United States
Department of
Agriculture
Forest Service
Rocky Mountain
Research Station
General Technical
Report RMRS-GTR-188
April 2007
Predict
post-fire
Erosion Risk Management
Tool (ERMiT) User Manual
(version 2006.01.18)
Peter R. Robichaud, William J. Elliot,
Fredrick B. Pierson, David E. Hall,
Corey A. Moffet, and Louise E. Ashmun
erosion
Year 1
Year 2
treatment
effectiveness
Year 3
and recovery
Robichaud, Peter R.; Elliot, William J.; Pierson, Fredrick B.; Hall, David E.; Moffet, Corey A.; Ashmun, Louise
E. 2007. Erosion Risk Management Tool (ERMiT) user manual (version 2006.01.18). Gen. Tech. Rep.
RMRS-GTR-188. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research
Station. 24 p.
Abstract
The decision of where, when, and how to apply the most effective post-fire erosion mitigation treatments requires
land managers to assess the risk of damaging runoff and erosion events occurring after a fire. To aid in this assessment, the Erosion Risk Management Tool (ERMiT) was developed. This user manual describes the input
parameters, input interface, model processing, and output files for version 2006.01.18.
ERMiT is a web-based application that uses Water Erosion Prediction Project (WEPP) technology to estimate
erosion, in probabilistic terms, on burned and recovering forest, range, and chaparral lands with and without the
application of erosion mitigation treatments. User inputs are processed by ERMiT to combine rain event variability with spatial and temporal variabilities of soil burn severity and soil properties, which are then used as WEPP
input parameters. Based on 20 to 40 individual WEPP runs, ERMiT produces a distribution of rain event sediment
delivery rates with a probability of occurrence for each of five post-fire years. In addition, event sediment delivery
rate distributions are generated for post-fire hillslopes that have been treated with seeding, straw mulch, and erosion barriers such as contour-felled logs or straw wattles.
Key words: erosion prediction model, FS WEPP, post-fire assessment, BAER treatments
The Authors
Peter R. Robichaud is a Research Engineer in the Soil and Water Engineering Research Unit of the Rocky
Mountain Research Station located at the Forestry Sciences Laboratory in Moscow, Idaho. He has developed
and implemented research protocols for measuring post-fire runoff and erosion and post-fire erosion mitigation
treatment effectiveness.
William J. Elliot is a Research Engineer and Project Leader in the Soil and Water Engineering Research Unit
of the Rocky Mountain Research Station located at the Forestry Sciences Laboratory in Moscow, Idaho. His research focuses on forest soil erosion processes and prediction.
Fredrick B. Pierson is a Research Soil Scientist with the USDA-Agricultural Research Service at the Northwest
Watershed Research Center in Boise, Idaho. His research focuses on rangeland hydrology and erosion.
David E. Hall is a Computer Programmer/Analyst in the Soil and Water Engineering Research Unit of the Rocky
Mountain Research Station located at the Forestry Sciences Laboratory in Moscow, Idaho. He has developed
numerous interfaces and the network technology to provide public Internet access to the natural resource models produced by the project.
Corey A. Moffet is a former post-doctoral associate with the USDA-Agricultural Research Service at the
Northwest Watershed Research Center in Boise, Idaho. He is currently a research rangeland scientist with the
USDA-Agricultural Research Service at the U.S. Sheep Experiment Station in Dubois, Idaho.
Louise E. Ashmun is a Civil Engineer in the Soil and Water Engineering Research Unit of the Rocky Mountain
Research Station located at the Forestry Sciences Laboratory in Moscow, Idaho.
You may order additional copies of this publication by sending your mailing
information in label form through one of the following media. Please specify the
publication title and series number.
Publishing Services
Telephone
FAX
E-mail
Web site
Mailing address
(970) 498-1392
(970) 498-1122
rschneider@fs.fed.us
http://www.fs.fed.us/rm/publications
Publications Distribution
Rocky Mountain Research Station
240 West Prospect Road
Fort Collins, CO 80526
Acknowledgments
The authors wish to acknowledge and thank the field crews who assisted with the data collection processes that
provided the values to populate this model. We also wish to thank the many people who used ERMiT and provided valuable feedback during the development process. Funding for the project was provided, in part, by the
Joint Fire Science Program (a collaborative program of the U.S. Department of Interior and U.S. Department of
Agriculture, Forest Service) and is greatly appreciated.
Contents
Preface....................................................................................................................................... ii
Purpose of Erosion Risk Management Tool........................................................................... 1
Accessing ERMiT...................................................................................................................... 1
Input Data................................................................................................................................... 1
Climate................................................................................................................................ 1
Soil Texture......................................................................................................................... 4
Rock Content...................................................................................................................... 4
Vegetation Type and Range/Chaparral Prefire Community Description............................. 4
Hillslope Gradient and Horizontal Length........................................................................... 4
Soil Burn Severity Class..................................................................................................... 4
Process...................................................................................................................................... 5
Overview............................................................................................................................. 5
Initial 100-year WEPP Run................................................................................................. 5
Variability of ERMiT Input Parameters................................................................................ 7
Multiple WEPP Runs........................................................................................................... 8
Combined Occurrence Probability.................................................................................... 10
Erosion Mitigation Treatments...........................................................................................11
Output....................................................................................................................................... 14
Summary of Input Selections and Initial 100-year WEPP Run......................................... 14
Sediment Delivery Exceedance Probability Graph for Untreated Condition..................... 14
Mitigation Treatment Comparisons Calculator.................................................................. 15
Supporting Tables............................................................................................................. 17
Saving Results.................................................................................................................. 18
Management Implications...................................................................................................... 18
References............................................................................................................................... 21
Appendix A. Model Assumptions........................................................................................... 22
Appendix B. Example.............................................................................................................. 23
Rocky Mountain Research Station
Natural Resources Research Center
2150 Centre Avenue, Building A
Fort Collins, CO 80526
Preface
The Erosion Risk Management Tool (ERMiT) uses Water Erosion Prediction Project (WEPP) technology as the
runoff and erosion calculation engine. WEPP simulates both interrill and rill erosion processes and incorporates
the processes of evapo-transpiration, infiltration, runoff, soil detachment, sediment transport, and sediment deposition to predict runoff and erosion at the hillslope scale (Flanagan and Livingston, 1995). The ERMiT interface
uses multiple runs of WEPP over a range of input parameters to predict event sediment delivery in probabilistic
terms on burned and recovering forest, range, and chaparral lands. This ERMiT User Manual provides the information needed to access, run, and interpret ERMiT output; however, the conceptual framework of the model
has not been included. The reader is directed to Robichaud and others (in press) for details of the underlying assumptions and probability calculations of the ERMiT model. This technical article describes: 1) the components
of the ERMiT model; 2) the variability of rainfall, soil burn severity, and soil properties (input parameters) that
influence postfire erosion; and 3) how the input parameter variabilities are combined to produce a probability distribution of event-based erosion rates with and without application of mitigation treatments.
ERMiT is a dynamic process-based model that can be readily updated as additional data and validation results
become available. User feedback is greatly appreciated.
Peter R. Robichaud
ii
Purpose of Erosion Risk
Management Tool
Erosion Risk Management Tool (ERMiT) (Robichaud
and others 2006) provides a distribution of rain event
erosion rates with the likelihood of exceeding these
values. This is unlike most erosion prediction models,
which typically have “average annual erosion” as output. ERMiT is a web-based application that uses Water
Erosion Prediction Project (WEPP) technology to predict
erosion in probabilistic terms on burned and recovering forest, range, and chaparral lands, with and without
the application of mitigation treatments (see Appendix
A for model assumptions). ERMiT combines weather
variability with spatial and temporal variabilities of
soil properties to model the range of post-fire erosion
rates that are likely to occur. Based on a single 100year WEPP run and 20, 30, or 40 ten-year WEPP runs,
ERMiT produces a distribution of runoff event sediment
delivery rates with corresponding exceedance probabilities for each of five post-fire years. In addition, sediment
delivery rate distributions are generated for hillslopes
that have been treated with seeding, straw mulch, straw
wattles, and contour-felled log erosion barriers.
ERMiT’s “event sediment delivery exceedance probability” output can help managers decide where, when,
and how to apply treatments to mitigate the impacts of
post-wildfire runoff and erosion on life, property, and
natural resources. With ERMiT, managers can establish a maximum acceptable event sediment yield and
use ERMiT to determine the probability of “higher
than acceptable” sediment yields occurring. The maximum acceptable event sediment yield will vary within
a burned area. For example, a short term decline in
water quality may be more acceptable than damage
to a cultural heritage site, and modeling the hillslopes
above these two resources would likely have different
user-designated exceedance probabilities and treatment
criteria. By modeling various hillslopes within a burned
area, managers can determine the probabilities of
erosion-producing runoff events occurring, the expected
event sediment deliveries, and rates of recovery for the
post-fire conditions that exist.
