Tahoe Stormwater Assessment and Management for Urban and Roadway Runoff

Alan C. Heyvaert1 John E. Reuter2 Robert G. Qualls3 John J. Sansalone4 Julie R. Midgette4

June 2016

prepared by 1Desert Research Institute, Reno, NV 2University of California, Davis, CA 3University of Nevada, Reno, NV 4University of Florida, Gainesville, FL

prepared for USDA Forest Service, Pacific Southwest Research Station This research was supported through a grant from the USDA Forest Service Pacific Southwest Research Station and 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. http://www.fs.fed.us/psw/partnerships/tahoescience/

The views in this report are those of the authors and do not ne cessary reflect those of the USDA Forest Service Pacific Southwest Research Station or the USDI Bureau of Land Management.

NOTE: The use of brand names and any mention or listing of commercial products or services in this report does not imply an endorsement by the project researchers or discrimination against similar products or services not mentioned.

Recommended citation:

Heyvaert, A.C., J.E. Reuter, R.G. Qualls, J.J. Sansalone, J.R. Midgette. 2016. Tahoe Stormwater Assessment and Management for Urban and Roadway Runoff. Final report. Prepared for the USDA Forest Service, Pacific Southwest Research Station. June 2016.

ii CONTENTS

LIST OF FIGURES ...... v LIST OF TABLES...... v LIST OF ACRONYMS ...... vi BACKGROUND ...... 1 Objectives ...... 2 Presentation Format ...... 2 Key Findings...... 3 Management Implications ...... 5 REFERENCES ...... 6 CONSTRUCTED WETLAND TREATMENT EVALUATION ...... 9 ABSTRACT ...... 9 INTRODUCTION ...... 10 METHODS ...... 12 Site Description...... 12 Sampling and Analysis ...... 13 Statistical Analyses ...... 14 RESULTS ...... 15 DISCUSSION...... 17 Accretion Rates...... 17 Origins of Material Accreted In the Wetland ...... 20 Accretion of Allochthonous vs. Autochthonous Organic Nutrient Elements ...... 21 Evidence of Highway Runoff ...... 22 Particle Size Distribution ...... 23 Management of Sediment Accumulation ...... 24 CONCLUSIONS ...... 25 REFERENCES ...... 26 BATCH SETTLING FOR SNOW AND SNOWMELT TREATMENT ...... 37 ABSTRACT ...... 37 INTRODUCTION ...... 38 OBJECTIVES...... 40 METHODS ...... 40 Site Description...... 40 Snow Sampling ...... 41 Laboratory Analyses ...... 41 Batch Settling Experiments...... 41

iii RESULTS AND DISCUSSION ...... 42 PM and PSD...... 42 Batch Settling Supernatants: Snow PM and PSD...... 43 Batch Settling Supernatants: Snowmelt PM and PSD ...... 44 Turbidity ...... 45 Batch Settling Supernatants: Snow Turbidity...... 46 Batch Settling Supernatants: Snowmelt Turbidity ...... 47 CONCLUSIONS ...... 48 REFERENCES ...... 49 APPENDIX A. CHARACTERIZATION OF RUNOFF & WETLAND SEDIMENT...... A-1

iv LIST OF FIGURES

CONSTRUCTED WETLAND TREATMENT EVALUATION 1. Diagram of stormwater treatment wetland located near intersection of highways SR 28 and SR 89 in Tahoe City, CA...... 33 2. Accretion rates of major plant and algal nutrient elements C, N, P, and S...... 34 3. Particle size distributions of sediment from cores of the wetland basin compared to those of suspended sediments from stormwater runoff...... 35

BATCH SETTLING FOR SNOW AND SNOWMELT TREATMENT 1. Sampling locations along US-50W in the Lake Tahoe watershed (California and Nevada)...... 57 2. PSDs of source area snow, with a cumulative gamma distribution fit to the median of source area PSDs...... 58 3. PSDs and corresponding turbidity relationships for PM batch settling of CALTRANS SMA snow and Firestation # 5 snow...... 59 4. PSDs and corresponding turbidity relationships for PM batch settling of Lake Parkway snowmelt and Takela Dr. snowmelt...... 60 5. PM fractions of snow supernatants and snowmelt supernatants after zero, one, six, and 24 hours...... 61 6. Probability density function of total PM of source area snow and snowmelt samples. .... 62

LIST OF TABLES

CONSTRUCTED WETLAND TREATMENT EVALUATION 1. Wetland basin and watershed characteristics...... 30 2. Concentrations or ratios of elements and ash in the mineral and O horizon of wetland soil...... 31 3. Average annual accretion rates and watershed yields of nutrients and metals over 16 years...... 32

BATCH SETTLING FOR SNOW AND SNOWMELT TREATMENT 1. Locations of sampling (site numbers correspond to marked locations presented in Figure 1)...... 54 2. Statistical summary of PM concentrations and turbidity for source area snow, source area snowmelt, Lake Tahoe ambient water, and control snow...... 55 3. Statistical summary of PM concentration, turbidity, and PSD cumulative gamma distribution parameters for batch supernatants of snow and snowmelt...... 56

v LIST OF ACRONYMS

BMP Best management practice CALTRANS California Department of Transportation CGD Cumulative gamma distribution DON Dissolved organic nitrogen DRI Desert Research Institute EPA United States Environmental Protection Agency FSP Fine sediment particle (<16 µm) HS Hydrodynamic separator LD Laser diffraction LOI Loss-on-ignition (550°C) LRWQCB Lahontan Regional Water Quality Control Board (CA) LSPA Laser particle size analysis NDEP Nevada Division of Environmental Protection NPDES National Pollutant Discharge Elimination System NTU Nephelometric turbidity units PC Percent Composition PM Particulate matter PSD Particle size distribution S.E. Standard error (of the mean) SMA Snow management area SS Suspended sediment SSC Suspended sediment concentration [mg L-1] TCWTS Tahoe City Wetland Treatment System TKN Total Kjehldahl nitrogen TMDL Total Maximum Daily Load TP Total phosphorus TRPA Tahoe Regional Planning Agency TSS Total suspended solids [mg L-1] UOP Unit operation

VS Volume of sample [L]

vi BACKGROUND Lake Tahoe has long been renowned for its exceptional clarity, which decreased over time with development and urbanization in the basin (Goldman 1988). Although urban areas in the Tahoe Basin occupy a relatively small amount of the total watershed, they contribute a substantial portion of pollutant loadings that have caused clarity loss in the lake. It is estimated that 72% of the total loading for fine sediment particles (FSP) derive from urban areas, while 38% of total phosphorus and 16% of total nitrogen derive from this same source (LRWQCB and NDEP, 2011). Since fine particles (<16 µm) and nutrients both contribute to clarity reductions in Lake Tahoe (Jassby et al., 1999; Swift et al., 2006), these are the focus of current pollutant reduction strategies, with an emphasis on reduction in FSP loading (LRWQCB and NDEP, 2008). However, the characterization of these particles in relation to other water quality parameters and in relation to effective methods for particle removal in stormwater runoff from roadways and urban areas are not well understood. Evaluating processes responsible for FSP and nutrient removal will allow these factors to be considered in the design of erosion control projects and BMPs to help reduce fine sediment and nutrient loading to Lake Tahoe. Overall BMP effectiveness in the Lake Tahoe Basin has been reviewed and synthesized in a number of documents (e.g., Reuter and Miller 2000, Reuter et al. 2001, Strecker et al. 2005, 2NDNATURE 2006). Although some BMPs have been extensively monitored for their performance in the Lake Tahoe Basin, these studies tend to be the exception. In particular, the fine sediment particles (<16 µm) that significantly affect lake water clarity have not been well studied, and a better understanding is needed of their characteristics and of the processes involved in fine sediment removal with typical treatment methods. Even at the national level, information on how these fine soil particles in stormwater runoff are trapped and processed in BMPs is largely unavailable (International Stormwater BMP Database). The success of both the Lake Tahoe TMDL and the Tahoe Environmental Improvement Program (EIP) will depend upon a more detailed understanding of the transport and fate of these fine particles within BMPs for effective water quality planning, prioritization for new BMP installations, quantification of BMP effectiveness, and for general BMP design, operation and maintenance. Given the significant expected cost associated with nutrient and FSP load reductions needed in the Tahoe Basin, and the fact that this effort will occur over a decadal timeframe (Lahontan and NDEP 2008), it is relevant to examine the characteristics of urban runoff water treatment associated with fine sediment particles and other important water quality characteristics, such as turbidity, total and size fractionated suspended solids and phosphorus loading. This information will be needed for improved management models and to determine the effectiveness of fine sediment removal by processes and unit operations that target this removal in different types of BMPs.

1 OBJECTIVES The overall goal of this project was to extend our understanding of treatment for urban and roadway runoff priority pollutants in relation to BMP effectiveness in the Tahoe Basin. These objectives included three primary areas of inquiry:  Assess functional relationships between fine sediment particle concentrations, turbidity, fractional suspended solids and phosphorus in urban stormwater runoff.  Provide information on the potential efficiency of fine particle removal from urban runoff in wetland treatment systems and in settling basins for roadway snow and snowmelt treatment.  Evaluate how well pollutant removal processes for sediment particles and nutrients perform over the typical life cycle period of a constructed treatment wetland.

Initially, the approach for this project was focused on evaluating performance characteristics in the Tahoe City Wetland Treatment System, as part of a constructed stormwater system implemented in 1998. Unfortunately, that wetland had reached its end-of-life cycle due to accumulated material and hydraulic short-circuiting, so it was taken off-line during the course of this project. Efforts were refocused on evaluating the material accumulated within this basin over its lifespan as a treatment wetland, and to use this information as an indication of performance and treatment capacity. A companion study examined the clarification of sediment particles in snowmelt as a function of batch settling time. Some additional analyses and tests on urban and highway runoff samples from the Tahoe Basin were conducted to evaluate characteristics relevant to treatment processes and pollutant removal. The overall approach taken in this project was to: 1) Analyze stormwater runoff samples for distribution of fine sediment particle concentration, turbidity and size-fractionated nutrient content. 2) Collect sediment cores from a stormwater wetland treatment basin at its end-of-life cycle and analyze these to establish nutrient and fine sediment retention characteristics. 3) Evaluate batch settling characteristics of sediment particles as a function of time for urban and highway snowmelt in the Tahoe Basin. 4) Develop recommendations for restoration and management of stormwater treatment wetlands and roadway snowmelt treatment systems in the Tahoe Basin.

PRESENTATION FORMAT The purpose of this report is to provide an assessment of specific factors relevant to stormwater treatment best management practices (BMPs) in the Lake Tahoe Basin. This document contains two professional papers prepared for publication in peer-reviewed

2 journals, and also presents results from ancillary analyses conducted during the course of this project that are not included in the professional papers. The first chapter is a study on the Tahoe City Wetland Treatment System (TCWTS). This wetland was constructed specifically to treat stormwater runoff from the urban core of Tahoe City and was brought on-line by Placer County Department of Public Works in 1998. The second chapter is a study on the clarification of particulate matter in snowmelt treatment systems as a function of batch settling time. Sampling sites were selected to represent transportation land use source areas and snow management within the Tahoe Basin. Figures and tables follow the text of each chapter, after references cited. In the Appendix we provide additional information on the analysis of Tahoe stormwater samples after laboratory fractionation by particle size, and similarly present the results from analysis of TCWTS sediment cores after size fractionation. Information on calibrated relationships between FSP and turbidity are provided in a previous report (Heyvaert et al. 2015).

KEY FINDINGS Sediment accumulation in the constructed treatment wetland averaged 3.2 centimeters per year. Over the 16-year period of active use this material accretion raised the sediment surface approximately 51 centimeters, which was sufficient to interfere with hydrologic function so that excavation ultimately was required to restore capacity, prevent bypass and reestablish proper flow paths. The wetland was taken offline and allowed to drain for about six months before cores were extracted in fall of 2014. Excavation in the following year removed about 2360 cubic yards of dewatered material from the wetland. This material consisted of dry senescent surface vegetation over an organic-rich layer (52% organic), which overlaid a predominately inorganic mineralized layer (7% organic). The inorganic layer represented about 91% of total accumulated mass by dry weight (excluding surface vegetation). Composition of major nutrients was higher in the organic horizon than in the mineral horizon of the accumulated sediments. Mean concentrations by dry mass in the organic and mineral horizons, respectively, were 0.153% and 0.045% for phosphorus, 1.4% and 0.18% for nitrogen, 27.6% and 2.6% for carbon. The majority of total sediment mass accumulated in the mineral horizon, however, so accretion rates of nutrients were higher in this layer than in the organic layer. Total soil accretion rates in the wetland basin by dry mass were 3.7 g m-2 y-1 for phosphorus, 17.7 g m-2 y-1 for nitrogen, and 280 g m-2 y-1 for carbon. Total inorganic matter accumulation in the cores averaged 7.0 kg m-2 y-1, of which approximately 54–60% consisted of fine sediment particles (FSP) less than 16 µm, indicating an FSP accretion rate of 3.8–4.2 kg m-2 y-1.

3 Accretion of nutrients, metals and inorganic sediments in the Tahoe City constructed treatment system was much higher than found in natural wetlands. This finding corresponds with the typical difference in yield between small urban drainages and larger more natural watersheds. Annual mass yield to the constructed wetland from the Tahoe City drainage, for example, was 1232 kg ha-1 yr-1 compared to yields ranging from 0.84–163 kg ha-1 yr-1 for the ten Lake Tahoe Interagency Monitoring Program (LTIMP) watersheds. Settleable (<75 µm) and suspended (<25 µm) particulate matter (PM) in roadway snow samples collected at sites in the south Tahoe region ranged from 1156–8512 mg L-1, while equivalent fractions of PM in roadway snowmelt runoff samples ranged from 1360–2270 mg L-1. These are very high concentrations compared to control samples of snow (distant from anthropogenic sources) and ambient lake water samples that averaged ~10 mg L-1. Furthermore, roadway snow and snowmelt PM concentrations are typically about an order of magnitude higher than PM concentrations in rainfall runoff. Most of the total mass for PM in roadway snow and snowmelt samples was contained in the sediment (>75 µm) fraction (85–95% by mass). Batch settling experiments were conducted to examine the characteristics of snow and snowmelt PM removal by gravity sedimentation processes. After one hour of settling the roadway source snow and snowmelt samples consisted almost entirely (99%) of suspended and settleable PM (<75 µm). With extended batch settling time the PM suspension became progressively finer until floc formation occurred. This resulted in a supernatant zone over a separate settling zone of coarser floc within six to 24 hours. Even after 24 hours of settling, however, the sample supernatants remained highly enriched with suspended PM <25 µm. The PM in roadway snow supernatants ranged from 146 to 341 mg L-1, while the snowmelt runoff supernatants ranged from 177 to 1572 mg L-1. Similarly, after 24 hours of batch settling, the turbidity of all snow and snowmelt supernatants remained high, ranging from 146 to 1572 NTU. Thus, discharge after detention treatment may exceed TRPA or LRWQCB standards. Size fractionated stormwater runoff samples showed that on average about 85% of turbidity and TP measured in unfiltered samples was associated with the <20 µm size fraction, while approximately 55% of turbidity and 57% of TP was associated with the <10 µm size fraction. For suspended sediment about 59% was in the <20 µm fraction and 40% was in the 10 µm size class. Thus, by interpolation only ~11% of suspended particle mass in these stormwater samples was contained in the 10–16 µm size fraction. Capturing the <10 µm fraction will be crucial for mitigating stormwater impacts on Lake Tahoe clarity. Fractionated size analysis of sediments cores from the Tahoe City wetland showed high percentage accumulations associated with the smaller size classes (<10 µm). For example, about 48% of total particle mass retained by the wetland system was in the <10 µm size fraction and 33% was in the <5 µm size fraction. Fine particle associations with TP were

4 even higher, with 73% and 41% contained in the <10 µm and <5 µm size fractions, respectively. Similarly, for turbidity in core suspensions, 65% was associated with the <10 µm fraction and 42% as associated with the <5 µm size class.

MANAGEMENT IMPLICATIONS Most of the phosphorus in urban stormwater runoff is associated with the FSP (<16 µm) fraction, and more than half of that is contained and transported by the <10 µm size fraction. Similar results were found with turbidity and sediment loading. This suggests that best management practices (BMPs) should place increasing emphasis on the retention of <10 µm particle size fractions. Wetland retention basins efficiently combine the physical properties of a retention basin with the biological characteristics of wetlands. The Tahoe City system was designed to remove nutrients and fine sediments from urban runoff through retention basin physical processes and wetland basin biological properties. It was so successful in meeting these goals that it ultimately accumulated too much material and needed to be restored after 16 years of useful performance. Retention of fine silt and clay-sized particles was much greater than would be expected from a stormwater retention basin without wetland function, which likely contributed to the relatively high rates of phosphorus and FSP removal, including the retention of sediment size fractions finer than 10 µm. Ultimately, accumulated soils in treatment systems must be excavated to maintain treatment performance and storage capacity. Experience with the Tahoe City treatment system suggests this would likely occur on a 15–25 year cycle, depending on input rates and management strategies. Bulk density of sediments in the Tahoe City treatment system was much lower than typical of most wetland soils. Subjecting wetland basins to periodic lowering of the water surface would create more consolidation and less elevation rise, which could increase life- cycle periods. Similarly, periodic draw-down followed by mechanical soil compaction could be useful at extending the time period before restoration maintenance is required. More frequent soil removal strategies could be applied in a block-wise manner to allow recolonization from existing vegetation. This would reduce revegetation costs and maintain treatment performance on a continual basis without incurring the high cost associated with less frequent full-basin excavations. Soils removed from wetland treatment systems in the Tahoe Basin could be used for creating compost or for soil amendments in restoration projects. While concentrations of metals and other elements (Zn, Cu, Fe, Mn, B, Na) in the Tahoe City wetland soils were somewhat elevated, they did not present harmful concentrations for these applications.

5 Roadway snow and snowmelt runoff are important sources of PM and other associated pollutants. Indeed, roadway snow and snowmelt PM concentrations are typically about an order of magnitude higher than PM concentrations in rainfall runoff. Sedimentation based BMPs are one of the most common applications for treatment of runoff water quality. However, clarification is often limited by time before flushing and available storage volume. While most of the total PM mass in Tahoe roadway snow and snowmelt consists of coarse particle sizes >75 µm, which is easily removed by gravity settling, both turbidity and fine particle concentrations are dominated by the <25 µm size fraction, which requires extended detention time for removal. Even after 24 hours of batch settling, the persistence of high levels of suspended PM demonstrates that sedimentation- based systems, hydrodynamic separation or primary clarification are generally insufficient for treatment of Lake Tahoe snow and snowmelt without secondary or advanced unit operations and processes designed and regularly maintained to separate suspended PM. It is critical to understand that removing mass-equivalent portions of coarser FSP grades does not translate into removing equivalent numbers of particles from the finer FSP grades. BMPs should be targeting particle removal efficiency at the smallest size practical, ranging up from 0.5 µm to 16 µm (rather than vice versa).

