Value of Ecosystems in Protecting Infrastructure

Coastal Wetlands and Storm Protection:
A spatially explicit estimate of ecosystem service value
Paul C. Sutton
Department of Geography
University of Denver
Workshop on Ecosystem Service Markets
And Associated Performance Measures
89th Annual TRB Conference
Washington DC
June 10, 2010
Ecosystem Services and
Market Failure
Market failures related to ecosystem services
include the facts that:
1) many ecosystems provide services that are public goods
2) many ecosystem services are affected by externalities
3) property rights related to ecosystems and their services
are often not clearly defined.
Consequently Ecosystem Services and their Economic
Valuation are frequently complex, contested, & controversial
* Adapted from http://www.ecosystemvaluation.org/1-02.htm
‘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
16
6800000
64
4
Caroline, Pennsylvania, Virginia
33
6000000
26
0
Florida, Pennsylvania, Maryland,
52
6000000
75
5
Virginia, Washington, West
21
5000000
72
5
(Florida state), North Carolina,
2
4400000
64
4
Virginia, Maryland, West
39
3400000
54
3
Florida, Georgia, Alabama
19
3000000
67
4
Texas
18
1650000
51
3
Panhandle
12
1500000
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
41
1
York, Rhode Island, Connecticut,
13
620000
51
3
Louisiana
0
260000
64
4
Alabama
32
250000
28
0
York, New Jersey
0
100000
36
1
Bahamas, Floride
6
100000
46
2
Texas
0
80000
36
1
Tennessee
3
70000
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
26
0
Florida Pandhandle
0
16000
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 associated with the hurricane (damage, fatalities, GDP in
swath, maximum wind speed, category, etc.).
Fleshing out the Table: 1) GDP in Swath
This image shows an
intersection analysis
NameYear
GDP in Swath
for Hurricane Hugo.
Hugo 1989
1.677 Billion
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. “Lather, Rinse, Repeat” for the rest
of the hurricanes to produce a 2nd column in the table.
Fleshing out the table: 2) 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
the Herbaceous wets in the
NameYear
Woody Wets in Swath
Herb Wets in Swath
same lather, rinse, repeat
Hugo 1989
2,306 km2
335 km2
Manner.
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.
‘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?
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
Year
States hit
1994 GA, MI, FL, AL
1983 TX
1980 TX
1989 TX, LA, FL, NC, PA, VA
2001 TX, LA, FL, NC, PA, VA
1992 FL, LA
2003 LA, MS, AL, FL
1991 NC, ME, NY, RI, CT, MA
1998 NC, SC, VA
1999 TX
1989 TX
1998 TX
2004 FL
1997 OH, PA, IL, NY, NJ
1999 NC
Elena
Emily
Erin
1985
1993
1995
Floyd
1999
Fran
Frances
Gaston
Gloria
Hugo
Irene
1996
2004
2004
1985
1989
1999
Isabel
Isidore
2003
2002
Ivan
Jeanne
Jerry
Katrina
Keith
Lili
Opal
2004
2004
1989
2005
1988
2002
1995
FL, AR, KY, SD, IO, MI, IN, MO
NC
FL, AL, MS
NC, FL, SC, VA, MD, PA, NJ, NY,
DE, RI, CT, MA, VT
NC, SC, VA, MD, VA, PA, OH,
Washington DC
FL, NC, SC, OH
VA, SC, NC
NC, NY, CT, NH, ME
SC
FL
NC, MD, VA, Washington DC
LA, MS, AL, TN
AL, LA, MS, FL, PA, MD, NJ, OH,
NC, VA, GA, TN
FL
TX
AL, LA, MS, GA, FL
FL
LA
FL, GA, AL
Mean
Median
S.D.
Max
Herbaceous GDP in swath Observed total
wind
Wetland
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
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
69.4
188,637
$420,940
$7,259
$56,214
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
72.0
56.6
37,942
574,157
$35,068
$64,990
$5,406
$79
$92,176
$547
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
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
α
β1
β2
-10.551
3.878
-0.77
3.29
0.706
0.16
-3.195
5.491
-4.809
0.003
<0.001
<0.001
= 0.60
Q: Why use
TD/GDP
For the dependent
Variable?
