Sediment Parameter and Calibration 

Guidance for HSPF 

United States Office of Water 

Environmental Protection 4305 January 2006 

Agency 

 BASINS Technical Note 8
ii

ACKNOWLEDGMENTS 

This BASINS Technical Note No. 8 was prepared by AQUA TERRA Consultants under Work 

Assignment No. 4-01 of EPA Contract No. 68-C-01-037. Mr. Paul Cocca was the EPA Work 

Assignment Manager (WAM) for this effort for most of the project schedule, and the individual 

who recognized the need for developing this Technical Note to make this information available 

to the BASINS user community. Mr. James Carleton assumed the role of WAM for this effort as 

the final draft was nearing completion. 

For AQUA TERRA, Mr. Tony Donigian and Mr. Brian Bicknell were the primary authors, with 

significant input and review provided by Mr. Jason Love and Mr Paul Duda. The sediment 

calibration and parameter guidance contained in this document was derived from numerous other 

HSPF modeling and source documents (e.g. ARM/NPS user manuals, USLE reports, ASCE 

Sedimentation Engineering) and based on many years of sediment modeling with HSPF. 

However, the material contained in this document should be viewed as general guidance which 

will continue to evolve and change as the sediment modeling technology and data bases expand 

and improve with time. 
 iii

CONTENTS

Introduction .....................................................................................................................................1 

Sediment Calibration Overview.......................................................................................................1 

Sediment Erosion Calibration..........................................................................................................3 

Instream Sediment Erosion Calibration...........................................................................................3 

Pervious Land Erosion (SEDMNT) Parameters..............................................................................6 

Impervious Land Washoff (SOLIDS) Parameters ....................................................................... 12 

Instream Transport (SEDTRN) Parameters ................................................................................. 14 

References .................................................................................................................................... 19 

Parameter and Value Range Summary Tables ............................................................................. 21 

Appendix ...................................................................................................................................... 24 
 1

BASINS Technical Note 8: 

Sediment Parameter and Calibration Guidance for HSPF 

Introduction

This technical note provides BASINS users with guidance on how to estimate the input 

parameters in the SEDMNT, SOLIDS, and SEDTRN sections of the Hydrological Simulation 

Program Fortran (HSPF) watershed model. It also outlines suggested procedures for sediment 

calibration, using a variety of graphical and statistical measures. For each input parameter, this 

guidance includes a parameter definition, the units used in HSPF, and how the input value may 

be determined (e.g. initialize with reported values, estimate, measure, and/or calibrate). Where 

possible, the note discusses how to estimate initial values using the data and tools included with 

BASINS. Also discussed, where appropriate, is the physical basis of each parameter and the 

corresponding algorithms as described in the HSPF Users Manual (Bicknell, et al, 2001) and 

earlier literature sources. In addition to the guidance provided below, model users are directed to 

other sources, including Sediment Calibration Procedures And Guidelines For Watershed 

Modeling (Donigian and Love, 2003) (Reproduced in the Appendix), the ARM Model User 

Manual (Donigian and Davis, 1978) and the HSPF Application Guide (Donigian et al., 1984). 

Summary tables are attached that provide ‘typical’ and ‘possible’ (i.e. maximum ‘expected’) 

ranges for the parameters discussed below, based on both the parameter guidance and experience 

with HSPF over the past two decades on watersheds across the U. S. and abroad (Donigian, 

1998). The overarching principal in parameter estimation should be that the estimated 

values must be realistic, i.e. make ‘physical’ sense, and must reflect conditions on the 

watershed. If the values estimated by the model user and/or derived from the guidance below, 

do not agree with the value ranges in the summary table, the user should question and reexamine the estimation procedures. The estimated values may still be appropriate, but the user 

needs to confirm that the parameter values reflect unusual conditions on the watershed. 

Another source of parameter information is the HSPF Parameter Database (HSPFParm) 

(US EPA, 1999) http://www.epa.gov/waterscience/ftp/basins/HSPFParm . HSPFParm consists 

of parameter values from previous applications of HSPF across North America assimilated into a 

single database, and with a customized graphical user interface for viewing and exporting the 

data. The pilot HSPFParm Database contains parameter values for model applications in over 40 

watersheds in 14 states. The parameter values, contained in the database, characterize a broad 

variety of physical settings, land use practices, and water quality constituents. The database has 

been provided with a simplified interactive interface that enables modelers to access and explore 

the HSPF parameter values developed and calibrated in various watersheds across the United 

States, and to assess the relevance of the parameters to their own watershed setting. 

Sediment Calibration Overview

Sediment is one of the most difficult water quality constituents to accurately represent in current 

watershed and stream models. Important aspects of sediment behavior within a watershed 
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system include loading and erosion sources, delivery of these eroded sediment sources to 

streams, drains and other pathways, and subsequent instream transport, scour and deposition 

processes. 

Sediment calibration for watershed models involves numerous steps in estimating model 

parameters and determining appropriate adjustments needed to insure a reasonable simulation of 

the sediment sources on the watershed, delivery to the waterbody, and transport behavior within 

the channel system. Rarely is there sufficient observed local data at sufficient spatial detail to 

accurately calibrate all parameters for all land uses and each stream and waterbody reach. 

Consequently, model users focus the calibration on sites with observed data and review 

simulations in all parts of the watershed to ensure that the model results are consistent with field 

observations, historical reports, and expected behavior from past experience. This type of 

‘weight-of-evidence’ approach is rapidly becoming the standard practice in watershed modeling. 

Sediment calibration must be done after the hydrologic calibration is completed, and it is 

extremely sensitive to the hydrology, particularly the amount and timing of surface runoff that is 

predicted by the model. Calibration of the parameters involved in simulation of watershed 

sediment erosion is more uncertain than hydrologic calibration due to less experience with 

sediment simulation in different regions of the country. The process is analogous; the major 

sediment parameters are modified to increase agreement between simulated and recorded 

monthly sediment loss and storm event sediment removal. However, observed monthly sediment 

loss is often not available, and the sediment calibration parameters are not as distinctly separated 

between those that affect monthly sediment and those that control storm sediment loss. In fact, 

annual sediment losses are often the result of only a few major storms during the year. 

As noted above, sediment calibration for watershed models involves numerous steps from initial 

estimates of model parameters, all the way to mimicking transport behavior within the channel 

system and at the watershed outlet. These steps usually include: 

1. Estimating target (or expected) sediment loading rates from the landscape, often as a 

function of topography, land use, and management practices 

2. Calibrating the model loading rates to the target rates 

3. Adjusting scour, deposition and transport parameters for the stream channel to mimic 

expected behavior of the streams/waterbodies 

4. Analyzing sediment bed behavior (i.e. bed depths) and transport in each channel reach as 

compared to field observations 

5. Analyzing overall sediment budgets for the land and stream contributions, along with 

stream aggrading and degrading behavior throughout the stream network 

6. Comparing simulated and observed sediment concentrations, including particle size 

distribution information, and load information where available 

7. Repeating steps 1 through 6 as needed to develop a reasonable overall representation of 

sediment sources, delivery, and transport throughout the watershed system 

Parameter guidance is given for each of the modules required to simulate sediment delivery from 

the landscape (i.e. SEDMNT, SOLIDS) and instream transport (i.e., SEDTRN) and the 

parameters are grouped as required in each UCI table. 
 3

Sediment Erosion Calibration 

Sediment loadings to the stream channel are estimated by land use category from literature data, 

local Extension Service sources, or procedures like the Universal Soil Loss Equation (USLE) 

(Wischmeier and Smith, 1978) and then adjusted for delivery to the stream with estimated 

sediment delivery ratios (SDRs). This delivery adjustment is needed because HSPF, like most 

watershed-scale (lumped parameter) models, represents landscape loadings to the stream 

channel, which are less than the field-scale estimates from USLE. These estimated loading rates 

then become ‘calibration targets’ for the watershed model. 

Model parameters are then adjusted so that model-calculated loadings are consistent with these 

estimated ‘calibration targets’ and loading ranges. The model-calculated loadings are further 

evaluated in conjunction with the instream sediment transport calibration (discussed below) that 

extends to a point in the watershed where sediment concentration and/or load data are available. 

The overall objective is to represent the overall sediment behavior of the watershed, with 

knowledge of the morphological characteristics of the stream (i.e. aggrading or degrading 

behavior), using sediment loading rates that are consistent with the calibration targets and 

modeled concentrations that provide a reasonable match with instream sediment data. 

In HSPF, the erosion process on pervious land areas is represented as the net result of 

detachment of soil particles by raindrop impact on the land surface, and then subsequent 

transport of these fine particles by overland flow. On impervious surfaces (e.g. parking lots, 

driveways), soil splash by raindrop impact is neglected and solids washoff is often controlled by 

the rate of accumulation of solid materials. The primary sediment erosion parameters are the 

coefficients in the soil detachment equation for pervious areas, the coefficients in the sediment 

washoff equations for pervious and impervious areas, and the accumulation rate of solids on 

impervious surfaces. 

In general, sediment calibration involves the development of an approximate equilibrium or 

balance between the accumulation and generation of sediment particles on one hand and the 

washoff or transport of sediment on the other hand. Thus, the accumulated sediment on the land 

surface (i.e., DETS and SLDS) should not be continually increasing or decreasing throughout the 

calibration period. Extended dry periods will produce increases in surface sediment 

accumulations, and extended wet periods will produce decreases. However, the overall trend 

should be relatively stable from year to year. This equilibrium must be developed on both 

pervious and impervious surfaces, and must exist in conjunction with the accurate simulation of 

monthly and storm event sediment loss, depending on the data available for calibration. 

Donigian and Love (2003) (see Appendix) provide additional discussion of sediment calibration 

procedures. 

Instream Sediment Transport Calibration

Once the sediment loading rates are calibrated to provide the expected input to the stream 

channel, the sediment calibration then focuses on the channel processes of deposition, scour, and 

transport that determine both the total sediment load and the outflow sediment concentrations to 
 4

be compared with observations. The initial steps in instream calibration involve dividing the 

input sediment loads into appropriate size fractions, estimating initial parameter values and 

storages for all reaches, and a preliminary model run to calculate shear stress timeseries in each 

reach to estimate critical scour and deposition values. 

The eroded material is fractionated into sand, silt, and clay prior to entering a model reach using 

available soils information; typically, a single fractionation scheme is used for all reaches unless 

soils and land surface variations within the watershed support use of reach-specific fractions. 

The fractions should reflect the relative percent of the surface material (i.e., sand, silt, clay) 

available for erosion in the surrounding watershed, but also should include an enrichment factor 

of silt and clay to represent the likelihood of these finer materials reaching the channel. 

The sand, silt and clay fractions of total eroded sediment are specified in the MASS-LINK block. 

Each unique fractionation scheme will require a separate table in the MASS-LINK block. An 

example MASS-LINK table containing the sediment fractionation is shown below, with the sand, 

silt, clay fractions shown as 0.05, 0.70, and 0.25, respectively. 

 MASS-LINK 1 

PERLND PWATER PERO 0.0833 RCHRES INFLOW IVOL 

PERLND SEDMNT SOSED 1 0.05 RCHRES INFLOW ISED 1 

PERLND SEDMNT SOSED 1 0.70 RCHRES INFLOW ISED 2 

PERLND SEDMNT SOSED 1 0.25 RCHRES INFLOW ISED 3 

PERLND PWTGAS POHT 1.0 RCHRES INFLOW IHEAT 

 END MASS-LINK 1 

For HSPF, initial sediment parameters, such as particle diameter, particle density, settling 

velocity, bed depth and composition, and beginning calibration parameter values can be 

evaluated from local/regional data, past experience, handbook values, etc., and then adjusted 

based on available site specific data and calibration. Bed composition data are especially 

important so that the model results can be adjusted to reflect localized aggradation (deposition) 

or degradation (scour) conditions within the stream system. 

In HSPF, the transport of the sand (non-cohesive) fraction is commonly calculated as a power 

function of the average velocity in the channel reach in each timestep. This transport capacity is 

compared to the available inflow and storage of sand particles; the bed is scoured if there is 

excess capacity to be satisfied, and sand is deposited if the transport capacity is less than the 

available sand in suspension within the channel reach. 