Accessing ERMiT
ERMiT can be run from the Internet on the FS WEPP
web page (http://forest.moscowfsl.wsu.edu/fswepp/),
which is maintained by the USDA Forest Service, Rocky
Mountain Research Station. To run ERMiT, select
metric or U.S. conventional units and click the “ERMiT”
graphic. The “personality” field is used to maintain
individual user information when groups of users share
a single Internet Protocol (IP) address. Agencies are
increasingly networking computer systems so that users
within the same forest may have the same IP address.
Input Data
•
•
•
•
•
•
•
User inputs for ERMiT are:
climate
soil texture
soil rock content
vegetation type (forest, range, chaparral)
hillslope gradient and horizontal length
soil burn severity class
for range and chaparral, pre-fire plant community description (relative distribution of shrub, grass, and
bare soil cover in percentages).
User inputs are entered on a single interactive browser screen (fig. 1). When the cursor hovers over the input
parameter name, short hints are provided in the status
bar found in the lower left corner of the monitor screen
(fig. 2). More extensive explanations related to the input
parameters are found on linked pages accessed by clicking on the “ ” icon next to the parameter name.
Climate
ERMiT is linked to Rock:Clime (version 2004.04.26)
(Elliot and others 1999; Elliot and Hall 2000), which
provides climate parameter files for more than 2600
weather stations across the United States. These parameter files specify:
• station name, latitude, longitude, and elevation
• statistical characterizations of:
historical daily precipitation
minimum, maximum, and dewpoint temperatures
solar radiation
• monthly probabilities of a wet day following a wet
day, and of a wet day following a dry day
• a time-to-peak distribution
• wind data
Rock:Clime allows the user to create a custom climate
parameter file by making modifications to monthly
precipitation depth, monthly maximum and minimum
temperature, and monthly number of wet days in an
existing climate parameter file.
USDA Forest Service RMRS-GTR-188. 2007
Figure 1. The ERMiT input screen with example user selections.
Figure 2. The browser screen “status bar” provides helpful
hints as the curser moves over the input screen, in this
case, over the hillslope top gradient box.
USDA Forest Service RMRS-GTR-188. 2007
Amount of monthly precipitation may be modified
using data generated by PRISM (Parameter-elevation
Regressions on Independent Slopes Model) (Daly and
others 1994; Elliot 2004) or user data. PRISM provides
elevation and monthly precipitation values on a 2.5
arc-minute grid across the conterminous United States
(PRISM gridded data, normals, 1971-2000. http://www.
ocs.orst.edu/prism/).
ERMiT uses the climate parameter file (with all
user modifications) as input to CLIGEN (version 4.31)
(Nicks and others 1995) to generate a WEPP formatted
stochastic daily weather data file. This weather data file
includes:
Viewing climate station files using the
“Climate” link
Climate files—status designations
Selecting and modifying climates using the
“Custom Climate” button
• daily precipitation amount, duration, time-to-peak,
and peak intensity
• minimum, maximum, and dewpoint temperatures
• solar radiation
• wind velocity and direction
The input page for ERMiT displays a short list of
standard climates and, in some cases, a list of “custom
climates” generated by users of that IP address. The
name of a climate station listed in the ERMiT climate
selection list may be preceded by a “source of data”
symbol. Lack of a preceding symbol indicates that the
climate station is one of the standard stations available
immediately to all users.
• A leading asterisk (*) indicates that the climate file is
a “custom climate” created by a user of the current IP
address. Each custom climate, available only on the
computer where it was created, is generally accessible for at least one week after creation. [Because the
linkage is through the Internet Protocol (IP) address
of the connection in place when the custom climate
is created, America Online (AOL) users may encounter difficulties as their IP connects are not persistent,
even within a single ERMiT run. Thus, AOL users
may be unable to use custom climates once they are
created. In addition, dial-in users may get different
IP addresses each time they connect to the Internet,
which limits the use of a custom climate to a single
session.]
• A leading dash (-) indicates that the climate file for
the station has been made available, for a period of
time, perhaps for an instructional session.
• A trailing plus (+) sign indicates a modified climate
file; in other words, some of the standard climate station parameter values within this file were modified.
• Click the underlined title, Climate, to link to a new
page.
The new page contains a summary of the monthly
mean maximum and minimum temperatures, precipitation, and number of wet days (in other words, days with
precipitation) for the selected climate station. A table of
the weather stations used to determine the wind, dewpoint, solar radiation, and time-to-peak parameter values
is shown as well. From the climate page, the user may
also view the entire climate parameter file and a simple
line map (based on U.S. Census Bureau TIGER/Line®
files) showing the location of the station based on the
listed latitude and longitude. The climate parameter file
can be saved as a *.par file in the WEPP Windows directory structure and used for other applications in addition
to ERMiT.
• Click “Custom Climate,” which opens Rock:Clime,
to select a climate station that is not in the ERMiT
selection list, or to generate a custom climate.
• Select the state of interest.
• Click “SHOW ME THE CLIMATES” to add a new
climate.
A list of available climate station parameter files
for the selected state is displayed. The user has three
choices:
1. Add any of the listed climate stations to ERMiT’s
climate selection menu by selecting the climate
station and clicking “ADD TO PERSONAL
CLIMATES.”
2. View the parameters for the selected climate station
by clicking “DESCRIBE CLIMATE.”
3. Modify climate parameter values by clicking
“MODIFY THE CLIMATE,” which will open an
interactive screen with access to PRISM monthly
precipitation values based on latitude and longitude
input, and will also allow the user to enter and
modify climate data from an existing parameter set.
Hint—after using a linked climate page, click “Retreat”
or “Return to input screen” from any climate screen.
The current ERMiT session will be lost if you close the
browser window.
To create a climate parameter file for areas outside the
Rock:Clime coverage area (in other words, outside the
United States), select a similar available climate within
USDA Forest Service RMRS-GTR-188. 2007
the United States and modify it to more closely match
the climate of the area to be modeled. For climates that
are substantially different from an existing parameter
file, it is best to start with a climate that is drier than the
target climate.
Soil Texture
Users can select from among four soil textures: clay
loam, silt loam, sandy loam, and loam (fig. 1), based on
the USDA soil texture classification system.
Click the “
” icon next to the “Soil Texture” title
to view soil descriptions and the corresponding ASTM
Unified Soil Classification System group symbols.
Click the “Soil Texture” title to view the soil parameter values used for ERMiT’s initial 100-year WEPP
run. [The available ranges of soil parameter values and
the use of these values for the different WEPP runs are
discussed in the Process section of this User Guide and
in Robichaud and others (in press).]
Rock Content
In ERMiT, rock content refers to the proportion of
rocks found in the upper soil profile. Values up to 50
percent may be specified within the “Rock Content” box
(fig. 1). There is no mechanism to adjust soil parameters
for rock outcrops or surface rock cover.
Vegetation Type and Range/Chaparral
Pre-fire Community Description
The user can select one of three vegetation types to
model: forest, range, or chaparral. If the user selects
“range” or “chaparral,” he or she may specify the proportion of shrub, grass, and bare soil in the “Range/chaparral
pre-fire community” boxes. The default values for range
communities are 15 percent shrub, 75 percent grass, and
10 percent bare ground. The default values for chaparral
communities are 80 percent shrub, 0 percent grass, and
20 percent bare ground. For values other than the default
values, the user enters percent shrub and grass cover and
ERMiT adjusts percent bare ground to total 100 percent,
if possible—if not, ERMiT adjusts shrub or grass values
to total 100 percent. These input fields are inactive when
“forest” vegetation is selected.
Hillslope Gradient and Horizontal Length
The topographic inputs for ERMiT are hillslope
horizontal length and hillslope top, middle, and toe gradients. Hillslope horizontal length is the length of the
Figure 3. Hillslope profile illustration viewed by clicking the
“explain” button on the hillslope gradient or hillslope horizontal length box titles.
hillslope being modeled and includes the three slope
sections—top, middle, and toe (fig. 3). Top gradient is
the steepness, in percent, of the upper 10 percent (by
length) of the hillslope. Middle gradient is the steepness
of the main portion (central 80 percent) of the hillslope.
Toe gradient is the steepness of the lower 10 percent of
the hillslope. These values may be obtained from field
surveys, digital elevation models (DEMs), topographic
maps, or geographical information system (GIS) data
layers. Enter zero for top gradient if the top of the slope
being modeled starts at the top of the hill. The maximum allowable hillslope horizontal length is 1000 ft
(300 m) with a gradient between 0 and 100 percent (45
degrees). ERMiT sediment delivery predictions refer to
the amount of sediment that leaves the modeled hillslope profile.
