College of Environmental and Resources Sciences, Zhejiang University, Hangzhou, 310058, PR China; Zhejiang Provincial Key Laboratory of Subtropical Soil and Plant Nutrition, Zhejiang University, Hangzhou 310058, PR China.
College of Environmental and Resources Sciences, Zhejiang University, Hangzhou, 310058, PR China; China Ministry of Education Key Lab of Environment Remediation and Ecological Health, Zhejiang University, Hangzhou 310058, PR China.
Water Res. 2021 Mar 1;191:116811. doi: 10.1016/j.watres.2021.116811. Epub 2021 Jan 6.
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) and the landscape shape index of forest (LSI) as the key landscape metrics at sub-watershed scale and buffer zone scale, respectively. The LPI > 7.0% at the sub-watershed scale and LSI < 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.
自然地理特征和土地利用方式通过影响非点源（NPS）污染过程来影响河流水质。然而，由于空间尺度和季节的变化，每个景观因素可能会以不同的方式影响 NPS 污染过程。因此，定量分析每个景观指标的尺度效应，并确定流域水质与指标之间关系的突变点，对于水质保护的景观规划非常有帮助。本研究基于中国东部一个典型源头流域 12 个子流域四年的水质监测数据，采用常规冗余分析和偏冗余分析方法，量化了各种景观指标对河流水质的空间尺度效应和季节依赖性，进而识别了景观指标梯度上水质的突变点。结果表明，不同类别景观指标对河流水质的纯效应大小依次为：景观格局指标（20.5-31.6%）>自然地理指标（4.0-15.9%）>景观组成指标（3.2-7.5%）。自然地理对河流水质的影响在空间尺度上的效应最显著，而景观格局对水质的季节敏感性最高。与流域尺度的景观特征相比，缓冲带尺度更能解释整体水质变化，这种现象在雨季比在旱季更为明显。在研究流域内，我们确定了农田最大斑块指数（LPI）和森林景观形状指数（LSI）分别为子流域尺度和缓冲带尺度的关键景观指标。建议在子流域尺度上 LPI>7.0%，在缓冲带尺度上 LSI<5.5%作为有效的景观规划参数，以保护河流水质。结果表明，为了保护水质，景观调控应遵循尺度适应性措施，并考虑引起水质突变的景观阈值。
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