Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC
Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sus...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-10-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651324010819 |
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| author | Yang Song Xiaoming Li Ying Zheng Gui Zhang |
| author_facet | Yang Song Xiaoming Li Ying Zheng Gui Zhang |
| author_sort | Yang Song |
| collection | DOAJ |
| description | Land Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources. |
| format | Article |
| id | doaj-art-736533dac94a4ebebff5d344cb2f9f9d |
| institution | OA Journals |
| issn | 0147-6513 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-736533dac94a4ebebff5d344cb2f9f9d2025-08-20T01:54:45ZengElsevierEcotoxicology and Environmental Safety0147-65132024-10-0128411700510.1016/j.ecoenv.2024.117005Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCCYang Song0Xiaoming Li1Ying Zheng2Gui Zhang3College of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China; ASEM Water Resources Research and Development Center, Changsha 410031, PR ChinaCollege of Environmental Science and Engineering, Hunan University, Changsha 410082, PR China; Corresponding author.College of Forestry, Central South University of Forestry and Technology, Changsha 410004, PR ChinaCollege of Forestry, Central South University of Forestry and Technology, Changsha 410004, PR ChinaLand Use/ Cover Change (LUCC) plays a crucial role in influencing hydrological processes, nutrient cycling, and sediment transport in watersheds, ultimately impacting water quality on both spatial and temporal scales. Accurately predicting changes in watershed water quality is beneficial for the sustainable management of water resources. Current models often lack the ability to effectively predict water quality changes in a dynamic spatio-temporal context, particularly in complex watershed environments. The overall purpose of the study is to establish a comprehensive and dynamic modeling framework that links LUCC with water quality, allowing for accurate predictions of future water quality under varying land use scenarios. The model, which uses water quality as the dependent variable and LUCC as the independent variable, was developed to quantitatively predict changes in watershed water quality. To achieve this, annual multi-period remote sensing images from Landsat-5, Landsat-8 or Sentinel-2 satellites spanning from 1992 to 2022 were analyzed. Random Forest (achieving a Kappa coefficient of 0.9468) were employed to classify land use within the watershed. Based on classification results, a Cellular Automata-Markov chain model (CA-Markov) was constructed to simulate and predict the spatio-temporal patterns of land use, incorporating driving factors such as proximity to water systems, roads, elevation, and slope. Validation of the model using LUCC data from 2020 yielded a high prediction accuracy with a Kappa coefficient of 0.9505. The CA-Markov model was further utilized to project LUCC under three different scenarios—natural development, ecological protection, and arable land protection—between 2023 and 2033. Based on these projections, the coupled water quality and LUCC model was employed to predict water quality changes in the watershed over the same period. Key findings indicate that water quality is likely to improve under ecological protection scenario, while deterioration is expected under natural development scenario and cropland protection scenario due to urban expansion, agricultural practices, and water diversion for irrigation. This study provides a robust framework for watershed management, offering scientific guidance for source management and water purification efforts, thereby contributing significantly to the sustainable development of water resources.http://www.sciencedirect.com/science/article/pii/S0147651324010819LUCCQuantitative predictionCA-MarkovDongjiang Lake Watershed |
| spellingShingle | Yang Song Xiaoming Li Ying Zheng Gui Zhang Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC Ecotoxicology and Environmental Safety LUCC Quantitative prediction CA-Markov Dongjiang Lake Watershed |
| title | Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC |
| title_full | Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC |
| title_fullStr | Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC |
| title_full_unstemmed | Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC |
| title_short | Quantitative prediction of water quality in Dongjiang Lake watershed based on LUCC |
| title_sort | quantitative prediction of water quality in dongjiang lake watershed based on lucc |
| topic | LUCC Quantitative prediction CA-Markov Dongjiang Lake Watershed |
| url | http://www.sciencedirect.com/science/article/pii/S0147651324010819 |
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