REFERENCES 2NDNATURE LLC. 2006. Lake Tahoe BMP Monitoring Evaluation Process. Prepared for USFS Lake Tahoe Basin Management Unit, October 2006. Goldman, C.R. 1988. Primary productivity, nutrients, and transparency during the early onset of eutrophication in ultra-oligotrophic Lake Tahoe, California-Nevada. Limnology and Oceanography, 33(6): 1321-1333. Heyvaert, A.C., 2NDNATURE, and J.E. Reuter. 2015. Analysis of Turbidity as a Surrogate Indicator for Fine Sediment Particle Concentrations in the Tahoe Basin. Final report. Prepared for the USDA Forest Service, Pacific Southwest Research Station. December 2015. Jassby, A.D., J.E. Reuter, R.C. Richards and C.R. Goldman. 1999. Origins and scale dependence of temporal variability in the transparency of Lake Tahoe, California- Nevada. Limnology and Oceanography, 44(2): 282-294. Lahontan Regional Water Quality Control Board (LRWQCB) and Nevada Division of Environmental Protection (NDEP). 2008. Lake Tahoe TMDL Pollutant Reduction Opportunity Report v2.0. Lahontan Water Board, South Lake Tahoe, CA, and Nevada Division of Environmental Protection, Carson City, NV. March 2008.

6 Lahontan Regional Water Quality Control Board (LRWQCB) and Nevada Division of Environmental Protection (NDEP). 2011. Final Lake Tahoe Total Maximum Daily Load. Lahontan Water Board, South Lake Tahoe, CA, and Nevada Division of Environmental Protection, Carson City, NV. August 2011. Reuter, J.E. and W.W. Miller. 2000. Aquatic resources, water quality and limnology of Lake Tahoe and its upland watershed. p.215-402. In: D.D. Murphy and C.M. Knopp (eds). The Lake Tahoe Watershed Assessment Vol 1. USDA Forest Service Pacific Southwest Research Station, Gen. Tech. Rep. PSW-GTR-178/176. Reuter, J.E., A.C. Heyvaert, M. Luck and S. Hackley, S. 2001. Land Use Based Stormwater Runoff Monitoring and Evaluation of BMP Effectiveness in the Tahoe Basin. In: Investigations of Stormwater Monitoring, Modeling and BMP Effectiveness in the Lake Tahoe Basin. Report prepared for the Tahoe Regional Planning Agency and the California State Water Resources Control Board, 205j Grant. November 30, 2001. Strecker, E., J. Howell, A. Thayumanavan and M. Leisenring. 2005. Lake Tahoe basin Stormwater BMP Evaluation and Feasibility Study. Prepared for Lahontan Regional Water Quality Control Board and UCD Tahoe Research Group, by GeoSyntec Consultants, July 2005. Swift, T.J., J. Perez-Losada, S.G. Schladow, J.E. Reuter, A.D. Jassby and C.R. Goldman. 2006. Water clarity modeling in Lake Tahoe: Linking suspended matter characteristics to Secchi depth. Aquatic Sciences, 68: 1-15.

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8

ACCRETION OF NUTRIENTS AND SEDIMENT BY A CONSTRUCTED STORMWATER TREATMENT WETLAND IN THE LAKE TAHOE BASIN

by

Robert G. Qualls1 Alan C. Heyvaert2

1University of Nevada, Reno, NV 2Desert Research Institute, Reno, NV

ABSTRACT A wetland stormwater retention basin was established in 1998 and reached its useful lifetime in 16 years. The wetland was designed to serve a particularly important role in removing sediment and nutrients from runoff to one of the world’s most well-known ultra­ oligotrophic lakes. We used coring to measure the accretion of major nutrients and metals over a period of 16 years. The wetland accumulated mass by sedimentation of allochthonous material and net primary production of organic matter. Accretion of C, N, P, and S was greater than in most natural wetlands. The average annual accretion rates in g m-2 yr-1 were as follows: C, 280; N, 17.7; P, 3.74; S, 3.80; Fe, 194; Mn, 2.68; Ca, 30.8; Mg, 30.7; K, 12.2; Na, 2.54; Zn, 0.858; Cu, 0.203; and B, 0.03. The sediment and O horizon together accreted 3.2 cm of depth per year and 7.0 kg m-2 yr-1 of inorganic material. Zn and Cu concentrations in the organic horizon were characteristic of heavy metal pollution from highway runoff. Comparison of the particle size distribution between sediment cores and suspended solids in stormwater runoff indicated that the wetland was efficient in trapping fine silt and even clay sized particles. Sediment cores contained 25% clay and 56% silt. These fine fractions have been implicated in the majority of P flux to Lake Tahoe. A majority of the accretion of most metals and P could be attributed to the efficient trapping of sediment while over half of the accretion of C and N could be attributed to the accumulation of autochthonous organic matter from net primary production. Consequently, the wetland retention basin very efficiently combined the physical properties of a detention basin with the biological properties characteristic of wetlands.

9 INTRODUCTION Stormwater retention basins of various types have become one of the most common small water bodies in urban, residential and agricultural landscapes throughout the developed world. For example, there were over 1000 stormwater detention ponds in one county alone (Hillsborough County, Florida, U.S.) in 2014 (Betts and Alsharif, 2014). The International Stormwater Best Management Practices Database lists 39 detention basins, 68 retention ponds, 30 wetland basins, and 25 composite basins that are intensively monitored for input and output concentrations (Leisenring et al., 2014). Stormwater retention ponds and detention basins are designed to have adequate volume to retain stormwater for long enough periods to settle suspended solids. Fine particle sizes are well known to contain a large portion of phosphorus and metals because of their higher adsorption capacity (Lilienfein et al., 2004). Wetlands are well known for the processes of denitrification, sedimentation, and plant uptake of N, P, and metals, as well as the process of C sequestration (Mitsch and Gosselink, 2015). However, wetlands without a long hydraulic retention time may suffer erosion and less net sedimentation (Mitsch et al., 2014), and many natural wetlands tend to reach equilibrium with elevation rise (Mitsch et al., 2014). A created wetland may combine the advantages of a stormwater retention pond and a wetland if the depth necessary for a long retention time can be balanced against the requirement that the depth not be too great for the growth of wetland plants and if bulk accumulation does not excessively raise water levels. Lake Tahoe, in the subalpine Sierra Nevada of the U.S., is an ultra-oligotrophic lake that is world renowned for its clarity (Goldman, 1988). As a consequence, the best management practices for protecting the lake from stormwater runoff are particularly demanding. Phosphorus is currently the primary limiting nutrient for algal production and thus protection from runoff of fine particulate matter is very important (Sahoo et al., 2013; Sinaj et al., 1997). Historically, concentrations of other nutrients such as N, Fe, and Mo have also been found to limit algal production at various times (Goldman, 1964). In part because of the clarity of the lake, the basin also attracts a great deal of traffic and the watershed area is about 2.4% impervious surface, with almost half of that comprised of roads which tend to be close to the lake shore. (O’Neil‐Dunne et al., 2014). In addition, the high snowfall creates the need for high rates of application of road deicing sand and salt (Fan et al., 2014). The Tahoe City stormwater detention wetland was constructed in 1997 to prevent runoff from the Tahoe City urban and suburban area into the lake. The requirements were particularly demanding because of the extreme sensitivity of Lake Tahoe to nutrient inputs of P and fine sediment that also impacts the clarity of the near-shore environment (Sahoo et al., 2012). A number of simple detention ponds and retention basins have been constructed around the Lake Tahoe Basin but success in retention of fine silt and clay has been limited, as has been the experience in such basins on a national scale (Goldman et al., 1986). The basin that is described in this article was a combination of the concepts of a retention basin and a constructed wetland. The concept is not unique since about 15 studies documented in the

10 International Wetland BMP study were some combination of basins and wetlands (Leisenring et al., 2014). The basin simultaneously combines sedimentation with a second- stage biological processing that can be likened to advanced wastewater treatment. However, this study is of unique importance because we were able to measure the result of 16 years of sedimentation and organic matter production over the useful lifetime of the wetland basin. Short term measurements of input and output inevitably suffer from the exponential influence of floods and fail to integrate the long term processes, such as vegetation growth, that may vary as the basin fills. Most impoundment lakes, created ponds and stormwater retention basins inevitably fill with sediment, and their depth becomes inadequate for the purpose (Morris and Fan, 1998). Consequently, one of the rationales of this study was to document the accretion rate of sediment over a time period that corresponded to the raising of wetland surface to a point that impeded flow. At the Tahoe City constructed wetland this rise in surface appeared to indicate an extraordinary rate of accretion of nutrients and metals. One initial concern when establishing a treatment wetland in the Lake Tahoe Basin was the subalpine winter precipitation and summer drought climate pattern in which most runoff occurs during winter and with snowmelt periods. It was feared that the biological processes that were important in wetlands (e.g. denitrification, and plant uptake) would be inactive during the winter when most small water bodies were frozen. In 2005 this question was the title of a published paper “Can constructed wetlands reduce the diffuse phosphorus loads to eutrophic water in cold temperate regions?” (Braksterud et al., 2005). The paper illustrated the international scope of the concern. A study in water year 2003 compared inflow and outflow concentrations of nutrients to the Tahoe City wetland. Not only were concentrations of nitrate, total N, total P and total suspended solids reduced by 83, 49, 66, and 74%, respectively, but the concentrations of NO3-N were reduced during winter storms and snowmelt runoff events (Heyvaert et al. 2006). Since snowmelt in the Sierra Nevada Mountains is accompanied by a pulse in nitrate concentrations (Sickman et al., 2003), nitrate removal may be most important during these periods. There was also a concern that ice would reduce the volume available for hydraulic retention. Consequently we believed the results of this study will be of interest to wetland scientists in subalpine or boreal climates throughout the world. Based on the rationale presented in the previous paragraphs we evaluated the following hypotheses: (1) Rates of accretion of C, N, P, Fe, Mn, Ca, K, Zn, Cu, and B are higher than is representative of natural wetlands (e.g. Mitsch et al., 2014). (2) Accretion of C and N is largely associated with in situ organic matter production. (3) Over 50% of the concentrations of P and most metals are spatially associated with inorganic matter concentrations.

11 (4) Concentrations of Zn and Cu are higher than normal for area soils and indicate metal contamination from highway runoff. (5) The wetland effectively removed fine sediment particles of clay and very fine silt. To evaluate these hypotheses, we cored through the sediment and O horizon that had accumulated over 16 years of stormwater treatment and analyzed for total concentrations of nutrient elements, including metals that are plant nutrients. We compared the particle size distribution of accreted soil to that of suspended solids in runoff to evaluate Hypothesis 5. Total elemental concentrations were used because they are related to requirements for total maximum daily loads (Sahoo et al., 2012) and they are a common tool for geochemical analysis of sediment origin (Ijmker et al., 2012) that takes into account the fact that many elements (e.g. N) undergo transformations in situ. We also measured the average annual rise in elevation of the surface. We used a form of end-member analysis to determine which nutrients were mainly associated with organic or inorganic matter. We also used concentrations of Zn and Cu to evaluate the role of contamination from highway runoff. Finally, we discuss the options for renewal of the wetland’s capacity for removing nutrients.

METHODS SITE DESCRIPTION The Tahoe City wetland was constructed in 1997 to treat stormwater runoff from 23 ha (56 acres) of commercial (24%), residential (38%), roadway (21%), and vegetated (17%) land use areas, with stormwater conveyed to the wetland site by underground culvert. Located near the intersection of highways 89 and 28 in Tahoe City, CA, this system was designed to store and treat the 20-year one-hour storm, equivalent to about 2.5 cm of precipitation. It consists of two treatment cells in series. The inlet forebay discharges to a detention basin followed by a larger wetland basin, with a 292 m sinuous flow path from inlet to outlet (Figure 1). The upper basin has a maximum design water surface area of 2,320 m2, while the lower basin has a maximum design surface area of 4,046 m2. Over time both cells evolved toward less open surface, with a mix of vegetation that includes macrophytes in the genera Typha, Scirpus, Juncus, and Carex and floating plants in the genus Lemna. Wet pool volume varies with season and events but is estimated to average about 600 m3 between events, with most of that in the lower basin. The soil in which the basin was excavated is one of the Tahoe series, a coarse-loamy, mixed, superactive, acid, frigid Cumulic Humaquept (Soil Survey Staff, 2016). The “superactive” nomenclature refers to the mixture of alluvium derived from an andesitic lahar and granodiorite. The material derived from the andesitic lahar may include allophane and short-range order iron oxyhydroxides, which have a high adsorption capacity for phosphate (Lilienfein et al., 2004). Our observations suggested that the basin was excavated to the C horizon. The aquic suborder indicates a seasonally high water table. The C horizon is described as containing redoximorphic concentrations of both reduced (low chroma) and

12 oxidized (high chroma) Fe. Another soil in the watershed is one of the Marla series, a sandy, mixed, frigid Aquic Dystroxerept. The Marla series is similar to the Tahoe series but tends to be derived from granitic parent alluvium and is slightly higher on the landscape. The Marla soil series has a geochemical characterization for total elemental content that comprises most of the elements included in our study (Soil Survey Staff, 2016). Other soils on more steeply sloping land of the watershed include: the Kingsbeach series, a fine-loamy, isotic, frigid Ultic Palexeralf; the Tahoma series, a fine-loamy, isotic, frigid Ultic Haploxeralf; and on the upper slopes the Jorge series, a loamy-skeletal, isotic, frigid Andic Haploxeralf. The isotic and andic nomenclature reflects the volcanic origins of the parent material. All soil series were described as free of carbonates and the pH ranged from 5 to 6. Another source of sediments in the basin could be pulverized fractions derived from traction sand (aggregates added to icy roads to improve traction). Typical sand sources include alluviual soils from adjacent regions that are difficult to distinguish from low-lying soils in the watershed of the treatment watershed.

SAMPLING AND ANALYSIS Seven soil cores 4.64 cm in diameter were taken along three transects of the wetland for physical characterization and chemical analysis. Companion cores were also collected at five of these locations for particle size analysis (Figure 1). The material lying above the original surface was unconsolidated and the transition was indicated by a very distinct and obvious change in the resistance to penetration of the coring device. The depth to the bottom of the core from the current surface was measured with a meter stick so that compression of the core would not affect the depth measurement. All cores suffered some degree of compression. Further excavation with a shovel indicated that there were two horizons: a histic epipedon of about 20 cm and a fine grained mineral sediment below. The histic epipedon consisted of an Oi horizon with plant litter, underlain by a combination of fibric and histic material, based on rubbed fiber content. The histic epipedon (referred to as the O horizon, hereafter) was later separated and analyzed separately from the underlying mineral sediment. The O horizon had a Munsell color value of 3 and the mineral sediment was gleyed, with a chroma of 1. When wet, the mineral sediment did not show any redoximorphic masses of oxidized iron, but when dried, it did show spots of high chroma (7.5 YR 6/6) indicating iron oxidation. Cores were sliced lengthwise in the lab and photographed. Distinct striations were observed in the sediment in the lower portion of the sediment cores. Soils were dried at 60˚C, sieved though a 500 µm hole size sieve and then weighed. The O horizon was ground in a Wiley Mill. A subsample was dried at 105˚C, weighed and then combusted at 450˚C to measure loss on ignition. Subsamples were analyzed for total C and total N using dry combustion in a LECO CN-2000 C-N Analyzer (LECO, St Joseph, MI, USA) at the Oklahoma State University Soil, Water, and Forage Analytical Laboratory (Stillwater, OK, USA). The same laboratory also digested the samples in nitric-perchloric

13 acid and measured the total content of P, S, Fe, Mn, Ca, Mg, K, Na, Zn, Cu, and B using a Spectro CirOs ICP (inductively coupled plasma emission) Spectrometer (SPECTRO Analytical Instruments Inc., Mahwah, NJ). Analytical variability (as indicated by the coefficient of variation) was less than 2% of the mean for C and N analyses and less than 5% for metals. The average annual accretion rate over 16 years for each element and the mass of ash was calculated as (g dry mass * depth of core) / (core area * 16 yr). While C content was used as a measure of organic matter content for most results, in one case (for end-member analysis discussed below) it was useful to estimate percent C in organic matter instead. The percentage loss-on-ignition (LOI) is often used as a measure of organic matter content. A linear regression of percent LOI versus C concentration gave the following result: %C = (0.548 * LOI) – 0.99, with a correlation coefficient (R) of 0.997. The slope of the regression was interpreted as an estimate of percent C in the end member of volatile organic matter alone (54.8 %C). Particle size analysis was conducted on companion cores that were not dried for analysis but instead washed in bulk with deionized water through a 2 mm sieve to remove coarse organic material. The particle size distribution (PSD) on resulting suspension was determined by laser diffraction analysis with a Saturn DigiSizer 5200 (Micromeritics Instrument Corp., Norcross, GA) equipped with a liquid sample handling unit and integrated sonication probe. Samples were dispersed with Na-hexametaphosphate. Laser particle size analysis (LPSA) data were interpreted by Mie theory algorithm, with a specified ‘real’ refractive index of 1.550 and an ‘imaginary’ refractive index of 0.100 for particles in suspension. Results are reported in frequency distributions as suspended particle percentage volumes at half-phi size breaks from 12 to 0 (Krumbein, 1934; Krumbein and Sloss, 1963), which correspond to size classes on a log scale from 0.24 to 1000 µm.

STATISTICAL ANALYSES To evaluate hypotheses 2 and 3, we used a simple mixing equation (binary mixing model):

F1 = (Cmix - C2) / (C1 - C2) * 100 (1)

where: F1 is the proportion of end-member 1 in the mixture (in percent),

C1 is concentration of end-member 1,

C2 is concentration of end-member 2, and

Cmix is concentration of the mixture.

(Houndslow, 1995). Also, F1 + F2 = 1.