For the silt and clay (cohesive) fractions, shear stress calculations are performed by the 

hydraulics (HYDR) module, and then in the SEDTRN module they are compared to user-defined 

critical, or threshold, values for deposition and scour for each size (shown in Figure 1). When 

the shear stress for a timestep is greater than the critical value for scour, the bed is scoured at a 

user-defined erodibility rate and transport through the reach occurs; when the shear stress is less 

than the critical deposition value, the silt or clay fraction deposits at a settling rate input by the 

user for each size. If the shear stress falls between the critical scour and deposition values, the 

incoming suspended material is transported through the reach. 

In HSPF, the hydraulic characteristics of a stream reach are represented by a function table 

(FTABLE) that includes the relationships between stage, storage (volume), surface area, and 
 5

discharge. The accuracy of the FTABLE for a specific reach will be a critical factor in 

adequately representing the average velocity and hydraulic radius and calculated shear values, as 

a function of the stage, or depth of flow. This is especially evident for simulations of flood flows 

that exceed bankfull discharges; improper extension of the FTABLES can lead to erroneous 

velocities and shears and scour conditions during high flow events, and have major impacts on 

the model simulations for those events. 

Figure 1. Shear Stress Algorithm for Silt and Clay Fractions in HSPF 

 

The velocity (the driving function of the non-cohesives) is not represented in the model as 

directly related to shear stress (the driving function of the cohesives), and thus it is possible to 

have an unreasonable relationship between the two size fractions, and the corresponding flow 

rate. It is therefore advised to calculate the resulting shear stresses and velocities for each 

“portion” of the FTABLE using the equations presented in the HSPF User’s Manual. Even with 

reasonable FTABLES you will find that it is possible to have the cohesives settle out at low 

flows while maintaining non-negligible sand concentrations. These issues can be resolved by 

careful inspection and adjustment of the FTABLES. Since changes to the FTABLEs can impact 

the hydrologic calibration, the model flow results may need to be re-visited and checked if 

FTABLES are adjusted during the sediment calibration. 

As part of the sediment parameterization, the model is run with the initial parameter estimates 

and shear stress values are output for each stream reach. For the silt and clay size particles, the 

critical shear stress parameters (one for scour and one for deposition) for each size are adjusted 

so that the model calculates scour during high flow events, deposition and settling during low 

flow periods, and transport with neither scour nor settling for moderate flow rates. 

TIME (hr) 

Scour Threshold

Deposition Threshold 

SCOUR

DEPOSIT

TRANSPORT 

BED SHEAR STRESS (lb/ft2) 
 6

The shear stress values are then adjusted more carefully in calibration so that scour occurs during 

storm periods and deposition occurs at low flows. Once the timing of scour and deposition 

processes is correct, the rate of scour is adjusted in an attempt to match either expected behavior 

within each reach, and/or the observed concentrations. During high flow periods, the amount of 

scour is adjusted with an erodibility factor for each reach that controls the rate of scour whenever 

the actual shear stress is greater than the critical shear stress value for scour. During low flow 

periods the silt/clay fall velocity parameter can be adjusted slightly to improve the agreement. 

The Donigian and Love (2003) paper in the Appendix provides further discussion of the potential 

impact of FTABLEs on the shear stress calculations and resulting sediment concentrations. Note 

also: In HSPF the units of shear stress are lb-force/ft2

. Typically, shear stress values are 

expressed in the literature as Pascals (Pa) or Newtons/m2 (N/m2). The conversion is 1 Pa = 1 

N/m2

 = 0.02088 lb/ft2. 

The calibration procedure generally involves comparison of model simulations (concentrations 

and loads) to available observed data. This is often limited to event mean concentrations of total 

suspended solids (TSS) for selected storm events and nonstorm (baseflow) periods, or 

pollutographs of TSS concentrations throughout a few events. However, other types of 

comparisons are also possible, such as load estimates and sediment rating curves; see the 

Appendix for examples of these types of comparisons. 

Parameter Guidance 

The parameter guidance below is listed in the order of the parameter tables required by each 

module section responsible for simulating sediment erosion and solids washoff from pervious 

and impervious surfaces, and instream sediment transport, deposition, and scour (i.e. SEDMNT, 

SOLIDS, and SEDTRN). The parameters are grouped as required in each UCI table. 

Pervious Land Accumulation and Removal of Sediment (SEDMNT) Parameters 

The SEDMNT section simulates the production and removal of sediment from a pervious land 

segment (PERLND).

SED-PARM1 Table: 

 CRV Flag to select constant (CRV=0) or monthly-variable (CFV=1) erosion-related 

cover, COVER. Monthly values are commonly used to reflect seasonal 

variability of the vegetation or other erosional cover, e.g., for agricultural areas to 

reflect the timing of cropping and tillage practices. 

 VSIV Flag to select a constant or monthly-variable rate of net vertical sediment input, 

NVSI. Constant annual values (VSIV=0) are commonly used since atmospheric 

sources are usually difficult to quantify and are a small fraction of surface storage 

for pervious land surfaces. However, monthly values (VSIV=1) can be used to 

reflect variability of NVSI as impacted by seasonal land surface activities, such as 

tillage, if data are available to estimate NVSI. 
 7

 SDOP Flag to select the algorithm used to simulate removal of sediment from the land 

surface; choose either the method used in predecessor models (HSPX, ARM, and 

NPS) (SDOP=1), or an alternative method as described in the HSPF User Manual 

(SDOP=0). Recommendation: set SDOP to 1. This method, used in the 

predecessor models is more commonly used, and has been subjected to more 

widespread application. 

 SED-PARM2 Table: 

This table includes parameters for estimating the production and reduction of detached sediment 

on the pervious land. 

 SMPF Supporting Management Practice Factor (unitless) (measure/estimate). SMPF is 

used to simulate the reduction in erosion achieved by use of erosion control 

practices. SMPF is analogous to the P factor in the Universal Soil Loss Equation 

(Wischmeier and Smith, 1978), and initial values should be set equal to the P 

value for practices such as contouring, terracing, strip-cropping, etc. Table 1 

shows values of P for alternative practices and slope conditions. Model users 

should note that the practices listed in Table 1 also affect other HSPF parameters, 

such as NSUR, UZSN, LSUR, and SLSUR. The impact of different agricultural 

practices can only be evaluated with changes in all relevant parameters. Renard et 

al., (1997) provide detailed discussions on the physical basis and estimation of 

each of the USLE parameters, and as such it is also a valuable source of guidance 

for HSPF sediment parameters. 

 Use of BASINS Data/Tools:

In cases where GIS coverages are available for alternative management practices, 

BASINS GIS capabilities can be used to identify land areas with similar practices 

each of which may be represented as a separate PERLND with its own P value. 

Alternatively, the P values of different practices may be weighted by area 

fractions within a single PERLND. 

 KRER Coefficient in the soil detachment equation (complex) (measure/estimate, then 

calibrate as needed to achieve target loading rates). This parameter is related to 

the erodibility or detachability of the specific soil type and surface conditions. 

Experience indicates that KRER is directly related to the K factor in the USLE 

and can be initially estimated as KRER = K. K values can be obtained with 

techniques published in the literature or from soil scientists familiar with local soil 

conditions. Table 2 shows representative K values by soil texture and organic 

matter content. Renard et al (1997) provide extensive discussion on estimation of 

K from soil and land surface characteristics. Since adjustments to KRER will 

affect the amount of detached sediment that can be delivered to streams, users 

should review the detached sediment storage (DETS) to ensure that the 

accumulated sediment on the land surface is not continually increasing or 

decreasing throughout the simulation period. 
 8

Table 1. Values of Support-Practice Factor, P 

Land Slope (%): 1.1–2.0 2.1–7.0 7.1–12. 12.1–18. 18.1–24.

Practice 

Contouring (PC) 0.60 0.50 0.60 0.80 0.90 

Contour Strip Cropping (PSC) 

 R-R-M-M (1) 0.30 0.25 0.30 0.40 0.45 

 R-W-M-M 0.30 0.25 0.30 0.40 0.45 

 R-R-W-M 0.45 0.38 0.45 0.60 0.68 

 R-W 0.52 0.44 0.52 0.70 0.70 

 R-O 0.60 0.50 0.60 0.80 0.90 

Contour Listing or Ridge Planting 0.30 0.25 0.30 0.40 0.45 

Contour Terracing (Pt) (2,3) 0.6/√n 0.6/√n 0.6/√n 0.6/√n 0.6/√n 

No Support Practice 1.0 1.0 1.0 1.0 1.0 

 

(1) R = row crop, W = fall-seeded grain, O = spring-seeded grain, M = meadow. The crops are grown in 

rotation, and are arranged on the field such that rowcrop strips are always separated by a meadow or 

winter-grain strip. 

(2) These Pt values estimate the amount of soil eroded to the terrace channels and are used for 

conservation planning. For prediction of off-field sediment, the Pt values are multiplied by 0.2 

(3) n = number of approximately equal-length intervals into which the field slope is divided by the 

terraces. Tillage operations must be parallel to the terraces.

Source: Stewart, et al., 1975 

 Use of BASINS Data/Tools:

 The State Soil (STATSGO) data layer contains data on ‘kffact’, defined as the soil 

erodibility factor that is fragment free for use in the USLE. Run the BASINS 

State Soil Characteristic Report and select mean estimate, area-weighted, surface 

layer, for ‘kffact’. The TMDL USLE Tool (Hummel et al., 2000; discussed in the 

Appendix) also provides guidance in selecting USLE parameters.

JRER Exponent in the soil detachment equation (complex) (initialize with reported 

value, then calibrate as needed). JRER approximates the relationship between 

rainfall intensity and incident energy to the land surface for the production of soil 

fines. Wischmeier and Smith (1978) proposed the following relationship for the 

kinetic energy produced by natural rainfall; 

 

 Y = 916 + 331 log X 

Where Y = kinetic energy, ft/ton/acre/in. 

 X = rainfall intensity, inches/hr 
 9

Table 2. Representative Values of the Soil Erodibility Factor, K 

 Organic Matter Content (%): < 0.5 2.0 4.0 

Texture Class 

Sand 0.05 0.03 0.02 

Fine Sand 0.16 0.14 0.10 

Very Fine Sand 0.42 0.36 0.28 

Loamy Sand 0.12 0.10 0.08 

Loamy Fine Sand 0.24 0.20 0.16 

Loamy Very Fine Sand 0.44 0.38 0.30 

Sandy Loam 0.27 0.24 0.19 

Fine Sandy Loam 0.35 0.30 0.24 

Loam 0.38 0.34 0.29 

Silt Loam 0.48 0.42 0.33 

Silt 0.60 0.52 0.42 

Sandy Clay Loam 0.27 0.25 0.21 

Clay Loam 0.28 0.25 0.21 

Silty Clay Loam 0.37 0.32 0.26 

Sandy Clay 0.14 0.13 0.12 

Silty Clay 0.25 0.23 0.19 

Clay 0.13–0.29 

These values are estimated averages of broad ranges of specific soil values. When a texture 

is near the borderline of two texture classes, use the average of the two K values. 

Source: Stewart et al., 1975. 

Using this relationship, various investigations have also shown that soil splash is 

proportional to the square of the rainfall intensity (Meyer and Wischeimer 1969, 

David and Beer 1974). Thus, a value of about 2.0 for JRER is predicted from 

these studies. Most HSPF applications have used the default value of 2.0 for this 

exponent. Use the default value of 2.0, and adjust only if supported by local data 

and conditions. 

 AFFIX The fraction by which detached sediment storage decreases each day during nonstorm periods (/day) (estimate, then calibrate as needed). AFFIX is a soil 

compaction factor that reduces the amount of detached soil particles available for 

transport. This parameter attempts to represent the natural aggregation and 

mutual attraction of soil particles and the compaction of the surface soil zone 

from which erosion occurs. These processes are a complex function of soil 

characteristics, meteorologic conditions, and tillage practices for which a detailed 

simulation is not possible. Values in the range of .001 to .1 are possible. 

Typically, AFFIX is adjusted in combination with the KRER and NVSI 

parameters to ensure that the accumulated detached sediment on the land surface 
 10

(DETS) does not continually increase or decrease throughout the calibration 

period. 

COVER The fraction of land surface which is shielded from rainfall (unitless) (measure/ 

estimate, then calibrate as needed), and is therefore not susceptible to soil fines 

detachment by raindrop impact. Seasonal/monthly values are often used. 

Overhead photographs at periodic intervals during the year are the most direct 

means of estimating the land cover fraction. 

COVER values are sometimes estimated as one minus the monthly C factor in the 

USLE. For cropland, the C factors for the various stages of crop growth should 

be used in estimating COVER. 

