Close
About
FAQ
Home
Collections
Login
USC Login
Register
0
Selected
Invert selection
Deselect all
Deselect all
Click here to refresh results
Click here to refresh results
USC
/
Digital Library
/
University of Southern California Dissertations and Theses
/
The characterization of Huntington Beach and Newport Beach through Fourier grain-shape, grain-size, and longshore current analyses
(USC Thesis Other)
The characterization of Huntington Beach and Newport Beach through Fourier grain-shape, grain-size, and longshore current analyses
PDF
Download
Share
Open document
Flip pages
Contact Us
Contact Us
Copy asset link
Request this asset
Transcript (if available)
Content
THE CHARACTERIZATION OF HUNTINGTON BEACH AND NEWPORT BEACH THROUGH FOURIER GRAIN-SHAPE, GRAIN- SIZE, AND LONGSHORE CURRENT ANALYSES by Craig Ellsworth Magnusen A Thesis Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree MASTER OF SCIENCE (Geological Sciences) August 1995 UNIVERSITY O F S O U T H E R N CA LIFO RN IA TH E GRADUATE SCH O O L U NIVERSITY PARK LOS A N G E LE 6, C A LIFO R N IA 8 0 0 0 7 This thesis, written by Craig Ellsworth Magnusen under the direction of h)$. Thesis Committee, and approved by its members, has been pre sented to and accepted by the Dean of The Graduate School, in partial fulfillment of the requirements for the degree of Master of Science Dtan _ August 21, 1995 D ate.......................................... TABLE OF CONTENTS Page FIGURES v TABLES ix ACKNOWLEDGEMENTS x ABSTRACT xi 1.0 INTRODUCTION 1 1.1 General Statement 1 1.2 Purpose 3 2.0 REGIONAL SETTING 4 2.1 Regional Geography 4 2.2 Location 4 3.0 THE LITTORAL CELL 9 4.0 HISTORIC SOURCES 11 4.1 Fluvial Sources 11 4.2 Marine Sources 16 5.0 BEACH EROSION 17 6.0 FIELD METHODOLOGY 19 6.1 Foreshore Sampling 19 6.2 Berm, Backshore, and Dune Sampling 19 6.3 Suspected Source Sampling 20 6.4 Current Data 20 7.0 LABORATORY TECHNIQUES 21 7.1 Shape Analysis 21 7.2 Size Analysis 22 8.0 DATA ANALYSIS 22 8.1 Fourier Grain-Shape Analysis 22 8.2 Data Preparation 24 9.0 STATISTICAL ANALYSES 9.1 Factor Analysis 9.11 Factor Analysis Discussion 9.12 Factor Analysis Results 9.2 ANOVA 9.21 One-way ANOVA Results: Grain-Shape Significance of Factor Loadings 9.22 Two-way ANOVA Results: Foreshore Grain-Size Significance 9.23 Two-way ANOVA Results: Size Analysis Core Study 9.3 Hotelling’s T2 Test 9.31 Hotelling’s T2 Results: Grain-Shape Significance Within Foreshore Sampling Stations - Factor 1 9.32 Hotelling’s T2 Results: Grain-Shape Significance Within Foreshore Sampling Stations - Factor 2 9.33 Hotelling’s T2 Results: Grain-Shape Significance Between Foreshore Sampling Stations - Factor 1 9.34 Hotelling’s T2 Results: Grain-Shape Significance Between Foreshore Sampling Stations - Factor 2 9.35 Hotelling’s T2 Results: Grain-Shape Signifiicnace Within Core Depth Stations - Factor 1 9.36 Hotelling’s T2 Results: Grain-Shape Significance Within Core Depth Stations - Factor 2 9.4 Discriminate Function Analysis 9.41 Discriminate Function Analysis Results- Foreshore Study 9.42 Discriminate Function Analysis Results- Core Study: Berm, Backsore and Dune 10.0 DATA INTERPRETATIONS 10.1 Foreshore - FGSA 10.11 Supporting Evidence 10.12 Shelf-sand : A Discussion 7 2 10.2 Foreshore - ANOVA Significant Grain-Size Changes 72 10.21 ANOVA Significant Grain-Size Changes Berm, Backshore, and Dune 73 10.3 Hotelling’ s Significant Grain-Shape Changes - Foreshore 80 10.31 Hotelling’s Significant Grain-Shape Changes - Berm, Backshore, and Dune 88 10.4 Discriminant Function Analysis of the Foreshore 88 10.41 Discriminant Function Analysis of the Berm, Backshore, and Dune 89 11.0 CONCLUSION 89 12.0 REFEREN CES 92 13.0 APPENDICES 97 Appendix A: Grain-Size Histograms of Potential Sources 98 Appendix B : Grain-Size Histograms of Foreshore Locations 111 Appendix C: Grain-Size Histograms of Berm Locations 151 Appendix D: Grain-Size Histograms of Backshore and Dune Locations 163 iv FIGURES Figure Page 1. The nine geomorphic provinces of Southern California. 5 2. Map showing the topographic features of the Los Angeles Basin. 6 3. Index map of the counties of California. 7 4. Index map of foreshore sampling locations. 8 5. Index map of the Santa Ana River watershed. 12 6. Mid-Pleistocene Santa Ana River. 13 7. Newport Beach features, also indicating location of observed erosion in the early 1970’ s. 18 8. Bivariate plot of factor 1 versus factor 2 scores emphasizing source influences on the foreshore. 27 9. Scanning Electron Microscopy showing low factor 1 characteristics. 32 10. Scanning Electron Microscopy showing high factor 2 characteristics. 33 11. Bivariate plot of foreshore phi mean grain size versus location for time of study. 36 12. Bivariate plot of foreshore phi standard deviation versus location for time of study. 37 13. Bivariate plot of foreshore phi standard deviation versus phi mean grain-size for December 6, 1991. 38 V 14. Bivariate plot of foreshore phi standard deviation versus phi mean grain-size for February 20, 1992. 39 15. Bivariate plot of foreshore phi standard deviation versus phi mean grain-size for April 27, 1992. 40 16. Bivariate plot of foreshore phi skewness versus phi standard deviation for December 6, 1991. 41 17. Bivariate plot of foreshore phi skewness versus phi standard deviation for February 20, 1992. 42 18. Bivariate plot of foreshore phi skewness versus phi standard deviation for April 27, 1992. 43 19. Bivariate plot of foreshore phi skewness versus phi standard deviation for time of study. 44 20. Bivariate plot of foreshore phi skewness versus phi mean grain-size for December 6, 1991. 45 21. Bivariate plot of foreshore phi skewness versus phi mean grain-size for February 20, 1992. 46 22. Bivariate plot of foreshore phi skewness versus phi mean grain-size for April 27, 1992. 47 23. Bivariate plot of foreshore asperity versus sample station emphasizing significant differences for time of study. 49 24. Bivariate plot of foreshore elongation versus sample station emphasizing significant differences for time of study. 50 25. Bivariate plot of foreshore asperity versus location, emphasizing significant differences between locations. 52 26. Bivariate plot of foreshore elongation versus location, emphasizing significant differences between locations. 54 vi 27. Bivariate plot of berm asperity versus sample station emphasizing grain-shape changes within locations. 55 28. Bivariate plot of berm elongation versus sample station emphasizing grain-shape changes within locations. 56 29. Bivariate plot of backshore and dune asperity versus sample station emphasizing grain-shape changes within locations. 57 30. Bivariate plot of backshore and dune elongation versus sample station emphasizing grain-shape changes within locations. 58 31. Bivariate plot showing percentages of shelf and river derived sand versus foreshore location for December 6, 1991. 60 32. Bivariate plot showing percentages of shelf and river derived sand versus foreshore location for February 20, 1992. 61 33. Bivariate plot showing percentages of shelf and river derived sand versus foreshore location for April 27, 1992. 62 34. Bivariate plot of changes of shelf-sand contributions versus foreshore location for the time of study. 63 35. Bivariate plot showing berm percentages of shelf-sand as determined by Discriminant Function Analysis. 67 36. Bivariate plot showing backshore and dune percentages of shelf-sand as determ ined by discriminant function analysis. 68 37. Bivariate plot of location versus percentages of southeast longshore current for January through April, 1992. 71 vu 38. Bivariate plot of phi standard deviations versus berm location. 39. Bivariate plot of phi standard deviations versus backshore and dune location. 40. Bivariate plot of phi standard deviations versus all core locations. 41. Bivariate plot of phi mean grain-size versus berm location. 42. Bivariate plot of phi mean grain-size versus backshore and dune location. 43. Bivariate plot of phi mean grain-sizes versus all core locations. 44. Bivariate plot of phi skewness versus berm location. 45. Bivariate plot of phi skewness versus backshore and dune location. 46. Bivariate plot of phi skewness versus all core locations. 47. Bivariate plot of phi standard deviation versus mean grain-size for berm locations. 48. Bivariate plot of phi standard deviation versus mean grain-size for backshore and dune location. 49. Bivariate plot of phi skewness versus phi standard deviation for berm locations. 50. Bivariate plot of phi skewness versus phi standard deviation for backshore and dune location. 74 75 76 77 78 79 81 82 83 84 85 86 87 viii TABLES Table 1. Locations, computer file names, and collection dates of beach and potential source samples. 2. Results of ANOVA end member testing 3. Percent composition of foreshore as determined through DFA 4. Changes in shelf-shape composition A ck n o w led g m en ts I am forever grateful to my grandparents, Wayne and Minerva Johnson, for their encouragem ent and financial assistance. My sincerest gratitude also goes out to Dr. Robert H. Osborne for his guidance and faith. This thesis is dedicated to my grandfather and the “ Doc” - two great men who will be forever remembered and appreciated. I would also like to thank the faculty and especially Dr. Donn S. Gorsline, Dr. Bernard Pipkin, and Dr. David Bottjer for serving on my thesis committee. My thanks also go to my comrades of the Sedimentary Petrology Laboratory, particularly Rory Robinson and Dr. A rthur Lee for their friendship and computer wizardry. My thanks also go to Dr. Craig Everts for graciously supplying longshore current data. Last, but certainly not least, I am forever grateful for my parents and their unwavering confidence, and especially for my wife Lauren, and my daughters Kara and Kelsey. Without their love and support this project would not have been possible. This research was sponsored in part by grants from the Sea Grant Institutional Program and the Departm ent of Geological Sciences Graduate Research Fund. x ABSTRACT A thorough understanding of the sources, tran sp o rt directions, deposition and erosion of beach sand is crucial for any coastal zone. An understanding of these processes is crucial for the preservation of valuable recreational areas, and perhaps even more so in highly developed areas which depend on the maintenance of a wide beach for protection against oceanic forces. Using Fourier grain-shape (FGSA), grain-size, and longshore current analyses, beach sand in the Southern San Pedro littoral cell has b e e n characterized in term s of sources and selective transport due to grain elongation and asperity. The study area has been cut off from its major sand source, the Santa Ana River, for several decades due to channelization and damming. This fact emphasizes the need for a thorough understanding of this system, for the area, especially downcoast of the Newport Submarine Canyon, is essentially starved and its survival depends on periodic dredging of the river bed for beach nourishm ent m aterial. The planning of such a project benefits from a com prehension of the active system. Prior to nourishm ent and groin construction, the area experienced property damage resulting from shore erosion. Through the FGSA of 16,200 quartz grains, observations reveal that the Southern San Pedro littoral cell is a complex, highly perturbed system. Grain- shape has been shown to change as a function of supply, selective transport, and selective removal. The dredge spoil placement of late 1991 has benefited a very limited stretch of the downcoast beaches. Future nourishm ent projects will benefit if the circulation patterns observed are considered. A correlation was found between mean grain-size and seasons. The increasing size of beach sand in the winter suggests the selective removal of finer grain sand by the more intense scouring of winter storm waves. Within locations, a trend of decreasing grain-size with depth was reported, likely due to the stratigraphy of seasonal deposition. The classic definition of the littoral cell does not apply in this area, for a net uni-directional current was not observed during the time period of this study. Based on current vector data, the sand in this area has a long residence time. Current data also validates previous work indicating that Newport Submarine Canyon is presently an ineffective sediment sink. The role of shelf sand as a source has historically been ignored in the calculation of beach sand budgets. Site wide, shelf sand enriched the foreshore by 15% from the end of summer, 1991, to the end of winter 1992. This study emphasizes the importance of considering this source in future sand-budget investigations. 