Measurement of Windblown Dust Emission Potential and Soil Characteristics at the Salton Sea in Support of the Programmatic Environmental Impact Report:

Final Report

8/18/06

Vic Etyemezian, Mark Sweeney, Eric McDonald, Todd Caldwell, John Gillies, George Nikolich, and Jin Xu

Desert Research Institute

755 E. Flamingo Rd Las Vegas, NV 89119

702 862-5569 vic@dri.edu

William Nickling and Torin Macpherson

Wind Erosion Laboratory Department of Geography

University of Guelph, Guelph, Ontario, Canada

Prepared for

California Department of Water Resources

Colorado River & Salton Sea Office 770 Fairmont Avenue Glendale, CA 91203

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EXECUTIVE SUMMARY The California Department of Water Resources (DWR) contracted the Desert

Research Institute (DRI) to conduct a field study at the Salton Sea in support of assessing air quality impacts from various restoration alternatives and preparation of the Salton Sea Ecosystem Restoration Study (ERS) and Programmatic Environmental Impact Report (PEIR). The field study, summarized in this report, was conducted between September 2005 and March 2006. The study focused on collecting and characterizing soil properties from sites along the Salton Sea shoreline that may serve as surrogates for future exposed shoreline. In addition, the potential for PM10 (Particulate matter with aerodynamic diameter smaller than 10 µm) dust emission was directly measured at the same sites to provide an estimate of the magnitudes and variability of windblown dust potential from shoreline to be exposed in the future.

The field study was completed during three sampling campaigns at the Salton Sea to represent seasonal variability in soil properties and dust emissions. Those were September 21, 2005 through September 30, 2005 (Test 1), January 24, 2006 through January 27, 2006 (Test 2), and March 20, 2006 through March 24, 2006 (Test 3). Fourteen sites around the Salton Sea were initially selected for measurements. Sites were selected to meet the following criteria: 1) accessibility for equipment and personnel, 2) landowner permission granted for measurements, 3) representative spatial coverage around the perimeter of the Sea, 4) inclusion of a wide variety of soil textures to represent variability occurring at the Sea, and 5) inclusion of several sites with qualities similar to playas. At the beginning of Test 2, three sites were added to the initial 14 to better represent PM10 dust emissions from salt-crusted soils. Considering all 17 sites, 10 were within 4 feet (~ 1 m) of the Sea level and all but one were within 25 feet (~9 m) of Sea level.

At each site, qualitative and quantitative techniques were used to characterize surface crust properties, crust strength, and PM10 emissions at varying amounts of wind shear. Soil samples were also collected for subsequent laboratory analysis of bulk density, texture, aggregate content, moisture content, carbonate content, salt content, and other chemical properties.

Two types of penetrometers were used to measure soil strength. A Proctor spring penetrometer was used to measure the amount of normal stress required to break the surface crust, while a cone penetrometer was used to characterize the soil strength at varying depth. Additionally, two soil pits (> 20 cm) were dug at each site in order to characterize near-surface properties such as crust hardness, thickness, and moisture content.

Soil samples were collected at each site for subsequent laboratory analysis. Samples were collected for bulk density and moisture analysis using a ring or vessel to remove a soil portion of known volume with minimal disturbance of the structure. Bulk density and water content were measured in the lab by gravimetric methods.

Bulk samples collected for particle size analysis were dry-sieved to determine the gravel and coarse sand fractions. Laser particle size analysis was performed on the material that passed through the coarse sand sieve. For a portion of the samples collected during the September field study, traditional pipette methods for particle sizing were also

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employed for comparison with the laser method. In comparing the two methods, some deviation was observed for clay-sized particles. However, overall the two methods agreed quite well and for the remaining samples, the laser method was used exclusively. Particle size distributions and textural classification were used to infer the size distributions of aggregates and soil hydrologic parameters such as wilting point, field capacity water content, saturation soil water content, and saturated hydraulic conductivity.

Chemical characterization of soil properties included measurement of percentage of calcium carbonate content by acid digestion, electrical conductivity as a surrogate for salt content, loss on ignition analysis for organic carbon, cation analysis (K, Mg, Ca, Na, and P), and cation exchange capacity by ammonium saturation method. Concentrations of extractable Na+, Ca2+, and Mg2+ were used to estimate the Sodium Adsorption Ratio (SAR).

Windblown dust emission potential was measured using the Portable In-Situ Wind ERosion Lab (PI-SWERL) at all sites. Unlike traditional, straight-line wind tunnels, the PI-SWERL is a cylindrical device that utilizes the principles of Couette flow to induce shear stress directly on the soil test surface. The rate of rotation (RPM) of a flat annular ring above the soil surface determines the equivalent amount of applied shear stress, quantified as a friction velocity u*. The PM10 dust emitted at varying values of friction velocity is quantified by multiplying the air flow rate through the PI-SWERL by the PM10 dust concentration measured with a fast-response nephelometer-style instrument. The PI-SWERL is advantageous over the straight-line wind tunnel because of its portability and because it is comparatively fast. Many of the sites where windblown dust emissions were measured were not accessible to the wind tunnel which is transported with a pickup truck and a trailer. In addition, wind tunnels require substantial setup time whereas a PI-SWERL measurement can be initiated within minutes of arriving at a site. Use of the PI-SWERL in this study allowed for inclusion of a large number of sites and completion of measurements over three sampling seasons.

To ensure that measurements with the PI-SWERL would be reasonably comparable to measurements using field wind tunnels that were utilized previously at Owen’s Lake and elsewhere, the PI-SWERL was collocated with the University of Guelph straight line field wind tunnel at 8 of the 17 sites selected for sampling at the Salton Sea. These side by side tests at the Salton Sea were appended to another side-by-side data set collected at 15 other locations in the Mojave Desert to help establish a correspondence between PI-SWERL dust emission measurements and those obtained with traditional straight-line wind tunnels. Comparing the measurements between the PI-SWERL and University of Guelph wind tunnel indicated that while both instruments exhibit large variability owing to natural spatial heterogeneities, when averaged over many sites and replicate measurements, the two methods were well correlated.

The PI-SWERL was operated by sequentially stepping through 4 to 6 pre-set RPM target values at each measurement location. Between 3 and 12 replicate measurements were completed at each site during each of the 3 field sampling seasons. PI-SWERL measurements were not conducted when the soil surface was visibly wet.

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The seventeen sites at the Salton Sea exhibited a large range of soil properties in terms of crust hardness, salt content, and soil texture. Sites with soils having low salt content (< 10,000 mg/kg, EC 0-4 mmhos/cm) and relatively fine texture (silt loam, silty clay loam, silty clay, and clay) included A31-1 and A101-1; sites with moderate to high salt content (> 10,000 mg/kg, EC > 4 mmhos/cm) and relatively fine texture included A32-1, A34-3, A200-1, A201-1, SS16-1, SS17-1, and SS23-1; sites with relatively coarse texture (sandy loam, loamy sand, and sand) and low salt content included A29-1, A100-1, SS2-1, and SS6-1; sites with coarse texture and high salt content were A34-1, A34-2, A100-2, and SS9-1. In addition, several sites exhibited a significant gravel, barnacle, or fishbone content including A29-1, A34-1, A100-1, SS2-1, and SS9-1. A100-1 and SS2-1 represented sandy dry washes whereas A29-1, A34-1, and SS9-1 were more like beaches.

Loose surface soil aggregates were only observed at three sites (A200, A201, A100-2), on salty crusts, during January testing. The aggregates appeared to be composed of sand or smaller particles cemented by salts. All other sites and sampling times had stable crusts with no discernible loose aggregates on the surface though some sites did exhibit loose gravel or wind-blown sand on the surface.

In general, sites with barnacles or clay-rich sites exhibited the lowest bulk densities due to the large amount of air space in barnacle-rich samples, and the potential for sample shrinkage during drying of clay-rich samples. Measurements of soil bulk density did not point to any obvious seasonal trends. The average coefficient of variation (COV), which provides an estimate of uncertainty and is equal to the standard deviation of replicate measurements divided by their average, was approximately 10%. At some sites, the COV was as high as 35% owing to the difficulty of sampling for bulk density when a salt crust is separated by air from the underlying soil. In most cases, differences in bulk density between the three field measurement campaigns were within the uncertainty of the measurement.

Chemical analysis of soil samples showed that properties such as organic matter content and pH were consistent within site and no changes were noted with season. Soils had low organic matter contents except for site SS23 where no direct wind erodibility measurements were completed during any of the field campaigns due to wet site conditions. At three sites (A34-2, A200-1, and A201-1), salt content measured by electrical conductivity was higher in January than in September or March. Those sites were close to the shoreline and either wicking of salt water followed by evaporation or else windblown deposition of salt contributed to elevated salt contents in January. Sites with high salt contents tended to form salt crusts when dry. Sites farther from the Sea tended to have low salt contents and crusts made of silt and clay. P, Ca, Mg, K, P, Na, and SO4 contents were also measured. Among those species, the most abundant were were Na, Ca, and SO4 with concentration ranges of 50 – 11,000 ppm, 1,000 – 30,000 ppm, and 10 – 38,000 ppm, respectively. Concentrations of K, Mg, and P were generally lower with values in the ranges of 50 – 1,900 ppm, 50 – 9,000 ppm, and 4 – 160 ppm, respectively. With the exception of Ca, these species were present in roughly proportional quantities with some variation from one site to the next. Sites with the lowest concentrations were the wash and inter-dune sites and sites with the highest concentrations were the salt playas and barnacle beaches.

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Surface crust hardness measured with a Proctor spring penetrometer varied considerably by season for some sites. In almost every case, crust strengths were lowest during the January sampling period. Sites with weaker crusts in January also had higher salt contents for the most part. Two exceptions to this were the dry wash bed sites, A100-1 and SS2-1, where crusts appeared to increase in strength from September to March. The low salt and clay contents at those two sites probably made them less susceptible to changes during the cooler and wetter winter conditions (January sampling period) than the other sites. The “ball drop” test conducted during the January sampling campaign qualitatively showed results that were similar to the spring penetrometer.

Soil strength measured at depth with a cone penetrometer also showed some changes over the three sampling periods. However, owing to the coarse vertical resolution of the instrument, it was not possible to use those data to analyze the surface crust properties, where dust emissions are prevalent.

Measurements with the PI-SWERL showed substantial increases in PM10 dust emission in January 2006 compared to September 2005. Eleven of the 16 sites where PI-SWERL measurements were completed exhibited their highest dust fluxes in January (A100-1, A31-1, A34-1, A34-2, SS17, SS2, SS6, SS9, A200, A201, A34-3). Of those, 4 had silt/clay crusts (low salt) and 7 had salt crusts. Four sites had their highest fluxes in September (A101, A29, A32, SS16; two silt/clay, two salt crust) and one site had its highest flux in March (A100-2; salt crust). At most sites, PM10 emissions had returned or were returning to September values by March 2006. These observations were qualitatively consistent with anecdotal evidence from residents in the area as well as prior work completed at Owens Lake, where dust emissions exhibited a peak during the cooler times of the year. This trend was also consistent with the generally reduced surface crust strengths observed in January compared to September and March. At 8 sites over the three seasons, emission strengths corresponded to crust strengths. Of the eight sites, 3 had silt/clay crusts (A101, A31, SS6) and 5 had salt crusts (A34-1, A34-2, SS17, SS9, A34-3). Two sites (A200, A201), where testing occurred only once, showed high emissions with low crust strength as well. The six remaining sites (A100-1, A100-2, SS2-1, A29-1, A32-1, and SS16-1 - 3 silt/clay crust, 3 salt crust) showed no apparent relation between crust strength and emissions

The PM10 temporal emission patterns from sites with relatively low salt content (<10,000 mg/kg, EC 0-4 mmhos/cm, A100-1, A101-1, A29-1, A31-1, SS2-1, and SS6-1) were roughly compared to those with relatively high salt content (>10,000 mg/kg, EC > 4 mmhos/cm, A100-2, A200-1, A201-1, A32-1, A34-1, A34-2, A34-3, SS16-1, SS17-1, and SS9-1). First, it was clear that PM10 emissions were higher for both salt-rich and low salt soils in January compared to September and March. Second, the magnitude of PM10 emissions in January did not seem to differ greatly between salt-rich and low salt sites. Third, PM10 emissions for salt-rich sites were lower than those at low salt sites in both September and March. This indicated that salt crusts that form during warmer months tend to stabilize the soil, compared to locations where salt crusts do not form. It was noted however that these conclusions were based on aggregated values across sites and did not weigh the relative prevalence of the different types of soils found around the Salton Sea. That is, the amount of shoreline surface area represented by each site was not taken into account.

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In summary, surface-crusted sites where measurements were conducted as part of this study can be grouped in to two main categories with respect to salt content: those that contained crusts composed of silt and/or clay particles, and those that contained crusts composed of salt. All salt crusted surfaces were located at sites adjacent to the Sea. Silt/clay crusts were located adjacent to the Sea in some cases and at higher elevations in other cases. Most salt-crusted surfaces had playa-like attributes in that they were frequently wetted, were fine-grained, and contained mud cracks or salt ridges when desiccated.

Soil moisture showed no relation to emission strength in this study, nor did estimated aggregate size. We note however that in most cases, surfaces at the sites were either visibly wet or clearly dry. When sites were visibly wet, dust emissions were not measured with the PI-SWERL. In some cases high values of measured soil moisture were associated with high values of PM10 dust emissions as measured by PI-SWERL. This was likely an artifact of the method used to collect samples for moisture analysis, where bulk soil material which includes the surface soil as well as material that is several centimeters below the surface was analyzed for moisture. For surfaces with a salt crust, it was observed that while the surface crust, the location where PM10 dust is emitted from, was dry, the soil underneath the surface was wet. Thus, moisture content analysis in those cases does not reflect surface conditions. It is important therefore not to draw conclusions regarding a soil’s potential to emit windblown PM10 based on bulk soil moisture measurements.

High emissions from salt crusted surfaces, especially in January, were linked to weak surface crusts, and in some cases, loose soil structure immediately below the crust that served as reservoir for abundant dust-sized particles. However, this study suggested that while salt-crusted surfaces did indeed emit dust, they were not the single predominant source of dust around the margin of the Sea. There was a decrease in crust strength in both silt/clay crusts with low salt components and salt-rich crusts. Higher PM10 dust emissions in January were not solely a result of the formation of salt fluff. In the warmer months (September and March), the highest PM10 dust emissions were measured at sites with silt/clay crusts. Therefore, at the Salton Sea, salt crusts appeared to be significant but temporary sources of dust, limited to cool, wet months, whereas silt/clay crusted sites (not only limited to playa-like environments) appeared to be significant sources of dust throughout the year.

Sites were also grouped by four landform types. Those were “playa-like”, “paleo-lake”, “barnacle-beach”, and “dry wash”. Playa-like sites were considered to most closely resemble what the sediment in the Salton Sea would be like immediately after water levels recede. Comparing PM10 emissions among these landform types at a common friction velocity of 0.56 m/s – a value chosen to represent conditions of highest one-hour winds at the Salton Sea and which is likely exceeded over shorter periods or during wind gusts – dry wash sites consistently exhibited the highest PM10 emissions during the three field sampling campaigns. However, average emissions from playa-like sites in January 2006 (0.9 mg/m2s) were comparable to those from dry wash sites (1.3 mg/m2s) owing to the formation of a friable crust at many of the playa-like sites in the cool, wet conditions of the season.

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Results from the PI-SWERL measurements at the Salton Sea were compared to earlier wind tunnel measurements conducted by Nickling et al. (2001) at Owens Lake, California. Several important differences in the methodology of these two studies and the interpretation of results were highlighted. Noting these differences, a tentative conclusion is that PM10 windblown dust emissions at the “playa-like” sites at the Salton Sea are likely lower than those at Owens Lake but the potential for the Salton Sea to be as emissive as Owens Lake cannot be completely ruled out.

It is important to place the results of this study in the context of the larger question of anticipated PM10 dust emissions as the Salton Sea shoreline recedes and to discuss the limitations that are inherent to the study. The response of a soil surface to shear stress by wind applied directly to that surface is only one of the factors that influences dust emissions from an area source like the Salton Sea. Additional factors that have a critical effect on the actual amount of PM10 windblown dust include the distribution of wind speeds and large-scale surface roughness. These factors were not considered here. Another major limitation of this study is that PM10 emissions at varying friction velocities were measured at locations around the Salton Sea shoreline. For obvious reasons, it was not possible to operate the PI-SWERL on the surfaces that are currently inundated but likely to be exposed in the future. As the Salton Sea recedes, rather complex physical and chemical processes will result in changes of soil texture, salt chemistry, crust formation, and the availability of new sources of sand for saltation. Therefore, while this work has provided substantial insight into the seasonal behavior of several different types of landforms that can be found at the Salton Sea today, the lake is likely to undergo changes that cannot be fully captured by this study.

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ACKNOWLEDGEMENTS The authors gratefully acknowledge the helpful comments, critiques and

suggestions provided by John Dickey, Carrie MacDougall, Mark Schaaf, and Pamela Vanderbilt (CH2M Hill), Pat Chavez (USGS), Cheryl Rodriguez (BOR), Ted Schade (GBUAPCD), and Nick Lancaster (DRI). We deeply appreciate the time that Mr. Al Kalin spent conveying valuable insights into locations of playas and seasonality of dust emissions. The assistance of Mr. Bruce Wilcox (IID) in obtaining permission to perform measurements on IID-managed property was critical to the timely completion of this study. We would like to express our gratitude for the administrative and logistical support that was necessary to complete this project and was provided by Charles Keene, Jerry Boles, John Vrymoed, and Jeanine Jones (DWR). This work was funded through contract with the California Department of Water Resources (Standard Agreement No 4600003670, Task Order No SS0405-3670-0003).

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TABLE OF CONTENTS EXECUTIVE SUMMARY ........................................................................................................................III ACKNOWLEDGEMENTS ....................................................................................................................... IX TABLE OF CONTENTS........................................................................................................................... XI LIST OF FIGURES.................................................................................................................................XIII LIST OF TABLES..................................................................................................................................XVII 1 INTRODUCTION............................................................................................................................... 1

1.1 REPORT ORGANIZATION............................................................................................................... 2 2 BACKGROUND ................................................................................................................................. 3

2.1 TECHNIQUES FOR MEASUREMENT OF WINDBLOWN DUST EMISSIONS............................................ 4 2.1.1 Tower Measurements of Emission Fluxes............................................................................... 4 2.1.2 Wind Tunnel Measurement of Emission Fluxes: Straight-Line Wind Tunnel ......................... 5 2.1.3 Portable In-Situ Wind ERosion Lab (PI-SWERL) .................................................................. 5

3 METHODS.......................................................................................................................................... 9 3.1 SITE LOCATIONS AND DESCRIPTIONS ............................................................................................ 9 3.2 ON-SITE MEASUREMENTS........................................................................................................... 12 3.3 BULK ANALYSIS MEASUREMENTS .............................................................................................. 12 3.4 PI-SWERL ................................................................................................................................ 13

3.4.1 PI-SWERL Data Processing ................................................................................................. 13 3.4.2 Collocation of PI-SWERL with University of Guelph Wind Tunnel ..................................... 16 3.4.3 Procedures for PI-SWERL Measurements at the Salton Sea................................................ 27

4 RESULTS .......................................................................................................................................... 29 4.1 SITE DESCRIPTIONS .................................................................................................................... 29 4.2 CRUST STRENGTH....................................................................................................................... 36 4.3 BULK ANALYSIS RESULTS........................................................................................................... 40

4.3.1 Particle size distribution....................................................................................................... 40 4.3.1.1 Soil texture................................................................................................................................. 40 4.3.1.2 Aggregate size distribution ........................................................................................................ 44

4.3.2 Bulk density .......................................................................................................................... 44 4.3.3 Soil Hydrologic Properties ................................................................................................... 45 4.3.4 Soil Chemical Properties...................................................................................................... 45

4.4 PM10 EMISSIONS......................................................................................................................... 48 4.4.1 Data trends: Season and Salt content................................................................................... 50 4.4.2 Magnitudes of PM10 emissions by site and by landform....................................................... 57 4.4.3 Comparison of PM10 emissions measurements at the Salton Sea and Owens Lake.............. 66

5 DISCUSSION AND CONCLUSIONS ............................................................................................ 69 5.1 FUTURE PROJECTS PLANNED ...................................................................................................... 72

6 REFERENCES.................................................................................................................................. 73 APPENDIX A: SUMMARY OF PI-SWERL DATA FROM THE SALTON SEA: SEPTEMBER 2005, JANUARY 2006, AND MARCH 2006.......................................................................................... A-1

APPENDIX B: SALTON SEA SITE PHOTOGRAPHS ...................................................................... B-1

APPENDIX C: SUMMARY OF BULK ANALYSIS OF SOIL PROPERTIES FROM THE SALTON SEA: SEPTEMBER 2005, JANUARY 2006, AND MARCH 2006. ..................................................... C-1

APPENDIX D: RESPONSE TO REVIEW COMMENTS ON DRAFT FINAL REPORT D-1

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LIST OF FIGURES FIGURE 1-1. FROM SCHOEDER ET AL. (2002), THE HISTORICAL MONTHLY SURFACE ELEVATIONS AND

SALINITY OF THE SALTON SEA............................................................................................................... 2 FIGURE 2-1. SCHEMATIC OF THE PI-SWERL. THE RATE OF ROTATION OF THE ANNULAR RING IS

CONTROLLED BY A DC MOTOR, WHICH IS IN TURN CONTROLLED BY A COMPUTER. THE DUST SUSPENDED BY THE SHEARING FORCE BENEATH THE ANNULAR RING IS MEASURED BY A PM10 MONITOR AND REPORTED TO THE COMPUTER. ....................................................................................................... 6

FIGURE 2-2. IDEALIZED ILLUSTRATION OF A PLATE MOVING OVER A SOIL SURFACE SIMILAR TO TURBULENT COUETTE FLOW. A SIMILAR PHENOMENON OCCURS WHEN THE ANNULAR RING OF THE PI-SWERL SPINS ABOUT ITS AXIS, THOUGH THE GEOMETRY IS MORE COMPLEX. ..................................................... 6

FIGURE 2-3. ILLUSTRATION OF SWIRLING FLOW INSIDE OF PI-SWERL CAVITY (A) AND CORRESPONDING SAND PARTICLE MOTION IN CUTAWAY TOP VIEW (B) AND CUTAWAY SIDE VIEW (C). ............................. 7

FIGURE 2-4. EXAMPLE OF PI-SWERL MEASUREMENT CYCLE. THE BLUE HORIZONTAL LINES REFLECT THE SETTING AND DURATION OF EACH ROTATION STEP (RIGHT Y-AXIS). THE GREEN AND RED LINES RESPECTIVELY ILLUSTRATE THE DUST CONCENTRATION OVER THE PI-SWERL CYCLE FOR A TEST PLOT THAT HAS BEEN TREATED WITH A STABILIZING AGENT AND A PLOT THAT HAS NOT BEEN TREATED AT THE NEVADA TEST SITE......................................................................................................................... 8

FIGURE 3-1. LOCATIONS OF SAMPLING SITES AROUND THE SALTON SEA. RED CIRCLES REPRESENT SITES WHERE THE PI-SWERL WAS COLLOCATED WITH THE UNIVERSITY OF GUELPH WIND TUNNEL IN SEPTEMBER 2005 WHILE BLUE CIRCLES CORRESPOND TO SITES WHERE ONLY THE PI-SWERL WAS OPERATED. YELLOW CIRCLES CORRESPOND TO SITES THAT WERE ADDED IN JANUARY 2006. NO PM10 EMISSIONS MEASUREMENTS WERE COLLECTED AT SITE SS23 (GREEN) BECAUSE THE SITE WAS EITHER INUNDATED AND/OR DISTURBED BY HEAVY OFF-ROAD VEHICLE TRAFFIC............................................ 11

FIGURE 3-2. SAMPLING SITES AT THE SALTON SEA GROUPED BY LANDFORM ACCORDING TO TABLE 3-1. A SMALL RED DOT INDICATES THAT THE MEASURED SALT CONTENT WAS GREATER THAN 10,000 MG/KG............................................................................................................................................................. 11

FIGURE 3-3. ILLUSTRATION OF PI-SWERL TEST AND THE CONCEPT OF CUMULATIVE PM10 EMISSIONS. EI FOR A GIVEN STEP I IN EQUATION (3-1) IS EQUAL TO THE AREA UNDERNEATH THE DASHED LINE FOR THAT STEP DIVIDED BY THE DURATION OF THAT STEP. .................................................................................. 14

FIGURE 3-4. RELATIONSHIP BETWEEN SHEAR STRESS MEASURED AT GROUND LEVEL WITH IRWIN SENSOR AND THE NUMBER OF RPM OF THE PI-SWERL ANNULAR RING. R/R = 0 AT THE CENTER AXIS, R/R = 0.78 AT THE INNER EDGE OF THE ANNULAR RING, R/R = 0.94 AT THE OUTER EDGE OF THE ANNULAR RING, AND R/R = 1 AT THE INNER WALL OF THE PI-SWERL CHAMBER................................................................. 15

FIGURE 3-5. RELATIONSHIP BETWEEN PI-SWERL RPM AND RESULTANT SHEAR STRESS AND FRICTION VELOCITY AS MEASURED BY IRWIN SENSORS MOUNTED ON FLAT PLYWOOD. SHEAR STRESS VALUES WERE CALCULATED AS THE AVERAGE OF MULTIPLE POINT MEASUREMENTS BETWEEN R/R = 0.78 AND R/R = 0.94 (SEE FIGURE 3-4). .............................................................................................................. 16

FIGURE 3-6. TERNARY PLOT OF SOIL TEXTURES FROM SITES WHERE THE PI-SWERL WAS COLLOCATED WITH THE UNIVERSITY OF GUELPH WIND TUNNEL. DOTS OF THE SAME COLOR REPRESENT REPLICATE SAMPLES FROM THE SAME SITE. ........................................................................................................... 17

FIGURE 3-7. PI-SWERL COLLOCATED WITH THE UNIVERSITY OF GUELPH WIND TUNNEL ON A DISTURBED PLAYA SURFACE ON SUPERIOR LAKE, CA............................................................................................ 18

FIGURE 3-8. ILLUSTRATION OF WIND TUNNEL AND PI-SWERL TESTS AND CALCULATION OF COMPARABLE PM10 EMISSIONS. THE TOP PANEL SHOWS THE MOST COMMON TYPE OF WIND TUNNEL TEST WHERE THE WIND TUNNEL IS RUN AT A SINGLE VALUE OF WIND SPEED. THE BOTTOM PANEL SHOWS HOW THE PI-SWERL TEST USES STEP VALUES OF RPM TO OBTAIN A MEASUREMENT OF EMISSIONS AT MULTIPLE VALUES OF FRICTION VELOCITY AT THE SAME TEST LOCATION. THE MIDDLE PANEL SHOWS HOW PM10 CONCENTRATIONS IN THE WIND TUNNEL WOULD VARY IN TIME IF THE WIND TUNNEL WAS OPERATED IN A MANNER SIMILAR TO PI-SWERL (I.E. STEPS WITH INCREASING FRICTION VELOCITY). ..................... 19

FIGURE 3-9. EXAMPLE OF APPLYING ROUGHNESS CORRECTION TO PI-SWERL DATA FOR SURFACES COVERED WITH DENSE NON-ERODIBLE ROUGHNESS ELEMENTS. A) PHOTOGRAPH OF GRAVEL COVERED SURFACE AT SITE L1 (NOT DISTURBED) AND B) EXAMPLE OF HOW APPLYING ROUGHNESS CORRECTION (40%) TO PI-SWERL DATA IMPROVES AGREEMENT WITH WIND TUNNEL MEASUREMENTS................. 22

FIGURE 3-10. SCATTER PLOT OF WIND TUNNEL PM10 EMISSIONS VERSUS PI-SWERL MEASURED PM10 EMISSIONS FROM 23 COLLOCATED TESTS. TRIANGLES REPRESENT DATA POINTS WHERE THE PI-

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SWERL FRICTION VELOCITY HAS BEEN CORRECTED FOR HEAVY GRAVEL COVER ON THE SURFACE. ERROR BARS FOR BOTH PI-SWERL AND WIND TUNNEL DATA ARE SHOWN IN THE HORIZONTAL AND VERTICAL DIRECTIONS, RESPECTIVELY AND REPRESENT ONE GEOMETRIC STANDARD DEVIATION....... 23

FIGURE 3-11. RELATIONSHIP BETWEEN PI-SWERL EMISSIONS AND WIND TUNNEL EMISSIONS WHEN THE GEOMETRIC MEANS OF PI-SWERL DATA ARE TAKEN OVER 0.25 DECADES AND COMPARED WITH THE CORRESPONDING WIND TUNNEL GEOMETRIC MEANS (R2 = 0.96). THE DASHED LINE REPRESENTS WHAT A 1:1 LINEAR RELATIONSHIP WOULD LOOK LIKE ON THE FIGURE. ........................................................ 24

FIGURE 3-12. COMAPRISON OF PI-SWERL AND WIND TUNNEL PM10 EMISSIONS AT U* = 0.56 M/S ON A SITE BY SITE BASIS. THE FIGURE SHOWS PM10 EMISSIONS OBTAINED BY BOTH ARITHMETICALLY (STANDARD AVERAGING) AND GEOMETRICALLY (AVERAGING OF LOGARITHMS) AVERAGING DATA FROM REPLICATE MEASUREMENTS. THE VERTICAL BARS REPRESENT STANDARD DEVIATIONS. DATA ORDERED FROM LEFT TO RIGHT BY INCREASING AVERAGE PM10 EMISSIONS. ................................................................ 25

FIGURE 3-13. SAME AS FIGURE 3-12 EXCEPT LEFT Y-AXIS IS LOGARITHMIC.................................................. 26 FIGURE 3-14. PHOTOGRAPH OF THE PI-SWERL MOUNTED ON AN ATV AND IN OPERATION AT THE SALTON

SEA. AN ELECTRIC WINCH IS USED TO LOWER AND RETRACT THE PI-SWERL UNIT FROM THE TEST LOCATION. ........................................................................................................................................... 27

FIGURE 4-1. SPRING PENETROMETER DATA BY SEASON. A COLUMN EXTENDING BELOW ZERO INDICATES THAT THE SITE WAS WET OR THAT DATA WERE NOT COLLECTED AT THAT SITE DURING THE SPECIFIED SEASON. VERTICAL ERROR BARS REPRESENT STANDARD ERROR......................................................... 37

FIGURE 4-2. SOIL STRENGTH AT DEPTH. ........................................................................................................ 38 FIGURE 4-3. COMPARISON OF SILT AND CLAY USING LASER AND PIPETTE METHODS. .................................... 41 FIGURE 4-4. SOIL TEXTURES FROM EACH SITE DISPLAYED ON A TERNARY DIAGRAM. ................................... 43 FIGURE 4-5. COMPARISON OF SOIL TEXTURES AT SITES WHERE PI-SWERL TESTS WERE COMPLETED (BLUE)

WITH GRAB SAMPLES FROM AROUND THE SALTON SEA SHORELINE (YELLOW) AND AT A DEPTH OF 1.5 M (ORANGE), 3.0 METERS (RED), AND 4.6 M (MAROON). GRAB SAMPLE DATA WERE COLLECTED AS PART OF AN EARLIER STUDY (AGRARIAN RESEARCH, 2003)......................................................................... 43

FIGURE 4-6. BULK DENSITY MEASUREMENTS COMPARED OVER THREE FIELD MEASUREMENT PERIODS. VERTICAL BARS REPRESENT STANDARD DEVIATIONS AMONG REPLICATE SAMPLES COLLECTED AT EACH SITE...................................................................................................................................................... 45

FIGURE 4-7. SOIL CHEMICAL SPECIES SUMMARY FROM BULK SAMPLES. LINES CONNECTING DOTS ARE INTENDED TO FACILITATE VIEWING OF DATA AND ARE NOT INTENDED TO CONVEY CONTINUITY OR INTERPOLATION. .................................................................................................................................. 47

FIGURE 4-8. SOIL CALCIUM CARBONATE FROM SOILS COMPARED OVER THE THREE TESTING INTERVALS. .... 48 FIGURE 4-9. SALT % (MG/KG) AND EC COMPARED OVER THREE SAMPLING INTERVALS. .............................. 48 FIGURE 4-10. COMPARISON OF PI-SWERL PM10 EMISSIONS FROM ALL SITES. A) FALL 2005, B) WINTER 2006,

AND C) SPRING 2006. INDIVIDUAL DATA POINTS REPRESENT GEOMETRIC MEANS OF SEVERAL REPLICATE MEASUREMENTS ................................................................................................................ 53

FIGURE 4-11. COMPARISON OF PM10 EMISSIONS AS MEASURED BY PI-SWERL DURING THE SEPTEMBER 2005 FIELD STUDY. DATA POINTS CORRESPOND TO GEOMETRIC MEAN EMISSIONS AT SPECIFIC VALUES OF FRICTION VELOCITY. OPEN CIRCLES CORRESPOND TO GEOMETRIC MEAN OF EMISSIONS FOR ALL SITES WHEN 4 OR MORE DATA POINTS FROM DIFFERENT SITES WERE AVAILABLE AT A SPECIFIC FRICTION VELOCITY. GEOMETRIC MEANS REPRESENTED BY OPEN CIRCLES ONLY INCLUDE SITES WHERE DATA WERE AVAILABLE FOR ALL THREE SAMPLING SEASONS. ...................................................................... 54

FIGURE 4-12. COMPARISON OF PM10 EMISSIONS AS MEASURED BY PI-SWERL DURING THE JANUARY 2006 FIELD STUDY. DATA POINTS CORRESPOND TO GEOMETRIC MEAN EMISSIONS AT SPECIFIC VALUES OF FRICTION VELOCITY. OPEN CIRCLES CORRESPOND TO GEOMETRIC MEAN OF EMISSIONS FOR ALL SITES WHEN 4 OR MORE DATA POINTS FROM DIFFERENT SITES WERE AVAILABLE AT A SPECIFIC FRICTION VELOCITY. GEOMETRIC MEANS REPRESENTED BY OPEN CIRCLES ONLY INCLUDE SITES WHERE DATA WERE AVAILABLE FOR ALL THREE SAMPLING SEASONS. ...................................................................... 55

FIGURE 4-13. COMPARISON OF PM10 EMISSIONS AS MEASURED BY PI-SWERL DURING THE MARCH 2006 FIELD STUDY. DATA POINTS CORRESPOND TO GEOMETRIC MEAN EMISSIONS AT SPECIFIC VALUES OF FRICTION VELOCITY. OPEN CIRCLES CORRESPOND TO GEOMETRIC MEAN OF EMISSIONS FOR ALL SITES WHEN 4 OR MORE DATA POINTS FROM DIFFERENT SITES WERE AVAILABLE AT A SPECIFIC FRICTION VELOCITY. GEOMETRIC MEANS REPRESENTED BY OPEN CIRCLES ONLY INCLUDE SITES WHERE DATA WERE AVAILABLE FOR ALL THREE SAMPLING SEASONS. ...................................................................... 56

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FIGURE 4-14. COMPARISON OF PM10 GEOMETRIC MEAN EMISSIONS FROM ALL FIELD CAMPAIGNS FOR SITES WITH HIGH (> 10,000 MG/KG, EC > 4 MMHOS/CM) AND LOW (< 10,000 MG/KG, EC = 0-4 MMHOS/CM) SALT CONTENT. DATA SHOWN CORRESPOND TO FRICTION VELOCITIES WHERE GEOMETRIC MEANS WERE AVAILABLE FOR ALL COMBINATIONS OF FIELD CAMPAIGN AND SALT CONTENT. ........................ 57

FIGURE 4-15. PM10 EMISSIONS BY SEASON AT U*=0.56 M/S . LEFT PANEL SHOWS RESULTS FROM INDIVIDUAL MEASUREMENT LOCATIONS AT A SITE AND RIGHT PANEL SHOWS THE AVERAGE (CIRCLE), STANDARD DEVIATION (VERTICAL BARS), AND THE NUMBER OF TEST LOCATIONS (SQUARE – RIGHT AXIS) FOR THE SAME SITE. ........................................................................................................................................... 59

FIGURE 4-16. COEFFICIENT OF VARIATION (COV) OF MULTIPLE REPLICATE SAMPLES AT THE SAME SITE BY SITE AND SEASON. THE COEFFICIENT OF VARIATION IS DEFINED AS THE STANDARD DEVIATION OF A SAMPLE DIVIDED BY THE AVERAGE. DATA ARE SHOWN FOR U* = 0.56 M/S. ........................................ 64

FIGURE 4-17. PI-SWERL PM10 EMISSIONS AT U*=0.56 M/S FOR ALL TEST LOCATIONS SEGEREGATED BY SEASON AND LANDFORM...................................................................................................................... 65

FIGURE 4-18. AVERAGE VALUES OF PI-SWERL PM10 EMISSIONS AT U*=0.56 M/S (OPEN CIRCLES), STANDARD DEVIATIONS OF EMISSIONS (VERTICAL BARS), AND NUMBER OF TESTS REPRESENTED IN AVERAGE (SQUARES – RIGHT Y-AXIS). ................................................................................................................. 65

FIGURE 4-19. SAME AS FIGURE 4-18 EXCEPT AVERAGES AND STANDARD DEVIATIONS INCLUDE ONLY SITES WHERE DATA ARE AVAILABLE FOR ALL THREE SAMPLING SEASONS. ................................................... 65

FIGURE 4-20. COMPARISON OF PI-SWERL EMISSIONS MEASURED AT THE SALTON SEA WITH WIND TUNNEL EMISSIONS MEASURED AT OWENS LAKE (NICKLING ET AL., 2001). FOR THE WIND TUNNEL, EACH DATA POINT REPRESENTS A SINGLE MEASUREMENT AT A SPECIFIC SITE. SOLID SQUARES INDICATE THAT AN ARTIFICIAL SAND FEED WAS USED. FOR THE PI-SWERL DATA, EACH POINT REPRESENTS AN ARITHMETIC AVERAGE OF SEVERAL REPLICATE MEASUREMENTS AT A SITE. GUELPH TUNNEL MEASUREMENTS AT SALTON SEA ARE SHOWN AS TRIANGLES IN PANEL A. .......................................... 68

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LIST OF TABLES TABLE 3-1. SITE LOCATIONS (COORDINATES IN DECIMAL DEGREES) AND CHARACTERISTICS........................ 10 TABLE 4-1. WESTERN REGIONAL CLIMATE CENTER DATA FOR MECCA 2 SE SITE. ...................................... 29 TABLE 4-2. BALL DROP TEST RESULTS FROM JANUARY 2006 FIELD CAMPAIGN. THE TEST IS CONSIDERED

PASSING IF THE BALL DROPPED FROM A HEIGHT OF 20 CM DOES NOT PENETRATE THE SURFACE CRUST............................................................................................................................................................. 40

TABLE 4-3. CORRELATION TABLE COMPARING LASER PARTICLE SIZE DATA TO THE STANDARD PIPETTE METHOD............................................................................................................................................... 42

TABLE 4-4. AGGREGATE DIAMETERS ESTIMATED FROM SOIL TEXTURE AFTER CHATENET ET AL. (1996)...... 44 TABLE 4-5. SURFACE ROUGHNESS HEIGHTS AS MEASURED BY UNIVERSITY OF GUELPH WIND TUNNEL. ....... 50 TABLE 4-6. APPROXIMATE EQUIVALENT VALUES BETWEEN FRICTION VELOCITY U* AND WIND SPEED

MEASURED AT 10 METERS USING MINIMUM, MAXIMUM, AND AVERAGE ROUGHNESS HEIGHTS (Z0) FROM WIND TUNNEL MEASUREMENTS AT THE SALTON SEA .......................................................................... 50

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1

1 INTRODUCTION The Salton Sea is a terminal lake that was formed inadvertently during the

diversion of the Colorado River in the early part of the 20th Century. Since then, its waters have been increasing in salinity (Figure 1-1). Agricultural water conservation practices combined with a constant inflow of salts suggests that lake levels will continue to decline while salinity will continue to increase. These conditions have already resulted in sporadic, often widespread death of fish and birds that rely on the Salton Sea as a source of water along their migration path. High levels of nutrients have also resulted in algal blooms and eutrophication, further degrading the quality of the Salton Sea as a fish and bird habitat. In partial fulfillment of the Colorado River Quantification Settlement Agreement (QSA), water inflow into the Salton Sea is slated to be further reduced over the coming decades. In preparation for this eventuality, the California Department of Water Resources (DWR) has been tasked with preparing a Salton Sea Ecosystem Restoration Study (ERS) along with a Programmatic Environmental Impact Report (PEIR). These efforts will consider, among other aspects, the air quality impacts that would result from restoration alternatives including a “No Action” alternative. An important air quality concern is the potential for increased windblown dust emission potential from locations where currently inundated areas would be uncovered periodically or permanently as the Salton Sea shoreline transforms under changing inflow conditions. DWR’s prime contractor for preparing the ERS and PEIR is CH2M Hill.

DWR contracted the Desert Research Institute (DRI) to conduct a field study at the Salton Sea in support of assessing air quality impacts from various restoration alternatives and preparation of the ERS and PEIR. The field study, summarized in this report, was conducted between September 2005 and March 2006. The study focused on collecting and characterizing soil properties from sites along the Salton Sea shoreline that may serve as surrogates for future exposed shoreline. In addition, the potential for PM10 (Particulate matter with aerodynamic diameter smaller than 10 µm) dust emission was directly measured at the same sites to provide an estimate of the magnitudes and variability of windblown dust potential from shoreline to be exposed in the future.

The field study was divided into three field campaigns representing the fall, winter, and early spring seasons to capture the dependence of dust emissions on environmental factors such as temperature and humidity. Characterization of soil properties included sample collection and analysis for bulk density, soil texture, soil moisture content, surface crust characteristics, salt content, and chemistry. Emissions of PM10 dust under varying wind conditions were measured with the Portable In-Situ Wind ERosion Lab (PI-SWERL), a device that was developed by the DRI. To ensure that measurements with the PI-SWERL would be comparable to measurements using field wind tunnels that were utilized previously at Owen’s Lake and elsewhere, the PI-SWERL was collocated with the University of Guelph straight line field wind tunnel at 8 of the 17 sites selected for sampling at the Salton Sea. These side by side tests at the Salton Sea were appended to another side-by-side data set collected at other locations in the Mojave Desert to help establish a correspondence between PI-SWERL dust emission measurements and those obtained with traditional straight-line wind tunnels.

2

This report summarizes the methods used and the results obtained over the three field sampling campaigns. The measurements conducted as part of this study are intended to support the development of the ERS and PEIR as well as for future analysis of the potential for windblown dust emissions at the Salton Sea.

