Coastal Wetlands and Storm Protection:Coastal Wetlands and Storm Protection: A spatially explicit estimate of ecosystem service valueA spatially explicit estimate of ecosystem service value

Paul C. SuttonPaul C. SuttonVisiting Research FellowVisiting Research Fellow

Department of Geography, Population, and Department of Geography, Population, and Environmental ManagementEnvironmental Management

Flinders University, Adelaide SAFlinders University, Adelaide SApsutton@du.edupsutton@du.edu

Collaborators & Co-conspiratorsRobert Costanza

Gund Institute of Ecological Economics, Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT 05405-1708, USA

Octavio Pérez-Maqueo & M. Luisa MartinezInstituto de Ecología A.C., km 2.5 antigua carretera a Coatepec no. 351,

Congregación El Haya, Xalapa, Ver. 91070, México.

Sharolyn J. AndersonDepartment of Geography, University of Denver, Denver, CO, 80208, USA

Kenneth MulderW. K. Kellogg Biological Station, Michigan State University, Hickory Corners,

MI 49060, USA

How many e-mails? 100s easily How long? Over a year.We will meet at Stonehenge…

Ecosystem Services: What are they?• Ecosystem Services are the processes by

which the natural environment produces resources useful to people, similar to economic services. Some Examples are:

– The cycling of nutrients and energy– Provision of clean air and water– Pollination of crops– Pest and disease control– Climate regulation– Mitigation of natural hazards (what are we talking about?)

• Ecosystem Services are often Public Goods in the economic sense in that they are non-rival in consumption (I can use a lighthouse to navigate my ship without impacting your ability to do the same), and non-excludable (in that if I owned the light house it would be difficult to provide the service only to those who paid for it).

• Public Goods are a recognized ‘Market Failure’ in that both building lighthouses and providing many ecosystem services are really not good ideas for private enterprise despite the fact that Lighthouses and many Ecosystems have costs of production and/or maintenance that are greatly exceeded by the value of the services they provide. (e.g. Benefits exceed Costs)

Ecosystem Services as ‘Public Goods’

• Because Ecosystem Services are often destroyed or diminished by human action it behooves us to make reasonable estimates of their economic value to inform policy decisions regarding their fate.

• Attempts at putting a dollar value on ecosystem services range from ‘squidgy’ contingent valuation studies (e.g. How much would you pay for the continued existence of [name your charismatic mega-fauna]? … To …

• Rigorous and precise estimates of tangible values based on hard empirical evidence as presented here .

Valuation of Ecosystem Services:Putting a dollar value on a ‘non-market’ service

‘Geography’ or ‘Why Space Matters?’:Spatially Explicit Valuation of Ecosystem Services

• Forests and other vegetated landcover provide an ecosystem service in the form of soil retention, or, mitigation of soil erosion. (that is reduced by fire)

• Q1: How might ‘where’ this service is provided influence the ‘value’ of the service provided?

• Q2: How might ‘the size of the burned patch’ influence the ‘value’ of the service provided?

Before and After photos northeast of Sage Ranch, Topanga Fire, California

‘Coastal wetlands reduce the damaging effects of hurricanes on coastal communities by absorbing storm energy in

ways that neither solid land nor open water can.’ *(Simpson and Riehl 1981).

• What is the dollar value of the ‘reduced damage’?• What DATA does one need to answer this question?

* Simpson, R.H. and Riehl, H. 1981. The hurricane and its impact. Louisiana State University Press, Baton Rouge, LA.

The Data Story…

• 1) Table of all Hurricanes that hit the U.S. since 1980 w/ Name, Year, $ Damage, # Dead, etc. ...

• 2) Shapefile of all the Hurricane Tracks since 1980 with windspeed, Category, Pressure, Temp

• 3) Population and Nighttime Imagery to obtain ‘people in swath’ and ‘GDP in swath’ (modeled)

• 4) LandCover (~30 Meter resolution - NLCD) to obtain ‘Wetlands’ information

The Damage Table (N = 34 Hurricanes)

Data Source: www.em-dat.net/index.htm

NAMEYEAR Storm Type Event Name location # People Killed Total Damage $ max wind speed Maximum Categoryandrew1992 Hurricane Andrew Florida, Louisiana, Bahamas 44 26500000 69 4floyd1999 Hurricane Floyd North Carolina, Florida, South 77 7000000 69 4jeanne2004 Hurricane Jeanne Florida 6 7000000 57 3charley2004 Hurricane Charley Florida 16 6800000 64 4allison2001 Hurricane Allison