1.0 INTRODUCTION 1.1 General Statem ent The purpose of this project is to study the southern portion of the San Pedro littoral cell to determine the source(s) of beach sand and the resultant mixtures due to contributions from different sources and current transport. Through the use of Fourier grain-shape analysis, the temporal and spatial changes of grain shape will be analyzed. To date, studies based on Fourier grain shape analysis of coastal and continental-shelf sand, collected from Point Conception to the southern part of Baja California, have been completed or are in progress (Bloom, 1979; Clark, 1980; Clark and Osborne, 1982; Bomer, 1985; Ahlschwede, 1988; Broadhead, 1988; Cho, 1989; Osborne and Cho, 1989; Osborne and Yeh, 1989; Osborne and others, 1990 and 1991; Osborne and Yeh, 1991; Yeh, 1991; Lu, 1992; Osborne and others. 1992; Lee and others, 1993; Murillo, 1993; Robinson, 1993; and Feffer, 1995). To complete this regional study, it is im portant that the Huntington Beach- Newport Beach area be included to evaluate the role of the Santa Ana River and the adjacent shelf deposits. The object of this project is to determine a sand budget for this area based on Fourier grain-shape analysis. Through this determination, a clearer understanding of the sources, transport directions, relative quantities, and rates of sediment transport can be attained. This project is important to further our understanding of the processes of supply, transport, and residence time of littoral sand. A greater understanding of the rate and volume of shelf sand contribution can clarify the role of the Holocene transgression in providing modem beach sand. 1 Through the use of Fourier grain-shape analysis, a more accurate understanding of the transport of sand by fair weather and storm-generated oceanic waves and wave-generated currents can be gained. Additionally, the role of the cross-shore vector in supplying sand to the beach from the adjacent inner continental shelf or from the beach to the adjacent shelf is poorly understood. This is likely to be a major source of error in current methods used for sediment budget analysis (Osborne and Yeh, 1991). The vectors associated with the rates of sediment supply and current transport can be quantified using Fourier grain-shape analysis, therefore the validity of the classic concept of a large littoral cell can be tested. With a greater understanding of the source and transport vectors used to compute sand budgets, and further evaluation of storm and fair weather processes, the scientific community as well as coastal engineering, planning, and management agencies benefit. Due to the increasing developm ent of coastal properties, it is crucial that the processes of sand sourcing, deposition, and erosion be more fully understood for effective planning of beach maintenance. The regional importance of this study rests largely on the fact that this coastline zone is characterized by some of the highest property values of the state. Due to the control of the Santa Ana River, a primary natural source of beach replenishment has been greatly reduced, thus leaving this coastline vulnerable to erosion and property damage. Therefore, a thorough evaluation of this coastal area is necessary to anticipate future beach nourishment requirements. An additional threat to this coast is the possibility of contamination due to toxic spills. The proximity to offshore oil wells and major shipping lanes 2 highlights the im portance of a thorough understanding of the vectors of transport to minimize property damage and adverse environmental impact. 1.2 Purpose The prim ary purpose of this study is to offer source and relative supply rates of sand delivered to the foreshore of Huntington Beach and Newport Beach, as determined through FGSA. The following hypotheses are to be tested: A) The inner shelf is an im portant source of beach sand, transported from depths by larger storm-driven swells shoreward; B) A net uni-directional current and the Newport Submarine Canyon define the study area as a littoral cell; C) There are significant differences between sampled grain- shape populations. To further characterize the beach environm ent, hand-augered berm, backshore, and dune samples were collected. Samples of various depths were examined with Fourier Grain-Shape and conventional size analyses in an effort to examine the following additional hypotheses: D) Beach sand, due to high-energy mixing, is homogeneous with respect to grain-shape; E) Grain-size is homogeneous due to the good sorting of grain- sizes attained in the high-energy beach environment as it approaches hydraulic equilibrium . To complete the characterization of the beach sand, grain-size analyses were performed on all beach and possible source samples. 3 2 .0 REGIONAL SETTING 2.1 Regional G eography The nine geom orphic provinces (Southern Coast Ranges, Great Valley, Sierra Nevada, Basin Ranges, Transverse Ranges, Mojave Desert, Los Angeles Basin, Peninsular Ranges, and Salton Trough) which are commonly used to classify the regional geology of Central and Southern California are illustrated in Figure 1 (Sharp, 1972). These provinces, grouped topographically and geologically, are based on landforms and late Cenozoic erosional and structural history (Norris and Webb, 1991). The coastline studied borders the Los Angeles Basin province, south of the Palos Verdes Peninsula and Los Angeles Harbor. The Los Angeles basin has accum ulated a great thickness of m arine and terrestrial mud and sand (Sharp, 1972). The present relief began to evolve in the early Miocene, when significant vertical movement acted as a control to subsequent deposition. Since the m iddle Miocene, the basin has experienced 7,000 meters of subsidence with contemporaneous deposition. The Los Angeles Basin is enclosed by the mountains of the Transverse and Peninsular ranges, and is divided by the Puente-Repetto Hills (Figure 2). Three of southern California’s larger rivers, the Los Angeles, San Gabriel, and Santa Ana rivers, drain this basin. 2 .2 LOCATION The study area, a portion of the coastline within Orange County (Figure 3), extends from First Street, Huntington Beach, south to the jetty seaward of Channel Road, Newport Beach (Figure 4). This jetty forms the northern 4 ........ .......... «. 0 50 100 150 200 Kilometers Figure 1. The nine geomorphic provinces of Southern California, which are: 1-Southem Coast Ranges, 2-Great Valley, 3-Sierra Nevada, 4-Basin Ranges, 5- Transverse Ranges, 6-Mojave Desert, 7-Los Angeles, 8- Peninsular Ranges, and 9-Salton Ranges (from Sharp, 1972). 5 Son Fernando V " * Son Gabriel Mtns. C -'-* *•, ,«u*| Santo Monica ... * £ • + '* * * • '» ,v y** . *5. © v % > '' X r -r v -4 . s Baldwin Hills ' " " C F / < ! I is f Coyote Hills Dominguez & a . / Santo J V V -d/>0 0V i fi * • S rtf//75 K ilom eters Figure 2. Map showing the topographic features of the Los Angeles Basin (from Sharp, 1972). 6 ORANGE COUNTY Figure 3. Index map of the counties of California, emphasizing the study area. Map is not to scale. 7 Figure 4. Index map of the study area, emphasizing the foreshore sampling locations. hunting . . . . FIRST ST. A ’ 3 Km 2 2 00 < u 04938735 boundary of Newport Harbor and the southernmost extent of the San Pedro littoral cell. For the purposes of this study, this littoral cell has been divided into four subdivisions, the northern, central, south-central, and southern cells (Figure 4). The sample sites are listed in TaMe 1. 3.0 THE LITTORAL C ELL A littoral cell, as defined by Emery (I960), is a section of the coast dominated by a net uni-directional current which transports sediments longshore from the source(s) to an ultimate sink. In this area, the principal sources are the Santa Ana River and the offshore inner-shelf deposits. The ultimate sediment sink has previously been assumed to be the Newport Submarine Canyon (Hand and Emery, 1964). Felix (1969), however, found this canyon to be largely inactive and undergoing agradation at approximately 1.4 cm/yr„ primarily trapping organic-rich sewage and silt-clay from sewage discharge and river input, and only trapping minor amounts of very fine sand. The sand is derived from submarine canyon walls, and is transported into the canyon by strong, storm-related wave surge. Also reported are divergent currents at the mouth of the Newport Submarine Canyon (Felix, 1969), which further acts to keep longshore-transported sediment from entering the canyon. Therefore, although the head of the submarine canyon lies only 150 meters from the shore, beach sand is not intercepted. One possible sediment sink exists downcoast of the canyon at the mouth of the Newport Harbor, where fine sediment, which leaks around the jetty, encounters deeper water and tidal flushing. The seaward reaches of the inner shelf may also be a sink for storm- transported sand. 9 Table 1. Locations, com puter file names, and collection daltes of beach and potential source samples. A. Beach Foreshore Foreshore Foreshore Berm Backshore and Dune 1 2 /0 6 /9 1 0 2 /2 0 /9 2 0 4 /2 7 /9 2 0 1 /2 1 /9 3 0 1 /2 1 /9 3 First St. A1 B1 Cl Newland St. A2 B2 C2 Brookhurst St. A3 B3 C3 Olive St. A4 B4 C4 BM4,BM5,BM6 BS1,BS2,BS3 Lugonia St. A5 B5 C5 56th St. A6 B6 C6 BM10.BM11 BS7,BS8,BS9 44th S t A7 B7 C7 32nd St. A8 B8 P C8 23rd St. A9 B9 C9 “ 1 BM15.BM16 BS12.BS13.BS14 19th St. 1 A10 B10 CIO BM19.BM20 BS17.BS18 15th St. A11 B11 C11 Main St. A12 B12 C12 Channel Rd. A13 B13 Cl 3 BM24.BM25 BS21,BS22,BS23,D26 B. Potential Sources 1 1 /1 3 /9 1 0 1 /0 3 /9 2 0 4 /2 4 /9 3 0 5 /1 1 /9 3 0 5 /1 3 /9 3 0 7 /0 7 /9 3 0 7 /1 1 /9 3 Santa Ana River R1,R2 Inner Shelf S1,S2,S3,S5 Catalina Grus CAT Santa Ana Sandstone SAS Huntington Cliffs HC San Gabriel River SGR San Gabriel River-East Fork SGRE San Gabriel River-West Fork SGRW Surfside Beach Prado Dam 1 SRF PD Sunset Beach Prado Basin SST PB 4 .0 HISTORIC SOURCES 4.1 Fluvial Sources Historically, the principal source of beach sand for the study area was sediment supplied by the Santa Ana River. The headwaters of the Santa Ana River originate in the San Bernardino Mountains and are supplemented by tributaries draining the San Gabriel and San Jacinto Mountains (Figure 5). Coursing over 160 km, the river drains an area of approximately 4,406 km^, encompassing San Bernardino, Riverside, Los Angeles, and Orange Counties (Wyman, 1939). The average slopes in the drainage basins are 0.05 in the mountains, 0.005 in the valley, and values of less than 0.001 near the coast (Brownlie and Taylor, 1981). Roughly one-third of the drainage area consists of mountainous terrain, where are