Soil Burn Severity Class
Soil burn severity is a description of the impact
of a fire on the litter, forest floor, and soil. The soil
burn severity of a fire varies widely in space, depending on fuel load, moisture conditions, weather
(at the time of the fire), and topography (Robichaud
and Miller 1999), and creates a mosaic pattern of low,
moderate, and high soil burn severity across the landscape. However, analyses of post-fire soil properties
(using rainfall simulation experiments) only differentiate two soil burn severity classes, high (H) and
low (L) (Brady and others 2001; Pierson and others
2001; Robichaud 1996; Robichaud 2000). In other
words, in terms of soil parameter values, only two
“levels,” or sets of values, can be distinguished. For
modeling purposes, the H and L parameter values are
USDA Forest Service RMRS-GTR-188. 2007
steps were used to incorporate parameter variability into
the model:
Figure 4. ERMiT input page graphic shows the four, six, or
eight spatial arrangements of high and low soil burn severity
overland flow elements (OFEs) that are modeled based on
the user-selected soil burn severity classification. Red represents high and yellow represents low soil burn severity
with bold color arrangements modeled for the first post-fire
year and faint color patterns modeled in subsequent years.
arranged on the hillslope in multiple configurations
to model the three possible user-designated soil burn
severity classifications (low, moderate, high).
A hillslope segment with uniform soil, vegetation, and
topography is called an overland flow element (OFE),
and each hillslope is conceptually modeled with three
OFEs—each representing about one-third of the slope.
The red and yellow graphic displayed under the “Soil
burn severity class” box in the burn severity portion
of the ERMiT input page represents the four (low), six
(moderate), or eight (high) spatial arrangements of high
and low soil burn severity parameters that are modeled
based on the user-selected soil burn severity classification (fig. 4). In figure 4, each OFE is represented as a
single square in the rectangular strip of three squares for
the hillslope. Red represents high (H) and yellow represents low (L) soil burn severity soil parameter values.
Patterns with bold colors are modeled for the first year
following fire. Patterns with faint colors are modeled for
succeeding years (fig. 4).
Process
Once the input data selections have been made and
entered, click “Run ERMiT” to begin the calculations.
The following is a description of the erosion calculation
processes that occur with each session.
Overview
ERMiT uses WEPP as its erosion calculation engine.
WEPP models the processes of interrill and rill erosion,
evapotranspiration, infiltration, runoff, soil detachment,
sediment transport, and sediment deposition to predict
runoff and erosion at the hillslope scale (Flanagan and
Livingston 1995). In addition, spatial and temporal variability in weather, soil parameter values, and soil burn
severity are incorporated into ERMiT. Three general
1. Establish a range of possible parameter values from
field measurements.
2. Select five representative values from within the
range.
3. Assign an “occurrence probability” to each selected
value.
Temporal variation, the change in soil parameter values
over time due to recovery, is modeled by changes in the
occurrence probabilities assigned to the selected values
for each year of recovery.
Initially, ERMiT runs WEPP for the user-specified
climate, vegetation, and topography using the “most
erodible” soil parameters and soil burn severity spatial
pattern with the 100-year weather file. ERMiT selects
the single event with the largest runoff value in each of
the 100 years. From the 100 selected runoff events, the
5th, 10th, 20th, 50th, and 75th largest runoff events (and
the year those events occurred) are chosen for further
analysis. Each selected event year and its preceding
year is run through WEPP multiple times using all
combinations of the 10 soil parameter sets and four, six,
or eight soil burn severity spatial arrangements. The
three sources of variation (climate, soil burn severity,
and soil parameters) are each assigned an independent
occurrence probability. These independent occurrence
probabilities are combined to determine the occurrence
probability associated with each of the 100, 150, or 200
sediment delivery predictions (fig. 5).
Initial 100-year WEPP Run
A 100-year weather file, generated using CLIGEN,
is used by WEPP to produce a 100-year runoff record
for the combination of soil and burn severity conditions that have the greatest potential to generate runoff
for the site—three high soil burn severity OFEs that use
the “most erodible” soil parameter set (Soil 5) values for
interrill erodibility (Ki), rill erodibility (Kr), effective hydraulic conductivity (Ke), and critical shear (τc). ERMiT
selects the single event with the largest runoff value in
each of the 100 years. From those 100 selected runoff
events the 5th, 10th, 20th, 50th, and 75th largest runoff events
(and the year those events occurred) are chosen for further analysis. The runoff values are not representative of
the modeled scenario; rather, these values are predicted
runoff under the most extreme high severity burn conditions. However, the precipitation characteristics of the
selected runoff events are representative of the range of
events that have the potential to generate runoff.
USDA Forest Service RMRS-GTR-188. 2007
Figure 5. Flow chart of the ERMiT modeling process used to calculate probabilistic sediment delivery using the CLIGEN weather
generator and the WEPP erosion prediction engine.
USDA Forest Service RMRS-GTR-188. 2007
Variability of ERMiT Input Parameters
Climate variability
ERMiT re-runs WEPP using shortened weather files
to predict event sediment deliveries. The shortened
weather file includes the years with the selected runoff
events, plus the preceding year, if they have not already
been selected. This ensures that, when the shortened
weather file is run, the modeled soil water content on
the day of the event is similar to what it was during
the 100-year run. The assigned runoff event occurrence
probabilities are 7.5, 7.5, 20, 27.5, and 37.5 percent for
the 5th, 10th, 20th, 50th, and 75th largest runoff events,
respectively. For the selected runoff events, ERMiT
records the date, runoff and precipitation amounts, and
duration, and calculates the 10- and 30-min peak intensity values, which are displayed in the output.
Spatial (soil burn severity) variability
ERMiT uses two different sets of soil parameter values—one set for low soil burn severity (L) and one set
for high soil burn severity (H). Hillslope topographic,
vegetation, and soil parameter values are applied in combination for each overland flow element (OFE). ERMiT
models each hillside with three overland flow elements,
and to incorporate spatial variability due to soil burn
severity, several patterns of OFEs are modeled. [For
computational efficiency, ERMiT combines contiguous
OFEs of the same burn severity and conceptually models the hillslope as either one or two OFEs (for example,
HHH=one OFE of H; LLH=one OFE of L and one OFE
of H).] For the user-selection of High soil burn severity,
four spatial arrangements of OFEs are modeled for the
first post-fire year:
HHH (10 percent occurrence probability)
LHH (30 percent occurrence probability)
HLH (30 percent occurrence probability)
HHL (30 percent occurrence probability)
The first letter of the triplet represents the upper OFE, the
second represents the middle OFE, and the third represents the lower OFE (fig. 6 and table 1). A user selection
of Moderate soil burn severity (table 1) models the first
year following a fire with the three OFEs arranged as:
HLH (25 percent occurrence probability)
HHL (25 percent occurrence probability)
LLH (25 percent occurrence probability)
LHL (25 percent occurrence probability)
A Low soil burn severity user selection (table 1) models
the first year after a fire with OFEs arranged as:
LLH (30 percent occurrence probability)
LHL (30 percent occurrence probability)
HLL (30 percent occurrence probability)
LLL (10 percent occurrence probability)
To model post-fire recovery (post-fire Year 2 to Year
5) for a High soil burn severity user selection, changes
in assigned occurrence probabilities are applied and the
LLH, LHL, HLL, and LLL spatial arrangements are
added. For a Moderate soil burn severity user selection,
post-fire recovery is modeled by changes in assigned occurrence probabilities and the addition of HLL and LLL
spatial arrangements (table 1 and fig. 6). Thus, eight OFE
arrangements are modeled for a High soil burn severity
user selection, six for a Moderate soil burn severity user
selection, and four for a Low soil burn severity user selection to predict the event sediment yield for each of
the five post-fire years (fig. 6). For each successive year
of post-fire recovery, changes in assigned occurrence
probabilities decrease the likelihood of the higher erosion parameters and increase the likelihood of the lower
erosion parameters (table 1).
Soil property variability
The variable effects of post-fire ground cover, soil
water repellency, and soil erodibility are modeled by
using selected values from a range of measured values for interrill erodibility (Ki), rill erodibility (Kr),
effective hydraulic conductivity (Ke), and critical shear
(τc). The range of values for each parameter depends
on soil texture and a high or low soil burn severity designation (table 2). From each value range, a cumulative
Figure 6. Graphic viewed by clicking
on “explain” from the soil burn
severity class box. The bold colored
squares (red is high and yellow is
low soil burn severity) represent
the upper, middle, and lower
OFEs that are modeled for the
first year following a fire. The faint
colors indicate OFE arrangements
modeled in subsequent post-fire
recovery years.
USDA Forest Service RMRS-GTR-188. 2007
soil parameter values that correspond to
highly erodible soil conditions.
