McManamay Ryan A, Parish Esther S, DeRolph Christopher R
Department of Environmental Science, Baylor University, Waco, TX 76798-7266, United States.
Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, United States.
Data Brief. 2020 Apr 28;30:105629. doi: 10.1016/j.dib.2020.105629. eCollection 2020 Jun.
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" [1]. 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.
本文所述的数据集为一个决策支持原型（DSP）工具包奠定了基础，该工具包旨在帮助利益相关者确定河流生态系统的哪些方面受到了水电的影响。DSP工具包及其应用在文章《基于证据的指标方法指导水电开发的初步环境影响评估》[1]中有所介绍和描述。DSP的开发以及决策支持的输出主要围绕42个河流功能指标展开，这些指标通过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这六个主要类别来描述河流生态系统的维度。DSP中有三种主要工具：基于科学的问卷（SBQ）、环境包络模型（EEM）和河流功能联系评估工具（RFLAT）。SBQ是一种结构化的调查式问卷，其目的是提供哪些指标受到水电影响的证据。基于全球文献综述，从关于大坝对河流影响的一般假设中提出了140个问题。EEM是一个基于多个变量预测水电影响指标可能性的模型。EEM的预期用途是在新水电开发的情况下，当SBQ的结果不完整或高度不确定时使用。EEM是通过汇编一个包含大坝、水库属性以及有关环境问题的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记录在这些地点的生态指标数据相结合。该模型通过247个“包络”和权重因子运行，代表每个变量对每个指标的个体影响，所有这些都可以通过电子表格获得。最后，RFLAT是一个用于检查指标之间因果关系的工具。基于文献综述假设了指标间的关系，并将其总结为节点和边数据集，以表示图形网络的结构。使用贝叶斯定理根据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。可以通过为SBQ添加更详细的问题来扩展和丰富数据集，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）将成为水电影响元分析的非常有用的数据集。