found Precambrian gneiss and schist, which have been intruded by Mesozoic granodiorite, monzonite, and other granitic rocks (Brownlie and Taylor, 1981). Due to the nearly continuous uplift since the early Tertiary, an ample supply of coarse sand-size sediment has been available. The Santa Ana River, whose lower reaches were considered to possess the characteristics of a braided stream, has historically changed course in its flood plain with periodic breaches of its channel, which periodically relocated the river entrance into the Pacific Ocean. The following, taken from Stevenson (1954), summarizes the river’s history from the Pleistocene to the recent: Erosion of the large filled channel which now forms the northern end of Newport Bay commenced in the mid-Pieistocene. At that time relief was considerable, with sea level being 60 m below that at present (Figure 6, from Felix, 1969). The Santa Ana River wandered over the alluviated Los Angeles Basin, changing its course frequently. During one of these swings it left the vicinity of the Bay and moved 11 west where it cut the “arroyo” now known as Newport Submarine Canyon. Uplift ceased at the end of the Pleistocene and the area was inundated by the sea. In the Holocene a series of uplifts began. One of these, only a few hundred years ago, diverted the river to an outlet in the vicinity of Seal Beach, 18 km northwest of the present river mouth. A large flood in 1825 (Sherman, 1931) swung the river to approximately its present position. The large quantity of sediment carried by the flood started to build the Newport barrier beach (Balboa Peninsula) and diverted the river to the western side of the Bay. A still greater flood in 1861 built the barrier to nearly its present size. Felix (1969) found many other channels which incise the outer portion of the continental shelf and slope, which indicates more extensive changes in the positions of the river channel in the Pleistocene and Holocene during lower sea-level stands. Following 1861, many damaging floods occurred. Changes in the location of the Santa Ana River created a fertile plain on which agricultural developm ents and urban communities were rapidly taking hold. This increased development resulted in higher economic losses due to floods. Additional flooding occurred in 1916, emphasizing the need for flood protection. The estim ated damage to agricultural land alone ex ceed ed $1,500,000 (Wyman, 1939) and it was obvious that the recurrence of a major flood in the future would greatly endanger human life and property. In 1920, the present artificial channel was constructed by private interests. In 1927, the Orange County Flood Control District was established, w hich enacted control measures, primarily the construction of the Prado Dam. Prior to the completion of this dam in 1941, the floods of 1937 and 1938 resulted in a loss of more than $18,750,000 and a death toll of 45. Subsequent channelization, percolation basins, and dams have essentially sealed off the Santa Ana River as a source of beach sand. The previous 14 sediment supply, estimated between 330,000-500,000 m3/yr., has been reduced to less than 140,000 m3 /y r. of sediment too fine-grained for beach nourishment (Brownlie and Taylor, 1981). The San Gabriel and Los Angeles rivers were also historic sources of beach sand; however, it is unlikely that they presently contribute sediment to the downcoast beaches. The engineering history of these rivers parallel that of the Santa Ana River; dams, catchment basins, and paved channels have drastically reduced the delivery of sediment to the coast. A major difference between the San Gabriel and Los Angeles rivers and the Santa Ana River is the coastal engineering at their mouths, the former being highly engineered and restricted by jetties, breakwaters, groins, piers, and the respective openings of Anaheim Bay and the Port of Los Angeles. The cumulative effect of these structures has been to greatly perturb the historic longshore transport of any river-derived sediment. Furthermore, considering the distance between the study area and the Los Angeles River, which is sheltered by extensive outer breakwaters, there is little likelihood of any contribution from this source. In the mid 1890's, the net longshore transport near the San Gabriel River mouth was from the southwest to the northeast, as indicated by observed erosion of what is present day West Beach and deposition on East Beach (ACE, 1986). Presently, the net longshore transport has reversed, with vectors trending northeast to southwest, resulting in an average recession of East Beach between 0.5 to 0.9 m/yr. (Everts, 1993). The dominant cause of the reversal of the longshore current is likely the shelter provided by the offshore breakwater of the Los Angeles-Long Beach Harbor. The breakwater insulates this coast from the onslaught of strong, 15 winter storms originating in the North Pacific which generate high surf and strong currents. Only those storms which originate in the south or west, occurring usually in the late summer, have the opportunity to impact this area. Medium-to-long period waves with periods of 10 to 24 seconds and heights up to 8.5 m originate from Equatorial storms. Such wave energies are capable of inflicting major damage to this area (Pipkin and Proctor, 1992) The combination of the control of the San Gabriel River and the net longshore current result in the unlikely possibility of downcoast sediment transport presently occurring. 4.2 MARINE SOURCES Gorsline and Grant (1972) described surficial sediments on the San Pedro Shelf in terms of grain-size characteristics and the distribution of major sediment groups, which they categorized as: 1) reddish-stained, coarse-grained sand, thought to be a relict Pleistocene sand deposited in a shoreline environment, derived from outcrops of Tertiary strata; 2) Holocene detrital silt and sandy silt. Osborne and others (1983) described three additional sediment groups as varieties of populations resulting from the mixture of Pleistocene and Holocene sand. A patch of the relict sand occurs south of Anaheim Bay, with mixed populations occurring adjacent to this patch (Osborne and others, 1983). Offshore, between the Santa Ana River and Newport Beach in water ranging from 5.5 - 42 m deep, the sand is assigned to the undifferentiated Holocene unit, consisting of olive-gray to olive brown, fossiliferous, moderately well sorted, silty, very fine to fine-grained sand (Osborne and others, 1983). 16 Felix (1969) described another patch of relict shelf sand off Newport Beach as sediment of a high-energy beach and beach dune which was deposited during the last sea level stand 12 m below present sea level. Grains were found to be subrounded to round, and coated with an orange iron oxide, indicating considerable subaerial exposure. Of the clear grains observed, frosting was visible on 50 percent of the grains, suggesting reworking by wind into beach dunes. The mineralogy is the same as sand from the Santa Ana River and modern beach sediment, however the grain size is slightly finer (Felix, 1969). 5.0 BEACH EROSION With the increased flood control of the Santa Ana River, the coastal zone suffered adverse effects. Presently, this coastal zone is essentially a starved beach, receiving shelf sand only from major storms, and river sand only during times of dredging of river bed supplies. Newport Beach is vulnerable to destructive late summer and fall storms spawned in the Antarctic and off Baja California. Due to its southern exposure and lack of screening borderland islands, summer erosion is a recurrent fact. During the summer of 1965, approximately 55 meters of beach was eroded, nearly destroying homes before stabilization of the shore by sand bags, and in the summer of 1968, several homes were undermined. Most of the erosion occurred in West Newport Beach between the Newport pier and the Santa Ana River mouth (Figure 7, from Pipkin, 1985). In an effort to control the longshore movement of beach sand, five groins were constructed in 1968 at 40th, 44th, 48th 52nd, and 56th streets. These range in length from 80 to 175 m (Shaw, 1980). In 1969, dredge spoil from the Santa Ana River was used to 17 PLEASE NOTE Page(s) not included with original material ana unavailable from author or university. Rimed as received. UMI restore the eroded beach (Pipkin, 1985). Three additional groins, which are from 155 to 180 meters long were constructed at 28th, 32nd, and 36th streets between 1969 and 1973 (Shaw, 1980). 6.0 FIELD METHODOLOGY 6.1 Foreshore Sampling In late December, 1991, a program of beach nourishment was undertaken by the Army Corps of Engineers (Everts, 1991). Approximately 1.2 to 1.3 million cubic yards of Santa Ana River sand was injected in depths of -2 to -8 m mean lower low water (MLLW) from 56th Street to the east side of the Santa Ana River mouth. A pre-injection sample set from thirteen sites, three upcoast of the Santa Ana River, three in the placement vicinity, and seven downcoast of the injection zone were collected at 0 m MLLW on December 6, 1991. Two more sets of samples were collected at 0 m MLLW on February 20 and April 4, 1992, which reflect the injection of dredge spoils and the end of the oceanographic winter. Injection was initiated in mid-December, 1991, and was terminated in early May, 1992. Foreshore sampling of surficial sand was accomplished by filling a cloth sample bag in the swash zone at 0 m M LLW . 6.2 Berm, Backshore, and Dune sampling A hand auger was used by the author to penetrate approximately 3 m below ground surface (bgs) for collection of berm and backshore samples. Samples 19 were collected at -0.3, -1.5, and -3.0 m bgs. The auger was advanced to the desired depth, the sand was retrieved, and poured into a cloth sampling bag held in place against the auger bit by Dr. Robert H. Osborne. The one dune sample was a surface sample collected with a trowel and placed into a cloth bag. 6.3 Suspected Source Sampling Pre-dredge samples of the Santa Ana River were obtained from Petra Geotechnical Incorporated of Costa Mesa. These samples were collected on November 15,1991. Shelf samples were collected by Dr. Robert H. Osborne from a Coast Guard Point Class vessel using a grab sampler, in water depths ranging from -12.5 to -17 m MLLW on January 3, 1992. River, cliff, and terrace deposits were collected by the author with a hand trowel and placed into a cloth sampling bag. All samples are tabulated in Table 1. 6.4 Current Data Longshore current vector data were supplied by Dr. Craig Everts, a coastal scientist with Moffatt and Nichol, Engineers located in Santa Ana, California. Current vectors were determ ined through visual observations of motion of a dye patch placed in the surf zone. The dye was placed daily at the time of mid- tide, and the distance and direction that the dye moved in 100 seconds was recorded. This data supplied the most accurate near-shore longshore current vectors available for the time frame of this study. 20 Net current directions were used in an effort to explain grain-shape variations. With an understanding of these vectors, selective transport from site to site may be better analyzed. Due to the lack of current measurements north of the Santa Ana River, an assumption must be made concerning the net longshore direction. Since winter months have a higher frequency of more severe storms than summer months, and since these storms primarily arrive from northern latitudes, this area is assumed to have a net north-to-south longshore current. 