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三峡库区农田面源污染典型区域制图及其研究现状评价
引用本文:温兆飞,吴胜军,陈吉龙,吕明权,王小晓,肖艳.三峡库区农田面源污染典型区域制图及其研究现状评价[J].长江流域资源与环境,2014,23(12):1684.
作者姓名:温兆飞  吴胜军  陈吉龙  吕明权  王小晓  肖艳
作者单位:(1.中国科学院重庆绿色智能技术研究院,中国科学院水库水环境重点实验室,重庆 401122; 2.中国科学院大学,北京 100049
基金项目:重庆市科技攻关计划项目(cstc2012ggB20001)“基于‘3S’技术的三峡库区农业面源污染动态监测技术研究与应用示范”;三峡后续规划项目“三峡库区生态屏障区面源污染防控关键技术与示范研究”
摘    要:利用GIS空间叠加分析方法,通过遴选影响库区农田面源污染关键因子,获得了3大类8小类库区农田面源污染典型区,从空间分布格局的角度对这些典型区做了深入的比较分析,并提出土壤类型是导致不同典型区分布差异的主导因素。在此基础上,对国内外已在库区内开展农田面源污染研究工作的相关研究区进行了典型性分布现状评价,研究认为:(1)目前针对“紫色土-旱地-林地-水田”农田面源污染典型区的研究区设置较完善,在整个库区中的分布格局也较合理,有利于相关研究成果在该区推广应用;(2)对“黄壤-旱地-林地”典型区研究较少,无法满足整个库区尺度下的应用需求,需加强对库区中部和库尾相关区域的研究区配置;(3)由于对“石灰(岩)土-林地”典型区的研究缺失,亟需开展该区内农田面源污染研究工作。研究获得的不同类型典型区分布图可为相关研究区的选择提供必要的决策依据。此外,研究虽只是针对农田面源污染开展了典型区制图分析,但研究方法具有一定的推广性,在必要数据支持下,可应用于三峡库区或其他区域农业面源污染典型区制图研究

关 键 词:GIS空间分析  农业面源污染  非点源污染  水土流失  研究区选择  典型性

TYPICAL REGION MAPPING OF AGRICULTURAL NON POINT SOURCE POLLUTION IN THE THREE GORGES RESERVOIR AREA: PATTERN ANALYSIS AND STATUS EVALUATION
WEN Zhao fei,,WU Sheng jun,CHEN Ji long,LV Ming quan,WANG Xiao xiao,XIAO Yan.TYPICAL REGION MAPPING OF AGRICULTURAL NON POINT SOURCE POLLUTION IN THE THREE GORGES RESERVOIR AREA: PATTERN ANALYSIS AND STATUS EVALUATION[J].Resources and Environment in the Yangtza Basin,2014,23(12):1684.
Authors:WEN Zhao fei    WU Sheng jun  CHEN Ji long  LV Ming quan  WANG Xiao xiao  XIAO Yan
Institution:(1.Key Laboratory of Reservoir Aquatic Environment, Chinese Academy of Sciences, Chongqing Insitute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 401122, China; 2.University of Chinese Academy of Sciences, Beijing 100049, China
Abstract:With more and more studies focusing on the agricultural non point source pollution (ANSP) in the Three Gorges Reservoir area(TGRA), promotion the applicability of these relevant research results is of great significant for the regional ecological environment protection. However, the key in this process is to make a comprehensive survey for the various typical areas of the ANSP in TGRA and their spatial distribution patterns. This can not only make full use of the related research resources, such as research data or results about the ANSP in the TGRA, but also can guide the control managements and policy orientations about ANSP in the TGRA. To these ends, we first screened out the key factors, namely soil type, land use type, and the soil erosion intensity, that can mainly affect the ANSP and prepared their corresponding spatial data in the TGRA. After then, 24 different types of regions of ANSP in TGRA, were mapped by using the GIS spatial analysis (i.e., overlay analysis) of the prepared data in the TGRA. In order to find out the type region of ANSP in TRGA, 8 types of region were selected from these 24 types under the hypothesis that the typical region generally has large area. Each of these 8 mapped typical regions was further fully analyzed from the spatial distribution pattern point of view, and it was found that these patterns are more decided by the soil type than the other two factors. Based on this fact, the 8 typical regions were then can be grouped in 3 categories, namely “purple soil dryland forest paddy field” typical region, “yellow soil dryland forest” typical region, and “limestone soil forest” typical region.Finally, the typicality of the numerous study areas which have been selected and studied in the previous researches about the ANSP in the TGRA, were used to evaluate the research status of the typical regions. It was found that the “purple soil dryland forest paddy field” typical region has been heavily considered, and the spatial distribution of these study areas in this region is reasonable. Moreover, although the “yellow soil dryland forest” typical region has been certainly concerned, it is still unable to meet the application requirements on the entire reservoir scale. Nevertheless, as there was seldom studies focused on the ANSP in the “limestone soil forest” typical region, it is suggested that more works should be put forward on this type region. Meanwhile, the maps of different classes of typical regions of ANSP in the TGRA can provide a basis for decision making in the study area selection in the future. Although this study is mainly focused on the typical region of ANSP in the TGRA, the method used in this paper can be further applied to other regions
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