Two-­‐dimensional Numerical Modeling of Suspended Sediment on the Trout Creek Floodplain SNPLMA Round 8 Final Report Stephen Andrews S. Geoffrey Schladow Daniel Nover Tahoe Environmental Research Center University of California, Davis Abstract This study seeks to quantify the deposition and retention of fine suspended sediment on a floodplain of Trout Creek (South Lake Tahoe, CA) using a two-­‐dimensional numerical model. Estimating and determining methods of increasing floodplain sediment deposition in the South Lake Tahoe area is important because of the large detrimental effect fine sediment loads from this area have on Lake Tahoe clarity. The 2D hydrodynamic flooding model BreZo was chosen for this study, and the model was applied to a previously restored reach of Trout Creek, between Pioneer Trail and Martin Avenue. Modifications were made to BreZo to improve the model’s treatment of scalar dispersion, turbulence, hydrology, and bottom boundary friction. Sub-­‐modules were added to simulate water temperature and suspended sediment. The model was calibrated on a snowmelt pulse from the spring of 2003, for which inflow condition data was known. The calibrated model indicated that, of the physical mechanisms leading to fine sediment removal considered, flocculation was the largest, with gravitational settling and sediment stranding by infiltration also being significant. This strongly suggests that floodplains have great potential for removing fine sediment, as these sediments are the ones most prone to removal by flocculation. Floodplain modification simulations indicated that changes made to the floodplain vegetation and the addition of small backwater depression areas will have minimal impacts on overall sediment retention. By contrast, small weirs placed in the channel during flooding will have large effects on fine sediment removal, with increases in sediment retention of 20%, if weir placement results in the flooding of previously dry areas. The testing of the ideal placement and sizing can be readily achieved using BreZo. Weirs, through their low costs and low planning demands may have value as an interim floodplain BMP while more extensive and more broadly beneficial stream/floodplain restoration projects are being developed. Acknowledgements This research was supported by an agreement from the USDA Forest Service Pacific Southwest Research Station. It was supported in part using funds provided by the Bureau of Land Management through the sale of public lands as authorized by the Southern Nevada Public Land Management Act. We thank Bill Sluis, Daret Kehlet, Bill Fleenor, and Russ Wigart for their help creating, deploying, and helping to maintain many of the field sensors used in this study. Scott Carroll and Cyndie Walck are gratefully acknowledged for their time and introductions to the field sites. We thank other floodplain researchers in the Tahoe area Virginia Mahacek, Eddy Langendoen, and Nicole Beck for their openness for collaboration, sharing of field data, and constructive comments on this work. Comments on the draft of the final report by Jonathan Long are also appreciated. Table of Contents Abstract........................................................................................................................................................ 2 Acknowledgements ..................................................................................................................................... 3 Introduction ................................................................................................................................................. 7 Importance of floodplains........................................................................................................................ 7 Objectives ................................................................................................................................................ 7 Field Site ...................................................................................................................................................... 8 Model description...................................................................................................................................... 10 Laboratory Experiments ............................................................................................................................ 13 Settling Experiments .............................................................................................................................. 13 Flume Experiments ................................................................................................................................ 16 Model Grid ................................................................................................................................................. 21 Field Data ................................................................................................................................................... 26 Model Calibration and Validation .............................................................................................................. 34 Model Simulations ..................................................................................................................................... 37 Discussion and Future Work ...................................................................................................................... 41 References ................................................................................................................................................. 42 Table of Figures Figure 1 Map showing Trout Creek floodplain field site location, South Lake Tahoe, CA. .......................... 8 Figure 2 USGS measured Trout Creek discharge at Pioneer Trail, 1990-­‐2009 (blue line). Red line indicates bankfull discharge........................................................................................................................................ 9 Figure 3 Example of unstructured grid with mesh refinement on the right half. ...................................... 10 Figure 4 Illustration of flow through and over submerged vegetation, and the resulting shear-­‐induced exchange between the layers. ................................................................................................................... 