Texas, Louisiana, Florida, North Caroline, Pennsylvania, Virginia 33 6000000 26 0

ivan2004 Hurricane Ivan Alabama, Louisiana, Mississippi, Florida, Pennsylvania, Maryland, 52 6000000 75 5

isabel2003 Hurricane Isabel North Carolina, Maryland, Virginia, Washington, West 21 5000000 72 5

frances2004 Hurricane Frances Martin, Plam Beach counties (Florida state), North Carolina, 2 4400000 64 4

fran1996 Hurricane Fran North Carolina, South Carolina, Virginia, Maryland, West 39 3400000 54 3

opal1995 Hurricane Opal Florida, Georgia, Alabama 19 3000000 67 4alicia1983 Hurricane Alicia Texas 18 1650000 51 3juan1985 Hurricane Juan

Louisiana, Mississippi, Florida Panhandle 12 1500000 39 1

elena1985 Hurricane Elena Florida, Arkansas, Kentucky, South Dakota, Iowa, Michgigan, 4 1100000 57 3

gloria1985 Hurricane Gloria East Coast 15 900000 64 4allen1980 Hurricane Allen Texas 0 860000 85 5erin1995 Hurricane Erin Florida, Alabama, Mississipi 11 700000 41 1bob1991 Hurricane Bob

North Carolina, Maine, New York, Rhode Island, Connecticut, 13 620000 51 3

lili2002 Hurricane Lili Louisiana 0 260000 64 4alberto1994 Tropical storm Alberto

Georgia, Michigan, Florida, Alabama 32 250000 28 0

danny1997 Hurricane Danny Ohio, Pennsylvania, Illinois, New York, New Jersey 0 100000 36 1

irene1999 Hurricane Irene Bahamas, Floride 6 100000 46 2chantal1989 Hurricane Chantal Texas 0 80000 36 1isidore2002 Hurricane Isidore

Louisiane, Mississippi, Alabama, Tennessee 3 70000 57 3

gaston2004 Hurricane Gaston Richmond, Lynchburg (Chesterfield County , Virginia 7 62000 31 0

allison1989 Hurricane Allison Texas, Louisiane 0 45000 23 0jerry1989 Hurricane Jerry Texas 2 35000 39 1bret1999 Hurricane Bret Texas 0 34000 62 4keith1988 Tropical storm Keith Floride 0 30000 33 1charley1998 Tropical storm Charley Texas 17 25000 26 0bill2003 Tropical storm Bill

Louisiana, Mississippi, Alabama, Florida Pandhandle 0 16000 26 0

bob1991 Storm Bob New England, New York, New Jersey 2 0 51 3

kate1985 Hurricane Kate Florida, Panhandle, Georgia 5 0 54 3

The Storm Tracks (N = ~100)

The image above gives one an idea of what the tracks of the Atlantic hurricanes look like when displayed In a Geographic Information System. These are represented as lines and had to be buffered to a width That reasonably approximates the damaging swath of a hurricane. We chose a swath width of 100km. (Data Source: www.grid.unep.ch/data/gnv199.php

The Flying Spaghetti Monster

Note:Each Storm Track consists of smaller sections Which have Category, Windspeed, Temp & Pressure Attributes

Buffer Existing Storms to 100 km (50 km per side) and Match to Damage Table (N=34)

Andrew is an interesting example hurricane in that is represents one of the problems associated with this Analysis. Andrew did most of its damage during its first landfall in Florida. However, it went into the gulf of Mexico and struck the gulf coast later in its path. This multiple landfall issue raises some questions with Respect to our analysis. We believe we have to separate the two landfalls and the other data associatedWith the hurricane (damage, fatalities, GDP in swath, maximum wind speed, category, etc.).