7.0 Laboratory Techniques 7.1 Shape Analysis 1. Sand samples were wet sieved (gently) to collect the 1.0 to 2.0 phi (0.25 to 0.50 mm) medium sand fraction. By restricting the grain-size, the possibility of grain-shape variation as a function of grain size is reduced. 2. The sieved samples were then individually washed in beakers with a concentration of 50% hydrofluoric acid for 3 minutes. This wash corrodes the feldspars, which aids quartz identification, and cleans the quartz grains of organic material and surface stains. Clean grains are imperative so that an accurate oudine of the grain-shape is obtained. 3. The samples were washed thoroughly with deionized water to remove any residue which may interfere with the grain outlines. 4. The samples were dried in a convection oven at 40^ C. 5. The samples were stored in labeled plastic vials. 6. When samples were readied for digitizing, they were examined under a petrographic microscope with a reflected light source and at least 200 quartz 21 grains were picked from each sample with an extremely fine-hair paint brush. 7. These quartz grains were then aligned on a slide and placed beneath a Leitz Dialux polarizing microscope which was connected to a video camera, which, in turn, was connected to a V A X 3200 microcomputer. 8. With the digitized information, normalized amplitude files were created to perform the necessary statistical tests, such as a factor analysis. 7.2 Size Analysis 1. One hundred grams of each sample were weighed on a Mettler P-1000 balance, calibrated to zero. 2. The sample was then placed in a sieving column of nested one half phi sieve intervals (Krumbein and Pettijohn, 1938). 3. The column was placed on a Fisher Wheeler Sieve Shaker and shaken for a minimum of one hour. 4. The column was then removed from the shaker and dismantled. 5. The grains retained in each sieve were then weighed on the same balance previously used. 6. The first, second, and third moments (mean, standard deviation, and skewness) were then calculated using standard moment measures (Folk, 1966). 8.0 DATA ANALYSIS 8.1 Fourier Grain-Shape Analysis Fourier grain-shape analysis (FGSA) analyzes the variation of a two 22 dimensional, maximum-projection, grain-shape area (Ehrlich, Orzeck, and Weinberg, 1974). By the expansion of the periphery radius as a function of an angle about a grain’s center o f gravity by a Fourier series, the grain shape is determined. FGSA partitions the shape of a grain into a series of harmonics, which are shape components. This is accomplished with the following equation: o c H(0) = Ao + ^/4ncos(rt0- < p n ) n - 1 Where R is the radius from the shapes center point to a peripheral point in a polar direction 6, where Ao is the mean radius of the shape, where An is the amplitude, and where < p n is the phase angle of the nth term of the series. The grain-shape is thus reflected in the amplitudes and phase angles. For example, the fourth harmonic describes the influence on shape of a four leaf clover shape. The harmonic which is most like the shape analyzed will have the highest value. Lower harmonics describe the gross, overall shape of the grains, whereas high harmonics describe fine characteristics, such as roughness (asperity). The first through twenty-fourth harmonics were utilized in this study. This report utilizes the Fast Fourier Transform methodology, which is discussed in Osborne and Yeh (1991). Interested readers are referred to their paper. For extensive treatments of this mathematically complex methodology, readers may also refer to Bendat Piersol (1971), Brigham (1974), and Bloomfield (1976). Additional papers which have applied Fourier analysis to a wide variety of petrofacies and natural tracer problems are: Ehrlich and others (1974), Mrakovich and others (1976), Van Nieuwenhuise and others (1978), Brown and others (1980),, Ehrlich and Chin (1980), Hudson and Ehrlich (1980), 23 Mazzullo and Ehrlich (1980,1983), Riester and others (1982),, and Mazzullo and Magenheimer (1987). Additional Fourier grain-shape studies, completed in California, include Ehrlich and others (1974), Bloom (1979), Porter and others (1979), Clark and Osborne (1982), Gaynor (1984), Bomer(1985), Osborne and others (1985), , Ahlschwede (1988), Broadhead (1988), Cho (1989), and Osborne and others (1990). 8.2 Data Preparation The Grain-Shape Analysis program (GSA), which consists of several complex FORTRAN routines, was used for Fourier calculations and shape recording. This program, written by Rory Robinson and Tim Foley of the University of Southern California Sedim entary Petrology Laborartory, examines, mathematically describes, and stores the 200 grain boundaries of each sample. Recorded samples are designated with a BPT. file extension label. BPT. files were converted to files with the AMP. file extension label. This conversion transforms the X -Y Cartesian coordinates, determined by GSA, into polar coordinates. The polar coordinates consist of the radius from the grain centroid to the projected grain boundary, and the angle formed by a horizontal line bisecting the centroid. Due to the Fast Fourier Transform mathematics requiring equally spaced data centered about zero, the polar coordinates are linearly interpolated to produce 128 evenly spaced points. Finally the data is divided by the amplitude of the zero harmonic to reduce possible effects of grain shape variation as a function of grain size. The resulting amplitudes are equal to the square root of the summation of the real and imaginary Fast Fourier Transform values, which equal 64 wave 24 numbers per grain. Of these 64 wave numbers, waves 1 through 24 were analyzed. Harmonics exceeding 24 were not analyzed, for it has been previously shown that little variance is explained by the higher harmonic num bers. The program CONVRTIT, was then used to transform AMP. files into IBM recognizable ASCII data files. This was necessary so that the data could be analyzed with the BioMedical Data Processing statistical program (BMDP), which is loaded on an IBM computer. The following statistical tests were perform ed with this statistical package, with slight input m odifications. Computer program s other than the BMDP package were written by Rory Robinson and Tim Foley of the University of Southern California Sedimentary Petrology Laboratory. 9 .0 STATISTICAL ANALYSES 9.1 Factor Analysis Q.-mode factor analysis was perform ed to identify sample end members using averaged amplitude values for harmonics 1 through 24. Factor analysis was developed in the 1930’s by experimental psychologists to analyze factors responsible for human behavior (Davis, 1986). The data was plotted on factors, or axes, determ ined from eigenvalues and eigenvectors obtained from the covariance matrix. This matrix is obtained from the original data matrix multiplied by its transpose (Davis, 1986). The num ber of factors obtained by this procedure is the num ber of variables analyzed minus one. In this study, this results in the generation of 25 23 factors. Fortunately, the first two factors usually identifies the majority of the variance in the system, thus allowing harmonic by harmonic significance testing. The Q. mode factor analysis maximizes variance, being a principle component analysis (Davis, 1986). 9.11 Factor Analysis Discussion Ideally, sources plot as the end-members on the bivariate factor graph. The resulting mixtures of the sources therefore plot somewhere within the source field. This situation makes the interpretation of shape changes, which is a function of source mixing, relatively simple. However, the first set of samples collected revealed that the sources plot near the center of the sample field, forcing a difficult interpretation. Following the initial time series sampling of the foreshore and factor analysis, it became apparent that an unaccounted population of sand may be responsible for the apparently anomalous shapes observed. Simple mixing of the sampled “sources” could not account for some of the observed shapes. With the logic that sources that are now isolated from the system may be responsible for these shapes, and that sand has a long residence time in the littoral cell, w hether due to seasonally varying converging or opposing longshore currents, a search was conducted to find a source of these grains, which are more elongate and rougher than present-day source sand. Upstream and immediately downstream of Hansen Dam, samples w ere obtained from the headwaters of the West and East fork of the San Gabriel River. Samples were also taken from the headwaters of the Santa Ana River, 26 FACTOR 2 SCORES INCREASED ELONGATION A I A SHELF ♦ RIVER FORESHORE O GRUS I FORESHORE: A=12/06/91 B=02/20/92 0=04/27/92 • CLIFF -1 -2 +---------- □ C5 GABRIEL ) RIVER ♦ SGRE SANTA B 8 i SI SHELF «:SAS -2 -1 0 INCREASED ASPERITY FACTOR 1 SCORES SGRW P O ■ N l Figure 8. Bivariate plot of factor 1 versus factor 2 scores. Delineated zones are strongly influenced by source end mem bers a t the 0.05 significance level. Refer to table 1 for sample identification. specifically at the Santa Ana Sandstone outcrop at Seven Oaks. The Prado Basin and Prado Dam terrace deposits were also analyzed to determine if this sediment could be responsible for the cemented grain surfaces found on some of the Pleistocene relict sand. Additional “source” samples were collected at Sunset Beach and Surfside to test the potential of an upcoast derivation of sediment. Grus from the Catalina schist, which is the rock used for groin construction in West Newport Beach, was also analyzed to determine if it may be a possible source of grain shape. Following another factor analysis, the data clearly indicates that, as illustrated in Figure 8, the search for the source of more elongate and rougher grains which may have, prior to controls, contributed to the beach sand population was successful. The previously ignored San Gabriel River sand, and the Santa Ana Sandstone, were found to be the likely sources of elongate and rough grains. Utilizing historic sediment sources and the posibility of a long residence time of sand delivered to the littoral cell, a feasible explanation of the observed shape populations was discovered. Of interest is the stronger influence of grain-shape exerted by the San Gabriel River on Sunset Beach when compared to the upcoast and adjacent Surfside. This is likely due to the well-documented wave refraction effects of the Anaheim Bay jetty. It has been shown that the jetty refracts and intensifies incoming swells to erode Surfside and result in deposition at Sunset Beach (Everts, 1993). This process may be acting to selectively sort rougher, more elongate grains from Surfside and deposit them downcoast at Sunset Beach. The artificial nourishment of Surfside, primarily from dredge spoils from the Anaheim Bay/Naval Weapons Station area are San Gabriel River- 28 derived. More recently, the 1990 offshore borrow of 1,822,000 cubic yards of sedim ent (United States Army Corps of Engineers, 1986), offers another explanation of the more elongate and rougher grains of Sunset Beach, which show the San Gabriel River signature, as compared to the sheif-sand dilution at the placem ent site of Surfside. Possibly the combination of shelf sand placement at Surfside, and the intensified downcoast longshore transport of selectively transported grains produces a lag deposit at Surfside of smoother, shelf population grains and a San Gabriel River sand-shape population of more elongate, rougher grains at Sunset Beach. 9.12 Factor Analysis Results The processed data reveals that factor one explains 76 percent and factor two explains 14 percent of the total variance of the 71 sample means. Figure 8 illustrates the plot of these two factors as X-Y Cartesian coordinates. This figure also illustrates sample groupings based on sources, determ ined by Hotelling T2 significance testing (see below). 