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, austin.troy@uvm.eduBrian Voigt*, PhD Candidate, brian.voigt@uvm.eduwww.uvm.edu/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 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

The Five D’s of UrbanSimThe Five D’s of UrbanSim

Data-intensiveDisaggregatedDynamicDisequilibriumDriven by

trends and forecasts

Model Coordinator

Database

Scenario Data

Control Totals

TDM

Exogenous Data

Output / Indicators

Modeling with UrbanSimModeling 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

UrbanSim Decision MakersUrbanSim 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

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 Indicators

Transport: 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

OutputsOutputs

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

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 workshops

1. Transportation corridor-oriented development

2. Investment for increased regional road connectivity

3. Population boom4. County-wide

growth center implementation

5. Green scenario: natural areas protection

Combined 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