Erosion Risk Management 

Tool (ERMiT) User Manual

(version 2006.01.18)

United States 

Department of 

Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical 

Report RMRS-GTR-188

April 2007

Peter R. Robichaud, William J. Elliot,

Fredrick B. Pierson, David E. Hall,

Corey A. Moffet, and Louise E. Ashmun

Predict 

post-fire erosion

treatment

effectiveness and recovery

Year 1

Year 3

Year 2
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 (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm/publications

Mailing address 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
USDA Forest Service RMRS-GTR-188. 2007 1

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 100-

year 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.

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2 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 3

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:

daily precipitation amount, duration, time-to-peak, 

and peak intensity

minimum, maximum, and dewpoint temperatures

solar radiation

wind velocity and direction

Climate files—status designations

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.

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Viewing climate station files using the 

“Climate” link

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.

Selecting and modifying climates using the 

“Custom Climate” button

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:

Add any of the listed climate stations to ERMiT’s 

climate selection menu by selecting the climate 

station and clicking “ADD TO PERSONAL 

CLIMATES.”

View the parameters for the selected climate station 

by clicking “DESCRIBE CLIMATE.”

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 

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4 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 

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 

Figure 3. Hillslope profile illustration viewed by clicking the 

“explain” button on the hillslope gradient or hillslope horizontal length box titles.
USDA Forest Service RMRS-GTR-188. 2007 5

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 

steps were used to incorporate parameter variability into 

the model:

Establish a range of possible parameter values from 

field measurements.

Select five representative values from within the 

range.

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.

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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.
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 7

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.
8 USDA Forest Service RMRS-GTR-188. 2007

distribution function is created and the 5th, 20th, 50th, 80th, 

and 95th percentile values are selected. The selected values for all four soil parameters are grouped by percentile 

ranking into five soil parameter sets:

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 

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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 

and grasses in the pre-fire vegetation and 

by burn severity. This is accounted for by 

weighting Ki and Ke soil parameter values 

within each value range based on the userspecified proportions of pre-fire shrub and 

grass cover with bare soil accounting for 

the remainder of the 100 percent pre-fire 

cover.

To model change over time, the occurrence probabilities of Soil 1 and Soil 2 

(the less erodible soil parameter sets) are 

increased, and Soil 3, Soil 4, and Soil 5 

(the more erodible soil parameter sets) 

are decreased for each year of post-fire 

recovery (table 3). Post-fire recovery is 

slower in areas affected by monsoons 

than in other environments because monsoon rains usually come in short bursts 

of rainfall and do not provide dependable 

wet cycles for seed germination. ERMiT 

uses an empirical relationship (total precipitation is less than 600 mm per year 

and total July, August, and September 

precipitation is greater than 30 percent 

of the annual precipitation) to determine 

if a particular climate is monsoonal. If 

rainfall data fit the monsoon rainfall definition, then 

the post-fire Year 2 occurrence probabilities for the soil 

parameter sets remain similar to Year 1 to reflect the 

slower recovery in these climates (table 3).

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 

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.

Hillslope -----------Occurrence probability (%)-----------

burn severity

OFEs Year 1 Year 2 Year 3 Year 4 Year 5

User selected High soil burn severity

HHH 10 0 0 0 0

LHH 30 25 0 0 0

HLH 30 25 25 0 0

HHL 30 25 25 25 0

LLH 0 25 25 25 25

LHL 0 0 25 25 25

HLL 0 0 0 25 25

LLL 0 0 0 0 25

User selected Moderate soil burn severity

HHH 0 0 0 0 0

LHH 0 0 0 0 0

HLH 25 0 0 0 0

HHL 25 25 0 0 0

LLH 25 25 25 25 25

LHL 25 25 25 25 25

HLL 0 25 25 25 25

LLL 0 0 25 25 25

User selected Low soil burn severity

HHH 0 0 0 0 0

LHH 0 0 0 0 0

HLH 0 0 0 0 0

HHL 0 0 0 0 0

LLH 30 25 25 25 25

LHL 30 25 25 25 25

HLL 30 25 25 25 25

LLL 10 25 25 25 25
USDA Forest Service RMRS-GTR-188. 2007 9

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 ---------------------- Occurrence probability (%) ---------------------

parameter

set Year 1 Year 2 (monsoon) Year 3 Year 4 Year 5

Soil 1 10 30 (12) 50 60 70

Soil 2 20 30 (21) 30 30 27

Soil 3 40 20 (38) 18 8 1

Soil 4 20 19 (19.5) 1 1 1

Soil 5 10 1 (9.5) 1 1 1

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.