Figure 1-1. From Schoeder et al. (2002), the historical monthly surface elevations and salinity of the Salton Sea

1.1 Report Organization A brief review of the processes affecting windblown dust emissions and the

techniques available to directly measure the effect of wind on dust emissions is provided in Chapter 2. In Chapter 3, we detail the methods used, both in the field and in the lab, to collect soil samples, measure soil bulk parameters, and directly measure PM10 dust emissions at the Salton Sea sites. This includes a summary of the correspondence found between the University of Guelph wind tunnel and PI-SWERL emissions measurements. A summary of the soil properties and site-specific windblown dust emission potentials is provided in Chapter 4. The effects of the presence of salt on playa surfaces, the hardness of the soil surface crust, and soil texture on windblown dust emissions potential are also discussed in this Chapter. Chapter 5 recaps the overarching trends in the data collected as part of this study and enumerates supporting research that is in progress.

While large-scale trends and findings are summarized in the body of this report, complete datasets and supplemental information are attached as appendices. Appendix A includes the results from all PI-SWERL tests as well as wind tunnel measurements. Appendix B contains photographs of the soil surfaces at the Salton Sea sites obtained during each of the three field sampling campaigns. Soil bulk properties are summarized in Appendix C. Review comments on the draft version of this report (5/22/06) along with the authors’ responses to those comments are attached as Appendix D.

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2 BACKGROUND Mineral aerosol particles composed of wind-blown dust from deserts and

anthropogenically disturbed soils comprise one of the highest atmospheric mass loadings of particulates on a planetary scale after sea-salt aerosol (Duce, 1995; Tegen and Fung, 1995). At the global scale, dust in the atmosphere scatters and absorbs solar radiation, causes atmospheric turbidity, and thus plays an important role in the global radiation balance (Tegen and Fung, 1995; Tegen et al., 1996; Sokolik and Toon, 1996; Schulz et al., 1997).

Dust particles are directly entrained by the shearing action of the wind (Garland, 1983) and through impacts from saltating sand particles that dislodge silt and clay particles for suspension transport (Bagnold, 1941; Shao et al., 1993; Rice et al., 1997; Rice et al., 1996). Often, dust particle entrainment is modeled as a process that only occurs when a threshold wind speed that imparts a high enough shear stress on the surface is reached. This value of shear stress is quantified through the threshold friction velocity (u*,th; Bagnold, 1941). Several surface properties and soil characteristics can affect the amount of dust emitted by a soil, though the mitigating effects of individual factors are frequently difficult to quantify. Among the most important factors are soil moisture, soil and surface roughness, soil texture, and surface crust formation.

Traditionally, windblown dust emission has been closely linked with the process of sand saltation (Bagnold, 1941; Shao et al., 1993; Rice et al., 1997; Rice et al., 1996), whereby sand grains are lifted by aerodynamic forces, carried by the wind a distance of several meters and then reimpact on the soil surface. When the sand grains impact the soil surface, they may dislodge additional sand grains and/or result in dust emission. Sand saltation is certainly a very important process in dust emission in areas where sand is available. However, recent studies have suggested that aggregates of small particles may play a role similar to sand on soils with finer textures (Alfaro et al., 2004).

Soil moisture can decrease windblown dust emission from a soil surface by increasing cohesiveness of sedimentary particles (Chepil, 1956; McKenna Neuman and Nickling, 1989; Saleh and Fryrear, 1995). A moisture content of 4% by weight is usually sufficient to prohibit particle entrainment (Bisal and Hsieh, 1966). Soil moisture also aids in crust development and aggregate formation, as well as creating conditions more conducive to vegetation growth that acts to protect the surface against wind erosion.

The soil aerodynamic roughness height (zo), or the physical roughness of the surface, can influence the threshold above which dust emission can occur. Surfaces that have zo greater than 0.001 m are not likely to emit dust under any but the most severe winds (Gillette, 1999). Low densities of roughness elements, such as pebbles on a sand surface, actually lower the threshold velocity by promoting local flow acceleration and scouring (Logie, 1982). High densities of roughness elements raise the threshold velocity due to increased surface protection and absorption of momentum from the wind (Logie, 1982; Nickling and McKenna Neuman, 1995; Gillies et al., 2000; Crawley and Nickling, 2003). Thus, adding a sparse gravel cover to an erodible soil surface can result in increased dust emissions compared to the bare surface exposed to the same wind conditions. However, if the density of the gravel is increased past a certain point, the susceptibility of the soil surface to wind erosion begins to decrease, eventually to a level

4

lower than that of the bare soil surface. This occurs because when roughness elements are densely packed, they tend to isolate the underlying surface from the shearing effects of wind.

Another property of the surface that has an influence on dust production is the dry size-distribution of the soil aggregates present in the loose, wind-erodible fraction of the soil (Chatenet et al., 1996; Alfaro et al., 2004). Alfaro et al. (2004) demonstrated using data from wind tunnel tests carried out by Nickling and Gillies (1989) that soils with a high potential for dust emissions contained a fine soil aggregate population. The soils were characterized by different silt and clay contents (Nickling and Gillies, 1989) and showed that, contrary to what is often assumed, texture is not the only parameter to predict the ability of a soil to emit dust. Soils with similar textures may be very emissive under some conditions and very stable under other conditions. Nonetheless, texture should be considered as one of several important parameters including the availability of loose sand, salt content, environmental conditions such as temperature and humidity, and surface roughness that may serve to alter the susceptibility of the bare soil to wind erosion.

The presence or absence of soil crusts can have a considerable effect on dust emissions. The strength of bonding between the soil particles or aggregates can influence the amount of dust generated. Even a weak crust has been shown to reduce the rate of dust emission significantly (Gillette et al., 1982), protecting the underlying less cohesive particles from erosion.

2.1 Techniques for measurement of windblown dust emissions Several techniques are available for measurement of windblown dust emissions.

Among the first to be used (Gillette, 1977) was a tower-based measurement, where the horizontal flux of windblown sediment is related to the vertical flux of suspended dust. Tower-based measurement systems require that measurements occur during periods of active eolian (windblown) sediment transport, a comparatively rare occurrence at most geographic locations. An alternative to sampling the horizontal flux during a dust storm is to artificially subject the soil surface to wind conditions that are similar to those during dust storms. Using wind tunnels that are field-portable, it is possible to simulate high winds and measure the resultant dust emissions. Building on a similar principal, and resulting in a much smaller measurement platform, other investigators have circumvented the need to simulate the atmospheric boundary layer (which has certain scaling requirements) and opted instead to create controlled shear stress above the soil surface. Since shear is the driving force behind windblown dust emissions, devices such as the DRI-developed PI-SWERL provide a repeatable measure of the wind erosion potential for a surface. Other techniques that utilize remote sensing (open-path FTIR and lidar) are slowly becoming viable measurement alternatives, though like the towers, they do require active eolian transport at the time of the measurements.

2.1.1 Tower Measurements of Emission Fluxes Vertical dust flux represents the rate at which fine particulates are lofted into the

atmosphere by wind. It is an important indicator of the strength of a dust source and its contribution to atmospheric loading. A critical component to assess the vertical flux of

5

dust into the atmosphere from surfaces that are being eroded by the wind is the concentration gradient of dust particles above the surface. The concentration gradient data are combined with the momentum gradient data, i.e., the vertical wind speed profile, to define the vertical PM10 flux (e.g., Gillette, 1977; López et al., 1998). The vertical flux can be estimated by the relationship:

( )( )12

1221D uu

CCuCF−−

= (2-1)

where F is the vertical flux of particles (µg m-2 s-1), CD is the surface drag coefficient (Priestly, 1959), u1 and u2, and C1 and C2 are the average wind speeds and dust concentrations at heights 1 and 2.

2.1.2 Wind Tunnel Measurement of Emission Fluxes: Straight-Line Wind Tunnel Portable wind tunnels that have been built and used for characterizing entrainment

thresholds and particulate fluxes vary widely in size and design, as does the instrumentation that is used within the sampling environment. Basically there are two size categories of portable straight line wind tunnels: 1) large tunnels with duct cross section areas in the order of 1×1 m and working lengths from 4 to 11 m, and 2) small tunnels with typical cross sections ranging from 0.15×0.15 m to 0.3×0.3 m and lengths between 3 to 3.5 m. There are also two flow designs, the suction-type where air is drawn from the inlet down the tunnel by a fan and the push-type where air is blown into the inlet. Of currently available portable wind tunnels for measuring dust emissions, the one that achieves the closest approximation to modeling atmospheric flow and sediment transport is the large straight-line suction type. These larger wind tunnels overcome constraints on the saltation system that are imposed by the physical dimensions of smaller tunnels (Owen and Gillette, 1985; White and Mounla, 1991). However, owing to their finite length, wind tunnels sometimes deplete the supply of sand that is available for saltation within a portion of the duration of the entire measurement. While it is possible to introduce sand into a field wind tunnel through a hopper or feeder, it is not known whether this artificial introduction of sand saltators is representative of real-world conditions.

2.1.3 Portable In-Situ Wind ERosion Lab (PI-SWERL) The PI-SWERL is composed of a DC motor, powered by 12-volt batteries, that

sits on top of an open-bottomed cylindrical chamber (Figure 2-1). The motor is coupled to an annular ring, which hangs parallel to and several centimeters above the soil surface within the chamber. As the annular ring rotates about its center axis, according to a prescribed cycle that is controlled by the computer, a velocity gradient forms between the flat portion of the ring (the outer 5 cm) and the ground. This results in the generation of wind shear close to the ground, similar to the effect of moving two parallel flat plates relative to one another as in Couette flow (see Figure 2-2). The magnitude of the shear stress increases with the rate of rotation. The shear induced by the PI-SWERL forces soil particles to begin to move along the ground surface (similar to saltation), in the process causing smaller particles in the PM10 size fraction to be dislodged and emitted as dust. The flow field at the ground surface and a schematic of how soil particles within the PI-SWERL chamber respond to the induced shear stress are shown in Figure 2-3. The

6

concentration of PM10 is monitored by a fast response instrument (DustTrak, TSI, Model 8520), which is attached to the top of the chamber using conductive tubing.

Side View Bottom View

Computer Controller/ Data System PM Monitor

Sample Tube

Open-bottomed Cylindrical Chamber

Variable Speed Motor

Annular Ring

60 cm

40 cm

Side View Bottom View

Computer Controller/ Data System PM Monitor

Sample Tube

Open-bottomed Cylindrical Chamber

Variable Speed Motor

Annular Ring

Side View Bottom View

Computer Controller/ Data System PM Monitor

Sample Tube

Open-bottomed Cylindrical Chamber

Variable Speed Motor

Annular Ring

60 cm

40 cm

Figure 2-1. Schematic of the PI-SWERL. The rate of rotation of the annular ring is controlled by a DC motor, which is in turn controlled by a computer. The dust suspended by the shearing force beneath the annular ring is measured by a PM10 monitor and reported to the computer.

Plate moving with respect to ground

Ground

Velocity gradient results in shear at ground, suspending loose particles

Plate moving with respect to ground

Ground

Velocity gradient results in shear at ground, suspending loose particles

Figure 2-2. Idealized illustration of a plate moving over a soil surface similar to turbulent Couette flow. A similar phenomenon occurs when the annular ring of the PI-SWERL spins about its axis, though the geometry is more complex.

Once sites for measurement are identified, operation of the PI-SWERL is straightforward. The instrument is placed on the ground at the desired location. In areas where seasonal grasses and shrubs exist, the vegetation is carefully cut close to the soil surface to avoid disturbing the soil. A “skirt” weighted down with lead shot serves to seal the PI-SWERL to the test surface. Using a laptop interface, the PI-SWERL cycle is started. A blower is used to vent the PI-SWERL chamber with clean air at a known flow rate (Q) for a period T0 that is long enough to ensure that any dust raised during the placement of the PI-SWERL on the soil surface has been removed from the chamber. With the blower still on, the annular ring within the PI-SWERL cavity begins rotating at a predetermined rate R1 for a predetermined period of time T1. At the end of the time period T1, the rate of rotation is increased to the next step (R2) where it is held for a time equal to T2. This step-wise increase in RPM continues until the final step (Rn) is reached.

7

After the final step is completed (Tn), the annular ring begins to slow to a stop while the PI-SWERL chamber is vented with clean air for a period Tn+1.

a.

b. c.

Figure 2-3. Illustration of swirling flow inside of PI-SWERL cavity (a) and corresponding sand particle motion in cutaway top view (b) and cutaway side view (c).

An example of a PI-SWERL measurement is shown in Figure 2-4. The figure depicts two tests performed with the PI-SWERL, one on a surface that has been treated with a stabilizing agent and one on a nearby soil that has not been treated. For the first 120 seconds (stage 0), the PI-SWERL cavity is flushed with clean air until the dust concentration approaches zero. As the rate of rotation increases to 900 RPM (stage 5), small amounts of dust are measured for both the treated and untreated surfaces. As the rate of rotation continues to increase, the dust emissions from the untreated surface begin to greatly exceed those from the treated surface (stages 7 through 10). Finally, the power to the motor that drives the annular ring is turned off and the chamber is vented (flush stage). These data can be used to obtain two different types of information. First, they can be used to estimate the minimum amount of shear necessary for dust emission to begin (quantified as a threshold friction velocity). Second, the areas underneath the dust concentration curve at each stage can be integrated to obtain the total amount of dust that is available for emission for each level of shear stress experienced by the soil.

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0

5

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15

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0 100 200 300 400 500 600 700

Time (s)

0

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UncontrolledTreatedTarget_RPM

PM10

Dus

t Con

cent

ratio

n (m

g/m

3 )N

ominal R

PM

flush

0

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21

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8

7

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ominal R

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ratio

n (m

g/m

3 )N

ominal R

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flush

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6

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8

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10

Figure 2-4. Example of PI-SWERL measurement cycle. The blue horizontal lines reflect the setting and duration of each rotation step (right y-axis). The green and red lines respectively illustrate the dust concentration over the PI-SWERL cycle for a test plot that has been treated with a stabilizing agent and a plot that has not been treated at the Nevada Test Site.

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3 METHODS The purpose of the Salton Sea field measurements was to provide insights into the

relationships between season, soil properties, and windblown dust emissions. Therefore, wherever possible, at all field sampling sites measurements were obtained to characterize the soil textural properties, soil bulk density, surface crust properties, soil salt content, and PM10 dust emissions. Measurements were completed during three separate field sampling campaigns representing three different seasonal conditions. The field measurement periods were: September 21, 2005 through September 30, 2005, January 24, 2006 through January 27, 2006, and March 20, 2006 through March 24, 2006.

At each location, two shallow soil pits were dug and used to characterize the surface crust and soil properties. The upper most surface crust was collected for grain size, physical soil properties, and chemical analysis. Crust strengths were measured using soil penetrometers. Soil hydraulic properties were estimated based on textural parameters.

Grain size distribution and hydraulic soil properties were determined once for each site, since these properties are unlikely to change from season to season. All other properties were determined for each of the three field measurement periods, since soil moisture and temperature can change bulk density and chemical properties of soils.

3.1 Site locations and descriptions Sites were selected to meet the following criteria: 1) accessibility to wind tunnel

and/or PI-SWERL, 2) permission of landowners, 3) coverage around the perimeter of the Sea, 4) inclusion of a wide variety of soil textures for wind tunnel calibration, and 5) inclusion of several playa-like sites. Some sites close to the Sea were inundated during one or more of the field campaigns and no PM10 emissions data were collected at those times.

Initially, 14 sites were selected for measurement of windblown dust emission potential. During the January 2006 field campaign, three additional sites were added to the preliminary list to emphasize salt crusted playas that may have been under-represented in the initial site selection. Of the fourteen sites that were initially selected for measurement of wind erodibility with respect to PM10 dust and soil bulk properties, at eight sites (A34-1, A100-1, A100-2, A34-2, SS2, SS16, A101-1, and A31), the University of Guelph field wind tunnel was collocated with the PI-SWERL. At eight sites total (A29, SS17, A32, SS9, SS6, A34-3, A200, and A201), only the PI-SWERL was used to measure wind erodibility. At one site (SS23) no measurements were conducted due to visible moisture on the soil surface, severe disturbance, and accessibility issues. The general site characteristics are provided in Table 3-1 and measurement locations are shown in Figure 3-1. Figure 3-2 shows the landform represented by each of the sites (as described in Table 3-1). Elevations reported for each site were determined using digital elevation models checked by field observations, so elevations are only approximate. Detailed site descriptions appear in Section 4.1.

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Table 3-1. Site locations (coordinates in decimal degrees) and characteristics

Site Latitude Longitude

Elevation (feet above

Salton Sea level) approximate

Landform Crust Type

SS2 33.323117 -115.9457 10 Dry wash Silt/clay SS6 33.18045 -115.852433 100 Dune-interdune Silt/clay SS9 33.089483 -115.7095 3 Barnacle beach Salt SS16 33.341 -115.667967 3 Playa-like Salt SS17 33.354167 -115.721067 3 Playa-like Salt A29 33.439917 -115.8432 3 Barnacle beach Silt/clay A31 33.36175 -115.66695 25 Paleo-lake Silt/clay A32 33.27812 -115.6022 1 Playa-like Salt A34-1 33.347933 -115.966467 4 Playa-

like/Beach Salt

A34-2 33.347617 -115.96625 4 Playa-like/Beach

Salt

A100-1 33.35265 -115.97265 8 Dry wash Silt/clay A100-2 33.351583 -115.970683 8 Playa-

like/Beach Salt

A101 33.350183 -115.655533 25 Paleo-lake Silt/clay SS23 33.4974 -116.079817 3 Playa-like Salt A34-3 33.34838 -115.96549 1 Playa-like Salt A200 33.20039 -115.59705 1 Playa-like Salt A201 33.11743 -115.69131 1 Playa-like Salt

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Figure 3-1. Locations of Sampling sites around the Salton Sea. Red circles represent sites where the PI-SWERL was collocated with the University of Guelph wind tunnel in September 2005 while blue circles correspond to sites where only the PI-SWERL was operated. Yellow circles correspond to sites that were added in January 2006. No PM10 emissions measurements were collected at site SS23 (green) because the site was either inundated and/or disturbed by heavy off-road vehicle traffic.

Figure 3-2. Sampling sites at the Salton Sea grouped by landform according to Table 3-1. A small red dot indicates that the measured salt content was greater than 10,000 mg/kg.

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3.2 On-site measurements

A proctor spring penetrometer was used to measure the strength of the surface crust. Fifteen replicate measurements were completed at each site. The spring penetrometer measures, in psi (converted to kPa), the amount of force required to break the surface crust. Additionally, a cone penetrometer was used to measure soil strength at depth. The cone penetrometer measures the force required to push a metal rod with a cone tip through the soil. It measures at 1.5 mm intervals in kPa. During the second field sampling period, the ball-drop method was also employed at each site to test crust strength.

Two soil pits >20 cm deep were dug at each site to describe near surface properties, such as crust thickness, crust type, moisture, hardness, textural changes, and subcrusts. Samples were collected for textural and chemical analysis. Additional samples were collected for bulk density using either a brass ring or a soil can of known volume, or by collecting an intact ped for in-lab measurements.

3.3 Bulk analysis measurements

In the lab, soil samples were weighed, dried for 24 hrs at 105°C, and weighed again to determine gravimetric moisture content. Bulk density was then calculated for samples that were collected in soil tins or brass rings of a known volume, or determined from intact peds by coating them with paraffin wax and determining the volume by displacement in water. To determine particle size, the >2mm fraction was removed using a mesh sieve and weighed. Particle size distribution of the fine fraction (< 2 mm) was determined using a combination of standard dry sieves and laser diffraction techniques (Gee and Or, 2002). The dry sieve method was used to determine the coarse to very fine sand fractions in the samples. Laser particle size analysis was used to determine the distribution of coarse sand and less (including silt and clay sized particles) within the samples using a Micromeritics Saturn DigiSizer 5200. The sample was internally dispersed using ultra-sonication in de-ionized water with 0.005% surfactant (sodium metaphosphate) and circulated through the path of the laser light beam. The Micromeritics Saturn DigiSizer 5200 calculates the diameter of a particle as if it were a sphere. Most laser particle size analysis techniques tend to underestimate the clay fraction of samples, so standard pipette analysis was performed on 16 samples to compare directly with the laser data. Percentages of silt and clay were calculated by drawing sample at different times based on the particle settling velocity (Gee and Or, 2002).

Aggregate size distribution were estimated from soil textural data (Chatenet et al., 1996) and measured directly from four samples. Aggregates were carefully collected from the surface. The samples were dried in the lab, and then analyzed using a Gilson Performer III three-inch sieve shaker for eight minutes at a vibration intensity of 5.5, similar to the method of Chatenet et al. (1996). We used 6 size fractions including > 1000 µm, 1000-500 µm, 500-250 µm, 250-125 µm, 125-63 µm, and < 63 µm. Sieved samples were weighed to determine the size fraction of aggregates.

Percentage of calcium carbonate in the soils was determined by acid digestion in a Chittick apparatus to measure the volume of CO2 gas evolved (Dreimanis, 1962; Machette, 1986). Salt content was estimated from electrical conductivity (EC) of aqueous

13

soil extracts. Water containing dissolved salts conducts current proportional to the amount of salt present. A soil-water extract of 1:5 was used in conjunction with a conductivity bridge to estimate the total amount of soluble salt. Hydrogen-ion activity (pH) of the soil was measured from a 1:1 soil-aqueous matrix suspension. Organic carbon was determined by loss on ignition (LOI), which determines a percent weight loss following combustion for 1 hr at 600°C (Ben-Dor and Banin, 1989). This loss was assumed to be soil organic matter.

Extractable cations (K, Mg, Ca, Na) and sulfate were measured using extraction and analysis. Phosphorus was measured using the sodium carbonate fusion method, which is a better method for soils with pH > 7.5 (in which case, extraction of P using the dilute acid fluoride method performs unsatisfactorily; Kuo, 1996). Cation exchange capacity (CEC) was determined using the ammonium saturation method (Sumner and Miller, 1996). The sodium adsorption ratio (SAR) was calculated as [Na+]/([Ca2+] + [Mg2+])1/2 (Sumner and Miller, 1996).

Hydrologic parameters such as wilting point, field capacity water content, saturation soil water content, and saturated hydraulic conductivity were estimated from textural data (Saxton et al., 1996), as was air entry potential (Rawls et al., 1982).

Descriptions of relative salt and carbonate content have been based on the numerical data and are explained as follows: Carbonate content: Low (<5%), Moderate (5-10%), High (10-20%), Very High (>20%); Salt content: Non saline (<1000 mg/kg, EC 0-2 mmhos/cm), Very slightly saline (1000-10,000 mg/kg, EC 2-4 mmhos/cm), Slightly saline (10,000-25,000 mg/kg, EC 4-8 mmhos/cm), Moderately saline (25,000-50,000, EC 8-16 mmhos/cm), and Strongly saline (>50,000 mg/kg, EC > 16 mmhos/cm). Salt contents are roughly equivalent to NRCS soil description guidelines (Richards, 1945; Soil Survey Division Staff, 1993).

3.4 PI-SWERL Windblown PM10 dust emissions from sites at the Salton Sea were measured

using the Portable In-Situ Wind ERosion Lab (PI-SWERL). The PI-SWERL, described in Section 2.1.3, was collocated with the University of Guelph straight-line field wind tunnel at 8 sites at the Salton Sea for comparison. Data from those 8 sites were combined with other collocation data obtained elsewhere in the Mojave Desert to establish a relationship between PI-SWERL measurements and traditional wind tunnel measurements which have been used previously in similar studies. Processing of the PI-SWERL data, results from collocation with the University of Guelph wind tunnel, and the procedures for using PI-SWERL at the Salton Sea are summarized in the ensuing sections.

3.4.1 PI-SWERL Data Processing Figure 2-4 shows an example of the PI-SWERL being operated at 12 distinct

RPM target levels. Level 0 corresponds to a flushing of the PI-SWERL chamber at clean airflow rate F (usually set to ~250 liters per minute) prior to the beginning of a test. At level 0, the PI-SWERL blade does not rotate. Flushing with clean air for a period of 90 seconds or more is necessary to rid the chamber of any PM10 dust that may have been suspended during the placement of the PI-SWERL on the surface. Starting at level 1, the

14

PI-SWERL blade begins to rotate until it reaches a preset RPM level (300 RPM in the case of the example figure). PM10 concentration C, the actual rate of rotation, and the clean airflow rate are monitored continuously with 1-second resolution. Level 1 is maintained for some predetermined period of time (typically 180 - 300 seconds for the tests at Salton Sea) before the preset RPM at level 2 is targeted. This continues through the highest measurement level (level 10 in the case of the figure). After the highest level is completed, power to the PI-SWERL motor is shut off while clean air continues to flush the chamber to remove PM10 dust in preparation for the next test.

Raw PI-SWERL data are converted into cumulative PM10 emissions by summing the flux of PM10 up to the RPM target value of interest according to:

eff

i

ieffibeginiend

iend

begincumi AEA

tt

FCE ∑

∑=

−

×=

1,,

,

1,, (3-1)

where the summation occurs over every 1-second measurement of clean air flow and PM10 concentration during level i, which begins at tbegin,i and ends at tend,i and where t is measured in integer seconds. Note that the PM10 emissions are calculated based on the effective area of the PI-SWERL measurement Aeff (0.07 m2) which is discussed below. The physical interpretation of the step emission Ei is illustrated in Figure 3-3 below.

0

10

20

30

40

50

60

70

80

90

0 200 400 600 800 1000 1200Time (Seconds)

PM 10

Con

c (m

g/m

3 )/

Cum

ulat

ive

flux

(mg/

m

2 )

.

0

200

400

600

800

1000

1200

1400

1600

1800

RPM

.

PM10-Conc (mg/m3)Cumulative PM10 Flux for Step (mg/m2)RPM

Clean Air Flush

First RPM Step

Initial peak at beginning

of step

Slow asymptotic

decline after peak

Clean Air Flush

Figure 3-3. Illustration of PI-SWERL test and the concept of cumulative PM10 emissions. Ei for a given step i in Equation (3-1) is equal to the area underneath the dashed line for that step divided by the duration of that step.

The distribution of shear stress within the PI-SWERL has been characterized and appears in Figure 3-4. The data in the figure have been obtained from measurements conducted with Irwin sensors mounted onto a smooth plywood surface. The Irwin sensor is a simple, omni-directional skin friction meter that measures the near-surface vertical pressure gradient (Irwin, 1981). The differential in dynamic pressure is measured

15

between two ports, one at the surface and the other at a height of 0.00175 m above the surface. Once calibrated, the Irwin sensor can be used to measure surface shear stresses at frequencies greater than 10 Hz (Irwin, 1981; Wu and Stathopoulos, 1994) and has been used successfully in a variety of flow conditions and surface roughness configurations (Irwin, 1981; Wu and Stathopoulos, 1994; Monteiro and Viegas, 1996; Crawley and Nickling, 2003).

In Figure 3-4, the panel on the right shows the distribution of ground-level shear stress from the center of the PI-SWERL out to the inner radius. The sharp rise in shear stress at r/R ~ 0.78 to 0.95 (between vertical lines) corresponds to the area immediately underneath the annular ring. The flat portion at the top of the curve corresponds to the radial distances that result in the greatest amounts of dust emission. The area underneath this portion of the annular ring (0.07 m2) is considered the effective test area Aeff at the given shear stress. The correspondence between RPM, average shear stress over the region (0.8 < r/R < 0.94), and friction velocity for the plywood test surface is shown in Figure 3-5. Note that surfaces substantially smoother or rougher than the plywood used to calibrate the PI-SWERL would require a correction to the curve shown in Figure 3-5 to account for roughness.

0

0.2

0.4

0.6

0.8

1

1.2

0 2000 4000Revolutions Per Minute (RPM)

She

ar S

tress

(Pa)

0

0.2

0.4

0.6

0.8

1

1.2

0 0.5 1r/R

She

ar S

tress

(Pa)

900 RPM1200 RPM1500 RPM1800 RPM2100 RPM2400 RPM

a. Shear Stress vs. Speed (r/R = 0.8) b. Shear Stress vs. r/R for various RPM Figure 3-4. Relationship between shear stress measured at ground level with Irwin sensor and the number of RPM of the PI-SWERL annular ring. r/R = 0 at the center axis, r/R = 0.78 at the inner edge of the annular ring, r/R = 0.94 at the outer edge of the annular ring, and r/R = 1 at the inner wall of the PI-SWERL chamber.

16

Friction Velocity:y = -5E-12x3 - 5E-08x2 + 0.0005x + 0.0165

R2 = 0.9998

Shear Stress: y = 0.0005x - 0.1711R2 = 0.9971

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400

2600

2800

3000

3200

PI-SWERL_RPM

Shea

r stre

ss (P

a)

0

0.2

0.4

0.6

0.8

1

1.2

u* (m

/s)Shearu*Poly. (u*)Linear (Shear)

Figure 3-5. Relationship between PI-SWERL RPM and resultant shear stress and friction velocity as measured by Irwin sensors mounted on flat plywood. Shear stress values were calculated as the average of multiple point measurements between r/R = 0.78 and r/R = 0.94 (See Figure 3-4).

3.4.2 Collocation of PI-SWERL with University of Guelph Wind Tunnel

The straight-line portable wind tunnel is currently the method that is closest to a “standard” instrument for direct measurement of PM10 dust emission fluxes from soils in lieu of measurements taken during actual wind erosion events. PI-SWERL does not simulate the atmospheric boundary layer in the same manner as the field wind tunnel, but has been developed to provide a comparable index of dust emission potential. To compare emissions between the PI-SWERL and the field wind tunnel, a series of collocated tests were conducted.

The University of Guelph’s suction-type straight-line field wind tunnel is approximately 1 m × 1 m × 11 m long. Large field wind tunnels such as this one achieve the closest approximation to measuring atmospheric flow and sediment transport. The tunnel is sufficiently long to develop a relatively thick boundary layer over natural surfaces (~0.25 m) and is large enough to overcome Froude Number effects that may pose a limit on the utility of smaller portable wind tunnels in experiments involving saltation (Owen and Gillette, 1985; White and Mounla, 1991). The wind velocity profile is measured using a series of pitot tubes connected to pressure transducers. DustTraks (TSI, 8520) are used to measure the dust concentration. Horizontal saltation flux (mg/m/s) is measured by a sediment trap within the tunnel (see Houser and Nickling, 2001). A single wind tunnel test usually consists of measurement at a specific friction velocity (u*). This friction velocity cannot be determined precisely prior to the initiation of the test. Rather, a specific speed is set for the diesel motor, a clutch between the motor drive shaft and the wind tunnel fan is engaged, air flow through the wind tunnel is driven by the fan, and the friction velocity is calculated at the end of the test based on the measured vertical velocity profile. For the calibration tests summarized here, ramp tests - where the motor speed is increased step-wise over time at the same location - similar to those of the PI-SWERL were also conducted.

17

Calibration tests between the PI-SWERL and the straight-line field wind tunnel were conducted at 23 different sites at the Ft. Irwin National Training Center, within the Mojave National Preserve, and at the Salton Sea in California between April 2005 and September 2005. Sites were chosen to reflect a spectrum of sand-, silt-, clay-rich, and texturally mixed soils (Figure 3-6) found on playas and lacustrine sediments, beach ridges, alluvial fans, washes, and eolian sands. Surface properties were also diverse and included salt crusts, silt-clay crusts, gravel cover, unconsolidated sediment, and artificially disturbed surfaces. To accommodate the physical constraints of the wind tunnel, sites had to be nearly free of vegetation and relatively flat. Testing occurred on both undisturbed and disturbed surfaces. Disturbance was generated by dragging large metal plates by ATV (All-Terrain Vehicle) across the surface three times. This broke surface crusts and mixed gravel into the soil where present.

Figure 3-6. Ternary plot of soil textures from sites where the PI-SWERL was collocated with the University of Guelph wind tunnel. Dots of the same color represent replicate samples from the same site.

PI-SWERL and wind tunnel tests were run side-by-side (Figure 3-7). During each wind tunnel test, PI-SWERL tests (1 to 5 per wind tunnel test location) were conducted alongside the tunnel. Following a test, the wind tunnel and PI-SWERL were moved to a fresh surface and the tests were repeated. Six or more wind tunnel tests were collocated with up to 16 PI-SWERL tests at each test location to help account for surface heterogeneity.

Test surfaces included: 1) gravel-covered surfaces 2) mud-cracked playa surfaces, 3) continuously crusted playa surfaces without cracks, or 4) artificially disturbed,

18

unconsolidated surfaces. Gravel-covered surfaces (F1, F2, L1, L6, L7, N2, A100-1) were found on alluvial fan, beach ridge, or wash settings. Mud-cracked surfaces (L5, N1, N3, N4, S1, S3, A101-1, A31-1) were found on playa or lacustrine sediments. These soils had higher percentages of clay with little to no gravel and were stabilized at the surface by silt-clay crusts. Some mud cracks had thin, loose, broken crusts. Continuously crusted surfaces (Z3, Z4, A100-2, A34-1, A34-2, SS16, SS2) were also found on playa and lacustrine sediments and had a significant salt component. Some crusts were smooth while others were rough and botryoidal. Soils were typically moist underneath the continuous crusts. Artificially disturbed sites were often paired with undisturbed sites, so that their textures were similar to their undisturbed counterparts. In most cases, disturbed surfaces were loose and powdery, sometimes containing granule to pebble-size aggregates of soil or rock.

Figure 3-7. PI-SWERL collocated with the University of Guelph wind tunnel on a disturbed playa surface on Superior Lake, CA.

Wind tunnel data were compared to PI-SWERL measurements on a site-by-site basis. For each test, the average cumulative PM10 emissions (mg/m2s) from PI-SWERL were calculated (See 3.4.1) for every RPM level. Measurements from multiple locations at the same site were then aggregated to obtain site geometric means of PI-SWERL emissions. In contrast to the PI-SWERL measurements, most wind tunnel tests represented a single wind speed (or shear stress/friction velocity). The difference between PI-SWERL and wind tunnel test protocols is illustrated in Figure 3-8. Although friction velocity settings for the wind tunnel did not exactly correspond to friction velocity settings represented by PI-SWERL RPM settings, PI-SWERL data were interpolated to estimate equivalent wind tunnel emissions. Thus, the PI-SWERL and wind tunnel data are not directly comparable. First, the PI-SWERL data represent PM10 emissions at a particular friction velocity that have been “reconstructed” using the measured PM10 flux from all prior friction velocity (RPM) steps. In contrast, most wind tunnel measurements represent only the emissions measured at some specific value of shear stress. Second, since the value of shear stress for the wind tunnel is not known prior to beginning a test and is calculated based on the velocity profile measured during a test, it was not possible to “dial in” the wind tunnel to exactly replicate PI-SWERL friction velocities. Thus wind tunnel data were interpolated during post-processing to estimate PM10 emissions at a friction velocity equal to the one exerted by the PI-SWERL.

19

Figure 3-8. Illustration of wind tunnel and PI-SWERL tests and calculation of comparable PM10 emissions. The top panel shows the most common type of wind tunnel test where the wind tunnel is run at a single value of wind speed. The bottom panel shows how the PI-SWERL test uses step values of RPM to obtain a measurement of emissions at multiple values of friction velocity at the same test location. The middle panel shows how PM10 concentrations in the wind tunnel would vary in time if the wind tunnel was operated in a manner similar to PI-SWERL (i.e. steps with increasing friction velocity).

In general, the relationship between PI-SWERL and wind tunnel measured emissions was consistent, though quite variable. This variability appears to be largely

20

random and probably results from the inherent spatial variability of dust emissions, a hypothesis corroborated by replicate measurements completed by PI-SWERL as well as the wind tunnel (when available). However, a systematic difference between PI-SWERL emissions and wind tunnel-measured emissions was noted for two sites, L1 and L6, for both the undisturbed surface and the artificially disturbed surface. At those two sites, PI-SWERL emissions were substantially higher at all values of friction velocity. In examining the photographs of the surfaces at those two sites and comparing with all the other sites, it was clear that L1 and L6 were the only two sites where there was a heavy (almost packed) cover of non-erodible, roughness elements – gravel in this case.

The underlying cause for the apparent mismatch between PI-SWERL and wind tunnel emissions at L1 and L6 was not that the PI-SWERL emissions were too high. Rather, it was that the relationship between RPM and friction velocity used for the PI-SWERL was underestimating the actual friction velocity. That relationship (Figure 3-5) was experimentally determined by operating the PI-SWERL over a plywood sheet and measuring the shear stress generated at the plywood surface. Plywood with a surface roughness z0 of approximately 2.7 µm (Borrmann and Jaenicke, 1987) is much smoother than a soil surface with a heavy gravel cover. Thus, a correction to the curve in Figure 3-5 would be required to more accurately represent shear stress dependence on RPM for a surface covered with non-erodible roughness elements. This correction was estimated by assuming that the shear stress generated by the PI-SWERL blade moving over the soil test surface is akin to the shear generation mechanism in turbulent Couette flow where two infinite plates, separated by some distance, move parallel to one another. This model for the PI-SWERL shear generation mechanism, combined with early empirical determinations of surface roughness parameters as a function of regular spacing of roughness elements (Schlichting, 1936) allowed for an estimation of the correction factor for friction velocity. Using the turbulent equations for Couette flow (Schlichting and Gersten, 2000) as a surrogate for PI-SWERL operation, at a specific RPM, the friction velocity generated between a smooth surface (PI-SWERL blade) and a rough surface (gravel cover, corresponding to ks = 0.31 cm or equivalent to hemispheres with a radius of 0.26 cm packed tightly) is 40% higher than the friction velocity between two smooth surfaces (PI-SWERL blade and plywood surface) at the same RPM and distance separation. Thus, for non-erodible densely packed gravel and a specific value of RPM, the friction velocity obtained from Figure 3-5 would have to be multiplied by 1.4 to represent a more realistic value. An example of a densely covered gravel surface, corrected and uncorrected PI-SWERL emissions and wind tunnel emissions is given in Figure 3-9.

While the type of empirical roughness correction to the friction velocity vs RPM curve described above is acceptable when the sites considered are very distinct from one another and a “rule of thumb” can be used and applied with the aid of site photographs, in general it would be desirable to measure the friction velocity directly at each site. Future improvements on the PI-SWERL will include an effort to incorporate a direct measure of shear stress (or friction velocity) into the unit. Note that the Guelph wind tunnel data did not require such a correction because a vertical profile of the wind speed gradient above the ground is obtained at each test location, offering a more direct measure of shear stress and friction velocity.

21

The relationship between wind tunnel measured PM10 emissions and PI-SWERL PM10 emissions at all sites where they were collocated and at all the levels of friction velocity at which the wind tunnel was operated, yields the correspondence shown in Figure 3-10. Note that these types of measurements are inherently noisy due to the natural heterogeneities of the soil surface. This is true for both the wind tunnel (vertical error bars represent geometric standard deviations from replicate measurements where available or estimated from sites with replicate measurements) and the PI-SWERL (horizontal error bars represent geometric standard deviations). When the data from Figure 3-10 are transformed into logarithm space and averaged appropriately (over 0.25 decades in the case of the figure), a more coherent relationship between wind tunnel and PI-SWERL measurements emerges (Figure 3-11). The regression performed in “log space” also allows for lower emissions values to be considered more equitably than when performed in linear space. Based on the regression in Figure 3-11, a power relationship can be used to transform emissions measured with the PI-SWERL (EPI-SWERL) into wind tunnel equivalent emissions (EWT) with an R2 of 0.96.

( ) ( )05.077.008.057.0 ±−×±= SWERLPIWT EE . (3-2)

This equation provides a useful method for comparing wind tunnel data with cumulative PI-SWERL PM10 emissions.

Note that the 1:1 line shown in Figure 3-11 also provides a reasonably good fit to the data, suggesting that within about a factor of two or so, PI-SWERL and wind tunnel measured emissions are approximately equal when considered over many sites and replicate measurements. This is an interesting finding since the PI-SWERL test area is approximately 0.6% of the wind tunnel test area, the PI-SWERL does not directly simulate the atmospheric boundary layer in the same manner that the wind tunnel does, and the physical dimensions of the PI-SWERL may impede saltation. If the correlation between PI-SWERL and the wind tunnel holds under other conditions not examined as part of this calibration effort (e.g. sand dunes), then the use of the PI-SWERL would not represent a significant compromise in accuracy compared to a large field wind tunnel. There may be great economy to be achieved by using PI-SWERL in place of the wind tunnel to measure dust emissions as a function of friction velocity in many field applications where time and monetary resources are scarce. However, it is important to note that the data from these collocation tests are not sufficient to conclude that the two methods yield the same results at each site tested. Rather, on the whole, they seem to produce comparable results when considered over a large number of sites and replicate tests. Figure 3-12 illustrates this point. Figure 3-13 shows the same information however the PM10 emissions scale (left hand y-axis) is logarithmic. For individual sites, PM10 emissions measured by PI-SWERL and wind tunnel are in some cases quite different (e.g. for sites L5-N and N4-D). The natural spatial heterogeneities – illustrated by the span of the “X” marks in the figures – are partly responsible for these differences. Simply, it is not possible to exactly collocate the wind tunnel with the PI-SWERL. Note that while the wind tunnel data also exhibit substantial variability – illustrated by the span of the red dots in the figure – the PI-SWERL data show larger within site variability. One possible reason for this is that the wind tunnel footprint (~ 10 m2) is substantially greater than the PI-SWERL footprint (~0.1 m2).

22

a.

0.001

0.01

0.1

1

10

0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6

Friction Velocity u* (m/s)

PM10

Dus

t Flu

x (m

g/m

2s)

PI-SWERL- Not corrected for roughness

Guelph Wind Tunnel

PI_SWERL- Corrected for roughness

b.

Figure 3-9. Example of applying roughness correction to PI-SWERL data for surfaces covered with dense non-erodible roughness elements. a) Photograph of gravel covered surface at site L1 (Not disturbed) and b) Example of how applying roughness correction (40%) to PI-SWERL data improves agreement with wind tunnel measurements.

23

y = 0.4127x0.7625

R2 = 0.7213y = 0.5061x0.7458

R2 = 0.5792

y = 0.5326x0.7518

R2 = 0.5613y = 0.5808x0.8055

R2 = 0.65980.0001

0.001

0.01

0.1

1

10

100

0.0001 0.001 0.01 0.1 1 10 100

PI-SWERL PM10 Emissions (mg/m2s)

Win

d Tu

nnel

PM

10 E

mis

sion

s (m

g/m

2 s)

Gravel Surfaces

Non Gravel Surfaces

Saltation Above 100 mg/ms

Power fit - Gravel Surfaces

Power Fit - All Data

Power fit (non-gravelsurfaces)Power (Saltation Above 100mg/ms)

Figure 3-10. Scatter plot of wind tunnel PM10 emissions versus PI-SWERL measured PM10 emissions from 23 collocated tests. Triangles represent data points where the PI-SWERL friction velocity has been corrected for heavy gravel cover on the surface. Error bars for both PI-SWERL and wind tunnel data are shown in the horizontal and vertical directions, respectively and represent one geometric standard deviation.

24

0.001

0.01

0.1

1

10

100

0.001 0.01 0.1 1 10 100

PI-SWERL PM10 Emissions (mg/m2s)

Win

d T

unne

l PM

10 E

mis

sion

s (m

g/m

2 s)

Y =(0.57 ± 0.08) X (0.77 ± 0.05)

Figure 3-11. Relationship between PI-SWERL emissions and wind tunnel emissions when the geometric means of PI-SWERL data are taken over 0.25 decades and compared with the corresponding wind tunnel geometric means (R2 = 0.96). The dashed line represents what a 1:1 linear relationship would look like on the figure.

