WEPP: MODEL USE, CALIBRATION,

WEPP: MODEL USE, CALIBRATION, AND VALIDATION D. C. Flanagan, J. R. Frankenberger, J. C. Ascough II ABSTRACT. The Water Erosion Prediction Project (WEPP) model is a process-based, continuous simulation, distributed parameter, hydrologic and soil erosion prediction system. It has been developed over the past 25 years to allow for easy application to a large number of land management scenarios. Most general or field agency users of WEPP rely upon existing or special databases and/or interfaces that have been developed, tested, and verified by others. This article describes WEPP model calibration and validation procedures, under ideal situations (where all necessary input data and runoff/sediment observations are available) as well as under more typical and less ideal conditions. Two case study applications of the model from the literature are highlighted and discussed in detail as examples of single storm hillslope profile and continuous simulation watershed applications. Current and future development efforts on WEPP are also described. Keywords. Erosion by water, Hydrology, Modeling, Soil loss, Validation, Verification, WEPP. The Water Erosion Prediction Project (WEPP) was developed by a large team of federal and university scientists, beginning in 1985. The project was initiated by Dr. George R. Foster, at that time a hydraulic engineer with the USDA-ARS National Soil Erosion Research Laboratory (NSERL) in West Lafayette, Indiana. WEPP process-based technology was intended to ultimately become a replacement for the mature, empirically based Universal Soil Loss Equation (USLE; Wischmeier and Smith, 1978), which was the standard tool used by the USDA Soil Conservation Service (SCS) for field conservation planning on private lands, as well as for conducting national soil resource inventories. Foster initiated the project, formed a core team of scientists, and worked with user agencies (SCS, Forest Service (FS), and Bureau of Land Management (BLM)) to develop a user requirements document to guide development of the model (Foster and Lane, 1987). The core team laid out the basic hydrologic and erosion process logic and designed extensive field experiments that were conducted in 1987-1988 to provide data for model parameterization (Laflen et al., 1991; Elliot et al., 1989). WEPP model coding was conducted at ARS locations in Tucson, Arizona, and West Lafayette, Indiana, mainly from 1985 to 1995, and the documented and validated hillslope Submitted for review in October 2011 as manuscript number SW 9458; approved for publication by the Soil & Water Division of ASABE in July 2012. The USDA is an equal opportunity provider and employer. The authors are Dennis C. Flanagan, ASABE Fellow, Research Agricultural Engineer, and James R. Frankenberger, Information Technology Specialist, USDA-ARS National Soil Erosion Research Laboratory, West Lafayette, Indiana; and James C. Ascough II, Research Hydraulic Engineer, USDA-ARS Agricultural Systems Research Unit, Fort Collins, Colorado. Corresponding author: Dennis C. Flanagan, USDA-ARS National Soil Erosion Research Laboratory, 275 S. Russell Street, West Lafayette, IN 47907; phone: 765-494-7748; e-mail: Dennis.Flanagan@ars.usda.gov. and watershed versions of WEPP were released at a special Soil and Water Conservation Society symposium in Des Moines, Iowa, in July 1995 (Flanagan and Nearing, 1995; Flanagan and Livingston, 1995). Since 1995, WEPP model, interface, and database development have continued at the USDA-ARS NSERL, although at a reduced level compared to the initial project efforts. Flanagan et al. (2001, 2007) present additional details on WEPP model development and science, field experimentation, and core scientific team composition. Other journal articles related to WEPP development, validation, and application include Nearing et al. (1989a), Zhang et al. (1996), Ascough et al. (1997), Liu et al. (1997), Tiwari et al. (2000), Laflen et al. (2004), Clark et al. (2006), Cruse et al. (2006), Pieri et al. (2007), Moore et al. (2007), and Abaci and Papanicolaou (2009). The current WEPP model software, technical documentation, and user documentation are publicly available from the NSERL website (www.ars.usda.gov/research/docs.htm?docid=10621). Model support or source code requests can be sent directly to the lead author of this article or to the e-mail address wepp@ecn.purdue.edu. WEPP DESCRIPTION WEPP is a distributed parameter model that can be run in either a hillslope profile or a small watershed configuration. The spatial scale for hillslope profile simulations is typically from 1 to ~100 m in length, although in some situations longer hillslopes of several hundred meters may be adequately simulated. For small watersheds in which the application interest is catchment sediment yield, the recommended maximum area of model application is about 260 ha, although again, in some situations, satisfactory performance may be achieved on somewhat larger areas. Alternately, for watersheds in which the main objective is to Transactions of the ASABE Vol. 55(4): 1463-1477 2012 American Society of Agricultural and Biological Engineers ISSN 2151-0032 1463

identify hillslope regions with relatively greater risks of runoff and soil loss occurrence (e.g., in a section of a forest burned by wildfire to target remediation efforts), substantially larger areas can be successfully simulated with the model. WEPP is meant to simulate the major processes of overland flow, sheet and rill erosion, and erosion from small channels, such as ephemeral gullies. As catchment areas increase and the dominant erosion processes controlling the sediment transported out of a channel network move toward more channel-specific undercutting, side-wall sloughing, etc., model predictions can become less robust since those processes are not accounted for. WEPP was also not originally designed to function with perennial streams, simulate classical gully soil losses, or simulate other erosion processes, such as landslides. However, the model simulates both surface and subsurface water movement, including percolation, deep seepage, subsurface lateral flow, and can also handle impervious subsurface layers, such as rock parent material below forest soils. The simplest type of WEPP model simulation is for a single storm event and a single hillslope profile. The four basic input files are slope, soil, management, and climate. The climate input for a single storm provides the storm depth, storm duration, and storm intensity information. The model can accept either breakpoint precipitation input for a storm (pairs of time and cumulative precipitation depth) or standard CLIGEN (CLImate GENerator; Nicks et al., 1995) storm format (depth, duration, time to peak intensity, ratio of peak intensity to average intensity). When the standard CLIGEN format is used, the WEPP model internally disaggregates the described storm into a single-peaked, double-exponential shape, and generates a set of internal breakpoints. When the WEPP model is executed, it calculates infiltration from the input rainfall event using the Green-Ampt Mein-Larsen (GAML) equation (Green and Ampt, 1911; Mein and Larsen, 1973) modified for unsteady rainfall (Chu, 1978; Stone et al., 1995) and determines if rainfall excess and runoff are generated. If runoff