How Landscape Patterns Affect River Water Quality Spatially and Temporally: A Multiscale Geographically Weighted Regression Approach
How Landscape Patterns Affect River Water Quality Spatially and Temporally: A Multiscale Geographically Weighted Regression Approach
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DOI：
10.3808/jei.202300503
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发表时间：
2023
影响因子：
7
通讯作者：
X. Li;J. Zhang;W. Yu;L. Liu;W. Wang;Z. Cui;R. Wang;Y. Li
中科院分区：
文献类型：
--
作者：
X. Li;J. Zhang;W. Yu;L. Liu;W. Wang;Z. Cui;R. Wang;Y. Li
河流的水质可以被认为是其周围景观的一个功能。了解景观格局与河流水质之间的关系对于优化景观格局以减少流域污染至关重要，但尚未得到解决。采用多尺度地理加权回归（MGWR）模型探讨景观格局与水质之间的关系。研究结果表明，景观指数与水质在不同季节的空间尺度上有不同的关系。NO_3 ~--N、NH_4 ~+-N和TN的独立影响最强的变量分别是茶园、居民地和季节变化。景观指标对TP的影响相对较弱，全年在流域尺度。景观因子对NO3--N的影响在汛期较为显著，而对NH 4 +-N的影响在非汛期较为显著。TN的影响景观指标的季节变化是不规则的。虽然景观组成比配置更显着影响水质，香农的多样性指数和斑块密度是重要的配置指标，显着影响水质。因此，在土地资源有限的情况下，在不改变流域组成的前提下，优化景观空间配置，是降低河流污染风险的关键。该研究进一步表明，MGWR模型可以很好地量化景观格局对流域水质的影响。
The water quality of a river can be considered a function of its surrounding landscape. Understanding the relationship between landscape patterns and river water quality is essential for optimizing landscape patterns to reduce watershed pollution and has not yet been solved. A multiscale geographically weighted regression (MGWR) model was used to explore the associations between the landscape patterns and water quality. Our results showed that landscape metrics have varied relationships with the water quality across spatial scales in different seasons. The strongest independent influencing variable for NO3–-N, NH4+-N, and TN was tea gardens, residential land, and varied seasonally, respectively. The impacts of the landscape metrics on the TP were relatively weak throughout the year at the watershed scale. The influence of landscape metrics on NO3–-N was more significant during the flood season, whereas that on NH4+-N was more notable during the non-flood season. Seasonal changes in the influencing landscape metrics of TN were not regular. Although landscape composition more significantly influenced water quality than configuration, the Shannon’s diversity index and patch density were important configuration indices that significantly impacted water quality. Therefore, with limited land availability, it is essential to optimize the landscape spatial configuration without changing the composition of the watershed to reduce the risk of river pollution. This study further indicated that the MGWR model can well quantify the effects of landscape pattern on water quality at the watershed scale.