Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface
Urban river water pollution is a serious environmental challenge confronting urban areas worldwide. The scientific quantification of the impact of landscape patterns on water quality provides essential support for pollution control and watershed landscape optimization. In this study, we onducted an...
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24014766 |
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| author | Kun Mei Haonan Shi Yupeng Wu Randy A. Dahlgren Xiaoliang Ji Minmin Yang Yueru Guan |
| author_facet | Kun Mei Haonan Shi Yupeng Wu Randy A. Dahlgren Xiaoliang Ji Minmin Yang Yueru Guan |
| author_sort | Kun Mei |
| collection | DOAJ |
| description | Urban river water pollution is a serious environmental challenge confronting urban areas worldwide. The scientific quantification of the impact of landscape patterns on water quality provides essential support for pollution control and watershed landscape optimization. In this study, we onducted an in-depth and meticulous exploration of the relationship between the landscape patterns and water quality in the Wen-Rui Tang River watershed at four distinct spatial scales (single subwatershed, dynamic subwatershed, riparian buffer, and reach buffer) and two time scales (dry season and rainy season). Regression models were used to investigate the quantitative impact of landscape patterns on water quality, redundancy analysis was employed to assess the overall explanatory power, and non-parametric point-of-change analysis was applied to evaluate variations in water quality along landscape gradients and identify critical landscape threshold ranges. The results indicate that the model performance of disolved oxygen at the reach buffer scale, pH and total nitrogen at the riparian buffer scale, and ammonia nitrogen, nitrate nitrogen, and total phosphorus at the dynamic subwatershed scale were better than at other scales. Overall, the dynamic subwatershed scale showed the strongest explanatory power for water quality indicators, with 91.2 % for the rainy season and 83.2 % for the dry season. The largest patch index of agricultural land (A_LPI) and water bodies (W_LPI) were identified as the most critical landscape indices influencing water quality at the dynamic sub-watershed scale, with threshold values for these indices established as A_LPI < 10 % and W_LPI > 1.5 %. These findings offer a comprehensive understanding of the spatiotemporal impacts of landscape patterns on water quality and offer valuable insights for watershed management and ecological planning. |
| format | Article |
| id | doaj-art-6768bfa91d444915956cd1b8e8335702 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-6768bfa91d444915956cd1b8e83357022025-01-31T05:10:31ZengElsevierEcological Indicators1470-160X2025-01-01170113019Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interfaceKun Mei0Haonan Shi1Yupeng Wu2Randy A. Dahlgren3Xiaoliang Ji4Minmin Yang5Yueru Guan6SuZhou Key Laboratory of Spatial Information Intelligent Technology and Application, School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China; Southern Zhejiang Water Research Institute (iWATER), Wenzhou 325035, China; Corresponding authors at: SuZhou Key Laboratory of Spatial Information Intelligent Technology and Application, School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSuZhou Key Laboratory of Spatial Information Intelligent Technology and Application, School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, China; School of Environment Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, China; Corresponding authors at: SuZhou Key Laboratory of Spatial Information Intelligent Technology and Application, School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSuZhou Key Laboratory of Spatial Information Intelligent Technology and Application, School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaDepartment of Land, Air and Water Resources, University of California, Davis, CA 95616, USASouthern Zhejiang Water Research Institute (iWATER), Wenzhou 325035, China; School of Public Health and Management, Wenzhou Medical University, Wenzhou 325035, ChinaSuZhou Key Laboratory of Spatial Information Intelligent Technology and Application, School of Geography Science and Geomatics Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Environment Science and Engineering, Suzhou University of Science and Technology, Suzhou 215009, ChinaUrban river water pollution is a serious environmental challenge confronting urban areas worldwide. The scientific quantification of the impact of landscape patterns on water quality provides essential support for pollution control and watershed landscape optimization. In this study, we onducted an in-depth and meticulous exploration of the relationship between the landscape patterns and water quality in the Wen-Rui Tang River watershed at four distinct spatial scales (single subwatershed, dynamic subwatershed, riparian buffer, and reach buffer) and two time scales (dry season and rainy season). Regression models were used to investigate the quantitative impact of landscape patterns on water quality, redundancy analysis was employed to assess the overall explanatory power, and non-parametric point-of-change analysis was applied to evaluate variations in water quality along landscape gradients and identify critical landscape threshold ranges. The results indicate that the model performance of disolved oxygen at the reach buffer scale, pH and total nitrogen at the riparian buffer scale, and ammonia nitrogen, nitrate nitrogen, and total phosphorus at the dynamic subwatershed scale were better than at other scales. Overall, the dynamic subwatershed scale showed the strongest explanatory power for water quality indicators, with 91.2 % for the rainy season and 83.2 % for the dry season. The largest patch index of agricultural land (A_LPI) and water bodies (W_LPI) were identified as the most critical landscape indices influencing water quality at the dynamic sub-watershed scale, with threshold values for these indices established as A_LPI < 10 % and W_LPI > 1.5 %. These findings offer a comprehensive understanding of the spatiotemporal impacts of landscape patterns on water quality and offer valuable insights for watershed management and ecological planning.http://www.sciencedirect.com/science/article/pii/S1470160X24014766Landscape patternsScale effectsStepwise multiple linear regression model (SMLR)Geographically weighted regression model (GWR)Redundancy analysis (RDA)Water quality mutation points |
| spellingShingle | Kun Mei Haonan Shi Yupeng Wu Randy A. Dahlgren Xiaoliang Ji Minmin Yang Yueru Guan Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface Ecological Indicators Landscape patterns Scale effects Stepwise multiple linear regression model (SMLR) Geographically weighted regression model (GWR) Redundancy analysis (RDA) Water quality mutation points |
| title | Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface |
| title_full | Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface |
| title_fullStr | Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface |
| title_full_unstemmed | Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface |
| title_short | Impact of landscape patterns on river water quality: Spatial-scale effects across an agricultural-urban interface |
| title_sort | impact of landscape patterns on river water quality spatial scale effects across an agricultural urban interface |
| topic | Landscape patterns Scale effects Stepwise multiple linear regression model (SMLR) Geographically weighted regression model (GWR) Redundancy analysis (RDA) Water quality mutation points |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24014766 |
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