流域景观格局与河流水质的多变量相关分析

流域内的景观格局改变是人类活动的宏观表现,会对河流水质产生显著影响,因此明确影响水质变化的关键景观因子,对于深入了解景观对水质的影响机制具有重要的研究价值。选择广东省淡水河流域为研究对象,以2007年ALOS卫星影像以及水质监测数据为基础,运用空间分析和多变量分析方法,分析淡水河流域景观格局与河流水质的相关关系。用包括流域和河岸带尺度的景观组成和空间结构信息的景观指数表征景观格局,用Spearman秩相关分析、多元线性逐步回归模型和典型相关分析(CCA)研究景观指数和水质指标的相关关系。研究结果表明:林地、城镇用地和农业用地占淡水河流域总面积超过90%,其中城镇用地超过20%。多元线性逐步回归分析和CCA结果说明水质指标受到多个景观指数的综合影响,反映了景观格局对水质的复杂影响机制。流域景观格局对河流水质有显著影响,流域尺度的景观指数比河岸带尺度的景观指数对水质影响更大。城镇用地比例是影响耗氧污染物和营养盐等污染物浓度最重要的景观指数,林地和农业用地对水质的影响较小。另外,景观破碎化对pH值、溶解氧和重金属等水质指标有显著影响。CCA的第一排序轴解释了景观指数与水质指标相关性的54.0%,前两排序轴累积能解释景观指数与水质指标相关性的87.6%,前两轴分别主要表达了城市化水平和景观破碎化水平的变化梯度。淡水河流域的景观格局特征从上游到下游呈现出城市—城乡交错—农村的景观梯度,水质变化也对应了这个梯度的变化,说明人类活动引起的流域土地覆盖及土地管理措施变化会对水质变化产生显著影响。

Multivariate correlation analysis between landscape pattern and water quality

Water quality variation is generally linked to the change of landscape pattern in watershed, which represents the main impact of human activities in macroscopic view. Therefore, identifying the crucial landscape factors that affect water quality variation is valuable for understanding the mechanism that landscape may affect water quality. Multivariate analysis tools are effective methods to deal with complex correlations between landscape pattern and water quality. Besides, advances of remote sensing (RS) and geographic information systems (GIS) technologies have made regional and watershed scale studies much more feasible. This study was conducted along Danshui River watershed, a branch of Dongjiang River in Guangdong Province. The correlation between landscape pattern and water quality of Danshui River was represented by using spatial analysis and multivariate analysis methods base on ALOS satellite image and water quality monitoring data in 2007. Landscape metrics, including information of landscape composition and spatial configuration, were used to represent landscape pattern. In order to cover overall landscape information, landscape metrics on both watershed scale and riparian scale were used. Spearman's rank correlation analysis, multiple linear regression models with step-wise and canonical correlation analysis (CCA) were used to reveal the linkage between landscape metrics and water quality. The results show that forest, urban and agriculture land use are accounted for more than 90% of the total area in Danshui River watershed, while the area proportion of urban land exceeds 20%. Results of multiple linear regression models with step-wise and CCA showed that water quality indicators were affected by more than one landscape metric. The variation of water quality was influenced by landscape pattern significantly. The landscape metrics in watershed scale revealed more information of water quality variation than landscape metrics in riparian scale. The proportion of urban land use proportion had the greatest impact on water quality. Spearman's rank correlation analysis and multiple linear regression models showed the proportion of urban land use was the most important contributing factor to cause variation of oxygen consuming pollutants and nutrients. However, forest and agriculture land use had less influence on water quality. On the other hand, landscape metrics about landscape fragmentation were crucial factors to affect indicators of water quality, such as pH, DO and heavy metals. The result of CCA indicated that the first ordination axis could explain 54.0% of the correlations between landscape metrics and water quality indicators, and the first two ordination axes could cumulatively explain 87.6% of the correlations between landscape metrics and water quality. The result of CCA revealed that water quality had an obvious trend with the varying landscape gradient. The first two ordination axes mainly represented urbanization gradient and landscape fragmentation gradient respectively. Landscape characteristics in the study area showed a gradient of urban, urban-rural fringe, rural from upstream to downstream of Danshui River watershed. The distribution of pollutants concentration was corresponded with the gradient of landscape pattern in the watershed. Land use and cover change is an integrated result due to human activities, and change the state of eco-system of river and watershed significantly. It's highly reasonable that the water quality must correspond to the change of watershed landscape.