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Coastal Wetlands and Storm Protection:
A spatially explicit estimate of ecosystem service value
Paul C. Sutton
Visiting Research Fellow
Department of Geography, Population, and
Environmental Management
Flinders University, Adelaide SA
[email protected]
Collaborators & Co-conspirators
Robert 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 Martinez
Instituto de Ecología A.C., km 2.5 antigua carretera a Coatepec no. 351,
Congregación El Haya, Xalapa, Ver. 91070, México.
Sharolyn J. Anderson
Department of Geography, University of Denver, Denver, CO, 80208, USA
Kenneth Mulder
W. 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 as ‘Public Goods’
• 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)
Valuation of Ecosystem Services:
Putting a dollar value on a ‘non-market’ service
• 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 .
‘Geography’ or ‘Why Space Matters?’:
Spatially Explicit Valuation of Ecosystem Services
Before and After photos northeast of Sage Ranch, Topanga Fire, California
• 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?
‘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
NAMEYEAR
andrew1992
floyd1999
jeanne2004
charley2004
allison2001
ivan2004
isabel2003
frances2004
fran1996
opal1995
alicia1983
juan1985
elena1985
gloria1985
allen1980
erin1995
bob1991
lili2002
alberto1994
danny1997
irene1999
chantal1989
isidore2002
gaston2004
allison1989
jerry1989
bret1999
keith1988
charley1998
bill2003
bob1991
kate1985
Storm Type
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Tropical storm
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Hurricane
Tropical storm
Tropical storm
Tropical storm
Storm
Hurricane
Event Name
Andrew
Floyd
Jeanne
Charley
Allison
Ivan
Isabel
Frances
Fran
Opal
Alicia
Juan
Elena
Gloria
Allen
Erin
Bob
Lili
Alberto
Danny
Irene
Chantal
Isidore
Gaston
Allison
Jerry
Bret
Keith
Charley
Bill
Bob
Kate
(N = 34 Hurricanes)
location
# People Killed Total Damage $ max wind speed Maximum Category
Florida, Louisiana, Bahamas
44
26500000
69
4
North Carolina, Florida, South
77
7000000
69
4
Florida
6
7000000
57
3
Florida Louisiana, Florida, North
16
6800000
Texas,
64
4
Caroline, Louisiana,
Pennsylvania,
Virginia
33
6000000
Alabama,
Mississippi,
26
0
Florida,
Pennsylvania,
Maryland,
52
6000000
North
Carolina,
Maryland,
75
5
Virginia,Plam
Washington,
West
21
5000000
Martin,
Beach counties
72
5
(Florida
state), North
2
4400000
North
Carolina,
SouthCarolina,
Carolina,
64
4
Virginia, Maryland, West
39
3400000
54
3
Florida, Georgia, Alabama
19
3000000
67
4
Texas
18
1650000
Louisiana,
Mississippi, Florida
51
3
Panhandle
12
1500000
Florida,
Arkansas, Kentucky,
39
1
South Dakota, Iowa, Michgigan,
4
1100000
57
3
East Coast
15
900000
64
4
Texas
0
860000
85
5
Florida,
Alabama,
Mississipi
11
700000
North
Carolina,
Maine,
New
41
1
York, Rhode Island, Connecticut,
13
620000
51
3
Louisiana
0
260000
Georgia, Michigan, Florida,
64
4
Alabama
32
250000
Ohio, Pennsylvania, Illinois, New
28
0
York, New Jersey
0
100000
36
1
Bahamas, Floride
6
100000
46
2
Texas
0
80000
Louisiane,
Mississippi, Alabama,
36
1
Tennessee Lynchburg
3
70000
Richmond,
57
3
(Chesterfield County , Virginia
7
62000
31
0
Texas, Louisiane
0
45000
23
0
Texas
2
35000
39
1
Texas
0
34000
62
4
Floride
0
30000
33
1
Texas
17
25000
Louisiana,
Mississippi, Alabama,
26
0
Florida
Pandhandle
0
16000
New
England,
New York, New
26
0
Jersey
2
0
51
3
Florida, Panhandle, Georgia
5
0
54
3
Data Source: www.em-dat.net/index.htm
The Storm Tracks (N = ~100)
The Flying Spaghetti Monster 
Note:
Each Storm Track consists of smaller sections Which have
Category, Windspeed, Temp & Pressure Attributes
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
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 Swath
This image depicts
the swath of Hurricane
Hugo (1989) as it hit
NameYear
Pop in Swath
South Carolina. The
Hugo 1989
1,735,806
shaded pink area is the
population density (from
LandScan2000) masked
to only those areas
within 100 km Of
the coast. The zone of
intersection of the swath of the hurricane and the population
density on the coast is roughly demarcated by the green
polygon. Sadly, these numbers have to be determined one at a
time because The swaths of hurricanes often overlap – so, in
essence we obeyed the instructions on the shampoo bottle:
“Lather, Rinse, Repeat” (GIS is FUN )
Fleshing out the Table: 2) GDP in Swath
This image shows a similar
kind of intersection analysis
for Hurricane Hugo except
NameYear
GDP in Swath
Hugo 1989
1.677 Billion
instead of population density
The 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 individual
pixels 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 rest
of the hurricanes to produce a 2nd column in our table
Fleshing out the table: 3) Wetlands in Swath
The 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
NameYear
Woody Wets in Swath
Herb Wets in Swath
the Herbaceous wets in the
Hugo 1989
2,306 km2
335 km2
same 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 Herbaceous
Wetlands and 2,306 km2 of Woody Wetlands. This image raises another question of
how 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.