14 Our hypothesis was related to the current spatial variation in elemental composition of two end members that comprised the soil: inorganic matter and organic matter. To evaluate hypotheses 2 and 3, we used measured values of F1, F2, and Cmix to calculate C1 and C2, the concentrations of each element in hypothetical end members (100% organic matter or 100% inorganic matter). In a first step, we dealt only with the current statistical association of elements with these two end members, realizing that some elements had been transformed after being deposited (e.g. N). A correlation was calculated between all elemental concentrations (and ash content). In a second step, the concentrations of ash and C were used as proxies for the concentrations of organic and inorganic matter, where 97% ash and 0% C corresponded to 100% inorganic matter. Then regressions were performed between % ash and the concentration of each element as well as between % C and the concentration of each element. In a third step, the intercepts of these regressions at 0% C and 54.8% C were used to calculate the concentrations, C1 and C2, of each element statistically associated with inorganic matter and organic matter as it varied spatially. Because ash and C concentration were inversely related we did not attempt multiple regression with the two as independent variables. Then we constructed an elemental composition of the two end members based on this analysis. The proportion of each, accounting for the accretion rate for each element due to “mixing” of these two end members, was then calculated. Two other end member analyses also used equation 1 to estimate the contribution of allochthonous C, and in a second case, sediment from highway runoff. For each or these analyses, we used assumptions to estimate C1 and C2 in the end-members and solved for F1 and F2 using equation 1. All errors associated with the mean are shown as standard error of the mean unless otherwise indicated and reflect spatial variation among cores with some contribution of analytical variation.

RESULTS The surface of the wetland soil rose in elevation by 3.2 (± 0.3) cm/yr, for a total of 51.0 (± 5.2) cm over the 16 yr period of accumulation. The depth was more variable than many other properties (presented subsequently) because of the shape of the basin (Figure 1 and Table 1). The unconsolidated nature of the sediment and O horizon was reflected in a low bulk density of only 0.24 (± 0.03) g/cm3. The average concentration of C in the O horizon was 27.6 (± 1.5)%, which was above that necessary to classify it as a histic epipedon, but there also was a significant amount of inorganic sediment mixed with the organic material as indicated by an ash content of 48.3 (± 3.8)% (Table 2). In the mineral horizon, the C concentration was only about 10% of that in the O horizon, but no sample of the mineral horizon had less than 1.67% C (not shown) and ash content averaged 93%. The ratios of C/N were 20.0 (± 1.9) in the O horizon,

15 and 14.1 (± 0.4) in the mineral horizon. The ratios of C/P, however, were much lower in the mineral horizon than in the O horizon, as indicated by a one-sided t-test (P< 0.05). Among the metals, Fe was by far the highest in concentration (Table 2) despite its susceptibility to reduction-oxidation (redox) reactions. Another metal sensitive to reduction and oxidation is Mn, which was 73 times lower in concentration in the mineral horizon than Fe. Sodium is used for road deicing in winter, but was only present in concentrations averaging 0.135% in the O horizon. The heavy metals Cu and Zn were present in concentrations over 100 µg/ g soil in the O horizon, but were at much lower concentrations in the mineral horizon (Table 2), as indicated by one sided t-tests (P < 0.05). The ratios of Zn/Cu, however, were similar in both horizons (ratios ranging from 3-4). The micronutrient B was only present in concentrations of less than 10 µg/ g soil (Table 2). The end-member analysis resulted in grouping the elements into those statistically (with a significant regression, P< 0.05) associated with organic matter (group “O”) and those associated mainly with inorganic matter (group “I”) as shown in Table 2. Elements for which the ratio on the two end members was less than 10 to 1, i.e. associated with both organic and inorganic matter, were termed group “M” (i.e. of “mixed” association). The macronutrients N and S were strongly associated with organic matter (Table 2). The metals Zn, Cu, and Na were also significantly associated with organic matter, although Na had the weakest correlation (R = 0.81, Table 2). In contrast, the metals Fe, Mn, and Mg were strongly associated with inorganic matter with R values for the correlation with ash content from 0.97 to 0.59 (Table 2). The comparison of Fe and Mg is relevant for the fact that Fe, but not Mg, is redox reactive (Table 2). Of the nutrient elements that are associated with both inorganic and organic matter concentrations, P is most notable because the intercept where C = 0 (Table 2) and the mean concentration of P in the mineral horizon were similar (0.038% vs. 0.045%) suggesting a substantial portion of the P in the mineral horizon is associated with inorganic matter. The wetland spatial distribution of the elements Mg, Fe, and Mn was fairly uniform among cores in the mineral horizon. The standard errors were only 5% of the mean for Fe and Mg, and 15% for Mn (see Table 2 for the S.E.). These were the elements most closely correlated with inorganic matter (ash). In contrast, the spatial distribution of C among cores was more variable. The end-member analysis, in which each hypothetical end-member would be either 100% inorganic matter or 100% organic matter, resulted in the following elemental compositions. For 100% organic matter the concentrations were: 2.8% N, 0.27% P, 0.81% S, 0.13% Fe, <0.001% Mn, 1.03% Ca, 0.42% Mg, 0.33% K, 0.27% Na, 1228 µg/ g Zn, 180 µg/ g Cu. For 100% inorganic matter the concentrations were: 0.05% N, 0.038% P, 0.003% S, 2.70% Fe, 0.035% Mn, 0.37% Ca, 0.23% Mg, 0.16% K, 0.018% Na, 45 µg/g Zn, and 18 µg/ g Cu. Boron was not significantly correlated with either. The end member compositions corresponded to the groupings indicated in Table 2 as “O”, “I”, or “M”.

16 Accretion rates for the four major nutrient elements are shown in Figure 2, with the relative contributions of both O and mineral (I) horizons to total accretion indicated. The organic horizon contributed from 28% to 41% of the total accretion for C, N, and S, but only about 15% of the P total accretion. Since dry mass of the inorganic (mineral) horizon comprised over 90% of the total core mass, however (not shown), its contribution to total accretion was still over 50% for each of these elements. The accretion rates of other elements are shown in Table 3. Watershed yields (accretion rates scaled up to the 22 Ha watershed area) are shown in Table 2 for comparison to literature sources (see Discussion). Results from laser particle size analysis (LPSA) were very similar between cores, showing a slightly bimodal distribution with peak concentration by volume at the 16 µm size class. In general this followed the pattern of PSD measured in stormwater inflow to the treatment system (Figure 3). Stormwater inflow samples collected from 2003–2012 were analyzed by LPSA and show that most of the suspended sediment was delivered as silt (63% by volume), with approximately equivalent amounts received as both sand (21%) and clay (16%). Size distribution shifted slightly toward smaller particles in sediment compared to mean stormwater particle distribution, with 19% by volume in sand, 56% in silt and 25% in clay. Coarse sand particles (>500 µm) presumably dropped out in either the forebay or detention basin, resulting in negligible concentrations in the wetland basin sediment cores. (Additional data on geochemical characteristics associated with particle size in the wetland sediment and Tahoe Basin stormwater are provided in the Appendix of this report.)

DISCUSSION ACCRETION RATES Stormwater retention basins are designed to remove sediments that are often mainly inorganic while wetlands are renowned for accumulating organic matter. The Tahoe City wetland combined both these properties and had high accretion rates of both C and inorganic matter compared to values for natural wetlands in the literature. A summary of values for C accretion rates in wetlands was compiled by Bernal and Mitsch (2012). Comparison of 23 temperate, inland wetlands from literature gave a median of 131 g C m-2 yr-1, which was lower than the value of 280 g C m-2 yr-1 in the current study. However, created wetlands with high hydraulic and nutrient loading rates can have much higher rates of C, N, and P accretion. Bernal and Mitsch (2013) contrasted two created wetlands (the Olentangy wetlands) with a natural wetland in the same region of Ohio and found that C accretion was 70% greater in the two created wetlands (267 ± 17 and 219 ± 15 g C m-2 yr-1). In the two created wetlands the N and P accretion rates were 18 and 21 g N m-2 yr-1 for N and 3.3 and 3.5 g P m-2 yr-1 (Mitsch et al. 2014). These values for the Olentangy riverine wetlands are very comparable to those found in the Tahoe City wetland for C, N, and P accretion (Table 3). The Olentangy wetlands were also sampled 15 years after creation, which is similar to the age of the Tahoe City wetland (16 yr). Because of the relatively narrow range of C/N ratios in wetland soils, we believe the N accretion rates of the natural wetlands in the literature

17 review (Bernal and Mitsch, 2012) were also lower than in these created wetlands. The rates of C burial in lakes and ponds of the conterminous U.S. is a mean of 46 g C m-2 yr-1 and a median of 31 g C m-2 yr-1, which are much lower than found in this study (Clow et al., 2015). However, rates of C burial in reservoirs where the sedimentation rate is much higher gave a mean of 149–363 g C m-2 yr-1 (Clow et al., 2015), which was more comparable to the rates of C accretion reported in this article. Phosphorus assimilation of wetlands summarized from a large database (including both natural and treatment wetlands) suggested that the long-term assimilative capacity was 1 g P m-2 yr-1, although they could exceed this capacity in the early stages (Richardson et al. 1996). The P accretion rate of both the Tahoe City wetland and the Olentangy wetlands (Mitsch et al., 2014) exceeded this rate by almost 4 times in their first 15–16 yr after creation. Chemical fractionation and digestion would be necessary to distinguish the forms of P in the Tahoe City wetland, but in suspended sediments of Ward Creek, near the study site, Ferguson (2006) found that 25% was bioavailable. Of total P, 67% was inorganic, and 33% was organic. In urban runoff from Tahoe City, however, 36% was bioavailable, while 64% was inorganic P. Thus, statistical correlation between organic matter and P in the soil of the wetland basin is similar to that found by fractionation of the suspended sediments. This distribution between organic P and inorganic P is typical for wetland soils (Qualls and Richardson, 1996). Consequently, our results lent support to hypothesis 3, but storage of organic P in the O horizon was still a significant portion of the accretion. The annual accretion rates for N and P from the wetland sediment cores correspond remarkably well with measurements of retention during a single year, (calculated from inflow minus outflow concentration expressed as g m-2 yr-1) (Heyvaert et al., 2006). The retention was 13 g total N m-2 yr-1 and 3.2 g total P m-2 yr-1 compared to 17.7 for N and 3.7 for P accretion in the cores (Figure 2). The slightly greater accretion of N could conceivably have been due to N fixation by cyanobacterial mats or simply difference in the loads over time. The inflow concentrations to the Tahoe City wetland in 2003 tended to be similar to those from other commercial and highway areas of the Tahoe Basin, but were relatively high compared to other watersheds with less urban land use (Heyvaert et al. 2006). Also, water year 2013 precipitation was about 90% of average over the 16-year period (NRCS SNOTEL site #809 in Tahoe City). Sediment accumulation rates in urban stormwater ponds and constructed wetlands vary considerably. More and Hunt (2012) reported accretion rates compiled from various studies ranging from 0.1 to 13 kg m-2 yr-1. High accretion rates can lead to a rapid rise in base elevation that ultimately limits the useful life of treatment systems. Slow elevation rise would be considered something less than 1.25 cm yr-1. In the absence of site-specific accretion data, the USDA Natural Resources Conservation Service recommends a minimum design standard of 2.54 cm yr-1 for constructed treatment basins (NRCS, 2010). The Tahoe City wetland

18 accretion rate averaged 3.2 cm yr-1. Sedimentation (accretion) rates in the Olentangy wetlands (Mitsch et al. 2014) ranged from 1.2 to 1.4 cm per year, or 6.0 kg m-2 yr-1, which was comparable to the rate of mass accretion found in the Tahoe City wetland (7.69 kg m-2 yr-1), although the rate of elevation rise was greater in the Tahoe City wetland. Mitsch et al. noted that their average rate was very high compared to most wetlands because the wetlands were created in excavated basins undergoing “primary succession” that were filling with sediments whose source was riverine water with substantial loads of sediment. They noted that created wetlands generally have very high hydraulic loading rates, high sediment loads, and are newly created basins in contrast with natural wetlands where elevation rise is more in equilibrium with periodic erosion or decomposition. The Tahoe City wetland fit into this pattern because it was also initially an excavated basin whose bottom was below the inflow and outflow elevations. Another example of seven constructed wetland basins in Sweden specifically designed to trap sediment and phosphorus from water with high sediment and water loads of agricultural watersheds was summarized by Johannesson et al. (2015). The sedimentation rates ranged from 1.3 to 10.8 kg m-2 yr-1, a range that encompassed the rate in the Tahoe City wetland but was far greater than most natural wetlands. Rates of sedimentation in reservoirs of the conterminous U.S. averaged 4.4 cm yr-1 (Clow et al., 2015). In the Tahoe City wetland, the low spatial variability of Fe, Mg, and Mn concentrations that were associated with the inorganic sediments filling the basin suggested that sediment entered the wetland, was evenly distributed, and then settled. Given the findings of Mitsch et al. (2014), that rates of sedimentation on the order of 6.0 kg m-2 yr-1 are high, and from our finding of association of metals with inorganic sediment accretion, we conclude that rates of accretion of metals are also high in the Tahoe City wetland compared to most. Thus, measured rates of accretion of C, N, P and metals in the Tahoe City wetland supported hypothesis 1. One factor contributing to the rise in surface elevation that eventually impeded flow through the system was low bulk density of the unconsolidated soil material (Table 1). The bulk density in the Tahoe City wetland (Table 1) was only about 52% of that found in the sediments and accumulated organic matter in the Olentangy wetlands (Mitsch et al. 2014). The buoyant force of the water in a continuously saturated wetland (or pond) as well as the content of low density organic matter contributed to low bulk density. Wetlands or stormwater retention basins that experience periodic lowering of the water surface may experience more consolidation and less elevation rise, but increased mineralization of C and N may be an associated cost. Watershed yield of total N (Table 3) was similar to that for an average for the State of California (Smith and Alexander, 2000). Sediment yield, however, substantially exceeded the watershed yields from Tahoe Basin streams. Rios et al. (2014) summarized data from ten USGS monitored streams and compared these to the suspended sediment yield from Barton Creek near Tahoe City, with values ranging from a low of 0.84 kg ha-1 yr-1 at Logan Creek to

19 a high of 163 kg ha-1 yr-1 at Third Creek. In Barton Creek, which is much less densely urbanized than Tahoe City, the sediment yield was 8.16 kg ha-1 yr-1 compared to 1232 kg ha-1 yr-1 into the Tahoe City wetland. This illustrates the tremendous load put onto relatively small treatment systems, compared to larger drainage areas (290–14,200 km2) with more natural conditions and less relative impervious area (0.5–7.9%). Despite the gleyed color of the soil, Fe concentrations were similar to those of surrounding lowland soils of the watershed. Wetland soils tend to lose Fe by reduction and leaching. There may have been some reduction and leaching of Fe, but the Fe content was still comparable to the surrounding soils at the time of sampling. The export of Fe, particularly reduced Fe, is regarded as critical since Fe has been found to be limiting to algal growth of phytoplankton in Lake Tahoe (Goldman, 1964). However, the wetland was responsible for trapping a very large load of Fe in the accreted inorganic matter before it could be transported to Lake Tahoe or the Truckee River. In this case the wetland’s role as a stormwater retention basin, which promoted the settling of sediments, was probably the most important factor. Mn is another redox reactive element but the concentrations were far lower than those of Fe (Table 2). Pollution of Na from salt applications to roads has resulted in elevated concentrations in lakes and damage of vegetation in cold climates over the developed world. Large quantities of Na are distributed on the roads of the Tahoe Basin each year, where the average annual snowfall is 4.67 m (in Tahoe City). Since 22% of the watershed of the Tahoe City wetland is comprised of roads, we might expect some excess salinity in the wetland. However, total Na is no higher than it is in the dominant soil type (the Tahoe series). Salt damage to roadside vegetation is common in the Lake Tahoe Basin (Fan et al., 2014). There was evidence that Na was more concentrated in the O horizon closer to the surface (Table 2), which might indicate aerial deposition. In fact, Fan et al. (2014) found that aerial spray from traffic and wind caused the greatest proportion of tree damage and was mostly confined to areas 10 m from roads. Boron is a plant micronutrient but it can be toxic to plants depending on the concentration. Boron concentration can reach toxic levels in geothermal areas of the region (Smith et al., 2013) but concentrations were relatively low in the wetland.

ORIGINS OF MATERIAL ACCRETED IN THE WETLAND Elemental composition is frequently used to infer the origin of sediments (e.g. Janneke et al., 2012). Elemental composition in the mineral horizon correlated best with the B horizon of the Marla soil series compared to other soil series in the watershed that had geochemical data (Soil Survey Staff, 2016, query, “geochemical data”). The correlation of concentrations of the elements listed in Table 2 vs. the concentrations in the Marla soil geochemical data were significant, with an R2 of 0.8 (Zn, Cu, and B were not included because they were not analyzed in the Soil Survey data). Concentrations of total Fe, Mg, and Mn, Ca, and K, in the wetland mineral horizon were lower than in soils from the backslope

20 areas of the watershed (the Tahoma and Jorge series). This comparison suggests that most of the material came either from alluvial soils of the watershed or from traction sands with similar alluvial origins, but not predominantly from erosion of the surrounding mountain slopes. The concentration of C in the mineral horizon of the wetland was lower than in the A horizons of the soils of the watershed, which also suggests that erosion of A horizon material was not the major source of sediment.