Tables 5.7 and 5.8 in the ARM User Manual (Donigian and Davis, 1978) pertain 

to the evaluation of C on undisturbed lands and have been reproduced from the 

paper by Wischmeier (1975). C factors for disturbed lands (croplands, 

agriculture, and construction areas) have been published in the USLE Report 

(Wischmeier and Smith 1965). The monthly COVER values estimated from C 

may need to be reduced since the C factor includes considerations other than crop 

canopy and raindrop interception since it represents a soil loss ratio, i.e. the ratio 

of soil loss for the current land surface conditions compared to clean-tilled 

continuous fallow conditions. Renard et al (1997) provide the most extensive 

recent guidance on estimation of the C factor. Users should avoid using COVER 

values of 0.98 to 1.0, even for dense forest or crop canopy conditions, since this 

will essentially eliminate any soil detachment and subsequent washoff of detached 

materials. In these cases, the lower COVER values allow for fringe and boundary 

areas of a PERLND that may contribute soil to the stream. 

 NVSI The rate at which sediment enters detached storage from the atmosphere 

(lb/ac/day), (estimate, then calibrate as needed). NVSI is often input with 

monthly variation. It represents any detached sediment accumulation processes 

not covered by rainfall impact or agricultural tillage operations that are generally 

specified using Special Actions. This can include the effects of wind-blown 

sediments, or land disturbance activities such as construction or landscaping. Note 

that NVSI can be negative, if the effects of the AFFIX parameter are not 

sufficient to represent all detached sediment reduction processes. 

 SED-PARM3 Table: 

This table contains parameters for estimating the sediment removal from the pervious land by 

washoff and gully erosion processes.

 KSER Coefficient in the soil washoff or transport equation (complex), (estimate, then 

calibrate as needed). It is an attempt to combine the effects of slope, overland 

flow length, sediment particle size, and surface roughness on the sediment 

transport capacity of overland flow into a single parameter. Consequently, 
 11

calibration is the major method of evaluating KSER. Terracing, tillage practices, 

and other agricultural management techniques will have a significant effect on 

KSER. Experience to date has indicated a possible range of values of 0.01 to 5.0. 

However, variations from this can be expected. 

 JSER Exponent in the soil washoff equation (complex), (initialize with reported value, 

then calibrate as needed). JSER approximates the relationship between overland 

flow intensity and sediment transport capacity. The vast majority of HSPF 

applications have used the default value of 2.0 for this exponent. Use the default 

value of 2.0, and adjust only if supported by local data and conditions. 

 KGER Coefficient in the matrix soil equation, which simulates gully erosion (complex), 

(estimate, then calibrate as needed). Unless there is evidence of gully erosion 

occurring in the watershed, KGER should be set to 0.0. 

 JGER Exponent in the matrix soil equation, which simulates gully erosion (complex), 

(estimate, then calibrate as needed). The vast majority of HSPF applications 

have used the default value of 2.0 for this exponent, but few applications even 

include gully erosion. Use an initial value of 2.5, since JGER is expected to be 

greater than JSER, and adjust if supported by local data and conditions. 

Monthly Input Parameter Tables: 

In general, monthly variation in selected parameters, such as COVER and NVSI should be 

included with the initial parameter estimates. The monthly values represent the variable on the 

first day of each month; the values other days are then interpolated from the values for the 

current and following months. All monthly values can be adjusted to calibrate for seasonal 

variations. 

MON-COVER Table: 

Monthly values for the fraction of land surface which is shielded from rainfall. Monthly values 

are often used, since COVER is primarily a function of the seasonal and vegetation canopy 

changes. 

MON-NVSI Table: 

Monthly values for the rate at which sediment enters detached storage from the atmosphere. 
 12

Impervious Land Accumulation and Removal of Solids (SOLIDS) Parameters

The SOLIDS section simulates the accumulation and removal of solids from an impervious land 

segment (IMPLND).

SLD-PARM1 Table: 

 VASD Flag to select a constant (VASD=0) or monthly-variable (VASD=1) rate of solids 

accumulation, ACCSDP. Monthly values are commonly used to reflect 

variability of the accumulation as impacted by climate and seasonal land surface 

activities. 

 VRSD Flag to select a constant (VRSD=0) or monthly-variable (VRSD=1) rate of solids 

removal, REMSDP. Monthly values are commonly used to reflect variability of 

the removal rate as impacted by seasonal land surface activities. 

 SDOP Flag to select algorithm used to simulate removal of sediment from the 

impervious surface; choose either the method used in predecessor models (HSPX, 

ARM, and NPS), (SDOP=1), or the alternative method as described in the HSPF 

User Manual (SDOP=0). Recommendation: Set SDOP to 1; this method is more 

commonly used, and has been subjected to more widespread application. 

 SLD-PARM2 Table:

 KEIM Coefficient in the solids washoff equation (complex), (estimate, then calibrate as 

needed). This parameter is an attempt to combine the effects of slope, overland 

flow length, sediment particle size, and surface roughness on the sediment 

transport capacity of overland flow into a single parameter. Consequently, 

calibration is the major method of evaluating KEIM. Experience to date has 

indicated a possible range of values of 0.01 to 5.0. However, variations from this 

can be expected. 

 JEIM Exponent in the solids washoff equation (complex), (estimate, then calibrate as 

needed). Values in the range of 1 to 2.5 are reasonable, with most models using a 

value of 1.6 to 2.0. Use an initial value of 1.8, and then adjust if supported by 

local data and conditions. 

 ACCSDP The rate solids accumulate on the impervious land surface (tons/ac/day), 

(estimate, then calibrate as needed). Data from street surfaces suggests values in 

the range of 0.0005 to 0.1, with most data in the range of 0.001 to 0.02. Note that 

ACCSDP is in units of ‘tons/ac/day’, whereas the corresponding rate for pervious 

surfaces (NVSI) is in lbs/ac/day. 

 REMSDP The fraction of solids storage which is removed each day when there is no runoff 

(per day) (estimate, then calibrate as needed). These removal processes include 
 13

wind, air currents from traffic, aggregation to larger, less transportable particles, 

and street cleaning activities. Values should range from 0.001 to 0.1, with typical 

values in the range 0.001 to 0.07. The effects of street cleaning can be estimated 

as: 

 R = P*(E/D) 

 where R = sediment removal by street cleaning 

 P = fraction of impervious area where cleaning is performed 

 E = efficiency of cleaning 

 D = frequency of cleaning 

For example, if cleaning is performed every seven days on 40% of the area with 

an efficiency of 80%, then 

 R = (.4) (.8)/(7) = 0.046 

If wind removal is estimated as 0.02, then REMDSP would be approximately 

0.066. Typically, removal rates are evaluated in conjunction with accumulation 

rates to establish a limit to the total sediment accumulation that can occur. This 

limit is given by ACCSDP/REMSDP. Consequently, joint calibration of 

accumulation and removal rates is recommended. 

 

Monthly Input Parameter Tables: 

In general, monthly variation in selected parameters, such as SACCUM and REMOV should be 

included with the initial parameter estimates. As noted above, the monthly values represent the 

variable on the first day of each month; the values for other days are then interpolated from the 

values for the current and following months. The monthly values can be adjusted to calibrate for 

seasonal variations. 

MON-SACCUM Table: 

Monthly values for the rate of solids accumulation on the land surface (ACCSDP). 

MON-REMOV Table:

Monthly values for the fraction of solids storage which is removed each day when there is no 

runoff (REMSDP).
 14

 Instream Sediment Transport (SEDTRN) Parameters 

The SEDTRN section simulates the transport, deposition, and scour of inorganic sediment from a 

free-flowing reach or mixed reservoir (RCHRES).

SANDFG Table: 

SDFG Flag to select the method that will be used for sandload simulation. 

1. Toffaleti – developed for wide rivers where hydraulic radius is 

approximately equal to depth; not often used due to lack of calibration 

parameters 

2. Colby – developed for wide rivers where hydraulic radius is 

approximately equal to depth; not often used due to lack of calibration 

parameters 

3. User Specified Power Function – most frequently used at the current 

time 

 

SED-GENPARM Table: 

 BEDWID The effective width over which bed sediment is deposited; the BEDWID is 

constant regardless of stage, top width, etc. (ft), (measure, estimate). BEDWID is 

used to calculate the depth of sediment in the bed in order to issue a warning 

whenever it exceeds BEDWRN (below). It may be estimated as the channel 

width at low flow. 

 Use of BASINS Data/Tools:

The RF1 coverage contains the mean stream width (Pwidth), but NHD does not. 

The BASINS delineation tools produce estimates of mean stream width as a 

function of upstream area using geomorphological relationships described in 

http://www.epa.gov/waterscience/basins/width_depth_FAQ.pdf. Digital 

orthophotos of the watershed are also a source of stream width data. 

 BEDWRN The depth of sediment in the bed, which, if exceeded (e.g., through sediment 

deposition) will cause a warning message to be printed in the echo/message file. 

(ft) (adjust as needed). These values will be dependent on the size of the water 

reach/body. For large rivers and lakes, these values will range from 2 to 15 ft, or 

even higher for large reservoirs. Smaller streams might range from 0.5 to 2 ft. 

Users should inspect the computed bed depth in the model output to verify that 

the results are reasonable, and that the temporal variation is consistent with 

expectations for each stream reach.

 POR The porosity of sediment in the bed (volume of voids / total volume) (unitless) 

(measure, estimate). POR is used to calculate the depth of sediment in the bed. 

For sand and/or gravel beds, porosities range between 0.35 and 0.5, with most 
 15

values near 0.45. For beds with more cohesives, the values can be higher, with 

values up to 0.9 for newly deposited muds and peat. As consolidation occurs, 

particularly if the channel dries out for extended periods, the porosity would 

decrease to values closer to 0.4 – 0.5. 

SED-HYDPARM Table: 

This table is only required if section HYDR is not active. If HYDR is active, as it is in most 

simulations, these quantities are specified in the HYDR section. 

 LEN Length of the stream reach (miles), (measure). Length is used in the computation 

of auxiliary variables, including hydraulic radius, flow velocity, and shear stress, 

which are used to simulate sediment transport in SEDTRN. 

 Use of BASINS Data/Tools:

 This is populated automatically by BASINS during model initialization. 

 DELTH Change in elevation from the upstream end of the stream reach to the downstream 

end (feet), (measure). DELTH is used to compute channel slope for (1) 

calculation of shear stress for cohesives (silt and clay), and (2) if sandload 

transport capacity is computed using either the Toffaleti or Colby method in the 

SEDTRN Block. 

 Use of BASINS Data/Tools:

This is populated automatically by BASINS, during model initialization, from the 

DEM by selecting the upstream and downstream elevations for the HSPF reach 

boundaries. Thus the accuracy of this slope calculation depends on the resolution 

and accuracy of the DEM. 

 DB50 Median diameter of the bed sediment (inches), (estimate/measure). DB50 is used 

to calculate: (1) the bed shear stress if the reach is a lake; and (2) the rate of sand 

transport if the Toffaleti or Colby method is used. Note: DB50 is not connected 

with the sand particle diameter (D) input in the SAND-PM table. Sediment 

diameter values can be obtained from texts on sedimentation/hydrology and from 

particle size analysis of the sediments in the watershed. Sand particles have 

diameters typically ranging from 0.05 – 2 mm (0.002 – 0.08 in). Table 3, 

reproduced from ASCE (1975) shows a sediment grade scale. 

SAND-PM Table: 

 D The effective diameter of the sand particles (in), (measure, estimate). Sediment 

diameter values can be obtained from texts on sedimentation/hydrology and from 

particle size analysis of the sediments in the watershed. Sand particles have 

diameters typically ranging from 0.05 – 2 mm (0.002 – 0.08 in); see Table 3 

above. Note: D is not used in the calculations; it is included in this table for 

consistency with the cohesive sediment data inputs. The Colby and Toffaleti 
 16

calculations for sand transport use the DB50 parameter, which is entered in the 

HYDR-PARM2 table. Therefore, enter a nominal value of 0.01 inches. 

 Table 3. Sediment Particle Diameters and Fall Velocities in Still Water 

Class Name Diameter1 

(mm) 

Fall Velocity2

(cm/sec) 

Very coarse sand 2.0 - 1.0 20. 

Coarse sand 1.0 - 0.5 12. 

Medium sand 0.5 - 0.25 5. 