Fleshing out the Table: 1) Population in SwathThis image depictsthe swath of Hurricane Hugo (1989) as it hit South Carolina. The shaded pink area is thepopulation density (from LandScan2000) masked to only those areaswithin 100 km Ofthe coast. The zone of intersection of the swath of the hurricane and the populationdensity on the coast is roughly demarcated by the green polygon. Sadly, these numbers have to be determined one at atime because The swaths of hurricanes often overlap – so, inessence we obeyed the instructions on the shampoo bottle: “Lather, Rinse, Repeat” (GIS is FUN )

NameYear Pop in SwathHugo 1989 1,735,806

Fleshing out the Table: 2) GDP in SwathThis image shows a similarkind of intersection analysis for Hurricane Hugo except instead of population densityThe underlying coastal data is an estimate of the year 2000 GDP mapped at 1 km2 spatial Resolution. This model is derived from allocating the aggregate GDP of the United States to the individualpixels of this image on a linear basis in which the image is a nighttime satellite image composite derived from the DMSP OLS.So in this case the Estimate of GDP impacted by Hugo in 1989 was almost 1.7 billion dollars. Again, “Lather, Rinse, Repeat” for the restof the hurricanes to produce a 2nd column in our table

NameYear GDP in SwathHugo 1989 1.677 Billion

Fleshing out the table: 3) Wetlands in SwathThe image above shows both Woody Wetlands and Herbaceous Wetlands from the National Land Cover Database that was derived primarily from Landsat imagery.It Is at 30 meter resolution. The woody wets have to be processed separately from the Herbaceous wets in thesame lather, rinse, repeat manner; however, we were getting Tired of making these images of Hurricane Hugo.In any case Hurricane Hugo passed Over 335 square kilometers of HerbaceousWetlands and 2,306 km2 of Woody Wetlands. This image raises another question ofhow to assess the mitigating influence of wetlands when one takes into account the direction of the hurricane. Do the Woody Wets behind Charleston Really provide as much protection as the herbaceous wetlands in ‘front’ of Charleston. A question To ponder……

NLCD Data Reference: Vogelmann, J. E. and Howard S. M. 2001. Completion of the 1990's National Land Cover Dataset for the conterminous United States from Landsat Thematic Mapper Data and Ancillary Data Sources. Photogrammetric Engineering and Remote Sensing 64: 45-57.

NameYear Woody Wets in Swath Herb Wets in SwathHugo 1989 2,306 km2 335 km2

This Tablewas built with the followingkey columns:

TD/GDPHerb WetsWindSpeed

to provide theRegression

info

Hurricane Year States hit

Max wind

speed (m/sec)

Herbaceous Wetland area in

swath (ha)

GDP in swath in year hit

(2004 $US) (millions)

Observed total damage (2004

$ US) (millions)

Estimated marginal value (2004 $US/ha)

Alberto 1994 GA, MI, FL, AL 28.3 4,466 $5,040 $305 $15,607Alicia 1983 TX 51.4 93,590 $100,199 $2,823 $14,449Allen 1980 TX 84.9 26,062 $13,151 $1,674 $127,090Allison 1989 TX, LA, FL, NC, PA, VA 23.1 167,494 $149,433 $63 $348Allison 2001 TX, LA, FL, NC, PA, VA 25.7 100,298 $185,610 $6,995 $1,611Andrew 1992 FL, LA 69.4 901,819 $83,450 $34,955 $699Bill 2003 LA, MS, AL, FL 25.7 642,544 $70,669 $17 $23Bob 1991 NC, ME, NY, RI, CT, MA 51.4 68,465 $122,358 $829 $30,683Bonnie 1998 NC, SC, VA 51.4 49,774 $15,840 $373 $6,984Bret 1999 TX 61.7 29,695 $2,043 $35 $4,557Chantal 1989 TX 36.0 104,968 $81,319 $111 $2,400Charley 1998 TX 25.7 55,126 $18,775 $33 $470Charley 2004 FL 64.3 358,778 $483,281 $6,800 $15,347Danny 1997 OH, PA, IL, NY, NJ 36.0 271,317 $66,711 $111 $367Dennis 1999 NC 46.3 22,752 $17,669 $45 $20,704Elena 1985 FL, AR, KY, SD, IO, MI, IN, MO 56.6 50,568 $14,240 $1,774 $8,835Emily 1993 NC 51.4 615 $6 $38 $5,795Erin 1995 FL, AL, MS 41.2 264,226 $132,138 $821 $1,278Floyd 1999