In range and chaparral environments,
field data indicate that post-fire values for
Ki and Ke vary by the proportions of shrubs
Hillslope
-----------Occurrence probability (%)----------and grasses in the pre-fire vegetation and
burn severity
OFEs
Year 1
Year 2
Year 3
Year 4
Year 5
by burn severity. This is accounted for by
weighting Ki and Ke soil parameter values
User selected High soil burn severity
within each value range based on the userHHH
10
0
0
0
0
LHH
30
25
0
0
0
specified proportions of pre-fire shrub and
HLH
30
25
25
0
0
grass cover with bare soil accounting for
HHL
30
25
25
25
0
the remainder of the 100 percent pre-fire
LLH
0
25
25
25
25
LHL
0
0
25
25
25
cover.
HLL
0
0
0
25
25
To model change over time, the occurLLL
0
0
0
0
25
rence probabilities of Soil 1 and Soil 2
(the less erodible soil parameter sets) are
User selected Moderate soil burn severity
HHH
0
0
0
0
0
increased, and Soil 3, Soil 4, and Soil 5
LHH
0
0
0
0
0
(the more erodible soil parameter sets)
HLH
25
0
0
0
0
are decreased for each year of post-fire
HHL
25
25
0
0
0
LLH
25
25
25
25
25
recovery (table 3). Post-fire recovery is
LHL
25
25
25
25
25
slower in areas affected by monsoons
HLL
0
25
25
25
25
than in other environments because monLLL
0
0
25
25
25
soon rains usually come in short bursts
User selected Low soil burn severity
of rainfall and do not provide dependable
HHH
0
0
0
0
0
wet cycles for seed germination. ERMiT
LHH
0
0
0
0
0
HLH
0
0
0
0
0
uses an empirical relationship (total preHHL
0
0
0
0
0
cipitation is less than 600 mm per year
LLH
30
25
25
25
25
and total July, August, and September
LHL
30
25
25
25
25
HLL
30
25
25
25
25
precipitation is greater than 30 percent
LLL
10
25
25
25
25
of the annual precipitation) to determine
if a particular climate is monsoonal. If
distribution function is created and the 5th, 20th, 50th, 80th, rainfall data fit the monsoon rainfall definition, then
and 95th percentile values are selected. The selected val- the post-fire Year 2 occurrence probabilities for the soil
ues for all four soil parameters are grouped by percentile parameter sets remain similar to Year 1 to reflect the
slower recovery in these climates (table 3).
ranking into five soil parameter sets:
Table 1. With each successive year of post-fire recovery, the assigned
occurrence probabilities and the selection of soil burn severity overland
flow element (OFE) arrangements (H=high soil burn severity overland flow
element; L=low soil burn severity overland flow element) are shifted toward
lower soil burn severity.
• 5th percentile values are grouped in Soil 1 (10 percent
occurrence probability)
• 20th percentile values are grouped in Soil 2 (20 percent occurrence probability)
• 50th percentile values are grouped in Soil 3 (40 percent occurrence probability)
• 80th percentile values are grouped in Soil 4 (20 percent occurrence probability)
• 95th percentile values are grouped in Soil 5 (10 percent occurrence probability)
The two soil parameter value ranges—one for low
and one for high soil burn severity—result in five soil
parameter sets for high soil burn severity and another
five parameter sets for low soil burn severity (table 2).
The current version of WEPP may internally limit some
Multiple WEPP Runs
ERMiT re-runs WEPP, using the shortened weather
file, for 10 soil parameter sets (High—Soil 1 through
Soil 5 and Low—Soil 1 through Soil 5) and for eight
soil burn severity spatial patterns. From the WEPP event
output, ERMiT determines an event sediment delivery
from each combination of rain events, soil parameter
sets, and soil burn severity spatial patterns. For the first
post-fire year, 100 event sediment delivery predictions
are used (table 4). To predict sediment delivery in postfire Year 2 through Year 5 (recovery), two additional
soil burn severity spatial patterns are used for the
user-selection of Moderate soil burn severity and four
additional spatial patterns are used for the user-selection
USDA Forest Service RMRS-GTR-188. 2007
Table 2. The post-fire value ranges for interrill erodibility (Ki), rill erodibility (Kr), effective hydraulic conductivity
(Ke), and critical shear (τc) by soil texture and high or low soil burn severity are shown. For range
and chaparral lands, user-designated pre-fire canopy cover proportions provide an additional level of
classification for Ki and Ke values.
Soil burn
severity
FOREST
Clay loam
Silt loam
Sandy loam
Loam
Ki (X 10 )
low
high
200 to 500
400 to 2,000
250 to 600
500 to 2,500
300 to 1,200
1,000 to 3,000
320 to 800
600 to 3,200
Kr (X 10-4)
low
high
0.010 to 2.5
2.0 to 8.0
0.020 to 3.5
3.0 to 9.0
0.030 to 4.5
4.0 to 10
0.015 to 3.0
2.5 to 8.5
Ke
low
high
25 to 8
13 to 2
33 to 9
18 to 3
48 to 14
22 to 5
40 to 18
27 to 4
τc
low
high
4
4
3.5
3.5
2
2
3
3
low
high
low
high
low
high
13 to 170
39 to 170
1.9 to 15
6.6 to 85
39 to 170
39 to 170
16 to 230
49 to 230
12 to 150
40 to 840
49 to 840
49 to 840
75 to 930
230 to 930
50 to 650
170 to 3,600
230 to 3,600
230 to 3,600
3.4 to 93
11 to 93
2.6 to 63
9.0 to 350
11 to 350
11 to 350
low
high
0.38 to 6.0
3.0 to 27
0.33 to 7.8
2.7 to 33
0.090 to 7.2
0.95 to 31
0.51 to 4.6
3.8 to 22
low
high
low
high
low
high
15 to 6
11 to 5
13 to 5
10 to 4
10 to 4
10 to 4
22 to 8
16 to 6
26 to 10
21 to 8
21 to 8
21 to 8
29 to 9
21 to 6
17 to 8
14 to 7
14 to 7
14 to 7
22 to 8
16 to 6
15 to 5
12 to 4
12 to 4
12 to 4
low
high
1.9
1.5
3.4
2.7
2.8
2.2
0.8
0.6
3
(kg-s m-4)
(s m-1)
(mm h-1)
(N m-2)
RANGE and CHAPARRAL
Pre-fire
cover
shrub
Ki (X 103)
(kg-s m-4)
grass
bare
Kr (X 10-4)
(s m-1)
shrub
Ke
(mm h-1)
grass
bare
τc
(N m-2)
Table 3. To model untreated recovery over time, the assigned occurrence probability
of Soil 1 and Soil 2 (the less erodible soil parameters sets) are increased and Soil
3, Soil 4, and Soil 5 (the more erodible soil parameters sets) are decreased for
each year of post-fire recovery.
Soil
parameter
set
---------------------- Occurrence probability (%) --------------------Year 1
Year 2 (monsoon)
Year 3
Year 4
Year 5
Soil 1
Soil 2
Soil 3
Soil 4
Soil 5
10
20
40
20
10
30 (12)
30 (21)
20 (38)
19 (19.5)
1 (9.5)
50
30
18
1
1
60
30
8
1
1
70
27
1
1
1
USDA Forest Service RMRS-GTR-188. 2007
of High soil burn severity (table 1). Thus, a total of
100, 150, or 200 possible predictions are incorporated
for Low, Moderate, or High soil burn severity userselection, respectively.
Combined Occurrence Probability
Each sediment delivery prediction has an associated probability of occurrence, which is calculated as
the product of the occurrence probabilities due to each
source of variation. For example, the occurrence probability for the event sediment delivery prediction given
the rain event associated with the 5th largest runoff (7.5
percent occurrence probability), the HHH soil burn
severity spatial arrangement (10 percent occurrence
probability), and the Soil 3 parameter set (40 percent
occurrence probability) is (0.075)*(0.10)*(0.40)=0.003,
or 0.3 percent (table 4). Sediment delivery predictions
Table 4. The assigned occurrence probabilities for the runoff events, soil burn severity spatial arrangements (H=high soil burn
severity overland flow element; L=low soil burn severity overland flow element), and the soil parameter sets are combined to
provide 100 occurrence probabilities associated with 100 event sediment delivery predictions for post-fire Year 1. Ten of the 100
permutations are shown completely.