25

0

2

4

6

8

10

12

14

16

18

20

L6-N

N2-

N

A10

1-1

L1-N

L5-N

Z4-D

Z4-N

N4-

N

N1-

N

Z3-N

A10

0-2

A34

-2

A31

-1

N3-

N

SS16

-1

S3-N

A34

-1

L7-N

L1-D

S1-N

L6-D

SS2-

1

N1-

D

A10

0-1

F1-D

N2-

DN

3-D

L5-D

N4-

D

S1-D

S3-D

Site

PM 10

Em

issio

ns (m

g/m

2 s)

/ N

umbe

r te

sts c

ompl

eted

Individual Test - PI-SWERLArithemtic Mean - PI-SWERLGeometric Mean - PI-SWERLIndividual Test - Wind Tunnel# of tests completed - PI-SWERL

Figure 3-12. Comparison of PI-SWERL and wind tunnel PM10 emissions at u* = 0.56 m/s on a site by site basis. The figure shows PM10 emissions obtained by both arithmetically (standard averaging) and geometrically (averaging of logarithms) averaging data from replicate measurements. The vertical bars represent standard deviations. Data ordered from left to right by increasing average PM10 emissions.

26

0.0001

0.001

0.01

0.1

1

10

100

L6-N

N2-

NA

101-

1

L1-N

L5-N

Z4-D

Z4-N

N4-

N

N1-

NZ3

-N

A10

0-2

A34

-2

A31

-1N

3-N

SS16

-1

S3-N

A34

-1

L7-N

L1-D

S1-N

L6-D

SS2-

1N

1-D

A10

0-1

F1-D

N2-

D

N3-

DL5

-D

N4-

DS1

-D

S3-D

Site

PM 10

Em

issio

ns (m

g/m

2 s)

Individual Test - PI-SWERLArithemtic Mean - PI-SWERLGeometric Mean - PI-SWERLIndividual Test - Wind TunnelT l2

Figure 3-13. Same as Figure 3-12 except left y-axis is logarithmic.

27

3.4.3 Procedures for PI-SWERL Measurements at the Salton Sea The PI-SWERL was operated at every site that was dry enough for testing during

each of the three field sampling campaigns. For ease of movement, the PI-SWERL unit, electronics, and battery were mounted onto the front end of an ATV (Figure 3-14). At each site, the ATV was driven to the sampling location, the PI-SWERL was lowered onto the test surface, and the measurement cycle was begun. At the end of the first measurement cycle, the PI-SWERL was retracted, the ATV was moved (usually by pushing it forward manually for a few meters) and another test was initiated. Most sites were accessible by van, but at three sites (A32-1, SS6-1, and A201-1), the measurement locations were accessible only by ATV.

Figure 3-14. Photograph of the PI-SWERL mounted on an ATV and in operation at the Salton Sea. An electric winch is used to lower and retract the PI-SWERL unit from the test location.

The PI-SWERL was operated at pre-set RPM intervals that were multiples of 400. Each RPM level was held for between 180 and 300 seconds (lower RPM setting held for shorter periods of time). The maximum RPM achieved during Salton Sea testing was 2800 at any site. However, in the large majority of cases, this would have resulted in PM10 concentrations far higher than the upper limit of the measurement range of the DustTrak (150 mg/m3) – the instrument used to measure PM10 in the PI-SWERL chamber - and the maximum RPM for most sites was substantially lower (usually 2000). During each measurement campaign, between 3 and 12 separate measurements were completed at each site. During the September 2005 tests, collocation measurements with the University of Guelph wind tunnel required a full day at each site. During those collocation measurements, it was possible to obtain comparatively more PI-SWERL measurements (8-12). For the remaining measurement campaigns, fewer measurements (~ 3-6) were completed at each site.

Figure 3-11 shows that a 1:1 relationship between the PI-SWERL and the University of Guelph tunnel fits the data reasonably well and that there would be little benefit in using the power fit of Equation 3-2 to calculate “wind tunnel” equivalent PM10

28

emission. Thus the PI-SWERL PM10 emissions reported in Chapter 4 are derived directly from Equation 3-1.

29

4 RESULTS Several different types of results were obtained from this study. First, a

qualitative description of the soil and crust properties for each site and each sampling season is presented (section 4.1). Second, quantitative measurements of crust strength by field sampling campaign are summarized in section 4.2. All measurements of bulk properties including soil texture, aggregate size distribution, bulk density, soil hydrologic properties, and soil chemical properties are presented in section 4.3. Finally, results from PI-SWERL measurement of wind erodibility with respect to PM10 dust emissions are given in section 4.4. Data from wind tunnel measurements are not discussed in this section, though those measurements are provided in Appendix A to this report.

4.1 Site descriptions

The following are brief descriptions that were completed at each site where wind erodibility measurements occurred. Testing occurred during three different field campaigns to represent seasonal variability in temperature and precipitation (Table 4-1): September 2005 (Test 1), January 2006 (Test 2), and March 2006 (Test 3). Collocated wind tunnel testing occurred only during Test 1. All other tests were performed with PI-SWERL. Elevations are given in feet above Salton Sea level (asl). Photographs of the soil surfaces from each site visit are presented in Appendix B of this report. Table 4-1. Western Regional Climate Center Data for Mecca 2 SE site.

Month Mean Monthly Temp Max (ºF)

Mean Monthy Temp Min (ºF)

Mean Monthly Precipitation (in)

January 69.9 38.3 0.57

March 80.2 42.8 0.25

September 102.2 68.2 0.29

A29: Windblown dust measurement: PI-SWERL only Elevation: ~3 ft asl Lat: 33.439917 , Lon: -115.8432 Site A29 is a barnacle beach immediately adjacent to the Sea. The soil texture is sand to loamy sand with 10% gravel in the form of rocks, barnacles, and fish bones. Test 1: The surface was smooth with no cracking and the top crust was 3-5 mm thick. The crust had no network and no relief (larger-scale bar and swale and beach ridge present). The crust was weak and dry. The soil had moderate carbonate and was very slightly saline. Test 2: The surface had a smooth crust up to 10 mm thick, sometimes hard to discern. The crust had no network, relief ranged from no relief to areas with up to 3 mm relief (barnacle protrusion), and was moderately hard, and dry. The soil had moderate carbonate content and was very slightly saline.

30

Test 3: No pits described. The surface looked similar to previous testing times. The soil had moderate carbonate content and was very slightly saline. A31: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~25 ft asl Lat: 33.36175 , Lon: -115.66695 Site A31 is similar to A101 on paleo-lake sediment. The texture of the soil is silty clay to silty clay loam and contains no gravel. Test 1: The surface was smooth with some cracking and the top crust was 2-3 mm thick. The crust had no network, had relief of 1-2 mm, ranged from weak to hard, and was dry. The soil had high carbonate content and was non saline to slightly saline. Test 2: The surface had a smooth crust 2 to 5mm thick with some thin mud cracks. The crust had no network, had a relief of 1 mm, was moderately hard, and dry. The subsoil had a crumbly texture. The soil had high carbonate content and was non saline. Test 3: The surface had a smooth crust 1 to 2 mm thick. The crust had no network, had a relief of 1 mm, was weak to moderately hard, and dry. The subsoil was crumbly. In some places a gravel rich layer was found 4 cm below the surface. The soil had high carbonate content and was slightly saline. A32: Windblown dust measurement: PI-SWERL only Elevation: < 1 ft asl Lat: 33.27812 , Lon: -115.6022 Site A32 is a playa-like surface adjacent to the Sea. The soil texture is loam to silt loam with no gravel. Test 1: The surface had a weak to botryoidal crust 1 mm thick. The crust had no network with a relief of 3 mm. The crust was weak and dry. The soil had high carbonate and was strongly saline. Test 2: Site was too wet for testing. Soil was completely saturated. Areas of standing water nearby. No sample was collected. Test 3: Site was too wet for testing. Soil was completely saturated. Areas of standing water nearby. Soil had high carbonate content and was strongly saline. A34-1: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~4 ft asl Lat: 33.347933 , Lon: -115.966467 Site A34-1 is a salt crusted playa/beach surface. The soil texture ranged from loamy sand to a sandy loam with an approximately 10% gravel component. Test 1: The surface had an irregular crust 2.5 to 7.5 mm thick. The crust had no network, had a relief of approximately 10 mm, was moderately hard, and slightly moist. The soil had low carbonate content and was slightly saline. Test 2: The site was strongly disturbed but testing was done on the least disturbed portions. The surface had a patchy crust. Non-crusted surfaces contained a gravel lag. Where crusted, it was smooth, 3 to 5 mm thick. The crust had no network, had a relief of

31

approximately 1 to 2 mm, was weak, and dry. The soil had low carbonate content and was very slightly saline. Test 3: The surface had a botryoidal to smooth continuous crust 2 mm thick. The crust had no network, had a relief of 2 to 3 mm (protruding gravel), was weak to moderately hard, and dry. The soil had low carbonate content and was strongly saline. A34-2: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~4 ft asl Lat: 33.347617 , Lon: -115.96625 Site A34-2 is near A34-1 and is a salt crusted playa/beach surface. The soil texture is loamy sand to sandy loam with little to no gravel. Test 1: The surface had a botryoidal crust 2 mm thick. The crust had no network, had a relief of 2.5 to 10 mm, was weak, and slightly moist. The soil had low carbonate content but was strongly saline. Test 2: The surface had a botryoidal crust 1 to 2 mm thick. Loose, wind blown sand covered the crust in patches. The crust had no network, had irregular relief of 1mm to 50 mm, was weak to moderately hard, and dry. Moist sand was found immediately underneath the crust. The soil had low carbonate content and was strongly saline. Test 3: The surface had a botryoidal crust 1 to 3 mm thick. The crust had no network, had relief of 2 to 3 mm, was weak to hard, and dry. Dry unconsolidated sand 1 cm thick was found under the crust, followed by moist sand. The soil had low carbonate content and was strongly saline. A34-3: Windblown dust measurement: PI-SWERL only Elevation: 0.5 ft asl Lat: 33.34838, Lon: -115.96549 Site A34-3 is along the shore near sites A34-1 and A34-2 and is playa-like. The soil texture is silt loam with no gravel. Testing at this site began in January 2006. Test 2: The surface had a smooth crust 1 to 2 mm thick. There was no network, the crust had 3 to 5 mm relief (barnacles and wind streaks), was loose to weak, and dry. The spongy crust was a low density green silt loam about 2 to 6 cm thick overlying barnacles and mud. The soil had very high carbonate content and was strongly saline. Test 3: The surface had a smooth to irregular to botryoidal crust in places 2 to 3 mm thick and up to 20 mm thick. There was no network and the crust relief was 3 to 5 mm. The crust was weak and dry. More of the area was wet during Test 3 compared to Test 2. Test 3 locations exhibited a thinner veneer of green fluff over barnacles than Test 2 locations. The soil had very high carbonate content and was strongly saline. A100-1: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~8 ft asl Lat: 33.35265 , Lon: -115.97265 Site A100-1 is located on a sandy, dry wash. The surface contains wind ripples, mud cracks, or gravel lag. The soil texture is sand and contained about 16% gravel.

32

Test 1: The surface had an irregular crust 5 mm thick. The crust had no network, had relief of approximately 1 to 3 mm, was weak and slightly moist. The soil had low carbonate and was non saline. Test 2: The surface had an irregular crust of variable thickness, 1 to 10 mm thick. The crust had no network, had a relief of 10 to 20 mm, was weak to moderately hard, and dry. The soil had moderate carbonate content and was non saline. Test 3: The surface had a smooth crust 2 to 10 mm thick. The crust had no network, had a relief of 2 to 3 mm (protruding gravel), was weak to moderately hard, and dry. Sediment was moist 5 cm below surface. The soil had moderate carbonate content and was non saline. A100-2: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~8 ft asl Lat: 33.351583 , Lon: -115.970683 A100-2 is a playa/beach site near A100-1 but has a salt crust and is similar to A34-1. The soil texture is loamy sand to sandy loam with no gravel. Test 1: The surface had a weak botryoidal crust 5 mm thick. The crust had no network, had a relief of 5 mm, was moderately hard, and dry. The soil had low carbonate content but was strongly saline. Test 2: The site was disturbed but crusted. Testing occurred on the least disturbed surfaces. The surface had a weak botryoidal to botryoidal crust 1 to 2 mm thick. The crust had no network, had a relief of 10 mm, was moderately hard and dry. Slightly moist unconsolidated sand (3 cm thick) was found immediately underneath the crust. Moist brown mud was present below the sand. The soil had low carbonate content and was strongly saline. Test 3: Surface was not as disturbed as during test 2. The surface had a weak botryoidal crust 2 mm thick. The crust had no network, had a relief of 2 mm, was moderately hard, and dry. The sediment was moist immediately underneath the crust. The soil had low carbonate content and was strongly saline. A101-1: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~25 ft asl Lat: 33.350183 , Lon: -115.655533 A101-1 is a plaeo-lake site near SS16 but further from the Sea. The area appears to be older lake sediments and contain small (1-3 mm) snail shells. The texture of the soil is silty clay to silty clay loam with little to no gravel. Test 1: The surface was smooth with some cracking and the top crust was 2-7 mm thick. The crust had no network, had relief of 2-3 mm, was hard and dry. The soil had high carbonate content and was very slightly saline. Test 2: The surface had a smooth to irregular crust 2 to 5 mm thick with hairline mud cracks. The crust had no network, had a relief of 1 to 5 mm, was moderately hard to hard, and dry. The soil below the surface was crumbly. The soil had high carbonate content and was very slightly saline.

33

Test 3: The surface had a smooth crust 1 to 3 mm thick with better defined mud cracks. The crust had no network, had a relief of 1 to 3 mm, was moderately hard to hard, and dry. The soil below the surface was crumbly. The soil had high carbonate content and was slightly saline. A200: Windblown dust measurement: PI-SWERL only Elevation: 0.5 ft asl Lat: 33.2004, Lon: -115.59705 Site A200 is a flat beach area (former boat launch) and is playa-like with salt crusts next to the Sea. The soil texture is a silty clay loam with no gravel. Testing at this site began during January 2006. Test 2: The surface had an irregular crust 1 to 2 mm thick. There was no network, crust relief was 3 to 5 mm, was weak and dry. The crust was composed of barnacles, salt, and sand particles. Sand 2.5 cm thick was found below the crust, and moist brown mud was found under the sand. The soil had high carbonate content and was strongly saline. Test 3: Site was too wet for testing. Soil was completely saturated. Wet irregular salt crust covered the surface in patches. The soil had high carbonate content and was strongly saline. A201: Windblown dust measurement: PI-SWERL only Elevation: 0.5 ft asl Lat: 33.11743, Lon: -115.69131 Site 201 is a flat beach area and is playa-like with salt crusts along the New River Delta. Access to the site was by ATV only. The soil texture is silt loam with no gravel. Testing at this site began during January 2006. Test 2: The surface had an irregular crust 1 mm thick. There was no network, crust relief was 3 to 5 mm, was weak and dry. The crust was composed mostly of salt. Dry to slightly moist sand and salt grains were found 0.5 cm thick below the crust, followed by a moist brown silty clay. Area may have been inundated with water recently by wind generated surges. The soil had moderate carbonate content and was strongly saline. Test 3: Site was too wet for testing. Soil was completely saturated. Wet irregular salt crust still existed. The soil had high carbonate content and was strongly saline. SS2: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~10 ft asl Lat: 33.323117 , Lon: -115.9457 Site SS2 is located within a dry wash that drains into the Sea. The texture of the soil is sand with about 13% gravel. Test 1: The surface had a loose gravel lag over a crust that was 2.5 mm thick. The gravel lag had a relief of 2-3 mm. There were multiple sub-layers of crust 1 to 2.5 mm thick that were weak and dry. The soil had low carbonate and was non saline. Test 2: The surface had a smooth crust 2 to 3 mm thick. The crust had no network, had a relief of 3 to 5 mm (protruding gravel), was weak, and dry. Loose sand and mud cracks

34

could be found in patches on the surface. A subcrust, where found, was 2 to 3 mm thick. The sediment was moist 5 to 10 cm below the surface. The soil had low carbonate content and was very slightly saline. Test 3: The surface had a smooth crust 3 to 8 mm thick. The crust had no network, had a relief of 2 to 3 mm (protruding gravel), was weak to moderately hard, and dry. The soil had low carbonate content and was non saline. SS6: Windblown dust measurement: PI-SWERL only Elevation: ~100 ft asl Lat: 33.18045 , Lon: -115.852433 Site SS6 is a sand dune and interdune area. The interdune surface is deflated to underlying fluvial or lacustrine sediment. The soil texture is sandy loam to loam with 4% gravel. Test 1: This sediment had a smooth surface, no cracks, and a crust that was 1-2 mm thick. The crust had no network with a relief of 1-4 mm, was loose to weak and dry. The soil had low carbonate and was non saline. The sand dunes were composed of unconsolidated sand with no crust. Test 2: The surface had a smooth crust 1 to 3 mm thick. The crust had no network, had a relief of 1 to 2 mm (gravel), was loose to weak, and dry. The surface had a gravel lag over dry dune sand. The soil had low carbonate content and was very slightly saline. Test 3: The surface had a smooth crust 1 to 2 mm thick. The crust had no network, had a relief of 0 to 5 mm (gravel), was weak, and dry. The surface had a gravel lag over dry dune sand. The soil had low carbonate content and was non saline. SS9: Windblown dust measurement: PI-SWERL only Elevation: ~3 ft asl Lat: 33.089483 , Lon: -115.7095 Site SS9 is a barnacle beach ridge adjacent to the Sea. The soil texture is loamy sand to sandy loam to loam with >30% gravel in the form of barnacles and fish bones. Test 1: The surface had a smooth to irregular crust (rough due to presence of barnacles) 3 mm thick. The crust had no network with a relief of 3-5 mm (barnacles). The crust was weak and dry. The soil had very high carbonate content and was slightly saline. Test 2: The surface had a smooth to irregular crust 3 mm thick. The crust had no network with a relief of 3 to 5 mm, was weak, and dry. The soil had very high carbonate content and was moderately saline. Test 3: The surface had a smooth to irregular crust 5 to 10 mm thick. The crust had no network with a relief of 5 mm (protruding barnacles), was moderately hard, and dry. The soil had very high carbonate content and was strongly saline. SS16: Windblown dust measurement: PI-SWERL and Wind Tunnel Elevation: ~3 ft asl Lat: 33.341 , Lon: -115.667967

35

Site SS16 is a salt crusted playa-like surface near the Sea. The texture of the soil is clay with no gravel. Test 1: The surface had a botryoidal crust that ranges from 2 to 15 mm thick. The crust had no network, had relief of up to 40 mm, was extremely hard to rigid, and dry to moist. Beneath the crust was very moist mud. The soil had a moderate carbonate content and was strongly saline. Test 2: The surface had a hummocky to botryoidal crust 3 to 4mm thick. The crust had no network, had a relief of 5 to 10 cm, was moderately hard, and dry. Dry soil 2 cm thick was found under the crust, then moist mud. The soil had low carbonate content and was strongly saline. Test 3: The surface had a botryoidal crust 3 to 30 mm thick. The crust had no network, had a relief of up to 5 cm, was weak to rigid, and dry. Dry soil 2 cm thick was found under the crust followed by moist brown, black, and green clay. The soil had moderate carbonate content and was strongly saline. SS17: Windblown dust measurement: PI-SWERL only Elevation: ~3 ft asl Lat: 33.354167 , Lon: -115.721067 Site SS17 is a playa-like surface near the Sea. The soil texture is silty clay with no gravel. Test 1: The surface was smooth with mud cracks and the top crust was 2-5 mm thick. The crust had no network with relief of 3-5 mm. The crust was moderately hard to hard and was dry, although some parts of the surface were moist. The soil had moderate carbonate content and was moderately saline. Test 2: The surface had a weak botryoidal to botryoidal crust 1 to 3 mm thick. The crust had no network, had relief of 3 to 5mm, was weak, and dry. The crust appeared to be broken up by natural processes and was spongy and fluffy. There was 1 cm of dry crust and granular soil over moist mud. The soil had high carbonate content and was strongly saline. Test 3: Site was too wet for testing. Surface was wet with salt precipitating in patches. The soil had moderate carbonate content and was strongly saline. SS23: Windblown dust measurement: None Elevation: ~3 ft asl Lat: 33.4974 , Lon: -116.079817 Site SS23 is a playa-like surface adjacent to the Sea. The soil texture is silt loam with no gravel. No PI-SWERL tests were performed at this site, but it was monitored with each visit. Test 1: The surface ws networked to smooth. The smooth surface had mud cracks. The crust thickness was 3-7 mm thick. Networked crust was fine to medium scale with relief of 30-40 mm. The crust was hard to very hard and moist to slightly moist. The soil had high carbonate content and was strongly saline. Test 2: The surface was heavily disturbed, but salt crusted. The area had been driven on extensively since the last visit. Beach area next to the water was a fluffy green silt-clay

36

with a very low density. Large (several cm) mud cracks were formed in the green sediment. The soil had very high carbonate content and was strongly saline. Test 3: The surface was similar to the last visit. Beach area next to the water was not as fluffy, and the sediment was moist. The soil had low carbonate content and was strongly saline.

4.2 Crust strength

The proctor spring penetrometer was used to estimate surface crust strength while a cone penetrometer was used to measure soil strength at depth. Spring penetrometer measurements were recorded at the point when the surface crust was broken under the pressure of the penetrometer. The hardest crusts were typically those with finer textures and higher salt content, although this was not always the case. Fifteen measurements were taken at each site. The measurements were averaged and are shown in Figure 4-1. In almost every case, crust strengths were lowest during the January sampling period. Consistent with field observations that crusts during this time were less stable than during the other two sampling periods, this was especially true for “playa-like” and “barnacle beach” sites. Sites with weaker crusts in January also had higher salt contents for the most part. Cooler temperatures and higher air and subsurface moisture may have contributed to the weaker crusts. Two sites (A100-1 and SS2-1) located in dry wash beds showed progressively stronger crusts with each sampling period. This progressive stability is likely due to the fact that water flow in these channels had not occurred since prior to testing. These soils were predominantly sandy with low clay and salt contents and were high above the influence of fluctuating Sea levels. Therefore, changes in temperature or humidity did not likely influence the soil stability at those two sites. Occasional rain events between September and March may have helped crust compaction through raindrop impact, and might help explain the progressive hardening of the crust through time.

The cone penetrometer did not allow adequate measurement of surface crust strength since the first measurement interval is 15 mm and most of the surface crusts at the Salton Sea were substantially thinner. However, the cone penetrometer did provide a measure of the strength of soil below the surface. Cone penetrometer data were obtained at 14 sites, with fifteen individual measurements performed at each site. The average soil strength with depth for each of the sites is shown in Figure 4-2. In many cases, the subsurface was more resistant to the cone penetrometer in January compared to other months. This is contradictory to the spring penetrometer measurement of surface crust strengths and may suggest that weakening of soil strength in January may be limited to the surface crusts only.

The ball drop method was also employed during the January field measurement period only. Five tests were conducted at each site (Table 4-2). Softer crusts in January resulted in mostly failed tests.

37

-200-100

0100200300400500600700800

A10

1-1

A31

-1A

100-

2A

200-

1A

201-

1A

32-1

A34

-1A

34-2

A34

-3SS

16-1

SS17

-1A

29-1

SS9-

1A

100-

1SS

2-1

SS6-

1

Site

Cru

st S

tren

gth

Inde

x (P

SI)

Sep-05 Jan-06 Mar-06

No DataWet Crust

Paleo Lake

Playa-Like Barnacle Beach

Dry Wash

Inte

r-dun

e

Figure 4-1. Spring penetrometer data by season. A column extending below zero indicates that the site was wet or that data were not collected at that site during the specified season. Vertical error bars represent standard error.

38

A29

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000

Crust strength (kPa)D

epth

(mm

)

JanMarch

A31

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000Crust strength (kPa)

Dept

h (m

m)

JanMarch

a. A29-1 b. A31-1

A34-1

0

100

200

300

400

500

600

0 1,000 2,000 3,000 4,000 5,000

Crust strength (kPa)

Dep

th (m

m)

SeptJanMarch

A34-2

0

100

200

300

400

500

600

0 1,000 2,000 3,000 4,000 5,000

Crust Strength (kPa)

Dep

th (m

m)

SeptJanMarch

c. A34-1 d. A34-2

A34-3

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000

Crust strength (kPa)

Dept

h (m

m)

JanMarch

A100-1

0

100

200

300

400

500

600

0 1,000 2,000 3,000 4,000 5,000

Crust Strength (kPa)

Dep

th (m

m)

SeptJanMarch

e. A34-3 f. A100-1 Figure 4-2. Soil strength at depth.

39

100-2

0

100

200

300

400

500

600

0 1,000 2,000 3,000 4,000 5,000

Crust Strength (kPa)

Dep

th (m

m)

SeptJanMarch

A101-1

0

100

200

300

400

500

600

0 1,000 2,000 3,000 4,000 5,000

Crust Strength (kPa)

Dep

th (m

m)

SeptJanMarch

g. A100-2 h. A101-1 A200

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000Crust strength (kPa)

Dep

th (m

m)

Jan

SS2

0

100

200

300

400

500

600

0 1,000 2,000 3,000 4,000 5,000

Crust strength (kPa)

Dep

th (m

m)

i. A200-1 j. SS2-1

SS6

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000

Crust strength (kPa)

Dep

th (m

m)

JanMarch

SS9

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000Crust strength (kPa)

Dept

h (m

m)

JanMarch

k. SS6-1 l. SS9-1

Figure 4-2. Soil strength at depth.(Cont.)

40

SS16

0

100

200

300

400

500

600

0 1,000 2,000 3,000 4,000 5,000

Crust Strength (kPa)D

epth

(mm

) SeptJanMarch

SS17

0

100

200

300

400

500

600

0 1000 2000 3000 4000 5000Crust strength (kPa)

Dep

th (m

m)

Jan

m. SS16-1 n. SS17-1

Figure 4-2. Soil strength at depth.(cont.)

Table 4-2. Ball Drop test results from January 2006 field campaign. The test is considered passing if the ball dropped from a height of 20 cm does not penetrate the surface crust.

Site Pass Fail SS2 0 5 SS6 2 3 SS9 5 0 SS16 3 2 SS17 1 4 A29 2 3 A31 3 2 A32 No Test A34-1 0 5 A34-2 0 5 A100-1 0 5 A100-2 1 4 A101 No Test SS23 No Test A34-3 0 5 A200 0 5 A201 0 5

4.3 Bulk analysis results

This section summarizes the results of bulk physical and chemical analysis of soil samples procured over the course of this study. Complete physical and chemical data from each site are tabulated in Appendix C.

4.3.1 Particle size distribution

4.3.1.1 Soil texture

Particle size distributions using laser and pipette methods were compared. Sixteen samples that have a wide range in texture (sand to clay) were compared using the two

41

methods. Overall, the results indicate that the two methods were comparable (Figure 4-3, Table 4-3). Sand fractions for both laser and pipette subsamples were determined using standard sieve methods. The correlation coefficients for various size fractions are shown in Table 4-3. A correlation of 1 indicates a perfect predictability of y if x is known. The greatest difference between the two methods can be seen for clay-sized particles, where the pipette method measured a higher percentage than the laser in many cases. However, most of the correlation coefficients are 0.9 and above, suggesting that data using the laser method are essentially comparable to the pipette method. Textural properties of each site are illustrated in Figure 4-4.

The sites tested at the Salton Sea display a wide range in textures from sand to clay, and multiple samples from the same site are relatively homogeneous in textural quality. Figure 4-5 shows the soil textures from sites used as part of this study overlaid on top of a more comprehensive dataset acquired by Agrarian Research (2003). In the latter study, several hundred “grab” samples were collected at regular intervals along the Salton Sea shoreline and at depths of 1.5 m, 3.0 m, and 4.6 m. Those grab samples were analyzed for barnacle content and size distribution in terms of sand, silt, and clay content. Figure 4-5 shows imperfect, but reasonable overlap between the textures encountered at sites that are part of this study and those reported for the grab samples at different depths. In general, sites from the present study exhibit a higher occurrence of sandy soils and silty clay soils and a lower occurrence of loam, clay loam, and clay soils compared to the grab samples. These differences may be partially attributed to variations in methodology in addition to actual differences in soil texture. The laser method (LPSA) used in the present study tends to overpredict silt and clay content compared to the hydrometer method used in the previous study (Agrarian Research, 2003), especially at low values of silt and clay content (< 20%).

y = 0.8495xR2 = 0.8347

y = 1.0418xR2 = 0.8469

0

10

20

30

40

50

60

70

80

90

0 20 40 60 80 100

Pipette (wt. %)

Lase

r (v

ol. %

)

SiltClayLinear (Silt)Linear (Clay)

Figure 4-3. Comparison of silt and clay using laser and pipette methods.

42

Table 4-3. Correlation table comparing laser particle size data to the standard pipette method.

1.0-

0.5

mm

%w

t0.

5-0.

25

mm

%w

t0.

2-0.

125

mm

%w

t

0.12

5-0.

0625

m

m %

wt

Co

Silt

%

wt

Fine

Silt

%

wt

Tota

l S

and

%w

tTo

tal S

ilt

%w

tC

lay

%w

tM

ean

D90

%D

10%

>500

um

250

um12

5 um

1.0-

0.5

mm

%w

t1

0.5-

0.25

mm

%w

t0.

597

10.

2-0.

125

mm

%w

t-0

.046

0.52

51

0.12

5-0.

0625

mm

%w

t-0

.115

0.37

30.

897

1C

o S

ilt %

wt

-0.3

09-0

.465

-0.5

39-0

.463

1Fi

ne S

ilt %

wt

-0.3

86-0

.726

-0.6

57-0

.609

0.13

51

Tota

l San

d %

wt

0.47

80.

832

0.83

90.

756

-0.6

19-0

.812

1To

tal S

ilt %

wt

-0.4

28-0

.706

-0.7

39-0

.655

0.90

40.

546

-0.8

741

Cla

y %

wt

-0.3

18-0

.611

-0.5

77-0

.536

-0.1

020.

808

-0.6

980.

262

1M

ean

0.91

00.

812

0.31

20.

172

-0.4

52-0

.596

0.74

7-0

.639

-0.5

411

D90

%0.

875

0.80

10.

287

0.21

7-0

.428

-0.6

060.

732

-0.6

23-0

.534

0.89

61

D10

%0.

685

0.44

30.

603

0.45

4-0

.415

-0.4

500.

620

-0.5

52-0

.406

0.66

80.

692

1>5

00 u

m0.

960

0.53

5-0

.169

-0.2

71-0

.239

-0.2

630.

345

-0.3

16-0

.219

0.87

50.

740

0.34

61

250

um0.

364

0.93

30.

731

0.53

3-0

.504

-0.7

250.

867

-0.7

39-0

.634

0.68

10.

645

0.59

00.

303

112

5 um

-0.0

200.

488

0.98

40.

907

-0.5

23-0

.641

0.83

4-0

.719

-0.5

980.

319

0.32

40.

697

-0.1

620.

703

162

.5 u

m-0

.176

0.30

50.

790

0.90

0-0

.210

-0.5

220.

637

-0.4

03-0

.670

0.12

00.

143

0.43

4-0

.299

0.47

80.

833

15 u

m-0

.333

-0.4

75-0

.441

-0.3

330.

926

0.20

7-0

.547

0.87

3-0

.198

-0.4

26-0

.440

-0.3

98-0

.261

-0.4

83-0

.406

3 um

-0.4

73-0

.831

-0.8

09-0

.692

0.50

80.

813

-0.9

660.

780

0.76

8-0

.751

-0.7

41-0

.669

-0.3

48-0

.881

-0.8

24To

tal S

and

%w

t0.

368

0.78

40.

878

0.77

3-0

.557

-0.7

690.

970

-0.8

03-0

.742

0.68

70.

640

0.67

90.

257

0.88

50.

892

Tota

l Silt

%w

t-0

.484

-0.7

90-0

.758

-0.6

250.

825

0.63

5-0

.916

0.97

20.

388

-0.7

13-0

.714

-0.6

51-0

.365

-0.8

27-0

.749

<3 u

m-0

.395

-0.6

96-0

.685

-0.6

240.

155

0.76

9-0

.825

0.46

30.

955

-0.6

50-0

.629

-0.5

37-0

.287

-0.7

38-0

.713

Pre-treated Mechanical Laser Diffraction

43

Figure 4-4. Soil textures from each site displayed on a ternary diagram.

Figure 4-5. Comparison of soil textures at sites where PI-SWERL tests were completed (blue) with grab samples from around the Salton Sea shoreline (yellow) and at a depth of 1.5 m (orange), 3.0 meters (red), and 4.6 m (maroon). Grab sample data were collected as part of an earlier study (Agrarian Research, 2003).

44

4.3.1.2 Aggregate size distribution

Loose surface soil aggregates were only observed at three sites (A200, A201, A100-2), on salty crusts, during January testing. The aggregates appeared to be composed of sand or smaller particles cemented by salts. All other sites and sampling times had stable crusts with no discernible loose aggregates on the surface though some sites did exhibit loose gravel or wind-blown sand on the surface. Two samples were collected from A201, and one sample each from A200 and A100-2.

Mean aggregate sizes are represented as CS (coarse sand, mean 690 µm), MS (medium sand, mean 520 µm), FS (fine sand, mean 210 µm), and FFS (very fine sand, mean 125 µm; Chatenet et al., 1996). A majority of the aggregates at the Salton Sea sites fall in the FS category. The finer-textured soils at A200 and A201 tended to have a larger proportion of FSS aggregates, compared to the coarser textured soil of A100-2, which had a larger proportion of coarser aggregates. This follows the observations made from soil texture and aggregate size (Chatenet et al., 1996). Aggregate diameters are estimated for all sites using Chatenet et al. (1996) and are presented in Table 4-4. Table 4-4. Aggregate diameters estimated from soil texture after Chatenet et al. (1996).

Site Major Aggregate diameter

Minor aggregate diameter

SS2 CS SS6 MS SS9 MS SS16 FS SS17 FSS A29 CS A31 FSS FS A32 FS

A34-1 MS A34-2 MS A100-1 CS A100-2 FS* or MS MS* A101 FSS SS23 FS A34-3 FS A200 FS* FSS* A201 FS* FSS* or MS*

CS= coarse sand, 690 um; MS = medium sand, 520 um; FS = fine sand, 210 um; FSS = very fine sand, 125 um. * measured in this study

4.3.2 Bulk density Measurements of bulk density (BD) for samples were in line with expected values

(Figure 4-6). Lower bulk densities for samples such as SS9 (barnacle) and SS16 (clay) were likely due to the large amount of air space in barnacle-rich samples, and the

45

potential for sample shrinkage during drying for clay-rich samples. The average coefficient of variation (COV), which provides an estimate of uncertainty and is equal to the standard deviation of replicate measurements divided by their average, was approximately 10%. At some sites, the COV was as high as 35% owing to the difficulty of sampling for bulk density when a salt crust is separated by air from the underlying soil. In most cases, differences in bulk density between the three field measurement campaigns were within the uncertainty of the measurement. Differences in BD at site SS16 may be related to BD measurements taken of the soil below the hard and irregular crust in September, whereas BD was measured from intact peds of crust using the wax method for January and March sampling. Large differences at A100-2 are more difficult to explain based on field or laboratory observations, and may simply reflect surface heterogeneity.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

A10

1-1

A31

-1

A10

0-2

A20

0-1

A20

1-1

A32

-1

A34

-1

A34

-2

A34

-3

SS16

-1

SS17

-1

SS23

-1

A29

-1

SS9-

1

A10

0-1

SS2-

1

SS6-

1 Site

Bulk

Den

sity

(kg/

l)

Sep-05 Jan-06 Mar-06

Paleo Lake

Playa-Like

Dry Wash

Bar

nacl

e B

each

Inte

r-Dun

e

Figure 4-6. Bulk density measurements compared over three field measurement periods. Vertical bars represent standard deviations among replicate samples collected at each site.

4.3.3 Soil Hydrologic Properties These parameters were estimated based on the average textural properties for each

site (Saxton et al., 1986; Rawls et al., 1982), and are summarized in Appendix C. Wilting point, field capacity, and saturated soil water content are presented in m3 water per m3 of soil, or mass fraction. Saturated hydraulic conductivity of the soil is reported as m/s. Air entry potential is reported as kPa. All of these properties depend upon soil texture. For example, finer textured soils have the ability to hold more water and therefore have higher wilting point and field capacity, but have lower saturated hydraulic conductivity.

4.3.4 Soil Chemical Properties A variety of soil chemical properties were measured. Properties such as organic

matter content and pH were consistent within site and no changes were noted with season. Soils had low organic matter contents except for site SS23 which contained

46

abundant fish parts. Soils were moderately alkaline with pH between about 8 and 9 at all sites during all seasons.

Soil chemistry varied from site to site (Figure 4-7) often by several orders of magnitude depending on the constituent. Variability among sites was significantly greater than variability from one season to the other at the same site. However, some differences in chemistry between different seasons at the same site were significant. Detailed discussion of the reasons for such differences is outside the scope of the present study, though we note that at least some of these differences may have been caused by spatial heterogeneity since the exact location of bulk samples may have changed from one sampling season to the next. Among P, Na, Ca, Mg, K, and SO4 the most abundant species were Na, Ca, and SO4 with concentration ranges of 50 – 11,000 ppm, 1,000 – 30,000 ppm, and 10 – 38,000 ppm, respectively. Concentrations of K, Mg, and P were generally lower with values in the ranges of 50 – 1,900 ppm, 50 – 9,000 ppm, and 4 – 160 ppm, respectively. With the exception of Ca, these species were present in approximately proportional quantities with some variation from one site to the next. Sites with the lowest concentrations were the wash and inter-dune sites and sites with the highest concentrations were the salt playas and barnacle beaches.

Calcium carbonate percentages were relatively stable from season to season (Figure 4-8). Soils with high carbonate values (>10%) typically contained barnacles (SS9, SS23, A200) or snail shells (A31, A101). The sites with snail shells were old lake deposits, and their higher carbonate contents could also be a function of their greater soil age. Two sites showed large changes between sampling times (SS23, A201). It is unknown if this change represents a real change in CaCO3 content or if it is only an artifact of spatial heterogeneity of soil samples.

Soluble salts measured by EC did exhibit some temporal trends (Figure 4-9). Three sites (A34-2, A200, A201) contained their highest salt content in January, when it was observed that these soils generated salt rich dust. The highest salt contents were found at sites adjacent to the Sea, where wicking and evaporation aided in their precipitation, or were contributed by sea spray or wave action. Wind likely aided in drying newly precipitated salts, which were then available for transport as dust. It was observed that during windy days, salt-rich fluff generated by the turbulence of the Sea blew or washed shoreward, covering soils and providing another source of salt. Sites with high salt contents tended to form salt crusts when dry. Sites further from the Sea tended to have low salt contents and crusts made of silt and clay. One exception to this rule was site A29, a beach site adjacent to the Sea which contained barnacles, low salts, and silt/clay crusts. Influx of fresh water and sediment via ephemeral washes at this site may have kept salt levels in these soils low.

47

1

10

100

1000

10000

100000

1000000

A10

1-1

A31

-1

A10

0-2

A20

0-1

A20

1-1

A32

-1

A34

-1

A34

-2

A34

-3

SS16

-1

SS17

-1

SS23

-1

SS9-

1

A29

-1

A10

0-1

SS2-

1

SS6-

1

Salt content (mg/kg) P (ppm) K (ppm)Mg (ppm) Ca (ppm) Na (ppm)SO4 (ppm)

a. September, 2005

1

10

100

1000

10000

100000

1000000

A10

1-1

A31

-1

A10

0-2

A20

0-1

A20

1-1

A32

-1

A34

-1

A34

-2

A34

-3

SS16

-1

SS17

-1

SS23

-1

A29

-1

SS9-

1

A10

0-1

SS2-

1

SS6-

1

b. January, 2006

1

10

100

1000

10000

100000

1000000

A10

1-1

A31

-1

A10

0-2

A20

0-1

A20

1-1

A32

-1

A34

-1

A34

-2

A34

-3

SS16

-1

SS17

-1

SS23

-1

A29

-1

SS9-

1

A10

0-1

SS2-

1

SS6-

1

c. March, 2006

Figure 4-7. Soil chemical species summary from bulk samples. Lines connecting dots are intended to facilitate viewing of data and are not intended to convey continuity or interpolation.

48

0

5

10

15

20

25

30

35

40

A10

1-1

A31

-1

A29

-1

A10

0-2

A20

0-1

A20

1-1

A32

-1

A34

-1 1

A34

-2

A34

-3

SS16

-1

SS17

-1

SS23

-1

SS9-

1

A10

0-1

SS2-

1

SS6-

1

Site

CaC

O3

(%)

Sep-05 Jan-06 Mar-06

Paleo Lake

Playa-Like

Dry Wash

Bar

nacl

e B

each

Inte

r-Dun

e

Figure 4-8. Soil calcium carbonate from soils compared over the three testing intervals.

0

50000

100000

150000

200000

250000

300000

350000

400000

A10

1-1

A31

-1

A10

0-2

A20

0-1

A20

1-1

A32

-1

A34

-1 1

A34

-2

A34

-3

SS

16-1

SS

17-1

SS

23-1

A29

-1

SS

9-1

A10

0-1

SS

2-1

SS

6-1

Site

Salt

(mg/

kg)

0

20

40

60

80

100

120

EC (m

mho

s/cm

)

Sep-05 Salt Jan-06 Salt Mar-06 Salt

Sep-05 EC Jan-06 EC Mar-06 EC

Paleo Lake

Dry Wash

Bar

nacl

e B

each

Playa-Like

Inte

r-Dun

e

Figure 4-9. Salt % (mg/kg) and EC compared over three sampling intervals.

