Ecological Indicators (Sep 2024)

Multiple scale impacts of land use intensity on water quality in the Chishui river source area

  • Jiaying Zhu,
  • Shuangyun Peng,
  • Xiangjin Shen,
  • Zhiqiang Lin,
  • Luping Gong,
  • Rui Zhang,
  • Bangmei Huang

Journal volume & issue
Vol. 166
p. 112396

Abstract

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There is a consensus on the impact of land use types and patterns on water quality. However, the specific relationship between human interventions on land resources, namely land use intensity (LUI), and water quality remains unclear. Therefore, it is important to investigate in-depth the concrete impacts of LUI on water quality and spatial scale effects to effectively mitigate and manage water quality degradation problems and guide the rational development and utilization of land resources. In this study, focusing on the streams in the headwaters of the Chishui River, Pearson correlation analysis, redundancy analysis (RDA) and stepwise multiple linear regression (SMLR) models were used to investigate how the LUI affects seasonal water quality at multi-spatial scales. The study revealed marked seasonal and regional discrepancies in water quality indicators, while also revealing significant between discrepancies scales and spatial heterogeneity in LUI. In terms of magnitude of impact, LUI had a significant effect on water quality variability, with a greater impact observed in the dry season than in the wet season. The specific impacts of different pollution sources on water quality are ranked as follows: domestic sewage and its waste > Livestock and poultry breeding > agricultural planting. From the point of scale effect, the LUI showed optimal effects on and predictive abilities for changes in water quality in smaller riverbank buffer zones. The SMLR analysis revealed that the LUI showed stronger predictive abilities for certain water quality indicators across various scales. For instance, BOD5, TP, CODMn, and NH3-N exhibited more significant predictive effects at smaller scales, whereas COD, EC, DO, and TN demonstrated better predictive accuracy at larger scales. The research conclusions offer valuable scientific insights into controlling environmental pollution in water bodies and have significant implications for the rational planning and implementation of land use development, management and conservation efforts.

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