9.2 Analysis of Variance (ANOVA) The Factor Analysis defined the factors responsible for explaining the majority of the variance in the system. However, Factor Analysis does not define the factors in a tangible way. The question arises: what, in terms of grain-shape, do the factors represent? The answer to this question is found by the use of ANOVA. Analysis of variance (ANOVA) algebraically partitions the sum of m ean squares into components, which are evaluated to determine their relative 29 contributions to the total variance in the system. The basic assumptions are that the data is randomly sampled, and the populations and variances are normally distributed. Both one-way and two-way ANOVA were utilized in this study. One-way ANOVA was used to analyze the significance of the harmonic amplitudes of factor one and two endmembers, explaining what the factors represent with respect to grain-shape. The significance of the shape-defining harmonics 1 through 24 for the endmember samples was tested using various Analyses of Variance (ANOVA) designs. An input file written by Dr. Arthur C. Lee of the University of Southern California Sedimentary Petrology Laboratory directed the BMDP program to perform three tests simultaneously. Levene’s F test was first calculated (Davis 1986). If this test showed that variances were assumed equal, then ANOVA for equal variances was calculated. Otherwise, when variances were not assumed to be equal, the Welch and Brown-Forsythe tests were performed by default. In one-way ANOVA, the total variance is analyzed with respect to variances among and within samples. Two-way ANOVA was utilized to examine the relationships between grain- size and temporal and spatial distribution of beach sand in the area. In two- way Anova, the procedure allows for the addition of and simultaneous analysis of an additional variable. For the detailed mathematics involved with this procedure, refer to Davis, 1986 or Dixon, 1991. 9.21 One-way ANOVA Results: Grain-Shape Significance of Factor Loadings ANOVA was performed on Factor 1 and Factor 2 end members. When 30 comparing the minimum factor 1 sample to the maximum factor 1 sample, harmonics 7 through 24 were found to be significant. The comparison of Factor 2 endmembers revealed harmonics 1 through 5 as significant. By the definition of FGSA, these results indicate that factor 1 describes grain asperity, whereas Factor 2 defines grain elongation, as illustrated in Figures 9 and 10 and Table 2. 9.22 Two-way ANOVA Results: Foreshore Grain-Size Significance The following results reflect the size characteristics of the foreshore. The results of analyzing means versus months are: 1. Highly significant differences were observed between foreshore mean grain-size and months. December samples were shown to be significantly different from April and February samples, however, April and February samples were not different from each other. The results of analyzing means versus stations are that significant differences were not seen between mean grain sizes and stations. The results of examining the relationships between standard deviation versus months and stations are that significant differences were not observed between standard deviations, months, and stations. 9.23 Two-way ANOVA Results: Size Analysis - Core Study The following are the results of two-way ANOVA tests of standard deviation versus stations, locations and depths in sediment of backshore and berm cores: 1. Very highly significant differences were reported between standard deviations and locations between the backshores and berms. 31 HRG- K 1 2 6 . PHOTO- 7453 Figure 9. Scanning Electron Microscopy showing low factor 1 characteristics. Figure 10. Scanning Electron Microscopy showing high factor 2 characteristics. Table 2. Results of ANOVA testing of the first 24 harmonics of the end members. Black Indicates significant differences at the 99.99% confidence level. M lnl/M axl refers to the lowest factor 1 loading com pared to the highest factor 1 loading. Maxl/M ax2 refers to the lowest factor 2 loading compared to the highest factor 2 loading. HARMONIC 1 2 3 4 5 6 MINI/MAXI MIN2/MAX2 2. Significant differences were found to exist between stations and locations with respect to the backshore and berm zones. The following are the results of two-way ANOVA tests of means versus stations, locations, and depths: 1. Very highly significant differences were reported between backshore and berm stations and means. 2. Very highly significant differences were observed between backshore and berm means and depths. 3. Significant differences were found to exist between backshore and berm stations, and depths. The grain-size trends are illustrated in Figures 11 through 22. Relationships illustrated but not noted above were found to be statistically insignificant. 9.3 Hotelling's Test To examine the statistical significance of the mean grain-shapes and mean grain-sizes of all beach and potential source samples, the Hotelling T^ test was performed. This test is a multivariate extension of the Student t-test which analyzes the means between two sample populations (Davis, 1986). This test determ ines if the endm em bers and sources, with respect to m ultivariate means, are statistically distinct, and if there is a distinction within and between locations and stations. The T^ value can be determ ined by the following relationship (Davis, 1986): 35 M E A N PH I VALUES 4 3.5 3 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5 -3 -3.5 -4 NORTHWEST SOUTHEAST 02 20:92 SHELF n - rt □ - 0 1 2 3 4 5 6 7 8 9 10 FORESHORE SAMPLE LOCATIONS Figure 11. Bivariate plot of phi mean grain-size versus sam ple locations for Decem ber 6, 1991 through April 27, 1992. O J < x > PH I STANDARD DEVIATION 4 3.5 3 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5 -3 -3.5 -4 SOUTHEAST 04/27.92 0 1 2 3 4 5 6 7 8 9 10 11 12 13 FORESHORE SAMPLE LOCATIONS Figure 12. Bivariate plot of phi standard deviation versus sample locations for December 6, 1991 through April 27, 1992. to ■vj PHI STANDARD DEVIATION 2 1.5 1 0.5 0 -0.5 -1 □ A 1 A13 □ LJA4D [A|^ 2 - ] [ | A ? A1° D A 1Z D A S D A 9 A7 [J jaE*11 -1.5 U J C O -2 0 0.5 1.5 2 2.5 PHI MEAN GRAIN-SIZE 3.5 Figure 13. Bivariate plot of phi standard deviation versus phi mean grain-size for December 6, 1991. PHI STANDARD DEVIATION O J < D 2 r - — 1.5 1 0.5 0 -0.5 -1 -1.5 -2 - i 1 -------- 1 -------- i--------r 0.5 ♦ B 1 1 ♦ B3 ♦ BE ♦ B12 ♦ E H 3 ♦ B7 >15 B9 4 B 8 B4, 1.5 2 2.5 PHI MEAN GRAIN-SIZE 3.5 Figure 14. Bivariate plot of phi standard deviation versus phi mean grain-size for February 20, 1992. PH I STANDARD DEVIATION 2 t- 1.5 1 0.5 : 0 ^ -0.5 ■ -1 -1.5 ; -2 - 0 ▲ CIO A C2 A Cl 2 A C13 A C4 AC3 A C7 A C5 Cl 1 A A C 9 C6 A A C8 A Cl 0.5 1 1.5 2 2.5 3 3.5 PHI MEAN GRAIN-SIZE Figure 15. Bivariate plot of phi standard deviation versus phi mean grain-size for April 27, 1992. • t* o PH I SKEWNESS 0.75 0.25 -0.25 □ A4DA3 I1A2 T11A 6 "A7 □ DAI □ AT3 □ AS : jai 2 □ All -0.75 □A10 □ A8 -1.25 □ A9 -1.75 - 0 0.5 1 PHI STANDARD DEVIATION Figure 16. Bivariate plot of phi skewness versus phi standard deviation for December 6, 1991. PHI SKEWNESS 0.75 0.25 -0.25 -0.75 -1.25 - -1.75 0 ♦ B1 6 1 2 ♦♦ 8 1 3 4B3 ♦ BIO ♦ B11 ♦ B9 ♦ B6 ♦ B4 ♦ B2 ♦ B5 ♦ B7 ♦ B8 0-5 1 1.5 PHI STANDARD DEVIATION a r-o Figure 17. Bivariate plot of phi skewness versus standard deviation for February 20, 1992. PHI SKEWNESS 0.75 0.25 -0.25 -0.75 -1.25 -1.75 0 ▲ C7 C13 a C11 ^ C 9 AC1| C3 A CIO A C2 A C4 A C6 A C5 A CB A Cl 0.5 1 PHI STANDARD DEVIATION Figure 18. Bivariate plot of phi skewness versus phi standard deviation for April 27, 1992. - P * 0 0 PH I SKEWNESS 4 3.5 3 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5 -3 -3.5 -4 NORTHWEST SOUTHEAST --[> ■ L 04'27:92 SHELF Q" *-D 0 1 2 3 4 5 6 7 8 9 FORESHORE SAMPLE LOCATIONS Figure 19. Bivariate plot of phi skewness versus sample locations for December 6, 1991 through April 27, 1992. 4* PH I SKEWNESS 1.5 1 0.5 0 -0.5 -1 -1.5 □ A4DA3 □A1 L'W a 5 DA13 DAS □ A ll DA12 □ A10 □ A8 □ A 9 A c n -2 ----- 0 0.5 1.5 2 2.5 PHI MEAN GRAIN-SIZE 3.5 Figure 20. Bivariate plot of phi skew ness versus phi m ean grain-size for December 6, 199 V PHI SKEWNESS 2 1.5 0.5 ♦ B 1 ♦ B 1 2 4 B 1 3 ♦ B3 ♦ BIO -0.5 -1 -1.5 ♦ B11 ♦ B2 ♦ B9 ♦ B6 ♦ B4 ♦ B5 ♦ B7 ♦ B8 -2 T~ 0 0.5 1.5 2 2.5 PHI MEAN GRAIN-SIZE 3.5 C l Figure 21. Bivariate plot of phi skewness versu phi mean grain-size for February 20, 1992. PHI SKEWNESS 2 1.5 1 0.5 0 -0.5 -1 -1.5 AC12 ACT3 CIO A AC2 AC7 AC3 c i 1 A AC9 AC4 A C 5 A C6 A C 8 AC1 4 * -2 T — t- ' r o t— t— r ~ ' !— • r - i — t - r * r - 0.5 1.5 2 2.5 PHI MEAN GRAIN-SIZE 3.5 Figure 22. Bivariate plot of phi skewness versus phi mean grain-size for April 27, 1992. where n is the number of samples, x is the sample means, fx is the population means, and is the inverse of the variance-covariance matrix. Once this value is determined, F may be calculated as: „ n - m * F = ------------- T~ m(n - 1 ) where n is the num ber of samples, and m is the num ber of variables, and allows one to reference a conventional F table for significance values. 9.31 Hotelling’s T2 Results: Grain-Shape Significance Within Foreshore Sampling Stations - Factor 1 With few exceptions, a general trend of increasing asperity characterizes the foreshore samples upcoast and in the vicinity of the Santa Ana River. As illustrated in Figure 23, significant increases of asperity were observed between First Street and Lugonia Street. The trend south of Lugonia Street reveals increasingly smoother grains populating the foreshore. Significantly smoother grains were observed at 44th Street, Main Street, and at Channel Road. 9.32 Hotelling’s T2 Results: Grain-Shape Significance Within Foreshore Sampling Stations - Factor 2 As illustrated in Figure 24, locations from First Street downcoast to 56th Street exhibit a significant increase of elongate grains in the foreshore sand population. Significant decreases in elongation were observed from 44th Street to Channel Road. 48 N O R T H W E ST t;i 12/06/91 ♦ 02/20/92 A 04/27/92 SO U TH EA ST □ A *+ A * ♦ A * + ft □ □ 2 O A “ A * ^ D D ♦* o ^ ♦ rj □ 2 - *+ ♦ A+ ^ g * ■ I < A A + fe > ^ h 2 n A V . I , | f- 2 I < ' H ^ S O T < 2 J z E - * H f- H n u fe ^ 5 ^ " O J2 2 * £ 2 £ " £ S B fe h z 5 § £ E < 2 f f l m o j $ 3 p j a 2 2 : 2 s 0 1 2 3 4 5 6 7 8 9 10 11 12 13 FORESHORE SAMPLE LOCATIONS Figure 23. Bivariate plot of asperity versus sample station. A * or + indicates that the sample is significantly different (alpha=0.05) from December or February samples, respectively. CHANNEL RD. NORTHWHST .............. ... _ . L„ I j SOUTHHAST A *+ Li 12/06/91 ♦ 02/20/92 A 04/27/92 ♦ * - .....— ...... ----------- - ............... ------------------- ... . i A *+ i * n A* A ♦ ♦ * n ♦ □ ♦ Cl u A ft A* A A ♦ p 2 C £ <• U A L I ♦ A + ♦ n n □ L U L I H C / 3 O S L U > 2 2 < ♦ * A* H ' S i Q 2 f- W 2 < z E- i-i C O < m Lj co & Q Q i J Q Z E --------------1 ---------- ■ J 1 2 — i---- O o 0£ ca --- -— £ < C O « L U > o 2 O o -J 56T H ST 44T H ST 32N D S T 23R D ST. p £ LU 1 9 T H ST. 1 5 T H ST. M AIN ST L U 2 2 < X r J 0 1 2 3 4 5 6 7 8 9 10 11 12 13 FORESHORE SAMPLE LOCATIONS Figure 24. Bivariate plot of elongation versus sample station. A * or + indicates that the sample is significantly different (alpha=0.05) from December or February samples, respectively. 