Development and Validation of the Tahoe Project Sediment Model. SNPLMA Project Number P052. Final Report. Investigators

Development and Validation of the Tahoe Project Sediment Model SNPLMA Project Number P052 Final Report Investigators Dr. William Elliot, Rocky Mountain Research Station Dr. Erin Brooks, University of Idaho Drea Traeumer, Em Hydrology December, 2014 1

Project Abstract Now more than ever there is a great need for scientifically defensible upland decision support tools in the Lake Tahoe basin. The recognition of the excess build-up of forest fuels and the continued struggle with maintaining or improving clarity in the lake has put enormous pressure on decision-makers in the basin. Managers are faced with stiff time constraints to meet TMDL requirements while thinning forests to reduce the risk or intensity of wildfire in the basin. The project developed an upland decision support tool to assist managers in the selection and assessment of site-specific management options to reduce forest fuel loads and to evaluate effectiveness of sediment mitigation practices. The online Tahoe Basin Sediment Model (TBSM) is based on the WEPP model and will be parameterized using existing Tahoe experimental databases. TBSM is a flexible web-interface tool which will assess the effects of site specific management practices on sediment transport and delivery from a treated hillslope to a channel. One of the primary focuses was to develop training materials that clearly describe how to apply the tool to many of the common fuel management and sediment management practices employed by decisionmakers in the basin. The web interface was developed such that new information can be incorporated into the tool as it becomes available from on-going research in or near the basin. TBSM will have an immediate impact in the basin as it provides the scientifically defensible predictions that watershed managers in the basin so desperately need. Goals and Objectives The goal of this project was to develop the Tahoe Basin Sediment Model (TBSM) online decision support tool to be used by forest managers and planners in the Tahoe Basin to assess hillslope-scale fine sediment (<20 um) loads for the most common forest upland management practices in the basin under current and future climate scenarios. The primary objectives of the project were to: 1. Compile a database of existing upland rainfall, runoff, and erosion experiments in the Tahoe basin. 2. Develop WEPP input files from existing datasets in the basin for the most common forest upland management practices. 3. Develop a climate generation tool that creates current and future climate files for any project location in the Tahoe basin. 4. Develop the TBSM with user friendly protocols for evaluating the effects of alternative management practices on fine sediment loads. 5. Validate TBSM loading estimates for fine sediment (<20um) from current and proposed monitoring projects within the basin). 6. Publish results in a peer reviewed journal Activities Survey for Tahoe Basin Needs The first phase of this project included two surveys. The first survey was to determine from Basin stakeholders their needs for soil erosion prediction technology. There were 20 surveys distributed, but there were only three returned. The returns did, however, represent the three main stakeholder groups in the basin, a regulatory agency, a management agency, and a fire protection agency. 2

The survey found that the respondents were most interested in impacts of mechanical fuel reduction systems and burning, some interest in the importance of road design, but low interest in recreational impacts. The respondents were all interested in predictions for average annual upland erosion, sediment delivery, surface runoff, lateral flow and base flow. The water board respondents were also interested in nutrients and snow water equivalent predictions, and the fire district respondents in soil water content (for fire risk) and vegetation regeneration. The water board was particularly interested in monthly distributions of predictions. Survey of Available Data A second survey was carried out to identify potential data sets for improving erosion models in the basin. This survey was targeted to 14 researchers who were believed to have collected data related to soil erosion in the Tahoe Basin in recent years. Three scientists responded to this request for information. The Forest Service Lake Tahoe Basin Management Unit provided a link to their database of publications, some hydraulic conductivity values from field measurements using permeameters; however, they did not have any data from rainfall simulations. El Dorado County provided similar information, with a database of what was considered relevant data. The county also had data related to properties of sediment leaving roads. Both of these Forest Service and county databases included gray literature as well as peer reviewed publications. The most useful information was provided by Dr. Randy Foltz, USFS Rocky Mountain Research Station and Drea Traeumer, co PI, who have been collecting rainfall simulation data in the Tahoe basin. Dr. Foltz provided information on the erodibility properties of roads from his SNPLMA Round 7 research project. Complementary Rainfall Simulation One of the sources of soil erodibility data was to be from the Traeumor round 8 project. Initial rainfall simulations carried out by Traeumor were not considered adequate for either the Round 8 study on jack pot burning or this study. Subsequent rainfall simulations by Dr. Randy Foltz were carried out. These results combined with other studies in the basin resulted in the soil properties for the Tahoe Basin summarized in the appendix. Soil Erodibility Studies There are four factors that describe soil erodibility in the WEPP Technology: Interrill erodiblity, rill erodibility, rill critical shear, and saturated hydraulic conductivity. In modeling soil erosion in the Tahoe Basin, there are generally three conditions to consider: forests, roads, and other disturbed conditions, such as road fills, constructions sites, or ski slopes. For forested areas, the disturbance associated with the area is critical for defining soil properties. The main disturbance categories are: 1) Undisturbed; 2) Mechanically disturbed; and 3) Burned. Within the Mechanically disturbed category, which is generally applied to thinning operations, the ground cover remaining after disturbance is considered as well as a reflection of the degree of disturbance. Typically, for simple thinning, a reduced ground cover is used with a shrub soil to describe the soil erodibility after the disturbance. For major understory thinning, which may even include removal of biomass for fuel, or a seed tree harvest, then a tall grass condition soil condition would be recommended along with the ground cover after the treatment. For a prescribed burn or low severity wildfire, a low severity fire condition is recommended, along with the 3