11 Figure 5 Illustration of heat transfer processes included in the temperature model................................ 12 Figure 6 LISST-­‐100X laser diffraction size analyzer. ................................................................................... 13 Figure 7 Typical particle size distribution for Trout Creek suspended sediment....................................... 14 Figure 8 LISST size analyzer with attached settling chamber. Particles fall a distance L before they are detected by the laser sensor. .................................................................................................................... 14 Figure 9 Theoretical time series of particle concentrations for 8 size classes during an experiment. Settling velocities are calculated as the length of the settling column divided by the time at which particle concentrations reach zero. ........................................................................................................... 15 Figure 10 Measured settling concentration histories for 8 particle size classes for Trout Creek suspended sediment samples. Median particle diameter for the class is given below each plot. Concentrations are given on the y-­‐axis in units of µL/L, and time is given on the x-­‐axis in seconds. ....................................... 16 Figure 11 Flume setup. Water is circulated in a clockwise direction through an array of wooden dowels and past an acoustic Doppler velocimeter (ADV) and the LISST particle size analyzer. ............................ 17 Figure 12 Particle size distributions shown as percent finer fraction (left) and bin concentration (right) for the three particle samples used in the flume particle capture study. ................................................. 17 Figure 13 Particle size distribution results obtained for flume experiments using three particle types and three flume treatments. Individual graphs are explained in the text........................................................ 18 Figure 14 Road dust particles trapped in dowel wakes for the without biofilm dowel treatment. .......... 19 Figure 15 Close-­‐up of dowel coated in organic biofilm (with organic filaments visible) and captured road dust particles for flume experiment with biofilm treatment..................................................................... 19 Figure 16 Model grid demarcation from Google Earth (left) and resulting .ply file (right) showing floodplain, channel, former channel, and near-­‐channel regions............................................................... 21 Figure 17 Trout Creek model grid. ............................................................................................................. 22 Figure 18 Locations of RTK GPS surveying data taken. .............................................................................. 23 Figure 19 Field site topography. X and y axes are given for the Easting/Northing UTM zone 11 grid. Elevation data is in m................................................................................................................................. 23 Figure 20 Typical vegetation at Trout Creek floodplain site. Photo taken spring 2009. ............................ 24 Figure 21 Google Earth aerial image of a floodplain and channel area, with vegetation community training pixels shown as colored boxes (top). The resulting fully classified image is shown below. Red pixels indicate pine community, cyan pixels indicate grass/sedge community, yellow pixels indicate willow community, and dark blue pixels indicate the channel.................................................................. 25 Figure 22 Trout Creek field monitoring schematic. ................................................................................... 27 Figure 23 Wind speed measured at meteorological station near the Trout Creek-­‐Cold Creek confluence, Nov 2007–Sep 2008. .................................................................................................................................. 28 Figure 24 Wind direction histogram, Nov 2007 -­‐ Sep 2008. ...................................................................... 28 Figure 25 Atmospheric pressure, Nov 2007 -­‐ Sep 2008............................................................................. 29 Figure 26 Shortwave radiation, Nov 2007 -­‐ Sep 2008................................................................................ 29 Figure 27 Air temperature, Nov 2007 -­‐ Sep 2008. ..................................................................................... 30 Figure 28 Relative humidity, Nov 2007 -­‐ Sep 2008. ................................................................................... 30 Figure 29 River bank and stream temperature, Nov 2007 -­‐ Sep 2008....................................................... 31 Figure 30 Trout Creek discharge (gray line, right axis) and fine suspended sediment concentration (blue line, left axis) measured at Marin Avenue. Time of LISST sampling is indicated with the arrow. Data and plot from 2nd Nature (2011). .................................................................................................................... 32 Figure 31 LISST measured particle size distributions for Trout Creek at Pioneer Trail (TCIN), just above the confluence with Cold Creek, and at Martin Ave (TCOUT). .................................................................. 33 Figure 32 LISST measured Cold Creek floodplain and channel water........................................................ 33 Figure 33 Locations of hydrodynamic model boundary conditions........................................................... 34 Figure 34 Trout Creek and Cold Creek boundary flows and suspended sediment inputs, spring 2003. ... 35 Figure 35 Measured and modeled Trout Creek outlet temperatures, spring 2003 calibration period. .... 36 Figure 36 Distribution of removal mechanisms according to mass of the finest sediment size class removed from suspension. ........................................................................................................................ 37 Figure 37 Floodplain deposition/loss maps due to (from left to right) gravitational settling, stranding, flocculation, and vegetative capture for the smallest size class................................................................ 