# Sediment Parameter and Calibration Guidance for HSPF United States Office of Water Environmental Protection 4305 January 2006 Agency 

# BASINS Technical Note 8ii 

### ACKNOWLEDGMENTS

This BASINS Technical Note No. 8 was prep ared by AQUA TERRA Consultants under Work Assignment No. 4-01 of EPA C ontract No. 68-C-01-037. Mr. Paul Cocca was the EPA Work Assignment Manager (WAM) for this effort for mo st of the project sche dule, and the individual who recognized the need for devel oping this Technical Note to make this information available to the BASINS user community. Mr. James Carlet on assumed the role of WAM for this effort as the final draft was nearing completion.

For AQUA TERRA, Mr. Tony Donigian and Mr. Bria n Bicknell were the primary authors, with significant input and review provided by Mr. Jas on 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. AR M/NPS user manuals, USLE reports, ASCE Sedimentation Engineering) and based on ma ny 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 se diment 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 ...................................................................................................................... ................ 241 

### BASINS Technical Note 8:
Sediment Parameter and Calib ration Guidance for HSPF

Introduction

This technical note provides BASINS users w ith 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 gra phical 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, measur e, and/or calibrate). Where possible, the note discusses how to estimate initial values using the data an d tools included with BASINS. Also discussed, where appropriate, is the physical ba sis 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 gu idance 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 ‘typi cal’ and ‘possible’ (i .e. maximum ‘expected’) ranges for the parameters discussed below, ba sed on both the parameter guidance and experience with HSPF over the past two decades on watershe ds across the U. S. and abroad (Donigian, 1998). The overarching principal in parameter es timation should be that the estimated values must be realistic, i.e. make ‘physical’ sense, and mu st 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 summ ary table, the user should question and reexamine the estimation procedures. The estimated values may still be ap propriate, but the user needs to confirm that the parameter values reflect unusual conditions on the watershed.

Another source of parameter information is the H SPF Parameter Database (HSPFParm) (US EPA, 1999) http://www.epa.gov/waters cience/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 vi ewing and exporting the data. The pilot HSPFParm Database contains parameter values for model applications in over 40 watersheds in 14 states. The parameter values, co ntained in the database , characterize a broad variety of physical settings, land use practices, and wate r quality constituents. The database has been provided with a simplified in teractive interface that enables modelers to access and explore the HSPF parameter values developed and calibra ted 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 aspe cts of sediment behavior within a watershed2 system include loading and erosion sources, de livery of these eroded sediment sources to streams, drains and other pathways, and subse quent instream transport, scour and deposition

processes.

Sediment calibration for watershed models i nvolves numerous steps in estimating model parameters and determining appropriate adjustment s needed to insure a reasonable simulation of the sediment sources on the watershed, delivery to the waterbody, and trans port behavior within the channel system. Rarely is there sufficient obs erved local data at sufficient spatial detail to accurately calibrate all parame ters for all land uses and each stream and waterbody reach. Consequently, model users focus the calibrati on 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, a nd expected behavior from past experience. This type of ‘weight-of-evidence’ approach is rapidly becoming the standard pr actice 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. Calib ration of the parameters involve d in simulation of watershed sediment erosion is more uncertain than hydrol ogic 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 increa se agreement between simulated and recorded monthly sediment loss and storm event sediment removal. However, observed monthly sediment loss is often not available, and th e sediment calibration parameters are not as distin ctly separated between those that affect monthly sediment and t hose 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 mi micking transport behavior within the channel system and at the watershed outle t. These steps usually include:

1. Estimating target (or expected) sediment lo ading 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 de pths) 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 beha vior 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 de velop a reasonable overall representation of sediment sources, delivery, and trans port 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 in stream 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 deliver y adjustment is needed because HSPF, like most watershed-scale (lumped parameter) models, re presents landscape loadings to the stream channel, which are less than the field-scale estim ates from USLE. These estimated loading rates
then become ‘calibration targets’ for the watershed model.