associated ground cover. In all these forest cases, data collected from different forest conditions by Robichaud were considered as the primary source of information to describe soil erodibility. For roads in the Tahoe Basin, data collected by Foltz (2008 and 2009) within the basin were combined with what was learned from his earlier research and that of Luce (1999) in Oregon to develop low and high traffic road erodibility values. For the other disturbed areas, data from two Grismer et al. publications were considered (See Reference Section). These papers contained management practices demonstrated by Hogan to be especially effective in mitigation, including mulching and incorporating mulch. As these sites were highly disturbed, the erodibility values observed by Grismer were tempered with agricultural soil erodibility values (Elliot et al., 1989). In all cases, the hydraulic conductivity reported from rainfall simulation was generally reduced about 40 percent. This has been found to be reasonable by a number of modelers and likely reflects the fact that simulations are carried out on very small areas of hill side, surrounded by large areas where there is no simulation. Thus the soil matric potential of the surrounding areas will tend to draw water into the soil. In addition, on steep slopes, there is likely considerable subsurface lateral flow from the simulation site to drier downslope areas that would not be occurring should the entire hill slope be experiencing the same rainfall or snow melt event. Once the main erodibility parameters were determined, they were compared to each other, and a continuum of properties developed so that the model will respond in the way the a user would expect. This adjustment is following the guidelines established by Foster et al. (1987) in the original WEPP User Requirements document, that the model should perform in the way that a user expects. Thus the ranking of hydraulic conductivity is from sand to silt to alluvium, and from Forest to Grass to disturbed to fire to roads in treatment. The interrill erodibility is similar, with silt the most erodible, followed by sand and alluvium, whereas with rill erodibility, sand is the most erodible, followed by silt and alluvium. These erodibility values have a similar ranking for vegetation for texture. The treatments, however, are different with the disturbed soils being the most erodible, and then fire, roads, grass and forest soil in decreasing erodibility. In all cases, the cover effects tend to overshadow the soil erodibility effects. A final inclusion for soil properties in the Tahoe basin database is that forest soils are considered the deepest, reflecting the fact that deeper soils are often needed to sustain forests, and the trees themselves add to the water holding capacity of a site, which can only be accounted for in the current version of WEPP by specifying a deeper soil. Deeper soils frequently have slightly less surface runoff, but may generate more lateral flow which can form spring lines at the base of hills, creating unexpected increases in surface runoff predictions. Future Climates Future climate scenarios were develop at for eight SNOTEL locations across the Tahoe Basin. Coates et al. (2010) used two Global circulation models, the Geophysical Fluid Dynamics Laboratory Model (GFDL) and the Parallel Climate Model (PCM) to provide 100 year records of daily future climate projections for the A2 and B1 emissions scenarios at a 12 km grid scale for the Tahoe basin. At this resolution there are 12 points with daily projections within or very close to the Tahoe Basin, see Figure 5-1 in Coates et al. (2010). These daily data were disaggregated into hourly data by John Riverson from Tetra-tech using observed hourly data trends using historic weather records at SNOTEL sites in the basin, see section 4.3 of Coates et al. (2010). These data were used to drive the LSPC hydrology and water quality model. The LSPC model breaks the Tahoe basin down into many subwatersheds and air temperature and precipitation data for the various watersheds LSPC are developed for each watershed using adiabatic lapse rates with elevation. 4

Annual Precipitation (mm/yr) Annual Avg. Temperature (C) 1800 1600 1400 Precip Tmax Tmin 20 15 1200 1000 800 600 400 200 0 1989 2009 2029 2049 2069 2089 10 5 0-5 -10 Figure 1. Observed and uncorrected future climate predictions of total precipitation, average annual maximum temperature, and average annual minimum temperature for the Heavenly SNOTEL. Thick solid lines are observed data for 1989-2005. Notice the shift in observed between forecasted temperatures. In order to be consistent with the Coates (2010) study we developed future climate files based on the same downscaled climate data. Rather than large watershed, the Tahoe Sediment Basin Model provides climate data at specific SNOTEL locations. Future climate scenario at eight SNOTEL sites in the basin were developed using scaling factors that relate the future climate scenarios at one of the 12 locations to the SNOTEL point through the use of 800 m resolution, 30 year average PRISM maps for Tmax, Tmin, and Precipitation following the approach of Brooks et al. (2010). When comparing these future climate forecasts to the observed data collected at the site we noticed in some cases that the observed trends in temperature and precipitation did not match. Figure 1 shows observed annual maximum temperature, minimum temperature, and precipitation as well as the forecasted data for the Heavenly SNOTEL. As indicated by this figure the trend in observed annual maximum temperature is a degree or two below the trend in the forecasted data. In contrast the observed minimum annual temperature is slightly warmer than the forecasted data. This discontinuity is likely caused by scaling problems using the average annual values from 800 m cells PRISM maps. Micro-topography near the SNOTEL station could play a factor in controlling the micro-climate. In order to provide continuous simulations that transition from observed data to future forecasts we applied a secondary correction to the future climate scenarios. The maximum and minimum temperature was shift by fixed, constant amount and the precipitation data was corrected using a fixed scaling factor. These corrections factors were calculated by comparing the average annual values over the previous 10 years to the forecasted data for the same period. Figure 2 shows the corrected future 5