ACCRETION OF ALLOCHTHONOUS VS. AUTOCHTHONOUS ORGANIC NUTRIENT ELEMENTS There is no simple way to measure the contributions of allochthonous C, N, and S to the accretion rate without a detailed budget of sediment in the inflow and outflow and net primary production over the 16 year period. But, with two assumptions, we can make an estimate. The mineral horizon had a minimum C concentration of 1.67%. If we assume this value represents the C concentration in incoming sediment, we can estimate the C accretion due to sediment inflow. Assumption number two is that the organic matter originating from allochthonous source material contained 4% ash based on another study (Qualls and Richardson, 2000). The additional C in the mineral horizon would then result primarily from root production. In the O horizon, there was also deposition of sediment, as indicated by the concentration of inorganic matter but the balance of C would result from net primary production. Using these two assumptions, a mixture of sediment with 1.67% organic matter in the mineral horizon with 2.6% C would indicate that the difference of 0.93% C concentration resulted from root litter production in the mineral horizon. In the O horizon, we used the concentration of ash in the O horizon as a conservative tracer in equation 1. The contribution of sediment in the O horizon was estimated to be 50% of the mass but only 3.1% of the total C. Thus, 96.9% of the C in the O horizon was estimated to come from autochthonous C. Using these percentages and the mass of C in each horizon (Tables 2, and 3), we estimated that 58.7% of the accretion of C in the whole soil profile had originated from autochthonous C. The estimate was most sensitive to assumption 1, while the presumption of the ash content of decomposed litter was not critical (<1% difference for a 50% difference in ash content). Consequently, the contribution of the plant production in the wetland was the most important component of C accretion, but sedimentation of allochthonous C was also a significant process. In the case of N, the origins and end state are more complex because it is likely that most N entered the wetland as allochthonous inputs, some of which were inorganic. The very close association of C and N (R = 0.94) and the representative nature of the C/N ratios (Mitsch and Gosselink, 2015) for the end state of the wetland suggest that most N is bonded in association with the C resulting from primary production. The rapid accumulation of the large O horizon resulting from net primary production required N to be taken up and deposited as organic matter that accumulated in the anaerobic conditions of the soil. The fact that the O horizon was histic is evidence of inhibited re-mineralization of the cycled N. If we do the same calculation (as outlined in the previous paragraph for the origins of C) for

21 nitrogen currently associated with the C originating from autochthonous production, we estimate that 53.8% of the N is associated with autochthonous C. This percentage is equivalent to an accretion rate of 11 g N m-2 yr-1. However, the N entering the wetland had more diverse origins. In the study of N inflow and outflow in 2003 (Heyvaert et al., 2006) about 50% of the total N in the inflow was dissolved inorganic N (mostly nitrate) and about 59% of that was retained by the wetland. The fate of this inorganic N could have included plant uptake and denitrification. The estimate of total N retained by the wetland from the inflow minus outflow budget was 13 g N m-2 yr-1, a rate that is similar to the estimate of accretion currently associated with autochthonous C (9.5 g N m-2 yr-1) and the balance can be more than accounted for by N bound to C in the allochthonous sediment. To account for the rapid accumulation of organic N associated with autochthonous C, the demand exerted by uptake of vegetation alone could have accounted for most removal of N from inflow. Perhaps the most comparable rate of denitrification in the literature would be from the Olentangy wetlands where the denitrification rate was 2.7 g N m-2 yr-1 which accounted for only 2% of the N inflow (Batson et al., 2010). In the Olentangy wetlands there was also evidence that plant uptake competed with denitrification for available nitrate. That denitrification rate was also within the range of the other freshwater wetland studies reviewed in that study. A review of N fixation rates in wetlands found them to mainly be less than 1 or 2 g N m-2 yr-1, but small scale measurements in cyanobacterial mats can be higher (Keddy, 2010). In summary, our findings support hypothesis 2, that a majority of C and N was eventually stored in association with plant production.

EVIDENCE OF HIGHWAY RUNOFF Since concentrations of major metals such as Fe and Mg may be similar in soils and highway runoff, Zn and Cu have been used instead as tracers for highway runoff (Sriyaraj and Shutes, 2001). Zn and Cu are products of tire and brake wear in particular, and occur in highway runoff in elevated concentrations (Caltrans, 2003). Concentrations in the O horizon of the Tahoe City wetland were much higher than in surrounding soils that have Zn and Cu analyses (Smith et al., 2013), and are in a range termed “high” for levels of metal contamination in sediments as given by the Swedish Environmental Protection Agency (reported in Sriyaraj and Shutes, 2001). The study of Sriyaraj and Shutes (2001) was particularly applicable since it concerned highway runoff from the M25 highway near London into a wetland and a retention pond. Concentrations of Zn and Cu in the sediment were elevated compared to a control wetland. They found that roots and rhizomes of Typha latifolia tended to concentrate Zn and Cu. However, the wetland and pond were effective in reducing the concentrations of heavy metals to acceptable concentrations in water flowing out. Other studies have found that both Zn and Cu are taken up as micronutrients by wetland plants in greatly elevated concentrations when exposed to elevated concentrations in water or sediment (Shutes, et al., 1993; Weis and Weis, 2004), and this effect is the basis for phytoremediation. In fact, the concentrations of Zn and Cu in the upper and lower depths of

22 soil in a study of a metal contaminated wetland in Spain are very similar to those in the O and mineral soil of the Tahoe City wetland (Weis and Weis, 2004). Decomposing Typha litter has also been found to concentrate Cu by a factor of four by bacterial immobilization during decomposition (Qualls and Richardson, 2000). Shutes et al. (1993) also found that concentrations of 10 mg/l Zn or Cu in solution in a dosing study were not toxic to growth of Typha latifolia, so we do not believe that concentrations of Zn and Cu found in the O horizon would be toxic to plant growth in the Tahoe City wetland. There is also a possibility that the higher concentration in the O horizon was caused by aeolian deposition or aerosols from traffic, but the findings of similar affinity for organic matter in other studies suggests that concentration by plant uptake may also be likely. As with other elements, the accretion of excess Zn and Cu prevented its transport to adjacent water bodies. The use of Zn and Cu as tracers may also provide a rough estimate of how much highway runoff contributed to the wetland, if several assumptions can be made. As one “end-member” representing highway runoff, we used concentrations of Zn and Cu in suspended sediments (1047 µg/g for Zn and 165 µg/g for Cu), which was the average for California highway runoff (Caltrans, 2003). The second end-member was the concentration of Zn and Cu in the mineral horizon, because it was in the range of local soils and sediments. Using these as “end members” we calculate that 3% of total mass in the O and mineral horizons may have originated as particulates from car wear sources in highway runoff. This estimate can only be approximate because of the assumptions that have to be made. It does not include suspended sediment derived from traction sand applied to road surfaces in winter or abrasion of the road surface, because the bulk of these materials are obtained from alluvial soils similar to those used for end member analysis, but it does provide an order of magnitude estimate of car wear particulates in highway runoff contributing to accretion of the wetland.

PARTICLE SIZE DISTRIBUTION The cores show high levels of fine particle retention by the wetland, with over 25% of the sediment concentration represented by clay (<4 µm) and an additional 56% represented by silt (<63 µm). This is much better than most BMPs can achieve based simply on sedimentation processes (see next chapter). The mechanism producing a bimodal peak in sediment core PSD (Figure 3) is not understood, but may represent preferential scavenging or disaggregation of silt-size particles during transport through the treatment system or during analysis. Clay size particle retention was notably efficient given the moderate hydraulic residence time of this stormwater basin (15–48 hr). In a study of two Norwegian wetlands constructed to mitigate arable field erosion, Braskerud (2003) showed that clay particle retention was better than expected from modeling predictions based on Stokes’ Law. He attributed this to particle aggregation during transport that effectively increased the

23 particulate settling velocities normally expected from clay size particles. That interpretation was subsequently confirmed by microscopic thin-section analysis (Sveistrup et al., 2008). Additional wetland properties, such as interception and capture by wetland plant fiber and attached biofilms, can also contribute to fine particle retention beyond what is expected simply from their settling characteristics. This was tested in laboratory flume experiments (Fauria et al., 2015) using synthetic vegetation with stormwater created from Tahoe road dust. They demonstrated that biofilm and vegetative stem density both increased fine particle capture rates.

MANAGEMENT OF SEDIMENT ACCUMULATION Like most stormwater retention basins or treatment wetlands with high sediment loading, the capacity of the basin must be renewed at some time due to sediment accretion. Options for renewal or maintenance may include the following, listed from more “passive” to more “active” and expensive ones. (1) Levee or berm height may be raised to increase the capacity. (2) Deep zones may be excavated parallel to flow in order to trap some additional sediment. (3) Water elevation may be drawn down to promote consolidation of the sediment and O horizon. (4) Water elevation may be drawn down and the soil mechanically compacted with a backhoe, rammer, or tracked vehicle. (5) Soil may be excavated leaving monoliths of vegetation that can recolonize the surface. (6) After drawdown of the water, soil may be excavated and placed on the berms so that leached water will drain back into the wetland basin. (7) After drawdown, soil may simply be excavated, windrowed to dry, loaded, and removed from the site. Each of these options has advantages, disadvantages and costs. Option 1 allows increased water depth and the vegetation can remain intact. But, the elevation of the inflow and outflow structures may limit the elevation of the wetland surface, as was the case with the Tahoe City wetland where the inflow culvert began backing up. The low bulk density of unconsolidated sediment was a factor that contributed to the rise in surface elevation in the Tahoe City wetland as it does in others (e.g. in the Lake Apopka wetland, Coveney et al., 2002). Drawdown is a frequently used strategy in management of lake impoundments to compact the sediment, but it may not be completely effective (U.S. Army Corps of Engineers, 1987). A version of option 4, drawdown with mechanical compaction to double the bulk density would allow a longer lifetime (about an extra eight years in the case of the Tahoe City Wetland) and the soil density would still be representative of many wetland soils. Many rhizomes would likely survive the compaction and provide for revegetation. Option 5, would remove a large percentage of the sediment but would also remove the cost of replanting the vegetation. Option 6 is a modification of the concept recommended by the U.S. Army Corps of Engineers (1987) for disposing of sediment in large reservoirs to simultaneously raise dikes. For a small wetland retention basin it would also have the advantage that it would remove the sediment but allow nutrients mineralized and leached from the excavated sediment to flow back into the wetland for reprocessing. However, there

24 must be sufficient area on berms and the perimeter to accommodate the excavated material. The slopes must be less than the angle of repose, and the piles would have to be seeded to prevent erosion. Excavation and removal from the site is probably the most expensive option but would renew the original capacity. As long as the soil is not placed back into an anaerobic environment, much of the C accumulated by the wetland would not be sequestered over the long term if disposed of in aerobic conditions. Under most of the options, drawdown or disturbance is likely to create a pulse of nutrients that must be contained for as long as possible before being allowed into the outflow (Kadlec and Wallace, 2009). The list of options presented above may serve as a useful guide to any form of treatment wetland or stormwater retention basin. Basins with high sediment loads are the ones with the most limited useful lifetimes, but they are also, perhaps, the ones that are most needed for water quality improvement. The Olentangy wetlands have high sedimentation rates similar to that in the Tahoe City wetland, and Mitsch et al. (2014) made a projection of the time remaining before they “fill up” at a couple of decades. The design of the Olentangy wetlands water delivery system allowed a degree of flexibility since the water was pumped up from the river to an elevation above the pool. Passive drainage inflow systems may not have that degree of flexibility. But if passive drainage systems can be designed to allow for eventual elevation of the wetland surface, it may allow for passive approaches such as option 1 above, to extend the useful lifetime.

CONCLUSIONS The Tahoe City wetland was designed to remove nutrients and sediments from urban and residential runoff before they entered Lake Tahoe to help preserve its ultra-oligotrophic status. It was so successful in meeting these goals that it accreted sediment and organic matter to the point that it reached its useful lifetime in 16 years. The opportunity to measure the accretion rates of elements over such a long period of time presented an unusual opportunity to quantify the long-term removal of nutrients and metals and compare it to the limited number of studies in which long term accretion of nutrients have been measured. The wetland accumulated mass by sedimentation of allochthonous material and net primary production of organic matter. Accretion of C, N, P, S and metals was greater than in most natural wetlands. A high rate of sedimentation and the growth of a large O horizon by net primary production together accreted 3.2 cm of depth per year and 7.0 kg m-2 yr-1 of inorganic material. The wetland was efficient in trapping fine silt and even clay sized particles, a result that may not be expected in most stormwater retention basins but was likely very important in removing P. The rates of sedimentation and accretion of C, N, and P were remarkably similar to those measured in the Olentangy wetlands; created wetlands which had a similar age (15 yr at the time of measurement, Mitsch et al., 2014), high inputs of sediment laden river water, and high rates of net primary production. That a created wetland in a sub-alpine climate could maintain similar rates is notable and has implications for other wetlands in cold temperate or sub-boreal regions of the world.

25 Concentrations of most metals indicated a source from the soils in the watershed, but Zn and Cu concentrations in the organic horizon were characteristic of heavy metal pollution from highway runoff. A majority of the accretion of most metals and P could be attributed of the efficient trapping of sediment, while a substantial proportion of the accretion of C and N could be attributed to the accumulation of autochthonous organic matter from net primary production. We conclude that wetland retention basins very efficiently combine the physical properties of a retention basin with the biological properties characteristic of wetlands.

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27 Lilienfein J, R.G. Qualls, S.M. Uselman and S.D. Bridgham, 2004. Adsorption of Dissolved Organic and Inorganic Phosphorus in Soils of a Weathering Chronosequence. Soil Science Society of America Journal 68:620-628. McCarty G., Y. Pachepsky, and J.A. Ritchie, 2009. Impact of Sedimentation on Wetland Carbon Sequestration in an Agricultural Watershed. Journal of Environmental Quality 38:804-13. doi:10.2134/jeq2008.0012. Mitsch, W.J., L. Zhang, E. Waletzko and B. Bernal, 2014. Validation of the Ecosystem Services of Created Wetlands: Two Decades of Plant Succession, Nutrient Retention, and Carbon Sequestration in Experimental Riverine Marshes. Ecological Engineering 72:11­ 24. Mitsch, W.J. and J.G. Gosselink. 2015. Wetlands. Fifth Edition. John Wiley and Sons, New York, New York. Mitsch, W.J., S.M. Nedrich, S.K. Harter, C. Anderson, A.H. Nahlik, and B. Bernal. 2014. Sedimentation in Created Freshwater Riverine Wetlands: 15 Years of Succession and Contrast of Methods. Ecological Engineering, 72:25–34. More, T.L.C., and W.F. Hunt. 2012. Ecosystem service provision by stormwater wetlands and ponds – A means for evaluation? Water Research, 46:6811-6823. Natural Resources Conservation Service (USDA NRCS). 2010. Conservation Practice Standard, Constructed Wetland. National Handbook of Conservation Practices, Code 656. July 2010. O’Neil‐Dunne, J., D. Saah, T. Moody, T. Freed, Q. Chen, and J. Moghaddas. 2014. Mapping hard and soft impervious cover in the Lake Tahoe Basin using LiDAR and multispectral images: a pilot study of the Lake Tahoe Land Cover and Disturbance Monitoring Plan. Report to U.S. Forest Service, Pacific Southwest Research Station. http://www.fs.fed.us/psw/partnerships/tahoescience/documents/p077_ImperviousSurface 2010_FinalReport.pdf Qualls, R.G, and C.J. Richardson, 2000. P enrichment affects litter decomposition, immobilization, and soil microbial P in wetland mesocosms. Soil Science Society of America Journal 64:799-808. Richardson C.J., S. Qian, C.B. Craft, and R.G. Qualls, 1996. Predictive Models for Phosphorus Retention in Wetlands. Wetlands Ecology and Management 4: pp. 159-175. Rios, D.T., S. Chandra, and A.C. Heyvaert. 2014. The importance of small urbanized watersheds to pollutant loading in a large oligotrophic subalpine lake of the western USA. Environmental Monitoring and Assessment, 186:7893-7907. Rowe, T.G., D.K. Saleh, S.A. Watkins, and C.R. Kratzer, 1998. Streamflow and Water Quality Data for Selected Watersheds in the Lake Tahoe Basin, California and Nevada, through September 1998. U.S. Geological Survey Water Resources Investigations Report 02-4030, Carson City, NV. 117 p.

28 Sahoo, G.B., D.M. Nover, J.E. Reuter, A.C. Heyvaert, J. Riverson, and S.G. Schladow, 2012. Nutrient and particle load estimates to Lake Tahoe (CA–NV, USA) for Total Maximum Daily Load establishment. Science of the Total Environment. 444:579–590. doi:10.1016/j.scitotenv.2012.12.019 Shutes, R.B.E, J.B. Ellis D.M. Revitt, and T.T. Zhang, 1993. The Use of Typha latifolia for Heavy Metal Pollution Control in Urban Wetlands. In: G.A. Moshiri (Editor). Constructed Wetlands for Water Quality Improvement. Lewis Publishers, Boca Raton, Fl., U.S. pp. 407-414. Sickman, J.O., A. Leydecker, C.C.Y. Chang, C. Kendall, J.M. Melack, D.M. Lucero and J.P. Schimel. 2003. Mechanisms Underlying Export of N from High‐Elevation Catchments During Seasonal Transitions. Biogeochemistry, 64:1‐32. Smith, D.B., W.F Cannon, L.G. Woodruff, F. Solano, J.E. Kilburn, and D.L. Fey, 2013. Geochemical and Mineralogical Data for Soils of the Conterminous United States: U.S. Geological Survey Data Series 801, 19 p., http://pubs.usgs.gov/ds/801/ Smith, R. A., and R. B. Alexander, 2000. Sources of Nutrients in the Nation's Watersheds. In Managing Nutrients and Pathogens from Animal Agriculture, Proceedings from the Natural Resource, Agriculture, and Engineering Service Conference for Nutrient Management Consultants, Extension Educators, and Producer Advisors, March 28−30, 2000, Camp Hill, PA. Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture, 2016. Web Soil Survey. http://websoilsurvey.nrcs.usda.gov/ Sriyaraj, K, and R.B.E. Shutes, 2001. An Assessment of the Impact of Motorway Runoff on a Pond, Wetland and Stream. Environment International 26:433- 439. Sveistrup, T.E., V. Marcelino, and B.C. Braskerud. 2008. Aggregates explain the high clay retention of small constructed wetlands: a micromorphological study. Boreal Environment Research, 13:275–284. Sinaj, S., E. Frossard, and J. C. Fardeau. 1997. Isotopically exchangeable phosphate in size fractionated and unfractionated soils. Soil Science Society of America Journal 61:1413– 1417. U.S. Army Corps of Engineers, 1987. Confined Disposal of Dredged Material, EM 1110-2­ 5027, Washington, D.C. http://www.publications.usace.army.mil/Portals/76/Publications/EngineerManuals/EM_1 110-2-5027.pdf Weis, J.S., and P.W. Weis, 2004. Metal Uptake, Transport and Release by Wetland Plants: Implications for Phytoremediation and Restoration. Environment International 30:685 – 700. Wetzel, R.G. 2001. Limnology of Lake and River Ecosystems. Third Edition. Academic Press, San Diego, Ca. 1006 p.

29 Table 1. Wetland basin and watershed characteristics. The first seven features are cited from Heyvaert et al. (2006) while the others were measured during this study.

