Landscape patterns regulate non-point source nutrient pollution in an agricultural watershed
Landscape patterns regulate non-point source nutrient pollution in an agricultural watershed
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景观格局调节农业流域面源养分污染
DOI：
10.1016/j.scitotenv.2019.03.014
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发表时间：
2019
影响因子：
9.8
通讯作者：
Lu Jun
中科院分区：
文献类型：
--
作者：
Wu Jianhong;Lu Jun
景观格局对水文循环和非点源营养盐污染过程有重要影响。然而，人们对景观特征与河流水质之间的定量关系知之甚少，很少有人研究河流水质随景观指标梯度的突变。本研究在中国东部一个典型的集约化农业流域进行，包括13个不同景观格局指数的子流域。采用冗余度分析、非参数偏差法、Bootstrap抽样等统计方法揭示了景观格局指标与水质变量之间的定量关系，并对不同景观格局梯度下河流水质突变现象进行了探讨。结果表明，景观格局对河流水质有显著影响，旱季和雨季景观格局对河流水质的影响有较大差异。在研究流域内，景观格局指数可以解释枯水期和雨季河流水质总变异的71.1%和55.3%。景观格局构型指标比其组成指标对水质变化的解释能力更强。在枯水期，最重要的景观指数林地最大斑块指数(LPIfor)解释了水质总变异的37.9%。而在雨季，最重要的景观指数是农田最大斑块指数，可以解释32.4%的变异。在所研究的流域中，当LPIfor值为35%或LPIfar值超过50%时，水质通常会发生突变，此时河水发生变化的可能性会大幅上升。
Landscape pattern critically affects hydrological cycling and the processes of non-point source nutrients pollution. However, little is known about the quantitative relationship between landscape characteristics and the river water quality, and very few studies have addressed the abrupt changes in river water quality with the gradient of landscape metrics. The present study was conducted in a typically intensive agriculture watershed of eastern China including 13 sub-watersheds with different landscape pattern metrics. We adopted redundancy analysis, nonparametric deviance reduction approach, bootstrap sampling and other statistical methods to reveal the quantitative relationship between landscape pattern metrics and water quality variables; then, the phenomenon of an abrupt change in river water quality was explored with different landscape pattern gradients. The results show that landscape pattern significantly affects river water quality, and this effect was quite different in dry and rainy seasons. In the studied watershed, landscape pattern metrics could respectively explain 71.1% and 55.3% of the total variance in the river water quality in dry and rainy seasons. The configuration metrics of landscape pattern had a stronger ability than their composition metrics to explain the variance in water quality. In the dry season, largest patch index of forestland (LPIfor), the most important landscape index, explained 37.9% of the total variance in water quality. While, in the rainy season, the most important landscape index was the largest patch index of farmland (LPIfar), and it could explain 32.4% of that variance. In the studied watershed, when theLPIforwas <35% orLPIfarwas over than 50%, water quality would typically change abruptly, at which the probability of a change in river water would suddenly rise substantially.