Annual Precipitation (mm/yr) Annual Avg. Temperature (C) 1800 1600 1400 Precip Tmax Tmin 20 15 1200 1000 800 600 400 200 0 1989 2009 2029 2049 2069 2089 10 5 0-5 -10 Figure 2. Observed and corrected future climate predictions of total precipitation, average annual maximum temperature, and average annual minimum temperature for the Heavenly SNOTEL. Thick solid lines are observed data for 1989-2005. Notice the shift in temperatures has been corrected. climate data for the same Heavenly SNOTEL, notice a much smoother transition from the observed data to the forecasted data. These corrections were made to both the A2 and B1 scenarios. One of the challenges with working with multiple climate scenerios is that they are independent predictions so simulations cannot be compared on a day to day basis nor even a year to year basis. Figure 3 compares the Future climate scenarios predicted for both the A2 and B1 scenarios. Notice that wet years in both scenarios do not match. It is for this reason that we will be recommending that users only make comparison between the climate scenarios using at least 10 year simulations. Figure 3 shows the 10 year running average for each scenario. The web interface provides the users with the ability to simulate specific time periods for each climate file and therefore provides the users with the ability isolate blocks of time. The online interface is currently programmed so that users select the nearest SNOTEL station and initial year to start their future climate scenario, like 2030, and the interface then abstracts the following 20- year predicted climate for the scenario (A2 or B1) desired. Phosphorus Data Phosphorous pathways in agricultural watersheds are mainly associated with surface runoff, detached sediments, and tile drainage water. The dominant pathway in most cases is associated with detached sediments, while phosphorous dissolved in surface runoff and tile drainage are of less importance. Agricultural phosphorus delivery models have tended to focus on how management practices such as 6

Annual Precipitation (mm/yr) Annual Avg. Temperature (C) 2000 1800 1600 10 per. Mov. Avg. (Precip. A2) 10 per. Mov. Avg. (Precip B1) 20 15 1400 1200 10 1000 800 5 600 400 0 200 0 2001 2021 2041 2061 2081-5 Figure 3. Future A2 and B1 climate predictions of total precipitation, average annual maximum temperature, and average annual minimum temperature for the Heavenly SNOTEL. Thick solid lines are B1 predictions. The solid black line and the dotted black line are the 10 year moving average trendline of the A2 and B1 precipitation forecasted data. manure spreading and chemical fertilizers affect the availability of soluble reactive phosphorous (SRP) for runoff, and the concentration of phosphorous adsorbed to soil aggregates and particles (particulate phosphorous). The concentrations of phosphorous in eroded sediments, surface runoff, and drain tile flows are then used in runoff and erosion models to predict phosphorous delivery. In forested watersheds, surface runoff and erosion are frequently minimal, are generally associated with wildfire. In the absence of wildfire, the dominant flow paths for runoff are either shallow lateral flow or base flow (Elliot, 2013; Srivistava et al., 2013). Phosphorous concentrations in the soil are usually much lower than in agricultural settings. Recent research has found that the concentration of SRP in the upper layers of the soil that are the source of shallow lateral flow are much greater than measured in surface runoff (Miller et al., 2005). These observations suggest that a phosphorous delivery model is needed for forest watersheds that can include the current surface runoff and sediment delivery vectors, as well as delivery from shallow lateral flow. In order to develop a model that can predict phosphorus delivery with lateral flow, a hydrologic model that includes shallow lateral flow as well as surface runoff and sediment delivery is needed. The Water Erosion Prediction Project (WEPP) model has such a capability (Dun et al., 2009; Srivistava et al., 2013). The WEPP Model is a physically-based distributed hydrology and erosion model. WEPP uses a daily time step to predict evapotranspiration, plant growth, residue accumulation and decomposition, deep seepage, and shallow lateral flow. Whenever there is a runoff event from precipitation and/or 7

snowmelt, WEPP predicts infiltration, runoff, sediment detachment and delivery (Flanagan and Nearing, 1995). WEPP has both a hillslope version and a watershed version. In recent years, the deep seepage has been used to predict groundwater base flow (Elliot et al., 2010, Srivastava et al., 2013), further increasing the model s hydrologic capabilities. The WEPP Model The WEPP model was originally developed to predict surface runoff and erosion and sediment delivery from agricultural, forest and rangeland hillslopes and small watersheds (Laflen et al., 1997). Inputs for the model include a daily climate file, soil file, topographic information, and a management or vegetation file. Within the model, WEPP completes a water balance at the end of every day by considering infiltration, deep seepage, lateral flow and evapotranspiration. Surface runoff is estimated on a subdaily time step using an input hyetograph based on the daily precipitation depth, duration, and peak intensity and the soil water content, using a Green and Ampt Mein Larson infiltration algorithm (Flanagan and Nearing, 1995; Dun et al., 2007). The deep seepage is estimated for saturated conditions for multiple soil layers if desired using Darcy s Law. Evapotranspiration is then estimated using either a Penmen method or Ritchie s Model. The lateral flow is then estimated for layers that exceed saturation using Darcy s law for unsaturated conditions as downslope conditions may not be saturated (Dun et al., 2007). Duration of surface runoff is dependent on storm duration and surface roughness (Flanagan and Nearing, 1995) and lateral flow duration is assumed to be 24 hours on days when lateral flow is estimated. If requested, WEPP generates a daily Water file that contains precipitation and snow melt amounts, surface runoff, lateral flow, deep seepage, and soil water content (Flanagan and Livingston, 1995). Table 1 shows a part of the water file for the Tahoe City, CA climate for Julien Days 70-78 (March 11-19). On day 70, precipitation (P) was all rain, on day with no snowmelt, on day 71 rainfall combined with melting snow, and days 77 and 78 were snowmelt only days. Surface runoff (Q) only occurred on day 78, while lateral flow was occurring every day. The soil became saturated on day 72 so that deep seepage occurred. During these 9 days, the total precipitation was 31.6 mm, total surface runoff was 14.73 mm, the lateral flow was 18.93 mm, the deep seepage was 0.13 mm, the soil water content increased by 73.38 mm and the snow water equivalent on the surface decreased by 106 mm. Development is ongoing to add the deep seepage to a temporary groundwater reservoir, and from that to use a linear flow model to predict base flow from a sub watershed as a fraction of the volume of that reservoir (Srivastava et al., 2013). WEPP predicts delivered sediment in five sized categories: primary clay, silt and sand particles, small aggregates made up of clay, silt and organic matter, and larger aggregates consisting of all three primary particles and organic matter. The sediment size classes and properties are summarized in Table 2 for a coarse sandy loam soil that is widespread in the Tahoe Basin. The fraction of sediment in each size class delivered from a hillslope or a watershed is presented appended to the main output file from WEPP. In addition, WEPP calculates a specific surface enrichment ratio (SSR) which is the ratio of the sediment surface area in the clay and organic matter fraction in the delivered sediment divided by this value for the soil on the hillslope. This ratio was intended to be used to assist water quality modelers to determine the increase in concentration of a pollutant in the delivered sediment compared to the sediment on the hillslope. For example if the phosphorus content in the soil was 500 mg/kg and the enrichment ratio was 2.2, the concentration of phosphorus in the delivered sediment would be 1100 mg/l 8