FOREST

 Soil burn

 severity Clay loam Silt loam Sandy loam Loam

Ki (X 103)

(kg-s m-4)

low 200 to 500 250 to 600 300 to 1,200 320 to 800

high 400 to 2,000 500 to 2,500 1,000 to 3,000 600 to 3,200

Kr (X 10-4)

(s m-1)

low 0.010 to 2.5 0.020 to 3.5 0.030 to 4.5 0.015 to 3.0

high 2.0 to 8.0 3.0 to 9.0 4.0 to 10 2.5 to 8.5

Ke (mm h-1)

low 25 to 8 33 to 9 48 to 14 40 to 18

high 13 to 2 18 to 3 22 to 5 27 to 4

τc (N m-2)

low 4 3.5 2 3

high 4 3.5 2 3

RANGE and CHAPARRAL

Pre-fire

cover

Ki (X 103)

(kg-s m-4)

shrub low 13 to 170 16 to 230 75 to 930 3.4 to 93

high 39 to 170 49 to 230 230 to 930 11 to 93

grass

low 1.9 to 15 12 to 150 50 to 650 2.6 to 63

high 6.6 to 85 40 to 840 170 to 3,600 9.0 to 350

bare low 39 to 170 49 to 840 230 to 3,600 11 to 350

high 39 to 170 49 to 840 230 to 3,600 11 to 350

Kr (X 10-4)

(s m-1)

low 0.38 to 6.0 0.33 to 7.8 0.090 to 7.2 0.51 to 4.6

high 3.0 to 27 2.7 to 33 0.95 to 31 3.8 to 22

Ke (mm h-1)

shrub low 15 to 6 22 to 8 29 to 9 22 to 8

high 11 to 5 16 to 6 21 to 6 16 to 6

grass

low 13 to 5 26 to 10 17 to 8 15 to 5

high 10 to 4 21 to 8 14 to 7 12 to 4

bare low 10 to 4 21 to 8 14 to 7 12 to 4

high 10 to 4 21 to 8 14 to 7 12 to 4

τc (N m-2)

low 1.9 3.4 2.8 0.8

high 1.5 2.7 2.2 0.6
10 USDA Forest Service RMRS-GTR-188. 2007

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]

(%)

Soil burn 

severity 

spatial 

arrangement

[occurrence 

probability]

(%)

Soil 

parameter 

set 

[occurrence 

probability]

(%)

100 permutations of the three sources of variability

Combined sources of variability

Combined occurrence 

probability (%)

Rain event 

associated with the 

5th largest runoff

[7.5]

HHH

[10]

Soil 1 [10] (5th RO rain event)a (HHH) (Soil 1) (0.075)*(0.10)*(0.10) *100=0.08

Soil 2 [20] (5th RO rain event) (HHH) (Soil 2) (0.075)*(0.10)*(0.20) *100=0.15

Soil 3 [40] (5th RO rain event) (HHH) (Soil 3) (0.075)*(0.10)*(0.40) *100=0.30

Soil 4 [20] (5th RO rain event) (HHH) (Soil 4) (0.075)*(0.10)*(0.20) *100=0.15

Soil 5 [10] (5th RO rain event) (HHH) (Soil 5) (0.075)*(0.10)*(0.10) *100=0.08

LHH [30]

HLH [30]

HHL [30]

Soil 1 [10]

15 combinations 15 calculated occurrence 

probabilities

Soil 2 [20]

Soil 3 [40]

Soil 4 [20]

Soil 5 [10]

Rain event 

associated with the 

10th largest runoff

[7.5]

HHH [10]

LHH [30]

HLH [30]

HHL [30]

Soil 1 [10]

20 combinations 20 calculated occurrence 

probabilities

Soil 2 [20]

Soil 3 [40]

Soil 4 [20]

Soil 5 [10]

Rain event 

associated with the 

20th largest runoff

[20]

