Lafarge Presque Island Quarry
InVEST Application
Prepared by
The World Resources Institute
10 G Street, NE Suite 800
Washington, DC 20002
Confidential – not for distribution
August, 2010
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Background
As part of the WWF-Lafarge Ecosystems Valuation Project, the World Resources Institute implemented
two InVEST modules on the Lafarge Presque Isle Quarry (PIQ) in Michigan.
InVEST can estimate the amount and value of ecosystem services that are provided on the current
landscape or under future scenarios. The models are spatially-explicit, using maps as information
sources and producing maps as outputs. InVEST returns results in either biophysical terms (e.g., tons of
carbon sequestered) or economic terms (e.g., net present value of that sequestered carbon). Models
currently offered are carbon sequestration, crop pollination, managed timber production, water
purification (for nutrients), reservoir hydropower production, and sediment retention for reservoir
maintenance. It also includes a biodiversity model so that tradeoffs between biodiversity and ecosystem
services can be assessed.
Models will be released soon for other services. These will include models for flood mitigation,
irrigation, agricultural production, open access harvest of timber and non-timber products, recreation
and tourism, and cultural benefits.
The results of the application of the Reservoir Sedimentation and Nutrient Retention models to PIQ are
described below. It is important to note that InVEST models are limited by the quality of the input data.
Sample input data was used due to the lack of more specific site data. For more accurate results, high
quality, detailed input data can be developed and verified with on-the-ground measurements.
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Sediment retention
At Lafarge Presque Isle Quarry the InVEST Avoided Reservoir Sedimentation module calculated that
1,271 tons of sediment are trapped yearly by vegetation, and the avoided annual costs of sediment
dredging are approximately US$20.3 million.
The 'sediment retention' service of a watershed is the amount of sediment that is trapped by the
watershed's vegetation. The module estimates the sediment retained by a landscape with particular
ecological and topographical features, and estimates the economic value of sediment retention as the
avoided costs of sediment dredging.
Model description and input data
The ability of a landscape to retain sediments is influenced by its land use and land cover. InVEST's
Avoided Reservoir Sedimentation model estimates sediment retention by using the Universal Soil Loss
Equation1, which integrates information on land use and land cover patterns, soil properties,
geomorphology, rainfall, and climate.
Erosion from a plot of land is given by the Universal Soil Loss Equation (USLE) as:
USLE = R x K x LS x C x P,
where R is the rainfall erosivity2, K is the soil erodibility factor3, LS is the slope length-gradient factor4, C
is the crop/vegetation and management factor5, and P is the support practice factor associated with the
crop/vegetation6.
The 'sediment retention' service of a watershed is defined as the amount of sediment that is trapped by
the watershed's vegetation. To estimate this service, the standard USLE (above) is compared to a
modified USLE (USLEb), which assumes bare soil (no vegetation present):
USLEb = R x K x LS.
The difference between the USLE and the USLEb is the amount of sediment trapped by vegetation at the
sediments' origin. Vegetation also captures sediment downstream from its origin. InVEST accounts for
the latter by estimating how much of the sediment eroded on all plots will be trapped by downstream
vegetation, based on the ability of vegetation in each plot to capture and retain sediment. The 'sediment
retention service' is equal to the sum of the sediment removed by the pixel itself and the sediment
removed by vegetation downstream. To assign a monetary value to the 'sediment retention' service, we
calculated the avoided cost of removing the sediment that would be deposited at streams if no
vegetation were present in the watersheds. For this calculation, the approximate costs of dredging (D)
at PIQ were used.
Table 1 presents the input data used in the estimation of the 'sediment retention' service at PIQ.
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Table 1: Input data for Presque Isle Quarry, Michigan
Variable
Digital Elevation
Model
Land use/Land cover
R
K
LS
C
P
D
Sediment Retention
Value
Threshold flow
accumulation
Slope threshold
Value
Values range
from 175 to
288 meters
above sea
level
Land cover
types given
integer
values
Values range
from 1332.36
to 1341.08
Values range
from 0 to .37
Values range
from 0 to
72.85 meters
Values range
from 0 to 20
Values
ranged from
0 to 12
US$16,000/t
on
Values range
from 0 to 100
percent
Default
model value:
1000
Default
model value:
25
Comments
National Elevation Data 30 meter (USDA/NRCS –
National Cartography & Geospatial Center). We filled
the sinks in this dataset using the Fill tool in ArcGIS.
