地理学报 ›› 2018, Vol. 73 ›› Issue (9): 1765-1777.doi: 10.11821/dlxb201809012

• 气候变化与生态环境 • 上一篇    下一篇

五华河流域非点源污染风险区和风险路径识别

陈裕婵1(),张正栋1(),万露文2,张杰1,杨传训3,叶晨1,李青圃1   

  1. 1. 华南师范大学地理科学学院,广州 510631
    2. 密歇根州立大学地球与环境科学系,美国 东兰辛 48823
    3. 广州地理研究所,广州 510070
  • 收稿日期:2017-07-10 出版日期:2018-09-25 发布日期:2018-09-19
  • 基金资助:
    国家自然科学基金项目(41471147)

Identifying risk areas and risk paths of non-point source pollution in Wuhua River Basin

CHEN Yuchan1(),ZHANG Zhengdong1(),WAN Luwen2,ZHANG Jie1,YANG Chuanxun3,YE Chen1,LI Qingpu1   

  1. 1. School of Geography, South China Normal University, Guangzhou 510631, China
    2. Department of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48823, USA
    3. Guangzhou Institute of Geography,Guangzhou 510070,China
  • Received:2017-07-10 Online:2018-09-25 Published:2018-09-19
  • Supported by:
    National Natural Science Foundation of China, No.41471147

摘要:

非点源污染是亟待解决的水环境问题之一,确定非点源污染过程中的潜在风险区和风险路径是解决非点源污染问题的关键。引入景观生态学中的最小累积阻力模型,以高污染负荷的耕地、建设用地为“源”,运用表示下垫面产流性质的地形湿度指数和CN值构建阻力面,对五华河流域的非点源污染高风险区和风险路径进行可视化识别和分析。结果显示:① 在下垫面产流作用下,五华河流域低产流区主要分布在流域西南部,高产流区呈“人”形贯穿全流域。② 最小累积阻力模型能够有效识别流域内非点源污染风险区和风险路径,五华河流域非点源污染高风险区分布于迥龙、田心、龙母、铁场、登云、通衢、鹤市、紫市、岐岭、华城、转水、潭下、水寨等地的河道两岸,以耕地为“源”的非点源污染风险路径与建设用地为“源”的非点源污染风险路径在空间分布上差异较大。③ 耕地对五华河水质的影响大于建设用地对五华河水质的影响,耕地中的富营养物质和沉积物更容易随地表径流进入受纳水体。④ 流域尺度上治理以耕地为“源”的非点源污染应在邻近耕地的河流两岸建立一定宽度的植被缓冲区,治理以建设用地为“源”的非点源污染宜围绕关键源区进行治理。本研究为非点源污染风险区和风险路径的识别提供一种新的思路,为进一步开展非点源污染治理提供理论依据。

关键词: 非点源污染, 风险区识别, 风险路径识别, 最小累积阻力模型, 五华河流域

Abstract:

Non-point source pollution is one of the most severe problems impacting water environments. Identifying potential risk areas and risk paths contributing to non-point source pollution is the soution to this problem. This study introduces the minimum cumulative resistance model of landscape ecology, which is based on land use and soil mapping at a scale of 1∶100000 and DEM data with a resolution of 30 m. The model takes high pollution-loaded cultivated land and construction land as the main sources and uses the Topographic Wetness Index and Runoff Curve Numbers, which can describe the underlying resistance surface runoff yield characteristics, to visually identify and analyze the risk areas and risk paths of the Wuhua River Basin. The results show that underlying surface runoff production results in low-yield flow areas that are mainly concentrated in the southwest of the basin, while high-yield flow areas herringbone throughout the study area. The minimum cumulative resistance model can effectively identify the risk areas and risk paths in this basin. The high-risk areas of non-point source pollution are mainly distributed in Jionglong, Tianxin, Longmu, Tiechang, Dengyun, Tongqu, Heshi, Zishi, Qiling, Huacheng, Zhuanshui, Tanxia and Shuizai, which are located along both sides of the river. The spatial distributions of the risk paths of cultivated land and construction land are significantly different. The effects of cultivated land on water quality of the river are greater than those of construction land on it, and the nutrients and sediments from cultivated land are more likely to run into the receiving water via surface runoff. Vegetation buffer zones should be set up on both sides of the river adjacent to cultivated land when we deal with non-point source pollution that originates from cultivated land, and the harnessment of non-point source pollution originating from construction land should be monitored around major source areas. This study provides a novel method for the identification of source areas and risk paths of non-point source pollution and a theoretical basis to formulate future management strategies.

Key words: non-point source pollution, risk areas identification, risk paths identification, minimum cumulative resistance model, Wuhua River Basin