38 Figure 38 Sediment retention relative to base case for floodplain modification model runs with all vegetation as grass, all vegetation as willows, and with original vegetation and the additional of a small weir. ........................................................................................................................................................... 38 Figure 39 Dependence of sediment removal efficiency gains from base case on weir placement. .......... 39 Figure 40 Dependence of sediment removal efficiency gains from base case on pond placement, depth, and number. .............................................................................................................................................. 40 Introduction Importance of floodplains Floodplains ecosystems are ecotones – areas located at the interface of aquatic and terrestrial environments. They typically have high species diversities and are responsible for numerous ecosystem and societal benefits, including water quality improvement, groundwater recharge, habitat for native species, air quality improvement, and downstream flood control. Because of the large expansion in available flow area that takes place when a river overtops its banks, flow velocities decrease, residence time increases, and the lower energy environment allows sediment deposition to take place. Many rivers in the South Lake Tahoe region have become incised due to channel straightening, flow control structures, or floodplain development. These rivers and their associated floodplains are now being targeted for restoration and improvement because of their sediment retention capacities and the associated benefits floodplains provide. Increases in floodplain sediment retention are important because of the corresponding decreases in downstream sediment loads to Lake Tahoe, the largest of which take place during times of flooding and could be ameliorated with improved floodplain restoration strategies. Objectives The objective of this study is to create and implement a two-­‐dimensional hydrodynamic and suspended sediment model in order to assess the sediment retention capacity of South Lake Tahoe floodplains. This model will then be used to explore what simple changes to a restored floodplain could be made (for example plantings of specific vegetation types, or small berm or weir placements) in order to maximize floodplain fine sediment retention. The model will separate the physical processes responsible for the sediment removal and elucidate why certain modifications result in higher sediment retention. The correctly calibrated and validated model may then be applied to additional field sites in the South Lake Tahoe area to estimate or predict sediment removal associated with a variety of flood events. Field Site The field site selected for this study is located on the Trout Creek River between Pioneer Trail and Martin Avenue, in South Lake Tahoe, CA. The site is the former location of the Trout Creek Stream Restoration and Wildlife Enhancement Project, completed in 2001. It is an excellent study site for two main reasons: the recent stream restoration work increases the probability of collecting overbank field data needed to validate the model, and the site has well defined inlets and outlets. USGS gaging stations measure flow at the upstream and downstream ends of the field site, and steep granite walls bound the field site to the east and west. Approximately halfway through the field site, Cold Creek enters Trout Creek, increasing the flow by approximately 40%, depending on the time of year. The length of the field site is approximately 1.7 km, and the width of the floodplain averages about 150 m. Figure 1 Map showing Trout Creek floodplain field site location, South Lake Tahoe, CA. Figure 2 shows the long term discharge record measured at the USGS station on the upstream (south) end of the field site. Peak yearly discharges typically occur in late May or early June and are associated with spring snowmelt, although three out of the top four highest discharges have been recorded during early winter (resulting from rain on snow events). Using the bankfull discharge threshold measured by Swanson Hydrology and Geomorphology (2004), determined based on the restored, post-­‐2001 upper reach channel, overbank flows have occurred or would have occurred in just under 50% of water years since 1990. Figure 2 USGS measured Trout Creek discharge at Pioneer Trail, 1990-­‐2009 (blue line). Red line indicates bankfull discharge. Model description The hydrodynamic model chosen for use in this study is BreZo, a two-­‐dimensional finite-­‐volume model built specifically to simulate flooding simulations (Begnudelli and Sanders, 2006). The model includes several flooding specific algorithms, including •
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Wetting and drying of computational cells using a method that prevents artificial concentration of sediment near wetting fronts The ability to handle subcritical to supercritical flow transitions, common in flow over varying topography The use of an unstructured grid (Figure 3), allowing the model to accurately represent complex boundaries found in floodplains and have higher resolution in areas near the Trout Creek channel Figure 3 Example of unstructured grid with mesh refinement on the right half. Several minor modifications were made to the model in order to more accurately simulate processes of interest to this study. In particular, the original treatment of bottom roughness in BreZo (using constant Manning’s n values) was deemed insufficient to predict flow and sediment deposition responses to changes in vegetation. The method of Baptist et al. (2007) was implemented in order to predict floodplain bottom roughness values based on field measured vegetation characteristics, including average vegetation stem diameter, height, and density (number of stems per unit area). The source term for the model continuity equation was modified to account for additions of water from precipitation, and losses due to evapo-­‐transpiration and seepage to groundwater. An Elder-­‐type parameterization for eddy viscosity was used to add a turbulence closure module to BreZo, and terms were added to the momentum equation to account for wind stress at the water surface. Because the dispersion and