eutrophication. However, existing remote sensing retrieval methods face limitations in addressing complex environmental variations. This study developed an innovative Dynamic Model Pool (DMP) framework to optimize water quality prediction performance dynamically. Using Sentinel-2 satellite imagery and monthly in-situ Chla measurement data from Erhai located in Southwest China spanning 2018 to 2020, this study tested the effectiveness of the DMP framework. The results demonstrated that: (1) The DMP framework dynamically selected the optimal model based on data-specific characteristics. In 2018, the CBR model achieved the highest accuracy, while in 2019, GBR and XGBR were the most accurate. In 2020, GBR outperformed other models. (2) Spatiotemporal Chla distribution maps recorded consistently higher concentrations in the south part of lake, while the central part showed minimal level and variation. (3) Seasonal precipitation and temperature variations and policy implementation were key drivers of Chla concentration changes. Seasonal variations in precipitation and temperature collectively influenced the nutrient input and dilution dynamics in Erhai. Meanwhile, policy interventions implemented between 2018 and 2022, such as pollution interception and wastewater treatment, substantially decreased nutrient inflows during flood seasons and effectively limited nutrient accumulation.http://www.sciencedirect.com/science/article/pii/S1569843225004558Chlorophyll-a (Chla)Machine learning modelWater quality monitoringSeasonal variabilitySpatial distribution |
| spellingShingle | Wei Si Zhixiong Chen Chi Yung Jim Ngai Weng Chan Mou Leong Tan Bingbing Liu Dong Liu Lifei Wei Shaoyong Wang Fei Zhang Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake International Journal of Applied Earth Observations and Geoinformation Chlorophyll-a (Chla) Machine learning model Water quality monitoring Seasonal variability Spatial distribution |
| title | Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake |
| title_full | Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake |
| title_fullStr | Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake |
| title_full_unstemmed | Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake |
| title_short | Decoding lake water eutrophication using an innovative dynamic model pool framework in Erhai Lake |
| title_sort | decoding lake water eutrophication using an innovative dynamic model pool framework in erhai lake |
| topic | Chlorophyll-a (Chla) Machine learning model Water quality monitoring Seasonal variability Spatial distribution |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225004558 |
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