1. Integrating water quality into the planning process using a land use simulation model Austin Troy*, Associate Professor, austin.troy@uvm.edu Brian Voigt*, PhD Candidate, brian.voigt@uvm.edu www.uvm.edu/envnr/countymode *University of Vermont Rubenstein School of Environment and Natural Resources Presented to NSF EPSCoR Water Workshop November 2008

2. Research Questions What will land use patterns in Chittenden County look like in 20- 30 years? What effect will future urban development patterns have on environmental indicators, including carbon footprint, water quality, and habitat fragmentation? How might alternative policies alter these outcomes? How can we develop a model framework that effectively integrates the (inter)actions of households, employers, developers, transportation, and the environment?

3. Integrated Model Framework

4. Model components UrbanSim: Land use model - www.urbansim.orgwww.urbansim.org TransCAD (Caliper Corp.): four step travel demand model Activity Model (RSG) Traffic Microsimulator (Adel Sadek and RSG) Suite of indicators and environmental modules

5. The Five D’s of UrbanSim Data-intensive Disaggregated Dynamic Disequilibrium Driven by trends and forecasts Model Coordinator Database Scenario Data Control Totals TDM Exogenous Data Output / Indicators

6. Modeling with UrbanSim Model parameters based on statistical analysis of historical data (same withTransCAD): ◦ Regression ◦ Choice modeling Integrates market behavior, land policies, infrastructure choices Simulates household, employment and real estate development decisions ◦ agent-based for household and employment location decisions ◦ grid-based for real estate development decisions from Waddell, et al, 2003

7. UrbanSim Decision Makers Grid_ID: 60211 Employment_ID: 427 Sector: 2 Employees: 135 Grid_ID:23674 HSHLD_ID: 23 AGE_OF_HEAD: 42 INCOME: $65,000 Workers: 1 KIDS: 3 CARS: 4 Grid_ID:23674 Households: 9 Non-residential_sq_ft: 30,000 Land_value: 425,000 Year_built: 1953 Plan_type: 4 %_water: 14 %_wetland: 4 %_road: 3

8. Input Data Economic land value, employment Structures Residential and non-residential, size, year built Biophysical topography, soils, wetlands, flood plains, water Infrastructure roads, transit, travel time to CBD, distance to Interstate Planning & zoning land use, development constraints Households age of head of household, income, race, # of autos, children Employment employment sector, number of employees Control Totals people: total population, # of households jobs: # of jobs by employment sector DATABASE

9. Land Price Real Estate Development Residential Land Share Accessibility Mobility & Transition Location Choice movers vacant units probabilities site selection Modeling with UrbanSim

10. Land Price Real Estate Development Residential Land Share Accessibility Mobility & Transition Location Choice Modeling with UrbanSim New land development events in response to insufficient supply

11. Standard Indicators Transport: VMT, accessibility Land use: vacancy, non-residential sq ft Land value: residential, commercial, industrial Population: total, density, summarize by area (e.g. block group, TAZ) Employment: count, type, sector Residential units: count, type, income

14. Residential units by 5 year time step

25. Environment Indicators Developing sub-modules that use UrbanSim output to estimate environmental impacts ◦ Carbon footprint analysis (Jen Jenkins/RSG) ◦ Mobile source pollutants (RSG) ◦ Habitat fragmentation (Troy/David Capen) ◦ Plant and soil impacts (Sarah Lovell/Deb Neher) ◦ Stormwater (Breck Bowden/Mary Watzin) To be integrated through Arc Objects framework

26. Water Quality Indicator Development (Bowden and Watzin) Instrumented 6 sub- watersheds to estimate the impact of development intensity and traffic on various measures of water quality 2 rural, 2 suburban, 2 highly developed

27. 6 Sampling Watersheds Alder Potash Muddy Allen Mill Snipe

28. Indicators sampled Stage, temperature, electrical conductivity, dissolved O 2 “Event loads” triggered by discharge events: ◦ Total N and P ◦ Sediment ◦ Chloride

29. Outputs Will have ability to ask ◦ How these metrics are influenced by development intensity ◦ How that changes seasonally ◦ How relationship changes with different storm event intensities and antecedent conditions

30. Linking water quality to UrbanSim UrbanSim grid-cell level outputs: ◦ # residential units ◦ Commercial sq. ft. These are being calibrated against impervious area data to yield coefficients These can vary as a function of population density, zoning, etc.

31. Percent impervious area by watershed: 1990 Predicted percent impervious area by watershed: 2030 Coefficients can be used to estimate impervious area given standard UrbanSim ouputs: predicted residential units and commercial square footage

32. Scenario Analysis

33. UrbanSim and Scenario Analysis What is a scenario? ◦ Alteration of baseline model inputs and assumptions for comparison * need TranSims for this analysis BASE YEAR – business as usual establish growth center(s) policy event 1 employment opportunity employment event alter transport infrastructure investment increase density policy event 2

34. Scenarios: types of things that can be modeled Constraints to development Rules for density, use, coverage, zoning Macro-scale transportation network (e.g. highways, onramps, roundabouts, etc.) Micro-scale transportation network (e.g. new lanes, turning rules, ITS, speed limits) Placement of public facilities (e.g. hospitals, schools, courts, parks, arena, airports, etc.) Infrastructure (e.g. sewer, water, electricity) Siting of major employers/employment centers Speculative behavior assumptions (e.g. response of commuters and land market to high oil prices)

35. Five scenarios Developed through two large stakeholder workshops 1. Transportation corridor-oriented development 2. Investment for increased regional road connectivity 3. Population boom 4. County-wide growth center implementation 5. Green scenario: natural areas protection Combined last two for preliminary scenario run

36. Sample scenario : Natural areas combined with growth centers

37. Scenario comparison

38. Baseline vs. alternate: Zoomed in How does this translate into different environmental outputs?

39. Scenario comparison: impervious area

40. Project Support Dynamic Transportation and Land Use Modeling ◦ Funder: USDOT Federal Highway Administration TRC Signature Project 1: Integrated Land-Use, Transportation and Environmental Modeling: Complex Systems Approaches and Advanced Policy Applications. ◦ Funder: UVM Transportation Center ◦ Co Lead Investigator: Adel Sadek

41. Team and Collaborators Graduate researchers: Brian Voigt, Alexandra Reiss, Brian Miles, Galen Wilkerson, Ken Bagstad Co-PIs and collaborators: Adel Sadek, Stephen Lawe, John Lobb, Lisa Aultman-Hall, Jun Yu, Yi Yang, Jen Jenkins, Breck Bowden, Jon Erickson, Sarah Lovell, Deborah Neher, Mary Watzin, Julie Smith, David Novak, Roel Boumans, Chris Danforth, David Capen, Peter Dodds Participants in Stakeholder Workshops Collaborating organizations: ◦ Resource Systems Groups, Inc, White River Junction, VT ◦ Chittenden County Regional Planning Commission ◦ Chittenden County Metropolitan Planning Organization ◦ University of Washington Center for Urban Simulation and Policy Analysis: Paul Waddell, Alan Borning, Hana Sevcikova, Liming Wang ◦ UVM Spatial Analysis Lab ◦ UVM Transportation Research Center ◦ More information: www.uvm.edu/envnr/countymodelwww.uvm.edu/envnr/countymodel