diffusion of sediment is so important to predicting its transport and deposition patterns in the floodplain, and improved method was implemented. The method chosen was described by Murphy et al. (2007) and accounts for the trapping effects of vegetation on longitudinal dispersion. In flow over submerged vegetation there are typically two zones – a lower quiescent zone at the bottom, and a faster moving zone above. The shear between these zones of different velocity creates vertical eddies that facilitate exchange between the two layers. This mechanism has predictable effects on longitudinal dispersion that were elucidated by Murphy et al. (2007) and have been implemented in BreZo for this study. Figure 4 Illustration of flow through and over submerged vegetation, and the resulting shear-­‐induced exchange between the layers. Finally, two water quality modules were added to BreZo – one to simulate water temperatures, and another to simulate fine suspended sediment. Water temperatures were chosen for addition to the model for a few reasons; (1) they are simple to measure in the field and can be used to calibrate parameters in the dispersion model, which will help inform the suspended sediment model, (2) some suspended sediment dynamics, namely flocculation, depend on the water temperature, and (3) water temperatures are important in their own right and control the growth and development of many aquatic organisms, play a large role in aquatic chemistry and nutrient dynamics, and influence evaporation calculations. Included in the module are mechanisms of heat transfer between the water and atmosphere that include penetrative shortwave radiation, downwelling and upwelling longwave radiation, and sensible and evaporative heat fluxes. Heat transfer mechanism between the channel or floodplain bed and the water column include conduction and friction (which is generally small except when shallow water is moving quickly over very rough surfaces). These mechanisms are illustrated in Figure 5. Figure 5 Illustration of heat transfer processes included in the temperature model. Five mechanisms of suspended sediment dynamics were included in the suspended sediment module: gravitational settling, vegetative capture, stranding due to infiltration, flocculation, and entrainment. Gravitational settling is the simple removal of fine sediment from suspension by gravitational deposition onto the channel bed or floodplain surface. Vegetative capture is the process whereby sediment in suspension is flowing through submerged vegetation, impacts a vegetative surface, sticks, and is effectively removed from suspension. This process has been demonstrated to be significant in flows through dense vegetation. Stranding due to infiltration is the process whereby water containing suspended sediment ponds, infiltrates, and the sediment is stranded on the floodplain. This process can also be considered as forced settling. The process of flocculation removes fine sediment particles only indirectly. Fine particles in suspension impact one another and may be retained to create aggregates. Although the aggregates are still in suspension, they are subject to faster settling rates and higher vegetative capture rates and are consequently more likely to quickly be removed from suspension. Additionally, the aggregates have a lower cross-­‐sectional area per unit volume than the individual fine particles and thus have a less significant impact on water clarity. Four mechanisms of particle collision (leading to flocculation) are simulated in the model – collisions due to Brownian motion, collisions in shear flows, differential settling, and inertial collisions. Size classes 0.5–4 µm, 4–8 µm, and 8–32 µm are simulated in the model. Laboratory Experiments There are two important coefficients involved in the calculations taking place in the suspended sediment module that have high uncertainties associated with them: settling velocities and vegetative capture efficiencies. In order to better inform the numerical model, a series of laboratory experiments were performed to obtain more accurate values for these coefficients. Settling Experiments Settling velocities experiments were performed using the LISST-­‐100X particle size analyzer (Figure 6). The LISST uses the principle of laser diffraction to measure suspended sediment concentrations (in units of volume concentration) for 32 logarithmically spaced size bins ranging from 1.25 to 250 µm. Figure 6 LISST-­‐100X laser diffraction size analyzer. An example of a typical particle size distribution for a Trout Creek sample is shown in Figure 7. Figure 7 Typical particle size distribution for Trout Creek suspended sediment. The LISST-­‐100X measures suspended sediment concentrations quickly (in fractions of a second) allowing for large sample sizes. It can also be used in situ, so that particle dynamics during sample handling (due to changes in water temperature or turbulent conditions) do not complicate sample analyses. It can be used to measure particle settling rates by attaching a settling column, similar to that shown in Figure 8. Figure 8 LISST size analyzer with attached settling chamber. Particles fall a distance L before they are detected by the laser sensor. A well mixed sample of Trout Creek water containing suspended particles was introduced into the settling column, and the LISST was started to record samples every 5 seconds. Over time, particles settled through the length of the settling column, L. At some time t, the last of the particles of a particular size class (the ones located at the very top of the settling column at the start of the experiment) settle past the laser detector, and no more particles of that size class are detected. The settling velocity for that size class can then be calculated as L divided by t. Figure 9 Theoretical time series of particle concentrations for 8 size classes during an experiment. Settling velocities are calculated as the length of the settling column divided by the time at which particle concentrations reach zero. An example of a close to ideal settling behavior is shown for 8 aggregated size classes in Figure 9. Figure 10 shows a typical example of the settling time histories of Trout Creek samples. Because of the way the LISST detects particles, there is some leakage from the smallest size bins into larger bins. This is the