- 1 CNH-L: Linking land-use decision making, water quality, and lake associations to understand human-natural feedbacks in lake catchments
- 2 News & Project Events
- 3 Important Team Resources
- 4 Project Information
- 5 Acknowledgments
- 6 MediaWiki Pointers
CNH-L: Linking land-use decision making, water quality, and lake associations to understand human-natural feedbacks in lake catchments
Worldwide, people benefit greatly from the irreplaceable services provided by freshwater lakes, such as drinking water, recreation, and fisheries. However, human activities in lake catchments contribute to eutrophication and the growth of harmful algal blooms that threaten the very waters upon which people depend. This degradation can generate incentives for behavioral change. For example, lake associations can initiate citizen-driven actions to protect and improve water quality. But will this action come in time? And will it focus on the key drivers of water quality?
This project examines the linkages between land-use decision making, fate-transport of nonpoint source pollution to lakes, lake water quality, the effects of water quality on property values, and the community responses that stimulate changes in land uses. In three lake catchments that vary in the intensity of agriculture, forested land and residential development we build the linkages from land use to water quality to identify the key drivers of lake water quality.
The insights from the three focal catchments will inform the understanding of human-natural system dynamics across thousands of lake catchments spanning the northeastern and midwestern U.S. An understanding of the relationships between and lake water quality and land-use policies will be leveraged to support science-based monitoring, advocacy and volunteerism to develop effective programs to protect and enhance lake water quality.
News & Project Events
- Check out the Virginia Tech press release about the project at this link.
- The video at this link provides an overview of our project.
Important Team Resources
- The authorship policy can be accessed here (as a PDF) and here (as a Word document).
- The Mendeley reference management group can be accessed at this link. You can read instructions for using Mendeley here.
- Information about data sharing and metadata can be found on the EDI website.
- The team directory can be accessed here.
- Access the project calendar at this link.
Contact us with questions or concerns about the project at firstname.lastname@example.org.
Our Multidisciplinary Team
(Left to right, back row) Pat Soranno, Hilary Dugan, Kevin Boyle, Mike Sorice, Joe Stachelek, Yu Zhang, Leah Fitchett, Armen Kemanian, Chris Duffy, Lars Rudstam, and Paul Hanson
(Left to right, front row) Kelly Cobourn, Aviah Stillman, Kait Farrell, Nicole Ward, Jen Klug, Weizhe Weng, Kathleen Weathers, and Cayelan Carey.
Photo credit: Cayelan Carey
This project builds on a strong collaboration among a diverse team of researchers from multiple disciplines and institutions, as well as citizen science groups. Our team's expertise spans the fields of freshwater ecology, environmental and resource economics, hydrology, and social science. Follow this link for a current directory of team members.
Research Objectives, Models, and Study Sites
See these pages for a description of:
To capture the two-way feedbacks between humans and lakes, this project couples multiple models together. View our modeling input-output table at this link.
Lake Association Partnerships
This project builds on an ongoing collaboration with our Lake Association partners in each of our focal lake catchments. These associations are civic organizations that engage in outreach and education within and among catchment communities. The lead for this portion of the project is Kathleen Weathers. More information on our lake association partners can be found at:
This work is supported as a grant from the National Science Foundation, Dynamics of Coupled Natural and Human Systems (CNH) program, award number 1517823.
This site is built with the Organic Data Science framework, which is developed using the MediaWiki and Semantic MediaWiki platforms.