Spatial scale effects of landscape metrics on stream water quality and their seasonal changes

被引:112
|
作者
Wu, Jianhong [1 ,3 ]
Lu, Jun [1 ,2 ]
机构
[1] Zhejiang Univ, Coll Environm & Resources Sci, Hangzhou 310058, Peoples R China
[2] Zhejiang Univ, Key Lab Environm Remediat & Ecol Hlth, China Minist Educ, Hangzhou 310058, Peoples R China
[3] Zhejiang Univ, Zhejiang Prov Key Lab Subtrop Soil & Plant Nutr, Hangzhou 310058, Peoples R China
基金
中国国家自然科学基金;
关键词
Stream water quality; Landscape metrics; Spatial scales effects; Seasonal sensitivity analysis; Partial redundancy analysis; Nonparametric change-point analysis;
D O I
10.1016/j.watres.2021.116811
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Physiography and land use patterns influence streams water quality by affecting non-point source (NPS) pollution process. However, each landscape factor may affect the NPS pollution process differently with the variations of the spatial scale and season. Thus, quantitative analysis of each landscape metrics scale effect and determination of the abrupt change-point in the relationship between stream water quality and the metrics is very helpful for landscape planning of water quality protection. Based on water quality monitoring data for four years in 12 sub-watersheds of a typical headwater watershed in Eastern China, we adopted regular and partial redundancy methods to quantify the spatial scale effects and seasonal dependence of various landscape metrics impact on stream water quality, and then to identify the abrupt change-point of the water quality along the gradient of landscape metrics. Results revealed that the pure effects of different categories of landscape metrics on stream water quality were in the following order: landscape configuration metrics (20.5-31.6%) > physiographic metrics (4.0-15.9%) >landscape composition metrics (3.2-7.5%). The spatial scale effect of physiography impact on stream water quality was the most significant, while the impact of landscape configuration on water quality had the highest seasonal sensitivity. The overall water quality variation was better explained by buffer zone scale than by catchment scale landscape characteristics, and this phenomenon was more obvious during the wet season than during the dry season. In the studied watershed, we identified the largest patch index of farmland (LPI far) and the landscape shape index of forest (LSI for) as the key landscape metrics at sub-watershed scale and buffer zone scale, respectively. The LPIfar > 7.0% at the sub-watershed scale and LSI for < 5.5 at the buffer zone scale were suggested as the preferred landscape planning parameters to protect the stream water quality efficiently. Results indicated that, to protect water quality, landscape regulation should follow the scale-adaptability measures and consider the landscape thresholds, which cause abrupt changes in water quality. (C) 2021 Elsevier Ltd. Allrightsreserved.
引用
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页数:11
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