reason particle concentrations for the intermediate size bins level out before reaching the zero concentration point. Settling velocities for these bins were calculated using the time at which the concentrations leveled off. For the smallest size class, the time required for particles to settle is not clear and does not occur during the course of the experiment. There may be a couple reasons for this: (1) the settling experiment was not set up properly, and small convection currents within the settling chamber kept the smallest particles in suspension, or (2) the smallest particles are of a density very close to that of water and are thus neutrally buoyant; this would be the case if the particles were of organic composition. For the smallest modeled size bin (0.5–4 µm), the average setting velocity of the first two size classes was used as the model coefficient. Figure 10 Measured settling concentration histories for 8 particle size classes for Trout Creek suspended sediment samples. Median particle diameter for the class is given below each plot. Concentrations are given on the y-­‐axis in units of µ L/L, and time is given on the x-­‐axis in seconds. Flume Experiments Vegetative capture efficiencies can be measured in a controlled laboratory setting using a recirculating flume setup. Sediment laden water is introduced into the flume and is circulated either through an array of dowels or past a single isolated dowel (see Figure 11). Capture efficiencies are calculated by either manually counting particles being retained on individual dowels, or by examining time series records of particle concentration. Capture efficiencies are known to be a function of the diameter of the dowels, the diameter of the particle, the velocity of the flow, and the density and spacing of the dowels. Figure 11 Flume setup. Water is circulated in a clockwise direction through an array of wooden dowels and past an acoustic Doppler velocimeter (ADV) and the LISST particle size analyzer. Previous experiments were done by Palmer et al. (2004) on isolated cylinders and Purich (2006) on arrays of dowels. However, the major drawback of their experiments was that plastic Pliolite particles were used, and impaction on plastic, grease-­‐coated cylinders was assessed. In floodplain conditions, particles will be a mix of different compositions (organic, inorganic, and heterogeneous flocs), and floodplain vegetation, after inundated for a period of time, will likely have a biofilm covering its surface. The effects of this biofilm on particle capture rates are unknown, and may be significant. Figure 12 Particle size distributions shown as percent finer fraction (left) and bin concentration (right) for the three particle samples used in the flume particle capture study. Three types of particle types were used in the flume experiments – water with dispersed clay, water with dispersed road dust obtained from a Washoe Country street sweeper, and water from a Davis, CA stagnant waterway containing large concentrations of fine organic particles. Their particle size distributions are shown in Figure 12. The three water samples were subjected to three different treatments using the flume setup, and the results of the nine experiments are shown in Figure 13. The graph in the upper left hand corner of Figure 13 shows the results of the first particle type (clay particles) subjected to the first flume treatment (60 min of recirculation without the dowel array present). The solid line shows the particle size distribution at the start of the run, the dashed line shows the size distribution 30 minutes into the treatment, and the dotted line shows the size distribution at the end of the treatment. A similar plot is shown for the no-­‐dowel treatment for the dispersed road dust particle sample in the middle plot on the left hand side (subplot D). Subplot G shows the no-­‐dowel treatment for the organic particles. The second treatment consisted of running water samples of the same particle types through the recirculating flume with the dowels present for 60 min. The results of this treatment are shown in subplots B, E, and H, for the particle types in the same order as the first column. The rightmost column, subplots C, F, and I show experimental results for the final flume treatment – recirculation through dowels with a pre-­‐grown biofilm on them. Figure 13 Particle size distribution results obtained for flume experiments using three particle types and three flume treatments. Individual graphs are explained in the text. The set of experiments yielded several interesting results. First, there was very little change seen in the sub 3 micron particles, for any treatment or particle type. For the organic particles (bottom row of plots), there were very few differences seen over the entire size range for any of the treatments. The largest treatment effect was seen in the road dust particles treated with the biofilm coated dowels (subplot F); there was a significant decrease in all intermediate and large particle sizes over the dowels without biofilm case. In the clay particles, the presence of a biofilm appears to have inhibited particle capture by the dowels, as removal rates increased from the without-­‐biofilm case. It was interesting to observe the mechanisms of particle removal change between treatments. For the road dust dowel-­‐without-­‐biofilm treatment, particles were mainly removed from suspension by trapping in the wakes behind the dowels (Figure 14). In the with-­‐biofilm treatment, road dust particles were directly removed by impaction on the dowels (Figure 15). Figure 14 Road dust particles trapped in dowel wakes for the without biofilm dowel treatment. Figure 15 Close-­‐up of dowel coated in organic biofilm (with organic filaments visible) and captured road dust particles for flume experiment with biofilm treatment. Although the flume experiments did highlight some of the dependencies of particle capture efficiencies on particle composition and the presence/absence of an organic biofilm on submerged surfaces, the results were not directly applicable to the modeling performed in this study. One reason for this was that the presence or absence of a biofilm on submerged vegetative surfaces in the Trout Creek floodplain during spring flooding is unknown. How fast the biofilm grows, its composition, and its effect on particle