COS 99-2 - Water clarity, land use, and human population density around lakes: a comparison of associations in the northeastern and upper midwestern United States

Tuesday, August 16, 2022

1:45 PM – 2:00 PM EDT

Location: 518B

Presenting Author(s)

KC

Kathryn L. Cottingham

Dartmouth College
Hanover, New Hampshire, United States

Slides (PDF)

Coauthor(s)

KJ

Kenneth M. Johnson

University of New Hampshire
Durham, New Hampshire, United States
Bethel G. Steele

Senior Research Specialist
Cary Institute of Ecosystem Studies
Millbrook, New York, United States
MD

Mark J. Ducey

University of New Hampshire
Durham, New Hampshire, United States
KW

Kathleen C. Weathers

Cary Institute of Ecosystem Studies
Millbrook, New York, United States
JB

Jennifer A. Brentrup

Cary Institute of Ecosystem Studies and Dartmouth College
Millbrook, New York, United States
BC

Barbara D. Cook

University of New Hampshire, United States
MP

Michael W. Palace

University of New Hampshire
Durham, New Hampshire, United States
MT

Michael Thompson

University of New Hampshire
Durham, New Hampshire, United States
JT

Jessica V. Trout-Haney

Dartmouth College
Hanover, New Hampshire, United States
CH

Christina Herrick

University of New Hampshire
Durham, New Hampshire, United States
FS

Franklin Sullivan

University of New Hampshire, United States
DL

David A. Lutz

Dartmouth College
Hanover, New Hampshire, United States

Background/Question/Methods

Freshwater lakes and reservoirs provide valuable ecosystem services that are threatened by anthropogenic change, including land-cover change. To date, most evaluations of the effects of land modification on water quality have relied on remotely sensed data, which often miss important features such as houses located underneath primarily forested cover. We hypothesized that incorporating housing density into land-cover classifications would improve our ability to predict Secchi depth, a key metric of water quality. However, when we tested this hypothesis for 51 lakes across Maine and New Hampshire, including housing density in our models provided only marginal improvements in fit. We are currently repeating these analyses for 28 lakes in forested watersheds across Michigan and Wisconsin as part of a regional comparison using Secchi depth data from the US Water Quality Portal (WQP, https://www.waterqualitydata.us/). As before, we will use general linear models and partial least squares to compare the explanatory power of two suites of land cover classifications – metrics derived directly from the 2011 National Land Cover Database (NLCD) and “demographically enhanced” metrics that incorporate housing density based on the 2010 US Census – within a 100 m buffer around each lake.

Results/Conclusions

Consistent with ecological expectations, clearer water (i.e., deeper Secchi depth) was associated with higher percent cover in forest and lower percent cover in wetlands and agricultural land use in both regions. In the Northeast, there was limited evidence to support the hypothesis that demographically enhanced land cover categorizations improve our ability to predict average summer Secchi depth. The best-fitting model with NLCD data included the percent cover in forest and wetland (F_2,48=26.5, P< 0.0001, R²=0.50); inclusion of high-density housing ( >80 houses/km²) under the forested land cover improved R² to 0.55 with little impact on AICc. However, the association with high-density housing was positive, not negative, which we interpret as indicating both human congregation around high-quality lakes and the initial resilience of lakes to development. The Midwest analysis is still underway, but preliminary findings suggest some intriguing differences from the northeastern lakes. If these results continue to suggest regional differences, an important next step will be to determine the geographic conditions under which it will be worthwhile to obtain the demographically enhanced land cover categorizations, which is time-consuming and can only be done on a decadal time scale following the US Census.