NOAA Coastal Great Lakes Land Cover Dataset (2006)
National rainfall erosivity dataset from Natural Capital
Institute/InVEST
Soil erodibility data from SSURGO soil database. Soil
type at the PIQ site was classified as “pits, quarry.” This
soil type and a few others did not have a given soil
erodibility value. We estimated an erodibility value of
0.1 for the quarry.
Dataset created by model. Derived from Digital
Elevation Model (DEM).
Default values used and vary with land cover type.
Default values used and vary with land cover type.
Estimate obtained from: US Army Corps of Engineers,
2009. Contract dredging report: Detroit District.
Identifies capacity of vegetation to retain sediment as
an integer percentage of the amount of sediment
flowing into a cell from upslope. Varies with land cover
type.
Number of upstream cells that must flow into a cell
before it’s considered part of a stream.
Describes landscape characteristics such as slope
management practices including terracing and slope
stabilization techniques. This value is subject to
calibration based on specific knowledge of the regional
landscape.
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Results
The two watersheds associated to PIQ are illustrated in Fig. 1. Figs. 2 and 3 show USLE and USLEb,
respectively.
Fig. 1: The two watersheds associated with Presque Isle Quarry, Michigan.
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Fig. 2: Current annual soil erosion (metric tons) at Presque Isle Quarry, Michigan, predicted by the
Universal Soil Loss Equation.
Fig. 3: Annual soil erosion (metric tons) at Presque Isle Quarry, Michigan, assuming bare soil (no
vegetation).
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Fig 4 illustrates the sediment (metric tons) retained by vegetation located downstream for the sediment
origin, and Fig. 5 shows the total sediment retained by the vegetation, i.e. the 'sediment retention'
service of the watersheds.
Fig 4: Annual retention of sediment (metric tons) by vegetation located downstream of the sediments'
origin at Presque isle Quarry, Michigan.
Fig. 5: The 'sediment retention' service (total sediment retained annually by vegetation, in metric tons)
at Presque Isle Quarry, Michigan.
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The total sediment retained annually by vegetation at PIQ is 1,271.31 metric tons. With dredging costs
of approximately US$16,000/ton7, the avoided dredging costs are US$20,340,960.
Discussion
InVEST's Avoided Reservoir Sedimentation model allows resource managers to see which parts of the
landscape are providing the greatest value in terms of avoided sediment removal costs. Because the
model, like all models, relies on simplifying assumptions about sediment-retention qualities of the
landscape, its greatest usefulness is in comparing the relative contribution of different parts of the
landscape to erosion control. Accurate values of avoided sedimentation would require in-situ studies, or
the calibration of available models, such as InVEST, to a particular site. For example, the USLE method
has only been verified in relatively flat areas, and its predictive ability in steep areas would need to be
tested. Finally, the InVEST module estimates potential soil loss and potential avoided costs of
sedimentation, but in areas such as PIQ, sediment accumulation may not present a problem to barge
transit, so sediment dredging could be avoided altogether in the short term.
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Nutrient retention
At Lafarge Presque Isle Quarry the InVEST Nutrient Retention module calculated that 3,379 kilograms of
nitrogen are retained yearly by vegetation, and the avoided water treatment costs are approximately
US$865,641.00.
The 'nutrient retention' service of a watershed is the amount of either nitrogen or phosphorus that is
trapped by the watershed's vegetation. The module estimates the nutrient retained by a landscape with
particular ecological and topographical features, and estimates the economic value of nutrient retention
as the avoided costs of water treatment.
Model description and input data
Vegetation and soil limit the amount of non-point pollutants that enter water bodies. InVEST's "Water
Purification: Nutrient Retention" model calculates the amount of nutrient pollutants (nitrogen and
phosphorous) retained on every pixel of a watershed, and assigns a monetary value to this ecosystem
service by calculating the avoided costs of eliminating these nutrients from the water.