Model parameters are then adjusted so that mode l-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 ca libration (discussed below) that extends to a point in the watershe d where sediment concentration and/ or load data are available. The overall objective is to represent the overa ll sediment behavior of the watershed, with knowledge of the morphological ch aracteristics of the stream (i.e. aggrading or degrading behavior), using sediment loading ra tes that are consistent with the calibration targets and modeled concentrations that pr ovide 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 imp act 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 raindr op 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 pe rvious 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 generati on 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 co ntinually increasing or decreasing throughout the calibration period. Extended dry periods will produce increases in surface sediment accumulations, and extended wet periods will produ ce decreases. However, the overall trend should be relatively stable from year to year . This equilibrium mu st be developed on both pervious and impervious surfaces, and must exis t in conjunction with the accurate simulation of monthly and storm event sediment loss, depe nding on the data available for calibration. Donigian and Love (2003) (see A ppendix) provide additional disc ussion 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 calibrati on then focuses on the channel processes of deposition, scour, and transport that determine both the total sediment lo ad and the outflow sediment concentrations to4 be compared with observations. The initial steps in instream calibration involve dividing the input sediment loads into appr opriate 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 fr actionation scheme is used for all reaches unless
soils and land surface variations within the watershed support us e 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 mate rials reaching the channel.

The sand, silt and clay fractions of total erode d 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, pa rticle density, settling velocity, bed depth and composition, and begi nning calibration parame ter values can be evaluated from local/regional data, past experi ence, handbook values, etc., and then adjusted based on available site specific data and calib ration. Bed composition data are especially important so that the model results can be adjust ed to reflect localized aggradation (deposition) or degradation (scour) conditions within the stream system.

In HSPF, the transport of the sand (non-cohesi ve) fraction is commonly calculated as a power function of the average velocity in the channel reach in each timest ep. 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 depos ited if the transport capacity is less than the available sand in suspension w ithin 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 comp ared to user-defined critical, or threshold, values fo r deposition and scour for each si ze (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 erodibili ty rate and transport thr ough 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 be tween the critical scour and deposition values, the incoming suspended material is tr ansported throu gh the reach.

In HSPF, the hydraulic characteristics of a st ream reach are represen ted by a function table (FTABLE) that includes the relationships betw een stage, storage (volume), surface area, and5 discharge. The accuracy of the FT ABLE for a specific reach will be a critical factor in adequately representing the average velocity and hydraulic radius and calcul ated 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 extens ion 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-co hesives) is not represented in the model as directly related to shear stress (t he driving function of the cohesi ves), 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 pres ented in the HSPF User’s Manual. Even with reasonable FTABLES you will find that it is possi ble to have the cohesives settle out at low flows while maintaining non-neglig ible sand concentrations. Thes e 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 m odel is run with the initi al parameter estimates and shear stress values are output for each stream r each. 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 sc our during high flow events, de position and settling during low flow periods, and transport with neither sc our nor settling for moderate flow rates. TIME (hr) Scour Threshold

Deposition Threshold SCOUR

DEPOSIT TRANSPORTBED SHEAR STRESS (lb/ft2)6 The shear stress values are then adjusted more caref ully in calibration so that scour occurs during storm periods and deposition occurs at low fl ows. Once the timing of scour and deposition processes is correct, the ra te of scour is adjusted in an attemp t to match either expected behavior within each reach, and/or the observed concentra tions. During high flow periods, the amount of scour is adjusted with an erodibili ty factor for each reach that cont rols 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 Appe ndix provides further discussion of the potential impact of FTABLEs on the shear stress calculations and resulting se diment concentrations. Note
also: In HSPF the units of shear stress are lb- force /ft 2 . Typically, shear stress values are expressed in the literature as Pascals (Pa) or Newtons/m 2 (N/m 2 ). The conversion is 1 Pa = 1
N/m 2 = 0.02088 lb/ft 2

.

The calibration procedure genera lly involves comparison of mode l simulations (concentrations and loads) to available observed data. This is often limited to event mean concentrations of total suspended solids (TSS) for selected storm ev ents 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 sedime nt 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 m onthly-variable (CFV=1) erosion-related cover, COVER. Monthly values ar e commonly used to reflect seasonal variability of the vegetation or other eros ional cover, e.g., for agricultural areas to reflect the timing of croppi ng and tillage practices.

VSIV Flag to select a constant or monthly-vari able rate of net vertical sediment input, NVSI. Constant annual values (VSIV= 0) are commonly used since atmospheric sources are usually difficult to quantify a nd are a small fraction of surface storage for pervious land surfaces. However, m onthly 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 met hod as described in the HSPF User Manual (SDOP=0). Recommendation: set SDOP to 1. This method, used in the predecessor models is more commonly us ed, and has been subjected to more widespread application.