9.33 H otelling’s R e s u lts : G rain-S hape S ig n ifican ce Betw een Foreshore Sampling Locations - Factor 1 December: Of all of the adjacent beach samples, only 56th Street and 44th Street exhibited a significant differences in asperity, with 44th Street possessing rougher grains. F eb ru a ry: Newland Street samples were found to have significantly rougher grains than First Street and Brookhurst Street. Lugonia Street sand had rougher grains than 56th Street. The asperity of 15 th Street sand grains was significantly higher than that of Main Street. A p ril: Newland Street was found to have significantly smoother grains than both First Street and Brookhurst Street. Lugonia Street exhibited significantly rougher grains than sand of 56th Street. Main Street was shown to have grains of higher asperity than those of Channel Road (Figure 25). 9.34 H otelling’s Results: Significance B etw een F o re s h o re Sampling Locations - Factor 2 December: Only 44th Street shows significantly more elongate grains when compared to adjacent locations. February: Newland Street had significantly more elongate grains when compared to adjacent locations. Lugonia Street has more elongate grains than 56th Street. 51 FACTOR 1 SCORES INCREASED ASPERITY 3 i l 2 1 0 -1 -2 -3 0 1 2 3 4 5 6 7 8 9 10 11 12 13 FORESHORE SAMPLE LOCATIONS Figure 25. Bivariate plot of factor 1 scores versus sam ple locations illustrating tem poral changes in grain-shape per location. Arrows indicate significant differences betw een adjacent localities a t th e 0.05 level. NORTHWEST 1 2 /0 6 /9 1 • 0 2 /2 0 /9 2 ~ - A - 04/27/92 SOITHEAST •cr' - o .. C \ / \ . \ f A \ a w > w i n a E - 1 - h1 7 1 Q Z < > < n te 2 6 0 OS < D Z X < * < o 2 o 5 S S < O C O V Z o V . u U J □ 2 - S C Q I ' H H 6 0 U 3 > J o H H w w X Q b ^ 3 r* " T h i H v; H D C 0 6 L U a z H W c / 3 X X fe in Q oi J E- P m Z Z Z $ < < X S u T _ in b J Main Street had more equant sand than observed at adjacent locations. ApriJ: First Street sand is significantly more elongate than Newland Street sand. Lugonia Street had, once again, more significantly elongate grains than did 56th Street. Main Street sand was significantly less elongate than both 15th Street and Channel Road. These results are illustrated in Figure 25. 9.35 Hotelling's T2 Results: Grain-Shape Significance Within Core Depth Stations- Factor 1 Site-wide significant differences of grain-shape asperity with core depth were minor. Overall, the general trend is that no significant differences of asperity with depths were observed between berm, foreshore, or dune samples (Figure 27 and Figure 28). 9.36 Hotelling’ s T2 Results: Grain-Shape Significance Within Core Depth Stations- Factor 2 As observed with factor 1 results, site-wide significant differences of grain-shape elongation with core depth were minor. Overall, the general trend is that of no significant differences of elongation with depths was observed between berm, foreshore, or dune samples (Figure 29 and Figure 30). 9.4 Discriminate Function Analysis (DFA) Discriminant Function Analyses (DFA) was utilized because it is the most appropriate multivariate statistical procedure for the estimation of source contributions to beach sand. DFA is a very powerful tool when used to 53 FACTOR 2 SCORES INCREASED ELONGATION S O IT H H A S T 1 2 /0 6 /9 1 — 0 2 /2 0 /9 2 0 4 /2 7 /9 2 3 l i 2 1 > — 0 1 -2 3 0 5 6 7 8 9 FORESHORE SAMPLE LOCATIONS 10 11 12 13 Figure 26. Bivariate plot of factor 2 scores versus sample locations illustrating changes in grain-shape per location. Arrows indicate significant differences between adjacent localities at the 0.05 level. C / 1 - P * FACTOR 1 SCORES INCREASED ASPERITY 3 I , 2 1 0 -1 -2 -3 Figure 27. Bivariate plot of asperity versus sample station showing changes in asperity with core depth. A * or + indicates significant difference (alpha=0.05) from -0.3 m or -1.5 m samples, respectively. N O R T H W K ST t" 1 -0.3m ♦ -1.5m A -3.0m ♦ BM11* SO ITH KAST ♦ BMS □ BM4 ABM6 B & U J > 2 < z -c < < C O W > O □BM10 H c o < U u Z BM1S □ 5 2 E C D c o f - ' ^ C O C Q | s 1 ♦ BM 2Q e/5 X £ BM24 □ BM2S ♦ § § Z u 0 5 G 7 8 9 BERM SAMPLE LOCATIONS 10 1 1 12 13 cn Or FACTOR 1 SCORES INCREASED ASPERITY 4 3 JL2 1 0 -1 -2 -3 Figure 28. Bivariate plot of asperity versus sample station, showing changes in asperity with core depth. A * or + indicates a significant difference (a!pha=0.05) from -0.3 m or -1.5 m samples, respectively. Ln N O R T H W E S T - 0 . 3 m ♦ - 1 . 5 m A - 3 . 0 m 0 EOLIAN DUNE ----------- SOtTHF.AST □ BS1 A B S3* ♦ BS2* & W > 5 < z , • • u u £ a < tn □ BS7 ♦ BS8 A B S9 H t n BS12 Cl 5 F - w Q oi a w Z □ B S 1 7 ♦ BS18 ♦ BS13* Z o H c o X D 26+ O BS21 r , RS23 k B S22 ♦ J L U Z z < X 'J 0 4 5 6 7 8 9 10 11 BACKSHORE AND DUNE SAMPLE LOCATIONS 12 13 FACTOR 2 SCORES INCREASED ELONGATION N O R T H W E ST L ! -0.3 m ♦-1.5 m A -3.0 m SOITHHAST k ♦ SMI 1* L n -s i -1 -2 -3 BM6 I BM4 a t u > 2 < ? * £ > c / i O ♦ BM5 □ BM10 w a z c u L U z ♦ BM20 a t < € k DBM19 at E ? u z E- m a fe 5 6 7 8 9 BERM SAMPLE LOCATIONS 10 11 BM2S + BM24 P Q a t 12 13 Figure 29. Bivariate plot of elongation versus sample station showing changes in elongation with core depth. A * or + indicates significant difference (alpha=0.05) from -0.3 m or -1.5 m samples, respectively. N O R T H W E ST 0 1 2 ! 1 -0.3 m ♦ -1.5 m A -3.0 m O E O U A N D U N E SOUTHEAST A n s i4 '+ OS in > t jb s i m H K in > c ♦ US8 □ BS7 4 B S 9 H w B S 1 . 1 4 B S 1 2 □ & a os a 2 C u : 2 2 □ BSI7 < S sc V . H Q i s & in 2 4 BSI8 f e W E & DZ6+ ') B S 2 2 ^ nsn n BS23 id 2 2 < ■ 0 3 4 5 6 7 8 9 10 BACKSHORE AND DUNE SAMPLE LOCATIONS 1 1 12 13 Figure 30. Bivariate plot of elongation versus sample station showing changes in elongation with core depth. A * or + indicates significant difference (alpha=0.05) from -0.3 m or -1.5 m samples, respectively. partition samples into previously characterized shape populations. In this study, each of the 200 grains per sample are assigned source designations. The DFA procedure consists of computing a transform which produces the maximum ratio of the difference between or among a set of multivariate means to the multivariate dispersion (variance) of the sources. If considering only two groups as consisting of points within multivariate space, DFA computes the equation which results in maximum spatial separation with the minimum degree of dispersion. This process insures that the spatial separation between clusters is maximized, thus allowing samples to be assigned to two or more sources (Davis, 1986). For this study, the International Mathematical Subroutine Library DFA was utilized for computation of the required transform. The harmonic amplitude values of each 200 grain sample were assigned to each source, thus allowing computation of the percentage of sand derived from each source. This process results in 340,800 individual computations. The assumption of DFA is that each source possesses a completely unique grain-shape. Since each source is a mixture of grain-shape components, the data has been recast to reflect the unique qualities of each individual source. 9.41 Discriminate Function Analysis Results - Foreshore Study From upcoast to downcoast locations, the following trends were observed, as illustrated in Figures 31 through 34 and in Tables 3 and 4: First Street: increased shelf-sand contribution Newland Street: increased shelf-sand contribution Brookhurst Street: increased shelf-sand contribution 59 70 3 60 2 | 50 Q ■ c c U J > 5 40 30 20 LU X t o L L . O LU O 2 a 10 c c LU a . 0 NORTHWEST ■ — RIV ER - SHELF .A SOUTHEAST A • - A - . ♦ — A -A . \ A £ £ c C E b Q Z _i \ * % * \ * ♦ z o h - co te g > 2 < z < < £ £ < W C O < 3 3 E - co X co x S P H w Q c 4 C " ) H :0 a < u w z 2 < 2 m 3 C O £ £ z > * \ / r V < f V * % H M X fe c o X fc H » Z < s 3 LU z z < X u 0 5 6 7 8 9 F O R E S H O R E S A M P L E L O C A T IO N S 10 11 12 13 C l o Figure 31. Bivariate plot showing percentages of shelf and river-derived sand versus foreshore sample locations for December 6,1991. PERCENTAGE O F SHELF A N D RIVER-DERIVED SAND 7 0 6 0 NORTHWEST — -A R IV E R SHELF SOI T I IF . AST 5 0 --------- 4 0 3 0 2 C 20 10 0 E — 05 H 05 O S E H M Q 2 < i s OS X T * a c S m os lu > 5 < 2 05 LU > J o E - 05 2 O s -I E - 05 X H tn X 5 H 05 Q 2 M r> te Q as ? 3 5 6 7 8 9 FORESHORE SAMPLE LOCATIONS LU 2 2 < 3 m D 05 £ 2 2 2 f-* C /1 X X 2 In 10 1 1 H 05 2 < s 12 13 tn Figure 32. Bivariate plot showing percentages of shelf and river-derived sand versus foreshore sample locations for February 20, 1992. CHANNEL RD. PERCENTAGE O F SHELF A N D RIVER-DERIVED SAND 7 0 6 0 5 0 4 0 3 0 20 10 0 Figure 33. Bivariate plot showing percentages of shelf and river-derived sand versus foreshore sample locations for April 27, 1992. < n r o NORTimT'ST RIVER SHELF •* jr LU c o 0 2 3 4 5 FORESHORE SAMPLE LOCATIONS CHANGE I N PERCENTAGE O F SHELF-DERIVED SAND 20 18 16 14 12 10 8 6 4 2 0 -2 -4 - 6 -8 -10 -12 -14 -16 -18 -20 NORTHWEST! ! B I L IN E OF NO CHANGE i® SHELF 1 2 /0 6 /9 1 -0 2 /2 0 /9 2 ♦ SHELF 0 2 /2 0 /9 2 -0 4 /2 7 /9 2 SOITHEAST i S ! IS ss -8L ♦ !H ! £ X E H u : D Z % \a z — !- ■ a s f- c f l X 3 a u o s ra 0 £ u j > Z < < CO f-1 C O I U > H c o < , Z O c D -J E - C O h- CO a E r H c o Q Z M f ) -1“ -r r O < u ♦ 1 Q ! < s f f l 3 w ( j 0 6 te g I Z s H I ♦ f- c o 3 fe H CO a to F- c o Z < 5 Q Ci 3 W z z •< T1 □ 4 5 6 7 8 9 F O R E S H O R E S A M P L E L O C A T IO N S 10 11 12 13 Figure 34. Bivariate plot of changes of shelf sand contributions versus sample locations for December 6, 1991 through April 27, 1992. 0 0 Table 3. Percentages of medium-sand size detrital quartz derived from sources indicated for each subcell. A 1, B l, Cl refer to upper foreshore sample collected at station 1 on 1 2 /0 6 /9 1 , 0 2 /2 0 /9 2 and 0 4 /2 7 /9 2 , respectively. SUBCELLS SAMPLE LOCATIONS A. Northern SubceH A1 Bl C1 SAN GABRIEL RIVER 54 40 44 INNER SHELF (SS) 46 60 56 B. Central Subcell A2 B2 C2 A3 83 C3 SANTA ANA RIVER 52 52 49 52 52 51 INNER SHELF (S3) 48 48 51 48 48 49 C. South-Central Subcell A4 B4 C4 A5 B5 CS A6 B6 C6 A7 B7 C7 A8 68 C8 A9 B9 C9 SANTA ANA RIVER 57 50 57 52 51 57 50 48 49 59 50 57 50 50 s o 57 46 51 INNER SHELF (S2) 43 50 43 48 49 43 50 52 51 41 50 42 50 50 50 43 54 4 8 D. Southern Subcell A10 BIO CIO A11 B11 C11 A12 B12 Cl 2 A13 Bl 3 Cl 3 SANTA ANA RIVER 47 53 49 54 53 49 47 50 55 59 55 57 INNER SHELF (S I) 53 47 52 46 47 52 53 50 45 41 45 43 $ Table 4. Changes in shelf-shape composition of foreshore samples. A + indicates an addition of shelf-shape sand, whereas a - indicates a reduction. ____ CHANGE IN SHELF-SHAPE COMPOSmON FORESHORE SAMPLE LOCATION 1 2 /0 6 /9 1 -0 2 /2 0 /9 2 0 2 /2 0 /9 2 -0 4 /2 7 /9 2 FIRST ST. + NEWLAND ST. - + BROOKHURST ST. - + SANTA ANA RIVER OLIVE ST. + LUGONIA ST. + 56TH ST. + 44TH ST. + 32ND ST. NO CHANGE NO CHANGE 23RD ST. + NEWPORT SUBMARINE CANYON 19THST. - + 15TH ST. + + MAIN ST. CHANNEL RD. + Olive Street: increased river-sand contribution Lugonia Street: increased river-sand contribution 56th Street: increased shelf-sand contribution 44th Street: increased sheif-sand contribution 32nd Street: no changes observed 23rd Streef: increased shelf-sand contribution 19th Street: increased shelf-sand contribution 15th Streef: increased shelf-sand contribution Main Street: increased shelf-sand contribution Channel Road: increased shelf-sand contribution 9.42 Discriminate Function Analysis Results - Core Study Berm A 16% increase of inner shelf sand was reported within the -0.3m and -1.5m depths, as illustrated in Figure 35. Berm samples were not obtained at the -3.0m depth due to lack of sample retrieval caused by water saturation. Backshore and Dune Immediately downcoast of the Santa Ana River, surface sand was found to contain 77% river sand at Olive Street. This concentration decreased with increasing depth. Progressing downcoast, a general trend of decreasing river sand in surficial beach sand is shown in Figure 36. A maximum shelf shape signature occurs at 23rd Street, immediately upcoast of the Newport Submarine Canyon. 