Characteristic Unit Measurement (±S.E)

Wetland Basin Area m2 4046

Watershed Area Ha 23

Impervious Surface Area % watershed 21

Wetland/Watershed Area % 1.76

Wetland Pool Volume m3 600

Residence Time during Storms hr 15-48

Average Annual Water influx (2003) m3 109,090

Average Sediment Depth cm 51.0 (± 5.2)

Average rise in sediment surface cm/yr 3.2 (± 0.3)

Bulk Density (total of O and mineral horizons) g/ cm3 0.24 (± 0.03)

30 Table 2. Concentrations or ratios of elements and ash in the mineral and O horizon of wetland soil. Standard error of the mean is shown to the right of the mean and reflects spatial variability between cores. The correlation (R value) is shown for cases where there was a significant positive or negative correlation with C or ash content. Absence of R indicates a non-significant relationship (n.s. at alpha 0.05) or non-applicability (n.a.) because of inter-correlation with C or ash. The intercept at an ash concentration corresponding to 0% C concentration is shown as an indication of the concentration associated with inorganic material alone. “Group” indicates elements associated mainly with: organic matter (O), mixed inorganic and organic matter (M), or inorganic matter (I).

Mineral O Intercept R Correlation R Correlation Element Unit Horizon S.E. Horizon S.E where Group With C With ash mean Mean C = 0

C % 2.60 0.31 27.6 1.5 n.a. -0.997 n.a. O N % 0.18 0.02 1.4 0.1 0.94 -0.93 0.05 O P % 0.045 0.02 0.153 0.01 0.93 -0.91 0.038 M S % 0.035 0.006 0.403 0.012 0.99 -0.97 0.003 O Fe % 2.57 0.05 1.22 0.07 -0.98. 0.97 2.70 I Mn % 0.035 0.002 0.018 0.005 -0.61 0.59 0.035 M Ca % 0.389 0.013 0.710 0.018 0.97 -0.96 0.37 M Mg % 0.404 0.008 0.320 0.016 -0.85 0.86 0.35 M K % 0.151 0.008 0.238 0.062 n.s. n.s. 0.16 M Na % 0.028 0.002 0.135 0.028 0.81 -0.82 0.12 O Zn µg/g 88 11 677 36 0.95 -0.93 45 O Cu µg/g 22 0.85 107 12 0.83 -0.80 18 O B µg/g 3.8 0.2 5.9 0.5 0.82 -0.85 n.a. M C/N ratio 14.1 0.4 20.0 1.9 n.a. n.a. n.a. n.a. C/P ratio 57 4 185 18 n.a. n.a. n.a. n.a. Ash % 93.0 0.5 48.3 3.8 -0.997 n.a. 0.97 I

31 Table 3. Average annual accretion rates and watershed yields of nutrients and metals over 16 years. The watershed yield is not shown (n.a.) for C because much of the C may have originated from autochthonous production.

Unit for Total Soil Watershed Yield Element S.E. S.E. Group Accretion Rate Accretion Rate (Kg Ha-1 yr-1)

C g m-2 yr-1 280.00 30.00 n.a. n.a. O N g m-2 yr-1 17.70 1.80 3.12 0.31 O P g m-2 yr-1 3.74 0.44 0.657 0.077 M S g m-2 yr-1 3.80 0.42 0.667 0.073 O Fe g m-2 yr-1 194.00 33.00 34.20 5.7 I Mn g m-2 yr-1 2.68 0.49 0.472 0.086 M Ca g m-2 yr-1 30.80 2.20 5.41 0.80 M Mg g m-2 yr-1 30.70 4.91 5.40 0.86 M K g m-2 yr-1 12.20 2.20 2.14 0.38 M Na g m-2 yr-1 2.54 0.35 0.447 0.061 O Zn g m-2 yr-1 0.858 0.12 0.151 0.022 O Cu g m-2 yr-1 0.203 0.035 0.036 0.006 O B g m-2 yr-1 0.03 0.005 0.005 0.001 M Inorganic kg m-2 yr-1 7.00 1.11 1232.00 195 I matter Total Mass kg m-2 yr-1 7.69 1.18 1353.00 208 n.a.

32

Figure 1. Diagram of stormwater treatment wetland located near intersection of highways SR 28 and SR 89 in Tahoe City, CA. Line diagram taken from design specification drawing, while colored overlay shows depth profile from 2006 survey. Cored sites in the basin are indicated by filled circles (site cored for both geochemical analyses and for particle size analysis) or unfilled circles (site cored only for geochemical analysis).

33

Figure 2. Accretion rates of major plant and algal nutrient elements C, N, P, and S. The units of g m2 yr-1 are an average over the 16 years of time since establishment of the basin. Note that the units of C accretion are plotted on the right axis to facilitate the presentation of rates that varied by a factor of 10. The error bars are the standard error of the mean of 7 cores for the total accretion rate taken across three transects to reflect spatial variability.

34

Figure 3. Particle size distributions of sediment from cores of the wetland basin compared to those of suspended sediments from stormwater runoff. Both were obtained from a laser scattering particle size analyzer. Vertical lines mark the borderline phi (Krumbein and Sloss, 1963) diameters of clay (< 3.9 µm), silt (3.9 to 63 µm), and sand (> 63 µm) fractions. The integrated areas indicated 25% clay, 56% silt, and 19% sand in the sediment. Stormwater inflow represents the average distribution (±S.E.) from samples (n = 89) collected between 2003–2012. The corresponding percentages in the stormwater runoff were: 16% clay, 63% silt, and 21% sand for suspended sediment.

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36

CLARIFICATION OF PARTICULATE MATTER IN SOURCE AREA SNOWMELT AS A FUNCTION OF BATCH SETTLING TIME

by

Julie R. Midgette1 John J. Sansalone1 Alan C. Heyvaert2

1University of Florida, Gaininesville, FL 2Desert Research Institute, Reno, NV

ABSTRACT For urban areas located in cold regions, transportation land use snow is exposed to particulate matter (PM) and anthropogenic chemical loadings from traffic and maintenance activities. This source area snow is a porous reservoir of PM and chemicals. Snowmelt can transport significant loads of these constituents to receiving waters, with deleterious impacts to pristine aquatic ecosystems such as Lake Tahoe. This study examines the clarification of hetero-disperse PM as a function of batch settling time from source areas in the Lake Tahoe watershed. After one hour of batch settling, approximately 99% of PM mass remaining suspended was less than 25 µm. With extended batch settling time the PM suspension became progressively finer until floc formation occurred, resulting in a separate settling zone of coarser flocs within six to 24 hours. After 24 hours of batch settling snowmelt remained highly enriched with PM and turbidity, ranging from (146-1572 mg L-1) and (143-436 NTU) in the suspension. Although sedimentation is effective at separation of coarser sediment PM, the persistence of high levels of suspended PM after 24 hours of batch settling demonstrates that sedimentation-based systems, hydrodynamic separation or primary clarification are insufficient for treatment of Lake Tahoe snow and snowmelt without secondary or advanced unit operations and processes designed and regularly maintained to separate suspended PM.

37 INTRODUCTION Snow, whether as snowfall or snow subject to management activities in the built environment of cold regions, is a repository of particulate matter (PM) and metal loadings, largely as a result of traffic and winter maintenance practices such as application of grit and deicing compounds. Although in the western United States (US) snowmelt is a vital water resource, urban snowmelt significantly contributes to annual loadings of PM and metals (Oberts 2000; Magill and Sansalone 2010). For a given diameter, a raindrop has lower specific surface area (SSA) than a snowflake of equivalent diameter (Ying and Wania 2004; Sansalone et al. 1998). Additionally, the slower settling velocity of snowflakes compared to spherical raindrops result in a more efficient scavenging of chemicals than that of rainfall (Glen et al. 2002; Hoff et al. 1998; UNESCO 2000). The large total surface area (SA) and slow velocity of snowflakes result in a more effective substrate for chemicals as compared to rainfall (Glenn and Sansalone 2002). Furthermore, the porous structure of a snowpack (0.4 to 0.9 porosity) and the extended residence times of plowed snow banks to traffic loads (hours to months) increase snow concentrations of PM and chemical species in comparison to rainfall-runoff (Kuroiwa 1998; Ozeki et al. 2002; Sansalone and Buchberger 1996; Sansalone and Glenn 2002). Subsequent snowmelt from urban areas transports significant loads of PM and chemicals into receiving waters, with acute and chronic impacts to aquatic ecosystems. Irrespective of chemical solutes and partitioning of PM-based chemical species, PM has an impact on aquatic organisms by retarding or preventing development of fish eggs by occluding spawning beds, resulting in high mortality rates (EPA 1986). Also, suspended PM attaches to developing eggs and prevents adequate exchange of oxygen and carbon dioxide between eggs and the surrounding water (EPA 1986). The relatively high heat absorbency of PM increases the temperature near surface waters which prevents vertical mixing and consequently decreases the solubility of dissolved oxygen and nutrients to the lower portions of the water column (EPA 1986). Of the suspended (< ~ 25 µm), settleable, and sediment (> 75 µm) fractions of PM, suspended PM in the water column results in light scattering and absorbance instead of transmission of light in straight lines, resulting in decreased optical clarity as indexed by turbidity (Rice et al. 2012). Turbidity is an important index parameter for assessing the health of freshwater ecosystems impacted primarily by suspended PM (Lloyd 1987). Decreased light penetration limits the growth of algae and other photosynthetic organisms which serve as food for fish, leading to decreased diversity and abundance of fish species (Lloyd 1987; Lloyd et al. 1987). In addition to the impacts of PM, rainfall-runoff and snow-snowmelt from transportation land use source areas is comprised of PM-bound metal species which also impact aquatic ecosystems. Loadings of metals in runoff can be up to 100 times higher than domestic wastewater loads (Wanielista et al. 1977). Similar to rainfall-runoff, sources of metals in snow-snowmelt from these source areas include brake and tire abrasion (Zn, Cd,

38 Pb, Cu), radiator fluid (Cu), abrasion, wear and oxidation of vehicular metal components (Fe, Mn, Al), application of deicing salts (Pb, Ca, Na, Cl, Fe, ferrocyanide), as well as leaching from infrastructure materials (Zn, Pb, Cd) (Droste and Johnston 1993; Oberts and Council 1994; Dong et al. 1984). The toxicity of metals for aquatic organisms and through the food web is directly related to metal species mobility, bioavailability, and water chemistry indices such as hardness, complexing or competitive species, partitioning to PM and metal distribution across the PM particle size distribution (PSD). The partitioning of metal ions between dissolved phase and the PSD of the PM phase occurs through a range of sorption mechanisms from ion exchange through chemical precipitation (Glenn and Sansalone 2002). Dissolved metal species can impart toxicity to aquatic organisms by sorption to exchange sites on biological membranes (for example, on the cell surface of algae or the gill mucous of fish), diffusing through lipid membranes, and once inside the cell denaturing cell proteins (Florence et al. 1992). Although dissolved metals are more acutely bioavailable than PM-based metals, PM-based metals can re-partition to the dissolved phase, and therefore also present a chronic toxicity risk (Herngren et al. 2005). In comparison with runoff, snow has higher equilibrium 3 6 -1 1 partitioning coefficients (Kd range: = 10 to 10 L kg for snowmelt, and Kd range: = 10 to 104 for runoff) indicating metals are preferentially PM-bound in snowmelt as compared to runoff for the same aqueous and PM chemistry and granulometry (Sansalone and Glenn 2002; Sansalone and Buchberger 1997). Glenn and Sansalone (2002) found that more than 90% of the metal mass of Pb, Cd, Cu, and Zn was PM-based (Glenn and Sansalone 2002; Droste and Johnston 1993). Previous studies on the partitioning of metals between PM-based and dissolved phase have concluded that while suspended PM (<25 µm) is the most mobile, bioavailable and difficult to separate and retain by Best Management Practices (BMPs), the predominance of metal mass is associated with the sediment PM fraction (>75 µm) of urban source area snowmelt and runoff (Glenn and Sansalone 2002; Sansalone and Ying 2008; Sansalone and Cristina 2004; Sansalone et al. 2010; Magill and Sansalone 2010). In contrast to the distribution of metal mass across the PSD, the PM-based concentrations of metals are greater for suspended PM as compared to sediment PM. Suspended PM also has greater direct contact with benthic organisms than coarser PM (Milligan and Loring 1997; Axtmann and Luoma 1991). The higher PM-based metal concentrations of suspended PM are a result of the relatively high specific surface area (SSA), longer residence time of transport as compared to sediment PM, and the definition of solid-phase concentration given that the relative mass of a suspended particle is much lower than a sediment particle. Additionally, while sediment PM dominates the granulometry of source area PM in terms of mass and SA, sediment is the only PM fraction that is generally separated by BMPs (ignoring common washout); with BMPs and deposition of sediment PM along the conveyance system, suspended PM can dominate the loading and the toxicity impact at the receiving water.

39 As a result of the National Pollutant Discharge Elimination System (NPDES) legislation, structural controls such as BMPs and non-structural source control or pollution prevention plans (P3) such as street sweeping are increasingly practiced (EPA 1999). Vacuum assisted street sweeping can be an effective non-structural control for PM (including finer PM fractions) and finer PM-based chemicals (Sutherland and Jelen 1997; Perkins and Yildiz 2010). Examining PM and PM-based metals clarification as function of batch settling time provides guidance with respect to the potential limit of efficacy for settling as a unit operation (UO). The quiescent settling batch mode of operations represents an upper limit for clarification at a given settling time.

OBJECTIVES Sampling sites along and adjacent to US-50W were chosen as representative transportation land use source areas within the Lake Tahoe watershed. Snow, snowmelt, ambient lake water, and control snow samples were collected. The first objective was to determine the changes in granulometric indices (PM fractions, PSD, particle density) and turbidity as a function of batch settling. The second objective was to determine the relationship between PM concentration and turbidity for snow and snowmelt samples.

METHODS SITE DESCRIPTION Lake Tahoe is a large oligotrophic alpine lake located between the Sierra Nevada and Carson mountain ranges at an altitude of approximately 1,898 meters above sea level (6223 feet) (Jassby et al. 1999). Lake Tahoe straddles the California and Nevada state boundaries, with approximately two-thirds of the basin in California and one third in Nevada. With a maximum depth of 505 meters, Lake Tahoe is the second deepest lake in the United States and the tenth deepest lake in the world (USGS 1997). The high transparency and low fertility of Lake Tahoe has been attributed to the large depth of the lake, the low ratio of watershed to lake area (800 km2, not including the lake, and 501 km2, respectively) and the lake’s granitic basin geology (Swift et al. 2006; Jassby et al. 1999). The oligotrophic nature of Lake Tahoe makes the algal populations especially vulnerable to fluctuations in nutrient levels. The lake is warm monomictic (mixing from top to bottom occurs once per year) and does not freeze. Mean precipitation ranges from 23.4 inches/year for watersheds to the east of the basin to 55.1 inches/year to the west of the basin, with the majority of precipitation in both regions occurring as snow between November and April. Pronounced snowmelt runoff events occur annually, typically during late spring and early summer. Rainstorms combined with rapid snowmelt often result in the highest flows and occasional flooding (Coats et al. 2008).

40 SNOW SAMPLING Samples were collected from transportation land use source areas listed in Table 1 and presented in Figure 1. In addition, direct snowfall control samples were collected; these control samples were spatially separated from any transportation, urban or direct human impacts other than background atmospheric deposition. Transportation land use samples were taken at plowed roadway and parking area snow banks. In this study “snow” refers to snow that has been accumulated or plowed and was sampled in the solid phase. “Snowmelt” refers to snow that has melted and was sampled in the process of conveyance by drainage. Snow or snowmelt was placed into 1 L and 4 L wide mouth polypropylene (PP) bottles. All snow samples were taken while snow was frozen in a solid phase, while snowmelt samples were sampled in a liquid phase. Samples were stored in ice chests with dry ice during transport to the laboratory where all samples were stored at or just below 0˚C and remained frozen until the time of analysis. Samples were allowed to melt at room temperature for 12 hours immediately prior to analysis.

LABORATORY ANALYSES Source area snow and snowmelt samples gathered in 2012 were analyzed for PM, PSD, turbidity, PM density and volatility and then used for batch settling experiments. After one, six, and 24 hours of batch settling, the supernatant was analyzed for PM, PSD, turbidity, PM density and volatility. PSDs were modeled as cumulative gamma distribution (CGD) functions for which scaling and shape parameters of the model were compared between sampling locations and settling times.

BATCH SETTLING EXPERIMENTS Whether by design or otherwise, sedimentation is the dominant mechanism for physical separation of PM in runoff and snowmelt clarification. Preliminary unit operations (UO) such as hydrodynamic separators (HS) and primary UOs such as volumetric clarifiers are sedimentation UOs. Furthermore, sedimentation is also an important mechanism that occurs in secondary UOs such as filters (Sansalone et al. 2009). In this study, batch settling was used to quantify the upper limit of physical separation with time for PM through batch quiescent sedimentation for the settleable and suspended fractions of source area snow and snowmelt. The settleable and suspended fractions are of particular interest because while sediment PM can be deposited during conveyance and is the primary PM fraction that can be removed by BMPs, settleable and suspended PM often gravimetrically dominates the PSD at the outfall of a watershed to receiving waters such as Lake Tahoe. Furthermore, the separation of suspended and settleable PM fractions (along with the associated PM-based chemicals) as a function of batch settling time provides an index of the potential treatability limit by sedimentation for this finer PM (Kim and Sansalone 2008). Therefore for the snow samples the sediment PM mass and PSD was quantified by size separation (sieving) prior to batch settling and the focus was settleable (<75 µm) and suspended (<25 µm) PM.

41 While sediment PM was separated and quantified for source area snow prior to batch settling experiments, source area snowmelt generally did not require the removal of sediment PM, which was separated in the conveyance process. The snowmelt samples were dominated by the suspended and settleable fractions and were analyzed directly for PSD without preliminary separation of sediment PM. The aqueous fraction of snow and snowmelt samples were analyzed for PM, PSD, turbidity, PM density and volatility. The effects of batch settling were determined by analyzing supernatant aliquots at one, six, and 24 hours for PM, PSD, turbidity, particle density and volatility.