Table 1. Example of information in the WEPP water.txt file output file. The climate was for Tahoe City, CA. The variables are: Day=julian day; P= precipitation; RM=rainfall +snowmelt; Q=daily runoff; Ep=plant transpiration; Es=soil evaporation; Dp=deep percolation; latqcc=lateral subsurface flow; Total Soil Water=Unfrozen water in soil profile; frozwt=frozen water in soil profile; SWE=Snow Water Equivalent on the surface. Day P RM Q Ep Es Dp latqcc Total-Soil frozwt SWE mm mm mm mm mm mm mm Water(mm) mm mm 70 8.4 8.4 0.00 0 2.05 0 0.28 129.73 0 258.79 71 2 42.68 0.00 0 3.09 0 0.88 168.43 0 218.12 72 0.3 17.74 0.00 0 3.2 0.01 1.89 181.08 0 200.67 73 6.9 30.09 0.00 0 2.35 0.02 2.77 206.03 0 177.49 74 13.7 4.57 0.00 0 2.08 0.02 2.77 205.69 0.04 186.62 75 0.3 0 0.00 0 1.27 0.02 2.54 201.89 0 186.92 76 0 0 0.00 0 3.52 0.02 2.26 196.09 0 186.92 77 0 11.48 0.00 0 1.73 0.02 2.77 203.06 0 175.44 78 0 22.69 14.73 0 1.12 0.02 2.77 207.11 0 152.75 Estimating Phosphorous Concentrations Phosphorus delivery from a hillslope will either be adsorbed to eroded sediment, (particulate phosphorus of PP) or be dissolved in surface runoff, subsurface lateral flow, or base flow (soluble reactive phosphorus or SRP). Concentration of phosphorus in sediment depends on the mineralogy of the soil and particle size. Phosphorus dissolved in solution depends on the geology and the flow pathways (surface, lateral or base flow) that runoff follows. For this paper, we will focus on developing concentration values that are typical of the Lake Tahoe Basin. A similar procedure can be applied to other watersheds. Within the Tahoe Basin, there is a long history of measuring phosphorus in streams. There are 13 major streams in the Lake Tahoe Basin with sample density ranging from 129 samples collected from Trout Creek to 1414 at Incline Creek (Figure 4). These samples were generally grab samples, often collected during peak flow times of the year. The subsequent analyses reported total phosphorus (TP), and SRP. We assumed that particulate phosphorus was the difference between TP and SRP. From these data, we were able to apply a USGS model, LOADEST, developed to transform point data into continuous flow data as a function of stream flow. With the LOADEST tool, we were able to estimate TP and SRP throughout the year for each of the Tahoe streams. 9

Table 2. For a forest sandy loam soil, properties of sediment size classes in eroded sediments in the WEPP model. Particle Composition (%) Class Diameter (mm) Specific Gravity Sand Silt Clay Organic Matter 1 0.002 2.60 0.0 0.0 100.0 250.0 2 0.010 2.65 0.0 100.0 0.0 0.0 3 0.030 1.80 0.0 80.0 20.0 50.0 4 0.300 1.60 85.4 7.1 7.5 18.8 5 0.200 2.65 100.0 0.0 0.0 0.0 Using the observed samples, and the LOADEST software, we developed a model for phosphorus concentrations throughout the year (Figure 2). Observed values tended to exceed LOADEST predictions as they were often collected during large runoff events, and were not representative of the more normal concentrations during each season or when averaged for a number of years. In addition to the sample concentrations we also had the hydrographs for each of the streams that were sampled. Figure 6 is a typical hydrograph where we have estimated the relative contribution of each of the flow paths (surface, lateral and base) using the WEPP model water file. Figure 6 shows that the base flow is the dominant flow path from July until snowmelt the following April, that surface runoff only occurs at times of peak flow rates, and that lateral flow is the dominant flow path during higher flow rates in the late spring. Combining this information with the results shown in Figure 5, it is apparent that the SRP in surface runoff is likely less than 0.01 mg/l, whereas SRP concentrations in lateral flow and base flow are likely to be around 0.02 mg/l. Concentrations are the lowest during March and April when surface runoff is contributing to runoff and diluting lateral and base flow, but higher from June onwards when lateral flow and base flow are the main sources of water in the stream system. Total phosphorus delivered, however, is likely to be the highest during the peak flow times associated with snow melt in April and May In addition to TP and SRP, we had the suspended sediment concentrations of the samples that had been collected from the streams in the basin. We were able to estimate the phosphorus concentration on suspended sediments by dividing the calculated PP concentration by the suspended sediment concentration. We found that the concentration varied within the Tahoe Basin depending on the geology, mainly volcanic in the northwest and decomposed granite in the southeast part of the basin (Figure 4). Estimating Fine Sediment Delivery In addition to phosphorus, stakeholders in the Lake Tahoe Basin are also concerned about fine sediment delivery (Coats, 2004). In this context, fine sediment is generally considered to be sediment particles and aggregates less than 10 to 20 microns in diameter. Such particles can remain suspended in lakes for a considerable period of time as vertical currents due to wind shear and temperature gradients are sufficient to prevent the particles from settling (Coats, 2004). It is these small particles combined with increased algal growth because of phosphorus enrichment that have caused the lake to lose some of its clarity in recent decades. 10