The InVEST model follows a series of steps. First, the model estimates how much pollutant is exported
from each parcel, based on export coefficients the user inputs8,9. Second, the model estimates how
much pollutant is retained by each downstream parcel by allowing each parcel downstream to retain
pollutant based on its land cover. The model also tracks how much pollutant reaches the stream in the
watershed. The model then aggregates the loading that reaches the stream from each pixel to
determine a total pollutant load at each point of interest. Once the total load is determined, the value of
the water purification service provided by each pixel is calculated based on the avoided treatment costs.
Table 2 presents the input data used in the estimation of nutrient retention at PIQ.
Table 2: Input data for Presque Isle Quarry, Michigan
Variable
Value
DEM
Annual average
precipitation
Potential
evapotranspiration
Values range
from 175 to
288 meters
above sea
level
737 mm
570 mm
Comments
National Elevation Data 30 meter (USDA/NRCS – National
Cartography & Geospatial Center). We filled the sinks in this
dataset using the Fill tool in ArcGIS. Because the scale of this
analysis is fairly small, a 30m resolution dataset may not be
detailed enough to provide highly accurate results.
Annual precipitation according to a model using point
precipitation and elevation data for the 30-year period of
1971-2000 (USDA/NRCS - National Cartography & Geospatial
Center, 2006).
Amount of water that would be removed from the surface of a
cell by evaporation and transpiration. (University of Wisconsin
Center for Sustainability and the Global Environment, 2001)
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Soil depth
Values range
from 0 to
1270 mm
Plant available
water fraction
Values range
from 0 to 0.4
Land use/Land
cover
Land cover
types given
integer values
Values range
from 1 to
7000 mm
based on land
cover type.
Values range
from 1 to
1000 based on
land cover
type.
Values ranged
from 0 to
11000 for N
and 0 to 3000
for P.
Values ranged
from 0 to 80
for N and 0 to
60 for P.
Root depth
ETK
Load_N/Load_P
Eff_N/Eff_P
Calib
1
Cost
$24
Annual Load
50
Time Span
Discount
Threshold flow
accumulation value
15 years
5
Default model
value: 1000
Measured as depth to any restrictive layer. Derived from
SSURGO database using Soil Data Viewer in ArcGIS. Many soil
types were not given depth values including most of the
quarry. We gave values of zero to these empty values which
assumes no soil – just rock. This will definitely impact our
results so more detailed, accurate soil data will be important
for this model.
Fraction of water stored in soil profile that is available for
plants’ use. Derived from SSURGO database using Soil Data
Viewer in ArcGIS.
NOAA Coastal Great Lakes Land Cover Dataset (2006)
Maximum root depth for vegetated land use classes in
millimeters. Values from sample data were used based on
similar land cover types.
Evapotranspiration coefficient for each land cover type. This
modifies the reference evapotranspiration which is typically
based on alfalfa or grass. Values from sample data were used
based on similar land cover types.
Nutrient loading for each land use. Different values were given
for nitrogen and phosphorus. Values from sample data were
used based on similar land cover types. These values need to
be calibrated to the site based on local knowledge.
Vegetation filtering value per pixel for each land cover type.
This identifies the capacity of vegetation to retain nutrients.
Given as a percentage of the amount of nutrient flowing into a
cell from upslope. Values from sample data were used based
on similar land cover types. These values need to be calibrated
to the site based on local knowledge.
Annual watershed loading calibration constant. Value from
sample data used.
Annual cost of nutrient removal in dollars per kilogram
removed. Value from sample data used.
Total critical annual nutrient loading allowed for the nutrient
of interest at the outlet of the watershed. Value from sample
data used.
Number of years for which net present value will be calculated.
Value from sample data used.
The rate of discount over the time span - used in net present
value calculations. Value from sample data used.
Number of upstream cells that must flow into a cell before it’s
considered part of a stream.
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Results
The figures below present the major outputs from the InVEST Nutrient Retention model.
Fig. 6: Nitrogen was chosen as the pollutant of interest for this running of the model. The Runoff Index
describes the potential for surface and subsurface runoff across the landscape. This is based on the
water yield and runoff coming from upstream pixels. The red and orange areas show high levels of
runoff potential on the non-vegetated land in the quarry.
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Fig. 7: Hydrologic Sensitivity Score raster. This output normalizes the runoff index seen above based on
the mean. Pixels that have a runoff index below the mean will export less than export coefficients and
pixels that have higher than mean runoff index will export more than export coefficients. High values
are shown in reds and oranges while low values are showing in blues and purples.
