Abstract
Uncertainty about environmental mitigation needs at existing and proposed hydropower projects makes it difficult for stakeholders to minimize environmental impacts. Hydropower developers and operators desire tools to better anticipate mitigation requirements, while natural resource managers and regulators need tools to evaluate different mitigation scenarios and order effective mitigation. Here we sought to examine the feasibility of using a suite of multi-faceted explanatory variables within a spatially explicit modeling framework to fit predictive models for future environmental mitigation requirements at hydropower projects across the conterminous U.S. Using a database comprised of mitigation requirements from more than 300 hydropower project licenses, we were able to successfully fit models for nearly 50 types of environmental mitigation and to apply the predictive models to a set of more than 500 non-powered dams identified as having hydropower potential. The results demonstrate that mitigation requirements are functions of a range of factors, from biophysical to socio-political. Project developers can use these models to inform cost projections and design considerations, while regulators can use the models to more quickly identify likely environmental issues and potential solutions, hopefully resulting in more timely and more effective decisions on environmental mitigation.
Original language | English |
---|---|
Pages (from-to) | 888-918 |
Number of pages | 31 |
Journal | Science of the Total Environment |
Volume | 566-567 |
DOIs | |
State | Published - Oct 1 2016 |
Funding
This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). We would like to thank R. McManamay for statistical advice, suggestions for predictor variables, and valuable comments on this manuscript. We also thank S.C. Kao for assistance with the NHAAP database and FERC licenses. This study was funded by the U.S. Department of Energy (DOE) Energy Efficiency and Renewable Energy Office , Wind and Water Power Technologies Program through Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-00OR22725 .
Funders | Funder number |
---|---|
U.S. Department of Energy | |
Office of Energy Efficiency and Renewable Energy | |
Oak Ridge National Laboratory | DE-AC05-00OR22725 |
Keywords
- Environmental
- Hydropower
- Mitigation
- Modeling
- Prediction
- Sociopolitical