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首页> 美国卫生研究院文献>Data in Brief >A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development
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A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development

机译：生态证据工具数据集可为水电开发的早期环境影响评估提供信息

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The datasets described herein provide the foundation for a decision support prototype (DSP) toolkit aimed at assisting stakeholders in determining evidence of which aspects of river ecosystems have been impacted by hydropower. The DSP toolkit and its application are presented and described in the article “Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development” . Development of the DSP and the output for decision support centralize around 42 river function indicators describing the dimensionality of river ecosystems through six main categories: biota and biodiversity, water quality, hydrology, geomorphology, land cover, and river connectivity. Three main tools are represented in the DSP: A science-based questionnaire (SBQ), an environmental envelope model (EEM), and a river function linkage assessment tool (RFLAT). The SBQ is a structured survey-style questionnaire whose objective is to provide evidence of which indicators have been impacted by hydropower. Based on a global literature review, 140 questions were developed from general hypotheses regarding the impacts of dams on rivers. The EEM is a model to predict the likelihood of hydropower impacting indicators based on a several variables. The intended use of the EEM is for situations of new hydropower development where results of the SBQ are incomplete or highly uncertain. The EEM was developed through the compilation of a dataset containing attributes of dams, reservoirs, and geospatial information on environmental concerns, which was combined with data on ecological indicators documented at those sites through literature review. The model operates through 247 “envelopes” and weighting factors, representing the individual effect of each variable on each indicator, all available through spreadsheets. Finally, the RFLAT is a tool to examine causal relationships amongst indicators. Inter-indicator relationships were hypothesized based on literature review and summarized into node and edge datasets to represent the structure of a graphical network. Bayes theorem was used estimate conditional probabilities of inter-indicator relationships based on the output of the SBQ. Nodes and edges were imported into R programming environment to visualize ecological indicator networks. The datasets can be expanded upon and enriched with more detailed questions for the SBQ, building upon the EEM with to develop more sophisticated models, and identifying new relationships for the RFALT. Additionally, once the tools are applied to numerous hydropower developments, the output of the tools (e.g. evidence of impacted indicators) becomes a very useful dataset for meta-analyses of hydropower impacts.
机译：本文所述的数据集提供了决策支持原型（DSP）工具箱的基础，该工具箱旨在帮助利益相关者确定河流生态系统的哪些方面已受到水电影响的证据。在“基于证据的指标方法来指导水电开发的初步环境影响评估”一文中介绍和描述了DSP工具包及其应用。 DSP的开发和决策支持的输出集中在约42个河流功能指标上，这些指标通过六个主要类别描述了河流生态系统的维度：生物群和生物多样性，水质，水文，地貌，土地覆盖和河流连通性。 DSP中包含三个主要工具：基于科学的调查表（SBQ），环境包络模型（EEM）和河流功能链接评估工具（RFLAT）。 SBQ是一种结构化的调查式问卷，其目的是提供证据证明哪些指标已受到水电影响。根据全球文献综述，从大坝对河流的影响的一般假设中提出了140个问题。 EEM是基于几个变量来预测水电影响指标的可能性的模型。 EEM的预期用途是用于SBQ结果不完全或高度不确定的新水电开发。 EEM是通过汇编包含有关环境问题的大坝，水库和地理空间信息的数据集而开发的，该数据集与通过文献综述在这些站点上记录的生态指标数据相结合。该模型通过247个“信封”和权重因子（代表每个变量对每个指标的单独影响）进行操作，所有这些均可通过电子表格获得。最后，RFLAT是检查指标之间因果关系的工具。基于文献回顾假设了指标间的关系，并将其汇总到节点和边缘数据集中以表示图形网络的结构。贝叶斯定理用于根据SBQ的输出估计指标间关系的条件概率。将节点和边导入R编程环境以可视化生态指标网络。可以针对SBQ扩展并充实更详细的问题，在EEM的基础上开发更复杂的模型，并为RFALT识别新的关系。此外，一旦将这些工具应用于众多水电开发项目，这些工具的输出（例如，受影响指标的证据）就成为对水电影响进行荟萃分析的非常有用的数据集。

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