4.4 PM10 emissions

This section summarizes the results from PI-SWERL wind erodibility measurements performed at the Salton Sea. Results are presented from several different perspectives. First, we summarize the PM10 emissions at different values of friction velocity u*. Aggregated values were achieved using geometric averaging (averaging of the logarithms of numbers). The geometric mean approach is useful for viewing the data

49

and trends from the study when those data vary by orders of magnitude in value. For estimating emissions from a site, it may be more appropriate to use standard arithmetic averaging; arithmetic averages are presented in Appendix A for completeness. Additionally, complete results from each individual measurement also appear in Appendix A. Second, we examine all PM10 emissions measured at all the sites at a specific value of u* (0.56 m/s) to provide a context for the measurements in terms of their relative magnitudes, both at individual sites and when grouped by landform. For these results, when averages are calculated, they are linear averages both on a per site basis and when data are aggregated by landform. Arithmetic averaging is more intuitive and is useful for quickly determining how important a specific value of PM10 emissions is in the context of all the data. Third, we provide a preliminary comparison between PM10 dust emissions measurements at the Salton Sea and earlier measurements at Owens Lake, California (Nickling et al., 2001)

Wind erodibility data are presented as PM10 emissions (milligrams of PM10 per square meter of surface per second – mg/m2s) at specific values of the friction velocity u*. The friction velocity is a measure of the amount of shear or frictional rubbing that the wind exerts on a surface. The friction velocity is related to ambient wind speed through the equation

0*

10 10ln4.0

1zu

u=

where u10 is the wind speed (m/s) at a height of 10 meters above the ground surface and z0 is the roughness height (m). In general, it is not possible to predict the often quite variable z0 a priori and an on-site wind profile measurement is required. Measurements of z0 were completed by the University of Guelph wind tunnel at 8 sites around the Salton Sea (A100-1, A100-2, A101-1, A31-1, A34-1, A34-2, SS16-1, and SS2-1) and those data are reported in Table 4-5. As indicated by the equation above, at a constant wind speed, the friction velocity increases with the roughness height. An approximate equivalence between friction velocity and 10-meter wind speed is shown in Table 4-6 using the minimum, maximum, and average roughness heights for all sites (z0 =4.3 x 10-7, 6.2 x 10-4, and 1.4 x 10-4 m, respectively).

Using the maximum roughness height for all sites from Table 4-5 (z0=6.2 x 10-4 meters) in conjunction with the maximum measured one-hour wind speed at the Salton Sea (30 miles per hour, 13.6 meters/second, CH2M Hill, 2006) provides a maximum reasonable value of average one-hour friction velocity for exposed soil surfaces at the Salton Sea of u* = 0.56 m/s. We have chosen this value of friction velocity for comparing emissions from all sites at the Salton Sea by landform. We note however that short-term sustained winds (e.g. 10 minutes) could result in substantially higher friction velocities (> 0.7 m/s) and that occasional wind gusts (5 – 30 seconds) may intermittently cause much higher equivalent friction velocities (> 0.9 m/s). Therefore, u*=0.56 m/s does not represent a strict upper limit on the reasonable range of friction velocities that can occur at the Salton Sea.

50

4.4.1 Data trends: Season and Salt content

Aggregated values of PI-SWERL PM10 emissions for all sites are segregated by field sampling season and shown in Figure 4-10. The individual points in the figure correspond to the geometric mean of PM10 emissions from all replicate tests at the specified friction velocity. Overall, the figure shows that PM10 emissions were generally lowest in September 2005, increased in January 2006, and returned or were returning to their September values in March 2006. This indicates that at the Salton Sea, there is a dependence of windblown PM10 emissions on season, with an apparent peak during cooler, wetter months. This observation is qualitatively consistent with anecdotal evidence from residents in the area as well as prior work completed at Owens Lake (St. Amand et al., 1987; Cochran et al., 1988; Gillette et al., 2001).

Table 4-5. Surface roughness heights as measured by University of Guelph wind tunnel.

Site Roughness height z0 (meters) A100-1 2.6E-05 5.0E-05 1.4E-04 3.4E-04 1.0E-04 Average 1.3E-04 geomean 9.1E-05 maximum 3.4E-04 minimum 2.6E-05 A100-2 3.6E-05 5.0E-04 4.4E-05 4.1E-04 5.8E-04 Average 3.1E-04 geomean 1.8E-04 maximum 5.8E-04 minimum 3.6E-05 A101-1 8.0E-07 6.5E-05 3.8E-06 8.5E-05 3.3E-05 2.0E-05 Average 3.4E-05 geomean 1.5E-05 maximum 8.5E-05 minimum 8.0E-07 A31-1 4.3E-07 1.1E-05 6.5E-07 1.7E-06 1.2E-05 1.2E-05

Average 6.2E-06 geomean 3.0E-06 maximum 1.2E-05 minimum 4.3E-07 A34-1 4.8E-04 1.3E-04 7.4E-05 2.4E-04 6.2E-05 2.3E-05

Average 1.7E-04 geomean 1.1E-04 maximum 4.8E-04 minimum 2.3E-05 A34-2 2.4E-04 6.2E-05 2.3E-05 1.3E-05 1.5E-04 3.5E-04

Average 1.4E-04 geomean 7.9E-05 maximum 3.5E-04 minimum 1.3E-05 SS16-1 4.1E-05 6.2E-04 1.9E-04 3.2E-04 Average 2.9E-04 geomean 2.0E-04 maximum 6.2E-04 minimum 4.1E-05 SS2-1 1.5E-04 4.3E-05 2.5E-05 3.0E-04 2.8E-04 7.6E-05

Average 1.5E-04 geomean 1.0E-04 maximum 3.0E-04 minimum 2.5E-05 All sites Average 1.4E-04 geomean 5.3E-05 maximum 6.2E-04 minimum 4.3E-07

Table 4-6. Approximate equivalent values between friction velocity u* and wind speed measured at 10 meters using minimum, maximum, and average roughness heights (z0) from wind tunnel measurements at the Salton Sea

Roughness height z0 (m) /u* (m/s) 0.21 0.31 0.4 0.48 0.56 0.7 0.81 0.9 0.96 Min m/s 8.9 13.1 17.0 20.4 23.7 29.7 34.3 38.2 40.7

4.30E-07 mph 19.9 29.3 37.8 45.4 53.0 66.2 76.6 85.1 90.8 Max m/s 5.1 7.5 9.7 11.6 13.6 17.0 19.6 21.8 23.3

6.20E-04 mph 11.3 16.7 21.6 25.9 30.2 37.8 43.8 48.6 51.9 Average m/s 5.9 8.7 11.2 13.4 15.6 19.6 22.6 25.1 26.8 1.40E-04 mph 13.1 19.3 24.9 29.9 34.9 43.6 50.5 56.1 59.8

51

In Figure 4-11 through Figure 4-13 sites have been separated by salt content and PM10 emissions have been plotted for each of the three sampling seasons. For the purposes of comparison, sites that were considered as having low salt content (salt by EC < 10,000 mg/kg, <3.1 mmhos/cm) included A100-1, A101-1, A29-1, A31-1, SS2-1, and SS6-1 whereas sites with relatively high salt content (salt by EC > 10,000 mg/kg, >3.1 mmhos/cm) included A100-2, A200-1, A201-1, A32-1, A34-1, A34-2, A34-3, SS16-1, SS17-1, and SS9-1. The open circles in the figures represent the geometric mean of PM10 emissions for all sites with similar salt content in a given field campaign, and where there were 4 or more data points at a given friction velocity. A true average of PM10 emissions across sites would be weighted by the representative area of each soil type along with other factors, but the present averaging scheme is illustrative for elucidating temporal patterns. The data corresponding to the open circles from each of the six combinations of salt content (high or low) and field season (September 2005, January 2006, or March 2006) are combined in Figure 4-14. Information is only shown for friction velocities where all six combinations have available data.

A number of interesting patterns are illustrated in Figure 4-14. First, it is clear that PM10 emissions are higher for both salt-rich and low salt soils in January compared to September and March. Second, the magnitude of PM10 emissions in January does not seem to differ greatly between salt-rich and low salt sites. Third, PM10 emissions for salt-rich sites are lower than those at low salt sites in both September and March. This indicates that salt crusts that form during warmer months tend to stabilize the soil, compared to locations where salt crusts do not form. It also suggests that the magnitude of the salt content as measured by EC does not seem to have an effect on the absolute values of PM10 emissions. We note however that this latter conclusion is somewhat tenuous because the geometric mean of PM10 emissions for low salt sites in January (See Figure 4-12) was driven largely by two sites (A100-1 and SS2-1) both of which were located in dry washes.

In examining the effect of crust strength (See Figure 4-1), in all cases where data are available for the three field sampling campaigns except for the wash sites A100-1 and SS2-1, crust strength was lowest in January. January also corresponded to the highest dust emissions for all sites (See Appendix A for site by site analysis of the three sampling campaigns). This tends to suggest – and agrees with intuition as well as prior work (Gillette et al., 1982; Gillette et al., 2001; Houser and Nickling, 2001; Rice and McKewan, 2001) – that crust strength is inversely correlated with dust emissions potential, except for the two wash sites (A100-1 and SS2-1) which were discussed earlier (Section 4.2).

Soil moisture is known to have an important role in windblown dust emissions. Generally, soils with higher water contents tend to be less emissive owing to the inter-granular cohesion offered by liquid water. It is interesting that the sites with some of the the highest PM10 dust emissions (A34-3 and A200-1 in January; A34-3 and A100-2 in March) exhibited rather large values of moisture content ranging from 0.10 – 0.35 g water/ g soil). This would seem to contradict the notion that wet surfaces do not emit dust. In fact, this result is an artifact of the procedure used to measure soil moisture content, where a bulk sample that includes the several top cm of soil are collected and

52

analyzed for moisture. For sites with high salt content, the surface crust is often quite dry while the underlying soil may be wet. Thus, while moisture in the soil tends to mitigate dust emissions, it should be emphasized that the moisture measured from bulk samples may not be representative of the conditions at the surface crust where emissions occur.

53

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PI-S

WE

RL

PM

10 E

miss

ions

(mg/

m2 s)

A100-1A100-2A101-1A200-1A201-1A29-1A31-1A32-1A34-1A34-2A34-3SS16-1SS17-1SS2-1SS6-1SS9-1

a. September 21, 2005 – September 30, 2005

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PI-S

WE

RL

PM

10 E

miss

ions

(mg/

m2 s)

A100-1A100-2A101-1A200-1A201-1A29-1A31-1A32-1A34-1A34-2A34-3SS16-1SS17-1SS2-1SS6-1SS9-1

b. January 24, 2006 – January 27, 2006

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PI-S

WE

RL

PM

10 E

miss

ions

(mg/

m2 s)

A100-1A100-2A101-1A200-1A201-1A29-1A31-1A32-1A34-1A34-2A34-3SS16-1SS17-1SS2-1SS6-1SS9-1

c. March 20, 2006 – March 23, 2006 Figure 4-10. Comparison of PI-SWERL PM10 emissions from all sites. a) fall 2005, b) winter 2006, and c) spring 2006. Individual data points represent geometric means of several replicate measurements

54

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PM10

Em

issi

ons

(mg/

m2s

)

A100-2

A200-1

A201-1

A32-1

A34-1

A34-2

A34-3

SS16-1

SS17-1

SS9-1

Geomean

a. September 2005, salt content greater than 10,000 mg/kg, EC > 4 mmhos/cm

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PM10

Em

issi

ons

(mg/

m2s

)

A100-1

A101-1

A29-1

A31-1

SS2-1

SS6-1

Geomean

a. September 2005, salt content less than 10,000 mg/kg, EC = 0-4 mmhos/cm Figure 4-11. Comparison of PM10 emissions as measured by PI-SWERL during the September 2005 field study. Data points correspond to geometric mean emissions at specific values of friction velocity. Open circles correspond to geometric mean of emissions for all sites when 4 or more data points from different sites were available at a specific friction velocity. Geometric means represented by open circles only include sites where data were available for all three sampling seasons.

55

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PM10

Em

issi

ons

(mg/

m2s

)

A100-2

A200-1

A201-1

A32-1

A34-1

A34-2

A34-3

SS16-1

SS17-1

SS9-1

Geomean

a. January 2006, salt content greater than 10,000 mg/kg, EC > 4 mmhos/cm

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PM10

Em

issi

ons

(mg/

m2s

)

A100-1

A101-1

A29-1

A31-1

SS2-1

SS6-1

Geomean

b. January 2006, salt content less than 10,000 mg/kg, EC = 0-4 mmhos/cm Figure 4-12. Comparison of PM10 emissions as measured by PI-SWERL during the January 2006 field study. Data points correspond to geometric mean emissions at specific values of friction velocity. Open circles correspond to geometric mean of emissions for all sites when 4 or more data points from different sites were available at a specific friction velocity. Geometric means represented by open circles only include sites where data were available for all three sampling seasons.

56

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PM10

Em

issi

ons

(mg/

m2s

)

A100-2

A200-1

A201-1

A32-1

A34-1

A34-2

A34-3

SS16-1

SS17-1

SS9-1

Geomean

a. March 2006, salt content greater than 10,000 mg/kg, EC > 4 mmhos/cm

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2

Friction velocity, u* (m/s)

PM10

Em

issi

ons

(mg/

m2s

)

A100-1

A101-1

A29-1

A31-1

SS2-1

SS6-1

Geoeman

b. March 2006, salt content less than 10,000 mg/kg, EC = 0-4 mmhos/cm Figure 4-13. Comparison of PM10 emissions as measured by PI-SWERL during the March 2006 field study. Data points correspond to geometric mean emissions at specific values of friction velocity. Open circles correspond to geometric mean of emissions for all sites when 4 or more data points from different sites were available at a specific friction velocity. Geometric means represented by open circles only include sites where data were available for all three sampling seasons.

57

0.001

0.01

0.1

1

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Friction Velocity u* (m/s)

PM10

Em

issio

ns (m

g/m

2s)

Sep-05 - SaltSep-05 - Low SaltJan-06 - SaltJan-06 - Low SaltMar-06 - SaltMar-06 - Low Salt

Figure 4-14. Comparison of PM10 geometric mean emissions from all field campaigns for sites with high (> 10,000 mg/kg, EC > 4 mmhos/cm) and low (< 10,000 mg/kg, EC = 0-4 mmhos/cm) salt content. Data shown correspond to friction velocities where geometric means were available for all combinations of field campaign and salt content.

4.4.2 Magnitudes of PM10 emissions by site and by landform

As mentioned earlier, examination of PM10 emissions at various sites on an absolute scale (linear scale using arithmetic averages) can provide insight into the relative importance of specific landforms in terms of overall emissions in an airshed. Figure 4-15 shows PM10 emissions measured at all locations (replicate measurements) at all the sites for each of the three sampling seasons at a friction velocity (u*) of 0.56 m/s including the standard deviations of replicate measurements. The value of 0.56 m/s for friction velocity was chosen to show these data because it represents a reasonable point of comparison corresponding to the highest one-hour wind speed (~30 miles/hour, 13.6 m/s) measured at the Salton sea (CH2M Hill, 2006). We have noted that wind speeds averaged over shorter durations will result in exceeding this value of friction velocity. The figure clearly shows that PM10 emissions at some sites are so high, that comparatively, emissions from other sites are nearly zero. In addition, the rather large variability in PM10 emissions over multiple measurements within the same site is conveyed by the magnitude of the standard deviation bar in the figure as compared to the value of the averages.

The coefficient of variation (COV) which is a measure of the variability within a dataset and is defined as the standard deviation divided by the average is shown in Figure 4-16 for each site and each season of sampling. The COV is not correlated with the number of replicate measurements performed or the magnitude of the emissions average and does not show any discernible trends in terms of the different landforms. At a number of sites where measurements were completed in September, the COV appears to

58

be elevated compared to other sites and compared to the same sites in January and March. The reason for this is unknown, though we note that for sites A100-2, A34-2, and SS16-1 – three of the sites with the highest COV values in September – the average emissions at u* = 0.56 m/s were quite low compared to other sites (See Figure 4-15). This suggests that these large values of COV for some of the September measurements would not have an impact on windblown dust emission estimates for the Salton Sea basin.

Similar data are shown in Figure 4-17 and average values are shown in Figure 4-18 by landform grouping rather than by individual site. Despite the large standard deviations associated with those data, the relatively large number of data points add confidence to the conclusion that the increases in PM10 emissions during the January tests were a result of the Playa-like and, to a lesser extent, the dry wash landforms. Figure 4-18 shows that on average, the highest PM10 emissions at u* = 0.56 m/s are at dry wash sites in January. Playa-like sites also exhibit substantially higher emissions in January compared to the other two months, though we note that some sites are not represented in all the averages for the three seasons. For example, site A201 is a significant contributor to the high average emissions in January. A201 was not tested in September and too wet to test in March. Thus it is difficult to determine from Figure 4-18 if all playa-like sites were more emissive in January than in March or September or if the high average emissions in January were driven by sites like A201. Figure 4-19 shows the same type of information as Figure 4-18 except that the averages and standard deviations are based only on those sites where data were available for all three sampling seasons. That is, the sites (A200-1, A201-1, A29-1, A32-1, A34-3, and SS17-1) are not included in the figure. Though there is a slight change in the average values of PM10 emissions when grouped by landform, Figure 4-19 exhibits the same temporal trends as when all sites are included in the calculation of the average.

59

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A101-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A101-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A31-1

0

1

2

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4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

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# of

tests

Averagecount

A31-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A100-2

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A100-2

Figure 4-15. PM10 emissions by season at u*=0.56 m/s . Left panel shows results from individual measurement locations at a site and right panel shows the average (circle), standard deviation (vertical bars), and the number of test locations (square – right axis) for the same site.

60

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A200-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A200-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A201-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A201-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A32-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A32-1

Figure 4-15 (cont.) PM10 emissions by season at u*=0.56 m/s . Left panel shows results from individual measurement locations at a site and right panel shows the average (circle), standard deviation (vertical bars), and the number of test locations (square – right axis) for the same site.

61

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A34-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A34-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A34-2

0

1

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4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

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# of

tests

Averagecount

A34-2

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A34-3

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A34-3

Figure 4-15 (cont.) PM10 emissions by season at u*=0.56 m/s . Left panel shows results from individual measurement locations at a site and right panel shows the average (circle), standard deviation (vertical bars), and the number of test locations (square – right axis) for the same site.

62

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

SS16-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

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20

25

# of

tests

Averagecount

SS16-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

SS17-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

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# of

tests

Averagecount

SS17-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A29-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

A29-1

Figure 4-15 (cont.) PM10 emissions by season at u*=0.56 m/s . Left panel shows results from individual measurement locations at a site and right panel shows the average (circle), standard deviation (vertical bars), and the number of test locations (square – right axis) for the same site.

63

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

SS9-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

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# of

tests

Averagecount

SS9-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

A100-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

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# of

tests

Averagecount

A100-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

SS2-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

SS2-1

Figure 4-15 (cont.) PM10 emissions by season at u*=0.56 m/s . Left panel shows results from individual measurement locations at a site and right panel shows the average (circle), standard deviation (vertical bars), and the number of test locations (square – right axis) for the same site.

64

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

SS6-1

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

5

10

15

20

25

# of

tests

Averagecount

SS6-1

Figure 4-15 (cont.) PM10 emissions by season at u*=0.56 m/s . Left panel shows results from individual measurement locations at a site and right panel shows the average (circle), standard deviation (vertical bars), and the number of test locations (square – right axis) for the same site.

-100%

-50%

0%

50%

100%

150%

200%

250%

300%

A10

1-1

A31

-1

A10

0-2

A20

0-1

A20

1-1

A32

-1

A34

-1

A34

-2

A34

-3

ss16

-1

SS17

-1

A29

-1

SS9-

1

A10

0-1

SS2-

1

SS6-

1

Site

CO

V (%

)

Sep-05 Jan-06 Mar-06

Paleo Lake

Playa-Like

Dry Wash

Bar

nacl

e B

each

Inte

r-Dun

e

Figure 4-16. Coefficient of variation (COV) of multiple replicate samples at the same site by site and season. The coefficient of variation is defined as the standard deviation of a sample divided by the average. Data are shown for u* = 0.56 m/s.

65

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

Paleo Lake Playa-Like Dry Wash Barnacle Beach

Figure 4-17. PI-SWERL PM10 emissions at u*=0.56 m/s for all test locations segeregated by season and landform.

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

10

20

30

40

50

60

# of

tests

Averagecount

Paleo Lake Playa-Like Barnacle BeachDry Wash

Figure 4-18. Average values of PI-SWERL PM10 emissions at u*=0.56 m/s (open circles), standard deviations of emissions (vertical bars), and number of tests represented in average (squares – right y-axis).

0

1

2

3

4

5

Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06 Sep - 05 Jan - 06 Mar - 06

PM 10

Emiss

ions

(mg/

m

2 s)

.

0

10

20

30

40

50

60

# of

tests

Averagecount

Paleo Lake Playa-Like Barnacle BeachDry Wash

Figure 4-19. Same as Figure 4-18 except averages and standard deviations include only sites where data are available for all three sampling seasons.

66

4.4.3 Comparison of PM10 emissions measurements at the Salton Sea and Owens Lake A study similar to this one was completed at Owens Lake, California in April and

May 2000 by Nickling et al. (2001). In the Owens Lake study, the University of Guelph portable field wind tunnel was used to estimate PM10 emissions from windblown dust at 13 sites in and around Owens Lake. Five of those sites (OL1 – OL5) were on the lakebed proper, where frequently wetted playa-like conditions existed. In the Nickling et al. (2001) report, a description of the site characteristics is given: “The lake sites were generally crusted with varying amounts of loose, coarse particles covering the surface. At all the sites the water table was relatively close to the surface (e.g. 30 cm depth at site [OL1]). Although the surface sediments were dry during testing, moisture content increased markedly with depth in the first 1-2 cm below the surface.” A very similar description can be applied to the “playa-like” surfaces that were tested with the PI-SWERL at the Salton Sea, especially during the January 2006 test conditions.

The data from the present study at the Salton Sea are compared side-by-side with those from the Owens Lake measurements in Figure 4-20. There are some important differences between the methodologies used in these two studies and what the data represent. We present a brief summary of those differences. In the Owens Lake study, the Guelph wind tunnel was used to measure PM10 emissions. At each location within a site, the wind tunnel was operated at a specific fan setting, the PM10 concentrations in the tunnel were measured at several heights, and the vertical velocity profile above the test surface was measured. After this measurement regime was stabilized, a hopper was used to artificially feed sand-sized grains into the tunnel test section. The introduction of sand into the tunnel was intended to simulate the dust emissions that would occur if the supply of saltating sand was unlimited. After the introduction of sand grains, the PM10 concentration and vertical wind speed profiles were measured again. Therefore, in Figure 4-20, there are two different sets of data for each of the Owens Lake sites. The first is the PM10 dust emissions measured initially – before sand was introduced into the tunnel - and the second is the emissions after sand was introduced into the tunnel. Each data point represents a single measurement at a single site.

For the PI-SWERL and Guelph wind tunnel measurements at the Salton Sea, sand was not artificially introduced into either device at any point during the measurement. The PI-SWERL data points shown in the figure represent arithmetic averages of several replicate measurements at each site. It is also worth noting that in addition to the inherent differences in the measurement methods, PM10 vertical dust flux emissions were calculated using different physical principles at Owens Lake than at the Salton Sea. At Owens Lake, classical mass transfer theory was used to estimate emissions based on the PM10 vertical concentration profiles. At Salton Sea, we simply used a mass balance approach. For the PI-SWERL, this involved summing the mass of measured PM10 that was being exhausted from an opening at the top of the chamber. For the wind tunnel, PM10 concentrations measured at different heights were multiplied by the volumetric flow rate at those heights and divided by the wind tunnel footprint area. Based on their extensive experience with wind tunnel data, the University of Guelph team postulated

67

that the emissions calculated using a mass balance approach (Salton Sea) are inherently higher than those calculated using mass transfer theory (Owens Lake).

Noting that the two measurements are not identically comparable, it is interesting to compare the PM10 dust emissions measured at the Salton Sea with the earlier measurements at Owens Lake. With respect to the wind tunnel measurements where sand was not artificially added to the test section, the September 2005 and March 2006 PI-SWERL measurements at the “playa-like” sites at the Salton Sea approximately span the same range as the measurements at Owens Lake. However, the relatively few wind tunnel measurements completed at the Salton Sea (triangles in Figure 4-20a) show lower emissions at moderate and high friction velocities (u* between 0.4 and 0.8 m/s) compared to wind tunnel measurements without the additions of sand at Owens Lake.

PI-SWERL measurements during the January 2006 field campaign appear to be somewhat higher than the Owens Lake data for measurements where sand was not introduced into the tunnel. Interestingly, though the values of friction velocity from the PI-SWERL tests at Salton Sea do not completely overlap those from Owens Lake, the PM10 emissions at Salton Sea in January appear to be only slightly lower than the Owens Lake data when sand was artificially introduced into the system (solid squares in Figure 4-20). Perhaps this is because in January, many of the crusts on “playa-like” surfaces at the Salton sea were friable and aggregates and sand that were bound into the crust during the September 2005 tests (and March 2006 to a lesser extent) were available for transport, resulting in an essentially unlimited reservoir over the duration of each PI-SWERL test (several minutes).

In summary, September wind tunnel measurements at Salton Sea were lower than wind tunnel measurements at Owens Lake when no sand was added to the tunnel and much lower than emissions at Owens Lake when sand was artificially introduced into the tunnel. PI-SWERL data for the same period showed comparable emissions between the Salton Sea and Owens Lake (no sand) and much lower emissions compared to Owens Lake when sand was added. PI-SWERL measurements at Salton Sea in January were significantly higher than emissions at Owens Lake (No Sand) and slightly lower than emissions at Owens Lake when sand was added to the wind tunnel. While this suggests that under some conditions, emissions at Salton Sea “playa-like” sites may be comparable to those at Owens Lake playas, the observation is mitigated somewhat by the fact that vertical dust fluxes (emissions) were calculated differently for the two locations. The mass balance approach used for Salton Sea is likely to yield higher emissions than the mass transfer approach used at Owens Lake. Thus, comparisons between the Salton Sea and Owens Lake should be undertaken with caution. With the data available and the caveat that measurement methods differed among the two locations, perhaps a reasonable, tentative conclusion is that PM10 windblown dust emissions at the “playa-like” sites at the Salton Sea are likely lower than those at Owens Lake but the potential for the Salton Sea to be as emissive as Owens Lake cannot be completely ruled out.

68

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4

u* (m/s)

PM 10

Em

issio

ns (m

g/m

2 s)

OL1 OL1-W/Feed

OL2 OL2-W/Feed

OL3 OL3-W/Feed

OL4 OL4-W/Feed

OL5 OL5-W/Feed

A100-2 A200-1

A201-1 A32-1

A34-1 A34-2

A34-3 SS16-1

SS17-1 SS_WT_A100-2

SS_WT_A34-1 SS_WT_A34-2

SS_WT_SS16-1

a. Salton Sea, September 2005 and Owens Lake April/May 2000 (Nickling et la, 2001)

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4

u* (m/s)

PM 10

Em

issio

ns (m

g/m

2 s)

OL1 OL1-W/Feed

OL2 OL2-W/Feed

OL3 OL3-W/Feed

OL4 OL4-W/Feed

OL5 OL5-W/Feed

A100-2 A200-1

A201-1 A32-1

A34-1 A34-2

A34-3 SS16-1

b. Salton Sea, January 2006 and Owens Lake April/May 2000 (Nickling et la, 2001)

0.0001

0.001

0.01

0.1

1

10

100

0 0.2 0.4 0.6 0.8 1 1.2 1.4

u* (m/s)

PM 10

Em

issio

ns (m

g/m

2 s)

OL1 OL1-W/Feed

OL2 OL2-W/Feed

OL3 OL3-W/Feed

OL4 OL4-W/Feed

OL5 OL5-W/Feed

A100-2 A200-1

A201-1 A32-1

A34-1 A34-2

A34-3 SS16-1

c. Salton Sea, March, 2006 and Owens Lake April/May 2000 (Nickling et la, 2001) Figure 4-20. Comparison of PI-SWERL emissions measured at the Salton Sea with wind tunnel emissions measured at Owens Lake (Nickling et al., 2001). For the wind tunnel, each data point represents a single measurement at a specific site. Solid squares indicate that an artificial sand feed was used. For the PI-SWERL data, each point represents an arithmetic average of several replicate measurements at a site. Guelph tunnel measurements at Salton Sea are shown as triangles in panel a.

69

5 DISCUSSION AND CONCLUSIONS Generally, playa surfaces that contain crusts with high salt contents tend to be

non-emissive, similar to clay-rich crusts because they form hard, cemented surfaces with high crust strengths (Kampf et al., 2005). It is generally true that soil surfaces that are disturbed, especially those with soft susceptible crusts, are more prone to emit windblown dust than if they were not disturbed (Gillette et al., 1982). However, certain soil surfaces are amenable to windblown dust emission even in the absence of disturbance. Ephemeral crusts, or those formed after rainfall, were studied at Owens Lake (Gillette et al., 2001). Soft crusts formed after winter rains form small cracks as they dry, generating aggregates that are small enough to be transported by the wind. Soft crusts were found to be significant producers of dust, along with areas composed of unbroken crusts that contained loose particles on the surface. Salts composed of sodium carbonate (NaHCO3) and sodium sulfate (Na2SO4) lose mineralogical structure and collapse, generating fluff , in response to changes in temperature and relative humidity (Cochran et al., 1988; Friedman et al., 1976; Saint-Amand et al., 1986). These types of crusts exist at Owens Lake and contribute to the dust problem there (Dahlgren et al., 1997). The continual removal of fluffed salts by wind erosion allows bare soil evaporation to maintain bicarbonate salt formation at Owens Lake (Cochran et al., 1988). This same process appears to be operating along exposed shores of the Salton Sea. A comprehensive study of salt mineralogy of crusts at the Salton Sea has not been completed. However, some useful information on the importance of salt crusts can be gleaned from the data collected as part of this study.

Sites where measurements were conducted as part of this study can be grouped in to two categories with respect to salt content: those that contained crusts composed of silt and/or clay particles, and those that contained crusts composed of salt. All salt crusted surfaces were located at sites adjacent to the Sea. Silt/clay crusts were located adjacent to the Sea in some cases and at higher elevations in other cases. Most salt-crusted surfaces had playa-like attributes in that they were frequently wetted, were fine-grained, and contained mud cracks or salt ridges when dessicated.

Our research plan allowed measurement of dust and soil properties three times throughout the year, to reflect potential seasonal changes in dust emissions. The data clearly show that dust emissions were higher in the winter compared to summer and spring at the Salton Sea. Eleven of the 16 sites where PI-SWERL measurements were completed exhibited their highest dust fluxes in January (A100-1, A31-1, A34-1, A34-2, SS17, SS2, SS6, SS9, A200, A201, A34-3). Of those, 4 had silt/clay crusts (low salt) and 7 had salt crusts. Four sites had their highest fluxes in September (A101, A29, A32, SS16; two silt/clay, two salt crust) and one site had its highest flux in March (A100-2; salt crust).

The higher dust fluxes in January can be attributed to crust strengths being lower at most sites during this time. At 8 sites over the three seasons, emission strengths correspond to crust strengths. Of the eight sites, 3 had silt/clay crusts (A101, A31, SS6) and 5 had salt crusts (A34-1, A34-2, SS17, SS9, A34-3). Two sites (A200, A201), where

70

testing occurred only once, showed high emissions with low crust strength as well. The six remaining sites (A100-1, A100-2, SS2-1, A29-1, A32-1, and SS16-1 - 3 silt/clay crust, 3 salt crust) show no apparent relation between crust strength and emissions.

Of the 10 sites with the overall highest emissions (A34-1, A200, A100-1, A31, SS17, SS2, A34-2, A34-3, SS6, A29), 5 had silt/clay crusts and 5 had salt crusts. For the most part, sites with silt/clay crusts showed the most consistent results from season to season with minor variation. Salt crusts showed the most variation, with the largest magnitude change occurring in January.

In some cases high values of measured soil moisture were associated with high values of PM10 dust emissions as measured by PI-SWERL. This was likely an artifact of the method used to collect samples for moisture analysis, where bulk soil material which includes the surface soil as well as material that is several centimeters below the surface was analyzed for moisture. For surfaces with a salt crust, it was observed that while the surface crust, the location where PM10 dust is emitted from, was dry, the soil underneath the surface was wet. Thus, moisture content analysis in those cases does not reflect surface conditions. It is important therefore not to draw conclusions regarding a soil’s potential to emit windblown PM10 based on bulk soil moisture measurements.

No clear correlation was evident between PM10 emission and soil texture. The highest emissions came from soils with textures ranging from sand to silty clay. Similarly, some of the lowest emissions came from sites with a similar spread in texture. This is additional supporting evidence that crusts are a major influence on dust production, perhaps far more important than soil texture in many cases.

High emissions from salt crusted surfaces, especially in January, were linked to weak surface crusts, and in some cases, loose soil structure immediately below the crust that served as reservoir for abundant dust-sized particles. Several surfaces clearly were visible sources of salt-rich dust. In January, sites A200, A201 had loose particles on top of salt crusts. The moist soil beneath combined with wicking and evaporation precipitated salt at the surface. Pulses of dust were observed to be emitted from these surfaces as salt continued to precipitate, dry and deflate. We witnessed puffs of dust coming off of a similar surface upwind of the barnacled site SS9 in March. These sites, immediately adjacent to the Sea and frequently wetted, are short-term but regenerated sources of dust that are potentially emissive from January through March.

Observations during the field campaigns at the Salton Sea suggested that surface salts may become available for deflation through other means as well. Foam generated on the surface of the Sea due to wave action and turbulence can be washed or blown ashore. This foam covers the beach sediment and barnacle berms and dries to a low density, green, organic-rich, salt-rich, fine-grained fluff several cm thick. Dust emissions were not directly observed from these surfaces, but wind streaks on some of the surfaces suggested that it had deflated recently. The PI-SWERL also confirmed that these surface could potentially be emissive. These surfaces would be considered short term sources of dust.

Sites were also grouped by four landform types. Those were “playa-like”, “paleo-lake”, “barnacle-beach”, and “dry wash”. Playa-like sites were considered to most closely resemble what the sediment in the Salton Sea would be like immediately after

71

water levels recede. When comparing PM10 emissions among these landform types at a common friction velocity of 0.56 m/s – a value chosen to represent conditions of highest one-hour winds at the Salton Sea and which is likely exceeded over shorter periods or during wind gusts– dry wash sites consistently exhibited the highest PM10 emissions during the three field sampling campaigns. However, average emissions from playa-like sites in January 2006 (0.9 mg/m2s) were comparable to those from dry wash sites (1.3 mg/m2s) owing to the formation of a friable crust at many of the playa-like sites in the cool, wet conditions of the season.

Our study suggested that while salt-crusted surfaces do indeed emit dust, they were not the single predominant source of dust around the margin of the Sea. January saw the decrease in crust strength in both silt/clay crusts with low salt components to salt-rich crusts. Therefore, higher PM10 dust emissions in January cannot be tied to the formation of salt fluff alone. Most types of crusts appeared to become weaker during this time as a function of temperature and humidity, not as a function of salt mineralogy or absolute salt content. It is worth emphasizing therefore, that at the Salton Sea, salt crusts appeared to be significant but temporary sources of dust, limited to cool, wet months, whereas silt/clay crusted sites (not only limited to playa-like environments) appeared to be significant sources of dust throughout the year.

Results from the PI-SWERL measurements at the Salton Sea were compared to earlier wind tunnel measurements conducted by Nickling et al. (2001) at Owens Lake, California. Several important differences in the methodology of these two studies and the interpretation of results were highlighted. Noting these differences, a tentative conclusion is that PM10 windblown dust emissions at the “playa-like” sites at the Salton Sea are likely lower than those at Owens Lake but the potential for the Salton Sea to be as emissive as Owens Lake cannot be completely ruled out.

Finally, it is important to place the results of this study in the context of the larger question of anticipated PM10 dust emissions as the Salton Sea shoreline recedes and to discuss the limitations that are inherent to the study. The response of a soil surface to shear stress by wind applied directly to that surface is only one of the factors that influences dust emissions from an area source like the Salton Sea. Additional factors that have a critical effect on the actual amount of PM10 windblown dust include the distribution of wind speeds and large-scale surface roughness. When present in sufficient amounts, large surface roughness elements - whether natural such as vegetation or artificial such as wind breaks - tend to reduce the amount of shear stress that is transferred from the atmospheric boundary layer to the soil surface. Thus, if everything else is held constant, then for a given friction velocity, the soil surface on a barren, roughness-free landscape will experience much more direct shearing by wind than a soil surface on a vegetated landscape. Vegetation is often absent from playa surfaces while it is present in other natural desert landscapes. Thus, the soil on a playa surface is more exposed to the shearing action of wind. In addition to the presence/absence of surface roughness elements, the frequency of occurrence of high winds is also an important factor in determining real-World windblown dust emissions. A soil surface that, according to wind tunnel or PI-SWERL measurements, is very prone to wind erosion may exist in a location where winds are rarely high and therefore, windblown emissions may not be a

72

concern. Likewise, a soil surface that is relatively stable under all but the highest shear stresses may be prone to windblown erosion owing to frequent high winds.

A major limitation of this study is that PM10 emissions at varying friction velocities were measured at locations around the Salton Sea shoreline. For obvious reasons, it was not possible to operate the PI-SWERL on the surfaces that are currently inundated but likely to be exposed in the future. As the Salton Sea recedes, rather complex physical and chemical processes will result in changes of soil texture, salt chemistry, and crust formation over time. It is unknown to what extent current measurements along the shoreline of the Salton Sea are applicable to areas that have yet to be exposed. Related to this point, as the lake level recedes and the landscape changes over time, there may be fresh, new sources of sand available for saltation on crusted surfaces. The measurements completed as part of this study indicate that for the sites along the shoreline, sand was either present in large amounts (such as at the wash and beach sites) or else almost completely absent (such as ta the silt/clay crusted sites). If silt/clay crusted playa surfaces become exposed to a supply of saltating sand that is currently not available but that will be as the water level recedes, then many of the conclusions of this study would no longer hold. Therefore, while this work has provided substantial insight into the seasonal behavior of several different types of landforms that can be found at the Salton Sea today, it is imperative that the Salton Sea be considered a dynamic system that is likely to undergo changes that cannot be fully anticipated by the results presented here.

5.1 Future projects planned

DRI is beginning the next phase of work that will attempt to identify playas or playa properties that are analogous to the Salton Sea shoreline. Data associated with the physical properties related to the emission potential of several playas in the region, including Silver Lake, Soda Lake, Owens Lake, Bristol Lake, Mono Lake, and Laguna Salada, among others, will be compiled. Scientific literature, anecdotal information and some field and lab data will be compiled for each playa. Comparisons to the Salton Sea will be made from this comprehensive assimilation of data and will shed light onto whether any playas or specific playa properties are analogous to the Salton Sea, and if these playas or properties can be used to estimate emission potential of the Sea.

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6 REFERENCES Agrarian Research (2003). Characterization of Shallow Sub-surface Sediments of the

Salton Sea. Prepared for The Salton Sea Science Office, La Quinta, California, USA. November 4, 2003. 76 pages.

Alfaro, S. C., Rajot, J. L. & Nickling, W. G. (2004) Estimation of PM20 emissions by wind erosion: main sources of uncertainties. Geomorphology, 59(1-4), 63-74.

Bagnold, R. A. (1941) The Physics of Blown Sand and Desert Dunes. Chapman and Hall, London, 265 pp.

Ben-Dor, E., and A. Banin. 1989. Determination of organic matter content in arid-zone soils using a simple “loss-on ignition” method. Commun. Soil Sci. Plant Anal. 20:1675-1695.

Bisal, F. & Hsieh, J. (1966) Influence of moisture on erodibility of soil by wind. Soil Science, 3, 143-146.

Borrmann, S., and R. Jaenicke (1987). Wind Tunnel Experiments on the Resuspension of Sub-micromemeter Particles from a Sand Surface. Atmospheric Environment 21: 1891 – 1898.

CH2M Hill (2005). Salton Sea Ecosystem Restoration Plan: Unified Executive Summary and Appended Final Air Quality Technical Memoranda Prepared to Support the Salton Sea Ecosystem Restoration Plan Programmatic Environmental Impact Report, February 2005. Report prepared by CH2M Hill for the California Department of Water Resources, Sacramento, CA, February, 2005.

CH2M Hill, (2006). DRAFT Programmatic Environmental Imapct Report.

Chatenet, B., Marticorena, B., Gomes, L., and Bergametti, G. 1996. Assessing microped size distributions of desert soils erodible by wind. Sedimentology 43: 901-911.

Chepil, W. S. (1956) Influence of moisture on erodibility of soil by wind. Soil Science Society Proceedings, 20, 288-292.

Cochran, G.F., T.M. Mihevc, S.W. Tyler, and T.J. Lopes. 1988. Study of salt crust formation mechanisms on Owens (dry) Lake, California Water Resources Publ. 41108, Desert Research Institute, Reno, NV. pp.

Crawley, D. & Nickling, W. G. (2003) Drag partition for regularly-arrayed rough surfaces. Boundary-Layer Meteorology, 107, 445-468.

Dahlgren, R.A., J.H. Richards, and Z. Yu. 1997. Soil and groundwater chemistry and vegetation distribution in a desert playa, Owens Lake, California. Arid Soil Research And Rehabilitation 11:221-244.

Dreimanis, A. 1962. Quantitative gasometric determinations of calcite and dolomite by using Chittick apparatus. J. Sediment. Petrol. 32: 520-529.

Duce, R.A. (1995). Sources, distributions, and fluxes of mineral aerosols and their relationship to climate, in Aerosol Forcing of Climate, edited by R.J. Charlson, and J. Heintzenberg, pp. 43-72, Wiley, 1995.

74

Friedman, I., G. Smith, and K.G. Hardcastle. 1976. Studies of Quaternary saline lakes: Isotopic and compositional changes during desiccation of the brines in Owens Lake, California, 1969-1971. Geochimica Et Cosmochimica Acta 40:501-511.

Garland, J. A. (1983) Some recent studies of the resuspension of deposited material from soil and grass. In: Precipitation Scavenging, Dry Deposition and Resuspension., Vol. 2, pp. 1087-1097. Elsevier,, Amsterdam.

Gee, G.W., and D. Or. 2002. Particle-size analysis, pp.255-293 in J.H. Dane and G.C. Topp (eds.), Methods of Soil Analysis: Part 4–Physical Methods, No. 5 Soil Sci. Soc. Am. Book Series. Soil Science Society of America, Madison, WI.

Gillette, D. A. (1977) Fine particulate emissions due to wind erosion. Transactions of the ASAE, 890-897.

Gillette, D. A. (1999) A qualitative geophysical explanation for "hot spot" dust emitting source regions. Contributions to Atmospheric Physics, 72(1), 67-77.

Gillette, D. A., Adams, J., Muhs, D. & Kihl, R. (1982) Threshold friction velocities and rupture moduli for crusted desert soils for the input of soil particles into the air. Journal of Geophysical Research, 87(C11), 9003-9015.

Gillette, D. A., T. C. Niemeyer, and P. J. Helm. 2001. Supply-limited horizontal sand drift at an ephemerally crusted, unvegetated saline playa. Journal of Geophysical Research, 106, (D16), 18,085-18,098.

Gillies, J. A. & Berkofsky, L. B. (2004) Eolian suspension above the saltation layer, the concentration profile. Journal of Sedimentary Research, 74(2), 176-183.

Gillies, J.A., N. Lancaster, W.G. Nickling, and D. Crawley (2000) Field determination of drag forces and shear stress partitioning effects for a desert shrub (Sarcobatus vermiculatus, Greasewood). Journal of Geophysical Research, Atmospheres 105 (D20), 24871-24880.

Gillies, J. A., Nickling, W. G. & McTainsh, G. (1996) Dust concentrations and particle-size characteristics of an intense dust haze event: Inland Delta Region, Mali, West Africa. Atmospheric Environment, 30(7), 1081-1090.