SED-PARM2 Table: This table includes parameters for estimating th e production and reduction of detached sediment on the pervious land.

SMPF Supporting Management Practi ce Factor (unitless) ( measure/estimate ). SMPF is
used to simulate the reduction in er osion 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 se t equal to the P value for practices such as contouring, terracing, strip-cropp ing, 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 diffe rent agricultural practices can only be evaluate d with changes in all releva nt 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 se parate 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 ac hieve 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 direct ly 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 representativ e K values by soil te xture and organic matter content. Renard et al (1997) pr ovide extensive discussion on estimation of
K from soil and land surface characteristics. Since adjustments to KRER will
affect the amount of detached sediment th at 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 (P C ) 0.60 0.50 0.60 0.80 0.90

Contour Strip Cropping (P SC ) 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 (P t ) (2,3) 0.6/ √ n 0.6/ √ n 0.6/ √ n 0.6/ √ n 0.6/ √ n

N o 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 fi eld such that rowcrop strips are always separated by a meadow or winter-grain strip. (2) These P t values estimate the amount of soil eroded to the terrace channels and are used for conservation planning. For prediction of off-field sediment, the P t 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 ‘kffac t’, defined as the soil erodibility factor that is fragment fr ee for use in the USLE. Run the BASINS State Soil Characteristic Report and sele ct 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) pr oposed 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/hr9 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 ra nges of specific soil values. When a texture
is near the borderline of tw o 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 rainfa ll 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 ad just only if supported by local data
and conditions.

AFFIX The fraction by which detached sedimentstorage 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 pr ocesses are a complex function of soil characteristics, meteorologic conditions, a nd 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 co mbination with the KRER and NVSI parameters to ensure that the accumulate d detached sediment on the land surface10 (DETS) does not continually increase or decrease throughout the calibration period.

COVER The fraction of land surface which is sh ielded from rainfall (unitless) ( measure/ estimate, then calibrate as needed ), and is therefore not susceptible to soil fines detachment by raindrop impact. Seas onal/monthly values are often used. Overhead photographs at pe riodic intervals during the ye ar are the most direct means of estimating the land cover fraction.

COVER values are sometimes estimated as one minus the monthl y C factor in the USLE. For cropland, the C factors for th e various stages of crop growth should be used in estimating COVER.

Tables 5.7 and 5.8 in the ARM User Manua l (Donigian and Davis, 1978) pertain
to the evaluation of C on undisturbed lands and have been reproduced from the paper by Wischmeier (1975). C factor s for disturbed lands (croplands, agriculture, and constructi on areas) have been publis hed 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 considera tions other than crop canopy and raindrop interception since it repr esents a soil loss ra tio, i.e. the ratio
of soil loss for the current land surface conditions co mpared to clean-tilled
continuous fallow conditions. Renard et al (1997) provide the most extensive recent guidance on estimation of the C f actor. 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 detachme nt and subsequent washoff of detached materials. In these cases, the lower COVER values allow fo r fringe and boundary areas of a PERLND that may c ontribute soil to the stream.

NVSI The rate at which sediment enters de tached 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 ope rations 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 e ffects of the AFFIX parameter are not sufficient to represent all detach ed 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 evalua ting 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 defa ult 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, wh ich simulates gully erosion (complex), ( estimate, then calibrate as needed ). The vast majority of HSPF applications
have used the default value of 2.0 for th is exponent, but few applications even include gully erosion. Use an initial value of 2.5, si nce JGER is expected to be greater than JSER, and adjust if supp orted 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. Th e monthly values repres ent 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 whic h 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 Re moval 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 co mmonly used to reflect variability of the removal rate as impacted by seasonal land surface activities.

SDOP Flag to select algorithm used to si mulate removal of sediment from the impervious surface; choose either the me thod used in predecessor models (HSPX, ARM, and NPS), (SDOP=1), or the altern ative 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 wa shoff 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 eval uating 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 ar e reasonable, with mo st 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 impe rvious land surface (tons/ac/day), ( estimate, then calibrate as needed ). Data from street su rfaces 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’, wh ereas the corresponding rate for pervious surfaces (NVSI) is in lbs/ac/day.