is generated, the model estimates the peak runoff rate using a solution of the kinematic wave equation, which becomes the characteristic runoff rate for use in the steady-state erosion equations. Additionally, the effective rainfall intensity impacting the soil and causing interrill soil detachment is computed, and the effective duration of both the peak runoff and the rainfall intensity are determined. A rectangular rill channel is assumed, and flow depth and flow shear stress acting on the soil are calculated (Gilley and Weltz, 1995). Sediment transport capacity is also predicted as a function of the flow shear stress. The slope profile is divided into a minimum of 100 sections, and sediment load in the flow is calculated as the sum of the interrill sediment detachment/delivery and the rill detachment or deposition. Where sediment load is below flow transport capacity and flow shear stress exceeds the adjusted critical shear stress of the soil, rill detachment will be calculated. A Runge- Kutta numerical solution is employed to compute sediment load moving downslope from point to point in detachment regions. When the sediment load exceeds the flow transport capacity, deposition of sediment in the rill is computed at the points, using an analytic solution to the deposition equation (Flanagan and Nearing, 2000). The erosion output from a single event simulation of the WEPP model consists of the computed sediment load at the last point on the hillslope profile and the change in sediment load with distance between each of the upslope computational points. Additional details on WEPP erosion science can be found in Foster et al. (1995) and Flanagan and Nearing (2000). Hillslope profiles may also be subdivided into multiple overland flow elements (OFEs). An OFE is a unique spatial region having homogeneous soils and cropping/ management. For example, figure 1 shows a common type of hillslope profile having three soil types, as well as an upslope cropped region and a grass buffer at the bottom of the profile. For this profile, a WEPP model simulation would need to be set up for four OFEs, as identified in the figure. In single event simulations, the initial conditions are specified by the user in the soil input file (initial profile saturation) and in the management file (initial plant residue cover, initial canopy cover, days since last tillage, soil roughness after last tillage, rainfall since last tillage, etc.). These input values are extremely important, as they are used by WEPP to adjust the infiltration and erodibility parameters for the day of simulation. Inattention to this critical detail is a very common problem encountered by novice model users, which often negatively impacts their WEPP model simulation results. For multiple OFE profiles, as in figure 1, correct initial conditions for each OFE are important. Continuous simulations with the WEPP model involve the use of climate input files that have a sequence of daily values for precipitation, temperatures, and wind for a minimum of one year up to a maximum of hundreds (or even thousands) of years. Typically, a string of generated climate is used, especially for conservation planning activities, as it is extremely uncommon to have detailed observed climate data for a particular location with all necessary information on rainfall intensities and other weather values. In WEPP, the default climate generation tool is CLIGEN, a stochastic weather generator that uses long-term monthly weather station statistics as input to produce a sequence of possible climate scenarios (Nicks et al., 1995). The WEPP model reads the input climate information for each day, and if there is rainfall, follows the same procedures described previously for a single storm simulation to determine runoff and sediment generation and losses. Additionally, a complete water balance for the slope profile (or for each OFE if the profile has multiple OFEs) is determined, including soil evaporation, plant transpiration, deep seepage, and subsurface lateral flow (Savabi and Williams, 1995). Plant growth provides canopy cover and residue cover and is simulated using a modified EPIC (Williams et al., 1989) approach. The simulated plants impact evapotranspiration, water content in soil profile layers that contain roots simulated by plant growth, and infiltration/runoff/erosion. WEPP also contains extensive capabilities for management of soil (tillage), crops (planting, harvesting, etc.), residue (removal, addition, burning, etc.), and irrigation water application (sprinkler, furrow). The model- 1464 TRANSACTIONS OF THE ASABE

Figure 1. Example WEPP hillslope profile with two management regions and three soils. This profile would be simulated with four unique overland flow elements (OFEs). predicted values for runoff, soil detachment and/or deposition at each computational point down the slope profile are stored in arrays, as well as summed and used in subsequent calculations for monthly, annual, and average annual outputs. Model-predicted values for storm precipitation depth, runoff depth, peak runoff rate, and storm sediment loss can also be used to conduct return period analyses. A number of temporal scales are employed in WEPP. An input precipitation event can range in duration from subhourly to multiple hours, while the infiltration and runoff computations may be as fine as seconds or minutes within a time step. The soil water balance is updated on a daily basis, as are the many internal model parameters (e.g., adjusted hydraulic conductivity, adjusted erodibilities, current residue cover, etc.). The time step for erosion calculations for a storm event is the effective duration of runoff, which is typically minutes to hours, with the maximum duration for both runoff and soil erosion calculations limited to a single 24 h day. In watershed simulations, the user must describe the spatial structure of the watershed in terms of hillslope regions, channel sections, and impoundments (fig. 2). Hillslopes can contribute water and sediment either to the top of a channel or laterally on either side. Channels can feed other channels or impoundments, and impoundments can be linked to channels downstream (fig. 2). Manually delineating hillslope, channel, and impoundment components within a watershed can be tedious and time-consuming; therefore, it is advisable to use one of the available WEPP geospatial tools, such as the web-based GIS interface (fig. 3) or GeoWEPP (Renschler, 2003). More details on watershed configurations and rules can be found in Flanagan and Livingston (1995). WEPP model simulations on the hillslopes are conducted first, and results (storm runoff and sediment loss information) are stored in pass files, which are subsequently used in the channel and impoundment runoff and sediment routing. The soil loss, sediment deposition, and sediment loss results from each watershed component are stored for each event, as well as used in summations to determine monthly, annual, and average annual results. Time scales in watershed simulations are very similar to those in hillslope profile simulations. Continuous water balances (and plant growth, residue decomposition, etc.) for channel section areas are updated on a daily basis, using the identical model subroutines as for hillslope OFEs. Infiltration is computed on channel areas using the same GAML equation as for hillslopes, with similar time steps. The channel peak runoff rate is calculated using either a modified rational equation or the Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS) Figure 2. Schematic of a small watershed containing the hillslope (with OFEs), channel, and impoundment components of the WEPP watershed model. 55(4): 1463-1477 1465