NC, FL, SC, VA, MD, PA, NJ , NY, DE, RI, CT, MA, VT 69.4 188,637 $420,940 $7,259 $56,214

Fran 1996NC, SC, VA, MD, VA, PA, OH, Washington DC 54.0 9,033 $10,471 $3,900 $114,389

Frances 2004 FL, NC, SC, OH 64.3 340,051 $150,986 $4,400 $5,272Gaston 2004 VA, SC, NC 30.9 100,502 $82,063 $62 $1,439Gloria 1985 NC, NY, CT, NH, ME 64.3 87,863 $188,531 $1,451 $72,229Hugo 1989 SC 72.0 32,906 $13,684 $1,391 $46,288Irene 1999 FL 46.3 692,219 $114,903 $104 $319Isabel 2003 NC, MD, VA, Washington DC 72.0 37,942 $35,068 $5,406 $92,176Isidore 2002 LA, MS, AL, TN 56.6 574,157 $64,990 $79 $547Ivan 2004

AL, LA, MS, FL, PA, MD, NJ , OH, NC, VA, GA, TN 74.6 504,033 $226,150 $6,000 $6,996

J eanne 2004 FL 56.6 404,769 $133,657 $7,000 $2,088J erry 1989 TX 38.6 98,540 $86,173 $49 $3,717Katrina 2005 AL, LA, MS, GA, FL 78.2 708,519 $214,277 $22,321 $4,363Keith 1988 FL 33.4 222,324 $55,856 $44 $328Lili 2002 LA 64.3 224,504 $24,439 $295 $1,779Opal 1995 FL, GA, AL 66.9 7,261 $12,652 $3,521 $465,730

Mean 52 218,995 $99,905 $3,561 $33,268Median 53 100,400 $75,994 $825 $4,914

S.D. 17 243,111 $110,816 $7,001 $83,466

ln (TDi /GDPi)= a + b1 ln(gi) + b2 ln(wi) + ui

TDi = total damages from storm i (in constant 2004 $US);GDPi = Gross Domestic Product in the swath of storm i (in constant 2004 $US). The

swath was considered to be 100 km wide by 100 km inland.gi = maximum wind speed of storm i (in m/sec)wi = area of herbaceous wetlands in the storm swath (in ha). ui = error

0.0001

0.0010

0.0100

0.1000

1.0000

0.0001 0.0010 0.0100 0.1000 1.0000 10.0000TD/GDP observed

TD/G

DP

pred

icte

d

Opal 2005

Jeanne 2004 Andrew 1992

Fran 1996

Katrina 2005

Allen 1980

Hugo 1989

Isabel 2003

Elena 1985

Erin 1995

Bill 2003

Allison 1989

Charley 1998

Isidore2002

Dennis 1999

Alberto 1994

Bob 1991

Gloria 1985

Gaston 2004

Keith 1988

Jerry 1989

Irene 1999Danny 1997

Chantal 1989

Allison 2001

Floyd 1999

Bret 1999

Emilly 1993

Alicia 1983

Frances 2004

Lili 2002Bonnie 1998

Charley 2004

Ivan 2004

Observed vs. predicted relative damages (TD/GDP) for each of the hurricanes used in the analysis.

Coefficient Std. Error t Pa -10.551 3.29 -3.195 0.003b

1 3.878 0.706 5.491 <0.001b

2 -0.77 0.16 -4.809 <0.001

Regression Parameters

R2 = 0.60Q: Why use

TD/GDPFor the dependant

Variable?

Regression Analysis

The Aggregate Conclusion• Mean Value of Coastal wetlands for

storm protection: $33,000/ha/year

• Coastal wetlands in the U.S. were estimated to currently provide $23 Billion/yr in storm protection services.

Q: Average Annual Damage from Hurricanes in the U.S. from 1980 to present is roughly ~$4.3 Billion per year. HowCan wetlands provide ~$23 Billion per year in protection?

Going Spatially Explicit:How does spatial context influence valuation?• Two approaches, two scales: States and Pixels

• The State based approach:– Hurricane frequency per state with associated data– Mean Value ~$37,000 per hectare per year

• The Pixel Based approach (at 1 km2 resolution):– Spatial context of nearby wetlands– Spatial context of nearby GDP– Frequency of Hurricanes (by category) at the pixel– Mean Value ~$ 31,000 per hectare per year

The Math gets wild – Don’t ask me about it.