Figure 4. Concentration of phosphorus (mg/kg) adsorbed to delivered sediment from watersheds within the Tahoe Basin. Figure 5. Observed and predicted phosphorus concentrations averaged across all years for a typical watershed. The distribution of particle size delivery from hillslopes or watersheds given in the WEPP output file (Table 2) can be parsed to determine the amount of sediment in each particle size category by summing the delivery of a given size primary particle with the fraction of that particle contained in the aggregates. In WEPP, clay primary particles are less than 4 μ diameter, and silt particles are between 4 and 62.5 μ. To simplify modeling, we assumed that within the silt textural category that the distribution of particle sizes was linear. Thus if the user needed to know the amount of sediment less than 10 μ, the number could be determined by adding all of the clay fraction as primary particles and in aggregates to the (10-2.5)/(62.5-2.5) fraction of the silt delivered as primary particles and in aggregates. 11

Figure 6. Example hydrograph based on WEPP hydrology for Blackwood Creek (Elliot et al., 2010) Interfaces In order to make this technology useful to mangers, an interface was developed similar to the Forest Service Disturbed WEPP online Interface for the WEPP model (Elliot, 2004). Figure 7 shows the input and output interfaces for the Tahoe Basin Sediment Model (TBSM). The user is asked to select a climate, dominant geology, vegetation conditions for the upper or treated part of a hill, and lower or stream side buffer part of the hill. In the case of an undisturbed condition, or a post wildfire condition, the upper and lower portions of the hill may have the same vegetation. The climate database for the TBSM includes one NOAA station within the Tahoe Basin as well as five nearby climates. In addition, climate statistics have been added to the database for seven NRCS Snow Telemtery (SNOTEL) stations located within the basin. The user is also asked to provide the phosphorus concentrations in the surface runoff, lateral flow, and sediment. Earlier versions of the interface were designed for the user to enter the phosphorus concentration in the soil, and the model would then adjust this value using the specific surface enrichment ratio from the WEPP output. We found, however, that it was easier to obtain the concentration of total and soluble reactive phosphorous from instream monitoring rather than concentrations of phosphorus in the soils themselves, so the current interface is designed to use the concentrations shown in Figure 4 based on instream data. The interface could be altered for other applications where onsite particulate phosphorus concentrations are readily available and be designed to use delivered sediment with a delivery ratio as previously discussed. Figure 7b shows the output screen for the TBSM. Each phosphorus path (sediment, surface runoff and lateral flow) is presented so that the users will be able to determine the dominant pathway for the condition they are modeling. In the example shown in Figure 7b for a prescribed burn with a buffer, the greatest source of phosphorus is in the delivered sediment. This is often the case in disturbed forests 12

Figure 7a. Input Screen for the Tahoe Basin Sediment Model using a SNOTEL Station from within the Tahoe Basin for the weather and phosphorus concentrations from Figures 4 and 5. Figure 7b. Output screen for the Tahoe Basin Sediment Model. (Stednick, 2010). In undisturbed forests, the greatest source of SRP is likely to be in the shallow lateral flow (Miller et al., 2005). The fine sediment category between 4 and 62.5 μ is specified on the input page (Figure 7a) and the total delivery per unit area is calculated from the predicted sediment delivery, and presented on the output page. Discussion This approach to modeling phosphorus and fine sediment delivery was developed for the Lake Tahoe Basin. The principals that are described here for estimating delivery of phosphorus can be applied to any condition where the input variables are known. The TSPM interface assumes a PP concentration attached to stream sediment. In other conditions, it may be more appropriate to link the PP concentration to the onsite concentration, and apply a specific surface enrichment ratio to the delivered sediment. With this interface, using the large PP concentrations in stream sediments (1000 2500 mg/kg), we may be over predicting the delivery of PP to the stream. Elliot et al. (2012) reported onsite concentrations of 4-22 mg/kg, and concentrations on coarse sediments collected from rainfall simulation of 160-475 mg/kg. The increasing concentrations of PP from soil to upland eroded sediments to stream sediments is due to the specific surface enrichment, and further work on the interface may be necessary to make sure the high instream concentrations are linked to the delivery of clay-size material. In the Tahoe basin, clay generally accounts for around 2 percent of the soil fraction. 13

