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Leveraging Big Data Towards Functionally-Based, Catchment Scale Restoration Prioritization.

John P Lovette1, Jonathan M Duncan2, Lindsey S Smart3

  • 1Department of Geography, University of North Carolina, Chapel Hill, NC, USA. jpl717@live.unc.edu.

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Summary
This summary is machine-generated.

Freshwater degradation requires better restoration planning. A new tool prioritizes stream and wetland restoration by assessing watershed health and restoration potential, guiding efforts to improve water quality and aquatic habitats.

Keywords:
CatchmentRestorationWatershed approachWatershed function

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Area of Science:

  • Environmental Science
  • Ecology
  • Water Resource Management

Background:

  • Freshwater degradation is widespread, leading to a growing restoration industry.
  • Current restoration efforts are often prioritized haphazardly across large areas.
  • Effective prioritization tools are needed for efficient stream and wetland restoration.

Purpose of the Study:

  • To develop a framework for prioritizing stream and wetland restoration at a catchment scale.
  • To integrate vetted, distributed data models for assessing watershed conditions and restoration potential.
  • To guide restoration efforts towards areas with the greatest potential for ecological improvement.

Main Methods:

  • Developed the River Basin Restoration Prioritization tool.
  • Utilized National Hydrography Dataset stream reaches and catchments in North Carolina.
  • Modeled hydrologic, water quality, and aquatic habitat conditions using peak flood flow, nutrient loading, and species distribution models.

Main Results:

  • Calculated baseline conditions and restoration potential for stream reaches and catchments.
  • Compared restoration potential across different catchments within river subbasins.
  • Identified areas where restoration efforts could be most effectively focused.

Conclusions:

  • The tool provides a data-driven framework for prioritizing restoration activities.
  • The modular design allows for future integration of improved datasets and broader scalability.
  • This approach can enhance the effectiveness of freshwater ecosystem restoration efforts.