Scale relationship between landscape pattern and water quality in different pollution source areas: A case study of the Fuxian Lake watershed, China
Understanding the scale relationship between landscape pattern and water quality is of great significance for improving water pollution and guiding the rational planning of land use in watersheds. At present, most existing studies take the watershed as a whole to explore the scale relationship betwe...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Elsevier
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/c526de06ce7142ee9cfedbfa75168bed |
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| Sumario: | Understanding the scale relationship between landscape pattern and water quality is of great significance for improving water pollution and guiding the rational planning of land use in watersheds. At present, most existing studies take the watershed as a whole to explore the scale relationship between landscape pattern and water quality. Research on different types of pollution sources is still lacking. According to the characteristics of the pollution source in the watershed, the study of the multiscale effects of landscape pattern of different pollution source areas on water quality will help better the understanding of the impact of the surrounding landscape on the water quality of rivers, which is very important for local river management. In this study, a typical plateau lake basin, the Fuxian Lake basin, is taken as the research area, and the scale relationship between the surface landscape pattern and water quality in different pollution sources is taken as the research object. The redundancy analysis and multiple regression modeling are used to explore the impact of landscape pattern on water quality. The results show that the key landscape metrics that affect water quality indices in different pollution source areas and at different scales are quite different. Shannon's diversity index (SDI) had a significant effect on total phosphorus (TP) across the entire watershed. In the urban region, SDI and the mean shape index (MSI) have the greatest impact on chemical oxygen demand (COD) at the sub-watershed scale. In the phosphorus mining region, the landscape metrics SDI, Shannon's evenness index (SEI), and patch cohesion index (COHES) have significant effects on water quality, especially on TP. In the farmland and village region, the other 7 landscape metrics excluding the number of patches (NumP) become the leading indicators affecting water quality at different scales, among which total nitrogen (TN) has a particularly significant influence. Overall, the factor with the greatest effect on water quality was SDI in different pollution source areas. In terms of the scale effect, the landscape pattern has the strongest effect on water quality changes in the urban and phosphorus mining regions at sub-watershed scales, while in the farmland and village region, 300 m and 500 m are the areas where the landscape pattern has the strongest effect on water quality. These findings can provide decisional support for improving the water quality of lakes and rivers from the perspective of sustainable landscape management. |
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