Another interesting hydrologic feature of coarse forest soils is that unless the soils are highly disturbed, there is little surface runoff. Comparing the hydrograph in Figure 3 to the SRP concentration variability in Figure 6 suggests that when surface runoff does occur, SRP concentrations are low, but when lateral flow or subsurface flow dominate the runoff, SRP concentrations increase. The net effect of integrating the runoff and concentrations values in these two figures suggests that total SRP delivery is the greatest when runoff is the greatest. It also suggests an interesting twist to managers: if managers seek to minimize surface runoff, lateral flow is likely to increase (Srivastava, 2013), and so will the concentration of SRP leaving the hillslope. Surface runoff itself will deliver less SRP, but it will also be the mechanism that delivers sediment, and may then dominate the TP budget. The TBSM does not consider channel processes. In steep forest watersheds, stream channels and banks tend to be coarse, having minimal impact on adsorbing or desorbing TP. Forests with finer textured or higher organic materials in stream beds or banks are more likely to have a moderating influence on TP delivery, adding to SRP during times of low stream SRP concentration and reducing SRP during times of high concentration. Work is ongoing with a SNPLMA Round 12 project to incorporate some of these channel processes. The interface clearly shows the link between sediment delivery and TP delivery. Past watershed research has shown that sediment budgets from forest watersheds are dominated by wildfire, with sediment delivery following wildfire as much as 100 times greater than associated with undisturbed forests (Elliot, 2013). Such sediment pulses will likely dominate delivery of phosphorus in the same way as they dominate the sediment budget. Managers will need to consider the effects of management practices not only on immediate sediment delivery, but also on the effects that management may have on reducing the probability or severity of wildfires because of those activities (Elliot, 2013). Workshops A one day training workshop was held at the Tahoe Regional Planning Agency on November 14, 2012, which was well attended by regulators, land managers, and consultants. A list of those that attended is presented as Table 3. Personnel from the USFS Lake Tahoe Basin Management Unit were unable to attend the 2012 workshop; therefore, a second training workshop was held for USFS personnel on March 14, 2013 at the USFS Lake Tahoe Basin Management Unit. A list of attendees for the 2013 workshop is presented as Table 4. Both were all day workshops. Attendees were first introduced to the WEPP model and the TBSM interface through a Power Point presentation. Attendees then ran TBSM simulations on line by working through worksheet exercises that applied TBSM to upland forest scenarios that included undisturbed forest, fuels management, roads/road decommissioning, cut and fill slopes, harvest treatments, ski areas, and snow making corridors. Worksheet examples were designed specifically to demonstrate to attendees how TBSM can be applied to evaluate baseline conditions and alternative treatment scenarios on fine sediment delivery. User Guide The TBSM User Guide was drafted, with worksheet exercises from the training workshops included as appendices to assist users. It is currently under review. 14

Table 3 TBSM Workshop Attendees at the November 14, 2012 Workshop at the Tahoe Regional Planning Agency. Name Cushman, Doug Byrne, Bryan Dugan, Maxwell Hagan, Tim Hirt, Brian Knust, Andrew Landy, Jack Pilgrim, Kevin Praul, Chad Sribe, Laurie Stucky, Daniel Van Huysen, Tiffany Vollmer, Mike Walck, Cyndie Agency/Company LRWQCB UNR UNR Carndo ENTRIX Consulting Califorina Tahoe Conservancy NDOT EPA USFS RMRS Environmental Incentives Consulting LRWQCB Atkins Consulting USFS TRPA CA State Parks Table 4. 2013 TBSM Training Workshop Attendees (March 14, 2013 at USFS Lake Tahoe Basin Management Unit). Name Agency/Company Craig Oehuti USFS Nicole Brill USFS Barbara Shanley USFS Genevieve Villamaire USFS Melanie Greene Hauge Brueck & Associates Technical Presentations Elliot, B. Brooks, E., Traeumer, D., Bruner, E. (2012) Predicting phosphorus from forested areas in the Tahoe Basin. Presented at the Conference on Environmental Restoration in a Changing Climate, Tahoe Science Conference, 22-24 May, 2012, Incline Village, NV. References Brooks, E.S., W.J. Elliot, J. Boll, and J. Wu. 2010. Final Report, Assessing the Sources and Tr4ansport of Fine Sediment in Response to Management Practices in the Tahoe Basin using the WEPP Model. Final Report, Round 7 SNPLMA, submitted USDA-FS Lake Tahoe Basin Management Unit. Accessed on 9/28/2012 at http://www.fs.fed.us/psw/partnerships/tahoescience/documents/final_rpts/p005finalreportjan2 011.pdf Coats, R. 2004. Nutrient and sediment transport in streams of the Lake Tahoe Basin: A 30-year restrospective, pages 143-147. USDA Forest Service Gen. Tech. Rep. PSW-GTR-193. USDA Forest Service Pacific Southwest Research Station. 15