RESULTS AND DISCUSSION PM AND PSD The PM concentrations of source area snow, source area snowmelt, ambient lake water, and control snow are summarized in Table 2a, while the PSDs for specific sites are shown in Figure 2. Based on the sampling and analysis, the mean PM concentrations of control snow and ambient lake water were 9.4 and 9.7 mg L-1. The total (suspended, settleable, and sediment) PM for source area snow ranged from 1,156 (CALTRANS Snow Management Area, SMA, S. Lake Tahoe, CA) to 77,402 mg L-1 (Douglas County, NV Firestation #5). In comparison, the total PM concentrations of the Lake Parkway snowmelt and Takela Drive snowmelt were 2,142 and 2,349 mg L-1, as shown in Figure 4a and 4b. (Note that the CALTRANS SMA collected, comingled and managed snow and managed snow from a diversity of transportation land use right-of-ways (ROW) throughout South Lake Tahoe, CA.) While PM concentrations in the source area snow varied by over an order of magnitude, the snowmelt PM concentrations were less variable, yet typically an order of magnitude higher than PM concentrations in rainfall-runoff. For example, rainfall-runoff values of total PM (as SSC) from I-10 in Baton Rouge ranged from 738 to 10,592 mg L-1 with a median value of 4,401 mg L-1 (Kim and Sansalone 2008a). ,Considering only the settleable and suspended PM fractions of source area snow, without sediment PM, the concentrations range from 1,156 to 8,512 mg L-1, as compared to snowmelt where these two finer PM fractions ranged from 1,360 to 2,270 mg L-1. The majority of PM mass and variability in PM mass in source area snow was therefore in the sediment PM fraction. Previous studies of rainfall-runoff PM granulometry have found similar correlations between high PM concentrations and high fractions of sediment (coarse) PM (Kim and Sansalone 2008b). The entire PSD profiles (0.02 to 9750 μm) from source area snow locations are presented in Figure 2 (with CALTRANS SMA snow presented separately in Figure 3 because of its distinct granulometry). Results from Figure 2 indicate that approximately 85-95% (by mass) of the particles present in source area snow are sediment sized, while the total PM concentrations vary by an order of magnitude (4569 – 77,402 mg L-1). Therefore sediment PM is consistently the dominant PM fraction, although the total PM contribution to source area snow is variable. Source area sites with higher PM concentrations have higher

42 relative standard deviations (RSD); for example site #1, with a mean PM concentration 77,402 mg L-1, has a RSD of 38.3% while site # 5, with a mean PM concentration of 4,569 mg L-1 has a RSD of 1.7%. Resulting from conditions of accumulation and exposure of the CALTRANS SMA snow, this source area snow did not have a significant sediment PM fraction as compared to other source areas and was examined separately. As shown in Figure 3, the PM in the SMA snow was comprised of PM predominantly smaller than 75 µm, which can be explained based on the snow residence time exposed to traffic. After a snow event, snow is plowed from the roadway surface as expeditiously as practical. This snow which is plowed, loaded onto trucks, and transported to the CALTRANS SMA has been exposed primarily to PM generated from vehicular contact with the snow-covered roadway. The level of traffic activity is significantly less during this snow-covered roadway period. In contrast, snow which is plowed to the curb or edge of the pavement remains as a longer-term porous reservoir for PM and chemicals generated from vehicular component wear and tire-pavement abrasion with the exposed pavement surface. This extended exposure, which can last for weeks or longer after the snow event, results in substantially higher PM concentrations, particularly in the sediment PM fraction (which typically comprises the majority of source area PM, by mass). Tire-pavement abrasion during the period of snow-covered roadways generates PM with a mean diameter of approximately 20 μm (Cristina et al. 2002); and abraded pavement can comprise between 40 to 50% of PM captured in the urban snow banks with longer exposure (Kobriger and Geinopolos 1984). While plowing snow from the pavement to the pavement edge is essential along urban pavements for transport of people, goods and services during winter storms, the subsequent longer term exposure of this snow bank/barrier results in an accretion of PM and traffic-generated chemical loads in source area snow.

BATCH SETTLING SUPERNATANTS: SNOW PM AND PSD After separating the sediment PM fraction of source area snow by wet sieving (which would typically be removed by deposition during snowmelt conveyance or at a BMP), the PSDs of snow samples were measured at time zero and the supernatant after one, six, and 24 hours of batch settling. For all snow samples with sediment PM separated, as settling time increased from zero to 24 hours, supernatant PM decreased and the fine PM fraction increased (as a proportion of supernatant PM) until floc formation occurred. By one hour, PM values decreased to 773 ± 309 mg L-1, as shown in Table 3a. As PM values decreased between zero and one hour, PSD in supernatant became finer (decreasing β values). The shape parameter, α, of the PSD CGD increased from time zero to one hour. By 1 hour, approximately 99% of PM is in the suspended (<25 µm) fraction, as shown in Figure 5, and therefore the PSD is less heterodisperse. Between one and six hours, PM supernatant concentrations decreased to 632 ± 156 mg L-1. With the exception of snow from Douglas County, NV Firestation #5, the supernatant became progressively finer between one and six hours. For all snow samples

43 other than Firestation #5 snow, the scaling parameter, β, of the PSD CGD decreased between one and six hours; the CGD β value of Firestation #5 snow increased from 1.45 at one hour to 2.06 at six hours, as shown in Figure 3. Therefore floc formation occurred between one and six hours for Firestation #5, as demonstrated by a shift towards coarser PSD. The scaling (size) parameter (β) of the CGD model of the PSD for time zero was much greater for Fire Station # 5 snow (β = 52.4) than the other snow samples (β = 13.0 - 20.5), indicating a higher ratio of settleable to suspended PM was correlated with earlier floc formation (by six hours instead of 24 hours). In solutions with higher ratios of settleable to suspended PM, floc formation is likely a result of differential sedimentation interactions. Additionally the shape parameter, α, of snow PSD continued to increase between one and six hours of settling as shown in Table 3a. Between six and 24 hours of batch settling, PM concentrations continued to decrease to 180 ± 102 mg L-1, as shown in Table 3a. PSD became coarser between six and 24 hours, indicating that floc formation had occurred, as shown in Table 3a and Figure 3e.

BATCH SETTLING SUPERNATANTS: SNOWMELT PM AND PSD As observed with source area snow, the batch supernatant of snowmelt progressively decreased over time in PM concentration, with increasingly finer PSDs (PSDs shifted towards smaller PM with smaller β values) until floc formation occurred (Table 3b). Between zero and one hour, PM concentrations decreased from 2246 ± 309 mg L-1 to 727 ± 243 mg L-1, as presented in Table 3b. PSD became finer (smaller β values) and less heterodisperse (higher α values). The supernatant PM was predominantly comprised of suspended PM (<25 μm) after one hour of settling, as shown in Figure 5b. From one to six hours, PM concentrations continued to decrease to 222 ± 15.4 mg L-1 (see Table 3b). For Lake Parkway snowmelt, PSD became increasingly finer and less heterodisperse, as shown in Figure 4a. For Takela Dr. snowmelt the PSD was increasingly finer, while β increased slightly from 1.37 to 1.77, and α decreased from 2.56 to 1.63, as shown in Figure 4b. Results suggest that for Lake Parkway snowmelt flocs had not yet formed by six hours, while for Takela Drive snowmelt some flocs had started to form, although not sufficiently to significantly shift the PSD. In contrast to the characteristic floc formation observed with source area snow between six and 24 hours, the snowmelt supernatant formed two visually discernible layers (in addition to the layer of completely settled PM). These supernatant layers had different PM concentration, turbidity and PSDs (Table 3b). For example, after settling the Lake Parkway snowmelt for 24 hours the top layer had a PM concentration of 276 mg L-1 and turbidity of 177 NTU, while the lower supernatant layer had a PM concentration of 911 mg L-1 and turbidity of 523 NTU. After 24 hours of batch settling for Takela Drive snowmelt the top layer had a PM concentration of 220 mg L-1 and turbidity of 228 NTU, while the bottom supernatant layer had a PM concentration of 1572 mg L-1 and turbidity of

44 1244 NTU. The PSDs of the bottom supernatant layers at 24 hours were coarser than the top supernatant layers at 24 hours (higher β values), as shown in Figure 4, indicating that flocs formed and settled to the lower layer of the supernatant. The PM concentrations in the top layer of the supernatant after 24 hours were similar to the supernatant PM concentrations at six hours (see Table 3b). For Lake Parkway snowmelt, supernatant PM concentration after six hours was 229 mg L-1, and at 24 hours the top layer PM concentration was 276 mg L-1. Similarly, for Takela Drive snowmelt, supernatant PM concentration of the supernatant after six hours was 214 mg L-1 and after 24 hours the top layer was 220 mg L-1. Conservation of mass between six and 24 hours implies that the supernatant at six hours was also comprised of higher PM concentration regions towards the bottom of the lower layer. Through conservation of mass and the continuous batch settling process at any time after settling begins, there will exist a vertical concentration gradient within the batch supernatant, with higher PM concentrations towards the bottom, regardless of whether this gradation is visually apparent. With respect to the PM fractions after one hour of settling; 99% of PM (by mass) in source area snow and also in snowmelt remained suspended and less than 25 µm in diameter, as shown in Figure 5; a result that matches similar findings for rainfall-runoff PM (Sansalone et al. 2010b). After 24 hours the settleable PM fraction in snow and snowmelt supernatants increased by approximately 3% as a result of floc formation, but the suspended fraction still dominated ( > 96% by mass). Table 3 summarizes settling results with increasing batch settling time. Results indicate the following trends: (1) PM supernatant concentrations decrease with increasing batch settling time, (2) Supernatant PSD becomes finer and less heterodisperse with batch settling time until floc formation occurs, (3) Once floc formation occurs PSD becomes slightly more coarse and heterodisperse, and (4) Supernatant of snowmelt at 24 hours was comprised of two layers, with the bottom layer containing higher PM concentrations, higher turbidity, and coarser PSD due to floc formation. After 24 hours of batch settling, snow and snowmelt samples were still high in suspended PM, with supernatant PM values ranging from 146 to 341 mg L-1 for snow and 177 to 1572 mg L-1 for snowmelt. For comparison, California restricts the suspended sediment concentrations (SSC) of tributaries into Lake Tahoe to a 90th percentile value of 60 mg L-1 (CRWCB 2011). Furthermore the SSC of Lake Tahoe must not exceed 10% of measureable natural conditions (CRWCB 2011). From Table 2, the mean PM concentration of Lake Tahoe’s near-shore (10 m) ambient water is 9.68 mg L-1. Based on this result, discharges into Lake Tahoe must not increase the SSC of the lake by more than 0.97 mg L-1.

TURBIDITY The turbidity of source area snow, source area snowmelt, ambient lake water, and control snow are summarized in Table 2b. The mean turbidity of ambient lake water and control snow was 1.7 and 3.09 NTU, respectively. The turbidity of source area snow (without

45 sediment PM) and snowmelt ranged from 840 to 9423 NTU and 1575 to 2948 NTU, as shown in Table 2b. The relationship(s) between turbidity and PM can be examined alongside PSDs to gain further insight into what changes in PM are occurring with settling and how they affect the optical properties of the solution (e.g., Figures 3 and 4). The relationships between PM concentration and turbidity were modeled with a power law:

T = a · PMb (1)

where T = turbidity [NTU], PM = particulate matter concentration [mg L-1], a = the normalization constant, and b = the scaling parameter (Clauset et al. 2009). When plotted on a set of logarithmic axes, a is the y-intercept, representing the minimum value of turbidity, and b is the linear slope, representing the rate of increase in turbidity with respect to PM concentration.

BATCH SETTLING SUPERNATANTS: SNOW TURBIDITY Between zero and one hour, supernatant turbidity of snow samples decreased from 4836 ± 3012 NTU to 1615 ± 1018 NTU, as shown in Table 3a. However, as finer PM becomes a more significant fraction (gravimetrically and numerically) of the supernatant, relative to coarser (settleable) PM, the effect of light scattering increases when normalized to PM on a mass basis. This effect is evident in Figure 3(c,d), where after one hour of batch settling time, the turbidity increased with respect to initial turbidity at each constant value of PM concentration. Thus, the finer the PM and the more numerous the particles in suspension, the more effectively that suspension scatters light as compared to a suspension of PM with the same mass concentration, but with larger settleable PM initially present that typically settle after one hour (Swift et al. 2006). Between one and six hours, turbidity of snow sample supernatant decreased to 475 ± 201 NTU. For CALTRANS SMA snow, the trend of increasing turbidity at each constant value of PM continued from one to six hours, as PSD became increasingly dominated by smaller particles, as shown in Figure 3(a, c). However, for Firestation # 5 snow, the turbidity decreased between one and six hours when normalized to gravimetric PM; this result is attributed to floc formation between one and six hours, as demonstrated in Figure 3(b, d). Therefore turbidity increases when normalized to PM by mass until floc formation occurs; with increased settling time and further floc formation the turbidity decreases for each value of PM as PM is comprised of larger flocs which are less effective in scattering light. Between six and 24 hours, supernatant turbidity decreases further to 290 ± 100 NTU as presented in Table 3a. Between six and 24 hours, the effect of changing PM size on light scattering is indistinguishable, despite the shift towards larger particles (flocs) in the PSD between six and 24 hours. Therefore the optical properties of supernatant PM are

46 distinguishable between six and 24 hours by laser diffraction (LD) but not by light scattering (nephelometry). Differences in diffractivity of similarly sized particles in two solutions may cause an apparent difference in PSD. Specifically, variations in morphology and mineralogy often cause deviations from the assumptions of Mie and Fraunhofer theory (Storti and Balsamo 2010; Ma et al. 2001), producing LD results confounded by factors other than particle size. For example, for non-spherical particles, a larger than representative particle size may be assigned as a result of the average cross sectional area of a non-spherical particle being larger than a sphere of equivalent volume (Eshel et al. 2004). Additionally, variations in refractive indices (RI) and density may cause apparent differences in PSD as measured by LD (Beuselinck et al. 1998). PM of similar sizes but different densities, RIs, or shape may preferentially settle between 6 and 24 hours, resulting in different patterns of LD despite similar turbidities. Although measurement of particle size by LD may be confounded in some cases by unusual particle properties, turbidity is still primarily determined by particle size. Results indicate the following trends: (1) Decreasing PM concentration is directly proportional to decreasing supernatant turbidity, (2) As smaller particles become a more significant fraction of supernatant PM, turbidity becomes higher when normalized to PM by mass because smaller particles are more effective at light scattering (Swift et al. 2006), (3) With further settling time, flocs form and consequently turbidity decreases when normalized to PM by mass, (4) The primary properties which determine light scattering as measured by nephelometry are PM concentration and PSD, and (5) Differences in PSD as measured by laser diffraction may not correspond to differences in light scattering and therefore may be the result of variability in diffraction measurement rather than actual differences in particle size.

BATCH SETTLING SUPERNATANTS: SNOWMELT TURBIDITY Between zero and one hour, snowmelt turbidity decreased from 2237 ± 699 NTU to 1776 ± 1076 NTU, as shown in Table 3b. Figure 4 demonstrates that as settling time increased from zero to one hour, Lake Parkway and Takela Drive snowmelt became finer in PSD and there was an increase in turbidity at each gravimetric value of PM. As settling time increased from one to six hours, turbidity decreased further to 337 ± 48.3 NTU (Table 3b). Between one and six hours, there was not a distinct change in the value of turbidity at each value of PM, although the PSDs appear to become finer between one and six hours (see examples in Figure 4). This may be a result of differences in light diffraction and not actual differences in PSD, as was suggested for snow between six and 24 hours. For both snowmelt samples, as batch settling time increased from six to 24 hours, two layers were formed within the supernatant; the top layer with turbidity of 220 ± 30.3 NTU, and the bottom layer with turbidity of 2903 ± 4286 NTU (Table 3b). Therefore the bottom layer of the supernatant had higher PM concentrations than the top layer at 24 hours (1247 ± 759 mg L-1 compared to 262 ± 28.4 mg L-1), as well as higher turbidity, and coarser

47 PSD (with CDG scaling parameter (β) of the bottom layer at 8.91 compared to 1.04 for the top layer). These results suggest that between six to 24 hours the flocs formed within the supernatant and settled into the bottom layer of the supernatant. The greatest turbidity changes for source area snow and for snowmelt occurred at up to one hour of batch settling time as a result of finer and more numerous fine particles dominating the suspension, compared to initial suspensions that also contained coarser settleable PM. After one hour, changes in PSD indicate floc formation, an interpretation supported by the decrease in turbidity for normalized PM (by mass). After 24 hours of batch settling, the turbidity of all snow and snowmelt samples remained high (146 to 1572 NTU) as compared to the TRPA and NPDES discharge standard of 20 NTU (TRPA 2013; CRWQCB 2011). These results indicate that secondary treatment such as coagulation/flocculation (C/F) and/or filtration may also be necessary after 24 hours of batch settling, an ideal settling condition that is not usually achieved by BMPs. Snowmelt in particular would require significant additional clarification for suspended PM.

CONCLUSIONS Snow and snowmelt were collected from transportation land use areas and snow management areas (SMA) in the Lake Tahoe watershed. Batch settling of source area snow and snowmelt was examined through measured changes in PM, PSD and turbidity over a 24 hour settling period. Source area snow contained higher and more variable PM concentrations than snowmelt, with the majority of mass and variability in mass associated with the sediment (>75 µm) PM fraction. CALTRANS SMA snow contained less PM than all other source areas, specifically sediment-sized PM. This was probably because that the majority of sediment PM is likely generated from post-plowing tire-pavement abrasion. After one hour of settling, the supernatant of snow and snowmelt were comprised mostly of PM less than 25 μm in diameter, and the PSD became increasingly finer with time until floc formation occurred. The supernatant of snow and snowmelt samples increased in turbidity as a function of gravimetric PM concentration (i.e., mass-normalized PM), with finer particles dominating the suspension. Snowmelt samples demonstrated a visible gradient in PM concentration as discernible layers of supernatant appeared, resulting from continuous floc formation and settling After 24 hours of batch settling, PM and turbidity remained above levels mandated by state and national discharge limits. Therefore batch settling, as a best-case-scenario for the performance of sedimentation BMPs, demonstrated that BMPs which rely on sedimentation alone are insufficient for treatment of PM in urban snowmelt. Adequate removal of suspended PM is particularly pertinent for pristine aquatic environments such as Lake Tahoe, which have very low natural turbidity and are therefore extremely sensitive to PM loads.

48 Although sedimentation is effective at separation of sediment PM, the persistence of high levels of suspended PM after 24 hours of batch settling demonstrates that sedimentation-based systems, hydrodynamic separation or primary clarification are generally insufficient for treatment of Lake Tahoe snow and snowmelt without secondary or advanced unit operations and processes designed and regularly maintained to separate suspended PM.