capture in the field are all unknowns that are currently being investigated in the Tahoe basin, and may be used in future modeling efforts. Similarly, little is known about the composition of the fine particles in the Trout Creek floodplain. Although the cold water temperatures and mostly granite basin suggest the finest stream particles are most likely terrigenous, the composition of these particles has not been definitely measured and the settling column results presented earlier suggest the finest particles may be organic. However, the flume setup holds much promise for evaluating particle removal rates for flow through vegetation, and will be used accordingly in future studies. Model Grid The BreZo model requires input files describing the two dimensional grid used in simulations. The grid for this study was generated using a free-­‐source Delauney Triangulation program, known as the Triangle Grid Generator (Shewchuk 2002). The field site was first outlined using polygons in Google Earth, and a standard format .ply file was generated. In this .ply file, regions were delineated to include the floodplain area, the channel area, former channel areas (cutoff from the main channel during the 2001 restoration project and now deep narrow pond areas), and the area near the stream. Stream, near-­‐
stream, and former channel areas were given high grid resolution (maximum triangle area 10 m2), and floodplain areas were given lower grid resolution (maximum triangle area 100 m2). The Google Earth demarcation of the field site and resulting .ply file are shown in Figure 16. The resulting grid, having approximately 13,000 nodes and 25,000 triangular cells, is shown in Figure 17. Figure 16 Model grid demarcation from Google Earth (left) and resulting .ply file (right) showing floodplain, channel, former channel, and near-­‐channel regions. Figure 17 Trout Creek model grid. Additional input files are necessary describing the two dimensional distributions of floodplain and channel bed elevations and vegetation heights. Because Lidar data of this area was not freely available at the time, floodplain elevations were obtained from a three day survey of the field site using a real-­‐
time kinetic GPS unit. The area covered during the survey is shown in Figure 19. Large areas that were not covered by the survey were inaccessible due to dense vegetation. The collected topography data were interpolated to the model grid using universal kriging techniques. The final topographic map is shown in Figure 20. Figure 18 Locations of RTK GPS surveying data taken. Figure 19 Field site topography. X and y axes are given for the Easting/Northing UTM zone 11 grid. Elevation data is in m. Additional data input files were required describing the two-­‐dimensional distribution of floodplain vegetation data. These vegetation community maps were generated using automated image processing routines, using images obtained from Google Earth. A typical photo of the vegetation communities at the Trout Creek floodplain site is shown in Figure 21. Figure 20 Typical vegetation at Trout Creek floodplain site. Photo taken spring 2009. Functionally, there are three main vegetation communities – short grasses/sedges, medium height willows, and tall pine trees. These three communities, and additionally the creek channel, can easily be distinguished in aerial images of the floodplain. Routines were written in Matlab to classify each pixel in a digital aerial image according to its red, green, and blue pixel intensities, and its pixel “texture,” a measure of the variation in surrounding pixel intensities. The grass/sedge class was typically brighter (higher pixel intensities in red, blue, and green), and the pine class was usually the darkest (lower pixel intensities). Although the creek and the pine vegetation class both had relatively dark pixels, the creek class had less variation in the intensities of the surrounding pixels and thus could be separated. Standard value ranges for intensities and textures were generated for each of the classes using “training data” (shown as the boxes in Figure 22), and then each pixel in the image was classified based on those data, using a fuzzy logic classification scheme. The resulting image decomposed each pixel from the aerial image into one of four classes (grasses/sedges, willows, pines, or water). Figure 22 shows the input aerial image, training data, and the resulting classified image for a floodplain and channel area. This was performed for images covering the entire field site, and the model grid was overlain with the classified aerial images. For each triangular cell, pixels falling within that cell were located, and the median vegetation class of those pixels was taken as being representative of the entire cell. All cells demarcated as channel cells in the .ply file, however, were forced to have no vegetation community data. This removed the problem of erroneously locating areas of high vegetation roughness in the middle of the channel. Figure 21 Google Earth aerial image of a floodplain and channel area, with vegetation community training pixels shown as colored boxes (top). The resulting fully classified image is shown below. Red pixels indicate pine community, cyan pixels indicate grass/sedge community, yellow pixels indicate willow community, and dark blue pixels indicate the channel. A vegetation survey was performed to obtain average values for vegetation stem density, stem diameter, and vegetation height for each of the classes. These characteristics were applied to all cells classified as having that vegetation type in the model input files. Field Data Attempts were made to monitor and measure flow (water depth and discharge) and water quality (temperature, turbidity, conductivity, deposited sediment) characteristics of overbank events during the spring runoff seasons of 2007–2009. Figure 23 shows the field instrument locations and densities. Figure 22 Trout Creek field monitoring schematic. Unfortunately, no overbank flow occurred in this reach of the Trout Creek floodplain for the years 2007–