Coats, R. J. Reuter, M. Dettinge, J. Riverson, G. Sahoo, G. Schladow, B. Wolfe, M. Costa-Cabral. 2010. The effects of climate change on Lake Tahoe in the 21 st Century: Meteorology, Hydrology, Loading, and Lake Response. Final Report, Round 8 SNPLMA, submitted USDA-FS Lake Tahoe Basin Management Unit. Accessed on 9/28/2012 at http://terc.ucdavis.edu/publications/p030climate_change_project_final_report_2010.pdf Copeland, N.S. and R.B. Foltz. 2009. Improving erosion modeling on forest roads in the Lake Tahoe Basin: Small plot rainfall simulations to determine sateruated hydraulic conductivity and interrill erodiblity. Presented at the Annual International Meeting of the American Soc. of Ag. & Bio. Engrs, 21-24 June, Reno, NV. St. Joseph, MI: ASABE. 11 p. Dun, S., J.Q. Wu, W.J. Elliot, P.R. Robichaud, D.C. Flanagan, J.R. Frankenberger, R.E. Brown and A.D. Xu. 2009. Adapting the Water Erosion Prediction Project (WEPP) Model for forest applications. Journal of Hydrology, 336(1-4):45-54. Elliot W.J. (2004) WEPP internet interfaces for forest erosion prediction. Jour. of the American Water Resources Association 40, 299-309. Elliot, WJ (2013) Erosion processes and prediction with WEPP technology in forests in the Northwestern U.S. Trans ASABE. 56(2): 563-579. Elliot, W. J., A. M. Liebenow, J. M. Laflen and K. D. Kohl. 1989. A compendium of soil erodibility data from WEPP cropland soil field erodibility experiments 1987 & 88. Report No. 3. USDA-ARS, National Soil Erosion Research Laboratory, W. Lafayette, IN. 316 p. Elliot, B. Brooks, E., Traeumer, D., Bruner, E. (2012) Predicting phosphorus from forested areas in the Tahoe Basin. Presented at the Conference on Environmental Restoration in a Changing Climate, Tahoe Science Conference, 22-24 May, 2012, Incline Village, NV. Elliot, W., E. Brooks, T. Link, S. Miller. 2010. Incorporating groundwater flow into the WEPP model. Procs. 2nd Joint Federal Interagency Conference, 17 June 1 July, Las Vegas, NV. 12 p Flanagan, D. C. and Livingston, S. J. (eds.). (1995) WEPP User Summary. NSERL Report No. 11. USDA Agriculture Research Service, National Soil Erosion Research Laboratory, W. Lafayette, IN. USA. 131 p. Flanagan D.C. and Nearing, M.A. (1995) USDA Water Erosion Prediction Project: hillslope profile and watershed model documentation. USDA-ARS National Soil Erosion Research Laboratory, NSERL Report No. 10. (West Lafayette, Indiana) Foltz, R.B., H. Rhee and W.J. Elliot. 2008. Modeling changes in rill erodibility and critical shear stress on native surface roads. Hydrological Processes. DOI: 10.1002/hyp.7092. Foster, G. R. and L. J. Lane, compilers. 1987. User Requirements: USDA-Water Erosion Prediction Project (WEPP). NSERL Report No. 1. W. Lafayette, IN: USDA-ARS, National Soil Erosion Research Laboratory. Grismer, M.E., A.L. Ellis and A. Fristensky. 2008. Runoff sediment particle sizes associated with soil erosion in the Lake Tahoe Basin, USA. Land Degrad. Develop. 19: 331-350. 16

Grismer, M.E. and M.P. Hogan. 2005. Simulated ranfall evaluation of revegetation/mulch erosion control in the Lake Tahoe Basin - 3: Soil treatment effects. Land Degrad. Develop. 13:1-13. Laflen JM, Elliot WJ, Flanagan DC, Meyer CR, Nearing MA (1997) WEPP-predicting water erosion using a process-based model. Jour. of Soil and Water Conservation 52(2), 96-102. Luce, C. H., and T. A. Black. 1999. Sediment production from forest roads in western Oregon. Water Resources Research 35(8): 2561-2570. Miller, W.W., D.W. Johnson, C. Denton, P.S.J. Verburg, G.L. Dana and R.F. Walker. 2005. Inconspicuous nutrient laden surface runoff from mature forest Sierran watersheds. Water, Air and Soil Pollution 163:3-17. Robichaud, P. R. 1996. Spatially-varied erosion potential from harvested hillslopes after prescribed fire in the interior northwest. Ph. D. dissertation. Moscow, ID:University of Idaho. 219 pp. Srivastava, A. 2013. Modeling of hydrological processes in three mountainous watersheds in the U.S. Pacific Northwest. PhD Dissertation. Pullman, WA: Washington State University. 170 p. Srivastava, A., M. Dobre, J. Wu, W. Elliot, E. Bruner, S. Dun, E. Brooks, and I. Miller. 2013. Modifying WEPP to improve streamflow simulation in a Pacific Northwest Watershed. Trans. ASABE 56(2): 603-611. Stednick, J.D. (2010) Effects of fuel management practices on water quality, Chapter 8. 149-163. In Elliot, W.J., I.S. Miller and L. Audin (eds.). Cumulative Watershed Effects of Fuel Management in the Western U.S. Gen. Tech. Rep. RMRS-GTR-231. Fort Collins, CO: U.S. Dept. of Agric., Forest Service, Rocky Mountain Research Station. Wagenbrenner, J. W., P. R. Robichaud, and W. J. Elliot (2010), Rill erosion in natural and disturbed forests: 2. Modeling Approaches, Water Resour. Res., 46, W10507, doi:10.1029/2009wr008315. Wikipedia. 2010. Albedo. Online at <http://en.wikipedia.org/wiki/albedo>. Accessed May, 2010. 17

Appendix 1. Soil Database for the Tahoe Basin Sediment Model The variables shown here are in the same format, with the same units as described in the WEPP User Summary (Flanagan and Livingston, 1995) Soil parameter database for Tahoe Basin model -- modified from Disturbed WEPP by W Elliot, May, 2010 Granitic 'Skid' 'Granitic' 1 0.2 0.75 2700000 0.001 4 10 300 90 2 4 3 rfg 'HighF' 'Granitic' 1 0.1 0.75 1800000 0.0005 4 15 300 90 2 3 3 rfg 'LowF' 'Granitic' 1 0.15 0.75 1000000 0.0003 4 20 300 90 2 4 3 rfg 'Sod_G' 'Granitic' 1 0.15 0.75 750000 0.0001 4 25 350 90 2 4 3 rfg 'BnchG' 'Granitic' 1 0.15 0.75 600000 0.00008 4 30 400 90 2 5 3 rfg 'Shrub' 'Granitic' 1 0.15 0.75 500000 0.00006 4 35 500 90 2 5 3.5 rfg 'YForst''Granitic' 1 0.1 0.5 400000 0.00004 4 40 600 90 2 5 4 rfg 'OForst''Granitic' 1 0.1 0.5 250000 0.00003 4 45 800 90 2 6 4 rfg 'Bare' 'Granitic' 1 0.2 0.75 300000 0.001 4 25 300 90 2 2 2 rfg 'Mulch' 'Granitic' 1 0.2 0.75 300000 0.001 4 30 400 90 2 4 2.5 rfg 'Till' 'Granitic' 1 0.15 0.75 300000 0.001 4 35 500 90 2 6 3 rfg 'Lowr' 'Granitic' 1 0.2 0.75 225000 0.0013 4 10 200 90 2 1 2 rfg 'Highr' 'Granitic' 1 0.2 0.75 900000 0.005 4 10 200 90 2 1 2 rfg Volcanic 'Skid' 'Volcanic' 1 0.2 0.75 3000000 0.0008 1.5 8 300 65 7 4 8 rfg 'HighF' 'Volcanic' 1 0.1 0.75 2000000 0.0004 1.5 10 300 65 7 3 7 rfg 'LowF' 'Volcanic' 1 0.15 0.75 1500000 0.0002 1.5 15 300 65 7 4 7 rfg 'Sod_G' 'Volcanic' 1 0.15 0.75 1000000 0.00008 1.5 20 350 65 7 4 8 rfg 18