Selected rain
event
[occurrence
probability]
(%)
Rain event
associated with the
5th largest runoff
[7.5]
Soil burn
severity
spatial
arrangement
[occurrence
probability]
(%)
HHH
[10]
LHH [30]
HLH [30]
HHL [30]
Rain event
associated with the
10th largest runoff
[7.5]
HHH [10]
LHH [30]
HLH [30]
HHL [30]
Rain event
associated with the
20th largest runoff
[20]
HHH [10]
LHH [30]
HLH [30]
HHL [30]
Rain event
associated with the
50th largest runoff
[27.5]
HHH [10]
LHH [30]
HLH [30]
HHL [30]
Rain event
associated with the
75th largest runoff
[37.5]
HHH [10]
LHH [30]
HLH [30]
HHL [30]
a
Soil
parameter
set
[occurrence
probability]
(%)
Soil 1 [10]
Soil 2 [20]
Soil 3 [40]
Soil 4 [20]
Soil 5 [10]
Soil 1 [10]
Soil 2 [20]
Soil 3 [40]
Soil 4 [20]
Soil 5 [10]
Soil 1 [10]
Soil 2 [20]
Soil 3 [40]
Soil 4 [20]
Soil 5 [10]
Soil 1 [10]
Soil 2 [20]
Soil 3 [40]
Soil 4 [20]
Soil 5 [10]
Soil 1 [10]
Soil 2 [20]
Soil 3 [40]
Soil 4 [20]
Soil 5 [10]
Soil 1 [10]
Soil 2 [20]
Soil 3 [40]
Soil 4 [20]
Soil 5 [10]
Soil 1 [10]
Soil 2 [20]
Soil 3 [40]
Soil 4 [20]
Soil 5 [10]
100 permutations of the three sources of variability
Combined sources of variability
(5th RO rain event)a (HHH) (Soil 1)
(5th RO rain event) (HHH) (Soil 2)
(5th RO rain event) (HHH) (Soil 3)
(5th RO rain event) (HHH) (Soil 4)
(5th RO rain event) (HHH) (Soil 5)
Combined occurrence
probability (%)
(0.075)*(0.10)*(0.10) *100=0.08
(0.075)*(0.10)*(0.20) *100=0.15
(0.075)*(0.10)*(0.40) *100=0.30
(0.075)*(0.10)*(0.20) *100=0.15
(0.075)*(0.10)*(0.10) *100=0.08
15 combinations
15 calculated occurrence
probabilities
20 combinations
20 calculated occurrence
probabilities
20 combinations
20 calculated occurrence
probabilities
20 combinations
20 calculated occurrence
probabilities
15 combinations
15 calculated occurrence
probabilities
(75th RO rain event) (HHL) (Soil 1)
(75th RO rain event) (HHL) (Soil 2)
(75th RO rain event) (HHL) (Soil 3)
(75th RO rain event) (HHL) (Soil 4)
(75th RO rain event) (HHL) (Soil 5)
(0.375)*(0.30)*(0.10) *100=1.13
(0.375)*(0.30)*(0.20) *100=2.25
(0.375)*(0.30)*(0.40) *100=4.50
(0.375)*(0.30)*(0.20) *100=2.25
(0.375)*(0.30)*(0.10) *100=1.13
RO rain event=rain event associated with the ranked runoff event
10
USDA Forest Service RMRS-GTR-188. 2007
are paired with their respective combined occurrence
probability, and sorted in descending order of sediment
delivery amounts. The “exceedance probability” for
each sediment delivery prediction is computed as the
sum of the occurrence probabilities for all greater sediment yield predictions plus one percent (table 5).
Table 5. The exceedance probability for each event sediment delivery prediction is computed as the sum of 1 plus the occurrence
probabilities for all greater sediment yield predictions. The boxed example below shows that an event sediment delivery of
20.6 t ha-1 has an exceedance probability of 9.9 percent. Note, only a portion of the 100 sediment delivery predictions are shown.
---------- Occurrence probability ---------Event
sediment
delivery
prediction
(t ha-1)
Permutation
(RO rain eventa,
soil burn severity OFE
arrangementb, soil
parameter set)
61.4
52.1
43.9
43.2
42.4
40.7
40.5
39.1
37.9
37.0
36.5
35.2
34.5
31.8
31.2
30.6
30.6
30.4
29.2
25.8
25.6
24.4
21.1
20.6
20.3
20.3
19.8
19.7
5, HHH, 5
10, HHH, 5
5, HHL, 5
5, HHH, 4
20, HHH, 5
5, LHH, 5
5, HLH, 5
10, LHH, 5
10, HHL, 5
10, HHH, 4
10, HLH, 5
5, HHH, 3
5, HHL, 4
20, LHH, 5
20, HHL, 5
20, HHH, 4
10, HHH, 3
20, HLH, 5
50, HHH, 5
5, HLH, 4
20, HHH, 3
10, HHL, 4
5, LHH, 4
20, HHL, 4
10, LHH, 4
10, HLH, 4
50, HHL, 5
10, HHL, 3
50 values
0.7
0.0
RO rain
eventa
(%)
Soil burn
severity
OFE spatial
arrangement
(%)
7.5
7.5
7.5
7.5
20
7.5
7.5
7.5
7.5
7.5
7.5
7.5
7.5
20
20
20
7.5
20
27.5
7.5
20
7.5
7.5
20
7.5
7.5
27.5
7.5
10
10
30
10
10
30
30
30
30
10
30
10
30
30
30
10
10
30
10
30
10
30
30
30
30
30
30
30
Soil
parameter
set
(%)
Permutation
combined
occurrence
probability
(%)
Exceedance
probability for
event sediment
delivery
prediction
(%)
10
10
10
20
10
10
10
10
10
20
10
40
20
10
10
20
40
10
10
20
40
20
20
20
20
20
10
40
0.075
0.075
0.225
0.150
0.200
0.225
0.225
0.225
0.225
0.150
0.225
0.300
0.450
0.600
0.600
0.400
0.300
0.600
0.275
0.450
0.800
0.450
0.450
0.450
0.450
0.450
0.825
0.900
1.08
1.15
1.38
1.53
1.73
1.95
2.17
2.40
2.63
2.78
3.00
3.30
3.75
4.35
4.95
5.35
5.65
6.25
6.53
6.98
7.78
8.23
8.67
∑+ 1=9.88
10.33
10.78
11.60
12.50
50 permutations and occurrence probabilities
5, HLH, 1
5, HHL, 1
20 values of
0.0
7.5
7.5
30
30
10
10
20 permutations and occurrence probabilities
50 values
0.225
0.225
69.58
69.80
20 values of
69.80
Total of 100
permutations
a
b
RO rain event=rain event associated with the ranked runoff event
H=high soil burn severity overland flow element (OFE); L=low soil burn severity overland flow element (OFE)
USDA Forest Service RMRS-GTR-188. 2007
11
PSC = 1342 + 0.0029* (diameter)2 + 272
slope
spacing
Erosion Mitigation Treatments
WEPP is not re-run to model mitigation treatments;
rather, treatment effects are modeled by increasing the
occurrence probabilities of the less erodible soil parameter sets and decreasing the occurrence probabilities of
the more erodible soil parameter sets.
Seeding
Robichaud and others (2000) reported that seeding
had little measured effect in reducing first year post-fire
erosion; seeding effects are more evident in the second
and subsequent years. In ERMiT, occurrence probabilities associated with the soil parameter sets are adjusted
to reflect the increase in ground cover and subsequent
small decrease in erosion after Year 2 (table 6). The
seeding rate is assumed to be approximately 8 lb ac-1
(9 kg ha-1).
Mulch
Four straw mulch application rates are modeled by
ERMiT. The sediment delivery predictions based on
each mulching rate are produced by adjusting the occurrence probabilities associated with soil parameter sets
(table 7), similar to the adjustments made for increases
in natural ground cover during post-fire recovery years
(table 3).
Log erosion barriers (contour-felled logs or
straw wattles)
ERMiT models straw wattles and contour-felled log
erosion barriers by applying a regression relationship,
based on user-specified mean log or wattle diameter
(in or cm), spacing between rows (ft or m) (fig. 7), and
hillslope gradient as entered on the input screen, to determine the potential storage capacity (PSC) for the
hillslope:
where diameter is in cm, spacing is in m, and PSC is
in m3 ha-1. Slope is in percent (0.05 to 100) and taken
from the hillslope gradient entered on the input screen.
Potential storage capacity (PSC) is converted to a weight
per unit volume based on measured sediment bulk densities (table 8).
Field observations indicate that the potential storage capacity is rarely fully utilized, and that sediment
trapping efficiency (sediment stored by erosion barriers
divided by the sum of the sediment leaving the hillslope
and the stored sediment) varies with rainfall intensity.
ERMiT calculates a weighted maximum 10-min rainfall
intensity (I10-W) based on the maximum 10-min rainfall
intensity (I10) estimated from each rain event associated
with the 5th-, 10th-, 20th-, 50th-, and 75th-ranked runoff
events. I10-W is calculated as the sum of the I10 for each
storm multiplied by its respective occurrence probability, such that:
I10-W = (I10-5th rank*0.075)+(I10-10th rank*0.075)+(I10-20th
*0.2)+(I10-50th rank*0.275)+(I10-75th rank*0.375)
rank
where I10-W (mm h-1) is the weighted maximum 10-min
rainfall intensity and I10-5th rank, I10-10th rank, I10-20th rank, I10-50th
, and I10-75th rank are the maximum 10-min rainfall intenrank
sity (mm h-1) estimated from each rain event associated
with the 5th-, 10th-, 20th-, 50th-, and 75th-ranked runoff
events, respectively. Rainfall intensity for snowmelt
events is taken to be zero.