This Table
was built with
the following
key columns:
TD/GDP
Herb Wets
WindSpeed
to provide the
Regression
info
Hurricane
Alberto
Alicia
Allen
Allison
Allison
Andrew
Bill
Bob
Bonnie
Bret
Chantal
Charley
Charley
Danny
Dennis
Max
Herbaceous GDP in swath Observed total
wind
W etland
in year hit
damage (2004
Estimated
speed
area in
(2004 $US)
$ US)
marginal value
(m/sec) swath (ha)
(millions)
(millions)
(2004 $US/ha)
28.3
4,466
$5,040
$305
$15,607
51.4
93,590
$100,199
$2,823
$14,449
84.9
26,062
$13,151
$1,674
$127,090
23.1
167,494
$149,433
$63
$348
25.7
100,298
$185,610
$6,995
$1,611
69.4
901,819
$83,450
$34,955
$699
25.7
642,544
$70,669
$17
$23
51.4
68,465
$122,358
$829
$30,683
51.4
49,774
$15,840
$373
$6,984
61.7
29,695
$2,043
$35
$4,557
36.0
104,968
$81,319
$111
$2,400
25.7
55,126
$18,775
$33
$470
64.3
358,778
$483,281
$6,800
$15,347
36.0
271,317
$66,711
$111
$367
46.3
22,752
$17,669
$45
$20,704
Year
States hit
1994
1983
1980
1989
2001
1992
2003
1991
1998
1999
1989
1998
2004
1997
1999
Elena
Emily
Erin
1985
1993
1995
56.6
51.4
41.2
50,568
615
264,226
$14,240
$6
$132,138
$1,774
$38
$821
$8,835
$5,795
$1,278
Floyd
1999
69.4
188,637
$420,940
$7,259
$56,214
Fran
Frances
Gaston
Gloria
Hugo
Irene
1996
2004
2004
1985
1989
1999
54.0
64.3
30.9
64.3
72.0
46.3
9,033
340,051
100,502
87,863
32,906
692,219
$10,471
$150,986
$82,063
$188,531
$13,684
$114,903
$3,900
$4,400
$62
$1,451
$1,391
$104
$114,389
$5,272
$1,439
$72,229
$46,288
$319
Isabel
Isidore
2003
2002
72.0
56.6
37,942
574,157
$35,068
$64,990
$5,406
$79
$92,176
$547
Ivan
Jeanne
Jerry
Katrina
Keith
Lili
Opal
2004
2004
1989
2005
1988
2002
1995
74.6
56.6
38.6
78.2
33.4
64.3
66.9
504,033
404,769
98,540
708,519
222,324
224,504
7,261
$226,150
$133,657
$86,173
$214,277
$55,856
$24,439
$12,652
$6,000
$7,000
$49
$22,321
$44
$295
$3,521
$6,996
$2,088
$3,717
$4,363
$328
$1,779
$465,730
52
53
17
218,995
100,400
243,111
$99,905
$75,994
$110,816
$3,561
$825
$7,001
$33,268
$4,914
$83,466
Mean
Median
S.D.
Regression Analysis
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
Regression Parameters
Coefficient
Std. Error
t
1.0000
Emilly 1993
P
Opal 2005
Allen 1980
-10.551
3.878
-0.77
R2
3.29
0.706
0.16
-3.195
5.491
-4.809
= 0.60
Q: Why use
TD/GDP
For the dependant
Variable?
0.003
<0.001
<0.001
Fran 1996
Hugo 1989
Bret 1999
0.1000
Isabel 2003
Gloria 1985
TD/GDP predicted
a
b1
b2
Elena 1985
Floy d 1999
Bonnie 1998
Dennis 1999
Lili 2002
Bob 1991
Charley 2004
Ivan 2004
Alicia 1983
Frances 2004
Katrina 2005
Alberto 1994
0.0100
Isidore2002
Jerry 1989
Jeanne 2004
Andrew 1992
Chantal 1989
Irene 1999
Gaston 2004
Danny 1997
Keith 1988
Charley 1998
Erin 1995
Allison 2001
0.0010
Allison 1989
Bill 2003
0.0001
0.0001
0.0010
0.0100
0.1000
TD/GDP observed
1.0000
10.0000
Observed vs. predicted relative damages (TD/GDP)
for each of the hurricanes used in the 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. How
Can 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 = state
sw = average swath in state s
gc = average wind speed of hurricane of category c
pc,s = the probability of a hurricane of category c striking state s in a given year
GDPsw = the GDP in state s in the average hurricane swath
wsw = the wetland area in state s in the average hurricane swath
Calculating Total Annual Value
We 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 = TV / Wetland
state
Wetlands Wetland
within 100 area in
km of
average
coast by
swath
state W
W
(ha)
(ha)
State
Alabama
16,759
Connecticut
21,591
Delaware
33,964
Florida
1,433,286
Georgia
140,556
Louisiana
1,648,611
Maine
60,388
Maryland
60,511
Massachusetts
49,352
Mississippi
25,456
New Hampshire
19,375
New Jersey
69,001
New York
5,306
North Carolina
64,862
Pennsylvania
7,446
Rhode Island
3,638
South Carolina
107,894
Texas
448,621
Virginia
71,509
Mean
Median
S.D.