'BnchG' 'Volcanic' 1 0.15 0.75 900000 0.00006 1.5 25 400 65 7 5 8 rfg 'Shrub' 'Volcanic' 1 0.15 0.75 800000 0.00004 1.5 30 500 65 7 5 8.5 rfg 'YForst''Volcanic' 1 0.1 0.5 700000 0.00003 1.5 35 600 65 7 5 9 rfg 'OForst''Volcanic' 1 0.1 0.5 600000 0.00002 1.5 40 800 65 7 6 9 rfg 'Bare' 'Volcanic' 1 0.2 0.75 750000 0.0008 1.5 20 300 65 7 2 7 rfg 'Mulch' 'Volcanic' 1 0.2 0.75 750000 0.0008 1.5 25 400 65 7 4 7.5 rfg 'Till' 'Volcanic' 1 0.15 0.75 750000 0.0008 1.5 30 500 65 7 6 7.5 rfg 'Lowr' 'Volcanic' 1 0.2 0.75 250000 0.001 1.5 8 200 65 7 1 7 rfg 'Highr' 'Volcanic' 1 0.2 0.75 1000000 0.004 1.5 8 200 65 7 1 7 rfg Alluvial 'Skid' 'Alluvial' 1 0.2 0.75 2500000 0.0006 1 6 400 60 10 5 12 rfg 'HighF' 'Alluvial' 1 0.1 0.75 1500000 0.0003 1 8 400 60 10 4 11 rfg 'LowF' 'Alluvial' 1 0.15 0.75 1000000 0.0002 1 10 400 60 10 5 21 rfg 'Sod_G' 'Alluvial' 1 0.15 0.75 900000 0.00006 1 15 450 60 10 5 22 rfg 'BnchG' 'Alluvial' 1 0.15 0.75 800000 0.00005 1 20 500 60 10 6 12 rfg 'Shrub' 'Alluvial' 1 0.15 0.75 700000 0.00003 1 25 600 60 10 6 13 rfg 'YForst''Alluvial' 1 0.1 0.5 600000 0.00002 1 30 700 60 10 6 13 rfg 'OForst' 'Alluvial' 1 0.1 0.5 500000 0.00001 1 35 900 60 10 7 13 rfg 'Bare' 'Alluvial' 1 0.2 0.75 600000 0.0006 1 15 400 60 10 3 10 rfg 'Mulch' 'Alluvial' 1 0.2 0.75 600000 0.0006 1 20 500 60 10 5 12 rfg 'Till' 'Alluvial' 1 0.15 0.75 600000 0.0006 1 25 600 60 10 6 13 rfg 'Lowr' 'Alluvial' 1 0.2 0.75 240000 0.0008 1 6 200 60 10 1 10 rfg 'Highr' 'Alluvial' 1 0.2 0.75 950000 0.003 1 6 200 60 10 1 10 rfg 19

Rock/Pavement 'Skid' 'Rock/Pave' 1 0.2 0.75 100 0.000001 20 0.1 300 60 5 1 4 rfg 'HighF' 'Rock/Pave' 1 0.1 0.75 100 0.000001 20 0.1 300 60 5 1 4 rfg 'LowF' 'Rock/Pave' 1 0.15 0.75 100 0.000001 20 0.1 300 60 5 1 4 rfg 'Sod_G' 'Rock/Pave' 1 0.15 0.75 100 0.000001 20 0.1 300 60 5 1 4 rfg 'BnchG' 'Rock/Pave' 1 0.15 0.75 100 0.000001 20 0.1 300 60 5 1 4 rfg 'Shrub' 'Rock/Pave' 1 0.15 0.75 100 0.000001 20 0.1 300 60 5 1 5 rfg 'YForst' 'Rock/Pave' Does not exist 00 'OForst' 'Rock/Pave' Does not exist 00 'Bare' 'Rock/Pave' 1 0.2 0.75 100 0.000001 20 0.1 300 60 5 1 4 rfg 'Mulch 'Rock/Pave' 1 0.2 0.75 100 0.000001 20 0.1 300 60 5 1 4 rfg 'Till' 'Rock/Pave' 1 0.15 0.75 100 0.000001 20 0.1 300 60 5 1 5 rfg 'Lowr' 'Rock/Pave' 1 0.2 0.75 100 0.000001 20 0.1 200 60 5 1 4 rfg 'Highr' 'Rock/Pave' 1 0.2 0.75 100 0.000001 20 0.1 200 60 5 1 4 rfg rfg = 0.5 for all Rock/Pavement soils Lower element for Rock/Pave to be Alluvial in all cases 20