Figure 3. Example small watershed located near Winnebago, Minnesota, delineated into channel sections and subcatchments (hillslope regions) with the WEPP web-based GIS interface. In WEPP watershed simulations, a representative slope profile must be developed for each subcatchment and can either have a single management and soil (single OFE) or contain multiple soils and managements (multiple OFEs). model peak runoff equation (Smith and Williams, 1980). Flow depth and hydraulic shear stress along the channel are computed by regression equations based on a numerical solution of the steady-state spatially varied flow equations. Detachment, transport, and deposition within constructed channels or concentrated flow ephemeral gullies are calculated by a steady-state solution to the sediment continuity equation. The impoundment component (Lindley et al., 1998a, 1998b) routes runoff and sediment through several types of impoundment structures, including farm ponds, culverts, filter fences, and check dams. See Ascough et al. (1995, 1997) for more details on WEPP watershed hydrology and erosion computations and model limitations. WEPP is currently solely a hydrologic and soil erosion model for predicting runoff, soil detachment and sediment deposition, and sediment yield at the hillslope profile and small watershed scales. Efforts are underway by a number of researchers to expand the capabilities of the model to include simulation of other water quality constituents (Savabi et al., 2011; Bhattarai et al., 2011) as well as to expand the maximum size of watersheds simulated (Wang et al., 2010; Dermisis et al., 2011). WEPP CALIBRATION AND VALIDATION An ideal dataset for calibration and validation of WEPP would contain detailed topographic, soil, plant/management, and climate data for an experimental plot or watershed to develop inputs for the model, as well as detailed observed soil moisture content, surface runoff, subsurface drainage, sediment loss, and sediment particle size characteristics for each storm event over a period of 10 to 20 years. A comprehensive dataset like this would allow testing of the various model components related to hydrology, ET, plant growth, and erosion and sediment transport, as well as the model as a whole, with some portion of the available climate data used for model calibration and the other portion used for model validation. However, in reality, there are almost no sets of comprehensive natural rainfall experimental field or watershed data like this. That is because most long-term monitoring sites, for either soil erosion (old USLE plots) or watershed discharge and sediment losses, were designed for other purposes, with procedures that often lack the detail necessary to calibrate and validate a process-based erosion model in depth. For some experimental datasets, particularly from undeveloped countries, rainfall depth may be the only available information on precipitation, so storm duration and intensities are unknown. The NSERL still houses all of the old U.S. erosion station experimental data that were used in the development of the USLE. However, the runoff data collected were for total runoff only (no rate information), the sediment loss data were for total storm soil loss (no rate information), and there was no information on sediment particle sizes. Sometimes, data from multiple storms were lumped together, depending upon when the collection tanks were serviced (fig. 4). At the plot scale, more modern rainfall simulation experiments can provide almost all of the information needed to conduct WEPP model calibration and/or validation for single storm simulations. Furthermore, modern natural rainfall erosion plot studies, although not completely comprehensive, also contain sufficiently detailed data on needed mod- 1466 TRANSACTIONS OF THE ASABE

Figure 4. Sediment collection from erosion plots at McCredie, Missouri, circa 1960. Multiple storm data were sometimes collected together, if plots could not be serviced in a timely enough manner. el inputs and outputs to allow acceptable calibration and validation. Erosion plots may also have unexplained variability in the observed data (Nearing et al., 1999), which should also be considered when determining the acceptability of model results. Calibration may not be needed for application of WEPP, and in fact this is one of the strengths of a process-based erosion model of this type. WEPP has been successfully applied in previous studies with no calibration. In particular, Tiwari et al. (2000) describe the application of WEPP, USLE, and RUSLE to a portion of the USLE dataset, with WEPP being applied in an uncalibrated mode. The model performed quite acceptably, at similar levels to both USLE and RUSLE. The type of model user as well as results from initial model runs will determine what, if any, calibration may be needed. For example, a field conservationist interested in making erosion model assessments may only wish to compare relative results from different farming management activities and likely would not conduct any model calibration. In addition, the conservationist probably would not have access to runoff/erosion data to conduct calibration/validation, although crop growth and management could be calibrated using observed crop yields and crop residue levels. On the other hand, a graduate student with a set of erosion experiment data might apply the model in an uncalibrated mode initially but then decide to examine how the simulation results might be improved through a calibration/validation exercise. SINGLE STORM WEPP MODEL CALIBRATION AND VALIDATION Single storm WEPP model simulations are the easiest to set up and run because only an individual storm event will be parameterized. This precludes the need for extensive and detailed temporal cropping and management operation data. The most basic model simulation with a single storm is for experimental bare and fallow (void of vegetation) plots, possibly used as a control treatment. In addition to any bare fallow plots, or instead of those (if none are available), other plot/field conditions can be simulated, such as seedbed (tilled or untilled with varying levels of crop residue on the soil surface) or vegetated (at some stage of plant growth and with some degree of aboveground biomass as canopy cover, and crop residue on the soil surface). Slope Input File If uniform erosion plots are to be simulated, create input files with the observed plot width, length, and uniform slope gradient. If plots are non-uniform, create a WEPP slope input file that most closely matches the fieldmeasured slope profile. This is accomplished by entering distance downslope and slope gradients at the points down a hillslope profile. Soil Input File Enter the plot or field site soil properties, as measured or as noted in the NRCS Soil Survey for the soil and county appropriate for your site. These properties include soil texture (% sand, % clay), organic matter, and rock fragments by depth layers (see Flanagan and Livingston, 1995). If soil-specific infiltration, i.e., baseline effective hydraulic conductivity (K be ), and erodibility parameters, i.e., baseline interrill erodibility (K i ), baseline rill erodibility (K r ), and baseline critical shear stress (τ c ), have been experimentally determined for the study soil, then those are the best values to use (for cropland soils, baseline conditions are for a freshly tilled soil with no vegetation or surface or subsurface crop residue materials). See Alberts et al. (1995) and Flanagan and Livingston (1995) for detailed information on these parameters and estimation procedures. More typically, measured values for K be, K i, K r, or τ c will not be available. In this case, use one of the existing soil files from the WEPP database as a starting point and modify it as necessary to match available field measured values. These four parameters are the most typical parameters 55(4): 1463-1477 1467