REMSDP The fraction of solids storage which is re moved each day when there is no runoff (per day) ( estimate, then calibrate as needed ). These removal processes include13 wind, air currents from traffic, aggregation to larger, less tran sportable particles, and street cleaning activities. Values s hould range from 0.001 to 0.1, with typical values in the range 0.001 to 0.07. The effect s of street cleanin g can be estimated

as:

R = P*(E/D) where R = sediment removal by street cleaning P = fraction of impervi ous area where cleaning is performed E = efficiency of cleaning D = frequency of cleaning

For example, if cleaning is performed ev ery 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 eval uated in conjunction with accumulation
rates to establish a limit to the total sediment accumulation that can occur. This limit is given by ACCSDP/REMSDP. C onsequently, joint calibration of
accumulation and removal rates is recommended.

Monthly Input Parameter Tables: In general, monthly variation in selected pa rameters, such as SACC UM and REMOV should be included with the initial parame ter 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 followi ng months. The monthly values can be adjusted to calibrate for
seasonal variations.

MON-SACCUM Table: Monthly values for the rate of so lids accumulation on the land surface ( ACCSDP ).

MON-REMOV Table: Monthly values for the fraction of solids storag e which is removed each day when there is no runoff ( REMSDP).14 Instream Sediment Transport (SEDTRN) Parameters

The SEDTRN section simulates the transport, de position, and scour of inor ganic 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 of ten used due to lack of calibration parameters 2. Colby – developed for wide rivers where hydraulic radius is approximately equal to depth; not of ten 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 stag e, 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 st ream width (Pwidth), but NHD does not. The BASINS delineation tools produce es timates of mean stream width as a function of upstream area using geomor phological relationships described in http://www.epa.gov/waterscien ce/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, wh ich, 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, thes e values will range from 2 to 15 ft, or even higher for large reservoirs. Smalle r streams might range from 0.5 to 2 ft. Users should inspect the computed bed dept h 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 (vol ume 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, porositie s range between 0.35 and 0.5, with most15 values near 0.45. For beds with more cohe sives, 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 eith er 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 downs tream elevations for the HSPF reach boundaries. Thus the accuracy of this sl ope 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 met hod 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 sediment s 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) s hows 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 sediment s in the watershed. Sand particles have diameters typically ranging from 0.0 5 – 2 mm (0.002 – 0.08 in); see Table 3
above. Note: D is not used in the calculat ions; it is included in this table for consistency with the cohesive sediment data inputs. The Colby and Toffaleti16 calculations for sand transport use the DB 50 parameter, which is entered in the HYDR-PARM2 table. Therefore, enter a nominal value of 0.01 inches.

Table 3. Sediment Particle Diamet ers and Fall Velocities in Still Water

Class Name Diameter 1 (mm) Fall Velocity 2 (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/cm 3 . 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 stil l 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 AS CE, (1975)) or screening procedure reference texts, for example Mills et al ., (1985). Table 3 shows sediment settling
velocities in cm/s. Sand settli ng velocities will typically be in the range of 0.1 to 4
in/sec.

RHO The density of the sand particles (g/cm 3 ), ( measure , obtain from literature,
estimate ). Sediment densities are used to calculate the bed depth along with the porosity (POR). Sediment density va lues can be obtained from texts on17 sedimentation/hydrology and from laborator y analysis of the sediments in the watershed. Typical values of RHO range from 1.5 to 2.8 g/cm 3 . If local data are not available, use 2.6 g/cm 3

.

KSAND The coefficient in the sandload power f unction, should be included if SDFG = 3. (complex) ( calibrate ). The sand transport power f unction is based on velocity. This equation will produce reasonable result s if the computed velocites, which are determined from the volume and discharge columns in the FTABLE, are
reasonable. This coefficient is a calibra tion 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 func tion, 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 oc currence provides parameters for silt; the second contains the clay parameters.

D The effective diameter of the si lt or clay particles (in), ( measure , estimate ).
Sediment diameter values can be obtain ed from texts on sedimentation/hydrology and from particle size analysis of the se diments 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 part icles 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 fo r drag for non-spherical particles. See texts on sedimentation or screening pr ocedure reference manuals, for example Mills et al., (1985). Table 3 c ontains estimated values of W (cm/s) as a function of particle diameter and density. In the abse nce 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/cm 3 ), ( measure , obtain from literature,
estimate ). Sediment densities are used to calculate the bed depth along with the porosity (POR). Sediment density va lues can be obtained from texts on sedimentation/hydrology and from laborator y analysis of the sediments in the watershed. Typical values of RHO range from 1.5 to 2.8 g/cm 3 . If local data are not available, use 2.3 g/cm 3 for silt and 2.0 for clay.18 TAUCD The critical bed shear st ress for deposition (lb/ft 2 ), ( calibrate ). Initial values of
TAUCD and TAUCS should be estima ted on a reach-by-reach basis by examining graphs of the simulated shear stress timeseries (TAU) graphed at the timestep of the simulation. Assign va lues 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 al so be inspected to verify they are reasonable. The FTABLE volume and discharge columns determine the shear stress along with the simulated discharg e, 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/ft 2 ), ( calibrate ). See the discussion for
TAUCD above to set initial values of TA UCS. Adjust TAUCS in concert with the erodibility coefficient (M) to calibr ate 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 cri tical shear stress values from Shields’ equation using bed and channe l properties, as follows:

τ c = θ ( γ s - γ ) D

where θ is the dimensionless Shields parame ter for entrainment of a sediment particle of size D, γ s 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 τ c values and assess channel stability issues in ur banizing watersheds using HSPF. These
same calculations can be used to deve lop initial TAUCS values which would subsequently be adjusted in calibration.

M The erodibility coefficient of the sediment (lb/ft 2 .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 reasonab le silt and clay concentrations during
scour conditions in the reach.19 REFERENCES

ASCE. 1975. Sedimentation Engineering, ASCE Ma nuals and Reports on Engineering Practice, No. 54, V.A.Vanoni, ed., American Societ y 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 - FORT RAN, User’s Manual for Version 12. U.S. EPA, National Exposure Re search Laboratory, Athens, GA.

David , W.P. and C.E. Beer. 1974. Simulation of Sheet Erosion, Part 1. Development of a Mathematical Erosion Model. Iowa Agri culture 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 Watershe d Modeling to address
Issues of Urbanization and Ch annel 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, No vember 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. Envi ronmental Protection Agency, EPA_ 600/3_78_080.

EPA, 1999. HSPFParm: An Interactive Databa se 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/waters cience/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 w ithin a Watershed Context. Prepared for: U.S. EPA National Exposure Re search Laboratory, Athens, GA. ( http://www.epa.gov/ceampubl/swater/usle ).

Meyer, L.D. and W.H. Wischmeier. 1969. Mathem atical 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 Assessmen t: A Screening Procedure for Toxic and20 Conventional Pollutants in Surface and Gr oundwater. 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. De partment of Agriculture, Agriculture, Handbook No. 703, 404 pp.

Rouse, H. 1937. Nomogram for the Settling Velo city 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 Engi neering Handbook. Section 3 - Sedimentation. United States Department of Agriculture, Washington, DC.

Stewart, B. A., D. A. Woolhiser, W. H. Wischmeier J. H. Car o, 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 a nd Prospective Technology for Predicting Sediment Yields Sources, USDA ARS-S-40

.

Wischmeier, W.H., and D.D. Smith. 1965. Predic ting Rainfall Erosion Lo sses from Cropland East of the Rocky Mountains: Guide for selection of pr actices for soil and water conservation. U.S. Department of Agriculture, Agricultural Handbook No. 282.

Wischmeier, W.H., and D.D. Smith. 1978. Predic ting 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 so il 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 calibrate22 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/cm 3 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/cm 3 1.8 2.7 1.5 3.0 Sediment properties Used for calculating bed depth.

TAUCD 

### * Critical bed shear stress for deposition lb/ft 2 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/ft 2 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/ft 2 .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 Fe deration 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 aspe cts of sediment behavior within a watershed system include loading and erosion sources, de livery of these eroded sediment sources to streams, drains and other pathways, and subse quent instream transport, scour and deposition

processes.

Sediment calibration for watershed models i nvolves numerous steps in estimating model parameters and determining appropriate adjustment s needed to insure a reasonable simulation of the sediment sources, delivery, and transport behavior within the ch annel 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 re ach. 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 ar e 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 HS PF 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, sedime nt rating curves, shear stress, TMDL, USLE25 INTRODUCTION

Sediment is a primary constituent of concern for many watershed assessments and Total Maximum Daily Load (TMDL) studies being perform ed 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 wate r quality constituents to accurately represent in current watershed and stream models. Importa nt aspects of sediment behavior within a watershed system include loading and erosion s ources, delivery of these eroded sediment sources to streams, drains and other pathways, and subs equent instream transport, scour and deposition

processes.