6 6 PERCENT SHELF-DERIVED SAND 45 NORTHWEST 4 0 “ ~ “ -0.3 m --------- ♦--------- -1 .5 m A -3 .0 m SOUTHEAST 'n , 35 30 25 20 Q A z o ! z < o U i z P i 2 w > < 3 5 § < t—1 1 w z < < f-* w u ts c5 £ < C/5 > J O X s k a & z t- V) L w— A § J u 2 Z < I u 0 5 6 7 8 9 BERM SAMPLE LOCATIONS 10 11 12 13 Figure 35. Bivariate plot showing the percentages of shelf-derived sand versus core locations as determined by discriminant function analysis. O ' -j PERCENT SHELF-DERIVED SAND 45 -0.3 m -3.0 m - -1.5 m 4 0 35 30 25 20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 BACKSHORE SAMPLE LOCATIONS Figure 36. Bivariate plot showing the percentages of shelf-derived sand versus core locations as determined by discriminant function analysis. < T > 00 Depths of -1.5 and -3.0 meters exhibit the same general trend as the -0.3 meter samples. River sand decreases with increasing distance downcoast from the Santa Ana River and with depth. At 23rd Street, shelf sand contributions decrease with depth. The dune sample collected at Channel Road shows a greater percentage of river sand than the backshore samples of the same location. 10.0 DATA INTERPRETATIONS 10.1 Foreshore - FGSA Prior to dredge-spoil placement, the sand of the study area, as previously illustrated in Figure 25, exhibited an overall statistically homogeneous grain- shape population between locations. Only 44th Street showed a unique population of more elongate and rougher grains. With the advent of the winter swells and the dredging of the Santa Ana River, the locations north of and in the vicinity of the river exhibited sand of increasing elongation and asperity, while southern beach sand became more equant and smoother. Plausible explanations of these trends are as follows: locations south of Lugonia Street were not influenced by the dredging operation. Either storm transported inner shelf-sand, selective removal of more elongate and rougher foreshore sand, reworking of grains in the surf zone, or a combination of these processes, led to a foreshore sand population enriched in smoother and rounder grains. Conversely, areas north of and including Lugonia Street were overwhelmed by the influence of Santa Ana River dredge spoils. Sites further upcoast and removed from the influences of sand dredged from the Santa Ana 69 River may be recipients of upcoast or offshore-derived sediment. Selectively removed elongate and rougher grains, either from sites proximal to the San Gabriel River, or from inner shelf deposits, may have been transported by longshore or offshore currents, thus enriching northern and central sub-cell foreshores with elongate and rougher sand grains. 10.11 Supporting Evidence Longshore current data supports the preceding hypothesis that little, if any, of the dredge spoils of the Santa Ana River influenced locations south of Lugonia Street. As illustrated in Figure 37, the dominant southeast-trending current near the mouth of the Santa Ana River reverses to a northwest- trending current near 56th Street. The convergence of these currents would result in reduced transport velocity and a sediment sink. In this area, the effects of the groin field between 56th Street and 28th Street upon current direction appears evident. From a southeast-dominant current near the Santa Ana River to 56th Street, the current vectors alternate from northwest at 56th Street to southeast to northw est again, until becoming predom inantly northw est north of the Newport Submarine Canyon. At the Newport Submarine Canyon, currents appear to funnel landward, up the canyon, and to splay out to both the northwest and southeast. This landward flushing likely reduces or eliminates the canyon’s ability to act as a littoral sediment sink. Offshore vectors of sediment transport are likely restricted to episodic rip- currents during periods of high waves (Everts, 1993). 70 PERCENT O F CURRENTS FLOW ING T O T H E SOUTHEAST 100 9 0 80 70 60 50 40 30 20 10 0 OBSERVATION LOCATIONS Figure 37. Bivariate plot of percent of so u th east trending January 1992 through April 1992 nearshore currents versus observation locations, showing positions relative to foreshore sample stations. O 01/92 ♦ 02/92 ■ 03/92 A 04/92 O b m A ♦ A O o Q £ L U > ce < z L O < < < L jj Z z > o tD < X 10 o -J C / J X h " 1 0 tn X * ■ £ NET southeast current above line NET NORTHWEST CURRENT BELOW LlNE' ♦ o & £ ♦ < / 7 O z C N j ro l o Q a: m r\j u o I — V ) X H to X 10.12 S h elf-san d : A D iscussion Offshore shelf-sand deposits are not uniform with respect to sand-shape composition. Shelf-sand sampled in the southern zone of the study area (SI) is more equant and smoother than samples upcoast, which become progressively elongate and rougher. Furthermore, sample SI is orange, oxidized, and a relict Pleistocene sand deposit, whereas upcoast samples bear the gray color of more recent fluvial deposits. Following the storm s of February, the distinctive orange sand was observed along the foreshore south of and in the vicinity of the Newport Submarine Canyon. This natural tracer supports the theory of onshore migration of inner shelf-sand as a contributor to the foreshore sand budget, and explains the trends revealed through FGSA. The characteristics of the shelf-sand upcoast also explain grain-shape changes observed at First Street and Newland Street. The increasingly elongate and rougher grains of the inner shelf upcoast act as a source of foreshore enrichm ent with respect to these shapes. Storm-generated swells agitate and suspend shelf sediment. Grains which possess high asperity and elongation, due to differential settling, are more likely to rem ain suspended longer than grains which are more spherical. Due to this longer suspension time, selective onshore transport and resultant foreshore enrichm ent of more elongate and rougher grains is likely to occur. 10.2 Foreshore - ANOVA Significant Grain-Size Changes The fact that December samples were found to be significantly different from February and April samples indicates that a correlation exists between mean grain-size and seasonal changes. The seasonal trend of increased grain- 72 size in the winter suggests inner shelf-sand input coupled with selective removal of finer-grain sediment driven by increased wave scouring of the foreshore. Sample sets of temporal equivalence were found to exhibit no significant differences with respect to mean grain-size. This suggests that in any given month during the study, mixing of the foreshore creates a relatively homogeneous sand-size population site-wide. 10.21 ANOVA Significant Grain-Size Changes - Berm, Backshore, and Dune The reported highly significant differences between standard deviations and locations between the berm and backshore indicate that the berm is better sorted than the backshore. The berm ranges from well sorted to moderately sorted, whereas the backshore and dune ranges from moderately-to-poorly sorted grain sizes (Figure 38 through 40). This indicates that hydraulic sorting is more efficient than aeolian sorting. Within locations, backshore and berm mean grain size and depths were found to be highly significantly different (Figures 41 through 43). A trend of decreasing grain-size with depth was reported. This may be a function of seasonal variability. If the beach deposits may be considered stratigraphic units, the progression with depth is analogous with a time regression. The deeper samples would therefore represent end of summer samples, which reflect lower wave energy and less transport capacity. In this environment of reduced energy, the sand would have a higher percentage of fine-grained sediment. 73 Figure 38. Bivariate plot o f phi standard deviation versus berm core sample locations. PHI STANDARD DEVIATION ( u» ro -» o O * ro u) ■& »L n Co L n ro c n — » c n © c n — ‘ c n r o c n c o c n ' - : ' > 1 ' ' ^ : ■ ■ ■ ] 1 1 ' ■ r 1 1 1 1 ' ’ ' T * i : : p I i i i r ■ l t r i ; 7 7 0 - FIRST ST. ro - N EW L A N D ST. U ) c /i n o x m £ c n s " D r~ m s " § O O O z IS) BROOKHURST ST. SA N T A ANA RIVER OLIVE ST. LUGONIA ST. - 5OTH ST. 44TH ST. - 32N D S T . 2 3 RD ST. NEWPORT SUBM ARINE CA N Y O N I9T H ST . ¥ t 3 • j\ 3 o 3 15TH ST. ro co MAIN ST. CHANNEL R D . V) Figure 39. Bivariate plot o f phi standard deviation versus backshore and eolian dune core sample locations. sz PHI STANDARD DEVIATION n O m > Z -a O n 3 o z C/1 L n (31 00 r o co ■ CO ro -ft cn co cn r o i/i — > cn O cn — » ’ ' ■ — ■ - ‘ .............. * ■ ' ' i i i i ............. —* r o c o I n r o C n CO I n -S t ro co FIRST ST. N E W L A N D ST. BRO OK HURST ST. SA N T A A NA RIVER O LIV E ST. LUGONIA ST. 56T H S T . 44T H ST. 3 2 N D S T . 23 RD ST. NEW PORT SU BM A R IN E C A N Y O N I'JTHST. I5TH ST. M AIN ST. CHANNEL R D . I i; ♦* \ i i i u z . o 7 3 3 Z . D 9 Z -n to' c 3 - t* o 2 0 3 % S ' 0 3 3. A t U m r+ O ' ® £ ■o n y x- o C O r t -s. n 2. 2 to 0 ) rt C O BJ £ C L zr £ n> o . 3 a. a > ( D w S. « E U I s - 3 = 4 g S 3 3 Z ” t / » Q 0 I : 3 s | z i 8 S S . ° q ro 3 co to 2 0 1 n *r •< 3 s ; C O 8 C /l £ o - > z -a i” m 5 ^ O 5 O as z m ID ro O J PHI STANDARD DEVIATION ■ o In O cn cn r o ro u> FIRST ST. N EW L A N D ST. BROOKHURST ST. SA N T A A NA RIVER O LIVE ST. N 2 E LL'GONIA ST. 56T H ST. £ ) * ♦ 44T H ST. 3 2 N D S T . 2 3 R D S T . ♦ ■)► NEW PORT SU BM A R IN E C A N Y O N 19T H ST . C 4 M I5TH ST. M A IN ST. CHANNEL RD. X ► 1 rr 'A U l b 3 ’ Z a z m C A w r - 1 _ • M E A N PH I VALUES 3.5 - -1 .5 m -3.0 m 2.5 n . D - - 0.5 u : 0 1 2 3 4 5 6 7 8 9 10 11 12 13 CORE SAMPLE LOCATIONS Figure 41. Bivariate plot of phi mean grain-size versus berm core sample locations. "si Figure 42. Bivariate plot o f phi mean grain-size versus backshore and eolian dune core sample locations. 8Z n 0 = o m > 1 I - m C D O o o Z C / 1 C O r o c o MEAN PHI VALUES d o in i - L p O r o i w u t n N u i - J ( f l O i n - ‘ i f l Nu i W 1 I ■ ■ 1 ■ 1 1 1 1 1 1 - 1 1 1 * - 1 — 1 ' 1 1 1 -L ‘ 1 1 1 - 1 J 1 1 1 T 1 1 1 1 ‘ 1 1 1 -* FIRST ST. ro - NEW LAND ST. oo - BROOKHURST ST. SANTA ANA RIVER OLIVE ST. on - LUGONIA ST. 56TH ST. 44TH ST. 32N D ST . 23RD ST. NEWPORT SUBMARINE CANYON I9THST. I5rH ST. - MAIN ST. CHANNEL RD. u 1 * 1 is 3 § □ 2 0 i — 1 n ’ j~. — ! t Figure 43. Bivariate plot o f mean phi values versus core sample locations. Black symbols indicate backshore cores, whereas white symbols indicate b erm cores. 6Z MEAN PHI VALUES o O i n tvj ro Ln co u > C n 8 7 3 > 2 -o G 5 § C O o - - FIRST ST. in j - NEW LAND ST. 6 ' j 3 B Vi H C O QROOKHURST ST. SANTA ANA RIVER ■ '* 1 B 1 OLIVE ST. ; ■ mm* □ c n LUGONIA ST. -1.5m cn - 56TH ST. ♦ ♦ 1 /1 B ~s] - 44TH ST. 00 32N D ST . O B CD - 23RD ST. ► G ■ NEWPORT SUBMARINE CANYON ► O 1VTH ST. 3 I5TH ST. o 3 __L ro MAIN ST. > ■ z 2 5 m w C i— • — a __i C O CHANNEL RD. □ □ > ■ Vl H The significant differences found between backshore and berm locations and depths is another expression of the variability of this system. It should be expected that in such a perturbed system that size differences would be experienced from location-to-location and from depth-to-depth. The comparisons of phi-skewness versus locations, phi-standard deviation versus phi-mean grain size, and phi-skewness versus phi-standard deviation resulted in no significant differences discovered (Figures 44 through 50). 