integrating water quality into the planning process using a land use simulation model
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Integrating water quality into the planning process using a land use simulation model. Austin Troy*, Associate Professor, [email protected] Brian Voigt*, PhD Candidate, [email protected] www.uvm.edu/envnr/countymode *University of Vermont Rubenstein School of Environment - PowerPoint PPT PresentationTRANSCRIPT
Integrating water quality into the Integrating water quality into the planning process using a land use planning process using a land use simulation modelsimulation model
Austin Troy*, Associate Professor, [email protected] Voigt*, PhD Candidate, [email protected]/envnr/countymode*University of VermontRubenstein School of Environment and Natural Resources
Presented to NSF EPSCoR Water WorkshopNovember 2008
Research QuestionsResearch 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?
Integrated Model Framework
Model componentsModel componentsUrbanSim: Land use model -
www.urbansim.orgTransCAD (Caliper Corp.): four step travel
demand modelActivity Model (RSG)Traffic Microsimulator (Adel Sadek and RSG)Suite of indicators and environmental
modules
The Five D’s of UrbanSimThe Five D’s of UrbanSimData-intensiveDisaggregatedDynamicDisequilibriumDriven by
trends and forecasts
Model Coordinator
Database
Scenario Data
Control Totals
TDM
Exogenous Data
Output / Indicators
Modeling with UrbanSimModeling with UrbanSimModel 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
UrbanSim Decision MakersUrbanSim Decision Makers
Grid_ID: 60211Employment_ID: 427Sector: 2Employees: 135
Grid_ID:23674HSHLD_ID: 23AGE_OF_HEAD: 42INCOME: $65,000Workers: 1KIDS: 3CARS: 4
Grid_ID:23674Households: 9Non-residential_sq_ft: 30,000 Land_value: 425,000Year_built: 1953Plan_type: 4%_water: 14%_wetland: 4%_road: 3
Input DataEconomic
land value, employmentStructuresResidential and non-residential, size, year built
Biophysicaltopography, soils, wetlands, flood plains, water
Infrastructureroads, transit, travel time to CBD, distance to Interstate
Planning & zoningland use, development constraints
Householdsage of head of household, income, race, # of autos, children
Employmentemployment sector, number of employees
Control Totalspeople: total population, # of householdsjobs: # of jobs by employment sector
DATABASE
Land PriceLand Price
Real Estate DevelopmentReal Estate Development
Residential Land ShareResidential Land Share
AccessibilityAccessibility
Mobility & TransitionMobility & Transition
Location ChoiceLocation Choice
• movers• vacant units• probabilities• site selection
Modeling with UrbanSimModeling with UrbanSim
Land PriceLand Price
Real Estate DevelopmentReal Estate Development
Residential Land ShareResidential Land Share
AccessibilityAccessibility
Mobility & TransitionMobility & Transition
Location ChoiceLocation Choice
Modeling with UrbanSimModeling with UrbanSim
New land development events in response to insufficient supply
Standard IndicatorsStandard IndicatorsTransport: VMT, accessibilityLand use: vacancy, non-residential sq ftLand value: residential, commercial,
industrialPopulation: total, density, summarize by
area (e.g. block group, TAZ)Employment: count, type, sectorResidential units: count, type, income
Residential units by 5 year time step
Environment IndicatorsEnvironment IndicatorsDeveloping 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
Water Quality Indicator Water Quality Indicator Development (Bowden and Development (Bowden and Watzin)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
6 Sampling Watersheds6 Sampling WatershedsAlder
Potash
Muddy
Allen
Mill
Snipe
Indicators sampledIndicators sampledStage,
temperature, electrical conductivity, dissolved O2
“Event loads” triggered by discharge events:◦Total N and P◦Sediment◦Chloride
OutputsOutputsWill 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
Linking water quality to Linking water quality to UrbanSimUrbanSim
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.
Percent impervious area by watershed: 1990Predicted 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
Scenario AnalysisScenario Analysis
UrbanSim and Scenario UrbanSim and Scenario AnalysisAnalysis
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
Scenarios: types of things Scenarios: types of things that can be modeledthat can be modeledConstraints to developmentRules for density, use, coverage, zoningMacro-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
centersSpeculative behavior assumptions (e.g.
response of commuters and land market to high oil prices)
Five scenariosFive scenariosDeveloped through two large stakeholder workshops1. Transportation corridor-oriented development2. Investment for increased regional road connectivity3. Population boom4. County-wide growth center implementation5. Green scenario: natural areas protectionCombined last two for preliminary scenario run
Sample scenario : Natural Sample scenario : Natural areas combined with growth areas combined with growth centerscenters
Scenario comparisonScenario comparison
Baseline vs. alternate: Baseline vs. alternate: Zoomed inZoomed in
How does this translate into different environmental outputs?
Scenario comparison: Scenario comparison: impervious areaimpervious area
Project SupportProject 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
Team and CollaboratorsTeam and CollaboratorsGraduate 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 WorkshopsCollaborating 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/countymodel