State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China.
State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
Sci Total Environ. 2018 Mar;616-617:376-385. doi: 10.1016/j.scitotenv.2017.10.316. Epub 2017 Nov 9.
It is important to assess river ecosystem health in large-scale basins when considering the complex influence of anthropogenic activities on these ecosystems. This study investigated the river ecosystem health in the Haihe River Basin (HRB) by sampling 148 river sites during the pre- and post-rainy seasons in 2013. A model was established to assess the river ecosystem health based on water physicochemical, nutrient, and macroinvertebrate indices, and the health level was divided into "very poor," "poor," "fair," "good," and "excellent" according to the health score calculated from the assessment model. The assessment results demonstrated that the river ecosystem health of the HRB was "poor" overall, and no catchments were labeled "excellent." The percentages of catchments deemed to have "very poor," "poor," "fair," or "good" river ecosystem health were 12.88%, 40.91%, 40.15%, and 6.06%, respectively. From the pre- to the post-rainy season, the macroinvertebrate health levels improved from "poor" to "fair." The results of a redundancy analysis (RDA), path analysis of the structural equation model (SEM), and X-Y plots indicated that the land use types of forest land and grassland had positive relationships with river ecosystem health, whereas arable land, urban land, gross domestic product (GDP) per capita, and population density had negative relationships with river ecosystem health. The variance partitioning (VP) results showed that anthropogenic activities (including land use and socio-economy) together explained 30.9% of the variations in river ecosystem health in the pre-rainy season, and this value increased to 35.9% in the post-rainy season. Land use intensity was the first driver of river ecosystem health, and socio-economic activities was the second driver. Land use variables explained 20.5% and 25.7% of the variations in river ecosystem health in the pre- and post-rainy season samples, respectively, and socio-economic variables explained 12.3% and 17.2% of the variations, respectively. The SEM results revealed that urban land had the strongest impact on water quality health and that forest land had the strongest impact on macroinvertebrate health. This study has implications for the selection of appropriate indicators to assess river ecosystem health and generated data to examine the effects of anthropogenic activities on river ecosystem health in a fast-growing region.
评估大型流域的河流生态系统健康状况非常重要，因为人类活动对这些生态系统的影响非常复杂。本研究于 2013 年在丰水期和枯水期分别采集了 148 个河流采样点，对海河流域的河流生态系统健康状况进行了调查。本研究建立了一个基于水质理化性质、营养盐和底栖无脊椎动物指标的河流生态系统健康评估模型，并根据评估模型计算的健康得分将健康水平划分为“很差”、“差”、“中”、“良”和“优”。评估结果表明，海河流域的河流生态系统健康状况总体上较差，没有流域被评为“优”。被评为“很差”、“差”、“中”和“良”的流域比例分别为 12.88%、40.91%、40.15%和 6.06%。从丰水期到枯水期，底栖无脊椎动物的健康水平从“差”改善到“中”。冗余分析（RDA）、结构方程模型（SEM）路径分析和 X-Y 图的结果表明，林地和草地的土地利用类型与河流生态系统健康呈正相关，而耕地、城市土地、人均国内生产总值（GDP）和人口密度与河流生态系统健康呈负相关。方差分解（VP）结果表明，人为活动（包括土地利用和社会经济）在丰水期共同解释了河流生态系统健康变化的 30.9%，在枯水期这一比例增加到 35.9%。土地利用强度是河流生态系统健康的第一驱动因素，社会经济活动是第二驱动因素。在丰水期和枯水期样本中，土地利用变量分别解释了河流生态系统健康变化的 20.5%和 25.7%，社会经济变量分别解释了 12.3%和 17.2%。SEM 结果表明，城市土地对水质健康的影响最大，而林地对底栖无脊椎动物健康的影响最大。本研究对选择合适的指标评估河流生态系统健康状况具有启示意义，并提供了评估人类活动对快速增长地区河流生态系统健康影响的数据。