Fine sand 0.25 - 0.125 2.2 

Very fine sand 0.125 - 0.062 0.75 

Coarse silt 0.062 - 0.031 0.16 

Medium silt 0.031 - 0.016 0.04 

Fine silt 0.016 - 0.008 0.01 

Very fine silt 0.008 - 0.004 0.0027 

Coarse clay 0.0040 - 0.0020 0.0006 

Medium clay 0.0020 - 0.0010 0.00015 

Fine clay 0.0010 - 0.0005 0.00004 

Very fine clay 0.0005 - 0.00024 0.00001 

Notes: 

1. Source: ASCE, 1975 

2. Fall velocity in still water; for diameters < 0.125 mm, estimated based on 

Stokes Law; assumed: median diameter from column 1, temperature = 24 

degC, and density = 2.65 g/cm3

. For larger particles, where Stokes Law does 

not apply, used estimated data for sand particles from Rouse (1937). 

 W The fall velocity of the sand particles in still water (in/sec), (measure, obtain 

from literature, estimate). Note: for sand transport, W is only used in the 

Toffaleti method; therefore, if the Colby method or power function method is 

being used, enter a nominal value of 0.4 in/sec. 

Particle velocities in still water can be estimated using simple equations such as 

Stokes’ Law for the terminal velocity of a spherical particle. See texts on 

sedimentation (e.g., SCS, (1983) and ASCE, (1975)) or screening procedure 

reference texts, for example Mills et al., (1985). Table 3 shows sediment settling 

velocities in cm/s. Sand settling velocities will typically be in the range of 0.1 to 4 

in/sec. 

 RHO The density of the sand particles (g/cm3

), (measure, obtain from literature,

estimate). Sediment densities are used to calculate the bed depth along with the 

porosity (POR). Sediment density values can be obtained from texts on 
 17

sedimentation/hydrology and from laboratory analysis of the sediments in the 

watershed. Typical values of RHO range from 1.5 to 2.8 g/cm3. If local data are 

not available, use 2.6 g/cm3. 

 KSAND The coefficient in the sandload power function, should be included if SDFG = 3. 

(complex) (calibrate). The sand transport power function is based on velocity. 

This equation will produce reasonable results if the computed velocites, which are 

determined from the volume and discharge columns in the FTABLE, are 

reasonable. This coefficient is a calibration parameter; start with a value of 0.1 

and adjust, in concert with EXPSND, to improve the comparison between 

simulated and observed sand concentrations. 

 EXPSND The exponent in the sandload power function, should be included if SDFG = 3. 

(complex) (calibrate). See the discussion for KSAND above. Begin with a value 

of EXPSND of 2.0 and adjust slightly, in concert with KSAND. 

SILT-CLAY-PM Table: 

This table should be entered twice. The first occurrence provides parameters for silt; the second 

contains the clay parameters. 

 D The effective diameter of the silt or clay particles (in), (measure, estimate). 

Sediment diameter values can be obtained from texts on sedimentation/hydrology 

and from particle size analysis of the sediments in the watershed. Table 3 contains 

a sediment grade scale. Silt particles have diameters typically ranging from 0.005 

– 0.05 mm (0.0002 – 0.002 in). Clay particles range from 0.0002 to 0.004 mm 

(8.E-6 – 0.00015 in). In the absence of measured data, use 0.0006 inches for silt 

and 0.0001 inches for clay. 

 W The fall velocity of the silt or clay particles in still water. (in/sec) (measure, 

obtain from literature, estimate). Particle velocities in still water can be 

estimated using simple equations such as Stokes’ Law for the terminal velocity of 

a spherical particle, with adjustments for drag for non-spherical particles. See 

texts on sedimentation or screening procedure reference manuals, for example 

Mills et al., (1985). Table 3 contains estimated values of W (cm/s) as a function of 

particle diameter and density. In the absence of detailed data and calculations, use 

a value of 0.0005 in/sec for silt and 0.00005 in/sec for clay. 

 RHO The density of the silt or clay particles, (g/cm3

), (measure, obtain from literature,

estimate). Sediment densities are used to calculate the bed depth along with the 

porosity (POR). Sediment density values can be obtained from texts on 

sedimentation/hydrology and from laboratory analysis of the sediments in the 

watershed. Typical values of RHO range from 1.5 to 2.8 g/cm3

. If local data are 

not available, use 2.3 g/cm3

 for silt and 2.0 for clay. 
 18

 TAUCD The critical bed shear stress for deposition (lb/ft2

), (calibrate). Initial values of 

TAUCD and TAUCS should be estimated on a reach-by-reach basis by 

examining graphs of the simulated shear stress timeseries (TAU) graphed at the 

timestep of the simulation. Assign values of TAUCS that are below the 

maximum of the curve, and TAUCD that are above the minimum of the curve. 

Calculated shear stress values should also be inspected to verify they are 

reasonable. The FTABLE volume and discharge columns determine the shear 

stress along with the simulated discharge, and unreasonable shear stress values 

should first be corrected by adjusting the FTABLE. Generally, TAUCS values 

will be greater than TAUCD, and the values of both parameters for silt will be 

greater than or equal to those for clay. Adjust TAUCD and W (particle fall 

velocity) to calibrate the timing and magnitude of silt and clay concentrations. 

Increasing TAUCD will result in increasing the occurrence and magnitude of 

deposition and vice versa. 

 TAUCS The critical bed shear stress for scour (lb/ft2

), (calibrate). See the discussion for 

TAUCD above to set initial values of TAUCS. Adjust TAUCS in concert with 

the erodibility coefficient (M) to calibrate the timing and magnitude of silt and 

clay concentrations. Increasing TAUCS will result in reductions in the occurrence 

and magnitude of scour and vice versa. 

Procedures are available to calculate critical shear stress values from Shields’ 

equation using bed and channel properties, as follows: 

 τc = θ (γs - γ) D 

where θ is the dimensionless Shields parameter for entrainment of a sediment 

particle of size D, γs is the unit weight of bed sediment, and γ is the unit weight of 

water. Donigian and Love (2005) have used these procedures to estimate τc values 

and assess channel stability issues in urbanizing watersheds using HSPF. These 

same calculations can be used to develop initial TAUCS values which would 

subsequently be adjusted in calibration. 

 M The erodibility coefficient of the sediment (lb/ft2

.d), (calibrate). This coefficient 

is entirely a calibration parameter. Set it to 0.01 and then adjust it (in concert with 

TAUCS, and with consideration of the balance between land sediment loading 

and channel scour) to result in reasonable silt and clay concentrations during 

scour conditions in the reach. 
 19

REFERENCES 

ASCE. 1975. Sedimentation Engineering, ASCE Manuals and Reports on Engineering Practice, 

No. 54, V.A.Vanoni, ed., American Society of Civil Engineers, New York.

Bicknell, B.R., J.C. Imhoff, J.L. Kittle Jr., A.S. Donigian, Jr., T.H. Jobes, and R.C. Johanson. 

2001. Hydrological Simulation Program - FORTRAN, User’s Manual for Version 12. 

U.S. EPA, National Exposure Research Laboratory, Athens, GA. 

David, W.P. and C.E. Beer. 1974. Simulation of Sheet Erosion, Part 1. Development of a 

Mathematical Erosion Model. Iowa Agriculture and Home Economics Experiment 

Station. Ames, Iowa. Journal Paper No. J-7897. 

Donigian, A.S. Jr., and J.T. Love. 2005. The Use of Continuous Watershed Modeling to address 

Issues of Urbanization and Channel Stability in Southern California. ASCE World Water 

and Environmental Resources Congress 2005. May 16-19, 2005. Anchorage, AK. (paper 

also attached in Appendix). 

Donigian, A.S. Jr., and J.T. Love. 2003. Sediment Calibration Procedures and Guidelines for 

Watershed Modeling. WEF TMDL 2003, November 16-19, 2003. Chicago, Illinois. 

Donigian, A.S., Jr. 1998. Personal communication, 1998. 

Donigian, A.S. Jr., J.C. Imhoff, B.R. Bicknell and J.L. Kittle. 1984. Application Guide for 

Hydrological Simulation Program Fortran (HSPF), prepared for U.S. EPA, EPA 600/3 

84 065, Environmental Research Laboratory, Athens, GA. 

Donigian, A.S., Jr. and H.H. Davis, Jr. 1978. User's Manual for Agricultural Runoff 

Management (ARM) Model, U.S. Environmental Protection Agency, EPA_ 

600/3_78_080. 

EPA, 1999. HSPFParm: An Interactive Database of HSPF Model Parameters, Version 1.0. 

EPA_823_R_99_004. U.S. EPA, Office of Water, Washington, DC. Available from the 

BASINS web site http://www.epa.gov/waterscience/ftp/basins/HSPFParm . 

Hummel, P.R., J.C. Imhoff, R. Dusenbury and M. Gray. 2000. TMDL USLE, A Practical Tool 

for Estimating Diffuse Sediment Source Loads within a Watershed Context. Prepared for: 

U.S. EPA National Exposure Research Laboratory, Athens, GA. 

(http://www.epa.gov/ceampubl/swater/usle). 

Meyer, L.D. and W.H. Wischmeier. 1969. Mathematical Simulation of the Processes of Soil 

Erosion by Water, Trans. Am. Soc. Agric. Eng. 12(6):754-762. 

Mills, W. B., D. B. Porcella, M. J. Ungs, S.A. Gherini, K.V. Summers, L. Mok, G.L. Rupp, and 

G.L. Bowie. 1985. Water Quality Assessment: A Screening Procedure for Toxic and 
 20

Conventional Pollutants in Surface and Groundwater. Vols. I and II. EPA/600/6-85/002. 

NTIS PB86-12249 6, U.S. EPA, Athens, Georgia. 

Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C. Yoder, coordinators. 1997. 

Predicting Soil Erosion by Water: A Guide to Conservation Planning With the Revised 

Universal Soil Loss Equation (RUSLE). U.S. Department of Agriculture, Agriculture, 

Handbook No. 703, 404 pp. 

Rouse, H. 1937. Nomogram for the Settling Velocity of Spheres, Division of Geology and 

Geography Exhibit D of the Report of the Commission on Sedimentation, 1936-37, 

National Research Council, Washington, DC. 

Soil Conservation Service. 1983. National Engineering Handbook. Section 3 - Sedimentation. 

United States Department of Agriculture, Washington, DC. 

Stewart, B. A., D. A. Woolhiser, W. H. Wischmeier J. H. Caro, and M. H. Frere. 1975. Control 

of Water Pollution from Cropland, Volume 1, A manual for guideline development. U.S. 

EPA and USDA, Report Nos. EPA-600/2-75 026a and ARS H-5-1, Washington, DC. 

Wischmeier, W.H. 1975. Estimating the Soil Loss Equation’s Cover and Management Factor for 

Undisturbed Areas. p118-124 in: Present and Prospective Technology for Predicting 

Sediment Yields Sources, USDA ARS-S-40.

Wischmeier, W.H., and D.D. Smith. 1965. Predicting Rainfall Erosion Losses from Cropland 

East of the Rocky Mountains: Guide for selection of practices for soil and water 

conservation. U.S. Department of Agriculture, Agricultural Handbook No. 282. 

Wischmeier, W.H., and D.D. Smith. 1978. Predicting rainfall erosion losses – a guide to 

conservation planning. The USDA Agricultural Handbook No. 537. 
 21

VALUE RANGES FOR HSPF SEDIMENT EROSION AND SOLIDS WASHOFF 

PARAMETERS 

 RANGE OF VALUES 

 NAME DEFINITION UNITS TYPICAL POSSIBLE FUNCTION OF ... COMMENT 

 MIN MAX MIN MAX 

PERLND 

SED - PARM2

SMPF Management Practice (P) 

factor from USLE 

none 0.0 1.0 0.0 1.0 Land use, Ag 

practices 

Use P factor from USLE 

KRER Coefficient in the soil 

detachment equation 

complex 0.15 0.45 0.05 0.75 Soils Estimate from soil erodibility 

factor (K) in USLE 

JRER Exponent in the soil 

detachment equation 

none 1.5 2.5 1.0 3.0 Soils, climate Usually start with value of 2.0 

AFFIX Daily reduction in detached 

sediment 

per day 0.03 0.10 0.01 0.50 Soils, compaction, ag 

operations 

Reduces fine sediments following 

tillage 

COVER Fraction land surface 

protected from rainfall 

none 0.0 0.90 0.0 0.98 Vegetal cover, land 

use 

Seasonal/monthly values often 

used 

NVSI Atmospheric additions to 

sediment storage 

lb/ac-dy 0.0 5.0 0.0 20.0 Deposition, activities, 

etc. 