Field data were used to determine erosion barrier sediment trapping efficiency functions based on I10-W for the
first two post-fire years:
Year 1: EFFy1 = -0.84 (I10-W) + 114
Year 2: EFFy2 = -1.4 (I10-W) + 116
where EFF is the trapping efficiency (percent) of the
erosion barriers and I10-W is the weighted maximum 10min rainfall intensity (mm h-1).
Table 6. The assigned occurrence probability of the seeded soil
parameter sets for each of five post-fire years.
Soil
parameter
set
Soil 1
Soil 2
Soil 3
Soil 4
Soil 5
12
--------------- Occurrence probability (%) --------------Year 1
Year 2
Year 3
Year 4
Year 5
10
20
40
20
10
50
30
18
1
1
60
30
8
1
1
70
27
1
1
1
70
27
1
1
1
USDA Forest Service RMRS-GTR-188. 2007
Table 7. The assigned occurrence probability for soil parameter sets in each year for four
application rates of straw mulch. The percent ground cover due to straw mulch is indicated
for each application rate.
Straw mulch application rate = 0.5 t ac-1 (1 t ha-1) for 47 percent ground cover
Soil parameter
set
Soil 1
--------------------occurrence probability (%)-----------------------Year 1
Year 2
Year 3
Year 4
Year 5
70
60
50
60
70
Soil 2
20
25
30
30
27
Soil 3
8
13
18
8
1
Soil 4
1
1
1
1
1
Soil 5
1
1
1
1
1
Straw mulch application rate = 1 t ac-1 (2 t ha-1) for 72 percent ground cover
Soil parameter
set
Soil 1
----------------------occurrence probability (%)---------------------Year 1
Year 2
Year 3
Year 4
Year 5
90
70
50
60
70
Soil 2
7
20
30
30
27
Soil 3
1
8
18
8
1
Soil 4
1
1
1
1
1
Soil 5
1
1
1
1
1
Straw mulch application rate = 1.5 t ac (3.5 t ha ) for 89 percent ground cover
-1
Soil parameter
set
Soil 1
-1
----------------------occurrence probability (%)---------------------Year 1
Year 2
Year 3
Year 4
Year 5
93
77
50
60
70
Soil 2
4
15
30
30
27
Soil 3
1
6
18
8
1
Soil 4
1
1
1
1
1
Soil 5
1
1
1
1
1
Straw mulch application rate = 2 t ac (4.5 t ha ) for 94 percent ground cover
-1
Soil parameter
set
Soil 1
-1
----------------------occurrence probability (%)---------------------Year 1
Year 2
Year 3
Year 4
Year 5
96
78
50
60
70
Soil 2
1
16
30
30
27
Soil 3
1
4
18
8
1
Soil 4
1
1
1
1
1
Soil 5
1
1
1
1
1
Figure 7. Erosion barrier diameter and spacing between
rows.
USDA Forest Service RMRS-GTR-188. 2007
13
Table 8. Observed sediment bulk density values used in
ERMiT to convert erosion barrier storage volume (m3 ha-1)
to mass (Mg ha-1).
Soil texture
Sediment
bulk density
(Mg m-3)
clay loam
silt loam
sandy loam
loam
1.1
0.97
1.23
1.16
The sediment trapping efficiency of erosion barriers
continues to decreases with time because of reduction in
potential storage capacity as well as settlement, decay,
and movement of the erosion barriers. After the second
year, efficiency is estimated as a percentage of the preceding year, such that:
Year 3: EFFy3 = 0.75 * EFFy2
Year 4: EFFy4 = 0.55 * EFFy3
Year 5: EFFy5 = 0.45 * EFFy4
Output
Summary of Input Selections and Initial
100-year WEPP Run
Summary of user selections
The top of the ERMiT output screen reports user inputs (fig. 8). The name of the selected standard climate
station is listed. If the climate was user-modified, the climate summary output (fig. 8) includes:
• maximum temperature by month (degrees Fahrenheit
or Celsius) [T MAX]
• minimum temperature by month (degrees Fahrenheit
or Celsius) [T MIN]
Figure 9. Precipitation and runoff values from the initial 100year high soil burn severity WEPP run.
• mean precipitation by month (in or mm) [MEANP]
• number of wet days by month [# WET]
User inputs for soil texture, rock content, hillslope gradient and length, soil burn severity, and vegetation type
are also reported.
Precipitation and runoff values from the initial
WEPP run
The average annual precipitation and runoff values,
as well as the total number of precipitation (rainfall and
snowmelt) runoff events, generated in the initial 100year WEPP run, are reported in the output screen (fig. 9).
This initial WEPP run used the “most erodible” soil parameters—in other words, soil parameter set High—Soil
5 and soil burn severity spatial arrangement HHH.
Selected storm characteristics
An output table shows some of the characteristics of
the five rain events associated with the five runoff events
selected for further analysis (fig. 10). The first table row
also reports the largest (ranked 1st out of 100 for runoff)
modeled runoff event, which is presented for user interest
only. The storms listed on rows two through six (ranked
5th, 10th, 20th, 50th, and 75th for runoff) are used to determine input for the 5- to 10-year weather file WEPP uses
for the multiple runs. Rain event descriptors include:
• Storm rank—rank of the total runoff amount from the
largest to smallest
• Storm runoff (in or mm)—total runoff modeled by
WEPP for the storm
• Storm precipitation (in or mm)—total precipitation
(rain or snow) for that event
• Storm duration (h)—length of the storm event
Figure 8. Summary of input parameters.
14
• 10-min and 30-min peak rainfall intensity (in h-1 or
mm h-1)—estimated values of rainfall intensity
for the given storm, calculated from information
CLIGEN provides for the storm [“N/A” indicates
that a value could not be calculated, and generally
USDA Forest Service RMRS-GTR-188. 2007
Figure 10. Rainfall event rankings (based on runoff) and characteristics from the selected storms.
indicates a snowmelt event in which no precipitation occurred].
• Storm date—month and day when the storm event occurred, and the nominal year (1 to 100). The storm
date can be useful in helping to determine what type
of event occurred—snowmelt, spring storm, etc.
Sediment Delivery Exceedance Probability
Graph for Untreated Condition
Below the inputs and selected storm event summaries,
a graphical output shows hillslope sediment delivery
exceedance probabilities plotted against the predicted
event sediment delivery amounts for each of the first five
post-fire years (fig. 11). The spacing between the plotted
lines represents the predicted natural (untreated) recovery rate for the hillslope being modeled.
• Click on the graph to display the sediment delivery
and exceedance probabilities in table format.
USDA Forest Service RMRS-GTR-188. 2007
Interpreting the sediment delivery exceedance
probability graph
As an example, draw an imaginary horizontal line
across the graph (fig. 11) at 10 percent probability. It
crosses the 1st year (furthest right) curve at about 20.5 t
ha-1 sediment delivery. Thus, there is a 10 percent probability that a single rain event will result in at least 20.5 t
ha-1 sediment delivery to the base of the hillslope during
the first year following a fire. The 2nd year curve crosses
the imaginary horizontal 10 percent probability line at
about 13 t ha-1 sediment delivery; the 3rd year curve at
about 5.5 t ha-1; the 4th year at about 2.5 t ha-1; and the
5th year curve at about 2 t ha-1 (fig. 11). Thus, there is a
decrease in predicted event sediment delivery (with a 10
percent chance of exceedance) for each year of post-fire
recovery.
Alternatively, choose a target sediment delivery
value and observe the trends through time. Draw an imaginary vertical line through the 5 t ha-1 sediment delivery
on the horizontal axis. The 1st year curve intersects the
15
Figure 11. Output graph showing exceedance probability versus event sediment delivery for five years after the
fire from the modeled, untreated hillslope.
5 t ha-1 line at about 42 percent probability—in other words, there is a 42 percent
probability that the modeled hillslope will
deliver at least 5 t ha-1 of sediment the first
year following the fire. The 5th year curve
intersects at about 1 percent probability.
Thus, the likelihood of delivering at least
5 t ha-1 of sediment has decreased from 42
to 1 percent between the 1st and 5th year following the fire (fig. 11).
Mitigation Treatment Comparisons
Calculator
The Mitigation Treatment Comparisons
Calculator (fig. 12) is an interactive screen
that allows the user to select an exceedance
probability and have the corresponding
event sediment delivery predictions displayed by year and by treatment. Values
listed for the untreated hillslope are analogous to drawing a horizontal line across the
16
Figure 12. ERMiT Mitigation Treatment Comparison Calculator.
USDA Forest Service RMRS-GTR-188. 2007
Figure 13. Text links to supporting tables.
Sediment Exceedance Probability graph (fig. 11) at a selected exceedance probability value.
