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首页> 外文期刊>Ecological indicators >Integrating multi indices for identifying priority management areas in lowland to control lake eutrophication: A case study in lake Gehu, China
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Integrating multi indices for identifying priority management areas in lowland to control lake eutrophication: A case study in lake Gehu, China

机译:集成多指标以识别低地优先管理区以控制湖泊富营养化:以中国格湖为例

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摘要

Best management practices (BMPs) for controlling non-point sources (NPS) nutrient loss at the watershed scale has become a global hotspot due to the severe eutrophication worldwide. Identifying priority management areas (PMAs) is particularly important for designing BMPs. However, such an endeavor is extremely challenging due to random spatio-temporal processes, particularly in lowland. In this study, a new model-based method was proposed to identify sensitive areas with the largest contribution to lake eutrophication. This method integrated three indices derived from three models to assess the risk due to nutrient dynamics in loss processes including sources, sinks and transformation of nutrients within the polders (lowland artificial watersheds) around Lake Gehu, eastern China and nutrient transport in the river and lake areas. A total of 67 polders in a 2 km wide buffer of Lake Gehu, as assessment units, were identified into four-level PMAs based on the integration. Results showed that 77% polders were Level II PMAs, and none were Level IV PMAs. The ratios of Level I and Level III PMAs was 18% and 5%, respectively. These values indicated that nutrient transport in rivers or lakes is vital for lake eutrophication and significantly influences the PMA map. This study demonstrated that the process-based model for risk assessment in identifying PMAs is useful in guiding decision-making for controlling lake eutrophication.
机译:由于世界范围内严重富营养化,在流域范围内控制非点源(NPS)养分流失的最佳管理方法(BMP)已成为全球热点。确定优先级管理区域(PMA)对于设计BMP尤其重要。但是,由于随机的时空过程,特别是在低地,这种努力极具挑战性。在这项研究中,提出了一种基于模型的新方法来识别对湖泊富营养化贡献最大的敏感区域。该方法综合了来自三个模型的三个指数,以评估损失过程中养分动态的风险,包括中国东部格湖周围田(低地人工流域)中养分的来源,汇和转换以及养分在河湖中的迁移地区。根据整合,在格湖2公里宽的缓冲区中共有67个田作为评估单位,被划分为四个级别的PMA。结果表明,77%的ders田为II级PMA,IV级PMA没有。 I级和III级PMA的比例分别为18%和5%。这些值表明,河流或湖泊中的养分迁移对于湖泊富营养化至关重要,并显着影响PMA图。这项研究表明,基于过程的风险评估模型可以识别PMA,可用于指导控制湖泊富营养化的决策。

著录项

  • 来源
    《Ecological indicators》 |2020年第5期|106103.1-106103.12|共12页
  • 作者

  • 作者单位

    Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci 73 East Beijing Rd Nanjing 210008 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci 73 East Beijing Rd Nanjing 210008 Peoples R China|Nanjing Hydraul Res Inst Ctr Ecoenvironm Res Nanjing 210098 Peoples R China;

    Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci 73 East Beijing Rd Nanjing 210008 Peoples R China;

    Changzhou Hydrol & Water Resources Survey Bur Jia 1 Xingye Rd Changzhou 213022 Peoples R China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Non-point sources; Nutrient loss; Priority management area identification; Model-based method; Lowland polder;

    机译:非点源;营养损失;优先管理区识别;基于模型的方法;低地田;

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