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51 Rice, E.W., Baird, R.W., Eaton, A.D., and Clescari, L.S. (2012). Standard methods for the examination of water and wastewater. American Public Health Association. 22nd ed. Washington, D.C. Sansalone, J. and Buchberger, S. (1996). “Characterization of metals and solids in urban highway winter snow and spring rainfall-runoff.” Transportation Research Record: Journal of the Transportation Research Board. 1523(1), 147-159. Sansalone, J. and Buchberger, S. (1997). “Partitioning and first flush of metals in urban roadway storm water.” Journal of Environmental Engineering, 123(2), 134-143. Sansalone, J. and Glenn, D. (2002). “Accretion and partitioning of heavy metals associated with snow exposed to urban traffic and winter storm maintenance activities.” Journal of Environmental Engineering, 128(2), 167-185. Sansalone, J. J., and Cristina, C. M. (2004). “First flush concepts for suspended and dissolved solids in small impervious watersheds.” Journal of Environmental Engineering, 130(11), 1301-1314. Sansalone, J.J., Liu, B., and Kim, J.-Y. (2009). “Volumetric clarifying filtration of urban source area rainfall runoff.” Journal of Environmental Engineering, 135(8), 609-620. Sansalone, J., Liu, B., & Ying, G. (2010a). “Volumetric filtration of rainfall runoff. II: Event- based and inter-event nutrient fate.” Journal of Environmental Engineering, 136(12), 1331-1340. Sansalone, J., Ying, G., and Lin, H. (2010b). “Distribution of metals for particulate matter transported in source area rainfall-runoff.” Journal of Environmental Engineering, 136(2), 176-184. Sansalone, J. J. and Tribouillard, T. (1999). “Variations in characteristics of abraded roadway particles as a function of particle size- implications for water quality and drainage.” Transportation Research Record, 1690(1), 153-163. Sansalone, J., and Ying, G. (2008). “Partitioning and granulometric distribution of metal leachate from urban traffic dry deposition particulate matter subject to acidic rainfall and runoff retention.” Water Research, 42(15), 4146-4162. Sutherland, R. C., and Jelen, S. L. (1997). “Contrary to conventional wisdom, street sweeping can be an effective BMP.” Advances in modeling the management of stormwater impacts, 5, 179-190. Swift, T.J., Perez-Losada, J., Schladow, S.G., Reuter, J.E., Jassby, A.D., and Goldman, C.R. (2006). “Water clarity modeling in Lake Tahoe: linking suspended matter characteristics to secchi depth.” Aquatic Sciences, 68(1), 1-15.

52 TRPA (2013). Code of Ordinances. Ch. 60- Water Quality. Adopted by the Tahoe Regional Planning Agency Governing Board: Retrieved from: http://www.trpa.org/documents/docdwnlds/ordinances/TRPA_Code_of_Ordinances.pdf. Last accessed August 5, 2013. USGS. Stream and Ground-Water Monitoring Program, Lake Tahoe Basin, Nevada and California. Fact Sheet FS-100-97. June 1997. UNESCO. (2000). Urban Drainage in Specific Climates: Vol. II. Ed. by S. Saegrov, J. Milina and S.T. Thorolfsson. IHP-V. Technical Documents in Hydrology. No. 40, Vol II, Paris. Wanielista, M. P., Yousef, Y. A., and McLellon, W. M. (1977). “Nonpoint source effects on water quality.” Journal (Water Pollution Control Federation), 441-451.

53 Table 1. Locations of sampling. Site numbers correspond to marked locations presented in Figure 1. R/W = right-of-way. SMA = snow management area. CALTRANS = California Dept. of Transportation.

Site No. Location Type of Site Samples Collected

US 50 W at Douglas Roadway 1 Snow in ROW County, NV Firestation #5 surface

US 50 W at Cave Rock, 2 Ambient lake Ambient lake NV

Roadway 3 US 50 W at Tunnel, NV Paved shoulder snow surface

US 50 W at Zephyr Cove, 4 Parking lot Parking lot snow and ROW drainage NV

US 50 W at Lake Parkway, Roadway 5 Snow in ROW and ROW drainage CA surface

US 50 W at Stateline, Roadway 6 Snow in ROW and gutterflow CA/NV surface

7 US 50 W at Marina, CA Ambient lake Storm sewer snowmelt and ambient lake

US 50 W at Ski Run Blvd, Roadway 8 Parking lot storm sewer snowmelt CA surface

US 50 W at Takela Dr., Roadway 9 Snow in ROW and drainage to ROW CA surface

Sierra Blvd. at Barbara CALTRANS 10 Disposal site snow Ave., CA SMA

Control snowfall- removed from any 11 State Rd. 28, CA Control snowfall anthropogenic impacts

54 Table 2. Statistical summary of PM concentrations (a) and turbidity (b) for source area snow, source area snowmelt, Lake Tahoe ambient water, and control snow. (a)

Ambient Control Total PM [mg L-1] Snow Snowmelt Lake Snow Mean 29,255 2,246 9.68 9.37

Median 15,860 2,289 9.10 9.26 St. Dev. 31,882 309 7.78 3.96 Range 1,156-77,402 1,856-2,550 1.18-19.0 3.70-13.0

(b) Ambient Control Turbidity [NTU] Snow Snowmelt Lake Snow

Mean 4,386 2,262 1.74 3.09

Median 3,996 2,262 1.62 2.94 St. Dev. 3,103 972 0.84 2.28 Range 840-9423 1575-2948 0.93-2.80 1.14-5.37

55 Table 3. Statistical summary of PM concentration, turbidity, and PSD cumulative gamma distribution (CGD) parameters for batch supernatants of snow (a) and snowmelt (b). The presence of flocs within the supernatant is indicated. (a)

PM CGD Floc Concentration Turbidity [NTU] Parameter Formation Time (hr.) [mg L-1]

Mean St. Dev. Mean St. Dev. α β Yes No

0 4351 2814 4836 3012 1.11 20.0 x

1 772 309 1615 1018 1.70 3.06 x

6 362 156 475 201 1.84 1.75 x

24 180 102 290 100 1.39 4.39 x

(b)

PM CGD Floc Concentration Turbidity [NTU] Parameter Formation Time (hr.) [mg L-1]

Mean St. Dev. Mean St. Dev. α β Yes No

0 2246 309 2237 699 0.83 69.7 x

1 727 243 1776 1076 1.89 3.05 x

6 222 15.4 337 48.3 1.67 1.91 x

24-top 262 28.4 220 30.3 1.80 3.83 x

24- bottom 1247 759 2903 4286 0.84 8.91 x

56

Figure 1. Sampling locations along US-50W in the Lake Tahoe watershed (California and Nevada).

57 100 Site No. 1 Site No. 3 80 Site No. 4 Site No. 5 Site No. 9 60 Median CGD

40

20 Percent finer by mass (%) mass by finer Percent

0

104 103 102 101 100

Particle Diameter (m) Figure 2. PSDs of source area snow. A cumulative gamma distribution (CGD) was fit to the median of all source area PSDs with the exception of CALTRANS SMA snow. The shape and scaling parameters, α and β, of the CGD and the median PSD are presented. The coefficient of determination for the fit of the CGD to the median PSD is R2 = 0.97.

58 100 100 (a) (b) PM 0 hr 80 Time 80 0 hr [mg L-1] 1 hr 1 hr 0 1156 6 hr 60 1 hr. 390 60 PM 6 hr Time 6 hr. 163 [mg L-1] 0 6959 40 40 1 hr. 448 6 hr. 238 20 Parameter 0 1 hr 6hr 20 Parameter 0 1 hr 6hr α 0.96 1.29 1.69 Percent finer by mass (%) (%) mass by finer Percent Percent finer by mass (%) mass finer by Percent α 0.68 1.98 1.39 β 20.5 5.74 2.32 β 52.4 1.45 2.06 0 0 100 10 1 100 10 1 Particle Diameter (m) Particle Diameter (m) 103 103 Time 0 (d) 1 hr (c) 6 hr 102 2 Time 0 b 10 T = a(PM) Turbidity 1 hr Time [NTU] 6 hr 1 0840 10 1 b 1 hr. 430 10 T = a (PM) 6hr. 240 Turbidity Time 0 Turbidity [NTU] [NTU] 10 Parameter 0 1 hr 6hr Turbidity [NTU] 0 10 0 5871 a 0.49 2.90 1.53 Parameter 0 1 hr 6hr b 1.06 0.91 0.99 a 0.50 0.87 0.82 1 hr. 717 -1 b 1.38 1.10 1.06 6 hr. 260 10 10-1 10-1 100 101 102 103 104 105 10-1 100 101 102 103 104 105 -1 -1 PM (as SSC) [mg L ] PM (as SSC) [mg L ]

3 100 10 Site Turbidity Site No. 1- 24 hr. No. [NTU] (f) 80 Site No. 10- 24 hr. 102 1 164 (e) 10 200 60 Site No. 1- 24hr. 1 Site No. PM [mg L-1] 10 Site No. 10 - 24 hr. 40 1199 T = a (PM)b 10 146 0 Parameter Site No. 1 Site No. 10 [NTU] Turbidity 10 Parameter Site No. 1 Site No. 10 20 α 0.84 1.28 a 0.38 0.93 Percent finer (%) by mass β 10.0 5.38 b 1.2 1.08 0 10-1 100 10 1 10-1 100 101 102 103 104 105 -1 Particle Diameter (m) PM (as SSC) [mg L ]

Figure 3. Plots (a,b) show PSDs while plots (c,d) show corresponding turbidity relationships for PM (<75 µm) after 0, 1, and 6 hours batch settling for CALTRANS SMA snow (left) and for Douglas County, NV Firestation # 5 snow (right). PSDs were modeled as cumulative gamma distributions (CGD). The shape parameter (α) and scaling parameter (β) of the CGDs are shown. Parameters “a” and “b” are the power law model parameters relating turbidity (T) and PM (as SSC) for snowmelt. Plots (e,f) show PSDs and turbidity relationships for PM after 24 hours of batch settling.

59

100 100 PM PM Time -1 Time Time 0 [mg L ] [mg L-1] 80 1 hr 0 2142 80 0 2349 1 hr. 518 1 hr. 936 6 hr 6 hr. 229 6 hr. 214 60 60 (a) Time 0 1 hr (b) 40 40 6 hr Parameter 0 1 hr 6hr 20 20 Parameter 0 1 hr 6hr α 0.61 1.83 1.68 α 1.00 2.56 1.63 Percent finer by mass (%) mass by Percent finer Percent finer by mass (%) by Percent finer β 164 2.06 1.64 β 17 1.37 1.77 0 0 1000 100 10 1 0.1 1000 100 10 1 0.1 Particle diameter (m) Particle diameter m)

104 104 Time 0 Time 0 1 hr (c) 1 hr (d) 3 3 10 6 hr 10 6 hr b T = a(PM)b Turbidity Turbidity 2 T = a(PM) Time 2 Time 10 [NTU] 10 [NTU] 0 1575 0 2945 101 1 hr 816 101 1 hr 2709 6 hr 349 6 hr 299 Turbidity [NTU] Turbidity [NTU] [NTU] Turbidity 0 10 Parameter 0 1 hr 6hr 100 Parameter 0 1 hr 6hr a 0.17 0.05 2.55 a 0.05 1.50 2.83 b 1.24 1.57 0.82 b 1.34 0.99 0.88 10-1 10-1 10-1 100 101 102 103 104 105 10-1 100 101 102 103 104 105 PM (as SSC) [mg L-1] PM (as SSC) [mg L-1]

4 100 10 24 hr. - top 24 hr. Parameter (e) 103 24 hr. - bottom layer ab 80 top 0.98 1.07 (f) 24 hr. PM 102 bottom 0.01 2.06 60 layer [mg L-1] 1 top 276 10 bottom 648 40 0 10 24 hr. - top

Turbidity [NTU] Turbidity 24 hr. Turbidity 24 hr. Parameter 24 hr. - bottom layer αβ -1 layer [NTU] 20 10 b top 2.77 1.04 top 213 T = a(PM) Percent finer (%) by mass bottom 529 bottom 0.84 8.91 10-2 0 10-1 100 101 102 103 104 1000 100 10 1 PM (as SSC) [mg L-1] Particle Diameter (m)

Figure 4. Plots (a,b) show PSDs while plots (c,d) show corresponding turbidity relationships for PM batch settling time for Lake Parkway snowmelt (left) and Takela Dr. snowmelt (right). PSDs were modeled as cumulative gamma distributions (CGD). The shape parameter (α) and scaling parameter (β) of the CGDs are shown. Parameters “a” and “b” are the power law model parameters relating turbidity (T) and PM (as SSC) for snowmelt. Plots (e,f) show PSDs and turbidity relationships for PM after 24 hours of batch settling of snowmelt.

60 100 (a) 80 Sediment 60 Settleable Suspended 40 PM Fraction (%) PM Fraction 20

0 Time 0 1 hr. 6 hr 24 hr

100 (b) 80 Sediment Settleable 60 Suspended

40 PM FractionPM (%) 20

0 Time 0 1 hr. 6 hr. 24 hr. top 24 hr. bottom

Figure 5. PM fractions of snow supernatants (a) and snowmelt supernatants (b) after zero, one, six, and 24 hours. Snowmelt samples formed two layers between 6 and 24 hours, which varied in PM composition.PM concentrations of snow and snowmelt supernatants are presented in Table 3.

61

0.30

0.25 Model; 0.20 R2 = 0.99 n = 38 0.15

0.10

0.05 Probability density function, pdf 0.00 102 103 104 105 106 Total PM [mg L-1]

Figure 6. Probability density function (pdf) of total PM of source area snow and snowmelt samples. Total PM was measured through a combination of laser diffraction (LD) and manual sieving.

62 APPENDIX A. CHARACTERIZATION OF STORMWATER RUNOFF AND WETLAND SEDIMENT ACCUMULATION. Urban runoff has been identified as the primary source (72%) of fine sediment particle (FSP) pollution to Lake Tahoe (LRWQCB and NDEP, 2011), as well as an important source for total phosphorus loading (38%), and a minor source for nitrogen loading (16%). Both phosphorus and turbidity often correlate with fine particle concentrations in urban runoff (Heyvaert et al., unpublished data). Phosphorus adsorbs onto particle surfaces (Ferguson J. 2006; Lilienfein et al. 2004), while turbidity increases with light scattering at higher particle concentrations (Heyvaert et al. 2015). Indeed, turbidity can serve as a useful surrogate measure of fine sediment particle concentration (see Heyvaert et al. 2015). Although there is a general understanding that phosphorus loading and turbidity will both increase with particle loading, data are not available to describe the relative contributions associated with different particle size classes. Therefore, we examined the characteristics of urban runoff associated with particle size classes and other important water quality characteristics, such as turbidity, suspended sediment, phosphorus and nitrogen concentrations. This should be useful information for management models and to help evaluate the effectiveness of fine sediment removal by processes and unit operations that target pollutant-specific removals with different types of BMPs. Following is a brief description of results obtained from differential size fraction analysis on runoff samples collected at several locations in the Lake Tahoe Basin (Table A1). Most of these runoff samples were obtained from highway sites during precipitation events (rain or snow). Also discussed are the results from analysis of size-fractioned sediments collected in the Tahoe City Wetland Treatment System in Tahoe City (near the intersection of Highways 89 and 28); these are the same sediment core samples discussed in Chapter 1 of this report. Particle size fractionation procedures were similar for both the stormwater samples and the wetland sediment core samples, except that sediment cores were first suspended in laboratory DI water before sieving and filtration. Samples were successively passed through a 1000 µm sieve, a 63 µm sieve, a 20 µm sieve, then through nylon mesh filters at 10 µm and 5 µm, as well as a final 0.45 µm nylon membrane filter (to yield dissolved components). Representative aliquots were drawn at each step for subsequent analysis of total phosphorus (TP), total Kjehldahl nitrogen (TKN), suspended sediment (SS) and turbidity (Figure A1). Analyses were conducted at the DRI Water Analysis Laboratory using Standard Methods (Eaton et al. 1998). Analysis of fractionated stormwater samples showed that on average the <20 µm size fraction contained about 85% of the TP, the total turbidity, and the TKN found in bulk stormwater (Table A2). Even the <10 µm fraction still contained more than half the amount

A-1 of TP and turbidity found in bulk samples, (57% and 55%, respectively). The <5 µm fraction represented about one-third of bulk measurements for TP and turbidity (38% and 31%, respectively). In contrast, suspended sediment concentrations decreased more rapidly: with 59% of bulk concentration in the <20 µm size fraction, 40% in the <10 µm fraction, and 25% in the <5 µm fraction. Fine sediment particles (FSP <16 µm) are a pollutant regulated by the TMDL for Lake Tahoe clarity. Interpolated from these results we estimate that the FSP fraction represents about 51% of bulk suspended sediment (by mass) in stormwater samples. Since only ~11% of suspended particle mass in stormwater samples was contained in the 10–16 µm size fraction, it becomes evident that capturing the <10 µm fraction will be crucial for mitigating stormwater impacts on Lake Tahoe clarity. Considering that particle numbers increase exponentially with decreasing particle size, and that particle numbers are the main factor affecting lake clarity (Swift et al. 2006), a BMP that removes 1 kg FSP over the size range from 10 to 16 µm would achieve much less for lake clarity compared to eliminating 1 kg FSP in the size range <10 µm. It is critical to understand that removing mass-equivalent portions of coarser FSP grades does not translate into removing equivalent numbers of particles from the finer FSP grades. BMPs should be targeting particle removal efficiency at the smallest size practical, ranging up from 0.5 µm to 16 µm (rather than vice versa). Interestingly, TKN concentrations did not decrease appreciably with stormwater size fractions. TKN in the dissolved phase (<0.45 µm) represented over half the amount measured in bulk samples. This is because stormwater TKN includes appreciable amounts of dissolved organic nitrogen (DON) and ammonium ion as well as some particulate nitrogen, primarily in the <5 µm size range. Wetland basin excavation removed about 2360 cubic yards of dewatered material from the Tahoe City Wetland Treatment System (J. Longo, personal communication), consisting of dry senescent surface vegetation over a predominately inorganic mineralized layer. Taking the <1000 µm fraction as representing bulk wetland sediment, after removal of coarse senescent vegetative material, we estimated relative percentages of constituents represented in the different size fractions (Table A3). The relative percentages of constituents by size class follow patterns similar to that observed with the runoff sample size fractions, but with a slight shift toward higher percentages in smaller size classes. Sediment in the <20 µm size range represented 68% of the bulk total (versus 59% in stormwater). Turbidity in the <20 µm fraction was 42% of turbidity in bulk suspension (versus 31% in stormwater), indicating a high percentage of very fine particles in wetland sediment. TP in the <20 µm size fraction was 73% of the total (versus 85% in stormwater). And much of the TKN in core suspension was present as particulates (93%), compared to the large amount of TKN in dissolved phase seen with stormwater (average 54%).