Houser, C. A. & Nickling, W. G. (2001) The emission and vertical flux of particulate matter <10 micrometers from a disturbed clay-crusted surface. Sedimentology, 48, 255-267.

Irwin, H.P. (1981) A simple Omnidirectional Sensor for Wind Tunnel Studies of Pedestrian-level Winds. Journal of Wind Engineering and Industrial Aerodynamics 7 (3): 219-239.

Kampf, S.K., S.W. Tyler, C.A. Ortiz, J.F. Munoz, and P.L. Adkins. 2005. Evaporation and land surface energy budget at the Salar de Atacama, Northern Chile. Journal of Hydrology 310:236-252.

Kuo, S. 1996. Phosphorous. In Methods of Soil Analysis Part 3: Chemical Methods. SSSA Book Series No. 5, p.869-920.

75

Logie, M. (1982) Influence of roughness elements and soil moisture on the resistance of sand to wind erosion. In: Aridic Soils and Geomorphic Processes, Catena Supplement 1 (Ed. by D. H. Yaalon), pp. 161-173. Catena Verlag GMBH, Ärmelgasse.

López, M. V., Sabre, M., Gracia, R., Arrúe, J. L. & Gomes, L. (1998) Tillage effects on soil surface conditions and dust emission by wind erosion in semiarid Aragon (NE Spain). Soil & Tillage Research, 45, 91-105.

Machette, M.N. 1986. Calcic soils of the southwestern United States. Geol. Soc. Am. Spec. Pap. 203: 1-21.

McDonald, E. and T. Caldwell. 2003. Soil evaluation in support of estimating carrying capacity and landscape degradation at the NTC- Fort Irwin, California. Final report for Charis Corporation. 76 p.

McKenna Neuman, C. & Nickling, W. G. (1989) A theoretical and wind tunnel investigation of the effect of capillary water on the entrainment of sediment by wind. Canadian Journal of Soil Science, 69(1), 79-96.

Monteiro, J.P., and Viegas, D.X. (1996). On the use of Preston Wall shear stress probes in turbulent incompressible flows with pressure gradients. Journal of Wind Wngineering and Industrial Aerodynamics 64 (1): 15-29.

Nickling, W. G. & Gillies, J. A. (1989) Emission of fine-grained particulates from desert soils. In: Paleoclimatology and Paleometeorology: Modern and Past Patterns of Global Atmospheric Transport, Vol. 282 (Ed. by M. Leinen and M. Sarnthein ), pp. 133-165. Kluwer Academic Publishers. Series C; Mathematical and Physical Sciences.

Nickling, W. G. & McKenna Neuman, C. (1995) Development of deflation lag surfaces. Sedimentology, 42(3), 403-414.

Nickling, W. G., McTainsh, G. H. & Leys, J. F. (1999) Dust emissions from the Channel Country of western Queensland, Australia. Zeitschrift für Geomorphologie, Supplementband 116, 1-17.

Nickling, W.G., C. Luttmer, D.M. Crawley, J.A. Gillies, and N. Lancaster (2001). Comparison of On- and Off-Lake PM10 Dust Emissions at Owens Lake, CA: Final Report. Report prepared by Wind Erosion Laboratory, University of Guelph, Ontario, Canada and Desert Research Institute, Reno, Nevada, USA. Report Prepared for Great Basin Unified Air Pollution Control District, Bishop, California, USA. February, 2001. 51 pages.

Owen, P. R. & Gillette, D. A. (1985) Wind tunnel constraint on saltation. In: The International Workshop on the Physics of Blown Sand, Vol. 8 (Ed. by O. E. Barndorff-Nielsen), pp. 253-269. University of Aarhus Press, Aarhus, Denmark.

Prandtl, L. (1935) Aerodynamic Theory Vol. III. Springer-Verlag, Berlin.

Priestly, C. B. (1959) Turbulent Transfer in the Lower Atmosphere. University of Chicago Press, Chicago.

76

Rawls, W.J., D.L. Brakensiek, and K.E. Saxton. 1982. Estimation of soil water properties. Trans. ASAE 25:1316-1320.

Rice, M. A., Mullins, C. E. & McEwan, I. K. (1997) An analysis of soil crust strength in relation to potential erosion by saltating particles. Earth Surface Processes and Landforms, 22(9), 859-884.

Rice, M. A., Willetts, B. B. & McEwan, I. K. (1996a) Observations of collisions of saltating grains with a granular bed from high-speed cine film. Sedimentology, 43(1), 21-32.

Rice, M.A., and I.K. McEwan. 2001. Crust strength: A wind tunnel study of the effect of impact by saltating particles on cohesive soil surfaces. Earth Surface Processes and Landforms 26:721-733.

Richards, L.A. (Ed.) (1954). Diagnosis and improvement of saline and alkali soils. Agricultural Handbook No. 60. U.S Department of Agriculture. (U.S. Government Printing Office: Washington, D.C.).

Saleh, A. & Fryrear, D. W. (1995) Threshold wind velocities of wet soils as affected by wind blown sand. Soil Science, 160, 304-309.

Saxton, K., Rawls, W., Romberger, J., and Papendick, R. 1986. Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal 50: 1031-1036.

Schichting, H. (1936). Experimentelle Untersuchungen zum Rauhigkeitsproblem. Ing.-Acrh., Bd. 7: 1 – 34.

Schlichting, H., and K. Gersten (2000). Boundary Layer Theory, 8th Edition, Springer-Verlag, Berlin, Germany, 2000. pp. 517 – 535.

Schroeder, RA, Orem, WH, and Kharaka, YK, (2002). Chemical evolution of the Salton Sea, California: nutrient and selenium dynamics. Hydrobiologia, v. 473, p.23-45.

Schülz, M., Y. Balanski, T. Claquin, W. Guelle, and F. Dulac (1997). Global simulation of the mineral aerosol distribution and its effects on radiation balance, Journal of Aerosol Science, 28 (Supplementum 1).

Shao, Y., Raupach, M. R. & Findlater, P. A. (1993) The effect of saltation bombardment on the entrainment of dust by wind. Journal of Geophysical Research, 98D, 12719-12726.

Soil Survey Division Staff. 1993. Soil Survey Manual. United States Department of Agriculture, 457 p.

Sokolik, I.N., and O.B. Toon (1996). Direct radiative forcing by anthropogenic airborne mineral aerosols, Nature, 381, 681-683, 1996.

St. Amand, P.A., L.A. Mathews, C. Gaines, and R. Reinking. 1986. Dust storms from Owens and Mono Valleys, California. Naval Weapons Center Tech. Publ. 6731, China Lake, CA,

77

St. Amand, P., C. Gaines, and D. St. Amand. (1987) Owens Lake, an ionic soap opera staged on a natric playa, in Centennial Field Guide- Cordilleran Section, M. L. Hills, (ed), Geological Society of America , Boulder, CO, p. 145-150.

Sumner, M.E., and W. P. Miller. 1996. Cation exchange capacity and exchange coefficients. In Methods of Soil Analysis Part 3: Chemical Methods. SSSA Book Series No. 5, p.1201-1230.

Tegen, I., and I. Fung (1995). Contributions to the atmospheric mineral aerosol load from land surface degradation, Journal of Geophysical Research, 100 (D9), 18707-18726.

Tegen, I., A.A. Lacis, and I. Fung (1996). The influence of climate forcing of mineral aerosols from disturbed soils, Nature, 380, 419-422.

White, B. R. & Mounla, H. (1991) Froude number effect on wind tunnel saltation. Acta Mechanica Supplementum, 1, 145-157.

Wu, H.Q., and Stathopoulos, T. (1994) Further Experiments in Irwins Surface Wind Sensor. Journal of Wind Engineering and Industrial Aerodynamics 53 (3): 441-452.

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A-1

APPENDIX A.

Summary of PI-SWERL Data from the Salton Sea: September

2005, January 2006, and March 2006.

The information presented on pages A-2 through A17 represents summaries of PI-SWERL and wind tunnel windblown dust emissions test for each site at the Salton Sea.

Each site is associated with tabular data that include wind tunnel measured emission fluxes for September 2005 and PI-SWERL measured emission fluxes for September

2005, January 2006, and March 2006. A figure appears below the table illustrating the dependence of geometric mean PM10 emissions on friction velocity segregated by field

sampling campaign and measurement method.

A-2

A100-1September, 2005 PI-SWERLu* (m/s) 2.19E-01 3.13E-01 4.01E-01 4.85E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 4.26E-03 1.01E-02 1.28E-02 1.66E-01 7.99E-01 2.33E+00Geometric Mean PM10 emissions (mg/m2s) 3.96E-03 8.79E-03 1.00E-02 1.15E-01 4.72E-01 2.32E+00

Guelph Tunnelu* (m/s) 2.75E-01 4.83E-01 5.66E-01 5.80E-01 6.39E-01PM10 emissions (mg/m2s) 3.41E-03 2.46E-02 1.07E-02 3.30E-03 9.57E-01

January, 2006 PI-SWERLu* (m/s) 2.19E-01 3.13E-01 4.01E-01 4.85E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 1.30E-02 2.64E-02 2.33E-01 4.15E-01 1.80E+00 1.62E+01Geometric Mean PM10 emissions (mg/m2s) 1.25E-02 2.51E-02 1.44E-01 3.49E-01 1.47E+00 1.62E+01

March, 2006 PI-SWERLu* (m/s) 2.19E-01 3.13E-01 4.01E-01 4.85E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 1.66E-03 1.98E-03 7.25E-03 1.89E-01 1.12E+00 3.76E+00Geometric Mean PM10 emissions (mg/m2s) 1.43E-03 1.97E-03 6.19E-03 1.80E-01 1.05E+00 3.76E+00

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

Em

issi

ons (

mg/

m2s

) PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A100-1

A-3

A100-2September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 1.16E-02 2.78E-02 5.36E-02 1.07E-01 1.32E-01 5.47E-01Geometric Mean PM10 emissions (mg/m2s) 5.24E-03 1.37E-02 2.74E-02 6.35E-02 9.46E-02 3.55E-01

Guelph Tunnelu* (m/s) 2.54E-01 2.94E-01 6.24E-01 6.41E-01 7.15E-01 7.61E-01PM10 emissions (mg/m2s) 1.81E-03 1.39E-02 1.30E-03 5.81E-03 2.10E-03 1.48E-03

January, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 4.01E-03 1.20E-02 6.40E-02 1.28E+00Geometric Mean PM10 emissions (mg/m2s) 3.93E-03 1.16E-02 6.05E-02 1.02E+00

March, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 1.28E-03 6.75E-02 3.42E-01 9.20E-01Geometric Mean PM10 emissions (mg/m2s) 1.20E-03 3.26E-02 2.02E-01 5.97E-01

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A100-2

A-4

A101-1September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 3.81E-03 8.19E-03 1.42E-02 2.11E-02 3.25E-02 1.14E-01Geometric Mean PM10 emissions (mg/m2s) 3.74E-03 8.10E-03 1.39E-02 2.08E-02 3.19E-02 8.13E-02

Guelph Tunnelu* (m/s) 2.18E-01 2.64E-01 3.64E-01 4.40E-01 4.82E-01 5.88E-01PM10 emissions (mg/m2s) 5.60E-03 3.52E-03 9.08E-03 5.74E-03 2.28E-02 1.94E-02

January, 2006 PI-SWERLu* (m/s) 5.63E-01 7.02E-01 8.15E-01Average PM10 emissions (mg/m2s) 3.29E-02 7.10E-02 1.66E-01Geometric Mean PM10 emissions (mg/m2s) 1.02E-02 5.08E-02 1.53E-01

March, 2006 PI-SWERLu* (m/s) 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 5.17E-03 9.45E-03 1.52E-02 3.38E-02 9.40E-02Geometric Mean PM10 emissions (mg/m2s) 4.93E-03 9.15E-03 1.35E-02 3.01E-02 8.39E-02

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A101-1

A-5

A200-1September, 2005 PI-SWERL Note: Site added during January 2006 field campaignu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERLu* (m/s) 2.19E-01 3.13E-01 4.01E-01 4.85E-01 5.63E-01Average PM10 emissions (mg/m2s) 1.03E-02 2.94E-02 2.39E-01 1.46E+00 2.21E+00Geometric Mean PM10 emissions (mg/m2s) 1.00E-02 2.90E-02 2.15E-01 1.09E+00 1.67E+00

March, 2006 PI-SWERL Note: Site inundated at time of testingu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A200-1

A-6

A201-1September, 2005 PI-SWERL Note: Site added during January 2006 field campaignu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 4.00E-03 9.70E-03 1.25E-01 1.01E+00Geometric Mean PM10 emissions (mg/m2s) 3.45E-03 9.50E-03 1.07E-01 8.17E-01

March, 2006 PI-SWERL Note: Site inundated at time of testingu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A201-1

A-7

A29-1September, 2005 PI-SWERLu* (m/s) 2.19E-01 3.13E-01 4.01E-01 4.85E-01 5.63E-01Average PM10 emissions (mg/m2s) 9.36E-04 1.87E-03 4.00E-03 6.20E-03 3.27E-01Geometric Mean PM10 emissions (mg/m2s) 8.52E-04 1.78E-03 3.77E-03 5.78E-03 9.14E-02

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 1.38E-03 1.05E-02 1.82E-01 1.75E+00Geometric Mean PM10 emissions (mg/m2s) 1.07E-03 4.30E-03 1.39E-01 1.43E+00

March, 2006 PI-SWERL Note: March data based on one measurement onlyu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01Average PM10 emissions (mg/m2s) 2.26E-03 5.92E-03 6.56E-02 4.57E-01Geometric Mean PM10 emissions (mg/m2s) 2.26E-03 5.92E-03 6.56E-02 4.57E-01

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A29-1

A-8

A31-1September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01Average PM10 emissions (mg/m2s) 1.24E-02 3.77E-02 1.33E-01 3.22E-01Geometric Mean PM10 emissions (mg/m2s) 1.00E-02 2.97E-02 8.52E-02 2.13E-01

Guelph Tunnelu* (m/s) 2.40E-01 2.46E-01 2.82E-01 3.88E-01 4.73E-01 6.22E-01PM10 emissions (mg/m2s) 1.31E-02 8.00E-03 4.42E-02 4.74E-02 5.19E-02 1.42E-01

January, 2006 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01Average PM10 emissions (mg/m2s) 1.41E-02 3.67E-02 1.49E-01 4.81E-01 1.39E+00Geometric Mean PM10 emissions (mg/m2s) 1.30E-02 3.39E-02 1.34E-01 4.36E-01 1.34E+00

March, 2006 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 2.02E-01 2.22E-01 2.62E-01 3.45E-01 4.67E-01 6.37E-01Geometric Mean PM10 emissions (mg/m2s) 3.37E-02 3.11E-02 6.00E-02 1.09E-01 1.88E-01 3.36E-01

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A31-1

A-9

A32-1September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01Average PM10 emissions (mg/m2s) 1.84E-03 8.52E-03 8.00E-02 3.21E-01 1.14E+00Geometric Mean PM10 emissions (mg/m2s) 1.39E-03 5.98E-03 3.39E-02 1.23E-01 4.03E-01

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERL Note: Site inundated at time of testingu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

March, 2006 PI-SWERL Note: Site inundated at time of testingu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A32-1

A-10

A34-1September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 1.89E-02 1.02E-01 4.52E-01 1.46E+00 5.76E-01 1.99E+00Geometric Mean PM10 emissions (mg/m2s) 1.18E-02 5.29E-02 1.54E-01 4.97E-01 5.13E-01 1.77E+00

Guelph Tunnelu* (m/s) 4.02E-01 4.56E-01 4.69E-01 5.50E-01 6.51E-01 6.64E-01PM10 emissions (mg/m2s) 4.92E-03 8.62E-03 5.19E-03 3.82E-03 7.65E-03 7.17E-02

January, 2006 PI-SWERLu* (m/s) 2.19E-01 3.13E-01 4.01E-01 4.85E-01 5.63E-01Average PM10 emissions (mg/m2s) 3.64E-03 7.21E-03 1.01E-01 5.72E-01 2.47E+00Geometric Mean PM10 emissions (mg/m2s) 3.53E-03 7.19E-03 8.84E-02 5.44E-01 2.46E+00

March, 2006 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 2.11E-02 5.57E-02 1.40E-01Geometric Mean PM10 emissions (mg/m2s) 8.37E-03 1.70E-02 6.58E-02

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A34-1

A-11

A34-2September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 1.80E-02 3.20E-02 5.95E-02 1.62E-01 3.51E-01 7.54E-01Geometric Mean PM10 emissions (mg/m2s) 3.21E-03 8.57E-03 2.25E-02 5.92E-02 1.09E-01 3.04E-01

Guelph Tunnelu* (m/s) 3.72E-01 3.91E-01 4.09E-01 4.97E-01 5.44E-01 8.52E-01PM10 emissions (mg/m2s) 4.54E-03 3.45E-03 5.98E-03 7.30E-03 2.62E-03 6.66E-03

January, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 1.34E-03 1.46E-02 4.27E-01 1.20E+00Geometric Mean PM10 emissions (mg/m2s) 1.17E-03 1.08E-02 3.59E-01 1.16E+00

March, 2006 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01Average PM10 emissions (mg/m2s) 2.28E-03 6.01E-03 1.42E-02 5.31E-02Geometric Mean PM10 emissions (mg/m2s) 2.28E-03 5.99E-03 1.40E-02 5.00E-02

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A34-2

A-12

A34-3September, 2005 PI-SWERL Note: Site added during January 2006 field campaignu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERLu* (m/s) 2.19E-01 3.13E-01 4.01E-01 4.85E-01 5.63E-01Average PM10 emissions (mg/m2s) 2.30E-03 9.58E-03 3.48E-02 8.29E-01 4.03E-01Geometric Mean PM10 emissions (mg/m2s) 2.20E-03 8.36E-03 2.81E-02 2.39E-01 3.98E-01

March, 2006 PI-SWERLu* (m/s) 0.4013576 0.562922 0.701505Average PM10 emissions (mg/m2s) 0.003649048 0.274085 2.820524Geometric Mean PM10 emissions (mg/m2s) 0.003449762 0.039978 0.152719

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

A34-3

A-13

SS16-1September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 4.49E-02 8.25E-02 1.28E-01 1.99E-01 3.08E-01 5.08E-01Geometric Mean PM10 emissions (mg/m2s) 6.12E-03 1.93E-02 4.20E-02 7.77E-02 1.39E-01 2.72E-01

Guelph Tunnelu* (m/s) 3.23E-01 3.98E-01 5.84E-01 7.68E-01PM10 emissions (mg/m2s) 2.47E-03 6.21E-04 1.51E-02 1.01E-02

January, 2006 PI-SWERLu* (m/s) 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 2.09E-02 2.21E-01 1.01E+00 4.68E-01 1.07E+00Geometric Mean PM10 emissions (mg/m2s) 1.59E-02 9.72E-02 3.46E-01 4.30E-01 9.94E-01

March, 2006 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 9.98E-04 3.65E-03 1.32E-02 2.40E-02 4.58E-02 7.74E-02Geometric Mean PM10 emissions (mg/m2s) 9.62E-04 3.49E-03 1.10E-02 2.02E-02 3.73E-02 6.35E-02

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

SS16-1

A-14

SS17-1September, 2005 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01 9.03E-01 9.61E-01Average PM10 emissions (mg/m2s) 2.99E-03 9.58E-03 2.76E-02 7.84E-02 7.21E-02 1.77E-01Geometric Mean PM10 emissions (mg/m2s) 2.96E-03 9.24E-03 2.26E-02 4.98E-02 6.25E-02 1.37E-01

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 8.69E-03 2.88E-02 1.70E-01 1.29E+00Geometric Mean PM10 emissions (mg/m2s) 7.71E-03 2.53E-02 1.50E-01 1.27E+00

March, 2006 PI-SWERL Note: Site inundated at time of testingu* (m/s)Average PM10 emissions (mg/m2s)Geometric Mean PM10 emissions (mg/m2s)

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

SS17-1

A-15

SS2-1September, 2005 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 2.43E-03 7.71E-03 5.09E-01 3.59E+00Geometric Mean PM10 emissions (mg/m2s) 2.20E-03 6.19E-03 3.37E-01 3.13E+00

Guelph Tunnelu* (m/s) 3.33E-01 3.70E-01 4.55E-01 5.48E-01 6.73E-01 6.80E-01PM10 emissions (mg/m2s) 1.65E-03 6.18E-03 5.77E-03 1.05E-02 7.40E-03 5.70E-01

January, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 1.97E-02 3.54E-02 8.66E-01 4.62E+00Geometric Mean PM10 emissions (mg/m2s) 1.77E-02 2.88E-02 8.26E-01 4.49E+00

March, 2006 PI-SWERLu* (m/s) 0.2186152 0.401358 0.562922 0.701505 0.8153Average PM10 emissions (mg/m2s) 0.000566152 0.002362 0.097929 1.203767 5.019917Geometric Mean PM10 emissions (mg/m2s) 0.000549515 0.002306 0.097066 1.160375 5.019917

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

SS2-1

A-16

SS6-1September, 2005 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 2.80E-03 4.98E-03 2.48E-02 1.77E-01Geometric Mean PM10 emissions (mg/m2s) 2.38E-03 4.76E-03 2.12E-02 1.32E-01

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERLu* (m/s) 4.01E-01 5.63E-01 7.02E-01 8.15E-01Average PM10 emissions (mg/m2s) 2.28E-02 2.73E-01 1.14E+00 2.94E+00Geometric Mean PM10 emissions (mg/m2s) 1.96E-02 2.69E-01 1.12E+00 2.85E+00

March, 2006 PI-SWERLu* (m/s) 0.2186152 0.401358 0.562922 0.701505Average PM10 emissions (mg/m2s) 0.001784685 0.026385 0.299269 1.211993Geometric Mean PM10 emissions (mg/m2s) 0.001743421 0.024463 0.279353 1.151324

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

SS6-1

A-17

SS9-1September, 2005 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01 7.02E-01Average PM10 emissions (mg/m2s) 1.56E-03 4.10E-03 1.08E-02 7.53E-01Geometric Mean PM10 emissions (mg/m2s) 1.49E-03 3.83E-03 1.02E-02 5.41E-01

Guelph Tunnelu* (m/s)PM10 emissions (mg/m2s)

January, 2006 PI-SWERLu* (m/s) 2.19E-01 4.01E-01 5.63E-01Average PM10 emissions (mg/m2s) 3.97E-03 3.06E-02 2.39E-01Geometric Mean PM10 emissions (mg/m2s) 3.97E-03 2.93E-02 1.85E-01

March, 2006 PI-SWERLu* (m/s) 0.2186152 0.401358 0.562922 0.701505Average PM10 emissions (mg/m2s) 0.002214362 0.01062 0.028325 0.22288Geometric Mean PM10 emissions (mg/m2s) 0.002162091 0.009024 0.023454 0.201389

0.0001

0.0010

0.0100

0.1000

1.0000

10.0000

100.0000

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

Nominal friction velocity (u*, m/s)

PM10

em

issi

ons (

mg/

m2 s)

PI-SWERL September 05PI-SWERL January 05PI-SWERL March 06Guelph Tunnel September

SS9-1

A-18

The Tables that appear on pages A19 – A34 contain the results from individual PI-SWERL measurements obtained at each site. Where available, data are shown for the September 2005, January 2006, and March 2006 field campaigns. The top rows in the tables correspond to the friction velocity. Data in the columns corresponds to cumulative PM10 flux measured at the specified friction velocity. The bottom seven rows contain the site and season aggregate averages, standard deviations, # of samples, standard error, geometric means, minimum and maximum values. For values with 2 or more replicate data points, the standard error is calculated as the standard deviation divided by the square root of the # of samples minus one. That is,

1_Re__#_tan_tan

−=

ValuesplicateofDeviationdardSErrordardS

A-19

A100-1 Date 09/21/05 Date 01/25/06 Date 03/21/06u* (m/s) 0.22 0.31 0.40 0.48 0.56 0.70 0.22 0.31 0.40 0.48 0.56 0.70 0.22 0.31 0.40 0.48 0.56 0.70

1.5E-02 3.0E-01 2.6E+00 1.4E-02 6.1E-01 3.5E+00 1.6E+01 3.4E-03 1.5E-02 5.9E-01 3.8E+003.2E-03 6.5E-03 1.4E-02 4.3E-02 2.1E-01 1.8E-02 3.8E-02 2.0E-01 7.8E-01 2.0E+00 1.4E-03 4.2E-03 1.1E+002.7E-03 5.2E-03 7.8E-03 4.2E-02 2.4E-01 8.8E-03 1.8E-02 4.8E-02 2.2E-01 5.8E-01 1.0E-03 2.1E-03 5.3E-03 1.3E-01 1.1E+002.7E-03 5.7E-03 8.7E-03 6.0E-02 4.1E-01 1.1E-02 2.3E-02 7.3E-02 2.5E-01 1.1E+00 8.9E-04 1.8E-03 4.4E-03 2.5E-01 1.7E+00

4.6E-03 3.0E-01 2.6E+005.3E-01

4.0E-03 3.1E-02 2.2E+002.3E-03 1.8E-02 1.9E+00

2.0E+002.0E+00

3.5E-03 8.3E-03 1.2E-02 5.5E-01 2.8E+007.9E-03 1.5E-02 2.4E-02 4.6E-01 2.1E+00

7.3E-017.0E-01

5.5E-03 1.0E-02 1.4E-02 9.5E-02 5.8E-016.9E-03 2.7E-02 3.3E-02 1.3E-01 7.3E-01

3.6E-03 7.4E-023.6E-03 6.6E-03 9.6E-03 1.7E-01 1.2E+00

3.4E-012.9E-01

3.6E-03 7.5E-03 2.2E-02 1.0E-01 6.1E-012.9E-03 8.1E-03 1.7E-02 1.1E-01 6.4E-01

Average 4.3E-03 1.0E-02 1.3E-02 1.7E-01 8.0E-01 2.3E+00 1.3E-02 2.6E-02 2.3E-01 4.2E-01 1.8E+00 1.6E+01 1.7E-03 2.0E-03 7.2E-03 1.9E-01 1.1E+00 3.8E+00Standard Deviation 1.9E-03 6.7E-03 8.5E-03 1.7E-01 7.7E-01 3.1E-01 4.0E-03 1.1E-02 2.6E-01 3.1E-01 1.3E+00 N/A 1.2E-03 1.9E-04 5.3E-03 7.9E-02 4.4E-01 N/A

# of Replicates 10 10 15 11 21 4 4 3 4 3 4 1 4 2 4 2 4 1Standard Error 6.2E-04 2.2E-03 2.3E-03 5.4E-02 1.7E-01 1.8E-01 2.3E-03 7.5E-03 1.5E-01 2.2E-01 7.3E-01 N/A 6.7E-04 1.9E-04 3.1E-03 7.9E-02 2.6E-01 N/A

Geo-mean 4.0E-03 8.8E-03 1.0E-02 1.2E-01 4.7E-01 2.3E+00 1.3E-02 2.5E-02 1.4E-01 3.5E-01 1.5E+00 1.6E+01 1.4E-03 2.0E-03 6.2E-03 1.8E-01 1.0E+00 3.8E+00Minimum 2.7E-03 5.2E-03 2.3E-03 4.2E-02 1.8E-02 1.9E+00 8.8E-03 1.8E-02 4.8E-02 2.2E-01 5.8E-01 1.6E+01 8.9E-04 1.8E-03 4.2E-03 1.3E-01 5.9E-01 3.8E+00Maximum 7.9E-03 2.7E-02 3.3E-02 5.5E-01 2.8E+00 2.6E+00 1.8E-02 3.8E-02 6.1E-01 7.8E-01 3.5E+00 1.6E+01 3.4E-03 2.1E-03 1.5E-02 2.5E-01 1.7E+00 3.8E+00

A-20

A100-2 Date 09/22/05 Date 01/25/06 Date 03/21/06u* (m/s) 0.40 0.56 0.70 0.82 0.90 0.96 0.22 0.40 0.56 0.70 0.22 0.40 0.56 0.70

8.6E-02 1.8E-01 3.1E-01 5.3E-01 5.5E-03 1.8E-02 5.4E-02 9.2E-01 8.3E-04 2.1E-01 9.9E-01 2.5E+003.6E-03 1.1E-02 2.2E-02 5.3E-02 3.7E-03 1.0E-02 9.8E-02 2.4E+00 1.7E-03 3.0E-02 1.8E-01 5.1E-012.7E-03 5.8E-03 9.8E-03 4.3E-02 3.4E-03 8.4E-03 6.5E-02 1.5E+00 1.3E-02 9.6E-02 3.2E-013.0E-03 6.4E-03 1.1E-02 8.7E-02 3.5E-03 1.2E-02 3.8E-02 3.4E-01 1.3E-02 9.7E-02 3.0E-016.6E-03 3.7E-02 1.0E-01 2.3E-01 3.7E-01 6.6E-014.5E-03 1.2E-02 3.6E-02 6.8E-02 2.1E-01 1.1E+004.6E-03 1.2E-02 2.4E-02 4.2E-02 8.6E-02 3.6E-014.1E-03 8.8E-03 1.7E-02 4.6E-02 1.2E-01 1.3E+005.3E-03 1.3E-02 2.4E-02 4.1E-02 6.7E-02 2.5E-013.1E-03 8.6E-03 1.5E-02 2.2E-02 3.5E-02 1.4E-013.5E-03 8.7E-03 1.6E-02 2.4E-02 3.6E-02 6.2E-02

Average 1.2E-02 2.8E-02 5.4E-02 1.1E-01 1.3E-01 5.5E-01 4.0E-03 1.2E-02 6.4E-02 1.3E+00 1.3E-03 6.7E-02 3.4E-01 9.2E-01Standard Deviation 2.5E-02 5.2E-02 8.9E-02 1.5E-01 1.2E-01 4.7E-01 1.0E-03 4.0E-03 2.5E-02 8.7E-01 6.4E-04 9.7E-02 4.4E-01 1.1E+00

# of Replicates 11 11 11 11 7 7 4 4 4 4 2 4 4 4Standard Error 7.8E-03 1.6E-02 2.8E-02 4.7E-02 4.9E-02 1.9E-01 5.9E-04 2.3E-03 1.5E-02 5.0E-01 6.4E-04 5.6E-02 2.5E-01 6.3E-01

Geo-mean 5.2E-03 1.4E-02 2.7E-02 6.4E-02 9.5E-02 3.5E-01 3.9E-03 1.2E-02 6.0E-02 1.0E+00 1.2E-03 3.3E-02 2.0E-01 6.0E-01Minimum 2.7E-03 5.8E-03 9.8E-03 2.2E-02 3.5E-02 6.2E-02 3.4E-03 8.4E-03 3.8E-02 3.4E-01 8.3E-04 1.3E-02 9.6E-02 3.0E-01Maximum 8.6E-02 1.8E-01 3.1E-01 5.3E-01 3.7E-01 1.3E+00 5.5E-03 1.8E-02 9.8E-02 2.4E+00 1.7E-03 2.1E-01 9.9E-01 2.5E+00

A-21

A101-1 Date 09/27/05 Date 01/27/06 Date 03/22/06u* (m/s) 0.22 0.31 0.40 0.48 0.56 0.70 0.40 0.56 0.70 0.82 0.56 0.70 0.82 0.90 0.96

1.5E-02 3.0E-01 2.6E+00 1.6E-03 7.2E-023.2E-03 6.5E-03 1.4E-02 4.3E-02 2.1E-01 8.9E-02 1.1E-01 1.6E-01 2.5E-01 3.6E-03 7.7E-03 1.5E-02 3.5E-02 1.4E-012.7E-03 5.2E-03 7.8E-03 4.2E-02 2.4E-01 7.6E-03 2.0E-02 1.6E-01 6.7E-03 1.3E-02 2.6E-02 5.3E-02 1.3E-012.7E-03 5.7E-03 8.7E-03 6.0E-02 4.1E-01 7.9E-03 2.8E-02 9.1E-02 7.6E-03 1.4E-02 3.4E-02 6.1E-02

4.6E-03 3.0E-01 2.6E+00 6.1E-03 1.3E-02 4.4E-025.3E-01

4.0E-03 3.1E-02 2.2E+002.3E-03 1.8E-02 1.9E+00

2.0E+002.0E+00

3.5E-03 8.3E-03 1.2E-02 5.5E-01 2.8E+007.9E-03 1.5E-02 2.4E-02 4.6E-01 2.1E+00

7.3E-017.0E-01

5.5E-03 1.0E-02 1.4E-02 9.5E-02 5.8E-016.9E-03 2.7E-02 3.3E-02 1.3E-01 7.3E-01

3.6E-03 7.4E-023.6E-03 6.6E-03 9.6E-03 1.7E-01 1.2E+00

3.4E-012.9E-01

3.6E-03 7.5E-03 2.2E-02 1.0E-01 6.1E-012.9E-03 8.1E-03 1.7E-02 1.1E-01 6.4E-01

Average 4.3E-03 1.0E-02 1.3E-02 1.7E-01 8.0E-01 2.3E+00 8.9E-02 3.3E-02 7.1E-02 1.7E-01 5.2E-03 9.4E-03 1.5E-02 3.4E-02 9.4E-02Standard Deviation 1.9E-03 6.7E-03 8.5E-03 1.7E-01 7.7E-01 3.1E-01 N/A 5.4E-02 6.6E-02 8.1E-02 2.2E-03 3.1E-03 8.1E-03 1.7E-02 4.8E-02

# of Replicates 10 10 15 11 21 4 1 4 4 3 2 3 4 4 4Standard Error 6.2E-04 2.2E-03 2.3E-03 5.4E-02 1.7E-01 1.8E-01 N/A 3.1E-02 3.8E-02 5.7E-02 2.2E-03 2.2E-03 4.7E-03 9.5E-03 2.8E-02

Geo-mean 4.0E-03 8.8E-03 1.0E-02 1.2E-01 4.7E-01 2.3E+00 8.9E-02 1.0E-02 5.1E-02 1.5E-01 4.9E-03 9.1E-03 1.3E-02 3.0E-02 8.4E-02Minimum 2.7E-03 5.2E-03 2.3E-03 4.2E-02 1.8E-02 1.9E+00 8.9E-02 1.6E-03 2.0E-02 9.1E-02 3.6E-03 7.6E-03 6.1E-03 1.3E-02 4.4E-02Maximum 7.9E-03 2.7E-02 3.3E-02 5.5E-01 2.8E+00 2.6E+00 8.9E-02 1.1E-01 1.6E-01 2.5E-01 6.7E-03 1.3E-02 2.6E-02 5.3E-02 1.4E-01

A-22

A29-1 Date 09/29/05 Date 01/26/06 Date 03/22/06u* (m/s) 0.22 0.31 0.40 0.48 0.56 0.22 0.40 0.56 0.70 0.82 0.40 0.56 0.70 0.82

1.9E-03 4.5E-03 1.4E-01 5.2E-04 7.7E-02 1.9E+00 2.3E-03 5.9E-03 6.6E-02 4.6E-016.7E-04 2.9E-03 6.1E-03 1.0E-02 1.5E-01 2.2E-03 3.2E-02 9.4E-02 1.9E+006.7E-04 1.7E-03 2.5E-03 4.3E-03 1.3E+00 4.1E-03 1.2E-01 4.2E-01 1.5E+007.2E-04 1.6E-03 4.4E-03 6.5E-03 3.2E-02 5.1E-04 5.0E-03 4.4E-01 2.7E+007.3E-04 1.3E-03 2.5E-03 4.0E-03 7.5E-03

Average 9.4E-04 1.9E-03 4.0E-03 6.2E-03 3.3E-01 1.4E-03 1.0E-02 1.8E-01 1.7E+00 1.5E+00 2.3E-03 5.9E-03 6.6E-02 4.6E-01Standard Deviation 5.3E-04 7.3E-04 1.5E-03 2.8E-03 5.5E-01 1.2E-03 1.5E-02 1.7E-01 9.6E-01 N/A N/A N/A N/A N/A

# of Replicates 5 4 5 4 5 2 4 4 4 1 1 1 1 1Standard Error 2.6E-04 4.2E-04 7.5E-04 1.6E-03 2.8E-01 1.2E-03 8.5E-03 1.0E-01 5.6E-01 N/A N/A N/A N/A N/A

Geo-mean 8.5E-04 1.8E-03 3.8E-03 5.8E-03 9.1E-02 1.1E-03 4.3E-03 1.4E-01 1.4E+00 1.5E+00 2.3E-03 5.9E-03 6.6E-02 4.6E-01Minimum 6.7E-04 1.3E-03 2.5E-03 4.0E-03 7.5E-03 5.1E-04 5.2E-04 7.7E-02 4.2E-01 1.5E+00 2.3E-03 5.9E-03 6.6E-02 4.6E-01Maximum 1.9E-03 2.9E-03 6.1E-03 1.0E-02 1.3E+00 2.2E-03 3.2E-02 4.4E-01 2.7E+00 1.5E+00 2.3E-03 5.9E-03 6.6E-02 4.6E-01

A-23

A31-1 Date 09/28/05 Date 01/27/06 Date 03/22/06u* (m/s) 0.40 0.56 0.70 0.82 0.40 0.56 0.70 0.82 0.90 0.40 0.56 0.70 0.82 0.90 0.96

2.6E-02 8.9E-02 1.6E-01 3.6E-01 2.2E-02 6.5E-02 2.6E-01 7.1E-01 4.0E-01 6.5E-01 9.6E-01 1.2E+00 1.4E+00 1.7E+001.0E-02 2.3E-02 4.1E-02 1.1E-01 1.2E-02 3.0E-02 8.6E-02 3.1E-01 1.0E+00 2.8E-03 8.1E-03 1.8E-02 3.7E-02 6.6E-02 1.3E-019.4E-03 3.3E-02 1.2E-01 5.2E-01 8.7E-03 2.6E-02 9.3E-02 2.5E-01 5.7E-03 1.3E-02 2.3E-02 4.2E-02 9.1E-021.1E-02 3.6E-02 1.4E-01 4.7E-01 2.6E-02 1.6E-01 6.6E-01 1.8E+00 5.8E-02 1.4E-01 3.1E-01 6.4E-011.7E-02 3.8E-02 8.8E-02 1.4E-011.1E-02 2.4E-02 6.0E-02 1.4E-011.5E-02 9.0E-02 7.0E-01 1.5E+005.2E-03 2.1E-02 5.0E-02 1.1E-012.9E-02 5.4E-02 9.1E-02 1.7E-014.1E-03 9.7E-03 4.0E-02 1.2E-012.2E-03 8.1E-03 3.0E-02 8.7E-021.0E-02 2.7E-02 6.3E-02 1.5E-01

Average 1.2E-02 3.8E-02 1.3E-01 3.2E-01 1.4E-02 3.7E-02 1.5E-01 4.8E-01 1.4E+00 2.0E-01 2.2E-01 2.6E-01 3.5E-01 4.7E-01 6.4E-01Standard Deviation 8.0E-03 2.7E-02 1.8E-01 3.9E-01 6.9E-03 1.9E-02 8.1E-02 2.3E-01 5.5E-01 2.8E-01 3.7E-01 4.7E-01 5.6E-01 6.7E-01 7.4E-01

# of Replicates 12 12 12 12 3 4 4 4 2 2 3 4 4 4 4Standard Error 2.4E-03 8.2E-03 5.6E-02 1.2E-01 4.9E-03 1.1E-02 4.7E-02 1.4E-01 5.5E-01 2.8E-01 2.6E-01 2.7E-01 3.2E-01 3.8E-01 4.3E-01

Geo-mean 1.0E-02 3.0E-02 8.5E-02 2.1E-01 1.3E-02 3.4E-02 1.3E-01 4.4E-01 1.3E+00 3.4E-02 3.1E-02 6.0E-02 1.1E-01 1.9E-01 3.4E-01Minimum 2.2E-03 8.1E-03 3.0E-02 8.7E-02 8.7E-03 2.6E-02 8.6E-02 2.5E-01 1.0E+00 2.8E-03 5.7E-03 1.3E-02 2.3E-02 4.2E-02 9.1E-02Maximum 2.9E-02 9.0E-02 7.0E-01 1.5E+00 2.2E-02 6.5E-02 2.6E-01 7.1E-01 1.8E+00 4.0E-01 6.5E-01 9.6E-01 1.2E+00 1.4E+00 1.7E+00

A-24

A32-1 Date 09/29/05 Date Dateu* (m/s) 0.40 0.56 0.70 0.82 0.90

4.6E-03 1.1E-02 1.2E-018.2E-04 3.5E-03 1.4E-02 5.5E-025.8E-04 2.2E-03 6.0E-03 1.7E-02 5.2E-021.1E-03 4.1E-03 1.8E-02 2.7E-01 4.3E-012.1E-03 2.2E-02 2.4E-01 9.5E-01 2.9E+00

Average 1.8E-03 8.5E-03 8.0E-02 3.2E-01 1.1E+00Standard Deviation 1.6E-03 8.1E-03 1.0E-01 4.3E-01 1.6E+00

# of Replicates 5 5 5 4 3Standard Error 8.1E-04 4.1E-03 5.0E-02 2.5E-01 1.1E+00

Geo-mean 1.4E-03 6.0E-03 3.4E-02 1.2E-01 4.0E-01Minimum 5.8E-04 2.2E-03 6.0E-03 1.7E-02 5.2E-02Maximum 4.6E-03 2.2E-02 2.4E-01 9.5E-01 2.9E+00

A-25

A34-1 Date 09/23/05 Date 01/25/06 Date 03/23/06u* (m/s) 0.40 0.56 0.70 0.82 0.90 0.96 0.22 0.31 0.40 0.48 0.56 0.40 0.56 0.70

3.8E-03 1.5E-02 5.1E-02 2.8E-01 5.0E-03 1.6E-01 2.6E+00 7.2E-02 2.0E-01 4.3E-016.1E-03 1.5E-02 5.1E-02 2.1E-01 5.4E-01 1.5E+00 3.0E-03 6.8E-03 4.4E-02 4.0E-01 2.1E+00 4.4E-03 8.7E-03 7.8E-026.5E-03 1.7E-02 3.6E-02 1.5E-01 9.3E-01 3.5E+00 2.9E-03 7.6E-03 9.7E-02 7.5E-01 2.7E+00 4.1E-03 6.7E-03 1.2E-021.2E-02 4.5E-02 9.0E-02 2.1E-01 6.1E-01 2.0E+00 3.8E-03 7.1E-03 4.6E-026.8E-03 1.3E-02 2.7E-02 5.1E-02 2.3E-01 9.3E-018.5E-02 1.9E-011.9E-02 1.3E-01 1.1E+00 4.9E+002.1E-02 3.0E-01 1.3E+00 3.6E+001.0E-02 2.0E-01 9.7E-01 2.3E+00

Average 1.9E-02 1.0E-01 4.5E-01 1.5E+00 5.8E-01 2.0E+00 3.6E-03 7.2E-03 1.0E-01 5.7E-01 2.5E+00 2.1E-02 5.6E-02 1.4E-01Standard Deviation 2.5E-02 1.1E-01 5.6E-01 1.9E+00 2.9E-01 1.1E+00 1.2E-03 6.0E-04 5.9E-02 2.5E-01 3.1E-01 3.4E-02 9.6E-02 1.9E-01

