Results for “Visualization Toolkits” — Page 9

DOE OSTI2022

Community Planning for Solar: Conducting a Community Solar Survey

This guide is designed to assist community officials, volunteers, and regional planning agency staff in conducting a survey of residents to learn about attitudes and development preferences towards solar energy within the community. The guide will provide the key steps, timelines, distribution options, ethics, and considerations that should be made in developing a survey distribution strategy. The guide also contains an overview of various question types and response categories, with considerations for the type of data that are needed for the study. The guide concludes with recommendations for managing data and databases, data visualization, and reporting results. The appendix includes samples of materials used in a solar energy survey, from invitation letters to the survey. This document is intended to provide a practical guide for implementing a survey in communities that are proactively planning for solar development. The guide offers a practical “how-to” plan for conducting a survey. It may be advantageous to consult with an expert in survey development who will be familiar with any methods and techniques described in this guide, but this is not necessary. This guide offers basic considerations and examples of questions and analyses to help a community understand preferences of the community.

14 SOLAR ENERGY↗

DOE OSTI2020

A dataset of eco-evidence tools to inform early-stage environmental impact assessments of hydropower development

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.

13 HYDRO ENERGY↗