不同尺度景观空间分异特征对水体质量的影响——以深圳市西丽水库流域为例

The influence of landscape spatial difference on water quality at differing scales:A case study of Xili reservoir watershed in Shenzhen city

由于景观格局对水体质量的影响是依赖于研究尺度、并且随着时间尺度和空间尺度的变化而变化的,因此探讨不同尺度上景观格局变化对水质的影响程度具有重要的研究价值。从深圳市水库流域水环境保护的需求出发,选择西丽水库及其汇水子流域为研究对象,以陈利顶等提出的景观空间负荷对比指数分析方法为基础,区分高度、坡度和距离3个空间要素计算了分区和累积景观空间负荷对比指数,继而利用该指数对比分析了不同时空尺度上"源""汇"景观空间分异特征变化对水质的影响程度。研究结果表明,不同尺度上的景观空间分异特征可以显著影响"源""汇"景观在非点源污染输出和削减方面贡献程度的对比关系,从而影响水体质量。由于不同尺度上景观空间分异特征的复杂性以及"源""汇"景观在数量和类型方面差异的显著性,景观空间负荷对比指数的对比关系并不能良好地反映不同空间尺度上景观空间分异特征对水质影响的差异状况,因此必须针对不同的研究尺度有选择的应用该方法来探讨景观空间分异特征对水质的影响程度。

The influence of landscape pattern on water quality will change depending upon scale, so it is meaningful to probe how changes in landscape pattern influence water quality at differing spatial-temporal scales. For water-environment protection of reservoir watersheds in Shenzhen city, this paper took Xili reservoir watershed and its sub-watersheds as research areas. This paper studied the influence of landscape spatial differences on water quality at differing temporal and spatial scales. At the temporal scale, this paper chose water quality monitoring data at the Xili reservoir outlet in 2000 and 2001, which was more heavily influenced by non-point pollution than point pollution. Spatially, due to the limits of data, this paper divided data into two spatial scales： the Xili reservoir watershed and its sub-watersheds. The Location-weighted Landscape Contrast Index （LCI） was developed and applied by Prof. Chen Li-Ding. It is used to analyze the relative influence of landscape spatial elements upon water quality. This paper first suggested some improvements to the LCI and then used those improvements to calculate elevation, slope and distance LCI with divisional or cumulative characteristics at the Xili reservoir watershed and its sub-watersheds. Then we used this index to analyze the influence of ‘Source＇ and ‘ink＇ landscape spatial difference changes upon water quality at differing temporal-spatial scales. The results show that LCI can clarify the differing roles of‘Source＇ and‘Sink＇ landscape in the non-point pollution process. We can use this method to analyze how landscape spatial differences influence water quality. Moreover, LCI reveals that three main spatial elements - elevation, slope and distance - limit human activities upon landforms. So we should exploit the ability of natural landforms to intercept and decrease non-point pollution. However, LCI also has some shortcomings in practical usage. It is influenced by the complexity of the landscape spatial difference and dramatically influenced by differences in quantity and type of‘Source＇ and ‘Sink＇ landscapes. It would therefore not be suitable for analysis at differing spatial scales for contrasting scale effects.So we should use the LCI method at appropriate scales, which will help the realization of research aims.