# of Replicates 9 9 8 8 4 4 3 2 3 2 3 4 4 4Standard Error 9.0E-03 3.7E-02 2.1E-01 7.2E-01 1.7E-01 6.4E-01 8.5E-04 6.0E-04 4.2E-02 2.5E-01 2.2E-01 2.0E-02 5.6E-02 1.1E-01

Geo-mean 1.2E-02 5.3E-02 1.5E-01 5.0E-01 5.1E-01 1.8E+00 3.5E-03 7.2E-03 8.8E-02 5.4E-01 2.5E+00 8.4E-03 1.7E-02 6.6E-02Minimum 3.8E-03 1.3E-02 2.7E-02 5.1E-02 2.3E-01 9.3E-01 2.9E-03 6.8E-03 4.4E-02 4.0E-01 2.1E+00 3.8E-03 6.7E-03 1.2E-02Maximum 8.5E-02 3.0E-01 1.3E+00 4.9E+00 9.3E-01 3.5E+00 5.0E-03 7.6E-03 1.6E-01 7.5E-01 2.7E+00 7.2E-02 2.0E-01 4.3E-01

A-26

A34-2 Date 09/24/05 Date 01/25/06 Date 03/23/06u* (m/s) 0.40 0.56 0.70 0.82 0.90 0.96 0.22 0.40 0.56 0.70 0.40 0.56 0.70 0.82

1.4E-01 2.3E-01 3.4E-01 5.5E-01 2.7E-03 3.6E-02 8.6E-01 2.3E-03 5.3E-03 1.7E-02 5.3E-021.9E-03 7.3E-03 7.1E-02 5.2E-01 1.8E+00 3.4E+00 9.2E-04 8.2E-03 4.0E-01 1.6E+00 6.1E-03 1.2E-02 3.1E-021.6E-02 2.8E-02 4.2E-02 7.3E-02 1.5E-01 3.3E-01 8.0E-04 5.2E-03 1.8E-01 1.1E+00 6.6E-03 1.4E-02 7.5E-021.5E-03 4.0E-03 1.0E-02 4.3E-02 1.8E-01 6.2E-01 9.2E-04 8.8E-03 2.6E-01 8.6E-011.8E-03 5.2E-03 1.2E-02 2.7E-02 7.3E-02 2.3E-011.5E-03 3.3E-03 6.3E-03 1.1E-02 2.1E-02 8.3E-021.2E-03 2.8E-03 5.0E-03 8.8E-03 1.4E-02 5.0E-021.3E-03 6.4E-03 4.1E-02 2.0E-01 5.5E-01 1.2E+001.3E-03 4.4E-03 9.3E-03 1.8E-02 3.5E-02 9.1E-02

Average 1.8E-02 3.2E-02 6.0E-02 1.6E-01 3.5E-01 7.5E-01 1.3E-03 1.5E-02 4.3E-01 1.2E+00 2.3E-03 6.0E-03 1.4E-02 5.3E-02Standard Deviation 4.4E-02 7.4E-02 1.1E-01 2.2E-01 6.0E-01 1.2E+00 9.3E-04 1.4E-02 3.0E-01 3.8E-01 N/A 6.5E-04 2.8E-03 2.2E-02

# of Replicates 9 9 9 9 8 8 4 4 4 3 1 3 3 3Standard Error 1.6E-02 2.6E-02 3.8E-02 7.8E-02 2.3E-01 4.4E-01 5.4E-04 8.3E-03 1.7E-01 2.7E-01 N/A 4.6E-04 2.0E-03 1.5E-02

Geo-mean 3.2E-03 8.6E-03 2.2E-02 5.9E-02 1.1E-01 3.0E-01 1.2E-03 1.1E-02 3.6E-01 1.2E+00 2.3E-03 6.0E-03 1.4E-02 5.0E-02Minimum 1.2E-03 2.8E-03 5.0E-03 8.8E-03 1.4E-02 5.0E-02 8.0E-04 5.2E-03 1.8E-01 8.6E-01 2.3E-03 5.3E-03 1.2E-02 3.1E-02Maximum 1.4E-01 2.3E-01 3.4E-01 5.5E-01 1.8E+00 3.4E+00 2.7E-03 3.6E-02 8.6E-01 1.6E+00 2.3E-03 6.6E-03 1.7E-02 7.5E-02

A-27

A34-3 Date Date 01/25/06 Date 03/23/06u* (m/s) 0.22 0.31 0.40 0.48 0.56 0.22 0.40 0.56 0.70 0.82

3.3E-03 6.2E-03 7.4E-02 3.0E+00 2.5E-03 5.3E-03 8.6E-03 1.8E-022.3E-03 1.9E-02 3.3E-02 9.5E-02 3.3E-03 9.2E-03 2.4E-02 5.3E-021.4E-03 8.0E-03 1.8E-02 7.4E-02 3.4E-01 2.3E-03 8.0E-01 8.4E+002.2E-03 5.2E-03 1.4E-02 1.6E-01 4.6E-01

Average 2.3E-03 9.6E-03 3.5E-02 8.3E-01 4.0E-01 2.5E-03 3.6E-03 2.7E-01 2.8E+00 5.3E-02Standard Deviation 7.8E-04 6.3E-03 2.8E-02 1.4E+00 8.6E-02 N/A 1.5E-03 4.6E-01 4.8E+00 N/A

# of Replicates 4 4 4 4 2 1 3 3 3 1Standard Error 4.5E-04 3.6E-03 1.6E-02 8.3E-01 8.6E-02 N/A 1.1E-03 3.2E-01 3.4E+00 N/A

Geo-mean 2.2E-03 8.4E-03 2.8E-02 2.4E-01 4.0E-01 2.5E-03 3.4E-03 4.0E-02 1.5E-01 5.3E-02Minimum 1.4E-03 5.2E-03 1.4E-02 7.4E-02 3.4E-01 2.5E-03 2.3E-03 8.6E-03 1.8E-02 5.3E-02Maximum 3.3E-03 1.9E-02 7.4E-02 3.0E+00 4.6E-01 2.5E-03 5.3E-03 8.0E-01 8.4E+00 5.3E-02

A-28

A200-1 Date Date 01/24/06 Dateu* (m/s) 0.22 0.31 0.40 0.48 0.56

7.5E-03 2.6E-011.3E-02 3.2E-02 3.4E-01 1.6E+001.3E-02 3.1E-02 1.8E-01 1.2E+00 3.7E+001.1E-02 3.3E-02 3.3E-01 2.7E+007.6E-03 2.2E-02 8.9E-02 2.6E-01 7.6E-01

Average 1.0E-02 2.9E-02 2.4E-01 1.5E+00 2.2E+00Standard Deviation 2.7E-03 5.3E-03 1.1E-01 1.0E+00 2.1E+00

# of Replicates 5 4 5 4 2Standard Error 1.3E-03 3.0E-03 5.3E-02 5.8E-01 2.1E+00

Geo-mean 1.0E-02 2.9E-02 2.2E-01 1.1E+00 1.7E+00Minimum 7.5E-03 2.2E-02 8.9E-02 2.6E-01 7.6E-01Maximum 1.3E-02 3.3E-02 3.4E-01 2.7E+00 3.7E+00

A-29

A201-1 Date Date 01/24/06 Dateu* (m/s) 0.22 0.40 0.56 0.70

2.4E-03 7.2E-03 2.3E-01 2.0E+002.6E-03 8.8E-03 6.2E-02 5.4E-012.7E-03 1.0E-02 1.5E-018.3E-03 1.2E-02 6.3E-02 5.0E-01

Average 4.0E-03 9.7E-03 1.2E-01 1.0E+00Standard Deviation 2.8E-03 2.3E-03 7.9E-02 8.5E-01

# of Replicates 4 4 4 3Standard Error 1.6E-03 1.3E-03 4.5E-02 6.0E-01

Geo-mean 3.4E-03 9.5E-03 1.1E-01 8.2E-01Minimum 2.4E-03 7.2E-03 6.2E-02 5.0E-01Maximum 8.3E-03 1.2E-02 2.3E-01 2.0E+00

A-30

SS16-1 Date 09/26/05 Date 01/27/06 Date 03/22/06u* (m/s) 0.40 0.56 0.70 0.82 0.90 0.96 0.56 0.70 0.82 0.90 0.96 0.40 0.56 0.70 0.82 0.90 0.96

4.1E-01 6.9E-01 9.2E-01 1.2E+00 1.5E+00 1.7E+00 7.3E-03 2.3E-02 7.1E-02 2.8E-01 6.8E-013.3E-03 7.8E-03 1.5E-02 2.4E-02 4.6E-02 1.1E-01 3.5E-02 5.7E-01 2.8E+00 1.3E-03 5.3E-03 2.4E-02 3.8E-02 7.2E-02 1.1E-013.0E-03 6.8E-03 1.2E-02 2.2E-02 3.3E-02 5.4E-02 7.0E-02 2.1E-01 6.5E-01 1.5E+00 7.3E-04 2.9E-03 6.6E-03 1.2E-02 2.2E-02 3.5E-028.2E-03 3.8E-02 1.3E-01 4.0E-01 8.4E-01 1.7E+00 2.7E-03 5.6E-03 1.0E-02 1.7E-02 3.1E-023.1E-03 1.3E-02 3.0E-02 6.2E-02 1.1E-01 2.1E-01 1.6E-02 3.5E-02 7.2E-02 1.3E-013.9E-03 1.0E-02 2.2E-02 4.1E-02 1.0E-01 1.9E-013.3E-03 1.5E-02 3.9E-02 8.6E-02 1.4E-01 3.9E-013.6E-03 1.5E-02 3.9E-02 7.0E-02 1.3E-01 3.0E-014.4E-03 1.6E-02 4.6E-02 9.6E-02 1.9E-01 3.6E-013.5E-03 1.0E-02 1.6E-02 2.2E-02 3.8E-02 7.7E-02

Average 4.5E-02 8.3E-02 1.3E-01 2.0E-01 3.1E-01 5.1E-01 2.1E-02 2.2E-01 1.0E+00 4.7E-01 1.1E+00 1.0E-03 3.7E-03 1.3E-02 2.4E-02 4.6E-02 7.7E-02Standard Deviation 1.3E-01 2.1E-01 2.8E-01 3.6E-01 4.7E-01 6.4E-01 1.9E-02 3.0E-01 1.5E+00 2.6E-01 5.4E-01 3.7E-04 1.4E-03 8.9E-03 1.5E-02 3.1E-02 5.1E-02

# of Replicates 10 10 10 10 10 10 2 3 3 2 2 2 3 4 4 4 4Standard Error 4.3E-02 7.1E-02 9.4E-02 1.2E-01 1.6E-01 2.1E-01 1.9E-02 2.1E-01 1.1E+00 2.6E-01 5.4E-01 3.7E-04 1.0E-03 5.1E-03 8.6E-03 1.8E-02 3.0E-02

Geo-mean 6.1E-03 1.9E-02 4.2E-02 7.8E-02 1.4E-01 2.7E-01 1.6E-02 9.7E-02 3.5E-01 4.3E-01 9.9E-01 9.6E-04 3.5E-03 1.1E-02 2.0E-02 3.7E-02 6.3E-02Minimum 3.0E-03 6.8E-03 1.2E-02 2.2E-02 3.3E-02 5.4E-02 7.3E-03 2.3E-02 7.1E-02 2.8E-01 6.8E-01 7.3E-04 2.7E-03 5.6E-03 1.0E-02 1.7E-02 3.1E-02Maximum 4.1E-01 6.9E-01 9.2E-01 1.2E+00 1.5E+00 1.7E+00 3.5E-02 5.7E-01 2.8E+00 6.5E-01 1.5E+00 1.3E-03 5.3E-03 2.4E-02 3.8E-02 7.2E-02 1.3E-01

A-31

SS17-1 Date 09/29/05 Date 01/27/06 Dateu* (m/s) 0.40 0.56 0.70 0.82 0.90 0.96 0.22 0.40 0.56 0.70

3.6E-03 1.5E-02 6.8E-02 2.4E-01 5.4E-02 2.1E-01 1.3E+002.4E-03 9.6E-03 2.4E-02 7.6E-02 5.3E-03 1.8E-02 1.7E-013.3E-03 7.8E-03 1.4E-02 2.6E-02 1.3E-01 3.6E-01 5.7E-03 1.5E-02 5.8E-02 1.0E+002.9E-03 8.5E-03 1.7E-02 2.6E-02 5.1E-02 9.3E-02 1.5E-02 2.8E-02 2.4E-01 1.5E+002.6E-03 7.1E-03 1.5E-02 2.5E-02 3.7E-02 7.6E-02

Average 3.0E-03 9.6E-03 2.8E-02 7.8E-02 7.2E-02 1.8E-01 8.7E-03 2.9E-02 1.7E-01 1.3E+00Standard Deviation 4.9E-04 3.1E-03 2.3E-02 9.2E-02 4.9E-02 1.6E-01 5.5E-03 1.8E-02 7.9E-02 2.6E-01

# of Replicates 5 5 5 5 3 3 3 4 4 3Standard Error 2.4E-04 1.6E-03 1.1E-02 4.6E-02 3.4E-02 1.1E-01 3.9E-03 1.0E-02 4.6E-02 1.8E-01

Geo-mean 3.0E-03 9.2E-03 2.3E-02 5.0E-02 6.2E-02 1.4E-01 7.7E-03 2.5E-02 1.5E-01 1.3E+00Minimum 2.4E-03 7.1E-03 1.4E-02 2.5E-02 3.7E-02 7.6E-02 5.3E-03 1.5E-02 5.8E-02 1.0E+00Maximum 3.6E-03 1.5E-02 6.8E-02 2.4E-01 1.3E-01 3.6E-01 1.5E-02 5.4E-02 2.4E-01 1.5E+00

A-32

SS2-1 Date 09/25/05 Date 01/25/06 Date 03/23/06u* (m/s) 0.22 0.40 0.56 0.70 0.22 0.40 0.56 0.70 0.22 0.40 0.56 0.70 0.82

5.1E-03 2.4E-02 5.6E-01 4.8E+00 1.2E-02 7.2E-01 4.0E+00 2.4E-03 7.9E-02 8.3E-01 5.0E+002.6E-03 5.0E-03 1.1E-01 2.0E+00 2.0E-02 9.1E-01 4.8E+00 4.3E-04 2.0E-03 1.1E-01 1.1E+001.4E-03 1.4E-02 1.4E+00 6.5E+00 1.1E-02 4.9E-02 1.3E+00 6.3E+00 7.0E-04 1.8E-03 1.1E-01 1.7E+002.1E-03 7.5E-03 2.6E-01 2.8E+00 2.9E-02 6.2E-02 5.6E-01 3.4E+00 3.2E-03 9.3E-02 1.2E+002.2E-03 4.4E-03 1.3E-01 2.3E+001.6E-03 3.9E-03 6.7E-02 9.1E-011.4E-03 3.2E-03 1.3E-01 2.3E+001.7E-03 3.3E-03 6.7E-01 4.7E+004.6E-03 1.0E-02 1.1E+00 5.6E+002.4E-03 6.5E-03 5.5E-01 4.0E+001.6E-03 3.5E-03 6.8E-01

Average 2.4E-03 7.7E-03 5.1E-01 3.6E+00 2.0E-02 3.5E-02 8.7E-01 4.6E+00 5.7E-04 2.4E-03 9.8E-02 1.2E+00 5.0E+00Standard Deviation 1.3E-03 6.2E-03 4.3E-01 1.8E+00 1.2E-02 2.4E-02 3.1E-01 1.3E+00 1.9E-04 6.2E-04 1.5E-02 3.9E-01 N/A

# of Replicates 11 11 11 10 2 4 4 4 2 4 4 4 1Standard Error 4.0E-04 2.0E-03 1.4E-01 6.0E-01 1.2E-02 1.4E-02 1.8E-01 7.3E-01 1.9E-04 3.6E-04 8.5E-03 2.2E-01 N/A

Geo-mean 2.2E-03 6.2E-03 3.4E-01 3.1E+00 1.8E-02 2.9E-02 8.3E-01 4.5E+00 5.5E-04 2.3E-03 9.7E-02 1.2E+00 5.0E+00Minimum 1.4E-03 3.2E-03 6.7E-02 9.1E-01 1.1E-02 1.2E-02 5.6E-01 3.4E+00 4.3E-04 1.8E-03 7.9E-02 8.3E-01 5.0E+00Maximum 5.1E-03 2.4E-02 1.4E+00 6.5E+00 2.9E-02 6.2E-02 1.3E+00 6.3E+00 7.0E-04 3.2E-03 1.1E-01 1.7E+00 5.0E+00

A-33

SS6-1 Date 09/30/05 Date 01/26/06 Date 03/20/06u* (m/s) 0.22 0.40 0.56 0.70 0.40 0.56 0.70 0.82 0.22 0.40 0.56 0.70

3.2E-03 5.7E-03 2.8E-02 3.0E-01 4.6E-02 3.1E-01 1.0E+00 2.1E+00 1.3E-03 4.2E-02 5.0E-01 1.9E+004.0E-03 5.8E-03 5.3E-02 3.5E-01 1.2E-02 1.9E-01 8.8E-01 2.5E+00 2.4E-03 2.8E-02 2.8E-01 9.6E-014.5E-03 6.8E-03 1.5E-02 9.5E-02 1.5E-02 2.8E-01 1.4E+00 4.0E+00 1.6E-03 2.1E-02 2.1E-01 8.0E-011.2E-03 3.2E-03 1.7E-02 9.3E-02 1.8E-02 3.1E-01 1.2E+00 3.2E+00 1.9E-03 1.4E-02 2.0E-01 1.2E+001.1E-03 3.3E-03 1.1E-02 4.3E-02

Average 2.8E-03 5.0E-03 2.5E-02 1.8E-01 2.3E-02 2.7E-01 1.1E+00 2.9E+00 1.8E-03 2.6E-02 3.0E-01 1.2E+00Standard Deviation 1.6E-03 1.6E-03 1.7E-02 1.4E-01 1.6E-02 5.5E-02 2.5E-01 8.5E-01 4.5E-04 1.2E-02 1.4E-01 4.7E-01

# of Replicates 5 5 5 5 4 4 4 4 4 4 4 4Standard Error 7.9E-04 8.1E-04 8.4E-03 6.9E-02 9.2E-03 3.2E-02 1.4E-01 4.9E-01 2.6E-04 6.8E-03 8.0E-02 2.7E-01

Geo-mean 2.4E-03 4.8E-03 2.1E-02 1.3E-01 2.0E-02 2.7E-01 1.1E+00 2.9E+00 1.7E-03 2.4E-02 2.8E-01 1.2E+00Minimum 1.1E-03 3.2E-03 1.1E-02 4.3E-02 1.2E-02 1.9E-01 8.8E-01 2.1E+00 1.3E-03 1.4E-02 2.0E-01 8.0E-01Maximum 4.5E-03 6.8E-03 5.3E-02 3.5E-01 4.6E-02 3.1E-01 1.4E+00 4.0E+00 2.4E-03 4.2E-02 5.0E-01 1.9E+00

A-34

SS9-1 Date 09/30/05 Date 01/26/06 Date 03/21/06u* (m/s) 0.22 0.40 0.56 0.70 0.22 0.40 0.56 0.22 0.40 0.56 0.70

2.5E-03 7.5E-03 1.7E-02 3.3E-01 3.4E-02 5.8E-021.4E-03 3.8E-03 8.6E-03 2.2E-01 1.9E-02 3.2E-011.4E-03 3.2E-03 6.3E-03 1.5E+00 4.0E-03 3.9E-02 3.4E-01 2.2E-02 5.9E-02 1.8E-011.3E-03 3.0E-03 1.4E-02 1.4E+00 6.4E-03 2.2E-02 3.2E-011.2E-03 3.0E-03 8.7E-03 3.1E-01 2.7E-03 8.6E-03 2.1E-02 3.0E-01

1.7E-03 5.5E-03 1.1E-02 9.8E-02

Average 1.6E-03 4.1E-03 1.1E-02 7.5E-01 4.0E-03 3.1E-02 2.4E-01 2.2E-03 1.1E-02 2.8E-02 2.2E-01Standard Deviation 5.6E-04 1.9E-03 4.3E-03 6.5E-01 N/A 1.0E-02 1.6E-01 6.8E-04 7.7E-03 2.1E-02 1.0E-01

# of Replicates 5 5 5 5 1 3 3 2 4 4 4Standard Error 2.8E-04 9.6E-04 2.2E-03 3.2E-01 N/A 7.3E-03 1.1E-01 6.8E-04 4.4E-03 1.2E-02 6.0E-02

Geo-mean 1.5E-03 3.8E-03 1.0E-02 5.4E-01 4.0E-03 2.9E-02 1.8E-01 2.2E-03 9.0E-03 2.3E-02 2.0E-01Minimum 1.2E-03 3.0E-03 6.3E-03 2.2E-01 4.0E-03 1.9E-02 5.8E-02 1.7E-03 5.5E-03 1.1E-02 9.8E-02Maximum 2.5E-03 7.5E-03 1.7E-02 1.5E+00 4.0E-03 3.9E-02 3.4E-01 2.7E-03 2.2E-02 5.9E-02 3.2E-01

APPENDIX B.

Salton Sea Site Photographs

A29 a) landscape, b) September, c) January, d) March

A31 a) landscape, b) September, c) January, d) March

A B

C D

A B

C D

A32 a) landscape, b) September, c) January, d) March

A34-1 a) landscape, b) September, c) January, d) March

A B

C D

A B

C D

A34-2 a) landscape, b) September, c) January, d) March

A34-3 a) landscape, b) January, c) March

A B

C D

A B

C

A100-1 a) landscape, b) September, c) January, d) March

A100-2 a) landscape, b) September, c) January, d) March

A B

C D

A B

C D

A101 a) landscape, b) September, c) January, d) March

A200 a) landscape, b) January, c) March

A B

C D

A B

C D

A201 a) landscape, b) January, c) March

SS2 a) landscape, b) September, c) January, d) March

A B

C D

A B

C D

SS6 a) landscape, b) September, c) January, d) March

SS9 a) landscape, b) September, c) January, d) March

A B

C D

A B

C D

SS16 a) landscape, b) September, c) January, d) March

SS17 a) landscape, b) January, c) March

A B

C D

A B

C D

SS23 a) landscape, b) September, c) January, d) March

SS17

A B

C D

A B

C D

C-1

Appendix C.

Summary of Bulk Analysis of Soil Properties from the Salton Sea: September 2005, January 2006, and March 2006.

C-2

Table C-1. Soil particle size distribution by laser particle size analysis (LPSA) and sieve methods expressed as fraction of sample

Lab ID # Field IDGravel >2mm

VC Sand

CO Sand

MD Sand

F Sand

VF Sand

Tot Sand

Tot Silt

Tot Clay

06-847 SS2-1 PIT A 0.086 0.116 0.004 0.133 0.411 0.217 0.887 0.082 0.03606-848 SS2-1 PIT B 0.219 0.313 0.176 0.187 0.188 0.069 0.938 0.045 0.02106-849 SS2-1 I BD 0.064 0.211 0.17 0.226 0.24 0.069 0.922 0.057 0.02806-850 SS2-1 II BD 0.148 0.295 0.222 0.252 0.163 0.031 0.970 0.021 0.01606-851 SS2-1 III BD 0.123 0.377 0.278 0.171 0.104 0.027 0.962 0.025 0.018

06-852 SS6-1 PIT A 0.121 0.166 0.004 0.113 0.236 0.131 0.654 0.187 0.16306-853 SS6-1 PIT B 0.015 0.007 0.002 0.119 0.386 0.151 0.670 0.182 0.15306-854 SS6-1 I BD 0.000 0.004 0.063 0.322 0.25 0.081 0.727 0.141 0.13806-855 SS6-1 II BD 0.070 0.007 0.005 0.166 0.41 0.132 0.726 0.154 0.12506-856 SS6-1 III BD 0.019 0.120 0 0.041 0.195 0.115 0.473 0.279 0.25

06-857 SS9-1 PIT A 0.443 0.159 0.06 0.209 0.12 0.087 0.639 0.248 0.11706-858 SS9-1 PIT B 0.264 0.144 0 0.095 0.128 0.076 0.446 0.348 0.21106-859 SS9-1 I BD 0.631 0.253 0.014 0.172 0.113 0.077 0.631 0.259 0.11306-860 SS9-1 II BD 0.047 0.113 0.194 0.3 0.169 0.035 0.818 0.112 0.07706-861 SS9-1 III BD 0.166 0.151 0.185 0.375 0.135 0.029 0.882 0.078 0.048

06-862 SS16-1 PIT A 0.000 0.000 0 0 0.005 0.008 0.013 0.353 0.63406-863 SS16-1 PIT B 0.000 0.000 0 0 0.013 0.013 0.026 0.358 0.61506-864 SS16-1 I BD 0.000 0.000 0 0 0.009 0.009 0.018 0.348 0.63206-865 SS16-1 II BD 0.000 0.000 0 0 0.002 0.004 0.006 0.37 0.62306-905 SS16-1 III 0.000 0.000 0 0 0.001 0.004 0.005 0.4 0.596

06-866 SS17-1 PIT A 0.000 0.000 0 0.001 0.009 0.025 0.035 0.443 0.52106-867 SS17-1 PIT B 0.000 0.000 0 0 0.004 0.012 0.016 0.406 0.57706-868 SS17-1 I BD 0.000 0.000 0 0 0.008 0.015 0.023 0.42 0.55506-869 SS17-1 II BD 0.000 0.000 0 0.002 0.016 0.037 0.055 0.426 0.51906-870 SS17-1 III BD 0.000 0.000 0 0.002 0.08 0.184 0.267 0.465 0.269

06-871 A29-1 PIT A 0.155 0.279 0.364 0.124 0.039 0.027 0.839 0.107 0.06106-872 A29-1 PIT B 0.158 0.098 0 0.105 0.085 0.053 0.343 0.411 0.24906-873 A29-1 I BD 0.083 0.172 0.652 0.133 0.015 0.005 0.985 0.015 0.00906-874 A29-1 II BD 0.040 0.194 0.617 0.097 0.01 0.006 0.932 0.047 0.02906-875 A29-1 III BD 0.059 0.192 0.656 0.098 0.005 0.002 0.961 0.025 0.02106-876 A29-1 IV BD 0.143 0.199 0.668 0.094 0.009 0.004 0.981 0.016 0.01

06-877 A31-1 PIT A 0.000 0.012 0 0.001 0.024 0.052 0.089 0.476 0.43606-878 A31-1 PIT B 0.000 0.012 0 0.006 0.048 0.09 0.157 0.455 0.38906-879 A31-1 I BD 0.000 0.000 0 0.004 0.027 0.055 0.086 0.447 0.46706-880 A31-1 II BD 0.000 0.000 0 0.005 0.042 0.066 0.114 0.43 0.45506-881 A31-1 III BD 0.000 0.000 0 0.001 0.025 0.051 0.077 0.466 0.45606-882 A31-1 IV BD 0.001 0.000 0 0.006 0.042 0.111 0.160 0.503 0.338

06-883 A32-1 PIT A 0.000 0.000 0 0.001 0.023 0.073 0.097 0.644 0.25906-884 A32-1 PIT B 0.000 0.001 0 0.003 0.026 0.081 0.111 0.633 0.25606-885 A32-1 I BD 0.000 0.000 0 0.005 0.094 0.23 0.330 0.477 0.19506-886 A32-1 II BD 0.000 0.001 0 0.005 0.05 0.153 0.209 0.565 0.226

C-3

Table C-1. Soil particle size distribution by laser particle size analysis (LPSA) and sieve methods expressed as fraction of sample(continued)

Lab ID # Field IDGravel >2mm

VC Sand

CO Sand

MD Sand

F Sand

VF Sand

Tot Sand

Tot Silt

Tot Clay

06-887 A32-1 III BD 0.000 0.000 0 0.002 0.031 0.126 0.160 0.591 0.249

06-888 A34-1 PIT A 0.087 0.034 0.001 0.113 0.323 0.202 0.677 0.239 0.08806-890 A34-1 PIT B 0.081 0.009 0.001 0.144 0.375 0.242 0.776 0.176 0.05306-1346 A34-1 A BD 0.178 0.035 0.001 0.137 0.371 0.222 0.766 0.173 0.06206-1347 A34-1 B 0.077 0.014 0 0.103 0.435 0.211 0.763 0.166 0.07206-1348 A34-1 C 0.047 0.036 0.002 0.11 0.329 0.214 0.691 0.224 0.08506-1349 A34-1 D 0.012 0.007 0.003 0.121 0.336 0.225 0.692 0.217 0.09106-889 A34-1 PIT A 0.090 0.064 0 0.172 0.486 0.151 0.880 0.085 0.04206-891 A34-1 PIT B 0.138 0.067 0.003 0.179 0.359 0.191 0.805 0.146 0.05506-1342 A34-1 I BD 0.093 0.043 0.077 0.276 0.338 0.149 0.883 0.082 0.03506-1343 A34-1 II 0.098 0.077 0.006 0.132 0.331 0.174 0.720 0.191 0.08806-1344 A34-1 III 0.056 0.028 0.001 0.163 0.401 0.197 0.790 0.157 0.05306-1345 A34-1 IV 0.241 0.071 0 0.13 0.393 0.21 0.804 0.148 0.047

06-892 A34-2 PIT A 0.000 0.000 0.001 0.133 0.359 0.248 0.746 0.197 0.06106-893 A34-2 PIT B 0.000 0.033 0.004 0.147 0.349 0.189 0.727 0.196 0.08306-1350 A34-2 I BD 0.018 0.010 0.008 0.158 0.357 0.228 0.761 0.18 0.0606-1351 A34-2 II 0.000 0.004 0 0.038 0.31 0.365 0.717 0.224 0.05906-1352 A34-2 III 0.003 0.019 0.005 0.121 0.291 0.221 0.657 0.251 0.09206-1353 A34-2 IV 0.000 0.014 0.004 0.152 0.345 0.177 0.692 0.217 0.092

06-894 A100-1 PIT A 0.234 0.042 0.104 0.294 0.293 0.132 0.872 0.094 0.04106-895 A100-1 PIT B 0.065 0.118 0.236 0.326 0.181 0.061 0.930 0.05 0.02806-1354 A100-1 A BD 0.221 0.163 0.139 0.288 0.209 0.08 0.879 0.084 0.03806-1355 A100-1 B 0.136 0.114 0.15 0.351 0.217 0.079 0.911 0.061 0.02906-1356 A100-1 C 0.213 0.218 0.225 0.262 0.148 0.063 0.916 0.057 0.02706-1357 A100-1 D 0.104 0.103 0.138 0.329 0.251 0.082 0.903 0.065 0.032

06-896 A100-2 PIT A 0.000 0.000 0.004 0.155 0.346 0.237 0.747 0.182 0.07606-897 A100-2 PIT B 0.000 0.004 0.014 0.236 0.369 0.198 0.827 0.127 0.05306-1358 A100-2 A BD 0.000 0.010 0.042 0.272 0.336 0.171 0.831 0.119 0.04906-1359 A100-2 B 0.000 0.002 0.006 0.177 0.375 0.215 0.775 0.164 0.06206-1360 A100-2 C 0.000 0.005 0.013 0.171 0.353 0.195 0.737 0.18 0.08306-1361 A100-2 D 0.000 0.002 0.001 0.139 0.371 0.253 0.766 0.172 0.06206-1362 A100-2 E 0.000 0.002 0.003 0.206 0.46 0.165 0.836 0.116 0.04906-1363 A100-2 F 0.000 0.007 0.003 0.264 0.456 0.132 0.862 0.094 0.044

06-898 A101-1 PIT A 0.002 0.000 0 0.001 0.023 0.047 0.071 0.526 0.40306-899 A101-1 PIT B 0.014 0.001 0 0.001 0.026 0.053 0.081 0.538 0.38106-900 A101-1 I BD 0.001 0.000 0 0.003 0.031 0.05 0.084 0.517 0.406-901 A101-1 II BD 0.000 0.000 0 0.001 0.015 0.03 0.046 0.522 0.43306-902 A101-1 III BD 0.000 0.000 0 0.001 0.016 0.034 0.051 0.539 0.41

06-903 SS23-1 0.000 0.000 0 0.001 0.045 0.146 0.192 0.655 0.15506-904 SS23-2 0.000 0.000 0 0.007 0.041 0.104 0.152 0.628 0.221

06-1765 A34-3 BD1 0.0708 0.028 0 0.003 0.049 0.114 0.302 0.307 0.19806-1768 A34-3 surface 0.0491 0.005 0 0.009 0.057 0.11 0.293 0.312 0.217

C-4

Table C-1. Soil particle size distribution by laser particle size analysis (LPSA) and sieve methods expressed as fraction of sample (continued)

Lab ID # Field IDGravel >2mm

VC Sand

CO Sand

MD Sand

F Sand

VF Sand

Tot Sand

Tot Silt

Tot Clay

06-1781 A200-1 Pit A 0.0509 0.005 0 0.002 0.031 0.069 0.178 0.386 0.3306-1782 A200-1 Pit B 0.0587 0.015 0 0.003 0.042 0.106 0.2 0.338 0.295

06-1786 A201-1 Soil pit A 0.0079 0.021 0 0.009 0.065 0.105 0.31 0.32 0.16906-1787 A201-1 Soil pit B 0.0012 0.005 0 0.008 0.07 0.108 0.308 0.323 0.177

C-5

Table C-2. Soil moisture, bulk density and crust thickness measured during September (1), January (2), and March (3)

Field ID

1 Moisture

(g/g)

2 Moisture

(g/g)

3 Moisture

(g/g) 1 BD 2 BD 3 BD

1 Crust thickness

(mm)

2 Crust thickness

(mm)

3 Crust thickness

(mm)SS2-1 PIT A 0.003 0.002 0.004 1.677 1.683 1.766 2 3 7SS2-1 PIT B 0.002 0.002 0.003 1.739 1.564 1.838 2 1 4SS2-1 I BD 0.003 0.002 0.002 1.696 1.726 1.812SS2-1 II BD 0.002SS2-1 III BD 0.002

SS6-1 PIT A 0.005 0.006 0.007 1.639 1.616 1.712 2 2 2SS6-1 PIT B 0.005 0.006 0.010 1.557 1.549 1.509 2 3 1SS6-1 I BD 0.004 0.001 0.011 1.573 1.575 1.658SS6-1 II BD 0.004SS6-1 III BD 0.012

SS9-1 PIT A 0.040 0.052 0.043 0.462 0.688 0.502 3 3 10SS9-1 PIT B 0.080 0.075 0.031 0.864 0.648 0.585 3 3 5SS9-1 I BD 0.026 0.075 0.964 0.773SS9-1 II BD 0.119SS9-1 III BD 0.047

SS16-1 PIT A 0.084 0.041 0.628 0.970 1.760 1.690 5 4 3SS16-1 PIT B 0.136 0.402 2.481 1.261 1.820 1.480 5 4 20SS16-1 I BD 0.280 0.042 1.011 1.066SS16-1 II BD 0.392SS16-1 III 0.421

SS17-1 PIT A 0.056 0.217 0.307 1.223 1.271 NS 4 3 0SS17-1 PIT B 0.007 0.117 1.348 1.085 NS 3 1SS17-1 I BD 0.259 0.158 1.456 1.165 NSSS17-1 II BD 0.231SS17-1 III BD 0.221

A29-1 PIT A 0.006 0.003 0.004 1.521 1.494 1.466 5 10 NSA29-1 PIT B 0.003 0.010 0.004 1.574 1.277 1.669 4 10 NSA29-1 I BD 0.005 0.003 1.424 1.611A29-1 II BD 0.004 1.392A29-1 III BD 0.004A29-1 IV BD 0.004

A31-1 PIT A 0.014 0.018 0.031 1.142 1.089 1.230 2 2 1A31-1 PIT B 0.007 0.026 0.031 1.293 1.028 1.245 2 5 2A31-1 I BD 0.014 0.028 0.016 1.428 1.094 1.107A31-1 II BD 0.012A31-1 III BD 0.012A31-1 IV BD 0.010

A32-1 PIT A 0.027 NS 0.329 1.429 NS NS 1 0 0A32-1 PIT B 0.018 1.139 NS NS 1A32-1 I BD 0.161 1.211 NS NSA32-1 II BD 0.017

C-6

Table C-2. Soil moisture, bulk density and crust thickness measured during September (1), January (2), and March (3) (continued)

Field ID

1 Moisture

(g/g)

2 Moisture

(g/g)

3 Moisture

(g/g) 1 BD 2 BD 3 BD

1 Crust thickness

(mm)

2 Crust thickness

(mm)

3 Crust thickness

(mm)A32-1 III BD 0.022

A34-1 PIT A 0.028 0.008 0.063 1.481 1.517 1.453 8 0 2A34-1 PIT B 0.043 0.008 0.103 1.380 1.507 1.581 6 5 2A34-1 A BD 0.010 0.005 0.013 1.300 1.607 1.453A34-1 B 0.016 1.328A34-1 C 0.028A34-1 D 0.026A34-1 PIT A 0.008 7A34-1 PIT B 0.004 2A34-1 I BD 0.007 1.624A34-1 II 0.087 1.191A34-1 III 0.013 1.220A34-1 IV 0.005 1.473

A34-2 PIT A 0.086 0.095 0.057 0.972 1.430 1.378 2 2 1A34-2 PIT B 0.030 0.117 0.099 0.984 1.235 1.415 2 2 3A34-2 I BD 0.052 0.111 0.080 1.206 1.270 1.336A34-2 II 0.110 0.045 1.216A34-2 III 0.123A34-2 IV 0.094

A100-1 PIT A 0.003 0.009 0.018 1.669 1.290 1.402 5 5 3A100-1 PIT B 0.003 0.005 0.015 1.557 1.410 1.464 5 2 10A100-1 A BD 0.003 0.005 0.005 1.817 1.479 1.595A100-1 B 0.004 1.765A100-1 C 0.003A100-1 D 0.004

A100-2 PIT A 0.074 0.031 0.122 1.174 1.086 1.576 5 1 2A100-2 PIT B 0.053 0.018 0.085 1.215 1.003 1.499 5 2 2A100-2 A BD 0.073 0.054 0.087 1.182 1.150 1.516A100-2 B 0.093 1.215A100-2 C 0.100 1.175A100-2 D 0.093 1.045A100-2 E 0.027A100-2 F 0.020

A101-1 PIT A 0.026 0.018 0.029 1.241 1.169 1.238 5 3 1A101-1 PIT B 0.025 0.020 0.028 1.143 1.366 1.210 5 5 10A101-1 I BD 0.024 0.019 0.029 1.267 1.411 1.261A101-1 II BD 0.026A101-1 III BD 0.024

SS23-1 0.038 0.068 0.313 1.087 NS NS 7 0 0SS23-2 0.140 1.389 NS NS 4

C-7

Table C-2. Soil moisture, bulk density and crust thickness measured during September (1), January (2), and March (3) (continued)

Field ID

1 Moisture

(g/g)

2 Moisture

(g/g)

3 Moisture

(g/g) 1 BD 2 BD 3 BD

1 Crust thickness

(mm)

2 Crust thickness

(mm)

3 Crust thickness

(mm)A34-3 BD1 NS 0.467 0.122 0.592 NS 1 20A34-3 surface NS 0.418 0.033 0.526 NS 2 3A34-3 NS 0.385 0.519 NSA34-3 NS 0.134

A200-1 Pit A NS 0.299 0.422 0.640 NS 2 0A200-1 Pit B NS 0.286 0.641 NS 2A200-1 NS 0.371 0.921 NSA200-1 NS 0.043A200-1 NS 0.088

A201-1 Pit A NS 0.323 0.398 0.937 NS 1 0A201-1 Pit B NS 0.281 1.026 NS 1A201-1 NS 0.311 0.811 NSA201-1 NS 0.125A201-1 NS 0.178

C-8

Table C-3. Soil organic matter, calcium carbonate, and salt content measured during September (1), January (2), and March (3)

Field ID OM 1 OM 2 OM 3

% CaCO3

1

% CaCO3

2

% CaCO3

3

1 Salt (mg/kg)

EC

2 Salt (mg/kg)

EC

3 Salt (mg/kg)

ECSS2-1 PIT A 0.002 0.003 0.004 4.61 3.55 3.17 274 1001 970SS2-1 PIT B 0.001 4.49 209SS2-1 I BD 4.25SS2-1 II BD 3.90SS2-1 III BD 4.51

SS6-1 PIT A 0.004 0.005 0.003 3.84 4.12 3.72 364 1218 443SS6-1 PIT B 0.005 5.06 341SS6-1 I BD 2.97SS6-1 II BD 3.43SS6-1 III BD 6.41

SS9-1 PIT A 0.023 NS NS 27.09 27.98 35.98 20237 40837 51674SS9-1 PIT B 21.72 17418SS9-1 I BD 37.84SS9-1 II BD 18.74SS9-1 III BD 17.36

SS16-1 PIT A 0.023 0.025 0.029 5.05 4.59 5.24 196793 300807 333532SS16-1 PIT B 0.023 5.62 216225SS16-1 I BD 7.99SS16-1 II BD 9.22SS16-1 III

SS17-1 PIT A 0.011 0.012 0.011 12.04 11.21 9.77 32451 81441 178401SS17-1 PIT B 0.011 12.38 24914SS17-1 I BD 5.55SS17-1 II BD 9.88SS17-1 III BD 8.72

A29-1 PIT A 0.005 0.005 NS 10.42 6.07 6.98 7863 5508 5431A29-1 PIT B 0.005 9.60 2983A29-1 I BD 8.47A29-1 II BD 7.49A29-1 III BD 4.30A29-1 IV BD 10.98

A31-1 PIT A 0.01 0.012 0.009 17.78 10.06 16.22 639 886 18554A31-1 PIT B 0.008 16.84 15504A31-1 I BDA31-1 II BD A31-1 III BD A31-1 IV BD

A32-1 PIT A 0.031 NS 0.017 13.74 NS 10.58 100777 NS 65044A32-1 PIT B 0.015 9.91 80090A32-1 I BD A32-1 II BD

C-9

Table C-3. Soil organic matter, calcium carbonate, and salt content measured during September (1), January (2), and March (3) (continued)

Field ID OM 1 OM 2 OM 3

% CaCO3

1

% CaCO3

2

% CaCO3

3

1 Salt (mg/kg)

EC

2 Salt (mg/kg)

EC

3 Salt (mg/kg)

ECA32-1 III BD

A34-1 PIT A 0.012 0.004 0.008 2.90 4.7595 2.7967 17530 4685 61855A34-1 PIT B 0.008 2.63 133052A34-1 A BD A34-1 BA34-1 CA34-1 DA34-1 PIT A 3.95 37320A34-1 PIT B 4.83 2828A34-1 I BD A34-1 IIA34-1 IIIA34-1 IV