that will be adjusted in any subsequent model calibration. A critical input in the soil file for a single storm simulation is the initial saturation of the soil profile. This parameter specifies the amount of the soil pore space that is filled with water and is used internally in the model to initialize the soil moisture content of the soil layers. Initial saturation input to WEPP needs to be as close as possible to the actual value in order for the hydrology component of the model to accurately predict the infiltration of rain water into the soil surface, movement of the wetting front, runoff generation, and water percolation. For some special situations in which subsurface lateral flow may be a dominant hydrologic process, e.g., in forested areas on shallow soils above an impermeable bedrock layer, use of the restrictive soil layer option in the WEPP soil input file may be necessary to properly simulate reduced surface runoff and increased subsurface water movement. Climate Input File The climate input for a single storm simulation should use an observed rainfall simulator or natural storm event information, preferably in breakpoint format, i.e., cumulative time and cumulative precipitation depth at that time. This will allow the hydrology component to most accurately simulate the rainfall pattern (times and intensities) and produce the most accurate prediction of infiltration, ponding, and surface runoff. Cropping/Management Input file The cropping/management input file to WEPP contains all the information related to plants/crops grown, growth parameters, residue characteristics, residue decomposition parameters, tillage implements and soil disturbance parameters, planting operations, harvest operations, residue management operations, temporal and spatial order of crops grown, tillage/residue operations, etc. However, almost all of the hundreds of input parameters in this file are not needed or relevant in a single storm simulation; there are only a very few that matter. This is because no temporal processes, such as plant growth or residue decomposition, are occurring for a single storm event, so the parameters associated with them do not apply. The most critical inputs in the cropping/management input file for a single storm calibration/validation simulation with WEPP are the initial conditions. These include the initial interrill and rill cover, initial canopy cover, days since last tillage, cumulative rainfall since last tillage, bulk density after last tillage, ridge height after last tillage, ridge random roughness after last tillage, initial dead root mass, initial submerged residue mass, initial snow depth, initial soil frost depth, initial soil thaw depth, rill spacing, and initial rill width (see Flanagan and Livingston, 1995). These parameters are used by WEPP to internally calculate adjustments to the baseline soil effective hydraulic conductivity and baseline soil erodibility values so that they reflect processes such as soil consolidation after tillage and reduction of erodibility from the freshly tilled baseline conditions. They are also used to back-calculate the plant biomass present and the plant height, which can impact runoff and erosion calculations on a storm event day. Alternately, the user has the option to turn off all temporal adjustments to the infiltration and erodibility parameters, which effectively reduces the importance of some of the initial conditions in the cropping/management input file, as parameter adjustments affecting K be, K i, K r, and τ c based on time since tillage or rainfall will no longer apply. However, this option is very rarely used, as it requires the WEPP user to provide the exact infiltration and erodibility parameters for each storm event at the time of the event, accounting for all possible adjustment factors. A few of the crop/residue specific parameters can also be important in single storm simulations, in addition to the initial conditions. These include the canopy cover coefficient (bb), canopy height coefficient (bbb), maximum canopy height (hmax), and the maximum Darcy-Weisbach friction factor for living plants (flivmx) (Flanagan and Livingston, 1995). The initial input value for canopy cover and the bb value are used internally in WEPP to backcalculate the aboveground live biomass on the day of simulation, and the bbb value is then used to compute the canopy height. Canopy cover and height are used in the erodibility adjustment equations. In addition, for small grain crops and sods, the hydraulic roughness of vegetation affecting water flow on the soil surface is modified by the model as a simple linear ratio of canopy height from a minimum value of zero to a maximum value of flivmx (see Gilley and Weltz, 1995). Typically, WEPP database default values are used for these crop/residue specific values, and they are not modified during calibration unless the user has better measured values available. Simulations A portion of the storm event data should be identified as a subset to use for model calibration, while the remainder of the dataset should be preserved for validation. The decision on how to divide the events into these two subsets is dependent upon the dataset itself. If sufficient numbers of years of observed data are available, and there is a good mix of average, dry, and wet years, then selection of a third or half of the storm data for calibration may be satisfactory, while the remaining two-thirds or half are kept for validation. However, if the years of record for the data are limited, or there is a skewed distribution toward either wet or dry weather sequences, then some other selection criteria may be necessary. One option would be to take all storm events (perhaps over a certain threshold, e.g., 10 mm or greater depth) and rank them from lowest to highest in terms of either total precipitation depth or total storm runoff depth. The ranked set can then be divided into quarter groups of storms and events from each quarter group randomly selected to go into either the calibration or validation sets. Calibration and Validation With the group of storms selected for calibration, develop the complete set of necessary climate, slope, soil, and cropping/management input files. The observed data for storm runoff and sediment loss also must be assembled. If there is observed information on the spatial soil loss and deposition on the plots/fields, that information can be com- 1468 TRANSACTIONS OF THE ASABE