The Sediment Delivery Prediction Calculator for
treatment with erosion barriers (contour-felled logs and
straw wattles) is embedded in the Mitigation Treatment
Comparisons Calculator (fig. 12). Predictions for contour-felled log or straw wattle erosion barrier treatments
require a user-designated mean diameter (0.15 to 3.3 ft
or 0.05 to 1 m) and spacing between rows of erosion barriers (10 to 165 ft or 3 to 50 m) (fig. 7).
By using the interactive input box in the upper
left corner of the Mitigation Treatment Comparisons
Calculator (fig. 12), the user may compare the predicted
sediment delivery for a range of occurrence probabilities (1 to 99.9 percent). In addition, by clicking on the
printer symbol to the right of each treatment label (or by
using the text link further down the page), a full table of
predicted event sediment deliveries and their occurrence
probabilities by year for an individual treatment are displayed on screen. The tabular output screen allows for
comparison of the predicted event sediment deliveries
between the untreated hillslope and the treated hillslope,
different treatment choices, and various treatment application rates for each of five post-fire years.
Supporting Tables
ERMiT provides supporting tables (four types—nine
individual), which are accessible by clicking either on
the small printer icons located within the Mitigation
Treatment Comparisons Calculator, or on the textual
links near the bottom of the output page (fig. 13). These
supporting tables include:
1. Sediment delivery—probability table: individual
WEPP sediment delivery predictions for each
combination of parameters and individual parameter
occurrence probabilities (untreated only) (fig. 14)
2. Sediment delivery statements (fig. 15)
3. Sediment delivery—probability of exceedance
tables:
Untreated
Seeding
Mulching at four rates:
USDA Forest Service RMRS-GTR-188. 2007
0.5 t ac-1 or 1 t ha-1 [47 percent ground cover]
1 t ac-1 or 2 t ha-1 [72 percent ground cover]
1.5 t ac-1 or 3.5 t ha-1 [89 percent ground cover]
(fig. 16)
2 t ac-1 or 4.5 t ha-1 [94 percent ground cover]
4. Erosion barrier efficiency tables (fig. 17)
Each ERMiT run is assigned an identification number (Run ID wepp-000000), which is displayed on the
screen with the graphs and supporting tables. This ID
number allows the user to track results from a single
run and compare results from different runs. In the
footer at the bottom of the ERMiT output page, the
ERMiT version, WEPP version, report on monsoon climate check, ERMiT run ID, and example citation are
listed (fig. 18).
Saving Results
All results and supporting tables may be printed using the web browser’s print function. Alternatively, the
user may copy and paste the ERMiT output into a word
processing document or spreadsheet. Generally, the mitigation treatment comparison table will not be active in
the applications where it has been pasted. Some browsers support “save as” “Web page, complete,” which will
preserve the functionality of the mitigation treatment
comparison table and retain the graphs and other images. If the output page is saved as a “Web page, HTML
only,” the functionality of the mitigation treatment
comparison table will be retained but the graph and other
images will be lost. No log file for accumulating ERMiT
results is available.
Management Implications
Federal land management agencies have spent tens
of millions of dollars on post-fire emergency watershed stabilization measures intended to minimize flood
runoff, peakflows, onsite erosion, offsite sedimentation,
and other hydrologic damage to natural habitats, roads,
bridges, reservoirs, and irrigation systems (General
Accounting Office 2003). The decision to apply post17
Figure 14. The individual WEPP event sediment delivery predictions (untreated hillslope) are
provided for each combination of three variability components—runoff event (arranged by
section), soil burn severity spatial arrangement (arranged by row), and soil parameter set
(arranged by column—Soil 1 through Soil 5 from left to right). Highlighted percentages
are the individual occurrence probabilities for each component of the permutation by
post-fire year.
18
USDA Forest Service RMRS-GTR-188. 2007
Figure 15. A portion of the sediment delivery statements from the ERMiT event
sediment delivery table.
fire treatments to reduce runoff and erosion is based on
a risk analysis—assessing the probability that damaging
floods, erosion, and sedimentation will occur; the values
that are at risk for damage; the need for reducing the
risk of damage; and the magnitude of risk reduction that
can reasonably be expected from mitigation treatments.
Potentially damaged resources can be identified and
the costs of post-fire erosion mitigation treatment can
be determined; however, the risk of damaging runoff,
erosion, and sedimentation occurring and the effectiveness of mitigation treatments are not well established.
Consequently, managers often must assign these probabilities and estimate treatment effectiveness based on
past experience and consensus of opinion.
Land managers need more information and tools to
determine hazard probabilities and balance the costs and
potential benefits of treatments. Unlike most erosion prediction models, ERMiT does not provide “average annual
erosion rates.” Rather, it provides a distribution of event
erosion rates with the likelihood of their occurrence.
Such output can help managers make erosion mitigation treatment decisions based on the probability of high
USDA Forest Service RMRS-GTR-188. 2007
sediment yields occurring, the value of resources at risk
for damage, cost, and other management considerations.
ERMiT is most useful when managers determine an event
sediment delivery that can be tolerated without sustained
damage to the resource(s) at risk and the probability of
that event occurring (see example in Appendix B). This
would likely vary throughout a burned area. For example,
short term declines in water quality may be tolerated without sustained damage, but not damage to a unique cultural
heritage site. Modeling the hillslopes above these two
resources would likely require different user-designated
exceedance probabilities and treatment criteria.
Application of post-fire erosion mitigation treatments
does not eliminate erosion, but treatments can reduce the
hillslope response to many rain events. After wildfires,
managers can use ERMiT to estimate the probabilities of
erosion-producing rain events occurring, expected hillslope event sediment deliveries, and predicted rates of
recovery for the burned area. In addition, realistic estimations of treatment effectiveness will allow managers
to make more cost-effective choices of where, when,
and how to treat burned landscapes.
19
Figure 16. Selected rows from the sediment delivery—probability of exceedance table for mulching at the
3.5 t ha-1 or 89 percent cover rate.
20
USDA Forest Service RMRS-GTR-188. 2007
Figure 17. ERMiT erosion barrier efficiency calculator.
Figure 18. Output screen footer information.
USDA Forest Service RMRS-GTR-188. 2007
21
References
Brady, J.A.; Robichaud, P.R.; Pierson, F.B. 2001. Infiltration
rates after wildfire in the Bitterroot Valley. Presented:
2001 ASAE annual international meeting: An Engineering
Odyssey; 2001 July 30-August 1; Sacramento, CA. Paper
Number 01-8003. St. Joseph, MI: American Society of
Agricultural Engineers. 11 p.
Daly, C.; Neilson, R.P.; Phillips, D.L. 1994. A statisticaltopographic model for mapping climatological precipitation
over mountainous terrain. Journal of Applied Meteorology.
33: 140-158.
Elliot, W.J. 2004. WEPP internet interfaces for forest erosion
prediction. Journal of the American Water Resources
Association. 40(2): 299-309.
Elliot, W.J.; Hall, D.E. 2000. Rock:Clime Beta CD Version
Rocky Mountain Research Station Stochastic Weather
Generator Technical Documentation [online at http://forest.
moscowfsl.wsu.edu/fswepp/docs/0007RockClimCD.
html]. Moscow, ID: U.S. Department of Agriculture, Forest
Service, Rocky Mountain Research Station [accessed 23
March 2006].
Elliot, W.J.; Scheele, D.L.; Hall, D.E. 1999. Rock:Clime
Rocky Mountain Research Station Stochastic Weather
Generator Technical Documentation [online at: http://
forest.moscowfsl.wsu.edu/fswepp/docs/rockclimdoc.
html]. Moscow, ID: U.S. Department of Agriculture, Forest
Service, Rocky Mountain Research Station [accessed 23
March 2006].
Flanagan, D.C.; Livingston, S.J. (eds.) 1995. WEPP user
summary. NSERL Report No. 11. West Lafayette, IN:
U.S. Department of Agriculture, Agricultural Research
Service, National Soil Erosion Research Laboratory.
123 p.
General Accounting Office. 2003. Wildland fires: better
information needed on effectiveness of emergency
stabilization and rehabilitation treatments. Report GAO-
22
03-430. Washington, D.C.: U.S. General Accounting
Office. 55 p.
Nicks, A.D.; Lane, L.J.; Gander, G.A. 1995. Weather
generator. In: Flanagan, D.C.; Nearing, M.A. eds. USDAWater Erosion Prediction Project Hillslope Profile and
Watershed Model Documentation. NSERL Report No.
10. West Lafayette, IN: U.S. Department of Agriculture,
Agricultural Research Service, National Soil Erosion
Research Laboratory. 2.1-2.22.
Pierson, F.B.; Robichaud, P.R.; Spaeth, K.E. 2001. Spatial
and temporal effects of wildfire on the hydrology of a
steep rangeland watershed. Hydrological Processes. 15:
2905-2916.