A ‘weight-of-evidence’ approach is rapidly becoming the standard practice in watershed modeling. Model performance and calibration/va lidation are evaluated through qualitative and quantitative measures, involving bot h graphical comparisons and st atistical tests. For flow
simulations where continuous records are availabl e, all these techniques are often employed, and the same comparisons are performed, during both the calibration and validation phases. For water quality constituents, includi ng 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 metric s (e.g., mean). However, consistency checks with expected value ranges for loading rates and stream morphology and behavior are critical when spatially distribute d 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 e xperience with the U. S. EPA Hydrological Simulation Program - FORTRAN (HSPF) (Bicknell et al., 2001). Model parameterization and calibration procedures are described, using exampl e applications and sample model results, to demonstrate recommended graphical and statistical procedures used to assess model performance for sediment loadings, concentrations, and budge ts within a watershed modeling framework. Although the results are specific to the EPA HS PF 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 pa rameters 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 simula ted and recorded monthly sediment loss and storm event sediment removal. However, observe d 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 sedi ment 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 i nvolves numerous steps in estimating model parameters then determining appropriate adjust ments needed to ensure a reasonable simulation of the sediment sources, deliver y, and transport behavior within the channel system. These steps usually include:

1. Estimating target (or expected) sediment lo ading 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. be d 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 beha vior 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 tran sport throughout the watershed system

Rarely is there sufficient observed local data at su fficient 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, mode l 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 observati ons, historical reports, and expected behavior from past experience. This is especially cr itical for sediment mode ling 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-s cale sediment processe s in a logical and reasonable fashion. Although the specific parame ter definitions and discussions and sample model results are based on the HSPF model, the overa ll 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 deliver y adjustment is needed because HSPF, like most watershed-scale (lumped parameter) models, re presents landscape loadings to the stream channel, which are less than the field-scale estim ates from USLE. These estimated loading rates
then become ‘calibration targets’ for the watershed model.

Model parameters are then adjusted so that mode l-calculated loadings are consistent with these
estimated ‘calibration targets’ and loading ranges. The model- calculated loadings are further27 evaluated in conjunction with the instream sediment transport ca libration (discussed below) that extend to a point in the watershed where sediment concentration and/or load data are available. The objective is to repres ent 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, la nd 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. Th e 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 regi onal 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 runo ff 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 Conser vation 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 facto r , where S is the slope gradient factor and
the L is the length of that slope. The USLE was creat ed 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 conditions28 relative to the st andard plot.

The C factor is the crop/vegetation and management factor. It is us ed to determine the relative effectiveness of soil and crop mana gement system or vegetation in terms of preventing soil loss. The C factor is a ratio compar ing 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 pr actices that will reduce the
amount and rate of the water runoff and thus redu ce 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 pr actices are cross slope cultivation, contour farming, and strip cropping. In cas es 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 fract ion 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 in cluding sediment source, texture, nearness to the stream, channel density, basin area, slope, land use/cover, and ra infall-runoff factors; however, several empirical formulas ex ist (e.g. see Greenfield et al., 2002).

Under EPA funding, AQUA TERRA C onsultants 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 ta bles for USLE factors, and other information useful in deriving appropriate va lues for the USLE and delivery rati os are provided, to the extent that it is practical, throughout th e U.S. The tool includes an on- line tutorial and active links to Internet web sites containing supplemental inform ation that can assist users in evaluating USLE
factors.

Figure 1 shows the computation screen for the TMDL USLE program, with highlighted comment ‘bubbles’ identifying th e source of values and information in each cell.

As an alternative to a spreadsheet based appro ach, using a GIS platform allows the USLE to be applied on a cell-by-cell basis, using watershed specific inform ation, for a more spatially accurate use of the equation and model land use sp ecific estimates of erosional rates. The USLE can be applied in a grid-based GIS environmen t 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. Numer ous 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 reso lution (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 ‘kffac t’ within the
STATSGO or SSURGO databa ses. The attribute ‘kffac t’ defines the soil erodi bility 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 si mplified 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 th e 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, in cluding formulas and tables provided within the TMDL USLE tool. Ultimately, once the gross er osional 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 proce ss of taking the input data (i.e., DEM, soils, land use/cover), calculating USLE factors, and deve loping 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 con cern. There will often be extreme variation in the calculated rates from year to year, and site to site, and users shoul d only expect that the
model rates will be consistent with the targets, not necessarily equal to them. Table 1 shows typical ranges of sediment loadi ng 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
Land31 Step 2: Sediment Erosion Calibration