pared to WEPP model outputs as well, although this type of observed data is very rare. Any observed data on sediment particle sizes leaving the field can also be compared to the WEPP-predicted values. Run each of the storm events with the same baseline value for the effective hydraulic conductivity for the soil, and record the predicted runoff values. The K be value is the main parameter typically used to calibrate WEPP runoff predictions. Compare the predicted runoff to the observed runoff using statistical evaluation criteria, such as those recommended by Moriasi et al. (2007), including examination of the means, standard deviation (SD), root mean square error (RMSE), percent bias (PBIAS), Nash-Sutcliffe model efficiency coefficient (E NS ; Nash and Sutcliffe, 1970), and the ratio of the RMSE to the SD of the measured data (RSR). In subsequent model simulations, modify the K be value to reduce the error observed between the observed and predicted runoff values for the calibration storm set. This can be done either manually if the dataset is relatively small, or automatically through some type of optimization software if the dataset is large. Typically, modifications to the calibration parameter will be made until one of the statistical criteria (e.g., E NS ) is maximized. Moriasi et al. (2007) provides recommended acceptable ranges for various statistical evaluation criteria. For example, for hydrologic performance of a model, they suggested that model simulations are satisfactory for runoff if E NS > 0.50, PBIAS < 25%, and RSR < 0.70 (Moriasi et al., 2007). Continue calibration until all evaluation criteria selected are within acceptable ranges. Once the hydrologic calibration is complete, conduct validation of the runoff using the other portion of the storm dataset with the final K be value obtained during calibration. Statistically compare the observed and predicted runoff values for the storms in the validation database, and calculate the appropriate statistical evaluation criteria (e.g., E NS, RSR). Then observe and record if the evaluation criteria for the validation dataset fall within the suggested ranges (Moriasi et al., 2007). Moving on to prediction of sediment losses using the same previously determined subsets of calibration and validation storms. Use the previously determined K be value for all simulations to produce the best runoff predictions. Since there are three soil erodibility factors (K i, K r, and τ c ) that can potentially be used for calibration, this can be a more difficult and challenging exercise than runoff estimation. Use field measurements (if available) to set the initial values for the soil erodibility factors. If measured erodibility values are not available, the WEPP database can be used to select the same or similar soil, and the values will be determined by WEPP parameterization equations (Flanagan and Livingston, 1995). Run an initial simulation with the calibration dataset, observe and record the sediment loss results, and compare the simulated sediment losses to the observed sediment losses. At this point, some decisions need to be made on which parameters to focus on during subsequent calibration and validation. If possible, identify the erosion processes that are dominant on the plots; for example, are rills present and actively eroding, or do the erosion processes appear to be dominated by sheet/interrill detachment? If interrill detachment is dominant, then one approach would be to fix K r and τ c at their default values and then calibrate further with only K i. Or, if it is known or thought that no rill detachment processes are occurring (for example, on very short plots), then a very high value (e.g., 20 Pa) for τ c could be set in the soil input file, which would effectively result in no rill detachment being predicted (since flow shear could never exceed the very high input critical shear). In this case, optimization would be conducted by altering only the input K i value for all storm events until the statistical evaluation criteria selected are maximized (or minimized). Alternately, if rill erosion processes are known to be dominant on the plots (e.g., observed extensive and active rill network forming and eroding), then it may be possible to conduct calibration by fixing K i and τ c at their default values, and focusing on the use of K r. For plots on which there is a mix of interrill and rill erosion processes, the procedure becomes less clear, although it is still probably easiest to fix two of the parameters (such as K i and τ c ) and attempt calibration using the other (K r ). If this approach proves unsuccessful, then an alternative approach can be attempted by adjusting one or both of the other erodibility parameters. Multi-parameter calibration of WEPP is possible and was performed by Nearing et al. (1989b) for rangeland conditions. Once the baseline erodibility parameters have been successfully set and/or calibrated, then conduct validation for sediment losses with the other set of storm data. Record the storm sediment losses, and compare them to the observed data. Determine the statistical evaluation criteria (E NS, PBIAS, and RSR), and identify where the validation set results fall within the recommended ranges. CONTINUOUS SIMULATION MODEL CALIBRATION AND VALIDATION Similar procedures to those previously described for single storms can be used for calibrating and validating the WEPP model in continuous simulations for hillslope profiles. A subset of years or storms should be selected to use in the model calibration, with the other subset to use for model validation. As mentioned earlier, correctly identifying the initial conditions for soil and cropping/management inputs is important, especially when the simulation period is short. Ideal climate inputs for a continuous simulation calibration/validation would be observed breakpoint precipitation data and other weather information on a daily basis for the entire period of record. However, comprehensive climate inputs can be difficult to obtain for any given time period. If all data are not available, it is possible to use the WEPP Windows interface (Flanagan et al., 1998) and/or CLIGEN to generate missing information. For example, many times the only information that a user (especially outside the U.S.) may have will be the daily precipitation depth and minimum and maximum daily air temperatures. CLIGEN can generate the daily storm intensity parameters, dewpoint temperature, solar radiation, and wind parameters if long- 55(4): 1463-1477 1469