Can be positive or negative 

SED - PARM3

KSER Coefficient in the sediment 

washoff equation 

complex 0.5 5.0 0.1 10.0 Soils, surface 

conditions 

Primary sediment Calibration

parameter 

JSER Exponent in the sediment 

washoff equation 

none 1.5 2.5 1.0 3.0 Soils, surface 

conditions 

Usually use value of about 2.0 

KGER Coefficient in soil matrix 

scour equation 

complex 0.0 0.5 0.0 10.0 Soils, evidence of 

gullies 

Calibration, only used if there is 

evidence of gullies 

JGER Exponent in soil matrix scour 

equation 

none 1.0 3.0 1.0 5.0 Soils, evidence of 

gullies 

Usually use value of about 2.5 

IMPLND

SLD – PARM2

KEIM Coefficient in the solids 

washoff equation 

complex 0.5 5.0 0.1 10.0 Surface conditions, 

solids charac. 

Primary solids Calibration

parameter 

JEIM Exponent in the solids 

washoff equation 

none 1.0 2.0 1.0 3.0 Surface conditions, 

solids charac. 

Usually use value of about 1.8 

ACCSDP Solids accumulation rate on 

the land surface 

lb/ac-dy 0.0 2.0 0.0 30.0 Land use, traffic, 

human activities 

Calibration, primary source of 

solids from impervious areas 

REMSDP Fraction of solids removed 

per day 

per day 0.03 0.2 0.01 1.0 Street sweeping, 

wind, traffic 

Usually start with value of about 

0.05, and calibrate 
 22

HSPF HYDRAULIC SEDIMENT PARAMETERS AND VALUE RANGES 

 RANGE OF VALUES 

 NAME DEFINITION UNITS TYPICAL POSSIBLE FUNCTION OF ... COMMENT 

 MIN MAX MIN MAX 

RCHRES 

SANDFG

SDFG Indicates Method Used for 

Sandload Simulation 

none 1 3 1 3 Type of stream; 

user experience. 

1 - Toffaleti, 2 - Colby, 

3 - Power Function 

SED-GENPARM

BEDWID Width of cross-section 

over which HSPF will 

assume bed sediment is 

deposited 

ft 10 500 5 1000 Reach \ 

Waterbody 

morphology 

Constant regardless of stage, 

top-width, etc 

BEDWRN Bed depth which, if 

exceeded (i.e., through 

deposition) will cause a 

warning message to be 

printed 

ft 0.5 10 0.5 20 Reach \ 

Waterbody 

morphology, User 

Needs 

Only affects when warning 

messages will be printed about 

high bed depth/deposition. 

Lakes/reservoirs will have 

higher values. 

POR Porosity of the bed 

(volume voids/total 

volume) 

none 0.3 0.6 0.25 0.9 Reach \ Sediment 

Bed 

Characteristics 

Only affects bed depth 

calculation. Can set to 0.5 if no 

data are available. 

SED-HYDPARM 

LEN Length of the RCHRES miles 0.1 1.0 0.01 100 Topography, 

stream 

morphology

If very large lengths are 

calculated, reach should be 

subdivided. 

DELTH Drop in water elevation 

from upstream to 

downstream extremities of 

the RCHRES 

ft 5 50 0.1 100 Topography, 

stream 

morphology

If large drops are calculated, 

the reach should be subdivided 

into multiple separate reaches. 

DB50 Median diameter of bed 

sediment (assumed 

constant) 

in 0.01 0.02 0.001 1.0 Channel bed 

properties Only used for lake shear stress 

and Toffaleti/Colby methods 

SAND-PM

D Effective diameter of the 

transported sand particles 

in .002 0.08 .0005 0.2 Sediment 

properties 

Not used in calculations. Set to 

0.01 in. 

W Fall velocity of transported 

sand particles in still water 

in/sec 0.2 4. 0.1 10. Particle diameter 

and density 

Used for Toffaleti method. 

RHO Density of sand particles g/cm3 2.2 2.7 1.5 3.0 Sediment 

properties 

Used for calculating bed depth.

KSAND Coefficient in sandload 

power function formula 

complex 0.01 0.5 0.001 10. Sand properties 

and hydraulics 

Calibration. Affects sand 

concentration. 

EXPSND Exponent in sandload 

power function formula 

complex 1.5 3.5 1.0 6.0 Sand properties 

and hydraulics 

Calibration. Affects sand 

scour. Usually start with 2.0 

SILT-CLAY-PM

D Effective diameter of silt, 

 or clay particles 

in .0002

.00001

.0025

.00015

.0001 

.000005

.004 

.00025

Sediment 

properties 

Used for calculating bed depth.
 23

W Fall velocity of transported 

silt or clay particles in still 

water 

in/sec .0001 0.01 0.0 0.1 Particle 

diameter and 

density 

Affects concentration during low 

flow. 

RHO Density of silt or clay 

particles 

g/cm3 1.8 2.7 1.5 3.0 Sediment 

properties 

Used for calculating bed depth.

TAUCD* Critical bed shear stress for 

deposition 

lb/ft2

 0.01 0.3 0.001 1.0 Silt/clay 

properties and 

hydraulics 

Calibration. Affects timing & 

magnitude of deposition. Initial 

values based on computed 

shear stress. 

TAUCS* Critical bed shear stress for 

scour 

lb/ft2

 0.05 0.5 0.01 3.0 Silt/clay 

properties and 

hydraulics 

Calibration. Affects timing & 

magnitude of scour. Initial 

values based on computed 

shear stress. 

M* Erodibility coefficient lb/ft2

.d 0.01 2. 0.001 5.0 Silt/clay 

properties and 

hydraulics 

Calibration. Affects magnitude 

of scour. 

* - Minimum values for lakes and other waterbodies may be 2 to 3 orders of magnitude lower than the 

table ranges in order to represent reasonable sediment trapping efficiency.
24

APPENDIX

SEDIMENT CALIBRATION PROCEDURES AND GUIDELINES 

 FOR WATERSHED MODELING 

A. S. Donigian, Jr. 

J. T. Love 

AQUA TERRA Consultants 

2685 Marine Way, Suite #1314 

Mountain View, CA 94043 

Presented at Water Environment Federation TMDL 2003 Conference, 

Chicago, IL November 16-19, 2003 

ABSTRACT 

Sediment is one of the most difficult water quality constituents to accurately represent in current 

watershed and stream models. Important aspects of sediment behavior within a watershed 

system include loading and erosion sources, delivery of these eroded sediment sources to 

streams, drains and other pathways, and subsequent instream transport, scour and deposition 

processes. 

Sediment calibration for watershed models involves numerous steps in estimating model 

parameters and determining appropriate adjustments needed to insure a reasonable simulation of 

the sediment sources, delivery, and transport behavior within the channel system. Rarely is there 

sufficient observed local data at sufficient spatial detail to accurately calibrate all parameters for 

all land uses and each stream and waterbody reach. Consequently, model users focus the 

calibration on sites with observed data and review simulations in all parts of the watershed to 

ensure that the model results are consistent with field observations, historical reports, and 

expected behavior from past experience. 

This paper explores a ‘weight of evidence’ approach for sediment calibration as part of overall 

watershed model calibration, using both graphical and statistical measures, based on recent 

experience with the U. S. EPA Hydrological Simulation Program - FORTRAN (HSPF). Model 

parameterization and calibration procedures are described, using sample model results, to 

demonstrate recommended graphical and statistical procedures to assess model performance for 

sediment loadings, concentrations, and budgets within a watershed modeling framework. 

Although the results are specific to the EPA HSPF model, the approach and procedures for 

sediment calibration are applicable to other watershed models that represent sediment processes 

and behavior at the watershed scale. 

KEYWORDS 

HSPF, erosion, sediment delivery, GIS, sediment rating curves, shear stress, TMDL, USLE 
25

INTRODUCTION 

 

Sediment is a primary constituent of concern for many watershed assessments and Total 

Maximum Daily Load (TMDL) studies being performed across the country. In addition to issues 

related to sediment impacts on stream habitats, sediment is also a carrier of many other 

pollutants, including metals, phosphorus, organics, and bacteria. Unlike many other pollutants, 

eliminating all sediments from the stream is not a solution since that will ultimately lead to 

channel scour and/or bank failures, as the stream attempts to reach a stable, dynamic equilibrium. 

Sediment is also one of the most difficult water quality constituents to accurately represent in 

current watershed and stream models. Important aspects of sediment behavior within a 

watershed system include loading and erosion sources, delivery of these eroded sediment sources 

to streams, drains and other pathways, and subsequent instream transport, scour and deposition 

processes. 

A ‘weight-of-evidence’ approach is rapidly becoming the standard practice in watershed 

modeling. Model performance and calibration/validation are evaluated through qualitative and 

quantitative measures, involving both graphical comparisons and statistical tests. For flow 

simulations where continuous records are available, all these techniques are often employed, and 

the same comparisons are performed, during both the calibration and validation phases. For 

water quality constituents, including sediment, model performance is often based primarily on 

visual and graphical presentations as the frequency of observed data is often inadequate for 

accurate statistical measures beyond basic metrics (e.g., mean). However, consistency checks 

with expected value ranges for loading rates and stream morphology and behavior are critical 

when spatially distributed field data are limited. 

This paper explores the ‘weight of evidence’ approach for sediment calibration as part of overall 

watershed model calibration based on recent experience with the U. S. EPA Hydrological 

Simulation Program - FORTRAN (HSPF) (Bicknell et al., 2001). Model parameterization and 

calibration procedures are described, using example applications and sample model results, to 

demonstrate recommended graphical and statistical procedures used to assess model performance 

for sediment loadings, concentrations, and budgets within a watershed modeling framework. 

Although the results are specific to the EPA HSPF model, the approach and procedures for 

sediment calibration are applicable to other watershed models that represent sediment processes 

and behavior at the watershed scale. 

SEDIMENT CALIBRATION OVERVIEW 

Sediment calibration follows the hydrologic calibration and must precede water quality 

calibration. Calibration of the parameters involved in simulation of watershed sediment erosion 

is more uncertain than hydrologic calibration due to less experience with sediment simulation in 

different regions of the country. The process is analogous; the major sediment parameters are 

modified to increase agreement between simulated and recorded monthly sediment loss and 

storm event sediment removal. However, observed monthly sediment loss is often not available, 

and the sediment calibration parameters are not as distinctly separated between those that affect 

monthly sediment and those that control storm sediment loss. In fact, annual sediment losses are 

often the result of only a few major storms during the year. 
26

Sediment calibration for watershed models involves numerous steps in estimating model 

parameters then determining appropriate adjustments needed to ensure a reasonable simulation 

of the sediment sources, delivery, and transport behavior within the channel system. These steps 

usually include: 

1. Estimating target (or expected) sediment loading rates from the landscape, often as a 

function of topography, land use, and management practices 

2. Calibrating the model loading rates to the target rates 

3. Adjusting scour, deposition and transport parameters for the stream channel to mimic 

expected behavior of the streams/waterbodies 

4. Analyzing sediment bed behavior (i.e. bed depths) and transport in each channel 

reach as compared to field observations 

5. Analyzing overall sediment budgets for the land and stream contributions, along with 

stream aggrading and degrading behavior throughout the stream network 

6. Comparing simulated and observed sediment concentrations, including particle size 

distribution information, and load information where available 

7. Repeating steps 1 through 6 as needed to develop a reasonable overall representation 

of sediment sources, delivery, and transport throughout the watershed system 

Rarely is there sufficient observed local data at sufficient spatial detail to accurately calibrate all 

parameters for all land uses and each stream and waterbody reach. In fact, for sediment 

modeling, users are often limited to observed data for monthly or storm periods at only selected 

sites within the watershed. Consequently, model users focus the calibration on those sites with 

observed data, and then must review simulations in all parts of the watershed to ensure that the 

model results are consistent with field observations, historical reports, and expected behavior 

from past experience. This is especially critical for sediment modeling due to the extreme 

dynamic behavior of sediment erosion and transport processes. 

Below we journey through each of the above steps to provide model users with general guidance 

and recommendations for modeling watershed-scale sediment processes in a logical and 

reasonable fashion. Although the specific parameter definitions and discussions and sample 

model results are based on the HSPF model, the overall procedures should prove useful to users 

of other watershed scale sediment model codes. 