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Fig. 8: The Adjusted Load raster shows critical areas of contaminant loading. This map shows where
pollutant loadings are of concern because of high loading rates in high scoring HSS areas. The total
adjusted load for the quarry area is 6,687 kilograms annually.
Fig. 9: This map shows the pollutant loading that reaches the stream from each pixel. This identifies
areas where managers can focus on controlling pollutant inputs. The total pollutant loading for the
entire quarry is 2,506 kilograms per year.
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Fig. 10: This map shows the cumulative amount of nutrient that is delivered to the stream from each
pixel along a flow path. It can be used to calibrate the model by comparing actual measured pollutant
levels at points along the flow path.
Fig. 11: Amount of nutrient retained by each pixel from what flows into it from upstream pixels. This
output represents the ecosystem service of water purification in biophysical terms. Vegetated areas
around the quarry contribute most to retaining nutrients. The total amount of nitrogen retained
annually by the quarry site is 3,379 kilograms.
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Fig. 12: This map shows the economic benefit of filtration by vegetation as avoided costs. This
represents the ecosystem service of water purification in economic terms. The value of the entire
quarry is $865,641 over 15 years based on a water treatment cost of $24 per kilogram of nutrient, an
allowed critical annual loading of 50 kilograms at the watershed outlet, and a 5% discount rate. Sample
input data was used for the economic analysis as a demonstration of the types of calculations the model
can perform.
Discussion
The InVEST Nutrient Retention model provides managers with a method of estimating the contribution
of vegetation and soil to purifying water through the removal of nutrients. This model has some
significant limitations, however. First, the model only provides retention estimates for two major
nutrients: nitrogen and phosphorus. Calculations for each nutrient must be run separately. For
example, to estimate both the nutrient retention of nitrogen and phosphorus, the model must be run
twice. In addition, the model does not account for chemical and biological interactions outside of
filtration by terrestrial vegetation. In-stream processes that may affect nutrient concentrations are not
measured. Another limitation particularly relevant to this analysis is that the model assumes that water
flows downslope along natural flowpaths. Therefore, these estimates may be less relevant in areas with
extensive ditching or other heavily engineered drainage. Lastly, the model is most accurate when
calibrated with actual measured levels of nutrient runoff at the outlet of the watershed. The more that
is known about the site, the more accurate the model can predict how well nutrients are being retained
by the land cover.
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Technical Notes
1. Wischmeier, W.H., Smith, D.D. 1978. Predicting rainfall erosion losses: A guide to conservation
planning. Agriculture Handbook No. 537. USDA/Science and Education Administration, US. Govt. Printing
Office, Washington, DC. 58pp.
2. Rainfall erosivity is the erosion potential of a plot of land at a given geographical location. The greater
the intensity and duration of rain storms in the location, the higher the erosion potential.
3. K is a measure of the susceptibility of soil particles to detachment and transport by rainfall and runoff.
4. The LS factor is the distance that a drop of rain/sediment runs until its energy dissipates. It represents
a ratio of soil loss under given conditions compared to a reference site with the “standard” slope of 9%
and slope length of 72.6 feet. The steeper and longer the slope is, relative to the conditions of the
reference site, the higher the risk for erosion will be. In InVEST, different LS equations are automatically
used for slope conditions that differ from the standard reference site conditions.
5. The C factor is a ratio comparing the soil loss from land under a specific crop and management system
to the corresponding loss from continuously fallow and tilled land.
6. P reflects the effects of practices to crop/vegetation that will reduce the amount and rate of water
runoff, and thus reduce the amount of erosion. It represents the ratio of soil loss by a support practice
to that of straight-row farming up and down the slope.
7. Dredging costs exclude a survey fee of US$2,000-$5,000.
8. Reckhow, K.H., Beaulac, M.N., Simpson, J.T. 1980. Modeling phosphorus loading and lake
response under uncertainty: A manual and compilation of export coefficients. U.S. EPA Report No.EPA440/5-80-011, Office of Water Regulations, Criteria and Standards Division, U.S. Environmental
Protection Agency, Washington, DC.
9. The export coefficients are modified by InVEST to account for differences between the characteristics
of the sites where the measures were developed and the characteristics of the sites where the user is
applying the model.
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