2009. Some useful spring meteorological base condition information was recorded (Figures 25–31), but baseline stream turbidity was typically below instrument detect levels. Figure 23 Wind speed measured at meteorological station near the Trout Creek-­‐Cold Creek confluence, Nov 2007–Sep 2008. Figure 24 Wind direction histogram, Nov 2007 -­‐ Sep 2008. Figure 25 Atmospheric pressure, Nov 2007 -­‐ Sep 2008. Figure 26 Shortwave radiation, Nov 2007 -­‐ Sep 2008. Figure 27 Air temperature, Nov 2007 -­‐ Sep 2008. Figure 28 Relative humidity, Nov 2007 -­‐ Sep 2008. Figure 29 River bank and stream temperature, Nov 2007 -­‐ Sep 2008. Because of this lack of field data, it was necessary to calibrate the hydrodynamic and suspended sediment models based on previous flooding data (described in the next section). In the spring of 2010, however, an overbank event lasting a couple of weeks occurred (Figure 32), and several particle size distributions were obtained at various locations throughout the field site using the LISST-­‐100X. These spot measurements indicated a couple of things. (1) Particle concentrations of all size classes are significantly reduced between Pioneer Trail and the confluence, an area with much overbank flow. These gains in particle attenuation are then partially lost as the water flows through the downstream section of the field site, where the channel is significantly more incised (Figure 30). (2) Measurements of floodplain water from Cold Creek, which was spilling into the Trout Creek channel near the confluence area, indicated significantly fewer particles of all size classes except those above 100 µm (which have the least effect on water clarity) than Cold Creek channel size distributions. This highlights the particle removal capabilities of the floodplain area. Figure 30 Trout Creek discharge (gray line, right axis) and fine suspended sediment concentration (blue line, left axis) measured at Marin Avenue. Time of LISST sampling is indicated with the arrow. Data and plot from 2nd Nature (2011). Figure 31 LISST measured particle size distributions for Trout Creek at Pioneer Trail (TCIN), just above the confluence with Cold Creek, and at Martin Ave (TCOUT). Figure 32 LISST measured Cold Creek floodplain and channel water. Model Calibration and Validation The model was calibrated in two ways. First, the completed model geometry was checked for the lowest discharge at which flooding took place (flows just above bankfull). Flow inputs at the upstream boundary were prescribed at a constant value of 76 cfs and those at the downstream boundary were prescribed at 100 cfs, values identified by the Swanson Hydrology and Geomorphology (2004) report as just above bankfull. Modifications in offset values in the model topographic data were made as needed to meet the initial flooding conditions. Locations of the boundary flows are shown in Figure 33. Figure 33 Locations of hydrodynamic model boundary conditions. Following this calibration, USGS gage data from Trout Creek (at Pioneer Trail and Martin Ave) and Cold Creek (at Pioneer Trail) taken in the spring of 2003 was used to run a simulation. Suspended sediment concentrations, taken as part of the LTIMP program were also recorded for this event and were used at the upstream boundaries (Figure 34). Inflowing water temperatures for Trout and Cold Creek were available as 15 minute event data during the simulation period. Outflow temperatures, however, were only available as daily minimum, maximum and average values. Factors such as those involved in the calculation of scalar dispersion, shading and sheltering values for emergent vegetation, and evaporation and infiltration rates were calibrated based on the correct matching of modeled to measured outlet discharge values and outlet temperatures (Figure 35). No data was available to calibrate the modeled outflowing suspended sediment concentrations to, so literature values were used for model coefficients, and these values were slightly modified to obtain floodplain fine sediment removal efficiencies in the range of 50–70%. This removal efficiency was obtained based on the work of Stubblefield et al. (2006) on a section of Trout Creek downstream of the field site, and has since been confirmed by the work of 2nd Nature (2011). Figure 34 Trout Creek and Cold Creek boundary flows and suspended sediment inputs, spring 2003. Figure 35 Measured and modeled Trout Creek outlet temperatures, spring 2003 calibration period. Model Simulations Simulations of the spring 2003 calibration flood event indicate that particle removal from the smallest size class (the one having the greatest impact on lake clarity) is mainly due to flocculation (Figure 36). However, this assessment is misleading, because those particles may not be removed by other processes, and later disaggregation processes may introduce the fine particles back into suspension. Spatial maps of where the fine sediment removal is taking place are shown in Figure 37. Settling and stranding removal is seen to take place in backwater areas away from the main channel, and vegetative removal is largest in flow through thick vegetation near the channel and in the cutoff channels. Flocculation is very high in the cutoff channels, due to high through flow volumes and the presence of vegetation. Se`ling 12% Vegetabon Impacbon 1% Flocculabon 64% Stranding 23% Figure 36 Distribution of removal mechanisms according to mass of the finest sediment size class removed from suspension. Figure 37 Floodplain deposition/loss maps due to (from left to right) gravitational settling, stranding, flocculation, and vegetative capture for the smallest size class. Several floodplain modification model runs were performed in order to assess whether some simple changes to the floodplain vegetation or topography could be made which would significantly increase floodplain fine sediment retention. Two vegetation modifications were first attempted – one case in which all vegetation was set as having the characteristics of the grass/sedge class, and another where all vegetation was set as having characteristics of the willow class. The resulting increase or decrease in sediment retention relative to the base case was not significant (Figure 38). Figure 38 Sediment retention relative to base case for floodplain modification model runs with all vegetation as grass, all vegetation as willows, and with original vegetation and the additional of a small weir. Modification of the floodplain topography so that a small weir obstructed the channel during spring flooding, however, produced noticeable increases in sediment retention. The location of the weir was