Abstract
The persistence of freshwater degradation has necessitated the growth of an expansive stream and wetland restoration industry, yet restoration prioritization at broad spatial extents is still limited and ad-hoc restoration prevails. The River Basin Restoration Prioritization tool has been developed to incorporate vetted, distributed data models into a catchment scale restoration prioritization framework. Catchment baseline condition and potential improvement with restoration activity is calculated for all National Hydrography Dataset stream reaches and catchments in North Carolina and compared to other catchments within the river subbasin to assess where restoration efforts may best be focused. Hydrologic, water quality, and aquatic habitat quality conditions are assessed with peak flood flow, nitrogen and phosphorus loading, and aquatic species distribution models. The modular nature of the tool leaves ample opportunity for future incorporation of novel and improved datasets to better represent the holistic health of a watershed, and the nature of the datasets used herein allow this framework to be applied at much broader scales than North Carolina.
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Acknowledgements
This project was funded by the North Carolina Department of Natural Resources, Ecosystem Enhancement Program. We would like to thank two anonymous reviewers for their helpful contributions to this manuscript, as well as Steve Preston and Chad Wagner of USGS for their review comments. We would also like to thank staff at NC DEQ for their guidance in developing, implementing, and continuing to improve this tool.
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Lovette, J.P., Duncan, J.M., Smart, L.S. et al. Leveraging Big Data Towards Functionally-Based, Catchment Scale Restoration Prioritization. Environmental Management 62, 1007–1024 (2018). https://doi.org/10.1007/s00267-018-1100-z
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DOI: https://doi.org/10.1007/s00267-018-1100-z