HHH [10]

LHH [30]

HLH [30]

HHL [30]

Soil 1 [10]

20 combinations 20 calculated occurrence 

probabilities

Soil 2 [20]

Soil 3 [40]

Soil 4 [20]

Soil 5 [10]

Rain event 

associated with the 

50th largest runoff

[27.5]

HHH [10]

LHH [30]

HLH [30]

HHL [30]

Soil 1 [10]

20 combinations 20 calculated occurrence 

probabilities

Soil 2 [20]

Soil 3 [40]

Soil 4 [20]

Soil 5 [10]

Rain event 

associated with the 

75th largest runoff 

[37.5]

HHH [10]

LHH [30]

HLH [30]

Soil 1 [10]

15 combinations 15 calculated occurrence 

probabilities

Soil 2 [20]

Soil 3 [40]

Soil 4 [20]

Soil 5 [10]

HHL [30]

Soil 1 [10] (75th RO rain event) (HHL) (Soil 1) (0.375)*(0.30)*(0.10) *100=1.13

Soil 2 [20] (75th RO rain event) (HHL) (Soil 2) (0.375)*(0.30)*(0.20) *100=2.25

Soil 3 [40] (75th RO rain event) (HHL) (Soil 3) (0.375)*(0.30)*(0.40) *100=4.50

Soil 4 [20] (75th RO rain event) (HHL) (Soil 4) (0.375)*(0.30)*(0.20) *100=2.25

Soil 5 [10] (75th RO rain event) (HHL) (Soil 5) (0.375)*(0.30)*(0.10) *100=1.13

a

RO rain event=rain event associated with the ranked runoff event

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 
USDA Forest Service RMRS-GTR-188. 2007 11

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.

Event 

sediment 

delivery 

prediction

(t ha-1)

Permutation

(RO rain eventa,

soil burn severity OFE 

arrangementb, soil 

parameter set)

---------- Occurrence probability ----------

Permutation 

combined 

occurrence 

probability

(%)

Exceedance 

probability for 

event sediment 

delivery 

prediction

(%)

RO rain 

eventa

(%)

Soil burn 

severity

OFE spatial 

arrangement

(%)

Soil 

parameter 

set

(%)

61.4 5, HHH, 5 7.5 10 10 0.075 1.08

52.1 10, HHH, 5 7.5 10 10 0.075 1.15

43.9 5, HHL, 5 7.5 30 10 0.225 1.38

43.2 5, HHH, 4 7.5 10 20 0.150 1.53

42.4 20, HHH, 5 20 10 10 0.200 1.73

40.7 5, LHH, 5 7.5 30 10 0.225 1.95

40.5 5, HLH, 5 7.5 30 10 0.225 2.17

39.1 10, LHH, 5 7.5 30 10 0.225 2.40

37.9 10, HHL, 5 7.5 30 10 0.225 2.63

37.0 10, HHH, 4 7.5 10 20 0.150 2.78

36.5 10, HLH, 5 7.5 30 10 0.225 3.00

35.2 5, HHH, 3 7.5 10 40 0.300 3.30

34.5 5, HHL, 4 7.5 30 20 0.450 3.75

31.8 20, LHH, 5 20 30 10 0.600 4.35

31.2 20, HHL, 5 20 30 10 0.600 4.95

30.6 20, HHH, 4 20 10 20 0.400 5.35

30.6 10, HHH, 3 7.5 10 40 0.300 5.65

30.4 20, HLH, 5 20 30 10 0.600 6.25

29.2 50, HHH, 5 27.5 10 10 0.275 6.53

25.8 5, HLH, 4 7.5 30 20 0.450 6.98

25.6 20, HHH, 3 20 10 40 0.800 7.78

24.4 10, HHL, 4 7.5 30 20 0.450 8.23

21.1 5, LHH, 4 7.5 30 20 0.450 8.67

20.6 20, HHL, 4 20 30 20 0.450 ∑+ 1=9.88

20.3 10, LHH, 4 7.5 30 20 0.450 10.33

20.3 10, HLH, 4 7.5 30 20 0.450 10.78

19.8 50, HHL, 5 27.5 30 10 0.825 11.60

19.7 10, HHL, 3 7.5 30 40 0.900 12.50

50 values 50 permutations and occurrence probabilities 50 values

0.7 5, HLH, 1 7.5 30 10 0.225 69.58

0.0 5, HHL, 1 7.5 30 10 0.225 69.80

20 values of 

0.0 20 permutations and occurrence probabilities 20 values of 69.80

Total of 100 

permutations

a

RO rain event=rain event associated with the ranked runoff event

b

H=high soil burn severity overland flow element (OFE); L=low soil burn severity overland flow element (OFE)
12 USDA Forest Service RMRS-GTR-188. 2007