225,691
60,388
475,157
GDP in
average
swath ($
millions/ye
ar)
6,388
12,601
12,089
186,346
29,120
370,299
15,500
16,011
16,801
6,048
9,905
21,864
2,117
21,295
2,994
1,759
39,177
79,110
23,588
9,499
65,673
10,488
70,491
7,356
36,250
14,670
21,924
67,266
3,890
23,051
78,703
90,770
13,023
93,117
12,810
15,367
63,661
27,786
45,948
16,011
89,175
38,200
23,051
31,083
state
Annual
Average
expected
annual
Probability of state being hit by a storm
Total annual value per state (TV ) for
marginal
value
of
of the given category in a year by Storm
k=10,000, 5,000, and 1,000
($
value per
wetlands
Category
millions/yr)
average
per ha per
swath
state (AV )
MV
at k=5,000
($/ha/yr)
k=10,000
k=5,000
k=1,000
($/ha/yr)
1
2
3
4
5
7.14% 3.25% 3.90% 0.00% 0.00%
14,155
40.9
133.6
749.5
7,970.4
2.60% 1.95% 1.95% 0.00% 0.00%
14,428
263.2
615.4
2,705.2
28,503.5
1.30% 0.00% 0.00% 0.00% 0.00%
222
3.7
8.7
38.6
255.8
27.92% 20.78% 17.53% 3.90% 1.30%
1,684
6,453.9
11,293.6 40,010.3
7,879.5
7.79% 3.25% 1.30% 0.65% 0.00%
630
72.3
140.0
542.2
996.2
11.04% 9.09% 8.44% 2.60% 0.65%
126
1,665.7
2,883.2 10,107.2
1,748.8
3.25% 0.65% 0.00% 0.00% 0.00%
715
21.3
46.5
196.0
770.1
0.65% 0.65% 0.00% 0.00% 0.00%
445
14.3
30.9
129.4
510.4
3.25% 1.30% 1.95% 0.00% 0.00%
8,422
301.2
643.3
2,673.1
13,035.3
1.30% 3.25% 4.55% 0.00% 0.65%
7,154
17.8
59.0
341.5
2,316.1
0.65% 0.65% 0.00% 0.00% 0.00%
1,095
10.7
28.1
131.7
1,451.2
1.30% 0.00% 0.00% 0.00% 0.00%
583
37.0
74.8
298.9
1,083.5
3.90% 0.65% 3.25% 0.00% 0.00%
586,845
79.5
271.2
3,473.3
51,106.9
13.64% 8.44% 7.14% 0.65% 0.00%
5,072
304.1
617.5
2,477.0
9,519.6
0.65% 0.00% 0.00% 0.00% 0.00%
11,651
4.1
14.1
141.3
1,890.4
1.95% 1.30% 2.60% 0.00% 0.00%
95,193
7.7
26.3
377.0
7,239.1
12.34% 3.90% 2.60% 1.30% 0.00%
1,281
265.0
498.0
1,880.0
4,615.3
14.94% 11.04% 7.79% 4.55% 0.00%
3,901
3,087.1
5,547.2 20,144.4
12,365.0
5.84% 1.30% 0.65% 0.00% 0.00%
1,555
115.6
230.8
914.1
3,227.6
6.39%
3.25%
7.01%
3.76%
1.30%
5.25%
3.35%
1.95%
4.39%
0.72% 0.14%
0.00% 0.00%
1.40% 0.35%
39,745
1,684
134,195
671.8
72.3
1,594.0
T ota ls
12,765.0
1,219.1
140.0
2,788.8
4,596.4
749.5
9,848.0
23,162.0 87,330.7
8,236.0
3,227.6
12,418.4
Pixel Level Analysis
Metamathemagical manipulations produce the equation below:
MVpxl is damage avoided ‘by the wetlands in that pixel’
e is 2.71828… α, β1, and β2 are the regression parameters, k=1000
gc is the average windspeed of category ‘c’ hurricanes
wpxl is the area of wetlands within 50 km of the pixel
GDPpxl is the GDP within 50 km of the pixel
Pc is frequency of hurricane of category c at that pixel
c 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)
“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”.
Deconstructing the Valuation Equation
and 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’ values
Focalsum( ): 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 (1km2)
Wetsinswath (1km2)
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 a
Representation 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 of
Storm protection service
Value from wetlands that
Is truly spatially explicit.
Just like the one to the
Right but based on using
Appropriately developed
Frequency maps that
Account for the spatial
And temporal variation of
Category (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.