The Science Behind Quantifying Urban Forest Ecosystem Services. David J. Nowak USDA Forest Service Northern Research Station Syracuse, NY, USA

Transcription

1 The Science Behind Quantifying Urban Forest Ecosystem Services David J. Nowak USDA Forest Service Northern Research Station Syracuse, NY, USA

2 Current Model Version 3.0

3 i-tree Version 4.0 (March 10, 2011) 5 New or Enhanced Tools Canopy Pest

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5 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.

6 Data Collection

7 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)

8 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 O 3, SO 2, NO 2 : multi-layer/big-leaf hybrid model PM, CO: average deposition velocity (Vd) Hourly Removal = Vd x C

9 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)

10 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

11 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)

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14 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. Regional Climate Vegetation Structure ---- Tree locations Species Canopy cover Leaf area Biomass Growth Mortality Diversity Health Site Bldg. data M Vegetation Structure ---- Tree locations Species Canopy cover Leaf area Biomass Growth Mortality Diversity Health Site Sequestration CO2 Released Bldg. data Emission Factors Meteor. Data Shade Air temp. Wind speed RH Energy Heating Cooling Air Quality Data NOx, SOx, O3, PM10 Dry deposition B.V.O.C. Isoprenes Monoterpenes Stormwater Runoff Interception Firewise Landscape s Aesthetics Other Sequestration CO2 Released Reduction in atmospheric CO2 Emission Factors Reduction in atmospheric CO2 Structure Avoided Emissions Energy & CO2 Air quality improvement Air Quality Hydrology Fire Other Structure

15 Research Process Functional Analysis Models use structural data (size at various ages). Vegetation To determine magnitude Structure of annual benefits: Energy saved Atmospheric CO 2 reduction Air pollutants removed Rainfall intercepted Aesthetics & other ---- Tree locations Species Canopy cover Leaf area Biomass Growth Mortality Diversity Health Site Sequestration CO2 Released Reduction in atmospheric CO2 Bldg. data Emission Factors Regional Climate Meteor. Data Shade Air temp. Wind speed RH Energy Heating Cooling Avoided Emissions Air Quality Data NOx, SOx, O3, PM10 Dry deposition Air quality improvement B.V.O.C. Isoprenes Monoterpenes Stormwater Runoff Interception L Structure Energy & CO2 Air Quality Hydrology

16 Research Process Value Analysis (Net Benefits) Convert resource units (kwh, lbs) to $ Annual Benefits: B = Energy + CO 2 + 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

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18 What Does Species Do? Ranks tree species based on their environmental benefits at maturity Complements existing tree selection programs

19 v. 4.0 Improvement

20 How Does Species Work? Utilizes local data Simple user interface Produces reports based on function

21 Using i-tree Species Input location, height, pollutant removal, and other functions Includes user input of importance values

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23 UFORE - Hydro Management model designed to be relatively easy to use Object-oriented, physical based, semidistributed, 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 soilvegetation-atmosphere transfer scheme (SVATS) C++ code with GIS inputs

24 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

25 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)

26 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

27 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)

28 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

29 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)

30 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

31 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

32 Baisman Run CRF1 = 0.56 CRF2 = 0.63 CRF3 = 0.70 Red Observed; Black - Modeled

33 Baisman Run 1,000, , , ,000 Runoff (m3/yr) 600, , , , , , Impervious Cover (%) Tree Cover (%)

34 Percent Change in Annual Stream Flow. Percent Change in Annual Stream Flow. Baisman Run Canopy held at 70% Impervious held at 10% Percent Impervious Cover Percent Tree Cover

35 Canopy

36 i-tree Canopy (v. 4.0)

37 You choose the cover classes

38 Classify random points

39 Statistics

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42 Pest

43 Pest Pest Detection Protocol Collect Pest & Disease Signs Symptoms Reports Associated pest & diseases Trends/patterns

44 I-PED Pest Evaluation & Detection (beta) IPED Goal- detect pest and diseases in urban environments as soon as possible

45 Reporting by species, zone or street

46 IPED Online Diagnostic Key

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48 What Does Vue Do? Utilizes existing land cover data maps for analysis Provides modeling for future planting scenarios Analyzes canopy cover Illustrates ecosystem services

49 How Does Vue Work? Utilizes existing public data sets Produces simple maps Can be used at various scales

50 Vue Home Page

51 Vue Home Page

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58 Main Navigation Window

59 Main Navigation Window

60 Analysis Tabs

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65 Map Output Carbon Storage

66 Save Options

67 Analysis Report Carbon Storage

68 Map Output Canopy Stocking

69 Map Output Canopy Stocking

70 Analysis Report Canopy Stocking

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72 Street Tree Storm Damage Estimates

73 The SDAP Process PLOT GENERATOR Random Plots Pre-Storm Sample Survey Estimating Engine Post-Storm Survey Final Damage

74 Questions? nrs.fs.fed.us/units/urban

75 How is an assessment done? i-tree Step 1 Determine Study Area

76 i-tree Step 2 Determine if inventory or sample

77 i-tree Step 2a Determine Number of Plots Typically 200 1/10 acre plots

78 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

79 i-tree Step 4 Lay sample points Random Pattern Random Grid Pattern Stratified by LU

80 Random with no Stratification

81 Random with Stratification

82 i-tree Step 5 Set up project

83 i-tree Step 6 Train crews and collect field data

84 i-tree Step 7 Enter data and analyze

85 i-tree analyses

86 i-tree Step 8 Use data and reports to make a difference Automatic Report Generator