term weather station statistics are available (or can be estimated from a surrogate location). Alternately, if a user has a set of observed daily rainfall and temperature data that is missing some days, the WEPP Windows interface has climate file tools that can fill in missing data based on the user s criteria (use zero value, use average monthly value, etc.). If daily information is available on multiple hydrologic measurements (runoff, soil moisture, ET, etc.), then the user has the option of calibrating/validating on more than just runoff and sediment losses. WEPP will simulate continuously through all parts of a year (fall/winter as well as spring/summer), so determining what time periods to compare in the model output is important. Annual runoff and soil loss would be typical values, but in many cases for small natural erosion plots or field watersheds, the measurement equipment was not operable during periods when freezing temperatures could occur. Thus, in those cases, the user possibly needs to select for comparison only the months when monitoring was active. Additional model input parameters are important in continuous model simulations that include tillage and vegetation growth. WEPP needs to be able to properly simulate the growth, senescence, and harvest of crops, any tillage operations used to cultivate the soil and plant crops, as well as decomposition of dead plant residues and any management of those residues. The model is supplied with a large number of default parameter values for tillage implements, plant growth, residue decomposition, and residue management operations. However, the default plant parameter sets are still relatively limited to major field crops. Thus in model applications, it is important for the user to ensure that WEPP is adequately simulating these crop growth, residue, and tillage processes. The suggested approach is for the user to select the closest match for crops, tillages, and other management from the WEPP default database, make any necessary changes to these to include known cropspecific information, and run some initial model simulations with the climate, soils, and slope profile at the location of interest. Model outputs can provide the annual and average crop yields, graphs of biomass and canopy development with time, graphs of residue cover production, residue decomposition with time, and residue burial by tillage, as well as other related information. In particular, it is critical to check that the biomass produced (or crop yield) is reasonably close to the range observed in the field, and that the residue cover produced and how it changes with time and tillage agree with field observations or accepted relationships (fig. 5). If in the preliminary model simulations the crop yields, biomass, or residue are unrealistic or unreasonable, then some modification of the crop/plant/tillage parameter values may be necessary before progressing on to the actual runoff and soil erosion calibration/validation. One of the most important crop growth parameters is the biomass energy ratio (BEINP). Default values are provided by Flanagan and Livingston (1995) and Arnold et al. (1995) and are also incorporated into the default WEPP software distribution. However, actual plant growth can be greatly impacted by the climate location and soil interactions. Flanagan and Livingston (1995) provide information on adjusting BEINP, which basically involves modifying this parameter until the long-term average annual biomass produced (or crop yield) predicted by the model matches that for the location of interest. Other important crop-specific parameters include the harvest index (HI) used to apportion vegetative biomass between that removed at harvest and that remaining on the field as crop residue, maximum growing degree days (GDDMAX) for the crop, a parameter to convert residue mass amount to cover percentage (CF), the decomposition rate for surface residue (ORATEA), and several senescence parameters. Once the biomass production/ yield is correct, the decomposition of the surface residue cover as well as the impacts of soil tillage operations on the residue cover and soil should be checked as well, and modifications to the relevant input parameters made until the predicted residue cover and soil roughness conditions through time are reasonable. Additional information on the Figure 5. Graphical output from WEPP continuous simulations provides information on crop growth through time. This screen capture shows aboveground live biomass in a 20-year corn-soybean rotation for a profile near Winnebago, Minnesota. Average predicted biomass for the corn is about 1.75 kg m -2 (red dashed line). If the actual field corn biomass production was known to be 2.2 kg m -2 (solid blue line), then modification should be made to the corn plant growth parameters, particularly the biomass energy ratio, and the simulations rerun until the average predicted biomass is near the field observed value. The green horizontal line near the bottom of the figure represents average predicted soybean biomass. 1470 TRANSACTIONS OF THE ASABE

WEPP plant growth model and associated parameters can be found in Flanagan and Livingston (1995). WATERSHED MODEL CALIBRATION AND VALIDATION Conducting WEPP model calibration and validation on watersheds is similar to that for individual hillslope profiles or plots, although there are many more input data requirements. The watershed structure and all necessary inputs for each component (multiple hillslopes, channels, impoundments) need to be constructed, either by hand or with one of the geospatial WEPP interfaces (e.g., GeoWEPP), using the best available information for topography, soils, and cropping/management. Climate input requirements are the same as previously discussed. Calibration procedures can become more complex for a watershed, as there is the likely possibility of considerable variation in soil types across a catchment, particularly as catchment size increases. One way to approach hydrologic calibration in this case is to modify all soil effective hydraulic conductivity values in soils present in a watershed by the same percentage amount during the optimization for runoff prediction. For example, if the initial predicted monthly runoff at the watershed outlet runoff averages 20% greater than the observed values, then the user should increase all soil values for K be by the same amount (e.g., 10%) in the next set of model runs in an attempt to compensate. Another alternative is to use a single representative soil for the entire watershed, most likely the soil occurring on the majority of the area, or a single composite soil with lumped parameters to represent the entire area. If other observed data are available in addition to channel flow (such as soil moisture, ET, etc.), then multiple-criteria calibration and validation for hydrology may be more appropriate here as well. For sediment calibration and validation, similar approaches are necessary. Additional complexities and uncertainties in a watershed simulation are present because, in addition to the hillslope regions supplying water and sediment, the channels can be in either detachment or deposition modes. Thus, in addition to possible modification of the K i, K r, and τ c values on each hillslope region, it is also possible to modify the channel erodibility and channel critical shear stress. Other channel parameters can also be important, such as Manning s roughness coefficient for a bare channel and Manning s roughness coefficient accounting for vegetation in the channel. Some knowledge of the watershed erosion processes and the contribution of sediment from hillslopes versus that from channels is helpful here to identify which areas are most important for calibration. For example, if there is information on smaller scale plot/field erosion (as discussed earlier), then use of the hillslope component calibration/validation results would allow concentration on channel erodibility