Isabel 2003
Gloria 1985
Elena 1985
Floyd 1999
Bonnie 1998
Dennis 1999
Lili 2002
Bob 1991
Charley 2004
R2
Fran 1996
Hugo 1989
Bret 1999
0.1000
Ivan 2004
Alicia 1983
Katrina 2005
Frances 2004 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 hard questions 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 s
W sw
(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 (TVs ) 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 (AVs )
MVsw
at k=5,000
k=10,000
k =5,000
k=1,000
($/ha/yr)
($/ha/yr)
1
2
3
4
5
7.14% 3.25% 3.90% 0.00% 0.00%
14,155
40.9
1 33.6
749.5
7,970.4
263.2
61 5.4
2,705.2
28,503.5
2.60% 1.95% 1.95% 0.00% 0.00%
14,428
3.7
8.7
38.6
255.8
1.30% 0.00% 0.00% 0.00% 0.00%
222
6,453.9
1 1 ,293.6 40,010.3
7,879.5
27.92% 20.78% 17.53% 3.90% 1.30%
1,684
72.3
1 40.0
542.2
996.2
7.79% 3.25% 1.30% 0.65% 0.00%
630
1,665.7
2,883.2 10,107.2
1,748.8
11.04% 9.09% 8.44% 2.60% 0.65%
126
21.3
46.5
196.0
770.1
3.25% 0.65% 0.00% 0.00% 0.00%
715
14.3
30.9
129.4
510.4
0.65% 0.65% 0.00% 0.00% 0.00%
445
301.2
643.3
2,673.1
13,035.3
3.25% 1.30% 1.95% 0.00% 0.00%
8,422
17.8
59.0
341.5
2,316.1
1.30% 3.25% 4.55% 0.00% 0.65%
7,154
0.65% 0.65% 0.00% 0.00% 0.00%
1,095
10.7
28.1
131.7
1,451.2
37.0
74.8
298.9
1,083.5
1.30% 0.00% 0.00% 0.00% 0.00%
583
79.5
271 .2
3,473.3
51,106.9
3.90% 0.65% 3.25% 0.00% 0.00%
586,845
304.1
61 7.5
2,477.0
9,519.6
13.64% 8.44% 7.14% 0.65% 0.00%
5,072
4.1
1 4.1
141.3
1,890.4
0.65% 0.00% 0.00% 0.00% 0.00%
11,651
7.7
26.3
377.0
7,239.1
1.95% 1.30% 2.60% 0.00% 0.00%
95,193
265.0
498.0
1,880.0
4,615.3
12.34% 3.90% 2.60% 1.30% 0.00%
1,281
3,087.1
5,547.2 20,144.4
12,365.0
14.94% 11.04% 7.79% 4.55% 0.00%
3,901
115.6
230.8
914.1
3,227.6
5.84% 1.30% 0.65% 0.00% 0.00%
1,555
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
T o ta ls
671.8
72.3
1,594.0
12,765.0
1 ,21 9.1
1 40.0
2,788.8
4,596.4
749.5
9,848.0
23,1 62.0 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.
Creating ‘Wets in Swath’ dataset
2
Wetsinpixel (1km )
2
Wetsinswath (1km )
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:
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…….
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.
Discussion
• Questions Specific to this study:
•
•
•
•
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?
• General Questions
•
•
•
•
•
This is a complex and controversial analysis that applies only to one aspect of the
ecosystem services provided by coastal wetlands. How useful is it for planning decisions
in the absence of complete consideration of the spatially variable nature of other
ecosystem services whose economic value has not been determined?
Is a simple benefits transfer model based on land cover useful despite its spatial
inaccuracies because of its simplicity and ease of implementation?
Which ecosystem services are highly spatially variable and which are not?
How important is it to characterize the spatial variability of the value of ecosystem
services?
How do we know when we are spending more time counting beans rather than cooking
them?