A34-2 PIT A 0.007 0.006 0.007 3.31 2.1191 2.9409 95577 190354 94287A34-2 PIT B 0.012 2.41 57405A34-2 I BDA34-2 IIA34-2 IIIA34-2 IV

A100-1 PIT A 0.004 0.004 0.003 4.39 7.3802 6.7147 320 658 888A100-1 PIT B 0.001 3.84 334A100-1 A BDA100-1 BA100-1 CA100-1 D

A100-2 PIT A 0.003 0.009 0.01 2.81 2.7777 3.8593 91073 87748 90284A100-2 PIT B 0.005 2.78 112524A100-2 A BDA100-2 BA100-2 CA100-2 DA100-2 EA100-2 F

A101-1 PIT A 0.009 0.011 0.011 19.48 13.455 15.645 1839 1074 6752A101-1 PIT B 0.011 17.87 689A101-1 I BD A101-1 II BD A101-1 III BD

SS23-1 0.13 0.088 0.041 22.89 22.34 3.30 127634 216829 314270SS23-2 0.05 4.98 434800

C-10

Table C-3. Soil organic matter, calcium carbonate, and salt content measured during September (1), January (2), and March (3) (continued)

Field ID OM 1 OM 2 OM 3

% CaCO3

1

% CaCO3

2

% CaCO3

3

1 Salt (mg/kg)

EC

2 Salt (mg/kg)

EC

3 Salt (mg/kg)

ECA34-3 BD1 0.072 0.074 20.113 21.111 176799 179257A34-3 surfaceA34-3 A34-3

A200-1 Pit A 0.054 0.044 13.692 14.834 306273 220062A200-1 Pit B A200-1 A200-1 A200-1

A201-1 Pit A 0.074 0.06 5.0242 10.677 326725 211472A201-1 Pit BA201-1 A201-1 A201-1

C-11

Table C-4. Soil chemistry data including pH, CEC, cations, sodium adsorption ration (SAR) and sulfate measured in September (1), January (2), and March (3)

Field ID pH 1 pH 2 pH 3 CEC 1 CEC 2 CEC 3P 1

(ppm)P 2

(ppm)P 3

(ppm)K 1

(ppm)K 2

(ppm)K 2

(ppm)SS2-1 PIT A 8.9 8.5 8.4 10.5 1.8 1.7 7 6 4 55 49 92SS2-1 PIT B 9.1 7.6 7 43

SS6-1 PIT A 8.1 7.8 8 11.5 4 5 19 6 11 107 103 206SS6-1 PIT B 8.1 13.7 6 127

SS9-1 PIT A 8 NS NS 16.7 NS NS 16 NS NS 97 NS NS

SS16-1 PIT A 7.8 7.8 7.9 15.1 8.2 7.2 8 13 15 1300 1536 1896SS16-1 PIT B 7.6 17.5 11 1422

SS17-1 PIT A 8 8.2 8 25.3 14.5 15 11 8 18 589 694 757SS17-1 PIT B 7.9 28.3 12 560

A29-1 PIT A 7.8 7.8 NS 8 1.9 NS 18 9 NS 105 46 NSA29-1 PIT B 7.9 9.4 23 152

A31-1 PIT A 8.4 7.7 7.7 18.7 11.8 14.7 10 7 13 371 332 394A31-1 PIT B 7.9 22.7 14 396

A32-1 PIT A 8 NS 8.6 21.6 NS 7.5 28 NS 25 411 NS 389A32-1 PIT B 8.2 26.5 19 294

A34-1 PIT A 8.5 8.2 8.5 14.3 3.3 3.3 26 11 8 157 175 152A34-1 PIT B 8.1 9.9 18 742

A34-2 PIT A 8.2 8.1 8.2 8.5 3 3.4 10 11 15 574 216 231A34-2 PIT B 8.4 8.9 15 222

A100-1 PIT A 8.1 8.3 8.5 9.6 3.5 3.8 9 5 7 57 98 76A100-1 PIT B 8.2 5.7 18 55

A100-2 PIT A 8.2 8.4 8.2 10.5 2 3.2 12 6 8 266 360 147A100-2 PIT B 8.5 10 8 232

A101-1 PIT A 8.3 7.9 7.9 26.9 13.7 14 13 11 20 622 388 701A101-1 PIT B 8.2 25.1 10 460

SS23-1 8.2 7.9 8.5 19.8 7.9 2.8 73 58 41 1476 1015 695SS23-2 8.4 11.7 65 1715

A34-3 BD1 8.6 8.4 10.9 13.1 79 161 932 936

A200-1 Pit A 8 8.4 4.4 6.2 56 114 1254 984

A201-1 Pit A 8 8.4 3.2 5.9 36 147 1100 1089

C-12

Table C-4. Soil chemistry data including pH, CEC, cations, sodium adsorption ration (SAR) and sulfate measured in September (1), January (2), and March (3) (Continued)

Field IDMg 1 (ppm)

Mg 2 (ppm)

Mg 2 (ppm)

Ca 1 (ppm)

Ca 2 (ppm)

Ca 2 (ppm)

Na 1 (ppm)

Na 2 (ppm)

Na 3 (ppm)

SS2-1 PIT A 49 208 226 997 1307 1496 86 1323 3084SS2-1 PIT B 43 1022 74

SS6-1 PIT A 101 146 320 1916 1644 2883 49 385 1654SS6-1 PIT B 110 2069 46

SS9-1 PIT A 189 NS NS 30250 NS NS 997 NS NS

SS16-1 PIT A 5511 4458 4551 4493 3526 3745 11010 10650 10430SS16-1 PIT B 5769 3409 10930

SS17-1 PIT A 954 1731 1727 4854 4915 5053 7635 10180 10200SS17-1 PIT B 562 5407 4804

A29-1 PIT A 142 139 NS 4881 4923 NS 946 230 NSA29-1 PIT B 182 3100 706

A31-1 PIT A 272 243 372 4237 3761 5285 251 1979 2392A31-1 PIT B 151 3696 3677

A32-1 PIT A 3095 NS 1968 6056 NS 6180 8994 NS 7505A32-1 PIT B 1817 6516 8073

A34-1 PIT A 320 205 556 19020 3168 8327 1810 808 4526A34-1 PIT B 1023 5114 9897

A34-2 PIT A 665 905 964 3795 8400 7295 9211 6871 8249A34-2 PIT B 851 2580 7773

A100-1 PIT A 86 255 142 1312 2799 1936 489 1186 654A100-1 PIT B 70 1082 289

A100-2 PIT A 683 2441 798 3601 2886 4892 9492 9319 7835A100-2 PIT B 785 3152 9882

A101-1 PIT A 447 472 453 4721 3896 4281 2941 1370 2366A101-1 PIT B 385 4715 959

SS23-1 5072 3609 4018 5657 3960 12000 10320 10310 10890SS23-2 11650 5190 10770

A34-3 BD1 3530 2368 9790 7503 10300 8869

A200-1 Pit A 4785 3901 9631 10970 10730 10730

A201-1 Pit A 4839 4404 7042 10890 10910 10880

C-13

Table C-4. Soil chemistry data including pH, CEC, cations, sodium adsorption ration (SAR) and sulfate measured in September (1), January (2), and March (3) (continued)

Field ID SAR 1 SAR 2 SAR 3SO4 1 (ppm)

SO4 2 (ppm)

SO4 3 (ppm)

SS2-1 PIT A 0.287 0.934 1.338 9 650 1060SS2-1 PIT B 0.264 8

SS6-1 PIT A 0.156 0.464 0.719 21 221 675SS6-1 PIT B 0.145 18

SS9-1 PIT A 0.181 NS NS 20140 NS NS

SS16-1 PIT A 1.049 1.155 1.121 22760 31120 30280SS16-1 PIT B 1.091 19490

SS17-1 PIT A 1.147 1.238 1.227 4922 7083 8383SS17-1 PIT B 0.897 2819

A29-1 PIT A 0.434 0.213 NS 2414 3326 NSA29-1 PIT B 0.464 796

A31-1 PIT A 0.236 0.703 0.650 156 676 1832A31-1 PIT B 0.978 1019

A32-1 PIT A 0.991 NS 0.960 5700 NS 8250A32-1 PIT B 0.984 7865

A34-1 PIT A 0.306 0.489 0.714 13450 1044 8514A34-1 PIT B 1.270 6163

A34-2 PIT A 1.437 0.859 0.999 4346 9412 7982A34-2 PIT B 1.505 8927

A100-1 PIT A 0.591 0.623 0.561 243 1141 172A100-1 PIT B 0.501 110

A100-2 PIT A 1.489 1.323 1.173 4876 10700 5392A100-2 PIT B 1.584 3896

A101-1 PIT A 0.754 0.560 0.707 479 550 650A101-1 PIT B 0.434 140

SS23-1 0.981 1.167 0.825 12440 11450 38470SS23-2 0.800 28230

A34-3 BD1 0.879 0.948 20690 10110

A200-1 Pit A 0.863 0.849 29680 24990

A201-1 Pit A 0.958 0.843 30000 29120

C-14

Table C-5. Soil particle size distribution by pipette and laser methods for comparison of selected samples

Pipette Results

Lab ID # Field ID% Gravel

>2mm

2.0-1.0 mm %wt

1.0-0.5 mm %wt

0.5-0.25 mm %wt

0.2-0.125 mm %wt

0.125-0.0625

mm %wt

Fine Silt %wt

Co Silt %wt

Total Sand %wt

Total Silt %wt

Clay %wt

06-847 SS2-1 PIT A 8.6 13.8 14.2 11.5 29.7 24.5 1.4 2.7 93.6 4.0 2.306-852 SS6-1 PIT A 12.1 14.7 8.5 14.1 26.1 18.4 3.0 4.1 81.7 7.1 11.206-857 SS9-1 PIT A 44.3 1.5 13.2 27.8 16.0 8.0 1.3 30.9 66.6 32.2 1.206-862 SS16-1 PIT A 0.0 0.1 0.3 2.0 1.8 1.1 25.3 8.4 5.5 33.7 60.806-866 SS17-1 PIT A 0.0 0.0 0.0 0.0 0.1 0.9 30.0 15.4 1.0 45.4 53.606-871 A29-1 PIT A 15.5 13.9 54.1 20.7 2.0 0.8 2.3 1.7 91.4 3.9 4.606-877 A31-1 PIT A 0.0 0.1 0.2 0.5 1.4 5.2 27.7 21.2 7.5 48.9 43.706-883 A32-1 PIT A 0.0 0.1 0.2 0.4 0.4 1.1 7.2 86.5 2.1 93.7 4.106-888 A34-1 PIT A 8.7 3.0 3.6 11.4 35.0 28.7 4.9 11.0 81.6 16.0 2.406-889 A34-1 PIT A 9.0 2.2 4.1 12.3 53.9 21.4 1.6 1.7 94.1 3.3 2.606-892 A34-2 PIT A 0.0 0.0 1.4 12.9 39.0 30.8 3.9 7.7 84.2 11.6 4.206-894 A100-1 PIT A 23.4 12.9 12.7 25.3 28.3 11.1 1.7 4.2 90.4 5.9 3.706-896 A100-2 PIT A 0.0 0.1 3.5 17.2 37.4 28.6 1.5 7.5 86.9 9.0 4.206-898 A101-1 PIT A 0.2 0.3 0.3 0.9 2.5 4.8 31.8 18.9 8.8 50.7 40.506-903 SS23-1 0.0 0.1 0.4 0.7 0.8 1.1 33.6 51.3 3.2 84.9 11.906-904 SS23-2 0.0 0.8 3.1 3.3 4.3 9.1 2.6 67.2 20.7 69.8 9.5

06-889 DUP Duplicate 9 2.0 4.5 12.1 55.8 19.8 1.4 1.1 94.2 2.5 3.3

Laser Results

Lab ID # Field ID% Gravel

>2mm

2.0-1.0 mm %wt

1.0-0.5 mm %wt

0.5-0.25 mm %wt

0.2-0.125 mm %wt

0.125-0.0625

mm %wt

Total Sand %wt

Total Silt %wt

Clay %wt

06-847 SS2-1 PIT A 8.6 11.6 0.4 13.3 41.1 21.7 88.7 8.2 3.606-852 SS6-1 PIT A 12.1 16.6 0.4 11.3 23.6 13.1 65.4 18.7 16.306-857 SS9-1 PIT A 44.3 15.9 6.0 20.9 12.0 8.7 63.9 24.8 11.706-862 SS16-1 PIT A 0.0 0.0 0.0 0.0 0.5 0.8 1.3 35.3 63.406-866 SS17-1 PIT A 0.0 0.0 0.0 0.1 0.9 2.5 3.5 44.3 52.106-871 A29-1 PIT A 15.5 27.9 36.4 12.4 3.9 2.7 83.9 10.7 6.106-877 A31-1 PIT A 0.0 1.2 0.0 0.1 2.4 5.2 8.9 47.6 43.606-883 A32-1 PIT A 0.0 0.0 0.0 0.1 2.3 7.3 9.7 64.4 25.906-888 A34-1 PIT A 8.7 3.4 0.1 11.3 32.3 20.2 67.7 23.9 8.806-889 A34-1 PIT A 9.0 6.4 0.0 17.2 48.6 15.1 88.0 8.5 4.206-892 A34-2 PIT A 0.0 0.0 0.1 13.3 35.9 24.8 74.6 19.7 6.106-894 A100-1 PIT A 23.4 4.2 10.4 29.4 29.3 13.2 87.2 9.4 4.106-896 A100-2 PIT A 0.0 0.0 0.4 15.5 34.6 23.7 74.7 18.2 7.606-898 A101-1 PIT A 0.2 0.0 0.0 0.1 2.3 4.7 7.1 52.6 40.306-903 SS23-1 0.0 0.0 0.0 0.1 4.5 14.6 19.2 65.5 15.506-904 SS23-2 0.0 0.0 0.0 0.7 4.1 10.4 15.2 62.8 22.1

Sand Fractions Silt Fractions

C-15

Table C-6. Summary of soil particle size distributions by site (for WEPS input) using LPSA and sieve methods

Field IDTest type (WT, P) Gravel

VC Sand

CO Sand M Sand F Sand

VF Sand

Sand % Silt % Clay %

SS2-1 WT+PI 0.128 0.262 0.170 0.194 0.221 0.083 0.936 0.046 0.024SS6-1 PI 0.045 0.061 0.015 0.152 0.295 0.122 0.650 0.189 0.166SS9-1 PI 0.310 0.164 0.091 0.230 0.133 0.061 0.683 0.209 0.113SS16-1 WT+PI 0.000 0.000 0.000 0.000 0.006 0.008 0.014 0.366 0.620SS17-1 PI 0.000 0.000 0.000 0.001 0.023 0.055 0.079 0.432 0.488A29-1 PI 0.106 0.189 0.493 0.109 0.027 0.016 0.840 0.104 0.063A31-1 WT+PI 0.000 0.004 0.000 0.004 0.035 0.071 0.114 0.463 0.424A32-1 PI 0.000 0.000 0.000 0.003 0.045 0.133 0.181 0.582 0.237A34-1 1 WT+PI 0.080 0.022 0.001 0.121 0.362 0.219 0.728 0.199 0.075A34-1 2 WT+PI 0.119 0.058 0.015 0.175 0.385 0.179 0.814 0.135 0.053A34-2 WT+PI 0.004 0.013 0.004 0.125 0.335 0.238 0.717 0.211 0.075A100-1 WT+PI 0.162 0.126 0.165 0.308 0.217 0.083 0.902 0.069 0.033A100-2 WT+PI 0.000 0.004 0.011 0.203 0.383 0.196 0.798 0.144 0.060A101-1 WT+PI 0.003 0.000 0.000 0.001 0.022 0.043 0.067 0.528 0.405SS23-1 None 0.000 0.000 0.000 0.004 0.043 0.125 0.172 0.642 0.188A34-3 PI 0.060 0.016 0.000 0.006 0.053 0.112 0.297 0.309 0.208A200-1 PI 0.055 0.010 0.000 0.002 0.037 0.088 0.189 0.362 0.312A201-1 PI 0.005 0.013 0.000 0.008 0.067 0.107 0.309 0.322 0.173

C-16

Table C-7. Summary of soil chemistry by site (for WEPS input) separated by season Test 1: September 2005

Field ID CaCO3 %Salt content (mg/kg) EC P (ppm) K (ppm) Mg (ppm) Ca (ppm) Na (ppm)

Na adsoprtion

ratioSO4

(ppm)SS2-1 4.35 242 7 49 46 1009.5 80 0.275 9SS6-1 4.34 352 12.5 117 105.5 1992.5 47.5 0.151 20SS9-1 24.55 18828 16 97 189 30250 997 0.181 20140SS16-1 6.97 206509 9.5 1361 5640 3951 10970 1.070 21125SS17-1 9.71 28682 11.5 574.5 758 5130.5 6219.5 1.022 3871A29-1 8.54 5423 20.5 128.5 162 3990.5 826 0.449 1605A31-1 17.31 8071 12 383.5 211.5 3966.5 1964 0.607 588A32-1 11.82 90434 23.5 352.5 2456 6286 8533.5 0.988 6783A34-1 1 2.77 75291 22 449.5 671.5 12067 5853.5 0.788 9807A34-1 2 4.39 20074 22 449.5 671.5 12067 5853.5 0.788 9807A34-2 2.86 76491 12.5 398 758 3187.5 8492 1.471 6637A100-1 4.12 327 13.5 56 78 1197 389 0.546 177A100-2 2.80 101798 10 249 734 3376.5 9687 1.536 4386A101-1 18.68 1264 11.5 541 416 4718 1950 0.594 310SS23-1 13.94 281217 69 1595.5 8361 5423.5 10545 0.890 20335

Test 2: January 2006

Field ID CaCO3 %Salt content (mg/kg) EC P (ppm) K (ppm) Mg (ppm) Ca (ppm) Na (ppm)

Na adsoprtion

ratioSO4

(ppm)SS2-1 3.55 1001 6 49 208 1307 1323 0.934 650SS6-1 4.12 1218 6 103 146 1644 385 0.464 221SS9-1 27.98 40837 NS NS NS NS NS NS NSSS16-1 4.59 300807 13 1536 4458 3526 10650 1.155 31120SS17-1 11.21 81441 8 694 1731 4915 10180 1.238 7083A29-1 6.07 5508 9 46 139 4923 230 0.213 3326A31-1 10.06 886 7 332 243 3761 1979 0.703 676A32-1 NS NS NS NS NS NS NS NS NSA34-1 1 4.76 4685 11 175 205 3168 808 0.489 1044A34-1 2 NS NS NS NS NS NS NS NS NSA34-2 2.12 190354 11 216 905 8400 6871 0.859 9412A100-1 7.38 658 5 98 255 2799 1186 0.623 1141A100-2 2.78 87748 6 360 2441 2886 9319 1.323 10700A101-1 13.45 1074 11 388 472 3896 1370 0.560 550SS23-1 22.34 216829 58 1015 3609 3960 10310 1.167 11450A34-3 20.11 176799 79 932 3530 9790 10300 0.879 20690A200-1 13.69 306273 56 1254 4785 9631 10730 0.863 29680A201-1 5.02 326725 36 1100 4839 7042 10910 0.958 30000

Test 3: March 2006

Field ID CaCO3 %Salt content (mg/kg) EC P (ppm) K (ppm) Mg (ppm) Ca (ppm) Na (ppm)

Na adsoprtion

ratioSO4

(ppm)SS2-1 3.17 970 4 92 226 1496 3084 1.338 1060SS6-1 3.72 443 11 206 320 2883 1654 0.719 675SS9-1 35.98 51674 NS NS NS NS NS NS NSSS16-1 5.24 333532 15 1896 4551 3745 10430 1.121 30280SS17-1 9.77 178401 18 757 1727 5053 10200 1.227 8383A29-1 6.98 5431 NS NS NS NS NS NS NSA31-1 16.22 18554 13 394 372 5285 2392 0.650 1832A32-1 10.58 65044 25 389 1968 6180 7505 0.960 8250A34-1 1 2.80 61855 8 152 556 8327 4526 0.714 8514A34-1 2 NS NS NS NS NS NS NS NS NSA34-2 2.94 94287 15 231 964 7295 8249 0.999 7982A100-1 6.71 888 7 76 142 1936 654 0.561 172A100-2 3.86 90284 8 147 798 4892 7835 1.173 5392A101-1 15.65 6752 20 701 453 4281 2366 0.707 650SS23-1 3.30 314270 41 695 4018 12000 10890 0.825 38470A34-3 21.11 179257 161 936 2368 7503 8869 0.948 10110A200-1 14.83 220062 114 984 3901 10970 10730 0.849 24990A201-1 10.68 211472 147 1089 4404 10890 10880 0.843 29120

C-17

Table C-8. Summary of soil hydraulic properties by site (for WEPS input)

Field IDWilting Point

Field Capacity

Saturated soil

Saturated hyd. Con.

Air Entry Ye (kPa)

SS2-1 0.041 0.108 0.313 3.9E-05 -0.1267SS6-1 0.116 0.214 0.44 3.0E-06 4.204SS9-1 0.094 0.193 0.417 6.5E-06 3.4197SS16-1 0.372 0.516 0.56 8.6E-07 8.296SS17-1 0.284 0.443 0.542 7.4E-07 7.6822A29-1 0.067 0.149 0.373 1.6E-05 1.9193A31-1 0.24 0.404 0.532 8.1E-07 7.3412A32-1 0.136 0.308 0.494 2.3E-06 6.0454A34-1 1 0.077 0.174 0.391 1.1E-05 2.5331A34-1 2 0.063 0.149 0.365 1.8E-05 1.6465A34-2 0.077 0.176 0.392 1.1E-05 2.5672A100-1 0.048 0.121 0.333 3.0E-05 0.5553A100-2 0.067 0.155 0.373 1.5E-05 1.9193A101-1 0.229 0.399 0.532 9.8E-07 7.3412SS23-1 0.117 0.295 0.482 3.7E-06 5.6362A34-3 0.153 0.311 0.472 2.528E-06 5.2952A200-1 0.142 0.326 0.484 1.25E-06 5.7044A201-1 0.196 0.341 0.474 3.584E-06 5.3634

C-18

Table C-9. Summary of crust properties by site (for WEPS input)

C-19

Test 1: September 2005

Field ID Moisture BD

Crust strength

PSI

Crust strength

kPa

Crust thickness

(mm)SS2-1 0.002 1.70 84.60 583.30 2SS6-1 0.006 1.59 206.67 1424.92 2SS9-1 0.062 0.76 90.00 620.53 3SS16-1 0.262 1.08 659.33 4545.94 5SS17-1 0.155 1.34 386.00 2661.38 3.5A29-1 0.004 1.48 214.00 1475.48 4.5A31-1 0.012 1.29 10.13 69.87 2A32-1 0.049 1.26 338.67 2335.03 1A34-1 1 0.025 1.37 20.13 138.81 7A34-1 2 0.021 1.38 NS NS 4.5A34-2 0.083 1.09 34.13 235.34 2A100-1 0.003 1.70 52.13 359.45 5A100-2 0.067 1.17 32.60 224.77 5A101-1 0.025 1.22 311.33 2146.57 5SS23-1 0.089 1.24 NS NS 5.5

Test 2: January 2006

Field ID Moisture BD

Crust strength

PSI

Crust strength

kPa

Crust thickness

(mm)SS2-1 0.002 1.66 451.33 3111.83 2SS6-1 0.004 1.58 132.00 910.11 2.5SS9-1 0.064 0.67 15.60 107.56 3SS16-1 0.162 1.55 52.00 358.53 4SS17-1 0.164 1.17 0.00 0.00 2A29-1 0.006 1.46 159.00 1096.27 10A31-1 0.024 1.07 45.00 310.26 3.5A32-1 NS NS 0.00 0.00 0A34-1 1 0.007 1.54 0.00 0.00 2.5A34-1 2 NS NS NS NS NSA34-2 0.092 1.31 1.96 13.48 2A100-1 0.006 1.39 256.00 1765.06 3.5A100-2 0.034 1.08 0.27 1.84 1.5A101-1 0.019 1.32 156.00 1075.58 4SS23-1 0.068 NS NS NS NSA34-3 0.351 0.55 0.00 0.00 1.5A200-1 0.318 0.73 0.20 1.38 2A201-1 0.305 0.92 0.00 0.00 1

Test 3: March 2006

Field ID Moisture BD

Crust strength

PSI

Crust strength

kPa

Crust thickness

(mm)SS2-1 0.003 1.81 598.00 4123.07 5.5SS6-1 0.009 1.63 210.67 1452.50 1.5SS9-1 0.050 0.62 43.87 302.45 7.5SS16-1 1.554 1.59 98.00 675.69 11SS17-1 0.307 NS 0.00 0.00 0A29-1 0.004 1.57 0.00 0.00 NSA31-1 0.026 1.19 208.33 1436.41 1.5A32-1 0.329 NS NS NS 0A34-1 1 0.060 1.50 18.58 128.09 2A34-1 2 NS NS NS NS NSA34-2 0.079 1.38 22.13 152.60 2A100-1 0.013 1.49 364.00 2509.69 6A100-2 0.098 1.53 15.53 107.10 2A101-1 0.029 1.24 196.00 1351.37 5.5SS23-1 0.313 NS NS NS 0A34-3 0.077 NS 0.00 0.00 12A200-1 0.422 NS 0.00 0.00 0A201-1 0.398 NS 7.56 52.09 0

C-20

D-1

APPENDIX D.

Response to Review Comments on DRAFT Final Report:

Measurement of Windblown Dust Emission Potential and Soil Characteristics at the Salton Sea in Support of the

Programmatic Environmental Impact Report

This Appendix contains the original review comments provided by staff at USGS, CH2M Hill, Great Basin Unified Air Pollution Control District, and DRI. For all reviewers, the format of the responses to comments is the same. The original comment is repeated in plain text and the response from the report authors follows immediately in italic text.

D-2

Response to Comments from Pat Chavez, USGS

D-3

D.1 Review Comments From Pat Chavez, USGS Desert Research Institute (DRI) report titled ‘Measurement of windblown dust emission potential and soil characteristics at the Salton Sea in support of the Programmatic Environmental Impact Report: DRAFT Final Report’ dated 5/22/06. Comments by: Pat Chavez Research Physical Scientist United States Geological Survey (USGS) Flagstaff, Arizona 928-556-7221 pchavez@usgs.gov June 20, 2006 I went through the report and made comments and asked questions on a section-by-section basis. Since several topics are discussed in more than one section of the report there is some overlap and similar comments made in different parts of the review. The general comment section highlights some of the questions encountered at several locations within the report. General comments:

• The purpose of the report is to present the results of data collected and analyzed to characterize the soil and surface at sites around the Salton Sea with emphasis on PM10 (dust) emission. One of the criteria of the sites selected was the potential to serve as ‘surrogates for future exposed shoreline.’ The data were collected during field surveys that occurred in September 21-30, 2005; January 24-27, 2006; and March 20-24, 2006. Initially there were fourteen sites with three more added during the January (second) field survey for a total of seventeen sites around the Salton Sea.

• The report was well written and I was glad to see that photographs of each of the sites were included in this version of the report. I was also glad that DRI was able to adjust their already full schedule on very short notice in January to add three sites (playa-like areas at the south end of the lake) during their January field campaign.

• A difficult question to answer at this stage is how representative these sites, or any other sites, are of areas that will be exposed in the future? One thing that could help with this question is, when possible, compare any on-shore study results with results that may be available of sites currently under water within the Salton Sea. For example, the soil texture results generated in this study should be compared to results generated by Agrarian Research for the 800 grab samples collected from within the Salton Sea from the shoreline to a water depth of 15 feet (a report was delivered to the Salton Sea Science Office dated November 4, 2003). Figure 4-4 of the DRI report is very similar to a figure in the Agrarian report and I suggest that the two results be compared and discussed (keeping in mind that this would be strictly related to particle size/texture information; that is,

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not surface crusting or salt contents). This is indeed a rather difficult question to answer at this time. It may not be possible to begin to address this issue until after the Salton Sea level has begun to subside at which point it may be possible to compare data collected while the sediment was inundated (e.g. soundings and grab samples) with what actually happens when the sediment is exposed. That said, the suggestion of comparing the soil textural properties at the sites where PI-SWERL was operated as part of this study to the grab samples collected as part of the Agrarian Research Study (2003) is very good. Figure 4-5 has been added to the report. The figure shows the sediment size distributions measured by Agrarian Research at depths of 0m (shoreline), 1.5 m, 3.0 m, and 4.5 m overlaid with the soil textures measured at the sites in this study. The Agrarian data exhibit a high occurrence of clay, clay loam, and loam soils that is not as strongly evident in the DRI samples. However, there appears to be good overlap in the sandy, sandy loam, and silt-clay regions of the ternary plot.

• I am concerned that constant wetting of low lying near-shore areas by waves that come off the waters during moderate to high winds may have impacted the results of this study (as it would any other study that uses near-shore test sites). Besides the potential that these results were impacted I am also concerned that this constant wetting may make it difficult to find sites that are representative of ‘to be exposed areas’ for future studies (more on this within the specific comment sections below). We agree that using shoreline sites to represent ‘to be exposed areas’ is not ideal. We would have preferred ‘recently exposed’ areas for performing our testing, but with the exception of a few ‘playa-like’ sites that appeared to be inundated at some time in the last few decades, but are now a few meters above water level, representative, non-vegetated ‘recently exposed’ sites were hard to come by except close to the shoreline. Nevertheless, the data collected during this study are useful for obtaining an estimate of PM10 emissions from soils with similar texture and somewhat similar salt chemistry to those that are likely to be exposed in the future. The fact that many of these areas are wetted over the course of the year is in a way advantageous for this study, because it ensures that the salt composition is not too different from the salt composition in the Salton Sea water. Having said that, we agree that the surface crusting properties may be altered at these sites once the lake level declines and the sites are out of reach of the water level (i.e. a few meters above water level). If this study is repeated when the water level declines, it will be possible to directly compare PM10 emissions at these sites when they were near the shoreline (present study) and when they become far from the shoreline (possible future study). Specific comments on this topic are addressed on a case by case basis below.

• Because of the limitation on the maximum PM10 emission that can be measured by the PI-SWERL system being on the low side the maximum RPM level varied

Response to Comments from Pat Chavez, USGS

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from site to site. This makes it difficult to compare sites with each other because the same maximum RPM (or equivalent u* and/or wind speed) was not used and the maximum value tested is not the same on the graphs showing the results. I think that values for the same u* need to be predicted using the dust emission model and the results for all the sites (during each field trip) graphed. Graphs with the data in linear (non-log) space should be included and the sites arranged within the graphs by the landforms presented in table 3-1 (more on this within the specific comment sections below). This has been addressed and specific details are provided below in response to specific comments. Figures have been added that compare emissions at all sites at a specific u*, linear scale plots have been added to the existing log-scale plots, and figures summarizing the findings by landforms have been included.

• Something that needs to be discussed further is the potential impact to the results by the availability (or lack) of sand within the relatively small PI-SWERL footprint. One of the surprising results was that ‘no clear correlation was evident between PM10 emission and soil texture’. I think that sand was not introduced into the PI-SWERL (or wind tunnel) test and since it is such a critical parameter for dust emission this needs to be address in more detail (more on this within the specific comment sections below). Specific comments are discussed below in the order in which they appear, but in general, all finite-length wind erosion measurement devices such as wind tunnels and PI-SWERL have difficulty with the issue of sand supply. The problem is that if the sand supply is limited in the footprint of the wind tunnel (or PI-SWERL), then once the sand has been moved out of the test area, saltation ceases. If one adds sand to the wind tunnel, of course, PM10 emissions increase. However, this results in an unrealistic scenario where there is in effect an unlimited supply of sand abrading the surface and causing dust emissions. Further, it is not clear that adding sand through – say a hopper – is at all representative of the saltation process. This is one reason that the University of Guelph research group led by William Nickling no longer uses a sand feed in their portable wind tunnel. We provide one additional observation on this topic. At the Salton Sea, most sites were either essentially free of sand or else where not sand supply-limited. This can be seen from the soil ternary diagram. At the sites where sand was limited dust emissions were driven by the movement and breakup of aggregates.

• With my background and interest in dust and landscape vulnerability to wind erosion I am interested in the new technology that the PI-SWERL system represents. However, I wonder if a lot of the discussion and explanation about the PI-SWERL and wind tunnel should perhaps be in an appendix? I am not sure if the level of detail given in the report will be of interest to most readers who will be primarily interested on the test results. This is a fair point, but a matter of preference. Since the PI-SWERL is a

Response to Comments from Pat Chavez, USGS

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relatively new device and many of the readers of the report will be wondering how it might compare with the wind tunnel, we have elected to keep the PI-SWERL/wind tunnel comparison in the main body of the report.

Specific comments: Pages 3-8 (Background and PI-SWERL sections):

• Soil characteristics and properties that affect the amount of dust emitted by a soil are discussed in the 2nd paragraph of page 3 and middle of page 4; there is no discussion about the need to have sand present. Sand saltation is one of the main ingredients for moderate to large amounts of dust emission (the other two are wind and fines) and my question is: was any sand introduced into the PI-SWERL (or wind tunnel) during the dust emission tests? If not, how might this affect the results (i.e., there might be a sand source near by that would saltate during windy conditions but sand was not available within the very small area covered by the PI-SWERL). Figure 2-3 on page 8 shows a schematic of how the PI-SWERL works and the importance of sand. In general, when you add sand to an aeolian system, the outcome is higher dust emissions than in the absence of sand. As mentioned previously, all finite-length wind erosion measurement devices such as wind tunnels and PI-SWERL have difficulty with the issue of sand supply. The problem is that if the sand supply is limited in the footprint of the wind tunnel (or PI-SWERL), then once the sand has been moved out of the test area, saltation ceases. If one adds sand to the wind tunnel, of course, PM10 emissions increase. However, this results in an unrealistic scenario where there is in effect an unlimited supply of sand abrading the surface and causing dust emissions. At the Salton Sea, most sites were either essentially free of sand or else where not sand supply-limited (Sometimes both conditions existed at the same site depending on season). This can be seen from the soil ternary diagram. At the sites where sand was limited dust emissions were driven by the movement and breakup of aggregates. That said, we have added a paragraph on the importance of sand. The gist of the discussion which can be found on page 3 is that conventional wisdom and our understanding of aeolian transport suggest that sand saltation is a very important aspect of dust emissions. However, we also have added citations to more recent work that suggests that on certain soil surfaces, sand saltation may play a minor role in dust emissions compared to the formation of aggregates from finer particles. This is especially true for silt and clay-crusted surfaces.

• Two of the surface properties and characteristics that affect the amount of dust emitted by a soil are given as soil moisture and texture in page 3 (which I agree with). My question is: later in the report it is stated that no correlation was seen between dust emission levels and soil moisture or texture; I would

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like the authors to comment as to why they think this happened (I have some suggestions about this later in the review). This is explained in greater detail elsewhere. To summarize, when a soil is moist, dust emissions are minimally small. However, methods for measuring soil moisture require a finite sample depth. At the Salton Sea, there were several sites where the top of the soil surfaces was dry (and emissive) but the underlying soil was wet. Thus, the normal method for measuring soil moisture indicated no relationship between soil wetness and dust emissions. With regard to texture, emissions were found to be high at both silt and clay-crusted sites (especially when a friable salt crust was present) and at sandy sites. At sandy sites, sand saltation probably dominates dust production while at crusted sites, loose aggregates break off at high wind shears and either saltate or break up further into clay-sized particles.

• Page 6 discusses the incremental increase in RPMs of the PI-SWERL during the testing and how this is related to shear stress (u*). Later in the report most/all the graphs related to dust emission are shown with u*. My question is: were RPMs related to wind speeds? It would be nice to be able to relate the graphs of dust emission to wind speed. From the report I did not get a sense for the types of winds that are needed at the various sites for dust emission to occur or the relative amounts of dust that could be produced at each site for a given wind speed (e.g., at 25 mph). This is a fair point. The draft report omitted any mention of how friction velocities relate to ambient wind speed, a quantity that most people have an intuitive understanding of. This is addressed in greater detail below in response to a specific comment, but our general approach was to provide a range of wind speeds that could correspond to the friction velocities (varies depending on assumption for surface roughness). We also use an estimate of the maximum wind speed to provide the reader with an understanding of a realistic maximum value of u* at the Salton Sea (This value turns out to be approximately 0.56 m/s which coincides with one of the RPM steps in the PI-SWERL sequence).

Pages 9-23 (Methods section):

• The first paragraph in section 3.1 on page 9 (site descriptions) states that ‘some sites close to the Sea were inundated during one or more of the field campaigns and no PM10 emissions data were collected at those times.’ It turns out that for the playa-like locations, especially at the south end of the Salton Sea, this might be a major problem. We recently installed two automatic digital camera stations to take photographs when the winds are above a pre-selected threshold at the SW and SE portions of the lake. The camera stations had been up for less than a week before we downloaded some interesting photographs (early April). During high wind events coming out of the west/NW a playa-like area we are monitoring in the SE portion of the

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Salton Sea was ‘completely’ covered with water (several times within a few days). It turns out that wind generated waves comes off the Salton Sea onto low lying near-shore areas, keeping these areas very wet. Of course, this shuts off any dust emission from the area, so the site becomes ‘not vulnerable’ to wind erosion due to the high winds that typically cause dust emission. This presents a problem because, in general, these types of sites (low lying near-shore exposed areas) which can be dust producer may not be representative of to be exposed areas until the water level drops enough that the area is far enough away from the water that it will not continually get covered with water during windy conditions. With the current conditions there are some areas that basically never dry out during the windy season, which of course, shuts off PM10 emission. We are considering options as to how to get around this problem, but in this has to be kept in mine and we need to realize that this could impact all studies related to testing and monitoring such sites. I would think that field measurements made during March could have been impacted by this and perhaps helps explain why a correlation between soil moisture and dust emission was not seen (i.e., several playa-like sites were so wet during the field surveys that no PI-SWERL test could be done, thus perhaps biasing the results?). These observations are very valuable and we agree that shoreline sites are not exact analogues for ‘to be exposed areas’. With regard to moisture, the conclusion as stated in the draft report is misleading and has been rephrased. Of course, when the surface soil is wet, there are no windblown dust emissions. The conclusion was intended to convey the point that measuring soil moisture using bulk techniques (where some depth of soil is collected, weighed, dried, and weighed again) does not reflect the erodibility of the very top surface, which is often dry enough to emit large amounts of dust even though the soil is quite wet a centimeter or less below the surface crust. .

• The site locations are shown in figure 3-1 and general characteristics given in table 3-1. I found the information in table 3-1 very useful and think that the authors should consider generating graphs that group the general landforms identified in table 3-1 together (i.e., playa-like, barnacle beach, dry wash, and Paleo-lake/barren desert). This could be done by just moving the position of the sites on the graphs so that similar landforms are next to each other and labeled. Doing this helped me with the interpretation of the results and to compare the different landforms with each other. I also think that presentation of the sites in figure 3-1 needs to be improved; I found it difficult to find the points and relate them to the site numbers and table 3-1. Perhaps using color dots and grouping the sites according to table 3-1 (e.g., red dots for playa-like sites, green dots for dry wash sites, etc.). Color could also be used to show low or high salt contents? This suggestion was adopted using two new/revised figures. Figure 3-1 shows color dots that represent the sites where PI-SWERL tests were completed,

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sites where the wind tunnel was collocated with the PI-SWERL, and sites that were added in January, 2006. This is essentially the same as figure 3-1 in the draft document except that it is now in color. We also added Figure 3-2 which shows the sites by landform as described in Table 3-1. Sites with a salt content > 10,000 mg/kg are delineated in the figure with a small red dot.

• Penetrometers were used to measure soil strength --- were any penetrometers that measures sheer stress used and related to the PI-SWERL measurements? No. The spring penetrometer used in this study was intended to provide a measure of curst strength (nominally the magnitude of normal stress that causes crust failure). The cone penetrometer provided data on soil strength at depth. No instruments were used to try to estimate shearing strength of the soil crust. We would be interested to learn more about such an instrument and its operational characteristics and performance if Dr. Chavez is aware of one. To our knowledge, there does not exist an instrument that measures shearing stress of the top few millimeters of the soil crust with accuracy.

• Albedo measurements were made at two of the seventeen sites and a comment was made that the average of the two sites (about 32%) would be applicable at all the sites. I am not sure I agree with this. Albedo is the ratio of the incoming solar radiation divided into the reflected radiation. If both incoming and reflected solar radiation values are measured at the same time, as done in this study, the albedo (or spectral reflectance) can generally be computed under either sunny or shaded conditions (the ratio is constant unless you get into sun elevation angles that are below about 35 degrees; at low sun elevation angles some surface features do not reflect solar radiation equally in all directions). In the work we have done in the Mojave Desert, and elsewhere, we have generated albedo maps at 30m resolution using satellite images and this might be something to consider for the Salton Sea area. The albedo of desert soils can vary from about 15 to 60 percent, with most being in the 20 to 35 percent range, with salts on playas often being in the 60 to 80% range. We agree that the albedo measurements conducted in this study were tenuous. They were originally intended to provide input into the WEPS model that was to be used to estimate dust emissions. The albedo measurements were unsuccessful partly due to equipment difficulties and partly due to the requirement that the measurements be completed around noon under a cloud-free sky. This is necessary because the pyranometer used to measure incoming radiation did not measure the full spectrum of incoming radiation. In any case, since the WEPS model will not be used to prepare the PEIR and since there were only two successful albedo measurements for the whole study, we have removed those data and related discussion from the final version of the report.

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• RPM levels of the PI-SWERL are discussed on page 13 and a question related to this and u* is: when graphs are shown later in the report (and in Appendix A) the maximum value for u* varies from site-to-site. This means that each site did not under go the exact same RPM range. I assume this was because at some sites the maximum threshold PM10 emission level of 150 mg/m**3 that the PI-SWERL system can measure was reached at lower RPMs than at other sites (implying that these sites are higher PM10 emission sources). If so, then to compare sites with each other a model that predicts PM10 emissions at a given u* (say 0.8) at all the sites needs to be used (typically a power function). The sites with the lower maximum u* are probably sites that will have the higher levels of PM10 emission and the larger prediction errors. In order to compare the sites against each other, graphs need to be generated showing the amount of PM10 emission at all the sites at the same maximum u* (wind speed); if this has not been done I suggest that it should be and the results added to the report. There are two separate issues/suggestions that arise from the above comment. They are: a. The question of whether or not reported aggregate values (i.e. averages or geometric means) for emissions across sites at a specific RPM value are representative of actual aggregate values since at some sites high emissions at specific RPM overloaded the measuring capacity of the DustTrak monitor – the instrument used to infer PM10 in the PI-SWERL chamber; This is a subtle but important point. As Dr. Chavez has observed, the upper limit of the DustTrak PM10 monitor is 150 mg/m3. At the most emissive sites, this upper limit can be reached at an RPM that is lower than the maximum RPM in the measurement cycle. In the field, when this PM10 maximum concentration is reached, the PI-SWERL control software toggles a flag in the data file to indicate that the PM10 measurements at the corresponding RPM are not valid. This has the effect of “chopping off” data points above a certain RPM level (or equivalently a certain u*) at the most emissive sites. Thus, if one was to aggregate emissions at a high RPM across a number of sites, those sites where emissions are highest would not be included in the aggregate and the aggregate value (whether an arithmetic average or a geometric mean) would thus be an underestimate of the “real” aggregate value. We note that this problem was avoided in the draft report or at least mitigated somewhat. In Figure 4-8 (Now Figure 4-10) we show all data at valid RPM levels (i.e. limit of 150 mg/m3 not exceeded). For some sites data are not shown for equivalent u* greater than a certain value (say 0.70 m/s) indicating that measurements above this value were not valid. We feel that this is a way to represent the data honestly to the reader. In Figures 4-9 through 4-12 (Now Figure 4-11 through 4-14), we show a geometric mean value only when the number of data points at a specific level of u* is greater than 4. With the exception of a few cases at the highest value of u* (>0.7 m/s), the geometric mean captures measurements from every site. Thus, we minimize the bias introduced into the aggregate values.