parameters for calibration and validation for sediment losses at the watershed scale. If knowledge of the channel types exists, e.g., the channel is a well-maintained grass waterway with no known detachment, then model calibration and validation could primarily focus on the contributing hillslope areas only, with perhaps some adjustments to the channel roughness coefficients if necessary (channel detachment could be negated by inputting a very high soil critical shear stress). CASE STUDIES Two case studies taken from recent WEPP model calibration/validation by other researchers are described here: (1) a hillslope profile application of the model using single storm runoff and sediment loss evaluations, and (2) a continuous simulation, large watershed application of WEPP. WEPP SIMULATION OF RUNOFF AND EROSION FROM NATURAL GAS WELL SITES IN TEXAS Wachal et al. (2008) applied the WEPP hillslope model to two natural gas well sites in north central Texas. One of the reasons that they chose WEPP was that, as an eventbased model, WEPP required much less observed runoff and soil loss data for successful calibration and validation compared to typical empirical average-annual soil erosion tools, such as the USLE. Their dataset from 2006 contained 15 storm events, resulting in 12 storm runoff events at their first site and eight events at their second site. The researchers selected three of these 20 events for model calibration. The natural gas well pads were instrumented with V-notch weirs, velocity flowmeters, and automated water samplers to measure runoff and collect sediment samples to determine soil erosion losses (Wachal et al., 2008). A tippingbucket rain gauge was also installed on site, and flow and rainfall data were recorded at 5 min intervals. Even though their recording rain gauge could have provided breakpoint rainfall data as climate input, Wachal et al. (2008) instead created standard CLIGEN format storms (storm depth, duration, time to peak intensity, and ratio of peak to average intensity). The three storms that were chosen for model calibration were selected to cover the range of soil loss events observed, i.e., small, medium, and large sediment yield occurrences. The researchers used observed slope, soil, and management information whenever possible. Where this information was not available or known, they selected input parameters from existing WEPP model databases or suggested in the literature (e.g., Laflen et al., 2001). The slope profiles were set up with two overland flow elements: the first was a cut slope on disturbed natural soil with limited vegetation present, and the second was the gas well pad surface consisting of nonerodible compacted rock with a very low infiltration rate (fig. 6). The four parameters adjusted during model calibration were K be, K i, K r, and τ c, as discussed earlier in this article. The final calibrated parameter values for the clay loam cut slope and the pad surface are reported in table 1. In this study, the model was evaluated using E NS, RSR, and PBIAS, as suggested by Moriasi et al. (2007) and Gupta et al. (1999). Wachal et al. (2008) also performed additional evaluations with modifications of E NS and RSR that accounted for the uncertainty in the measured runoff and sediment validation data using procedures described by Harmel and Smith (2007) and Harmel et al. (2006). For the first site, WEPP performance was good for runoff (E NS = 55(4): 1463-1477 1471

Figure 6. One of the natural gas well sites used in the Wachal et al. (2008) study. WEPP model simulations were conducted using single storms and two OFE hillslope profiles (OFE 1 = erodible cut slope to left; OFE 2 = nonerodible pad surface in center). Runoff and sediment were monitored at a weir on the right. (Figure adapted from Wachal et al., 2008). Table 1. Calibrated WEPP model parameter values for the two case studies. Critical Hydraulic Shear Stress Baseline Effective Hydraulic Conductivity Reference Site Location Interrill Erodibility (K i, kg s m -4 ) Rill Erodibility (K r, s m -1 ) (τ crit, Pa) (K be, mm h -1 ) Wachal et al. Clay loam hillslope Texas 9.58 10 6 0.03 2.35 0.75 (2008) Flex base pad Texas 1.0 10 6 0.0001 50 0.10 South Amana subwatershed Iowa 4.0 10 6 0.005 5.6 0.15 Abaci and Papanicolaou (2009) 0.68 and RSR = 0.56) and satisfactory for sediment yield (E NS = 0.63 and RSR = 0.61). For the second site, WEPP performance was very good for runoff (E NS = 0.68 and RSR = 0.56) but unsatisfactory for sediment loss (E NS = 0.32 and RSR = 9.83). However, when the versions of E NS and RSR modified to account for validation data uncertainty were used instead, model performance increased to very good for all runoff and sediment loss comparisons (for site 1, runoff E NS = 0.90 and RSR = 0.28, sediment loss E NS = 0.86 and RSR = 0.38; for site 2: runoff E NS = 0.99 and RSR = 0.12; sediment loss E NS = 0.86 and RSR = 0.38). Results from this study demonstrate the ability of WEPP to effectively simulate runoff and soil loss, with relatively little calibration data, as well as the importance of accounting for measured data variability when determining model performance. WEPP SIMULATION OF EROSION IN A CLEAR CREEK SUBWATERSHED IN IOWA Abaci and Papanicolaou (2009) used WEPP to examine the effects of land management practices on soil erosion in the South Amana subwatershed (SASW), a catchment of about 26 km 2 that drains to Clear Creek in east central Iowa (fig. 7) in Johnson and Iowa counties. Elevation above sea level in this watershed ranges from 235 to 275 m, and slope gradients range from 1% to 10%. The USGS National Ele- Figure 7. Location of Clear Creek watershed and the South Amana subwatershed in east central Iowa (from Abaci and Papanicolaou, 2009). 1472 TRANSACTIONS OF THE ASABE

vation Dataset digital elevation model (DEM) at a resolution of 30 m was used to delineate the SASW into 135 hillslopes and 87 channels. Soil information was obtained from the Iowa Soil Properties and Interpretation database and the NRCS SSURGO database, both of which also are at a resolution of 30 m. The three main soil series in the SASW were Tama silty clay loam and Downs silt loam on the well-drained uplands, and Colo silty clay loam on the poorly drained floodplains. This location was purposely selected in order for the dominant soil series in the watershed to exhibit similar or almost identical critical hydraulic shear stress values (Abaci and Papanicolaou, 2009). In addition to the soil database information, over 200 field soil samples were collected for detailed ground-truthing, and measurements of soil hydraulic conductivity, interrill erodibility, rill erodibility, and critical shear stress were experimentally determined at a number of field sites. Detailed documentation of land use practices was available in the SASW for a period of almost 100 years. Three row crop rotations comprised the majority of the existing agricultural land use: fall till corn and no-till soybeans (FTC-NTB), no-till soybeans and spring-till corn (NTB- STC), and no-till corn and fall-till soybeans (NTC-FTB). These together with hay and pasture fields and prairie bromegrass on conservation reserve acreage made up about 90% of the total area (fig. 8). The majority of the crop parameters were obtained from the WEPP database. The harvest index values for the corn and