Each of the calibration steps identified in the ov erall 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, th e 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 repres ented as the net result of detachment of soil particles by raindrop impact on th e land surface, and then subsequent transport of these fine particles by overland flow. On impervious surf aces (e.g. parking lots, driveways), soil splash by raindrop impact is neglected and solids wa shoff is often controlled by the rate of accumulation of solid materials. The primary sedi ment erosion solids parameters are as follows:

KRER - Coefficient in soil de tachment 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 ar e 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 prim ary ones controlling sediment loading rates. KRER is usually estimated as equal to the erodib ility 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 th e accumulation rate, ACCSDP. Table 2 lists the sediment and solids washoff parameters in H SPF, along with typical a nd possible minimum and maximum ranges based on application experience over the past 20 years. In addition, the HSPFParm database (U. S. EPA, 1999) provide s 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 generati on 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 s hould be relatively stable from year to year. This equilibrium must be deve loped on both pervious and impervious surfaces, and must exist in conjunction with the accurate simu lation of monthly and storm event sediment loss, depending on the data available for calibrati on. Additional guidance in sediment erosion calibration is provided in the HSPF App lication Guide (Donigi an 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 so il 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 calibrate33 INSTREAM SEDIMENT TR ANSPORT 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 calibrati on then focuses on the channel processes of deposition, scour, and transport that determine both the total sediment lo ad and the outflow sediment concentrations to be compared with observations. In practice, in stream calibration involves steps 3, 4 and 5 as listed and discussed above; these steps involve bo th initial parameterizati on, to establish initial parameter values, and a subsequent adjustment pr ocess. For HSPF, the initial parameterization includes the following:

• Divide input sediment loads in to 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 to tal input, it must be divided into sand, silt, and clay fractions for simulation of instr eam 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 fr actionation scheme is used for all reaches. However, if the resolution of the data and spatia l diversity of the soils warrant alternate schemes, it is possible for each reach to use separate frac tions. 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 enrichme nt factor of silt and clay to represent the
likelihood of these finer materials reaching the channel. Thus, th e sand particles are more likely to be deposited in the overland fl ow plane, in swales, ditches, de pressions, etc. and therefore the sand would be somewhat transport limited, compar ed 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 appropr iate fractionation values.

Estimate Initial Parameter Values And Storages For All Reaches

For HSPF, initial sediment parameters, such as particle diameter, pa rticle density, settling velocity, bed depth and composition, and begi nning calibration parame ter values can be evaluated from local/regional data, past experi ence, handbook values, etc., and then adjusted based on available site specific data and calib ration. Bed composition data are especially important so that the model results can be adjust ed to reflect localized aggredation (deposition) or degradation (scour) conditions within the stream system.

In HSPF, the value of bed depth represents the am ount of material (calcula ted from input values for bed width and porosity) that can be scoured from the stream reach; in effect it provides a34 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 ma ke appropriate parameter changes if needed. We often set initial bed depths to 2.0 to 5.0 feet for the natura l (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 timest ep. 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 depos ited if the transport capacity is less than the available sand in suspension with in the channel reach. For the silt and clay (cohesive)
fractions , shear stress calculations are performe d by the hydraulics (HYDR) module and are compared to user-defined critical, or threshol d, 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 a nd 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 ou tflow concentrations a nd fluxes are calculated for each timestep. These simulations are perfor med 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 functi on 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 user35 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 st ream reach are represen ted by a function table (FTABLE) that includes the relationships betw een stage, storage (volume), surface area, and discharge. From the equations shown above, it is clear that the accur acy of the FTABLE for a specific reach will be a critical factor in adequately representi ng 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 ba nkfull discharges; improper extension of the
FTABLES can lead to erroneous shear and scou r conditions during high fl ow events, and have major impacts on the model simulations for those events.