SEDIMENT EROSION CALIBRATION 

Sediment loadings to the stream channel are estimated by land use category from literature data, 

local Extension Service sources, or procedures like the Universal Soil Loss Equation (USLE) 

(Wischmeier and Smith, 1978) and then adjusted for delivery to the stream with estimated 

sediment delivery ratios (SDRs). This delivery adjustment is needed because HSPF, like most 

watershed-scale (lumped parameter) models, represents landscape loadings to the stream 

channel, which are less than the field-scale estimates from USLE. These estimated loading rates 

then become ‘calibration targets’ for the watershed model. 

Model parameters are then adjusted so that model-calculated loadings are consistent with these 

estimated ‘calibration targets’ and loading ranges. The model-calculated loadings are further 
27

evaluated in conjunction with the instream sediment transport calibration (discussed below) that 

extend to a point in the watershed where sediment concentration and/or load data are available. 

The objective is to represent the overall sediment behavior of the watershed, with knowledge of 

the morphological characteristics of the stream (i.e. aggrading or degrading behavior), using 

sediment loading rates that are consistent with the calibration targets and modeled 

concentrations that provide a reasonable match with instream sediment data. 

Step 1: Estimating Sediment Loadings from the Landscape 

Sediment concentrations measured at a particular gage reflect the combined affects of nonpoint 

source contributions from multiple land uses, any point sources upstream from the gage, and 

instream processes (e.g., deposition, scour, bank erosion). Consequently, the calibration target 

sediment loading rates need to be developed to help guide the calibration effort and ensure that 

the simulated erosional rates from each land use category are reasonable, and thereby provide a 

sound basis for calibrating the instream processes. 

Erosion is primarily a function of the amount of soil exposed directly to rainfall and surface 

runoff, which in turn is affected by rainfall, land cover, land slope, soil disturbance, and transport 

properties of the soil. The USLE is an empirical equation commonly used to estimate erosional 

rates as a function of these factors. The USLE formula is expressed as follows: 

 A = R * K * L * S * C * P 

A = annual soil loss in tons per acre per year 

R = rainfall erosivity factor 

K = soil erodibility factor 

L = slope length factor 

S = slope gradient factor 

C = cover management factor 

P = erosion control practice factor

The R factor is typically obtained from a national or regional isoerodent map, readily available 

in many soil engineering handbooks (e.g. Renard et al, 1997), and accounts for the amount and 

intensity of rainfall and runoff typical of a region. 

The K factor is a measure of the susceptibility of soil particles to detachment and transport by 

rainfall and runoff. Texture is the principal factor affecting K, but structure, organic matter and 

permeability also contribute. Determining K values can be performed either from handbook 

tables, or acquisition of accurate geo-spatial soils data from the U.S. Department of Agriculture 

(USDA), Natural Resource Conservation Service (NRCS) if a GIS approach is employed. The 

most common forms of these data are the STATSGO and SSURGO databases and/or GIS 

coverages. 

The L factor is very closely associated with the S factor, where S is the slope gradient factor and 

the L is the length of that slope. The USLE was created to predict soil erosion delivered to the 

base of a 22-meter agricultural plot with a uniform slope of 9 percent. The S and L factors are 

typically combined, defined as the topographic factor LS, to account for site specific conditions 
28

relative to the standard plot. 

The C factor is the crop/vegetation and management factor. It is used to determine the relative 

effectiveness of soil and crop management system or vegetation in terms of preventing soil loss. 

The C factor is a ratio comparing the soil loss from land under a specific crop and management 

system to the corresponding loss from continuously fallow and tilled land. 

The P factor is the support practice factor and reflects the effects of practices that will reduce the 

amount and rate of the water runoff and thus reduce the amount of erosion. The factor represents 

the ratio of soil loss by a support practice to that of straight-row farming up and down the slope. 

The most commonly used supporting cropland practices are cross slope cultivation, contour 

farming, and strip cropping. In cases where the conservation practice factor is not relevant, it is 

set as 1.0 for all areas, which does not negatively or positively influence the output of the model. 

As the USLE was developed at a field scale, depositional processes that occur in overland flow 

prior to reaching a stream channel are not included. Therefore, it is necessary to reduce the gross 

erosion by a fraction. This fraction or portion of sediment that is available for delivery is 

referred to as the Sediment Delivery Ratio (SDR). This ratio is then multiplied by the predicted 

or gross erosion rate to estimate the percent of eroded material to reach a specific point or 

location (e.g., outlet, waterbody, channel). There is no generally accepted procedure to estimate 

the SDR, which is affected by numerous factors including sediment source, texture, nearness to 

the stream, channel density, basin area, slope, land use/cover, and rainfall-runoff factors; 

however, several empirical formulas exist (e.g. see Greenfield et al., 2002). 

Under EPA funding, AQUA TERRA Consultants developed a Visual Basic spreadsheet tool 

named TMDL USLE (Hummel et al., 2000) for use in sediment associated TMDL studies 

(http://www.epa.gov/ceampubl/swater/usle). This spreadsheet is useful for estimating the 

expected relative magnitude of land surface sediment loadings (tons per year) from different land 

use types within a watershed, and thereby can be used to develop sediment calibration targets for 

watershed models. Maps, recommended value tables for USLE factors, and other information 

useful in deriving appropriate values for the USLE and delivery ratios are provided, to the extent 

that it is practical, throughout the U.S. The tool includes an on-line tutorial and active links to 

Internet web sites containing supplemental information that can assist users in evaluating USLE 

factors. 

Figure 1 shows the computation screen for the TMDL USLE program, with highlighted 

comment ‘bubbles’ identifying the source of values and information in each cell. 

As an alternative to a spreadsheet based approach, using a GIS platform allows the USLE to be 

applied on a cell-by-cell basis, using watershed specific information, for a more spatially 

accurate use of the equation and model land use specific estimates of erosional rates. The USLE 

can be applied in a grid-based GIS environment where map algebra can be performed with the 

GIS layer values. 
29

Figure 1 - TMDL USLE tool 

 

Typically, in a GIS environment the S and L factors are combined together and defined as the 

topographic factor (LS) with the slope length normalized to a standard plot length of 22 meters 

and the slope normalized to 9 percent. Numerous empirical formulas exist such as the one 

below. 

LS = [0.065 + 0.0456(slope) + 0.006541(slope)2 ] * [resolution / normlength] n

 slope = slope steepness (%) 

 resolution = cell resolution (meters or feet) 

 normlength = 72.5 ft or 22.1 meters 

 n = function of slope (see table below) 

 

slope < 1 1 < slope < 3 3 < slope < 5 > 5 

n 0.2 0.3 0.4 0.5 

The K factor can be obtained from a descriptor or attribute, referred to as ‘kffact’ within the 

STATSGO or SSURGO databases. The attribute ‘kffact’ defines the soil erodibility factor that is 

fragment free for use in the USLE. 

 

Within a GIS, the SDR can be calculated in a manner to try and account for all the 

aforementioned controlling factors, or in a simplified manner based on the drainage area of the 

channel segment as defined by the model setup. This simple approach is referred to as a 

watershed area-based method. The equation below was converted from a curve presented in the 

National Engineering Handbook produced by the Soil Conservation Service in 1983 (USDANRCS, 1983). 

 

SDR = 0.417762 * A -0.134958 – 0.127097 

A = drainage area (sq. miles) 
30

Numerous additional empirical formulas exist, including formulas and tables provided within the 

TMDL USLE tool. Ultimately, once the gross erosional rates are adjusted by the SDR, it is 

possible to use the GIS to summarize the range of erosional rates on a model land use specific 

basis. Figure 2 graphically depicts the process of taking the input data (i.e., DEM, soils, land 

use/cover), calculating USLE factors, and developing estimates of the erosional rates. 

Figure 2 – GIS Framework for USLE 

Calibration targets developed from the TMDL USLE spreadsheet, a GIS-based approach, or 

other procedures should be used only to define approximate ranges of loading rates to help guide 

the calibration for the watershed or region of concern. There will often be extreme variation in 

the calculated rates from year to year, and site to site, and users should only expect that the 

model rates will be consistent with the targets, not necessarily equal to them. Table 1 shows 

typical ranges of sediment loading rates for various land categories. 

Table 1 - Typical Ranges of Expected Erosion Rates 

Tons/ac Tonnes/ha 

Forest 0.05 - 0.4 0.1 - 0.9 

Pasture 0.3 - 1.5 0.7 - 3.4 

Conventional 1.0 - 7.0 2.2 - 15.7 (crop dependent) 

 Tillage

Conservation 0.5 - 4.0 1.1 - 9.0 (crop dependent) 

 Tillage

Hay 0.3 - 1.8 0.7 - 4.0 

Urban 0.2 - 1.0 0.4 - 2.2 

Highly Erodible > ~ 15.0 > ~ 33.6 

 Land 
31

Step 2: Sediment Erosion Calibration 

Each of the calibration steps identified in the overall procedures involve first a parameterization 

component followed by the actual calibration, or parameter adjustment, component to improve 

agreement between model values and various field observations. Clearly, the specific parameters 

to adjust for soil erosion calibration will depend on the specific model being used. In HSPF, the 

erosion process on pervious land areas is represented as the net result of detachment of soil 

particles by raindrop impact on the land surface, and then subsequent transport of these fine 

particles by overland flow. On impervious surfaces (e.g. parking lots, driveways), soil splash 

by raindrop impact is neglected and solids washoff is often controlled by the rate of 

accumulation of solid materials. The primary sediment erosion solids parameters are as follows: 

 KRER - Coefficient in soil detachment equation (pervious areas) 

 KSER - Coefficient in sediment washoff equation (pervious areas) 

 KEIM - Coefficient in impervious area solids washoff equation 

 ACCSDP - Accumulation rate of solids on impervious surfaces 

Although a number of additional parameters are involved in sediment erosion and solids 

calibration, such as those related to vegetal cover, agricultural practices, rainfall and overland 

flow intensity, etc., KRER and KSER are the primary ones controlling sediment loading rates. 

KRER is usually estimated as equal to the erodibility factor, K, in the USLE (noted above), and 

then adjusted in calibration, while KSER is primarily evaluated through calibration and past 

experience. For impervious surfaces, the rate of washoff is controlled by the KEIM parameter, 

but the net washoff is most often limited by the accumulation rate, ACCSDP. Table 2 lists the 

sediment and solids washoff parameters in HSPF, along with typical and possible minimum and 

maximum ranges based on application experience over the past 20 years. In addition, the 

HSPFParm database (U. S. EPA, 1999) provides calibrated parameter values for numerous 

watersheds across the US. 

In general, sediment calibration involves the development of an approximate equilibrium or 

balance between the accumulation and generation of sediment particles on one hand and the 

washoff or transport of sediment on the other hand. Thus, the accumulated sediment on the land 

surface should not be continually increasing or decreasing throughout the calibration period. 

Extended dry periods will produce increases in surface sediment accumulations, and extended 

wet periods will produce decreases. However, the overall trend should be relatively stable from 

year to year. This equilibrium must be developed on both pervious and impervious surfaces, and 

must exist in conjunction with the accurate simulation of monthly and storm event sediment loss, 

depending on the data available for calibration. Additional guidance in sediment erosion 

calibration is provided in the HSPF Application Guide (Donigian et al., 1984). 
32

Table 2 - Value Ranges for HSPF Sediment Erosion and Solids Washoff Parameters 

 RANGE OF VALUES 

 NAME DEFINITION UNITS TYPICAL POSSIBLE FUNCTION OF ... COMMENT 

 MIN MAX MIN MAX 

SED - PARM2

SMPF Management Practice (P) 

factor from USLE 

none 0.0 1.0 0.0 1.0 Land use, Ag 

practices 

Use P factor from USLE 

KRER Coefficient in the soil 

detachment equation 

complex 0.15 0.45 0.05 0.75 Soils Estimate from soil erodibility 

factor (K) in USLE 

JRER Exponent in the soil 

detachment equation 

none 1.5 2.5 1.0 3.0 Soils, climate Usually start with value of 2.0 

AFFIX Daily reduction in detached 

sediment 

per day 0.03 0.10 0.01 0.50 Soils, compaction, ag 

operations 

Reduces fine sediments following 

tillage 

COVER Fraction land surface 

protected from rainfall 

none 0.0 0.90 0.0 0.98 Vegetal cover, land 

use 

Seasonal/monthly values often 

used 

NVSI Atmospheric additions to 

sediment storage 

lb/ac-dy 0.0 5.0 0.0 20.0 Deposition, activities, 

etc. 