soybeans were based on direct measurements and literature values. Detailed crop and tillage operation rotation information was developed for the three crop rotations with assistance from local NRCS and county Soil and Water Conservation District (SWCD) personnel. The performance of CLIGEN version 5.2 was evaluated against long-term rain gauge and Iowa MESONET information, and it was determined that the generator performed well at reproducing the monthly rainfall statistics. In the WEPP model calibrations for the study site, a 100-year generated series of daily weather input to WEPP was generated by CLIGEN version 5.2 and used along with the delineated channel and watershed topographic structure, management, and soil assigned to each hillslope and channel region. Calibration of catchment flow discharge was conducted by varying the key soil parameters of K be within the accepted range for the soils, based on the database and field-measured values. WEPP-predicted values from the 100-year simulations were compared to average annual field data for water and sediment loads at the SASW outlet for 1997-2007 (Abaci and Papanicolaou, 2009). A consistent underprediction of runoff from the catchment resulted in setting K be to the minimum value (0.15 mm h -1 ) of the range to maximize runoff. Soil erosion on the hillslope regions was calibrated to an average value of 10.9 t ha -1 year -1, which compared closely with the NRCS Natural Resource Inventory estimate of 11.6 ±0.7 t ha -1 year -1 (USDA, 2009) for average soil loss in the state of Iowa. Calibrated values for the soil erosion parameters were K i = 4,000,000 kg s m -4, K r = 0.005 s m -1, and τ c = 5.6 Pa (table 1). Calibration of the sediment leaving the SASW outlet was achieved by varying Manning s roughness coefficients for bare soil (thus allowing for vegetation in the channels), the channel erodibility factor, and the critical hydraulic shear stress in the channel so that the results from the 100-year model simulations closely approximated the mean estimated field values for Clear Creek. An additional check comparing sediment delivery ratios (SDR) for the SASW with typical values for other watersheds was also conducted, with good agreement noted, especially in plots of SDR versus drainage area comparing WEPP results with those from a study by Rochi (1962). Abaci and Papanicolaou (2009) next conducted a quasi-validation of the WEPP model using monthly and yearly time series of water and sediment loads for the 1997-2007 time period, with no additional modifications to the model input parameters. In this part of their analysis, they computed monthly and annual field estimates of sediment losses by using newly established sediment rating Figure 8. Spatial land use identified in the South Amana subwatershed (from Abaci and Papanicolaou, 2009). 55(4): 1463-1477 1473

Figure 9. WEPP-predicted sediment yields by hillslopes in the South Amana subwatershed (from Abaci and Papanicolaou, 2009). curves based on sediment concentration results from a field study at the outlet of the SASW with streamflow time series from a linked USGS streamgauge at Oxford, Iowa. WEPP simulations for the 11-year period were conducted using detailed observed precipitation data containing detailed observed rainfall intensity information. Comparisons between the field-observed and the model-estimated flow discharge and sediment yields in both monthly and annual evaluations were deemed to be satisfactory (R 2 > 0.81 for discharge and R 2 > 0.92 for sediment discharge comparisons). Following their calibration and quasi-validation, Abaci and Papanicolaou (2009) also used the WEPP model results to quantify runoff and sediment loss by hillslope region (fig. 9), as this was important to identify regions in the SASW that could be targeted for additional conservation practices. They also used the model results to examine causes of high soil loss rates under different crop management systems. It should be noted that this successful model application involved a watershed approximately 10 the recommended maximum size. Reasons for the researchers success could be the relative lack of channel detachment processes in this watershed, as well as relatively high contributions of sediment from hillslopes. DISCUSSION WEPP is a process-based model that can be used for erosion and runoff estimation on small hillslopes and watersheds up to a recommended size of about 260 ha. It was designed for typical agricultural lands as a replacement for the USLE/RUSLE technologies. The Forest Service and the Bureau of Land Management are actively applying WEPP on a daily basis, particularly in studies of wildfire impacts and forest road management, but the model has not yet been implemented by NRCS. The strengths of the model include its ability to simulate spatially varying land use and soil properties over different hillslope and watershed components, and calculate sediment deposition and delivery in addition to soil detachment. Furthermore, since it is a process-based model, WEPP responds to storm event climate variations. Studies can be conducted examining erosion, sediment yield, runoff, crop growth, and soil moisture due to different climate and land use scenarios. Long simulation period applications of the model provide the ability to conduct risk analysis and calculate return periods for runoff and sediment yields. Some of the weaknesses of the model are that the channel processes simulated may only apply to relatively small scales, and erosion processes in classical gullies and perennial streams are not accounted for. In simulations of larger watershed areas, the hillslope applications of WEPP can be used to estimate runoff and sediment delivery to channels, but another model may be needed to simulate the large channel and stream hydrology and erosion processes. This approach can require considerable processing outside of WEPP to correctly set up and access the inputs and outputs to a coupled model (Dermisis et al., 2011). The land use databases used by WEPP are targeted to a relatively few agricultural areas. A model user often needs to develop new vegetation and management scenarios to apply the model to a wider range of conditions. The cropland plant growth component of WEPP only simulates a single plant community at a time within an OFE; when simulating more complex plant communities, a single crop parameter set must be used to approximate the actual field conditions. The spatial soil loss estimates at 100 points per OFE allow the model to be used in a GIS environment; however, considerable post-processing of model outputs is required. The detailed daily outputs from the model allow further analysis, such as to check if the biomass production is reasonable. The flexible climate inputs to the model allow WEPP to be used in single storm mode or over very long time periods, with either observed or generated climate sequences. Climate inputs to the model can also be modified to approximate changes in future climate due to global climate change (different frequency, amounts, and intensities of precipitation; different daily temperatures), and WEPP model applications can be conducted to examine the impacts on possible future runoff and soil loss (Zhang et al., 2011). Much of the WEPP scientific model development since 1474 TRANSACTIONS OF THE ASABE