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Integrating Water Quality into Planning: Land Use Simulation Model

This study explores the integration of water quality considerations into the land use planning process using a simulation model. It examines the potential impacts of future urban development patterns on environmental indicators such as carbon footprint, water quality, and habitat fragmentation, and explores alternative policies. The study aims to develop a model framework that effectively integrates the interactions of households, employers, developers, transportation, and the environment.

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Integrating Water Quality into Planning: Land Use Simulation Model

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

  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. 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 Grid_ID: 60211 Employment_ID: 427 Sector: 2 Employees: 135 UrbanSim Decision Makers

  8. DATABASE • 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

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

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

  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

  12. Residential units by 5 year time step

  13. 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

  14. 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

  15. 6 Sampling Watersheds Alder Mill Allen Potash Muddy Snipe

  16. Indicators sampled • Stage, temperature, electrical conductivity, dissolved O2 • “Event loads” triggered by discharge events: • Total N and P • Sediment • Chloride

  17. 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

  18. 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.

  19. 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

  20. Scenario Analysis

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

  22. 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)

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

  24. Sample scenario : Natural areas combined with growth centers

  25. Scenario comparison

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

  27. Scenario comparison: impervious area

  28. 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

  29. 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, HanaSevcikova, Liming Wang • UVM Spatial Analysis Lab • UVM Transportation Research Center • More information: www.uvm.edu/envnr/countymodel

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