Can be positive or negative 

SED - PARM3

KSER Coefficient in the sediment 

washoff equation 

complex 0.5 5.0 0.1 10.0 Soils, surface 

conditions 

Primary sediment Calibration

parameter 

JSER Exponent in the sediment 

washoff equation 

none 1.5 2.5 1.0 3.0 Soils, surface 

conditions 

Usually use value of about 2.0 

KGER Coefficient in soil matrix 

scour equation 

complex 0.0 0.5 0.0 10.0 Soils, evidence of 

gullies 

Calibration, only used if there is 

evidence of gullies 

JGER Exponent in soil matrix scour 

equation 

none 1.0 3.0 1.0 5.0 Soils, evidence of 

gullies 

Usually use value of about 2.5 

SLD – PARM2

KEIM Coefficient in the solids 

washoff equation 

complex 0.5 5.0 0.1 10.0 Surface conditions, 

solids charac. 

Primary solids Calibration

parameter 

JEIM Exponent in the solids 

washoff equation 

none 1.0 2.0 1.0 3.0 Surface conditions, 

solids charac. 

Usually use value of about 1.8 

ACCSDP Solids accumulation rate on 

the land surface 

lb/ac-dy 0.0 2.0 0.0 30.0 Land use, traffic, 

human activities 

Calibration, primary source of 

solids from impervious areas 

REMSDP Fraction of solids removed 

per day 

per day 0.03 0.2 0.01 1.0 Street sweeping, 

wind, traffic 

Usually start with value of about 

0.05, and calibrate 

 
33

INSTREAM SEDIMENT TRANSPORT CALIBRATION 

Parameterization of Instream Sediment Transport Processes 

Once the sediment loading rates are calibrated to provide the expected input to the stream 

channel, the sediment calibration then focuses on the channel processes of deposition, scour, and 

transport that determine both the total sediment load and the outflow sediment concentrations to 

be compared with observations. In practice, instream calibration involves steps 3, 4 and 5 as 

listed and discussed above; these steps involve both initial parameterization, to establish initial 

parameter values, and a subsequent adjustment process. For HSPF, the initial parameterization 

includes the following: 

• Divide input sediment loads into appropriate size fractions 

• Estimate initial parameter values and storages for all reaches 

• Run HSPF to calculate shear stress in each reach to estimate critical scour and 

deposition values 

Since the sediment load from the land surface is calculated in HSPF as a total input, it must be 

divided into sand, silt, and clay fractions for simulation of instream processes. Each sediment 

size fraction is simulated separately, and storages of each size are maintained for both the water 

column (i.e. suspended sediment) and the bed. 

Fractionating the Eroded Material 

The eroded material is fractionated into sand, silt, and clay prior to entering a model reach using 

available soils information; typically, a single fractionation scheme is used for all reaches. 

However, if the resolution of the data and spatial diversity of the soils warrant alternate schemes, 

it is possible for each reach to use separate fractions. The fractions should reflect the relative 

percent of the surface material (i.e., sand, silt, clay) available for erosion in the surrounding 

watershed, but also should include an enrichment factor of silt and clay to represent the 

likelihood of these finer materials reaching the channel. Thus, the sand particles are more likely 

to be deposited in the overland flow plane, in swales, ditches, depressions, etc. and therefore the 

sand would be somewhat transport limited, compared to the silt and clay. For example, if 

surface soils demonstrate a 40/50/10 distribution for sand/silt/clay, the fractionation for input to 

the reach might be 15/55/30. Investigation of the bed material composition will also help to 

provide insight into appropriate fractionation values. 

Estimate Initial Parameter Values And Storages For All Reaches 

For HSPF, initial sediment parameters, such as particle diameter, particle density, settling 

velocity, bed depth and composition, and beginning calibration parameter values can be 

evaluated from local/regional data, past experience, handbook values, etc., and then adjusted 

based on available site specific data and calibration. Bed composition data are especially 

important so that the model results can be adjusted to reflect localized aggredation (deposition) 

or degradation (scour) conditions within the stream system. 

In HSPF, the value of bed depth represents the amount of material (calculated from input values 

for bed width and porosity) that can be scoured from the stream reach; in effect it provides a 
34

limit so that the model will inform the user, through a warning message, when the channel has 

been completely scoured so that the user can make appropriate parameter changes if needed. We 

often set initial bed depths to 2.0 to 5.0 feet for the natural (i.e. non-channelized) stream 

segments, and 0.5 to .05 feet for the channelized segments to allow a reasonable amount of 

scour in the upstream natural channel and limit the scour to scattered localized deposits in 

channelized sections. 

Setting Initial Critical Scour and Depositional Shear Stresses 

In HSPF, the transport of the sand (non-cohesive) fraction is commonly calculated as a power 

function of the average velocity in the channel reach in each timestep. This transport capacity is 

compared to the available inflow and storage of sand particles; the bed is scoured if there is 

excess capacity to be satisfied, and sand is deposited if the transport capacity is less than the 

available sand in suspension within the channel reach. For the silt and clay (cohesive) 

fractions, shear stress calculations are performed by the hydraulics (HYDR) module and are 

compared to user-defined critical, or threshold, values for deposition and scour for each size. 

When the shear stress for a timestep is greater than the critical value for scour, the bed is scoured 

at a user-defined erodibility rate; when the shear stress is less than the critical deposition value, 

the silt or clay fraction deposits at a settling rate input by the user for each size. If the calculated 

shear stress falls between the critical scour and deposition values, the suspended material is 

transported through the reach. After all scour and/or deposition fluxes have been determined, the 

bed and water column storages are updated and outflow concentrations and fluxes are calculated 

for each timestep. These simulations are performed by the SEDTRN module in HSPF, complete 

details of which are provided in the HSPF User Manual (Bicknell et al., 2001). 

In HSPF, if the model reach being simulated is a stream or river, the bed shear stress is 

determined as a function of the slope and hydraulic radius of the reach, as follows: 

 TAU = SLOPE*GAM*HRAD 

where: 

 TAU = stream bed shear stress (lb/ft2 or kg/m2) 

 SLOPE = slope of the RCHRES (-) 

 GAM = unit weight, or density, of water (62.4 lb/ft3 or 1000 kg/m3) 

 HRAD = hydraulic radius (ft or m) 

The hydraulic radius is calculated as a function of average water depth (AVDEP) and mean top 

width (TWID): 

 HRAD = (AVDEP*TWID)/(2.*AVDEP + TWID) 

Average depth is computed as: AVDEP = VOL/SAREA 

 

The mean top width is found using: TWID = SAREA/LEN 

where: 

 LEN = length of the RCHRES, supplied by the user 
35

If the stream reach is a lake, alternative methods are used for the shear calculations (see Users 

Manual for details). 

In HSPF, the hydraulic characteristics of a stream reach are represented by a function table 

(FTABLE) that includes the relationships between stage, storage (volume), surface area, and 

discharge. From the equations shown above, it is clear that the accuracy of the FTABLE for a 

specific reach will be a critical factor in adequately representing the hydraulic radius and 

subsequent shear values, as a function of the stage, or depth of flow. This is especially evident 

for simulations of flood flows that exceed bankfull discharges; improper extension of the 

FTABLES can lead to erroneous shear and scour conditions during high flow events, and have 

major impacts on the model simulations for those events. 

As part of the sediment parameterization, the model is run with the initial parameter estimates 

and shear stress values are output for each stream reach. For the silt and clay size particles, the 

critical shear stress parameters (one for scour and one for deposition) for each size are adjusted 

so that the model calculates scour during high flow events, deposition and settling during low 

flow periods, and transport with neither scour nor settling for moderate flow rates; this is shown 

schematically in Figure 3. In general, the values are set so that scour of clay occurs at lower 

shear values than for silt (i.e. clay scours before silt), and deposition of silt occurs at higher shear 

values than clay (i.e. silt deposits before clay). 

Figure 3 – Shear Stress Calculations in HSPF

Figure 4 shows an example of setting the critical shear values based on both flow and shear 

stress simulations for a small eastern US watershed; the top plot show the flow simulation 

corresponding to the shear simulations on the bottom curve. We’ve also included, on the righthand scale of the shear curve, the percent exceedance values to help quantify how often scour 

and deposition conditions occur at different shear values. 

TIME (hr) 

Scour Threshold

Deposition Threshold 

SCOUR

DEPOSIT

TRANSPORT 

BED SHEAR STRESS (lb/ft2) 
36

Figure 4 - Example calculations for setting critical shear values for HSPF

Step 3: Adjust Instream Scour, Deposition, and Transport parameters 

Step 4: Analyze Bed Behavior and Transport Fluxes 

Step 5: Analyze Overall Sediment Budgets and Stream Behavior 

These 3 steps are listed together as they normally are performed while reviewing the same 

tabulations of model results; bed behavior and sediment budgets need to be reviewed to establish 

the basis for parameter adjustments on a reach-by-reach basis. Table 3 shows an example 

tabulation of reach-by-reach model results for a small eastern US watershed. The watershed 

includes 2 tributary reaches and 6 mainstem reaches, and the tabulations include sediment 

erosion (nonpoint) loads, point loads, upstream and total inflow loads, total outflow loads, and 

both cumulative and reach trapping efficiencies. For example, note that the tributaries 

demonstrate net scour behavior (deposition/scour column values are negative), while the 

mainstem is depositional throughout. This information can be compared with historical accounts 

or field observations to identify the ‘expected’ behavior for those stream segments. If this 

information is contrary to the model representation, i.e., the model simulates deposition when the 

reach is primarily being scoured, reach parameters and/or inflows need to be adjusted to correct 
37

the simulated behavior. 

Table 3 – Example Tabulation of Stream Sediment Fluxes and Behavior 

 

Nonpoint 

Point 

Source 

Upstream 

In 

Total 

Inflow 

 

Outflow 

 Deposit (+) 

Scour (-) 

Cumulative 

Point/NonPt 

Cumulative 

Trapping 

Efficiency 

Reach 

Trapping 

Efficiency 

Reach 

Segment (tons) 

 

(tons) (tons) (tons) (tons) (tons) (tons) (%) (%) 

Mainstem 1 212.5 107.4 6,453.7 6,785.3 6,186.3 599.7 10,566.9 41.5 8.8 

Mainstem 2 68.8 0.0 6,186.3 6,255.0 5,384.8 870.6 10,635.7 49.4 13.9 

Tributary 1 102.4 0.0 0.0 102.2 125.0 -22.7 102.2 -22.0 -22.0 

Mainstem 3 5.8 0.0 5,509.8 5,515.6 4,916.3 599.9 10,744.0 54.2 10.9 

Tributary 2 281.1 0.0 0.0 280.5 352.6 -72.1 280.5 -25.5 -25.5 

Mainstem 4 215.4 0.0 5,268.9 5,483.9 4,269.8 1,215.1 11,240.4 62.0 22.1 

Mainstem 5 54.1 0.0 4,269.8 4,323.8 3,507.1 826.2 11,294.5 68.9 18.9 

Mainstem 6 93.9 0.0 3,507.1 3,600.8 2,190.8 1,421.3 11,388.4 80.8 39.2 

Table 4 shows the corresponding detailed behavior of bed depth and sediment fractions in 

selected reaches within the watershed (Tributary 1 and Mainstem reaches 2 and 3). The 

tabulations in Table 4 include, annual inflow loads, outflow loads, and deposition/scour in the 

reach, along with the composition of these loads and the bed behavior and composition 

throughout the simulation period. Field observations of bed depth changes, expected deposition 

rates, bed sediment composition fractions, etc. can be used to assess the validity of the model 

results and identify needed changes. 

These results demonstrate the types of analyses performed as part of the sediment calibration 

effort. In this example, sand comprises a small fraction of the total sediment concentration and 

discharge, and thus the sand parameters are set to values to reflect this small contribution. In 

other watersheds, the non-cohesive (sand) fractions may be more critical and thus require greater 

focus and calibration effort. 

The primary focus of this example calibration is the silt and clay parameters. As noted above, 

the shear stress values are adjusted so that scour occurs during storm periods and deposition 

occurs at low flows. Once the timing of scour and deposition processes is correct, the rate of 

scour (i.e. erodibility factor in the model) is adjusted in an attempt to match either the expected 

behavior within each reach, from review of the type of information shown in Tables 3 and 4, 

and/or the observed concentrations (discussed below in the next step). During high flow periods, 

the amount of scour is adjusted with an erodibility factor for each reach that controls the rate of 

scour whenever the actual shear stress is greater than the critical shear stress value for scour. 