As part of the sediment parameterization, the m odel is run with the initi al parameter estimates and shear stress values are output for each stream r each. 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 sc our during high flow events, de position and settling during low flow periods, and transport with ne ither scour nor settling for modera te 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 critic al shear values based on both flow and shear
stress simulations for a small eastern US wate rshed; the top plot s how the flow simulation corresponding to the shear simula tions on the bottom curve. We’ve also included, on the righthand scale of the shear curve, the percent exc eedance values to help quantify how often scour and deposition conditions occur at different shear values. TIME (hr) Scou r Threshold

Deposition Threshold SCOUR

DEPOSIT TRANSPORTBED SHEAR STRESS (lb/ft2)36 Figure 4 - Example calculations for se tting critical shea r 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 nor mally are performed while reviewing the same tabulations of model results; bed behavior and sedi ment 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 r eaches, and the tabulations include sediment erosion (nonpoint) loads, point lo ads, 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 inform ation can be compared with historical accounts or field observations to identify the ‘expected’ behavior for thos e stream segments. If this information is contrary to the model representa tion, i.e., the model simula tes deposition when the reach is primarily being scoured, reach parameters and/or inflows need to be adjusted to correct37 the simulated behavior.

Table 3 – Example Tabulation of Strea m 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 (Tribu tary 1 and Mainstem reaches 2 and 3). The tabulations in Table 4 include, annual inflow load s, 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. Fi eld observations of bed depth changes, expected deposition rates, bed sediment composition fractions, etc. ca n be used to assess th e 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 cr itical 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 a nd deposition processes is correct, the rate of scour (i.e. erodibility factor in th e model) is adjusted in an atte mpt 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 belo w 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 greate r 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 th e 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 4,913.339 Step 6: Compare Results with Available Data

The remaining step in the calibration proce dure is to compare model simulations of
concentrations and loads to available observed da ta. 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 c oncentrations throughout a few events. However, other types of comparisons are al so possible, such as load esti mates 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 s hows 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 inc onsistencies demonstrated; the simulated flow peak is higher than the observed and precedes it, identif ying a possible time lag in the peak that is not well represented in the model. In addition, th e 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 obse rvations and needed accuracy of the model,
but model results need to be vi ewed with a critical eye toward demanding consistent behavior,
i.e. if flows are over-simul ated, TSS should be over-s imulated, and vise-versa.

Figure 5 - Example comparison of simula ted and observed storm flow and TSS
concentrations40 Figure 6 shows an annual plot of simulated a nd estimated sediment loads for the watershed
outlet. In this case the ‘Load Estimates’ were ex trapolations from available TSS data, and were

not the results of continuous, or even daily samp ling. So this comparison is really between two models: a simulation model (HSPF) and a regressi on model. However, even this type of comparison is useful, recognizing th at 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 se diment calibration. The average annual values in Figure 6 indicate a very good simulation even t hough there are significant differences year to year. For sediment modeling thes e 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 simu lated and estimated annual TSS loads

Figure 7 shows an example of comparing sedime nt 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 co ncept in this comparison is to evaluate whether
the model and the data demonstrate similar relati onships. 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 th e 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 regre ssion 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 ra tionale 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 gr oss channel representation, with long reach lengths, that tends to eliminate localized turbulence and scour conditions that would likely contribute to unde r-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 re latively 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 consis tency between the model and the observed data. If large consistent differences existed, it w ould justify continued42 calibration efforts to minimize such differences.

Step 7: Repeat Calibration Steps, as N eeded, To Improve Sediment Representation

Modeling tends to be a circular, or iterative, pr ocess. For sediment calibration, Steps 3 through 6 often need to be repeated until all the component s of the calibration exer cise are in reasonable balance. In some cases, the process may need to reconsider the target lo ading rates developed in Step 1, and then re-calibrate the model rates. Th is might occur if the surf ace loadings appear to dominate unrealistically the overall watershed simulation results.

The iteration process doesn’t re quire that every comparison be brought to the same level of agreement, but only that the entire process be re peated until the entire ‘weight-of-evidence’ from
the simulations indicates either th at the model is ‘as good as it can be’ or that it can not meet the specific needs of the watershed assessment. Th is should produce suffici ent evidence that the model is either acceptable for the intended purpo se, 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 experien ce with the U. S. EPA HSPF. The steps in the overall sediment calibration proc ess are identified and discusse d, along with specific issues related to model parameterization and calibration procedures. Using ex ample applications and sample model results, we demonstrate recommende d graphical and statistical procedures used to assess model performance for sediment loadings, concentrations, and budgets within a watershed modeling framework. Although the results are sp ecific to the EPA HSPF model, the approach and procedures for sediment calibration are appli cable to other watershed models that represent sediment processes and behavior at the watershed scale. A lthough sediment calibration remains one of the most difficult components of waters hed-scale water quality assessments, it is hoped that the procedures outlined herein will provid e some guidance and assistance to model users who attempt this often daunting task.

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