State Level Analysis• Data for each of the 19 states in the US that have been hit by a hurricane since 1980 (267

total hits) were used by Blake et al. (2005) to calculate the historical frequency of hurricane strikes by storm category. We calculated the average GDP and wetland area in an average swath through each state using our GIS database. We then calculated the annual expected marginal value (MV) for an average hurricane swath in each state using the following variation of the aforementioned regression equation:

where:S = statesw = average swath in state sgc = average wind speed of hurricane of category cpc,s = the probability of a hurricane of category c striking state s in a given yearGDPsw = the GDP in state s in the average hurricane swathwsw = the wetland area in state s in the average hurricane swath

Calculating Total Annual ValueWe then estimated total annual value of wetlands for storm protection

as the integral of the marginal values over all wetland areas (the “consumer surplus) from the first or “marginal” hectare in the swath down to a value k, as:

The above Equation estimates the avoided damages for each hurricane category in an average swath and multiplies by the probability of a given storm striking a state in a year. Value is thus only ascribed to wetlands that are expected to be in the swath of a hurricane. Residual analysis of our regression results suggested that our model over-predicted the damage mitigation for smaller wetland areas, so we only integrated MV down to k = 10,000 ha. This yields a conservative estimate of total damage avoided or total value.

Average Annual Value per Hectare of wetland per state as: AVstate = TVstate / Wetlandstate

State 1 2 3 4 5k=10,000 k=5,000 k=1,000

Alabama 16,759 6,388 9,499 7.14% 3.25% 3.90% 0.00% 0.00% 14,155 40.9 133.6 749.5 7,970.4Connecticut 21,591 12,601 65,673 2.60% 1.95% 1.95% 0.00% 0.00% 14,428 263.2 615.4 2,705.2 28,503.5Delaware 33,964 12,089 10,488 1.30% 0.00% 0.00% 0.00% 0.00% 222 3.7 8.7 38.6 255.8Florida 1,433,286 186,346 70,491 27.92% 20.78% 17.53% 3.90% 1.30% 1,684 6,453.9 11,293.6 40,010.3 7,879.5Georgia 140,556 29,120 7,356 7.79% 3.25% 1.30% 0.65% 0.00% 630 72.3 140.0 542.2 996.2Louisiana 1,648,611 370,299 36,250 11.04% 9.09% 8.44% 2.60% 0.65% 126 1,665.7 2,883.2 10,107.2 1,748.8Maine 60,388 15,500 14,670 3.25% 0.65% 0.00% 0.00% 0.00% 715 21.3 46.5 196.0 770.1Maryland 60,511 16,011 21,924 0.65% 0.65% 0.00% 0.00% 0.00% 445 14.3 30.9 129.4 510.4Massachusetts 49,352 16,801 67,266 3.25% 1.30% 1.95% 0.00% 0.00% 8,422 301.2 643.3 2,673.1 13,035.3Mississippi 25,456 6,048 3,890 1.30% 3.25% 4.55% 0.00% 0.65% 7,154 17.8 59.0 341.5 2,316.1New Hampshire 19,375 9,905 23,051 0.65% 0.65% 0.00% 0.00% 0.00% 1,095 10.7 28.1 131.7 1,451.2New J ersey 69,001 21,864 78,703 1.30% 0.00% 0.00% 0.00% 0.00% 583 37.0 74.8 298.9 1,083.5New York 5,306 2,117 90,770 3.90% 0.65% 3.25% 0.00% 0.00% 586,845 79.5 271.2 3,473.3 51,106.9North Carolina 64,862 21,295 13,023 13.64% 8.44% 7.14% 0.65% 0.00% 5,072 304.1 617.5 2,477.0 9,519.6Pennsylvania 7,446 2,994 93,117 0.65% 0.00% 0.00% 0.00% 0.00% 11,651 4.1 14.1 141.3 1,890.4Rhode Island 3,638 1,759 12,810 1.95% 1.30% 2.60% 0.00% 0.00% 95,193 7.7 26.3 377.0 7,239.1South Carolina 107,894 39,177 15,367 12.34% 3.90% 2.60% 1.30% 0.00% 1,281 265.0 498.0 1,880.0 4,615.3Texas 448,621 79,110 63,661 14.94% 11.04% 7.79% 4.55% 0.00% 3,901 3,087.1 5,547.2 20,144.4 12,365.0Virginia 71,509 23,588 27,786 5.84% 1.30% 0.65% 0.00% 0.00% 1,555 115.6 230.8 914.1 3,227.6