Table 6. The assigned occurrence probability of the seeded soil 

parameter sets for each of five post-fire years.

Soil

parameter --------------- Occurrence probability (%) ---------------

set Year 1 Year 2 Year 3 Year 4 Year 5

Soil 1 10 50 60 70 70

Soil 2 20 30 30 27 27

Soil 3 40 18 8 1 1

Soil 4 20 1 1 1 1

Soil 5 10 1 1 1 1

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:

PSC = 1342 + 0.0029* (diameter)

2

 + 272 

slope spacing

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 

rank*0.2)+(I10-50th rank*0.275)+(I10-75th rank*0.375)

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 

rank, and I10-75th rank are the maximum 10-min rainfall intensity (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 10-

min rainfall intensity (mm h-1).
USDA Forest Service RMRS-GTR-188. 2007 13

Figure 7. Erosion barrier diameter and spacing between 

rows.

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

--------------------occurrence probability (%)------------------------

Year 1 Year 2 Year 3 Year 4 Year 5

Soil 1 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

----------------------occurrence probability (%)----------------------

Year 1 Year 2 Year 3 Year 4 Year 5

Soil 1 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-1 (3.5 t ha-1) for 89 percent ground cover

Soil parameter 

set

----------------------occurrence probability (%)----------------------

Year 1 Year 2 Year 3 Year 4 Year 5

Soil 1 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-1 (4.5 t ha-1) for 94 percent ground cover

Soil parameter 

set

----------------------occurrence probability (%)----------------------

Year 1 Year 2 Year 3 Year 4 Year 5

Soil 1 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
14 USDA Forest Service RMRS-GTR-188. 2007

Table 8. Observed sediment bulk density values used in 

ERMiT to convert erosion barrier storage volume (m3 ha-1) 

to mass (Mg ha-1).

Sediment 

bulk density

Soil texture (Mg m-3)

clay loam 1.1

silt loam 0.97

sandy loam 1.23

loam 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]

•

•

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 100-

year 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

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 

•

•

•

•

•

•

•

Figure 8. Summary of input parameters.

Figure 9. Precipitation and runoff values from the initial 100-

year high soil burn severity WEPP run.
USDA Forest Service RMRS-GTR-188. 2007 15

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.

•

•

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 
16 USDA Forest Service RMRS-GTR-188. 2007

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 Figure 12. ERMiT Mitigation Treatment Comparison Calculator.
USDA Forest Service RMRS-GTR-188. 2007 17

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:

Sediment delivery—probability table: individual 

WEPP sediment delivery predictions for each 

combination of parameters and individual parameter 

occurrence probabilities (untreated only) (fig. 14)

Sediment delivery statements (fig. 15)

Sediment delivery—probability of exceedance 

tables:

Untreated

Seeding

Mulching at four rates:

1.

2.

3.







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]

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 post-









4.

Figure 13. Text links to supporting tables.
18 USDA Forest Service RMRS-GTR-188. 2007

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.
USDA Forest Service RMRS-GTR-188. 2007 19

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 

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.
20 USDA Forest Service RMRS-GTR-188. 2007

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.
USDA Forest Service RMRS-GTR-188. 2007 21

Figure 17. ERMiT erosion barrier efficiency calculator.

Figure 18. Output screen footer information.
22 USDA Forest Service RMRS-GTR-188. 2007

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 GAO03-430. 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 23

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)

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Appendix A. Model Assumptions

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.

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24 USDA Forest Service RMRS-GTR-188. 2007

Figure B1. Input screen for example scenario.

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.
USDA Forest Service RMRS-GTR-188. 2007 25

Figure B2. Mitigation Treatment Comparison Calculator for example scenario.

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