varied in order to assess the sensitivity of its location on sediment retention (Figure 39). Weir locations which caused flooding in areas which were previously not inundated (locations 2 and 4) produced the greatest gains in sediment removal. These gains were largely the result of increased settling and stranding mechanisms. Weir location 1 did not produce much additional sediment removal because the area directly upstream of that location was already extensively flooded in the base case. Figure 39 Dependence of sediment removal efficiency gains from base case on weir placement. A final floodplain modification explored was the addition of floodplain depressions, one and two meters deep, and approximately 100 m2 in area. These ponds were placed at the locations shown in Figure 40, and the depth of the ponds was varied, but total increases in sediment removal, even using 8 of these ponds was small compared to the sediment retention gains that the weirs provided. Figure 40 Dependence of sediment removal efficiency gains from base case on pond placement, depth, and number. Discussion and Future Work If the results of this study are to guide future restoration efforts, small weirs are the recommendation of the authors to significantly increase floodplain sediment retention. It is clear that these weirs must be carefully placed so that the resulting flooding inundates previously dry areas and creates the greatest benefits. The decision making process for weir placement and height could best be performed using the modeling tool developed as part of this project, along with decisions on the recurrence interval of present flows and climate-­‐change impacted flows in the future and the use of recently acquired LiDAR data. By contrast, changes in floodplain vegetation or the addition of backwater pond areas are not likely to produce significant effects on sediment retention although they may have other benefits related to habitat, scenic value etc. While the original project scope called for data collection on the Trout Creek floodplain, overbank flows did not occur in either of the winter/spring periods that the project embraced. As a result, sediment capture rates could only be estimated from literature values and should not be used as the basis of cost estimates. Their value is in allowing for a comparison of the relative magnitudes of sediment removal mechanisms. What they do suggest is that aggregation is the most important mechanism on a floodplain, and this is the mechanism that is most effective in the removal of fine sediment. The recommended action of installing small weirs would appear to be a relatively cost effective approach, particularly if the target is fine sediment. The extent of the weirs needed would be small compared to the area of additional floodplain that could be inundated. In addition, inundation would occur at much lower flows than it currently occurs at. The construction of weirs could also be considered as an interim, low-­‐cost step to be undertaken during the often protracted period when a larger and more comprehensive stream restoration project is being planned, designed, permitted and constructed. Such projects have many benefits including the correction of deeply entrenched sections, increased stream sinuosity, and most of all increased frequency of overbank events. However, they can take many years (even decades) to bring to completion. Judiciously placed weirs may temporarily achieve many of the desired benefits, but without actually restoring the stream. Many aspects of this work suggest future studies. In particular, it will be necessary to compare the model results given boundary conditions collected in the spring 2010 and spring/summer 2011 flooding to observed sediment retentions. One of the major limitations of this work in its current state is that model suspended sediment predictions have not been rigorously validated. Additionally, it will be necessary to perform a sensitivity study on the suspended sediment model coefficients. Many of the coefficients were taken from literature values and many have high uncertainties associated with them. The flume experiments described in this report suggest several future studies. Some of these are currently taking place in Cattleman’s Detention basin as part of an associated project, examining the growth and sediment retention characteristics of biofilm coated objects in a field environment. New flume studies that are currently being commenced will address some of the flume study drawbacks described earlier as well as inform future suspended sediment model refinements. References Agrawal, Y. C., and Pottsmith, H. C. 2000. Instruments for particle size and settling velocity observations in sediment transport. Marine Geology 168:89–114. Begnudelli, L. and Sanders, B. 2006. Unstructured grid finite-­‐volume algorithm for shallow water flow and scalar transport with wetting and drying. Journal of Hydraulic Engineering 132:371–384. Baptist, M.J., Babovic, V., Uthurburu, J. R., Keijzer, M. Uittenbogaard, R.E., Mynett, A., and Verwey, A. 2007. On inducing equations for vegetation resistance. Journal of Hydraulic Research 45:435–
450. Murphy, E., Ghisalberti, M., and Nepf, H. 2007. Model and laboratory study of dispersion in flows with submerged vegetation. Water Resources Research 43. Purich, A. 2006. The capture of suspended particles by aquatic vegetation. MS Thesis. The University of Western Australia. Palmer, M. R., Nepf, H. M., and Pettersson, T. J. R. 2004. Observations of particle capture on a cylindrical collector: implications for particle accumulation and removal in aquatic systems. Limnology and Oceanography 49(1):76–85. 2nd nature. 2011. Methodology to Predict Fine Sediment Load Reductions as a Result of Floodplain Innundation in Lake Tahoe Streams. SNPLMA Round 7 Final Technical Report. Shewchuk, J.R. 2002. Delaunay refinement algorithms for triangular mesh generation. Computational Geometry-­‐-­‐-­‐Theory and Applications 22:21–74. Stubblefield, A. P., Escobar, M. I., and Larsen, E. W. 2006. Retention of suspended sediment and phosphorus on a freshwater delta, South Lake Tahoe, California. Wetlands Ecology and Management 14(4): 287–302. Swanson Hydrology and Geomorphology. 2004. Trout Creek Meadow Restoration, 2001–2003 Geomorphic Monitoring: Final Report. Prepared for the City of South Lake Tahoe.