潮河流域景观格局与非点源污染负荷关系研究

以密云水库上游潮河流域为研究区,在HSPF模型模拟的基础上,利用CCA排序和路径分析等多种统计分析方法,分析不同水平景观格局与非点源污染过程的相关关系,确定各景观指数对非点源污染的影响和贡献程度.结果表明:潮河流域景观格局与非点源污染过程的关系密切,二者的关系与空间尺度也有着紧密的关联.景观格局指数能累积解释55％的流域非点源污染负荷变化,土地利用面积比例的影响要大于景观格局指数,耕地是污染负荷的主要贡献源,而林地和草地较能有效控制污染物的输出.污染负荷受景观的破碎化、多样性和蔓延度的影响较大,流域内的景观越破碎、类型越丰富,斑块分布越零散,污染物的输出也就越多.斑块类型水平上,影响污染物负荷的指标因景观类型不同而异,其中,斑块密度(PD)和边缘密度(ED)是影响流域非点源污染负荷的共性指标.路径分析的结果表明,边缘密度(ED)、香农多样性指数(SHDI)、聚集度指数(AI)和蔓延度指数(CONTAG)是影响流域非点源污染负荷输出的主要景观格局变量,其中,香农多样性指数对TN、TP负荷的解释能力最大.较少的人类活动干扰和斑块类型的团聚分布能减少污染物输出的风险.

Relationship between landscape pattern and non-point source pollution loads in the Chaohe River Watershed

Chaohe River Watershed, located in the upstream area of Miyun Reservoir, was selected to study the relationship between landscape pattern and non-point source pollution (NPS) loads simulated from Hydrologic Simulation Program Fortran (HSPF) model. Canonical correspondence analysis (CCA) and path analysis were used to identify the key landscape types and patterns, and to determine the impacts of various landscape factors on non-point source pollution loads at different scales. The results showed that, between NPS pollution loads and landscape characteristics in the study area, there was a significant correlation, which was closely associated with the spatial scale. Landscape variables can explain 55% variation of nutrient exports. Compared to landscape pattern metrics, percentage of land use type area may predict pollution loads better. Agriculture land was the most contributor on NPS pollution in Chaohe river watershed. The agriculture land area was positively correlated with nutrient losses. The forest and grassland area variation had negative correlation with TN and TP losses. At the landscape level, the fragmentation metrics, contagion metrics and diversity metrics were the main pattern indices effectively affecting the variation of nutrient losses. More fragmentation, diversity and scattered distribution of the landscape pattern would result in more nutrient losses. At the patch-class level, landscape metrics affecting the spatial variation of pollution process varied with land use types. Among them, the patch density (PD) and edge density (ED) were the common indicators affecting the pollution loads. The result of path analysis suggested that edge density (ED), Shannon's diversity index (SHDI), aggregation index (AI) and contagion index (CONTAG) were the main pattern indices effectively affecting the nutrient pollution process. The interpretation capacity of SHDI to TN and TP loads was greater than the other indicators. Less disturbance of human activities and more aggregation of patches can reduce the risk of pollutant loss.