Robichaud, P.R. 1996. Spatially-varied erosion potential from
harvested hillslopes after prescribed fire in the interior
Northwest. Moscow, ID: University of Idaho. 219 p.
Dissertation.
Robichaud, P.R. 2000. Fire effects on infiltration rates after
prescribed fire in northern Rocky Mountain forests, USA.
Journal of Hydrology. 231-232: 220-229.
Robichaud, P.R.; Miller, S.M. 1999. Spatial interpolation
and simulation of post-burn duff thickness after prescribed
fire. International Journal of Wildland Fire. 9(2):
137-143.
Robichaud, P.R.; Beyers, J.L.; Neary, D.G. 2000. Evaluating
the effectiveness of post fire rehabilitation treatments.
Gen. Tech. Rep. RMRS-GTR-63. Fort Collins, CO:
U.S. Department of Agriculture, Forest Service, Rocky
Mountain Research Station. 85 p.
Robichaud, P.R.; Elliot, W.J.; Pierson, F.B.; Hall, D.E.; Moffet,
C.A. 2006. Erosion Risk Management Tool (ERMiT) Ver.
2006.01.18 [Online at <http://forest.moscowfsl.wsu.edu/
fswepp/>.]. Moscow, ID: U.S. Department of Agriculture,
Forest Service, Rocky Mountain Research Station [accessed
5 June 2006].
Robichaud, P.R.; Elliot, W.J.; Pierson, F.B.; Hall, D.E.;
Moffet, C.A. [In press]. Predicting post-fire erosion and
mitigation effectiveness with a web-based probabilistic
erosion model. CATENA.
USDA Forest Service RMRS-GTR-188. 2007
Appendix A. Model Assumptions
ERMiT runs WEPP (version 2000.100) in cropland
mode with the following parameter (WEPP variable
names in parentheses) values in the management and
soil files:
• The model year begins the day after the wildfire occurs and ends on the anniversary day of the fire.
• Ground cover effects are modeled by adjusting soil
erodibility/cover values based on field measurements
from a variety of soil types and soil burn severity conditions.
• Management file:
No biomass
▪ “biomass energy ratio (beinp)” set to zero
No decomposition
▪ “decomposition constant to calculate mass
change of above-ground biomass, surface, or
buried (oratea)” set to zero
▪ “decomposition constant to calculate mass of
change of root-biomass (orater)” set to zero
Initial conditions set to give 1 percent cover
▪ “initial canopy cover, 0 to 1 (cancov)” set to
0.01
▪ “days since last tillage (daydis)” set to 9999
▪ “days since last harvest (dsharv)” set to 900
▪ “initial interrill cover, 0 to 1 (inrcov)” set to
0.01
▪ “initial residue cropping system (imngmt)” set
to perennial
▪ “initial rill cover, 0 to 1 (rilcov)” set to 0.01
No surface effects
Annual planting date set to May 1 (Julian day
121)
Annual harvest date set to September 1 (Julian day
244)
USDA Forest Service RMRS-GTR-188. 2007
• Soil Input file:
One soil layer
“percentage of organic matter (orgmat) in the layer”
set to 5 percent by volume in forest vegetation type
and 1 percent by volume in range and chaparral
vegetation types
“albedo of the bare dry surface soil (salb)” set to
0.2
“Initial saturation level of the soil profile porosity
(sat)” set to 0.75 m/m
“depth of soil surface to bottom of soil layer
(solthk)” set to 400 mm
“percentage of sand in the layer (sand)” varies by
soil texture
“percentage of clay in the layer (clay)” varies by
soil texture
“cation exchange capacity in the layer (cec)”
varies by soil texture
“rock fragment amount (rfg)” is user-specified,
maximum 50 percent
“interrill erodibility (ki)” varies by soil texture
and <bs> and <sp>1 (table 2)
“rill erodibility (kr)” varies by soil texture and
<bs> and <sp>1 (table 2)
“baseline critical shear (shcrit)” varies by soil
texture and <bs> and <sp>1 (table 2)
“effective hydraulic conductivity (avke)” varies
by soil texture and <bs> and <sp>1 (table 2)
1
<bs> represents the discrete soil burn severity spatial arrangement (“HHH,” “HHL,” “LHH,” etc.) used in a WEPP run.
<sp> represents the discrete soil parameter set (High “Soil
1” to “Soil 5” and Low “Soil 1” to “Soil 5”) used in a WEPP
run. Individual runs of WEPP use each applicable soil burn
severity spatial arrangement with each soil parameter set,
generating a WEPP output file for each combination of
these two variable sets for each selected rain event.
23
Appendix B. Example
An example ERMiT run is presented to illustrate
the user interface and model output formats and to
describe the sediment delivery prediction analyses.
The context for this example run is the 2000 Valley
Complex Fires that burned in the Bitterroot National
Forest of Montana. These large wildfires burned many
steep hillslopes at high severity. The water quality of
the streams and rivers within the burned area are highly
valued resources that were at risk from large increases
in sedimentation. This example run is for an 800 ft
slope above Rye Creek, which has a sandy loam soil
with 20 percent rock content. The hillslope gradients
are 10 percent at the top, 40 percent at mid-slope, and
20 percent at the toe (fig. B1).
The post-fire assessment team will determine the risk
of post-fire erosion that exceeds a tolerable limit for
event sediment delivery at the base of the hillslope. To
use the Mitigation Treatment Comparisons Calculator,
the post-fire assessment team established the following
decision criteria: 1) 3 t ac-1 was the maximum tolerable
single event sediment delivery in post-fire Year 1; and 2)
straw mulch treatment will be applied if the Year 1 risk
of exceeding the event sediment delivery limit (3 t ac-1)
is greater than 10 percent and straw mulch application
will reduce that risk to 10 percent or less.
By setting the output table to 10 percent exceedance
probability (circled in fig. B2), the post-fire assessment
team was able to compare the effects of mulching at
different rates. On the untreated hillslope, sediment
delivery estimates with 10 percent exceedance probability
are nearly 9 t ac-1, which is well above the 3 t ac-1
tolerable limit set by the assessment team. Mulching at a
rate of 0.5 t ac-1 lowers the sediment delivery prediction
with a 10 percent exceedance probability to 3.4 t ac-1,
which is still above the tolerable limit set by the postfire assessment team. However, mulching at a rate of
1.0 t ac-1 lowers the predicted sediment delivery with a
10 percent exceedance probability to 2.4 t ac-1, which is
within the acceptable limits set by the team. Mulching
at 1.5 t ac-1 does not lower the predicted event sediment
delivery enough to justify the additional mulch (fig. B2).
These ERMiT predictions support the assessment team’s
recommendation to apply straw mulch at a 1 t ac-1 rate
on the burned hillslope.
Figure B1. Input screen for example scenario.
24
USDA Forest Service RMRS-GTR-188. 2007
Figure B2. Mitigation Treatment Comparison Calculator for example scenario.
USDA Forest Service RMRS-GTR-188. 2007
25
RMRS
ROCKY MOUNTAIN RESEARCH STATION
The Rocky Mountain Research Station develops scientific
information and technology to improve management, protection,
and use of the forests and rangelands. Research is designed to
meet the needs of the National Forest managers, Federal and State
agencies, public and private organizations, academic institutions,
industry, and individuals.
Studies accelerate solutions to problems involving ecosystems,
range, forests, water, recreation, fire, resource inventory, land
reclamation, community sustainability, forest engineering
technology, multiple use economics, wildlife and fish habitat, and
forest insects and diseases. Studies are conducted cooperatively,
and applications may be found worldwide.
Research Locations
Flagstaff, Arizona
Fort Collins, Colorado*
Boise, Idaho
Moscow, Idaho
Bozeman, Montana
Missoula, Montana
Reno, Nevada
Albuquerque, New Mexico
Rapid City, South Dakota
Logan, Utah
Ogden, Utah
Provo, Utah
*Station Headquarters, Natural Resources Research Center,
2150 Centre Avenue, Building A, Fort Collins, Colorado 80526.
The U.S. Department of Agriculture (USDA) prohibits discrimination in
all its programs and activities on the basis of race, color, national origin,
age, disability, and where applicable, sex, marital status, familial status,
parental status, religion, sexual orientation, genetic information, political
beliefs, reprisal, or because all or part of an individual’s income is derived
from any public assistance program. (Not all prohibited bases apply to
all programs.) Persons with disabilities who require alternative means for
communication of program information (Braille, large print, audiotape,
etc.) should contact USDA’s TARGET Center at (202) 720-2600 (voice
and TDD). To file a complaint of discrimination, write to USDA, Director,
Office of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC
20250-9410, or call (800) 795-3272 (voice) or (202) 720-6382 (TDD).
USDA is an equal opportunity provider and employer.
Federal Recycling Program
View publication stats
Printed on Recycled Paper