Mean 225,691 45,948 38,200 6.39% 3.76% 3.35% 0.72% 0.14% 39,745 671.8 1,219.1 4,596.4 8,236.0Median 60,388 16,011 23,051 3.25% 1.30% 1.95% 0.00% 0.00% 1,684 72.3 140.0 749.5 3,227.6

S.D. 475,157 89,175 31,083 7.01% 5.25% 4.39% 1.40% 0.35% 134,195 1,594.0 2,788.8 9,848.0 12,418.4

Totals 12,765.0 23,162.0 87,330.7

Annual expected marginal value per average swath MVsw

($/ha/yr)

Average annual value of wetlands per ha per state (AVs) at k=5,000 ($/ha/yr)

Total annual value per state (TVs) for k=10,000, 5,000, and 1,000 ($

millions/yr)

Wetlands within 100

km of coast by state Ws

(ha)

Wetland area in

average swath Wsw (ha)

GDP in average swath ($

millions/year)

Probability of state being hit by a storm of the given category in a year by Storm

Category

Pixel Level Analysis

“I came to Casablanca for the waters” “There is no water in Casablanca”“I was told there would be no math involved” “There is math involved.”

“I was misinformed”.

MVpxl is damage avoided ‘by the wetlands in that pixel’e is 2.71828… α, β1, and β2 are the regression parameters, k=1000gc is the average windspeed of category ‘c’ hurricaneswpxl is the area of wetlands within 50 km of the pixelGDPpxl is the GDP within 50 km of the pixelPc is frequency of hurricane of category c at that pixelc varies from 1 to 5 (the categories of the hurricane)Avg windspeeds (meters/ sec): 77 (cat 5), 65(cat 4), 53(cat 3) , 45 (cat 2), 36 (cat 1)

Metamathemagical manipulations produce the equation below:

Deconstructing the Valuation Equationand clarifying the ‘spatial explicitness’

Hurricane Frequency, GDP in Swath, and Wetlands in Swath all vary spatially thus we have a spatially explicit valuation method.

The ArcGIS ‘FocalSum’ Function:How we get the ‘GDP in Swath’ and ‘Wetlands in Swath’ valuesFocalsum( ): for each cell location on an input grid, adds the values within a

specified neighborhood and sends the sum to the corresponding cell location on the output grid. We used it on both the GDP dataset and the wetlands dataset. Similar ‘Focal Function’ exist for mean, max, min, etc.

Creating ‘Wets in Swath’ dataset

Wetsinpixel (1km )2 Wetsinswath (1km )2

Wetsinswath = focalsum(wetsinpixel, circle, 50, data)

Creating ‘GDP in Swath’ dataset

Creating the Hurricane Frequency Dataset

At the pixel level, count the number of hurricanes that hit you for each category of storm (storms change category in space and time), five datasets (one for each category) smooth with a mean filter (focal mean)

A Single Pixel’s Value Calculation

To the right is aRepresentation of a single pixel in the

Mississippi Delta.It’s value is calculated below:

NOTE: BUT THIS IS WRONG

Frequency Data Whacked…Equations below are also incomplete.

Once We get Frequency Maps…

We can make maps ofStorm protection serviceValue from wetlands thatIs truly spatially explicit.Just like the one to theRight but based on usingAppropriately developedFrequency maps that Account for the spatialAnd temporal variation ofCategory (e.g. windspeed)Of storms through their Course…….

Discussion• Where is the ‘maximum wind speed’?

• What about Woody Wetlands?

• What about spatial orientation of wetlands with respect to Economic Activity?

• What about multiple landfalls?

Conclusions• Coastal wetlands provide storm protection as an

ecosystem service on the order magnitude of 10’s of BILLIONS of dollars per year in the U.S. alone.

• A single hectare of wetlands provides a variable

amount of services depending upon its spatial context with respect to GDP in space, other wetlands in space, and the frequency with which it is hit by hurricanes of various categories. Values range from 10s of dollars to Millions of dollars per hectare.