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Assessing the Vulnerability of Resource Lands in the Chesapeake Bay Watershed ReVA-MAIA Conference King of Prussia, PA May 13-15, 2003 Peter Claggett U.S. Geological Survey Chesapeake Bay Program Claire Jantz University of Maryland Department of Geography

The Chesapeake Bay Watershed

What does “vulnerability” mean? “Susceptible to physical or emotional injury” (Webster);“Susceptible to physical or emotional injury” (Webster); Combination of threat (exposure to stressors) and sensitivity (susceptibility to impairment)Combination of threat (exposure to stressors) and sensitivity (susceptibility to impairment)

X Threat Low High Sensitivity X X Resource Value High Relating vulnerability to land conservation: Resource X Highly vulnerable resource Conservation Priority! X

“Habitat loss and degradation is a major problem throughout the Mid-Atlantic region. In the eastern half of the region, urban sprawl is contributing to this loss and degradation” ~ “What is the State of the Environment in the Mid-Atlantic Region?”, EPA-MAIA, 2001 (EPA/903/F-01/003) “If recent trends continue, the area of developed land in the (Bay) watershed will increase by more than 60% by 2030” ~ “Chesapeake Futures: Choices for the 21 st Century”, STAC Vulnerability to what?

Integrated modeling approach Theobald supply/demand/ allocation model Supply: developable land Demand: housing unit projections Allocation and spread SLEUTH pattern based cellular automaton Calibrated on historic patterns of urban land cover change Forecasts patterns into the future

Integrating the Models Overlay and aggregate modeling results to a grid of one square mile cells Categorical classification of modeling results  Development potential = very high, high, moderate, or low Combine the results from each model (threat map) Overlay threat map on sensitive resource lands to prioritize conservation activities

Study Area: Washington, DC-Baltimore Region

SLEUTH modeling of impervious surfaces Mapped and modeled impervious surface 1990 mapped 2000 mapped 2010 modeled

SLEUTH’s Accuracy: Area of Developed Land

Mapped Change in Impervious Surface ( ) Modeled Change in Impervious Surface ( )

Correlation coefficient: 0.85 R 2 = 0.73 Y = 0.88x P < Theobald’s Accuracy: Housing Units

Measured change in housing units (1990 – 2000)

Determining mean lot size based on percent development

Land consumption: # of housing units * lot size ( ) Land consumption: # of housing units * lot size ( )

Results from SLEUTH and Theobald divided into five classes using “natural breaks” method “Very high” = top 2 quintiles from both models “High” = top 2 quintiles from either model “Low” = bottom quintile from both models “Moderate”= all other combinations Determination of threat level

Relative threat level

Resource Lands Sensitive Resource Lands Low Very High

Conclusions Combination of SLEUTH and Theobald models provide an adequate forecast of short-term, relative development potential (10-year timeframe) Long-term forecasts (20+ years) require the incorporation of economic data and a better representation of growth rates and patterns Incorporating lot size as a factor enables the exploration of low density growth and alternative future scenarios Aggregating model output within a large overlay grid enables the combination of multi-temporal and multi- spatial scale data

Future Research Validate analysis results Include more data in assessment of mean lot sizes Forecast long-term (20 – 30 year) urban, urbanizing, and rural growth rates and patterns, incorporating economic data and forecasts

Acknowledgements Resource Lands Assessment Technical Team, Chesapeake Bay Program Debbie Weller, Lynda Eisenberg, and Richard Hall, Maryland Office of Planning John Schlee and Katherine Nelson, Maryland National Capital Park and Planning Commission Portions of this research were funded by NASA grants NAG and NAG , principle investigator Dr. Scott Goetz, Woods Hole Research Center/University of Maryland

For more information about David Theobald’s supply/demand/allocation model –Western Futures Mapping: For more information about SLEUTH –USGS Gigalopolis website: –UMD RESAC implementation of SLEUTH in the Baltimore-Washington region: