The Science Behind Quantifying Urban Forest Ecosystem Services

David J. Nowak

USDA Forest Service

Northern Research Station

Syracuse, NY, USA

Current Model – Version 3.0

i-Tree Version 4.0 (March 10, 2011)

5 New or Enhanced Tools

Canopy Pest

Assessing Urban Tree Populations i-Tree Eco assesses:

Structure

Function

Energy

Air pollution

Carbon

VOC emissions

Value

Management needs

Pest risk

Tree health

Exotic/invasive spp.

Data Collection

i-Tree Eco Methods - Structure

No. trees, species composition, tree sizes, tree condition

Direct measures, statistical estimates with standard errors

Leaf area Formula based on species factors and crown measurements; adjusted based on crown missing

Leaf biomass Converts leaf area to leaf biomass based on species conversion factors

Data can be stratified (e.g., land use)

i-Tree Eco Methods - Functions Carbon

Biomass equations (spp, dbh, ht)

Adjusted downward for open-grown trees

Annual growth based on dieback, competition and length of growing season

Air Pollution Removal

Leaf area index, canopy cover by evergreen or deciduous; in-leaf season length

Local hourly weather & pollution conc. (C) data

O3, SO2, NO2: multi-layer/big-leaf hybrid model

PM, CO: average deposition velocity (Vd)

Hourly Removal = Vd x C

i-Tree Eco Methods - Functions

Building Energy Use

Based on work by McPherson and Simpson

Tree size by distance and direction from building

Average effects for region (heating and cooling)

VOC emissions (not reported)

Local hourly weather data

Species leaf biomass

Genera specific emission factors adjusted by NCAR and EPA formulas based on hourly light intensity and temperature (BEIS approach)

i-Tree Eco Methods - Valuation Air Pollution Removal

National average externality values from literature (updated to 2007)

Converting to EPA BenMap estimates

Carbon Storage and Sequestration Global externality estimates (Fankauser, 1994) = $22.8/metric ton

Energy Use State average electricity and heating fuel costs (oil, wood, natural gas)

Structural Value CTLA formula

Project Equipment and Costs Crew salary Transportation Project oversight (QA/QC, training) Equipment

Aerial photographs and street map to locate plots Clinometer Diameter tape Clipboard; data sheets, pens/pencils (or digital recorders-PDA) 50/100 ft tape measure (or electronic measuring device) Species ID guide Compass Camera (if taking pictures of plot) Chalk/Flagging (to mark trees that have been measured in plots with many trees)

Vegetation

Structure

---- Tree locations

Species

Canopy cover

Leaf area

Biomass

Growth

Mortality

Diversity

Health

Site

Sequestration CO2 Released

Reduction in

atmospheric

CO2

Bldg.

data

Meteor. Data Shade

Air temp.

Wind speed

RH

Regional

Climate

Energy Heating

Cooling

Emission

Factors

Avoided

Emissions

Air Quality

Data NOx, SOx, O3,

PM10

Dry

deposition

Air quality

improvement

B.V.O.C. Isoprenes

Monoterpenes

Stormwater

Runoff Interception

Firewise

Landscapes

Other

Aesthetics

Structure Hydrology Air Quality Energy &

CO2 Fire Other

Research Process—Structural Analysis

Data collection –

900 trees, 20 predominant species

age, species, dbh, ht., crown dia., condition, digital photos, foliar biomass samples, etc.

Calculate leaf area and foliar biomass

Regression models predict growth.

Vegetation

Structure

---- Tree locations

Species Canopy cover

Leaf area Biomass Growth Mortality Diversity Health Site

Sequestration CO2 Released

Reduction in

atmospheric

CO2

Bldg.

data

Meteor. Data Shade

Air temp. Wind speed

RH

Regional

Climate

Energy Heating Cooling

Emission

Factors

Avoided

Emissions

Air Quality

Data NOx, SOx, O3,

PM10

Dry

deposition

Air quality

improvement

B.V.O.C. Isoprenes

Monoterpenes

Stormwater

Runoff Interception

Firewise

Landscapes

Other

Aesthetics,

...

Pavement

durability

Structure Hydrology Air Quality Energy &

CO2 Fire Other

Research Process—Functional Analysis

Models use structural data

– (size at various ages).

To determine magnitude

of annual benefits:

Energy saved

Atmospheric CO2 reduction

Air pollutants removed

Rainfall intercepted

Aesthetics & other

Vegetation

Structure

---- Tree locations

Species Canopy cover

Leaf area Biomass Growth Mortality Diversity Health Site

Sequestration CO2 Released

Reduction in

atmospheric

CO2

Bldg.

data

Meteor. Data Shade

Air temp. Wind speed

RH

Regional

Climate

Energy Heating Cooling

Emission

Factors

Avoided

Emissions

Air Quality

Data NOx, SOx, O3,

PM10

Dry

deposition

Air quality

improvement

B.V.O.C. Isoprenes

Monoterpenes

Stormwater

Runoff Interception

Firewise

Landscapes

Other

Aesthetics,

...

Pavement

durability

Structure Hydrology Air Quality Energy &

CO2 Fire Other

Research Process—Value Analysis (Net Benefits)

Convert resource units (kWh, lbs) to $

Annual Benefits:

B = Energy + CO2 + AQ + Hydrology + property value

Annual Costs:

C = Plant + Trim + Removal + IPM + Irrigation + Clean-Up + Sidewalk + Liability + Admin + Other

Net Benefits = B – C

Benefits/Costs ratio = B/C

What Does Species Do?

Ranks tree species based on their environmental

benefits at maturity

Complements existing tree selection programs

v. 4.0 Improvement

How Does Species Work?

Utilizes local data

Simple user interface

Produces reports based on function

Using i-Tree Species

Input location, height, pollutant removal, and other functions

Includes user input of importance values

UFORE - Hydro Management model designed to be relatively easy to use

Object-oriented, physical based, semi-distributed, topographic model

TOPMODEL theory is used to simulate saturation excess overland flow (for forest area), base flow and ET process

Warm weather, semi-distributed urban soil-vegetation-atmosphere transfer scheme (SVATS)

C++ code with GIS inputs

UFORE Hydro Strengths

Specifically designed to incorporate urban tree and impervious surface effects on stream flow and water quality

Built to simulate the dynamic forest interception, infiltration and ET processes as well as urban impervious effect on runoff generation.

Calibrated against measure stream flow data

Relatively easy to use

UFORE Hydro Weaknesses

Lacks capabilities of fully-distributed model

Currently does not allow for specific locational designs of tree cover, impervious cover, or retention/detention ponds (operates on general cover types)

Works on watershed basis (with gauging station)

Model Inputs Hourly discharge data (USGS)

Digital elevation map (USGS)

Hourly weather and evaporation data

Evaporation data calculated from weather data

Structural information on watershed (NCLD and UFORE data) e.g.,

Tree cover

Impervious cover

Shrub and grass cover

LAI

Model Calibration

Auto calibrator (DOS Parameter Estimation (PEST) program)

Iterative process

Calibration results

Peak flow weighted (CRF1)

Base flow weighted (CRF2)

Balanced flow (peak and base) (CRF3)

Model Calculations Topographic index with tree and impervious cover

Interception routine

Canopy parameters (throughfall, storage capacity, daily leaf and trunk area)

Depression storage (impervious)

Evaporation and transpiration from vegetation, soil and water surfaces

Infiltration into soils

Subsurface, overland and impervious runoff

Model Outputs

For each time step (1 hour for these simulations):

Canopy interception

Depression storage

Infiltration

Evapotranspiration

Surface and subsurface (base flow) runoff

Channel discharge (total runoff)

Water Quality

Separate program with inputs from UFORE Hydro files

Multiple options that incorporate universal soil loss equation; buildup wash off routines

Currently only using EMC

Many other options need more input data

Dissolved sediment / solid pollutant load

Septic load

Dissolved pollutant concentration

Baisman Run Watershed Area (m2) 3,844,800

Percent Impervious cover 0.2

Percent Tree Cover 68.7

Percent of Tree Cover over Impervious Area 5

Percent Water Cover 0

Average Tree Leaf Area Index (LAI) 3.5

Percent Shrub Cover 7.8

Percent Grass Cover 20

Percent Evergreen Trees 4.2

Percent Evergreen Shrubs 21

Shrub LAI 3.9

Leaf on Day 80

Leaf off Day 294

Baisman Run

CRF1 = 0.56 CRF2 = 0.63 CRF3 = 0.70

Red – Observed; Black - Modeled

100

80

60

40

20

0

020

4060

8095

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

900,000

1,000,000

Runoff

(m3/yr)

Tree Cover

(%)

Impervious Cover (%)

Baisman Run

Baisman Run

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0 10 20 30 40 50 60 70 80 90 100

Percent Impervious Cover

Perc

ent C

hange in

Annual S

tream

Flo

w .

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

0 10 20 30 40 50 60 70 80 90 100

Percent Tree Cover

Perc

ent C

hange in

Annual S

tream

Flo

w .

Impervious held at 10% Canopy held at 70%

Canopy

i-Tree Canopy (v. 4.0)

You choose the cover classes

Classify random points

Statistics

Pest

Pest Detection Protocol

Collect Pest & Disease

Signs

Symptoms

Reports Associated pest & diseases

Trends/patterns

Pest

I-PED Pest Evaluation & Detection (beta)

IPED Goal- detect

pest and diseases in

urban environments

as soon as possible

Reporting by species, zone or street

IPED Online Diagnostic Key

http://wiki.bugwood.org/IPED

What Does Vue Do?

Utilizes existing land cover data maps for analysis

Provides modeling for future planting scenarios

Analyzes canopy cover

Illustrates ecosystem services

How Does Vue Work?

Utilizes existing public data sets

Produces simple maps

Can be used at various scales

Vue Home Page

Vue Home Page

Main Navigation Window

Main Navigation Window

Analysis Tabs

Map Output – Carbon Storage

Save Options

Analysis Report – Carbon Storage

Map Output – Canopy Stocking

Map Output – Canopy Stocking

Analysis Report – Canopy Stocking

Street Tree Storm Damage Estimates

Pre-Storm Sample Survey

PLOT GENERATOR

Post-Storm Survey

Random Plots

Estimating Engine

Final Damage Estimate

The SDAP Process

nrs.fs.fed.us/units/urban

Questions?

dnowak@fs.fed.us

How is an assessment done? i-Tree – Step 1

Determine Study Area

i-Tree – Step 2

Determine if inventory or sample

Typically 200 1/10 acre plots

i-Tree – Step 2a

Determine Number of Plots

i-Tree – Step 3

Determine what data to collect

Required core variables (spp, dbh)

Optional variables

Crown parameters

Tree health

Distance to buildings

Shrub data

Ground cover data

i-Tree – Step 4 Lay sample points

Random Grid Pattern

Stratified by LU Random Pattern

Random with no Stratification

Random with Stratification

i-Tree – Step 5 Set up project

i-Tree – Step 6

Train crews and collect field data

i-Tree – Step 7

Enter data and analyze

i-Tree analyses

Automatic Report Generator

i-Tree – Step 8

Use data and reports to make a difference