The need to analyze the model simulations on a reach-by-reach basis is mandated by the extreme 

variability in sediment processes. If upstream reaches are depositing more than expected, then 

inflows to downstream reaches will be less than what really occurs, requiring parameter 

adjustments that may not be reasonable for the downstream reaches. The opposite would occur 

if upstream reaches are eroding much more than expected; inflows to downstream reaches will 

be too large, resulting in more deposition than would be expected. If the reach parameters are set 

so that deposition does not occur, then the upstream eroded load will be transported and will 

subsequently impact other downstream reaches. Thus, an upstream to downstream analysis, on a 

reach-by-reach basis, is required to adequately assess model simulations. 
38

Table 4 - Example Bed and Stream Reach Sediment Simulations 

Mainstem 2 Final Average Arithmetic 

 Init Val 1995 1996 1997 1998 1999 2000 Fraction Fraction Average 

Bed Depth (ft) 2.00 2.10 2.10 2.10 2.20 2.20 2.30 2.20 

Bed Storage (tons) 

 Sand 0.79 34,684.7 34,775.7 34,795.8 34,805.4 34,828.3 34,840.5 0.70 0.73 34,788.4 

 Silt 0.15 7,623.7 8,780.0 9,310.2 9,867.1 10,581.5 11,573.3 0.23 0.20 9,622.6 

 Clay 0.07 3,112.9 3,165.8 3,181.9 3,201.7 3,222.9 3,255.5 0.07 0.07 3,190.1 

 Total 45,421.3 46,721.6 47,287.9 47,874.2 48,632.8 49,669.2 47,601.2 

Inflow (tons) 

 Sand 152.6 273.5 115.0 133.0 130.6 219.8 0.03 0.03 170.7 

 Silt 4,705.1 5,024.8 2,503.7 2,571.7 3,357.7 4,437.0 0.61 0.60 3,766.7 

 Clay 3,152.5 3,270.2 1,354.6 1,528.3 1,939.2 2,638.8 0.36 0.37 2,313.9 

 Total 8,010.2 8,568.5 3,973.3 4,232.9 5,427.4 7,295.6 6,251.3 

Dep(+)/Scour(-) (tons) 

 Sand 4.7 -0.4 22.9 12.1 25.6 14.9 13.3 

 Silt 1,038.4 1,135.4 530.0 556.7 714.9 991.9 827.9 

 Clay 39.8 44.4 16.0 19.8 21.3 32.5 29.0 

 Total 1,082.8 1,179.4 568.9 588.5 761.8 1,039.4 870.1 

Outflow (tons) 

 Sand 148.0 273.8 92.2 120.9 104.9 204.9 0.03 0.03 157.5 

 Silt 3,668.1 3,889.2 1,974.0 2,015.0 2,642.8 3,445.1 0.55 0.55 2,939.0 

 Clay 3,113.9 3,225.5 1,339.0 1,508.6 1,917.8 2,606.2 0.42 0.43 2,285.2 

 Total 6,929.9 7,388.5 3,405.2 3,644.4 4,665.6 6,256.2 5,381.6 

Tributary 1 Final Average Arithmetic 

 Init Val 1995 1996 1997 1998 1999 2000 Fraction Fraction Average 

Bed Depth (ft) 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 

Bed Storage (tons) 

 Sand 0.9 28,369.1 28,440.4 28,434.4 28,415.9 28,407.7 28,417.6 0.90 0.90 28,414.2 

 Silt 0.05 1,569.4 1,563.8 1,561.0 1,559.1 1,554.2 1,544.3 0.05 0.05 1,558.6 

 Clay 0.05 1,563.0 1,549.4 1,544.4 1,538.6 1,529.9 1,513.4 0.05 0.05 1,539.8 

 Total 31,501.5 31,553.6 31,539.8 31,513.6 31,491.9 31,475.4 31,512.6 

Inflow (tons) 

 Sand 32.1 49.3 24.3 14.9 29.2 64.6 0.35 0.35 35.8 

 Silt 45.9 70.5 34.8 21.4 41.8 92.3 0.50 0.50 51.1 

 Clay 13.8 21.2 10.4 6.4 12.5 27.7 0.15 0.15 15.3 

 Total 91.8 141.0 69.5 42.7 83.6 184.6 102.2 

Dep(+)/Scour(-) (tons) 

 Sand -0.7 -6.8 -7.3 -19.3 -8.7 9.4 -5.6 

 Silt -6.6 -10.0 -2.8 -2.0 -4.8 -9.9 -6.0 

 Clay -13.1 -17.8 -5.0 -5.7 -8.7 -16.5 -11.2 

 Total -20.4 -34.6 -15.1 -27.0 -22.2 -17.0 -22.7 

Outflow (tons) 

 Sand 32.8 56.1 31.6 34.3 37.9 55.2 0.27 0.33 41.3 

 Silt 52.6 80.5 37.5 23.3 46.6 102.2 0.51 0.46 57.1 

 Clay 27.0 39.0 15.5 12.2 21.2 44.2 0.22 0.21 26.5 

 Total 112.4 175.6 84.6 69.7 105.7 201.6 124.9 

Mainstem 3 Final Average Arithmetic 

 Init Val 1995 1996 1997 1998 1999 2000 Fraction Fraction Average 

Bed Depth (ft) 2.00 2.10 2.10 2.10 2.20 2.20 2.20 2.10 

Bed Storage (tons) 

 Sand 0.79 27,713.8 28,089.4 28,204.8 28,346.9 28,480.0 28,720.2 0.74 0.75 28,259.2 

 Silt 0.15 5,710.9 6,354.4 6,552.4 6,789.1 7,078.2 7,578.0 0.19 0.18 6,677.2 

 Clay 0.07 2,481.1 2,537.1 2,547.4 2,564.5 2,582.8 2,617.0 0.07 0.07 2,555.0 

 Total 35,905.8 36,981.0 37,304.6 37,700.6 38,141.1 38,915.2 37,491.4 

Inflow (tons) 

 Sand 182.5 332.6 125.4 156.1 144.5 263.5 0.04 0.04 200.8 

 Silt 3,723.3 3,973.5 2,013.7 2,039.7 2,691.8 3,552.2 0.55 0.54 2,999.0 

 Clay 3,141.7 3,265.6 1,355.1 1,521.2 1,939.8 2,651.9 0.41 0.42 2,312.6 

 Total 7,047.5 7,571.7 3,494.2 3,716.9 4,776.1 6,467.7 5,512.3 

Dep(+)/Scour(-) (tons) 

 Sand 164.8 300.0 116.0 142.4 133.3 240.1 182.8 

 Silt 480.4 627.7 198.1 236.6 289.1 499.6 388.6 

 Clay 40.0 49.2 10.2 17.3 18.3 34.2 28.2 

 Total 685.2 976.9 324.4 396.3 440.7 773.9 599.6 

Outflow (tons) 

 Sand 17.7 32.6 9.3 13.7 11.2 23.4 0.00 0.00 18.0 

 Silt 3,244.6 3,345.3 1,816.1 1,803.0 2,402.7 3,052.6 0.54 0.53 2,610.7 

 Clay 3,103.1 3,215.9 1,345.6 1,503.9 1,921.4 2,617.7 0.46 0.47 2,284.6 

 Total 6,365.4 6,593.8 3,171.1 3,320.6 4,335.3 5,693.8 4,913.3 
39

Step 6: Compare Results with Available Data 

The remaining step in the calibration procedure is to compare model simulations of 

concentrations and loads to available observed data. In many cases, this may be limited to event 

mean concentrations of total suspended solids (TSS) for selected storm events and nonstorm 

(baseflow) periods, or pollutographs of TSS concentrations throughout a few events. However, 

other types of comparisons are also possible, such as load estimates and sediment rating curves; 

each of these is discussed below. 

Figure 5 shows an example of the conventional storm event comparison for TSS for the same 

small eastern US watershed. Clearly, such comparisons need to be made for as many storm 

events as there are available data. Figure 5 shows a very good simulation for both flow and TSS 

concentrations; most simulations will not be this good, and will show large variations from storm 

to storm. Even with this storm, there are inconsistencies demonstrated; the simulated flow peak 

is higher than the observed and precedes it, identifying a possible time lag in the peak that is not 

well represented in the model. In addition, the flow peak is about 25% higher than observed, 

whereas the TSS peak is only about 10% higher. Both of these differences may be entirely 

acceptable, considering the uncertainty in the observations and needed accuracy of the model, 

but model results need to be viewed with a critical eye toward demanding consistent behavior, 

i.e. if flows are over-simulated, TSS should be over-simulated, and vise-versa.

 

Figure 5 - Example comparison of simulated and observed storm flow and TSS 

concentrations 
40

Figure 6 shows an annual plot of simulated and estimated sediment loads for the watershed 

outlet. In this case the ‘Load Estimates’ were extrapolations from available TSS data, and were 

not the results of continuous, or even daily sampling. So this comparison is really between two 

models: a simulation model (HSPF) and a regression model. However, even this type of 

comparison is useful, recognizing that differences do not necessarily detract from the validity or 

utility of the simulation model. This is simply one additional type of comparison that can be 

included in the weight-of-evidence approach to sediment calibration. The average annual values 

in Figure 6 indicate a very good simulation even though there are significant differences year to 

year. For sediment modeling these types of differences are to be expected, since, as noted 

earlier, sediment is one of the most difficult water quality constituents to model accurately. 

Figure 6 – Example comparison of simulated and estimated annual TSS loads

Figure 7 shows an example of comparing sediment rating curves, simulated and observed, for a 

single site at the watershed outlet. These curves essentially demonstrate the relationship between 

flow rate and sediment concentrations, and the concept in this comparison is to evaluate whether 

the model and the data demonstrate similar relationships. The top curve shows the flow versus 

load relationship, corresponding to the bottom curve of sediment concentration versus flow. 

Regression lines have been fitted to both the data and model results, and are shown on the 

curves. The log scale is used, as is typical for, and usually required for sediment rating analyses. 
41

Figure 7 – Example of observed and simulated sediment rating curves 

It should be noted that the regression curves are fitted to a subset of the observed and simulated 

values; the solid dots were excluded from the analyses, corresponding to TSS concentration 

values less than 1.0 mg/l. The rationale for this was as follows: 

• The observations were performed primarily at moderate to high flow conditions, 

so very low concentrations would not be well represented. 

• The HSPF model employs a relatively gross channel representation, with long 

reach lengths, that tends to eliminate localized turbulence and scour conditions 

that would likely contribute to under-simulating the low concentrations. 

• The extremely low concentrations contribute to a small fraction of the total annual 

sediment load, approximately less than 3 to 5 percent of the annual load in this 

watershed. 

The overall results in Figure 7 show a relatively good simulation of the flow-sediment 

relationship demonstrated in the sediment rating curves. Both the range of concentration and 

flow values, and the slopes of the regression lines, demonstrate consistency between the model 

and the observed data. If large consistent differences existed, it would justify continued 
42

calibration efforts to minimize such differences. 

Step 7: Repeat Calibration Steps, as Needed, To Improve Sediment Representation 

Modeling tends to be a circular, or iterative, process. For sediment calibration, Steps 3 through 6 

often need to be repeated until all the components of the calibration exercise are in reasonable 

balance. In some cases, the process may need to reconsider the target loading rates developed in 

Step 1, and then re-calibrate the model rates. This might occur if the surface loadings appear to 

dominate unrealistically the overall watershed simulation results. 

The iteration process doesn’t require that every comparison be brought to the same level of 

agreement, but only that the entire process be repeated until the entire ‘weight-of-evidence’ from 

the simulations indicates either that the model is ‘as good as it can be’ or that it can not meet the 

specific needs of the watershed assessment. This should produce sufficient evidence that the 

model is either acceptable for the intended purpose, a recommendation that the model or data 

input improvements may be needed, or that a different model should be considered. 

CLOSURE 

This paper explores the ‘weight of evidence’ approach for sediment calibration as part of overall 

watershed model calibration based on recent experience with the U. S. EPA HSPF. The steps in 

the overall sediment calibration process are identified and discussed, along with specific issues 

related to model parameterization and calibration procedures. Using example applications and 

sample model results, we demonstrate recommended graphical and statistical procedures used to 

assess model performance for sediment loadings, concentrations, and budgets within a watershed 

modeling framework. Although the results are specific to the EPA HSPF model, the approach 

and procedures for sediment calibration are applicable to other watershed models that represent 

sediment processes and behavior at the watershed scale. Although sediment calibration remains 

one of the most difficult components of watershed-scale water quality assessments, it is hoped 

that the procedures outlined herein will provide some guidance and assistance to model users 

who attempt this often daunting task. 

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