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b. The suggestion that emissions be compared at a specific value of u* for all sites – u* =0.8 m/s is given as an example. A related suggestion is to use power law profiles to fit u* vs emission curves in order to estimate the emissions at high RPM for sites where the maximum PM10 concentration of 150 mg/m3 is reached. This comment underscores a major oversight in the DRI draft report. DRI did not provide an adequate context for friction velocity in terms of the equivalent wind speeds at the Salton Sea. This has now been addressed in the report, but we provide a brief summary here in the context of Dr. Chavez’ comments. Based on the wind speeds summarized in the draft PEIR, the highest one-hour wind speed measured at either the Niland or the Indio met towers sites was 31 miles per hour. Using the range of roughness lengths measured by the University of Guelph wind tunnel at the Salton Sea in September 2005, this wind speed is very closely represented by a friction velocity of 0.56 m/s. Actually, in most cases, the actual friction velocity is lower, but using the higher value of 0.56 m/s is conservative in terms of dust emissions while being realistic in terms of winds measured in the vicinity of the Salton Sea. Emissions of PM10 at this value of friction velocity were measured at almost all the sites – the 150 mg/m3 limit is not exceeded until u* gets higher than about 0.6 m/s at any of the sites. Thus, instead of the u* value of 0.8 m/s suggested as an example by Dr. Chavez, we have elected to use a more realistic value of 0.56 m/s to compare PM10 emissions among sites. This also precludes the need to fit a power law model to the data since data are available for all sites at u* = 0.56 m/s. As an aside, power law fits for most of the sites result in underestimates of emissions at high values of u*. E.g. it is clear that if you use data up to u* =0.7m/s to fit a power law and estimate emissions at u* =0.8 m/s, the fitted value is systematically lower than the actual measured value of emissions at u* = 0.8. Thus, we do not agree that curve fitting is a viable method for comparing emissions from different sites. That said, the point is moot since u* = 0.56 m/s is the realistic upper value for friction velocity at the Salton Sea.

• With the large amount of heterogeneities seen at the sites I am also concerned that the number of locations used at each site to get an average varied from 3 to 12 locations. For one site during one of the field trips only one location could be used and zero locations for several sites because they were to wet. How might this affect the results? Clearly, the greater the number of measurements, the more confident we can be of the estimate provided – with the uncertainty decreasing roughly in proportion to the square root of the number of replicate tests. During the September tests, more time was spent at a number of sites owing to the collocation with the University of Guelph wind tunnel which has a comparatively long setup time. Therefore, at some sites, up to 12 replicate measurements were completed in September. In January and March fewer replicate tests were completed at each site. One of the goals of this study was to obtain an understanding of how emissions vary along the Salton Sea by

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texture, landform, and salt content. With limited resources, we chose to include more sites (i.e. fewer replicate measurements at each site) rather than obtaining a higher confidence (i.e. by making a greater number of measurements) in the PM10 emissions at fewer sites. In retrospect, this was the correct choice as PM10 emissions varied more from site to site than they did among replicate measurements at the same site. The main conclusions of the report are not based on data from individual sites, but rather on several replicate measurements at each of several sites. Thus, while the uncertainty bounds for a specific site are larger in January and March than they were for September, the main conclusions of the report relating to salt crust, texture, and season are not affected by the lower number of replicate measurements in January and March. Perhaps in future work, a specific subset of sites will be used to represent a landform type and the spatial heterogeneity can be quantified with greater confidence. In any case, many of the new figures (which also address Dr. Chavez’ concern about using log scales) also include the number of replicate measurements completed at each site during each season. This should help the reader determine how much confidence to have in data for any specific site, season, or combination of sites.

• In pages 16 through 21 wind tunnel and PI-SWERL testing is discussed. It states that wind tunnel tests represent ‘a single wind speed (or shear stress/friction velocity)’ while the PI-SWERL is ‘the average cumulative PM10 emissions for every RPM’; both methods use results from multiple locations at the same site and aggregated them to get a site geometric mean. A few comments on this are:

o I would like to hear more about the aspects of the wind tunnel value representing PM10 emission at a single (high) wind speed while the PI-SWERL represents a cumulative value of PM10 emission at the different RPM levels. I saw the graphs showing the correlation but perhaps the text needs to be expanded? We have added two illustrative figures to show how the PI-SWERL data differ from the wind tunnel data. Figure 3-3 (revised draft) shows a PI-SWERL test and relates the variables in Equation 3-1 to data traces shown in the figure. Figure 3-8 (revised draft) shows the difference in the raw data between a wind tunnel test conducted normally (i.e. single wind speed), the PI-SWERL test conducted normally (i.e. multiple steps), and a wind tunnel test that was conducted following the PI-SWERL protocol (i.e. wind tunnel speed was increased incrementally similar to PI-SWERL). In general, the PI-SWERL –style steps were difficult for the wind tunnel to emulate because the software running the tunnel was designed for a single set of measurement data (e.g. wind profiles). Thus the “stepped” wind tunnel test shown in Figure 3-8 was a rarity and was intended to help elucidate how the tunnel would behave if operated similarly to the PI-SWERL. We have also added a paragraph summarizing the two major

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differences in data acquisition and processing between the wind tunnel and PI-SWERL. With regard to the testing protocol, it is difficult to say whether the wind tunnel or PI-SWERL more closely resembles what happens in nature. One could argue that the wind tunnel is unrealistic because it immediately exerts a wind speed above a surface. In the real World, winds increase over a finite period of time (perhaps seconds in the case of thunderstorms and more like many minutes in the case of a pressure front).

o Using multiple test locations at each site and using the average value in geometric space (log scale) typically gives a better correlation between two parameters or methods. I would like to see not only the average geometric mean of all the locations at each site on a single graph with the same u*, but also the arithmetic values for all locations tested at each site at the same u* in linear space (i.e., all the test at all the sites without averaging or taking the log). This will help show how non-homogenous most sites are when it comes to landscape vulnerability and the large range of emission levels at each site and all sites combined. A graph showing the standard deviation of the locations tested at each site will show the variability in linear space (realizing that the number of locations tested varies from 3 to 12). This suggestion has been adopted. As explained elsewhere, a friction velocity of u* = 0.56 m/s is a reasonable value for comparing emissions between the PI-SWERL and the wind tunnel (and among PI-SWERL sites) since this corresponds approximately to the highest value of wind speed recorded at either the Niland or Indio weather stations. We have added a figure that compares individual measurement values, arithmetic averages, geometric means, the number of PI-SWERL replicate tests, and the standard deviations for all PI-SWERL tests at each collocation site. On the same figure (3-12), we have included individual wind tunnel tests (in most cases only one test was available per site). Figure 3-13 is similar to 3-12, but the scale for the emissions is logarithmic to illustrate the differences between PI-SWERL and the wind tunnel over the whole range of measurements and not just at the locations where emissions were highest (a consequence of using a linear scale in Figure 3-12). We have also added a paragraph referencing these two figures and stressing that the two methods are not identically comparable, but rather are correlated when considered over many sites and replicate measurements.

o In page 18 the report states that ‘the types of measurements are inherently noisy due to the natural heterogeneities of the soil surface’. When the data are ‘transformed into logarithm space and averaged appropriately a more coherent relationship …..emerges’. This natural

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heterogeneities is one of the reasons why it can be difficult to use current near-shore small areas to predict the behavior of areas that will be exposed in the future. We agree with this statement. We do not ascertain that it is easy to determine how representative specific near-shore areas are of the Salton Sea as a whole. In general, estimating windblown dust emissions from any surface, especially one that has not been exposed yet is a difficult task. However, current, near-shore areas are the best available surrogates for “to be exposed” areas, though they are admittedly imperfect for a number of reasons.

o The report states that logarithm space allows for lower emission values to be considered more equitably than in linear space (page 18); since a main interest is the impact to air quality I would think that moderate to high emission levels are of more interest that low levels. Therefore, I would suggest that methods that compromise the level of detail at the higher emission rates be carefully considered (i.e., use both methods --- with and without logs). Note that the data presented in figures 3-7 and 3-8 covers a dynamic range having factors of 500 and 1000, respectively; these rather large differences may be lost to readers not familiar with log scales and should perhaps be stated in the text and/or also shown in linear space. We do not agree that the moderate to high emissions are of more interest – at least not in the context of this report. The purpose of this work was to provide emission data as input for potential modeling efforts and, more importantly in our view, to better understand differences in emissions among sites based on seasonality, soil properties, and salt crust formation. Since there is large spatial variability in the data – orders of magnitude in some cases – we may also agree that the appropriate aggregate value to consider for comparing emissions over time and among landforms is the geometric mean and not the arithmetic mean. When a variable is log-normally distributed – i.e. assumes positive values that can range in orders of magnitude, the arithmetic mean of a sample picked from the distribution is not a good metric for comparing distributions – it would be if the variable is normally distributed. We do not claim that the distribution of PM10 emissions is precisely log-normal. However, the range of magnitudes indicates that it is a geometric and not an arithmetic distribution. That is, there are infrequent but very large values in the distribution. Thus, to compare, for example, emissions from a site on two different sample days, it is important to consider the geometric mean. Otherwise, the sample size (i.e. number of replicates) would a have a profound effect on the aggregate emissions estimate.

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We have noted in the body of the report that for the purposes of estimating emissions in the context of applying a model, for example, the reader may wish to use arithmetic means which are given in an Appendix. However, as a matter of opinion, we do not think it is helpful to use arithmetic means in estimating emissions with a model either. The reason is that by using arithmetic means, trends in emissions associated with crusting vs not crusting or barnacle beach vs. paleo-lake landforms would be washed out. From our view, the PM10 emissions measured on site are useful in a limited way for estimating emissions semi-quantitatively. However, they are most useful for understanding the differences among soils and their potential to emit windblown dust. We know a priori that any modeling effort is going to be approximate in terms of the magnitude of emissions estimated. At least, it would be helpful in decision making if the model used was able to delineate differences in emissions based on site properties. If the magnitudes of the model estimates are wrong, they can be scaled upward or downward as needed. However, the relative magnitudes of emissions from different areas – based on soil properties , etc – would at least be correct.

• Section 3.4.3 in pages 22 and 23 the PI-SWERL procedures at the Salton Sea is discussed and the report states that a ‘maximum RPM of 2800 was achieved, however, in the large majority of cases, this would have resulted in PM10 concentrations far higher than the measurement range of the DustTrak (150 mg/m**3)’. It also states that ‘during each measurement campaign, between 3 to 12 separate measurements were completed at each site’. I have the following comments/questions:

o I assume the 150 mg/m**3 is an equipment (DustTrak) limitation? Yes. The text on page 22 (of the draft report) has been altered slightly to more clearly convey this point. It now reads “However, in the large majority of cases, this would have resulted in PM10 concentrations far higher than the upper limit of the measurement range of the DustTrak (150 mg/m3) – the instrument used to measure PM10 in the PI-SWERL chamber - and the maximum RPM for most sites was substantially lower (usually 2000).”

o Again, it is difficult to compare the vulnerability level and/or dust emission potential at the different sites around the lake when different u* values (or equivalent wind speeds) were used during the testing because of the 150 mg/m**3 maximum threshold. Given two or more u* values at a site a model can be used to predict PM10 emission at the same u* value (say 0.8) for all the sites. The model is typically a power function so sites with the lower u* maximum levels (those that are probably the most emissive) will probably have the largest prediction errors. However, doing this will allow values to be

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generated at the same maximum u* for each site and graphed together, which will be useful for comparing the sites with each other. Other than the soil crust strength and ball drop test there are no figures comparing the various sites to each other for PM10 emissions. I think this should to be done and presented in the report. All elements of the above comment have been addressed earlier. We summarize them here, but for a detailed response, please see pp 10-11. In summary, u*=0.8 m/s is an unrealistically high value of friction velocity for the Salton Sea conditions. According to wind speed data collected at Niland and Indio and roughness lengths (z0) measured with the Guelph wind tunnel, u*=0.56 m/s provides a conservative upper end estimate for friction velocities at the Salton Sea. For almost every location tested at every site during every season, PI-SWERL data exist for u* = 0.56 m/s. So, we are able to use actual data in adopting Dr. Chavez’ suggestion of comparing emissions for all sites at a specific u*. This precludes the need to use power law fits to estimate emissions.

o It is not unusual for PM10 levels at the Niland and Westmorland CARB stations to reach levels in the 400 to 700 mg/m**3 during dust storm events. These represent values when the dust has been dispersed, so the concentrations are probably a lot higher closer to the dust sources. In Las Vegas during a dust storm on April 15, 2002 the PM10 level recorded for several hours by CARB type stations was around 1400 mg/m**3. At Owens Lake during a recent major dust event (Feb 2006) the PM10 sensors were pegged at 10,000 mg/m**3. I am concerned that because the maximum PM10 emission level that could be measured during the tests was only 150 mg/m**3 it will be difficult to get a complete picture of the potential for high dust emission levels at the various sites. Perhaps at some sites the tests may have stopped well before the maximum high emission potential could be reached or modeled (dust flux models are typically power functions and I wonder if points high enough up on the curve were reached to allow accurate predictions?). Could the authors comment on this. We have been identifying ways to extend the upper range of the PI-SWERL PM10 concentration measurement beyond the 150 mg/m3 limit imposed by the TSI DustTrak precisely because we share Dr. Chavez’ concern. However, in practice, this limitation has not compromised the measurements at the Salton Sea. As mentioned previously, the realistic upper limit for u* at the Salton Sea is 0.56 m/s. There were no instances when the 150 mg/m3 limit was exceeded at this value of friction velocity. Thus, while we agree that in future studies, this instrument limitation may pose a problem, for the present study and

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for other completed studies it has been inconsequential. We note as an aside that the units in Dr. Chavez’ comment require clarification due to quirks in word processing software (where the Greek letter mu (µ) and the letter m are considered equivalent). The DustTrak upper limit is 150 milligrams of PM10 per cubic meter (mg/m3). We presume Dr. Chavez meant that the concentrations at the CARB stations reach levels as high as 400 or 700 micrograms of PM10 per cubic meter (µg/m3). Likewise at Owens Lake PM10 concentrations reach 10,000 µg/m3 and fortunately not 10,000 mg/m3. The appearance of the units mg/m3 in Dr. Chavez’ comment above are probably a result of a word processing transcription error, but it is important to note the discrepancy since the difference between a microgram and a milligram is a factor of one thousand.

o Looking at the raw data in Appendix A shows the variability in the number of locations used to test at each site. It is important to keep in mind that there are sites where 3 to 4 locations were tested and averaged and others where 9 to 12 locations were used. This is particularly important for sites where the reason fewer locations were tested on the second or third visits was due to how wet it was at the site (i.e., playa-like). Again, this could help explain the lack of correlation to soil moisture and texture with PM10 emission levels. During the September 2005 field campaign, the PI-SWERL and Guelph wind tunnel crew were moving together from site to site. Since PI-SWERL tests are much faster than wind tunnel tests, the DRI crew performed as many tests as possible while waiting for the Guelph wind tunnel crew to finish their work at the site (up to 12). During the January and March 2006 field campaigns, a site was either too wet for any testing in which case no tests were completed or a minimum of three tests were completed as was dictated in the Work Plan for the study. One exception was testing in March at site A29-1, where for logistical reasons we were unable to complete the three replicate measurements. That said, this comment makes a good point. What is the uncertainty associated with the PI-SWERL measurement? This is addressed indirectly by adding figures to the report that show the values of PM10 emissions measured at each location at each site during each season of testing. We have also included graphs showing the values for the average emissions and the standard deviations by site and season. This will help convey the uncertainty associated with the PI-SWERL measurement.

Pages 25 to 50 (Results Section):

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• The site description subsection on pages 25 to 31 is well done and reads good. The only thing I would consider adding is the information shown in table 3-1 on page 11 dealing with landform (i.e., dry wash, barnacle beach, playa-like, and paleo-lake). The site descriptions appearing on pages 25-31 of the draft report have been modified as suggested.

• Section 4.2 discusses and shows the crust strength results at the different sites during the times visited and I find the graph shown in figure 4-1 combined with table 4-2 interesting. As mentioned earlier, to me it was useful to view the sites on a landforms basis (as shown in table 3-1) and think that it helps with the interpretation if the graph in figure 4-1 are organized using these landforms. Most of the playa-like sites had relatively low crust strength (which is correlated to high dust emission within the report) and sites with high crust strength, generally low dust emission, are mostly non-playa like. Because in figure 4-1 the sites are labeled only with the site identification (Axxx and SSxxx) I had to go back and forth between this figure and table 3-1, twenty pages earlier, to be able to see this relationship. Organizing figure 4-1 using the landform categories would highlight the fact that even though the crust strength was lowest at most sites in January, it was still relatively low in March for most playa-like sites and shows that perhaps they were still vulnerable. The figure has been modified as suggested.

• Figure 4-1 on page 33 also highlights the fact that most sites where test could not be done because they were too wet were at playa-like sites. As mentioned earlier, photographs collected by an automatic digital camera station we recently put up in the SE portion of the lake (Davis Road/close to Niland) that the playa-like site we are monitoring gets ‘very wet’ (completely covered with water) during most moderate to high wind events. This means that dust may be emitted during the first wind event of the season, but dust emission quickly gets turned off because of the constant wetting. I would guess that this may happen around the lake during the winter/spring winds (less so in the north because of the wind direction during the winter). As stated earlier, this implies that currently there may not be good ‘representative’ playa-like sites of to be exposed areas available around the lake until the water level lowers enough that this constant wetting of playa-like areas stops. This will be a problem for all studies using sites around the lake aim at monitoring and/or characterizing areas around the Salton Sea. As noted, there are few playa-like sites around the Lake that are truly far from the water levels (i.e. more than 5 meters above liquid water in the soil) and there are some sites that are wetted frequently by the Lake. This may affect the way these sites behave with respect to dust emissions and it is difficult to

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estimate the magnitude or even the direction of this effect.

• The graphs shown in figure 4-2 (pages 34 to 36) are difficult to compare with each other because they have different max scales (both vertical and horizontal axis). The cone penetrometer graphs have been modified so that the x- and y- axis scales are identical for all sites.

• I found the information shown in table 4-2 (ball drop test results) interesting because it was one of the few data sets that made it easy to compare the various sites with each other (realizing that this is a very general data set). Eleven out of the fourteen sites failed (79%); eight out of the ten playa-like sites failed (80%); with 35 out of the 45 drop test failing (77.8%); failed means vulnerable to dust emission. It looks like all the ball drop tests were done in January, were any done in March? Ball drop tests were only performed during the January tests. A statement to this effect has been added to the caption of the Table.

• Figure 4-4 is used to show the soil texture (particle size) results and is very similar to a figure in the Agrarian Research report that shows the same information for the 800 grab samples collected in waters depths ranging from the shore line to 15 feet. As suggested in the general comments the results shown in the two figures should be compared to look for possible correlations from a texture point of view between the 17 sites used and future ‘to be exposed’ areas currently under water. This relates directly to the suggestion of comparing, when ever possible, on-land results with currently under water area results. Figure 4-5 has been added to the report. The figure shows the sediment size distributions measured by Agrarian Research at depths of 0m (shoreline), 1.5 m, 3.0 m, and 4.5 m overlaid with the soil textures measured at the sites in this study.

• Section 4.4 presents the PI-SWERL results and it states at the bottom of page 43 ‘geometric averaging (averaging of the logarithms of numbers) was used because the magnitudes of emissions at a site are highly varied and average values can be misleading’. It also states that ‘for estimating emissions from a site, it may be more appropriate to use standard arithmetic averaging; arithmetic averaging is presented in Appendix A for completeness’. I have the following comments:

o As stated earlier, I would like to see graphs generated using the individual values (not averages) of the test results for all the locations at all the sites (individually and combined) in linear space at the same predicted u* (0.8); with the graphs combining sites according to

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landform categories shown in table 3-1. I think this would help show the differences on the amount of dust emission from each site and the different landforms. A graph could also be generated of the standard deviation at each site to show the variability. The report has been modified as suggested. Figures have been added that show (in linear space with the same y-axis scale) the individual measurements of PM10 emissions at each site during each season, the average and standard deviations of those measurements, and the number of data points included in the average. We have also aggregated the same information by landform as suggested. See Figures 4-15 through 4-19 in the revised report.

o If possible, a graph that shows the relationship between wind speed and u* should be generated. Currently, I have no idea what wind speeds would generate what level of PM10 emission at the different sites. This would help document what range of wind speeds were used at the various sites since the RPM maximum values were not the same, plus it would help put the upper limit of 150 mg/m**3 in perspective (i.e., perhaps at some sites a wind speed of 25 mph generated a dust emission of 140 mg/m**3 while at another site this might have occurred at 12 mph, implying that at 25 mph the emission would have been much higher). Instead of a graph, we have inserted a Table in the Results section (Table 4-6) that shows the approximate equivalent values between u* and 10-meter wind speed. A Table is more flexible as it allows the calculation for several different values of roughness height (z0) and in both meters per second and miles per hour (Units of miles per hour help to relate reported wind speeds in the draft PEIR to the present work.). In any case, a u* of 0.56 m/s appears to be the maximum likely achievable value based on one-hour wind speed measurements at the Salton Sea.

o The discussion on page 44 about January versus March and September results states that ‘PM10 emissions were generally lowest in September and increased in January, then decreased in March. This indicates that at the Sea there is a dependence of windblown PM10 emissions on season with an apparent peak during cooler, wetter months’. I wonder if the constant very high amount of wetting, that we have seen using our automatic digital camera station, might have affected the differences between the January and March testing? Since tests were not run at several sites because of wetting I would think that perhaps this could be the case? The discussion on page 44 and the related figures (4-9 through 4-12 in

Response to Comments from Pat Chavez, USGS

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the draft report) have now all been modified so that the aggregate value for emissions by seasons and salt content includes only those sites where data were available for all three sampling seasons. Though it is important to explicitly consider the effect that Dr. Chavez highlights above, in the present case it has no impact on the conclusions regarding changes in emissions over the three seasons. The data remain essentially unchanged as do the resultant conclusions.

Pages 51 to 53 (Discussion and conclusions, future projects sections):

• At the top of page 51 it states ‘low crust strength can be highly emissive if disturbed, compared with harder crust’. Yes, but the low crust strength can be highly emissive even if it is not disturbed. This statement implies that ‘if you keep people off the surface there will not be a problem’, which may or may not be the case. Of course, if a site is disturbed it can become more emissive, but a site can be highly emissive without ‘human’ disturbance. We have replaced that statement with a more accurate one: “It is generally true that soil surfaces that are disturbed, especially those with soft susceptible crusts, are more prone to emit windblown dust than if they were not disturbed (Gillette et al., 1982). However, certain soil surfaces are amenable to windblown dust emission even in the absence of disturbance.”

• Towards the bottom of page 51 it states that ’Eleven of 16 sites where PI-SWERL measurements were completed exhibited their highest dust fluxes in January’. Again, I wonder if the constant ‘very high wetting’ caused by high wind events during February and March might have had something to do with the lower reading in March (note, several of the playa-like sites did not have any testing in March because they were to wet). Also, the graph shown in figure 4-1 indicates that even though the crust strengths were generally higher in March than January, the crust strength at some sites were still quite low in March. As mentioned earlier, examination of the data for only those sites where measurements were completed during each of the three sampling periods indicated that this conclusion was not affected by the sites that were too wet to test in March.

• At the top of page 52 it states that ‘soil moisture showed no relation to emission strength’. This is a big surprise and again I wonder if this was not impacted by the fact that some of the near-shore sites under go high amounts of wetting during windy conditions. This is highlighted by the fact that test were not run at several sites because they were to wet (again, perhaps this

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biased the results?). The paragraph discussing the effects of soil moisture has been changed to more correctly reflect the intended meaning. Wet soil surfaces do not emit dust. However, because of the salt crust properties, sometimes the surface curst (1 few mm at the most) can be dry enough to emit dust while the soil below is quite wet. When a bulk sample is retrieved for analysis, it is collected to a depth of a few centimeters, which includes the wet underlying soil. This is a measurement issue and the text in the conclusions has been altered to reflect that.

• Middle of page 52 states that ‘No clear correlation was evident between PM10 emission and soil texture. The highest emissions came from soils with textures ranging from sand to silty clay’. This is a surprise. I have to wonder if the lack of sand introduction into the PI-SWERL test and/or the fact that the maximum RPM stops at emission levels below 150 mg/m**3 did not impact these results. If no sand is present within the relatively small footprint of the PI-SWERL there was nothing to saltate and cause fine dust particles to be emitted. Also, if the RPM value (wind speed) is to low to get to the higher end of sand saltation the amounts of fines emitted could be affected. These issues have been addressed earlier and we provide a summary here. The 150 mg/m3 limit was not an issue for the Salton Sea tests though we are examining ways of bypassing this shortcoming. The lack of correlation between soil texture and emissions is not surprising. Sandy surfaces tend to be emissive because of saltation and comparatively weak inter-particle bonds. The silty-clay surfaces usually are not emissive if the crust is stable. In the case of the Salton sea, at many of the ‘playa-like’ sites with salt present, the crust was highly friable, especially during the January tests. The absence of a sand feed in the PI-SWERL is not directly relevant to the relative magnitudes of the emissions from fine-textured versus coarse-textured soils. If sand was introduced at a silty clay site, dust emissions would likely have increased. However, since sand is not present at those sites, it would be somewhat artificial to induce sand saltation.

• At the bottom of page 52 it states ‘Our study suggested that while salt-crusted surfaces do indeed emit dust, they were not the single predominant source of dust around the margin of the Sea’. Again, I would have to wonder how the results may have been impacted either by the potential lack of sand within the very small footprint and/or the constant high amount of wetting that may occur at playa-like sites. The ideas in this comment have been addressed previously.

• In the future project planned section it states ‘the next phase of work will attempt to identify playas or playa properties that are analogous to the Salton

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Sea shoreline’. I would suggest that more work needs to be done in the Salton Sea to better characterize the conditions and landscape, especially of areas that are currently under water and will become exposed. This could include getting more information about the soils under the lake and the salt characteristics, plus the shallow hydrology characteristics under the Salton Sea (i.e., will future exposed areas be considered wet or dry playas? Note, this is different than the wetting that currently occurs due to high winds). If other playas are going to be studied I suggest that playas in southern New Mexico and Arizona also be considered because of the similar climate to the Salton Sea region. We agree that additional characterization of the distribution of soil particle sizes and locations is needed for areas that are currently inundated. We note however that the section is only intended to summarize the future projects that are planned to try to give the reader some context on the work discussed in the present report.

The authors wish to express their gratitude for your time and thoughtfulness in compiling these comments and suggestions. We believe that they have substantially improved the final quality of the revised report.

Response to Comments from CH2M Hill

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D.2 Response to Comments from CH2M Hill M E M O R A N D U M Comments on Report TO: Mark Sweeney

Vic Etymezian

COPIES: Pamela Vanderbilt

FROM: John Dickey

DATE: May 24, 2006

The easiest way to communicate comments is into the pdf file. However, the size of the file has posed some problems with this approach. So, I’m including some of the larger comments in this memo as a backup.

The actual comments did not appear in the pdf file, though the locations of those comments were retained. Therefore, we cross-referenced the location of the comment and the comment number to the original version of this document. Where appropriate, we have added clarification to the comment in brackets to provide context for the reader who does not have access to the pdf file.

1. p. iv on saline soil cutoff: Can you reference a comparable level of saturation extract EC, since that is how saline soils are defined elsewhere? Based on saturation percentage and generic relationships of ECe to % salts(Table 1 and Figure 3 of USDA, Agriculture Handbook No. 60), the conversion is readily made.

This suggestion has been implemented for the Executive Summary, methods, and the results sections of the report.

p. v 2. confidence limits? [With regard to the conclusion that bulk density measurements did

not vary in any consistent way from one season to the next]

We have added text to the Executive Summary to provide some insights into the uncertainties associated with bulk density measurements. As Figure 4-5 in the draft report (now Figure 4-6) shows, there are minor, but generally insignificant differences in bulk density from one season to the next for most sites. Note that since the analytical portion of the bulk density

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measurement simply involves weighing the dried soil sample – precise to less than 1% usually -, the differences among replicate samples are due almost entirely to surface heterogeneity and subtle changes in sampling technique from one season to the next. Sampling on salt-crusted playa was especially tricky since in many cases, the crust was separated from the underlying soil by a layer of air of variable thickness.

3. Sentence unclear. Implies that salt evaporates. Pls clarify.

The sentence has been changed. It now reads “Those sites were close to the shoreline and either wicking of salt water followed by evaporation or else windblown deposition of salt contributed to elevated salt contents in January.”

4. ...farther...

Agreed. “Further” was replaced with “farther”.

5. Comment on relative ranges? [With respect to the P, Ca, Mg, K, Na, and SO4 content of soil samples collected during the field study]

The ranges of concentrations have been added to the Executive Summary. As expected, salt playas and barnacle beaches were the saltiest and the wash sites were the least salty.

6. Higher than sites with stronger crusts?

The two “dry wash” sites – A100-1 and SS2-1 exhibited among the highest resistances to the spring penetrometer measured during the entire field study. This was not true for the September measurements, but certainly true for January and March.

p. vi 7. While I agree with your finding, there is critical context that is not immediately

captured, so that the actual result of the conditions you describe, when expressed on a playa, are not as well anticipated as they could be. I think that if we agree in principle, that the issue is very easily addressed in the text of the current report. This understanding of the annual cycle in surfaces' intrinsic emissions potential corresponds to what I've observed in data and field. On playas, however, extrinsic factors (wind, sand) can be extreme. This has the effect of making surface stability levels much more important, and the intrinsic emissions potential much more strongly expressed, than at similar, non-playa sites with less pronounced extrinsic stresses. This would likely be my major comment on DRI's interpretation of the results relative to playa environments.

This is a very important point and we have added a paragraph in the Executive Summary and a more extensive discussion in the Conclusions to convey to the reader that the findings of this study are limited to the response of the soil surface to shear stress applied directly to that surface (i.e. does not consider ambient wind conditions or vegetation) and that the data were

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collected along the shoreline which may differ significantly from the areas to be exposed in the future.

8. Why not use conventional wind tunnel systems? Is the PI-SWERL superior to straight-line wind tunnels, and if so, how?

We have added a brief paragraph in the Executive Summary to clarify why the PI-SWERL was used for the majority of the measurements instead of the straight-line wind tunnel. The paragraph reads “The PI-SWERL is advantageous over the straight-line wind tunnel because of its portability and because it is comparatively fast. Many of the sites where windblown dust emissions were measured were not accessible to the wind tunnel which is transported with a pickup truck and a trailer. In addition, wind tunnels require substantial setup time whereas a PI-SWERL measurement can be initiated within minutes of arriving at a site. Use of the PI-SWERL in this study allowed for inclusion of a large number of sites and completion of measurements over three sampling seasons.” The paragraph that follows describes the collocation with the University of Guelph wind tunnel at a subset of sites in September as a way of comparing the two methods.

p. vii 9. This characterization is right on. Efflorescent salts and saline dust are too often distractions from the larger impact of seasonally friable crusts, which is the exposure and loss of this reservoir of erodible material when it is not protected by a competent crust.

We view this as one of the most interesting findings of this study.

p. 1

10. It should be noted that the Salton Sea is naturally salinizing outside of the range habitable by species found there during preceding decades, and relied upon by, among others, sportsmen and fish eating bird species. Eutrophication also poses challenges to existing habitat and species that depend on it. Agricultural water conservation, even in the absence of the water transfer, would have resulted in declines in Sea level and further increased salinity. These problems exist(ed) before and in conjunction with impending water transfers, and contribute significantly to the need for ecosystem restoration.

We have added some text at the beginning of the Introduction along with a Figure to show that the Salton Sea was already salinizing.

p. 3

11. Are sand-sized particles not also sand also dislodged by these impacts?

Yes. A paragraph has been added after the referenced paragraph that describes the importance of sand saltation. This discussion also mentions that saltating sand grains can dislodge other sand grains from the soil in addition to silt and clay particles.

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12. Exposed soil at this moisture range is quickly dessicated to drier state in a windy desert. So while this is true for the immediate, exposed soil at this level of wetness will not be long stabilized by this moisture.

This is true in general. There is some difficulty associated with reconciling soil moisture at the immediate surface with the methods generally used to measure soil moisture. At the Salton Sea, there were a number of emissive sites where the measured soil moisture was substantially higher than 4%. However, the very top layer of soil (a few mm) was dry enough to be emissive. In responding to this comment and a similar comment from another reviewer, this point is elaborated on in the conclusions section of the report.

13. any

Agreed. “All” was changed to “any” to correctly reflect the meaning intended.

14. Are these are effectively the same thing?

The different effects of low and high packing densities of roughness elements on wind erosion were not delineated well in the draft version of the report. We have added the following sentences to clarify the point: “Thus, adding a sparse gravel cover to an erodible soil surface can result in increased dust emissions compared to the bare surface exposed to the same wind conditions. However, if the density of the gravel is increased past a certain point, the susceptibility of the soil surface to wind erosion begins to decrease, eventually to a level lower than that of the bare soil surface. This occurs because when roughness elements are densely packed, they tend to isolate the underlying surface from the shearing effects of wind.”

15. But they form over a wide range of surface textures in practice, partly because on playas, the underlying soil is often heavy textured anyway. Also, the sandy surfaces may be more erodible because of the potential for generating erosive sand locally. [In reference to the paragraph at the end of page 3 in the draft report that suggests that soil texture may not be the best indicator of the potential for dust emissions]

The referenced paragraph was misleading in the draft report. It has been changed to state that soil texture should be considered as only one of several important factors that contribute to a soil’s susceptibility to wind erosion.

p. 7

16. You note that the PI-SWERL can be used to determine the threshold friction velocity for surfaces. Was this done for the test sites? I would be interested in learning more about the variability in u*t on various surfaces.

While we note that the PI-SWERL data can in principal be used to estimate threshold friction velocity, the data from the sites at the Salton Sea indicate that in the absence of a specific definition of what constitutes a threshold value for friction velocity (either operational definition or physically based), the assignment of a threshold value is somewhat arbitrary. One physical reason for this is that while wind speeds and friction velocities are reported as scalar, time-

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averaged values, in practice, on a micrometeorological scale, the wind direction, wind speed, and friction velocity fluctuate about some mean value. Physically, this means that there is always some probability – perhaps very small, but positive – that some portion of the surface is experiencing wind shear above the threshold value – say for sand saltation. This concept and its implications for reporting threshold friction velocities is discussed in a recent paper by Lu et al (2005). The data from the Salton Sea show that in most cases, PM10 dust emissions increase monotonically with induced shear stress (related to friction velocity). Thus, there does not appear to be a specific value of friction velocity below which the emissions are zero and above which dust emissions are non-zero.

Lu, H.,; Raupach, M. R., and K. S. Richards (2005). Modeling entrainment of sedimentary particles by wind and water: A generalized approach. Source: Journal of geophysical research. 110, no. D24, (2005): D24114

p. 11

17. Assume "playa-like", not “paleo-like” would be correct.

“Paleo-lake” is used in the report to describe sites with silt/clay sediments that were originally submerged under the ancient lake in the Salton Sea basin. These sites were farther away from the shoreline and higher above the current lake level than those described as “playa-like”. As expected, the “paleo-lake” sediments did not exhibit an appreciable salt content.

p. 13

18. Again, pls provide estimated EC of these soil salinity class limits

Suggestion has been adopted. The text includes reference to the EC limits as does the figure where salinities from the various sites are summarized.

p. 14

19. The shear stress varies continuously with radial distance. Even within the range of the annular ring, there are differences in the shear stress. For purposes of displaying results, how have you characterized the shear stress within the chamber at each RPM level? Average across all radial distances? Average within the range occupied by the ring? Peak shear stress?

We agree that this was not explained well in the draft report. The shear stress at a specific RPM is calculated as the average shear stress over the region r/R = 0.78 to r/R = 0.94 (Those limits are shown in Figure 3-2 of the draft report.) This region of the PI-SWERL exhibits a relatively flat shear stress vs r/R dependence. It is also the area where shear stress is maximal. It is refered to in the draft report as the “effective” test area. While the shear stress is non-zero elsewhere in the PI-SWERL chamber (i.e. r/R < 0.78 or r/R > 0.94), it does fall off quickly in either direction from its maximum value. Since windblown emissions tend to follow a power law with respect to friction velocity (generally a u*^3 or more dependence after threshold is reached), the emissions from the area that is not underneath 0.78 < r/R < 0.94 are expected to be much smaller than those underneath the ring where shear stress is at a maximum value. This of course was the intended design of the PI-SWERL and the reason that an annular ring was chosen instead of

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a disc. The report has been modified to clarify the quantification of the shear stress at varying RPM. The caption of Figure 3-3 in the draft report and the text immediately before Figure 3-2 now reflects this information.

20. I don’t see mention of the time-dependence of PM10 concentrations within the chamber. I would expect that PM10 concentration would follow an s-shaped curve over time, at least once the threshold friction velocity was crossed. Within the 2-5 minute equilibration period, what is the shape of the PM10 concentration profile?

Two figures have been added to the report to illustrate the time-dependence of the PM10 concentration. Figure 3-3 shows the different parts of a typical PI-SWERL test including the time-dependent change in PM10 concentration and the step-accumulated PM10 flux. Figure 3-8 shows the same information for the University of Guelph wind tunnel compared to the PI-SWERL. In both cases, the PM10 concentration initially increases to some peak value and then declines quickly. This occurs in the first half minute or so. Thereafter, the PM10 value usually – though not always – continues to decline but much more slowly. The curve is not S-shaped because in both the wind tunnel and the PI-SWERL “clean air” is constantly introduced into the test section at a known flow rate and serves to flush out the PM10.

21. Do you have data available to relate shear stress to sand flux?

At the time of the field measurements for this study, we did not have a reliable method for gauging the saltation extent underneath the PI-SWERL. We have been developing methods to obtain a measure of saltation in future deployments.

p. 46

22. I would add a discussion of the variability in PM10 emissions vs. friction velocity at individual sites over time. What factors lead to high variability at some sites (A34-1, SS17-1), but low variability at others (A31-1, SS2-1)?

We have added Figure 4-16 which shows the coefficient of variation by site and season along with some discussion of the variability. There does not seem to be a discernible relationship between the variability in PM10 emissions and attributes of a site. Nevertheless, this was a good suggestion.

p. 51

23. If the data were available, a discussion of u*t differences could be used to back up your observations about the effect of crust strength on emissions. Increasing u*t is understood to be one of the main effects of crusting, but it would have been interesting to see the data. Other soil properties could explain a high u*t in spite of low crust strength (for example, high binding energy of clay soils).

As mentioned in the response to a previous comment, u*t was not calculated in this study, owing in part to ambiguity in its definition within the context of the data collected at the Salton Sea.

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p. 53

24. Can you address a possible modification of the PI-SWERL to allow measurement of sand flux within the chamber (along with shear stress)?

At the time of the field measurements for this study, we did not have a reliable method for gauging the saltation extent underneath the PI-SWERL. We have been developing methods to obtain a measure of saltation in future deployments.

[In a separate communication, CH2M Hill pointed out that for the Tables in Appendix A – pp A-19 through A-34 – the row labels at the bottoms were incorrect. That is, the minimum, maximum, average, and geometric means were not shown in the order indicated]

Those tables have been corrected and we have added rows for the standard deviation, number of replicate measurements, and the standard error.

Thank you for your thoughtful comments on the draft report.

Response to Comments Great Basin APCD

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D.3 Comments from Great Basin Air Pollution Control District From: Ted Schade [mailto:tedschade@yahoo.com] Sent: Monday, June 05, 2006 10:28 AM To: Keene, Chuck Cc: Grace A. McCarley Holder Subject: RE: Review of Draft Final DRI Report on Soil Properties from Sites Around the Salton Sea Chuck – Nice report. Great Basin APCD staff has taken a look at it and we have no major comments. We believe the conclusions of most interest are:

1. Even though the emission rates measured at Salton were much lower than emissions rates at Owens,

2. There will very likely be significant dust emissions from exposed sea bed when the sea recedes.

3. There is no clear correlation between PM10 emissions and soil texture (just like Owens Lake), because

4. Crusts (or lack of crusts) are the major influence on dust production, and 5. Crust formation (and corresponding emission conditions) is seasonal with the most

emissive conditions occurring in the winter (like Owens). Finally, the similarity between some of the photos of the exposed Salton bed and our experience with conditions at Owens is striking. Good luck, please let us know if we can be of any assistance. Ted Schade Air Pollution Control Officer Great Basin APCD www.gbuapcd.org

Thank you. We appreciate GBUAPCD reviewing the report. With respect to bullet item 1. above, a comparison of PM10 dust emissions from playa-like sites at the Salton Sea to those at Owens Lake has been added to the revised report following a suggestion from Nick Lancaster (DRI). While we have noted that comparing the two measurements is not a straight “apples to apples” comparison, the data seem to show that we cannot rule out that the Salton Sea may be in the same ballpark as far as windblown PM10 emissions as Owens Lake.

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D.4 Comments from Nicholas Lancaster (Desert Research Institute) Vic -

Comments on Draft Final Report for the Salton Sea

Well written and informative. Plenty of data, especially for soils. It appears that there is more material on soils than dust emissions, but this may be the result of most of the dust results being in the appendix.

It would be useful to have a graphic that compares the Salton Sea emissions (=F) vs u* data with that from other studies (e.g. Owens Lake, Dale Gillette and Nickling data etc). This would give the reader some idea of how the potential emissions from the Salton Sea area compare with other dust hot spots.

Fig 4-12 is the key figure in this report. It should be emphasized as much as possible.

Nick

Nicholas Lancaster,

Research Professor,

Division of Earth and Ecosystem Sciences,

Director: Center for Arid Lands Environmental Management

Desert Research Institute,

2215 Raggio Parkway,

Reno,

NV 89512-1095.

Your suggestion to include a graphic comparing emissions at the Salton Sea and Owens Lake was very good and we have done so. While we have noted that comparing the two measurements is not a straight “apples to apples” comparison, the data seem to show that we cannot rule out that the Salton Sea may be in the same ballpark as far as windblown PM10 emissions as Owens Lake. Thank you for your comments.

Response to Comments Great Basin APCD

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