A-2 From Chapter 1 of this report we know that total inorganic matter accumulation in the cores averaged 7.0 kg m-2 y-1, of which we estimate approximately 54–60% consisted of fine sediment particles (FSP) less than 16 µm (from LPSA analysis in Figure 1 and from Table A3 interpolation, respectively). This would indicate an FSP accretion rate of 3.8–4.2 kg m-2 y-1. About 48% of total particle mass retained by the wetland system was in the <10 µm size fraction and 33% was in the <5 µm size fraction. Therefore, a substantial portion of the FSP that accumulated in this wetland basin was in these smaller size classes (<10 µm). Most of the wetland particulate matter consisted of inorganic material, as measured by loss-on-ignition at 550°C. The amount of combusted organic matter increased slightly with smaller size fractions (from ~8% in bulk to 12% in the <5 µm fractions). Although not shown here, particulates in runoff samples averaged ~25% organic material, with almost all of it in the <20 µm fraction. Presumably, much of this would have been mineralized in the wetland or passed through as low-density particles moving with the flow during events.

REFERENCES Eaton, A.D., L.S. Clesceri, A.E. Greenberg, and M.A.H. Franson. 1998. Standard Methods for the Examination of Water and Wastewater. American Public Health Association, Washington, DC. Ferguson, J.W., 2006. The Bioavailability of Sediment and Dissolved Organic Phosphorus Inputs to Lake Tahoe. M.S. Thesis. University of Nevada, Reno, NV, U.S.A Heyvaert, A.C., 2NDNATURE, and J.E. Reuter. 2015. Analysis of Turbidity as a Surrogate Indicator for Fine Sediment Particle Concentrations in the Tahoe Basin. Final report. Prepared for the USDA Forest Service, Pacific Southwest Research Station. December 2015. Lilienfein J, R.G. Qualls, S.M. Uselman and S.D. Bridgham, 2004. Adsorption of Dissolved Organic and Inorganic Phosphorus in Soils of a Weathering Chronosequence. Soil Science Society of America Journal 68:620-628.Longo, John. 2015. On-site discussion after excavation of accumulated wetland material to restore original hydrologic capacity of the Tahoe City Wetland Treatment System. John Longo Construction Co., Reno, NV. Lahontan Regional Water Quality Control Board (LRWQCB) and Nevada Division of Environmental Protection (NDEP). 2011. Final Lake Tahoe Total Maximum Daily Load. Lahontan Water Board, South Lake Tahoe, CA, and Nevada Division of Environmental Protection, Carson City, NV. August 2011. Swift, T.J., J. Perez-Losada, S.G. Schladow, J.E. Reuter, A.D. Jassby and C.R. Goldman. 2006. Water clarity modeling in Lake Tahoe: Linking suspended matter characteristics to Secchi depth. Aquatic Sciences, 68: 1-15.

A-3

Figure A1. Fractional filtration of stormwater runoff samples and TCWTS sediment core suspensions. Aliquot analyses included suspended sediment (SS), turbidity (NTU), total phosphorus (TP), and total Kjehldahl nitrogen (TKN).

A-4 Table A1. Locations and site IDs of stormwater runoff samples collected for size- fractioned analysis.

Sample Site ID Description Location

1 SB Speedboat Drive at Lake Street Crystal Bay, NV 2 48 Highway 28 at National Avenue Kings Beach, CA 3 SB Speedboat Drive at Lake Street Crystal Bay, NV 4 56 Highway 89 intersection Highway 28 Tahoe City, CA 5 88 Country Club Drive at Miners Ridge Incline Village, NV 6 37 Highway 50 east of Glenbrook Road Glenbrook, NV 7 J-In Highway 431 at Country Club Drive Incline Village, NV 8 83 Highway 267 at Stuart Way Kings Beach, CA 9 C-In Highway 431 at Country Club Drive Incline Village, NV 10 88 Country Club Drive at Miners Ridge Incline Village, NV 11 J-In Highway 431 at Country Club Drive Incline Village, NV 12 49 Highway 28 at Stag Drive Tahoe Vista, CA 13 C-In Highway 431 at Country Club Drive Incline Village, NV 14 C-In Highway 431 at Country Club Drive Incline Village, NV

A-5 Table A2. Size-fractioned analysis of stormwater runoff samples collected from the Tahoe Basin. Analyses included suspended sediment (SS), total phosphorus (TP), total Kjehldahl nitrogen (TKN), and turbidity (NTU). Also shown are percentages of analyte represented in each size fraction (-ƒ) relative to the bulk sample (assume <1000 µm ≈ 100% of original sample).

Size Sample Turbidity TKN NTU-ƒ vs SS–ƒ vs TP-ƒ vs TKN-ƒ vs Sample Site ID SS (mg/L) TP (μg/L) Fraction Date (NTU) (μg/L) NTU-1000 SS-1000 TP-1000 TKN-1000

1 SB <1000µm 11/18/11 87 140 433 ------2 48 <1000µm 11/18/11 445 352 835 ------3 SB <1000µm 1/20/12 176 150 480 ------4 56 <1000µm 1/20/12 134 207 855 ------5 88 <1000µm 3/1/12 128 153 143 ------6 37 <1000µm 3/14/12 135 121 445 ------7 J-In <1000µm 3/25/14 495 630 2101 4151 ------8 83 <1000µm 4/25/14 123 147 712 1196 ------9 C-In <1000µm 5/20/14 106 87 452 999 ------10 88 <1000µm 7/20/14 29 24 169 1989 ------11 J-In <1000µm 10/31/14 70 78 286 3023 ------12 49 <1000µm 10/31/14 75 66 205 1865 ------13 C-In <1000µm 12/3/14 82 133 603 2011 ------14 C-In <1000µm 2/7/15 353 465 982 439 ------Average < 1000 µm: 100% 100% 100% 100% 1 SB < 63μm 11/18/11 87 136 422 -- 100% 97% 97% -- 2 48 < 63μm 11/18/11 426 267 826 -- 96% 76% 99% -- 3 SB < 63μm 1/20/12 178 118 420 -- 101% 79% 88% -- 4 56 < 63μm 1/20/12 134 174 850 -- 100% 84% 99% -- 5 88 < 63μm 3/1/12 118 104 132 -- 92% 68% 92% -- 6 37 < 63μm 3/14/12 126 103 359 -- 93% 85% 81% -- 7 J-In < 63μm 3/25/14 481 524 2083 3904 97% 83% 99% 94% 8 83 < 63μm 4/25/14 123 129 709 1043 100% 88% 100% 87% 9 C-In < 63μm 5/20/14 103 63 435 994 97% 72% 96% 99% 10 88 < 63μm 7/20/14 29 23 161 1999 100% 96% 95% 101%

A-6 Size Sample Turbidity TKN NTU-ƒ vs SS–ƒ vs TP-ƒ vs TKN-ƒ vs Sample Site ID SS (mg/L) TP (μg/L) Fraction Date (NTU) (μg/L) NTU-1000 SS-1000 TP-1000 TKN-1000

11 J-In < 63μm 10/31/14 64 57 285 2932 91% 73% 100% 97% 12 49 < 63μm 10/31/14 68 63 204 1852 91% 95% 100% 99% 13 C-In < 63μm 12/3/14 81 119 593 2051 99% 89% 98% 102% 14 C-In < 63μm 2/7/15 346 438 926 431 98% 94% 94% 98% ------­ ------­ ------­ ------­ Average < 63 µm: 97% 84% 96% 97% 1 SB < 20μm 11/18/11 81 98 416 -- 93% 70% 96% -- 2 48 < 20μm 11/18/11 326 234 801 -- 73% 66% 96% -- 3 SB < 20μm 1/20/12 171 77 370 -- 97% 51% 77% -- 4 56 < 20μm 1/20/12 103 97 690 -- 77% 47% 81% -- 5 88 < 20μm 3/1/12 118 101 121 -- 92% 66% 85% -- 6 37 < 20μm 3/14/12 111 57 336 -- 82% 47% 76% -- 7 J-In < 20μm 3/25/14 436 385 1833 3175 88% 61% 87% 76% 8 83 < 20μm 4/25/14 92 97 526 759 75% 66% 74% 63% 9 C-In < 20μm 5/20/14 84 45 398 760 79% 52% 88% 76% 10 88 < 20μm 7/20/14 28 12 144 1805 97% 50% 85% 91% 11 J-In < 20μm 10/31/14 63 48 216 2481 90% 62% 76% 82% 12 49 < 20μm 10/31/14 54 36 198 1785 72% 55% 97% 96% 13 C-In < 20μm 12/3/14 63 82 516 1842 77% 62% 86% 92% 14 C-In < 20μm 2/7/15 331 353 811 441 94% 76% 83% 100% ------­ ------­ ------­ ------­ Average < 20 µm: 85% 59% 85% 85% 1 SB < 10μm 11/18/11 58 65 154 -- 67% 46% 36% -- 2 48 < 10μm 11/18/11 227 133 475 -- 51% 38% 57% -- 3 SB < 10μm 1/20/12 96 61 310 -- 55% 41% 65% -- 4 56 < 10μm 1/20/12 68 85 340 -- 51% 41% 40% -- 5 88 < 10μm 3/1/12 69 50 71 -- 54% 33% 50% -- 6 37 < 10μm 3/14/12 77 43 155 -- 57% 36% 35% -- 7 J-In < 10μm 3/25/14 320 306 1762 2973 65% 49% 84% 72% 8 83 < 10μm 4/25/14 62 55 322 622 50% 37% 45% 52%

A-7 Size Sample Turbidity TKN NTU-ƒ vs SS–ƒ vs TP-ƒ vs TKN-ƒ vs Sample Site ID SS (mg/L) TP (μg/L) Fraction Date (NTU) (μg/L) NTU-1000 SS-1000 TP-1000 TKN-1000

9 C-In < 10μm 5/20/14 59 37 257 640 56% 43% 57% 64% 10 88 < 10μm 7/20/14 16 10 131 1687 55% 42% 78% 85% 11 J-In < 10μm 10/31/14 37 36 216 2383 53% 46% 76% 79% 12 49 < 10μm 10/31/14 44 26 166 1691 59% 39% 81% 91% 13 C-In < 10μm 12/3/14 47 54 367 1844 57% 41% 61% 92% 14 C-In < 10μm 2/7/15 144 118 298 496 41% 25% 30% 113% ------­ ------­ ------­ ------­ Average < 10 µm: 55% 40% 57% 81% 1 SB < 5μm 11/18/11 31 42 151 -- 36% 30% 35% -- 2 48 < 5μm 11/18/11 135 87 327 -- 30% 25% 39% -- 3 SB < 5μm 1/20/12 58 44 280 -- 33% 29% 58% -- 4 56 < 5μm 1/20/12 34 51 260 -- 25% 25% 30% -- 5 88 < 5μm 3/1/12 45 27 45 -- 35% 18% 31% -- 6 37 < 5μm 3/14/12 44 28 109 -- 33% 23% 24% -- 7 J-In < 5μm 3/25/14 198 161 607 1983 40% 26% 29% 48% 8 83 < 5μm 4/25/14 38 33 156 539 31% 22% 22% 45% 9 C-In < 5μm 5/20/14 35 22 129 600 33% 25% 29% 60% 10 88 < 5μm 7/20/14 8 7 106 1676 28% 29% 63% 84% 11 J-In < 5μm 10/31/14 19 22 161 2249 27% 28% 56% 74% 12 49 < 5μm 10/31/14 25 16 141 1361 33% 24% 69% 73% 13 C-In < 5μm 12/3/14 28 29 149 1968 34% 22% 25% 98% 14 C-In < 5μm 2/7/15 41 98 198 502 12% 21% 20% 114% ------­ ------­ ------­ ------­ Average < 5 µm: 31% 25% 38% 75% 1 SB < 0.45μm 11/18/11 0.6 n.a. 19 -- 0% -- 4% -- 2 48 < 0.45μm 11/18/11 0.7 n.a. 8 -- 0% -- 1% -- 3 SB < 0.45μm 1/20/12 0.6 n.a. 19 -- 0% -- 4% -- 4 56 < 0.45μm 1/20/12 0.6 n.a. 20 -- 0% -- 2% -- 5 88 < 0.45μm 3/1/12 0.6 n.a. 7 -- 1% -- 5% -- 6 37 < 0.45μm 3/14/12 0.2 n.a. 6 -- 0% -- 1% --

A-8 Size Sample Turbidity TKN NTU-ƒ vs SS–ƒ vs TP-ƒ vs TKN-ƒ vs Sample Site ID SS (mg/L) TP (μg/L) Fraction Date (NTU) (μg/L) NTU-1000 SS-1000 TP-1000 TKN-1000

7 J-In < 0.45μm 3/25/14 0.3 n.a. 11 803 0% -- 1% 19% 8 83 < 0.45μm 4/25/14 0.2 n.a. 6 311 0% -- 1% 26% 9 C-In < 0.45μm 5/20/14 0.4 n.a. 13 440 0% -- 3% 44% 10 88 < 0.45μm 7/20/14 0.2 n.a. 6 1224 0% -- 4% 62% 11 J-In < 0.45μm 10/31/14 0.2 n.a. 5 1465 0% -- 2% 48% 12 49 < 0.45μm 10/31/14 0.3 n.a. 8 932 0% -- 4% 50% 13 C-In < 0.45μm 12/3/14 0.2 n.a. 5 1562 0% -- 1% 78% 14 C-In < 0.45μm 2/7/15 0.3 n.a. 19 469 0% -- 2% 107% ------­ ------­ ------­ ------­ Average < 0.45 µm: 0% -- 3% 54%

A-9 Table A3. Size-fractioned analysis of sediment cores collected from the Tahoe City Wetland Treatment System (TCWTS). Percentages of analyte are shown for each size fraction (-ƒ) relative to bulk suspension (<1000 µm). Note that concentrations are listed for comparison between fractions only; these are not core concentrations, since core samples were substantially diluted to disperse and sieve sediments. Analyses included suspended sediment (SS), total phosphorus (TP), total Kjehldahl nitrogen (TKN), turbidity (NTU), and loss-on-ignition (LOI).

Core Size Turbidity SS TP TKN NTU-ƒ vs SS-ƒ vs TP-ƒ vs TKN-ƒ vs Site LOI (%) Suspension Fraction (NTU)* (mg/L)* (µg/L)* (µg/L)* NTU-1000 SS-1000 TP-1000 TKN-1000

TCWTS Core 1 < 1000µm 1803 2694 1918 6825 10% 100% 100% 100% 100% TCWTS Core 2 < 1000µm 3201 3845 2105 7587 8% 100% 100% 100% 100% TCWTS Core 3 < 1000µm 2816 3556 1907 5721 8% 100% 100% 100% 100% TCWTS Core 4 < 1000µm 2333 3062 1450 5389 8% 100% 100% 100% 100% TCWTS Core 5 < 1000µm 1553 2474 1126 2946 8% 100% 100% 100% 100% ------­ ------­ ------­ ------­ Average < 1000 µm: 100% 100% 100% 100% TCWTS Core 1 < 63µm 1752 2156 1753 5334 10% 97% 80% 91% 78% TCWTS Core 2 < 63µm 3031 3669 1919 6454 8% 95% 95% 91% 85% TCWTS Core 3 < 63µm 2582 2964 1705 4001 8% 92% 83% 89% 70% TCWTS Core 4 < 63µm 2246 2619 1332 4631 7% 96% 86% 92% 86% TCWTS Core 5 < 63µm 1504 2011 1046 2883 8% 97% 81% 93% 98% ------­ ------­ ------­ ------­ Average < 63 µm: 95% 85% 91% 83% TCWTS Core 1 < 20µm 1593 1719 1416 4115 12% 88% 64% 74% 60% TCWTS Core 2 < 20µm 2857 2934 1546 4901 11% 89% 76% 73% 65% TCWTS Core 3 < 20µm 2489 2322 1381 4014 10% 88% 65% 72% 70% TCWTS Core 4 < 20µm 2037 2073 1079 3942 10% 87% 68% 74% 73% TCWTS Core 5 < 20µm 1372 1631 812 2644 10% 88% 66% 72% 90% ------­ ------­ ------­ ------­ Average < 20 µm: 88% 68% 73% 72% TCWTS Core 1 < 10µm 1187 1181 1076 3008 13% 66% 44% 56% 44% TCWTS Core 2 < 10µm 2051 1926 1208 4072 12% 64% 50% 57% 54% TCWTS Core 3 < 10µm 1746 1600 1037 2909 12% 62% 45% 54% 51% TCWTS Core 4 < 10µm 1572 1642 839 2974 12% 67% 54% 58% 55% TCWTS Core 5 < 10µm 1007 1193 641 2038 12% 65% 48% 57% 69%

A-10 Core Size Turbidity SS TP TKN NTU-ƒ vs SS-ƒ vs TP-ƒ vs TKN-ƒ vs Site LOI (%) Suspension Fraction (NTU)* (mg/L)* (µg/L)* (µg/L)* NTU-1000 SS-1000 TP-1000 TKN-1000

------­ ------­ ------­ ------­ Average < 10 µm: 65% 48% 56% 55% TCWTS Core 1 < 5µm 785 828 795 2518 13% 44% 31% 41% 37% TCWTS Core 2 < 5µm 1242 1251 848 3452 12% 39% 33% 40% 45% TCWTS Core 3 < 5µm 1156 1122 776 2544 12% 41% 32% 41% 44% TCWTS Core 4 < 5µm 1031 1132 636 2649 12% 44% 37% 44% 49% TCWTS Core 5 < 5µm 648 742 451 1754 12% 42% 30% 40% 60% ------­ ------­ ------­ ------­ Average < 5 µm: 42% 33% 41% 47% TCWTS Core 1 < 0.45µm 0.77 n.a. 8 112 n.a. 0% -- 0% 2% TCWTS Core 2 < 0.45µm 0.85 n.a. 5 492 n.a. 0% -- 0% 6% TCWTS Core 3 < 0.45µm 0.65 n.a. 4 271 n.a. 0% -- 0% 5% TCWTS Core 4 < 0.45µm 0.77 n.a. 4 408 n.a. 0% -- 0% 8% TCWTS Core 5 < 0.45µm 1.10 n.a. 4 446 n.a. 0% -- 0% 15% ------­ ------­ ------­ ------­ Average < 0.45 µm: 0% -- 0% 7% * Core samples were substantially diluted to disperse and sieve sediments. Thus, values do not represent original core concentrations.

A-11