10.3 Hotelling’s Significant Grain-Shape Changes - Foreshore Considering that the majority of the system’s variance is explained by Factor 1 loadings, this factor will be the focus of Hotelling T^ d a ta interpretation. December Samples: Given that the net longshore current is towards the southeast at 44th Street, the following scenario may be a plausible explanation of the observed higher asperity of 44th Street sand compared to 56th Street sand. Both samples were collected immediately downcoast of groins. However, north of 56th Street groin, the net longshore current is to the northwest. This may be the effect of northwesterly swell rebound upon contact with the groin. The rebounding current collides with the dominant southeast-trending flow, thus creating a sediment sink. Grains with slower settling velocities (grains with higher asperity and elongation) continue along with the rebounding current, while more spherical and smoother grains settle out. This creates a groin deposit enriched with smoother and rounder grains, which likely leaks around the groin to the sample site. The dominant southeast current downcoast of this site results in selective transport of rougher grains to the 44th Street sampling site. NORTHWEST -0.3 m 3.5 - -I.5 m -3.0 m 2.5 co 1 CO m 0.5 z £ LU C O —D - □ - - - -n -0.5 x Q . -1.5 -2.5 -3.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 CORE SAMPLE LOCATIONS Figure 44. Bivariate plot of phi skewness versus berm core sample locations. PHI SKEWNESS 4 3.5 3 2.5 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 -2.5 -3 -3.5 -4 -n - -0 .3 m - 3 .0 m EiOl.IAN DUNK v P-' 2 3 4 6 1 5 0 7 8 0 CORE SAMPLE LOCATIONS Figure 45. Bivariate plot of phi skewness versus backshore and eolian dune core sample locations. 00 r o £8 PHI SKEWNESS i/i p L n O tn c n r o c a c <5 4 k CD C O cr " co St- n “ w « 2 o C D ! “ * ■ n o o -f 3 -o - w 2 : * £■ S ' ® a * 3 a B J (0 U ) i / > § < S ' I w g I- 8 g 3 v > tn u 3 3 q . 3 js* y _ 2 as' S> g r ff 8 1 1 ' 3 « 2 0 3 C D c n 0 1 o X - •< 3 g cn S c? r o co L n n O 3 0 m £ cn •o t~ m O 2 O oo z C / 1 C O ro co FIRST ST. N E W L A N D ST. BROOKHURST ST. SA N T A A NA RIVER o l i v e s t . □ m o - ► LUG ONIA ST. 56TH ST. «►< » 44TH ST. 3 2 N D S T . 2 3 R D S T . a ♦ NEWPORT SU BM A R IN E C A N Y O N I9THST. n 15TH ST. M AIN ST. ai p ♦ V i 3 u » © 3 m o z a z m CHANNEL R D . □ ► « £ C l C A c r r i / 3 H PH I STANDARD DEVIATION 2 - 1.5 f.1 -0.3 m ♦ -1 .5 m A -3 .0 m 1 0.5 H 0 -0.5 -1 □ ♦ □ -1.5 o o -2 0 0.5 - t r- 1.5 2 2.5 PHI MEAN GRAIN-SIZE 3.5 Figure 47. Bivariate plot of phi standard deviation versus phi mean grain-size for berm core samples. PH I STANDARD DEVIATION 2 1.5 1 0.5 0 -0.5 -1 -1.5 -2 0 0.5 1 1.5 2 2.5 3 3.5 PHI MEAN GRAIN-SIZE Figure 48. Bivariate plot of phi standard deviation versus phi mean grain-size for backshore and eolian dune core samples. 00 C n ''■0.3 m ♦ -1 .5 m A -3.0 m EOLIAN DUNE ri ♦ L \ A * A ♦ E l PHI SKEWNESS 0.75 r i -0.3 m ♦ -1.5 m A -3.0 m 0.5 0.25 0 -0.25 -0.5 □ k □ -0.75 c o a -1 . J ------ -7— 1 -T- 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 PHI STANDARD DEVIATION Figure 49. Bivariate plot of skewness versus phi standard deviation for berm core samples. PH I SKEWNESS 1 0.75 0.5 0.25 • 0 : -0.25 ^ -0.5 : -0.75 : -1 ----- 0 | [ J -0.3 m ♦ -1.5 m * -3.0 m & EOLIAN DUNE ♦ □ 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 PHI STANDARD DEVIATION Figure 50. Bivariate plot of phi skewness versus standard deviation for backshore and dune core samples. C O - v l February Samples: The significantly higher Factor 1 loadings indicate that Newland Street accepts selectively transported grains from upcoast First Street. Main Street sand was likely smoother than 15th Street sand due to dilution by offshore inner shelf-sand. April Samples: The reversal of Newland Street trends, which shows smoother grains than downcoast Brookhurst Street, may be attributed to selective removal of rougher grains and resultant longshore transport to Brookhurst Street. 10.31 Hotelling’s Significant Grain-Shape Changes - Berm, Backshore, and Dune The fact that many of these samples exhibit no appreciable change with depth indicates a possibility that grain shape is homogeneous with depth. This may be accounted for if the population of grains at various depths are well mixed through wave action, rather than being considered distinct populations demarcated by depth. Another explanation may be that through wave action and selective sorting, distinctive shape characteristics have been eliminated, resulting in a seemingly homogeneous mixture. 10.4 Discriminant Function Analysis Of The Foreshore The site-wide 15% increase of shelf-derived sand indicates that the onshore transport of inner shelf sand is a reality. This highlights the necessity to consider this source in the overall sand budget. Only Olive and Lugonia streets showed a net increase of river sand, which is obviously due to the contemporaneous dredging of the Santa Ana River. Elsewhere, the influences 88 of dredge spoils, if any, were overwhelmed by the onshore migration of inner shelf-sand. 10.41 D iscrim inant Function Analysis Of The Berm, Backshore, and D une The reported increase with depth of inner-shelf sand with respect to the berm and backshore is a function of the increased river sand input at Olive and Lugonia streets coupled with the longer residence time that the deeper sand has had within the surf zone. The longer the sediment resides in this high-energy environment, the greater the sorting and removal of river sand like shapes. The resulting lag deposit would therefore be enriched with shelf- shape components. This would, with time, lead to a stratification of m ore river-shape enriched sand on the surface as compared to sand found deeper in the beach. 11.0 CONCLUSION The primary mechanism of foreshore deposition observed in the study area is onshore migration of inner shelf-sand. The effects of this deposition on the grain-shape characteristics of the foreshore have been revealed through FGSA. The dilution and overprinting of this trend with dredge spoils of the Santa Ana River have only been detected between Brookhurst Street and Lugonia Street. Elsewhere, the grain-shape populations of the foreshore responded to offshore supply, coarser and more elongate in the northern subcell, becoming smoother and rounder towards the southern subcell. 89 Episodic pulses of storm-driven shelf-sand is a likely mode of sand input into the foreshore. Site-wide, the inner shelf-sand composition of the foreshore increased 15% from the end of summer to the end of winter. The sand that fails to reach the foreshore during storm events lags behind in the ridge and runnel “conveyor belt,” transported shorew ard through fair- weather summer swells. The fact of net foreshore winter erosion cannot be ignored. However, this study indicates that the foreshore is a dynamic system of simultaneous give and take. It has been shown that the shape characteristics of beach sand are represented by the first two factor loadings, which explain 90% of the variance in the system. The tem poral and spatial changes in shape composition reveal grain populations which can be explained as functions of selective transport of grains possessing higher asperity and elongation, net current vectors, and shelf and river-sand contributions. The concept of a littoral cell does not exist in this study site. Short-term current vectors do not reveal a net uni-directional current, which is a key ingredient in this theory. Due to the damming of the Santa Ana River, the natural sand supply has been reduced greatly. From 1919 to 1973, over 8,611,000 m3 of dredge material have been deposited on West Newport Beach in an effort to m aintain a beach wide enough to protect adjacent co astal properties (Shaw, 1980). Groins and jetties have been constructed in an attempt to keep the dredge material in place. This study is a study of a drastically perturbed natural system. The longshore current obstructing groins and jetties have created a situation where the residence time of the beach sands may be greatly increased. This 90 study validates previous works which found divergent currents at the mouth of the Newport Submarine Canyon, further enforcing the belief that the canyon is presently inactive. 91 12.0 References Ahlschwede, K.S., 1988, Sources and net littoral transport of sand in San Diego and Southern Orange Counties, Southern California: Fourier grain- shape analysis: Unpublished M.S. Thesis, University of Southern California, Los Angeles, California, 135 p. Bendat, J.S., and Piersol, A.G., 1971, Random data: Analysis and measurement procedures, Wiley-Interscience. Inc., New York, 407 p. Bloom, Laurie, 1979, The relationships among river, beach and submarine canyon sands in the southern Santa Barbara littoral cell, Ventura County, California: Fourier grain-shape analysis: Unpublished M.S. Thesis, University of Southern California, Los Angeles, California, 115 p. Bloomfield, Peter, 1976, Fourier analysis of time series: An introduction, Wiley- Interscience Inc., New York, 258 p. Bomer, E.J., 1985, Quartz grain-shape modification in high-gradient fluvial environments: Fourier grain-shape analysis: Unpublished M.S. Thesis, University of Southern California, Los Angeles, California, 139 p. Brigham, E.O., 1974, The Fast Fourier Transform, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 252 p. Broadhead, S.D., 1988, Fourier grain-shape analysis of the sources and littoral transport of sand, San Elijo Lagoon to Point La Jolla: Unpublished M.S. Thesis, University of Southern California, Los Angeles, California, 235 p. Brown, P.J., Ehrlich, Robert, and Colquhoun, D.J., 1980, Origin of patterns of quartz sand types on the southeastern United States continental shelf and implications on contemporary shelf sedimentation - Fourier grain- shape analysis: Journal of Sedimentaiy Petrology, v. 50, p. 1095-1100. Brownlie, W.R., and Taylor, B.D., 1981, Sediment management of Southern California mountains, coastal plains, and shoreline-Part C, Coastal sediment delivered by major rivers in Southern California: California Institute of Technology, Environmental Quality Laboratory Report No. 17-C, Pasadena, California, COE Ref#36, 314 p. Cho, K.H., 1989, Sedimentology of a composite inner-shelf sand body resulting from the resuspension of nearshore sediment by episodic, storm- generated currents, Oceanside California: Unpublished M.S. Thesis, University of Southern California, Los Angeles, California, 104 p. Clark, R.A. and Osborne, R.H., 1982, Contribution of Salinas River sand to the beaches of Monterey Bay, California, during the 1978 flood period: Fourier grain-shape analysis: Journal of Sedimentary Petrology, v. 52, p. 807-822. 92 Davis, J.C.,1986, Statistics and data analysis in geology, second edition, John Wiley and Sons, New York, 646 p. Dixon, W.J., 1991, BMDP statistical software for VAX/VMS systems, Volumes 1-2, BMDP Statistical Software Inc. Los Angeles, CA, 629 p. Ehrlich, Robert, Brown, P.J., Yarus, J.M., and Przygocki, R.S., 1980, The origin of shape frequency distributions and the relationship between size and shape: Journal of Sedimentary Petrology, v. 50, p. 475-484. Ehrlich, Robert, and Chin, Maureen, 1980, Fourier grain-shape analysis: A new tool for sourcing and tracking abyssal silts: Marine Geology, v. 38, p. 219-232. Ehrlich, Robert, Orzeck, J.J., and Weinberg, Bernhard, 1974, Detrital quartz as a natural tracer - Fourier grain shape analysis: Journal of Sedimentary Petrology, v. 44, p. 145-150. Emery, K.O., 1960, The sea off Southern California: A modem habitat for petroleum, John Wiley and Sons, New York, 366 p. Everts, Craig E., 1993, Wave and Current Data Summary: West Newport Beach, 6 Jan 1992 - 5 Mar 1993: Public Works Department, City of Newport Beach, p. 15-30. Feffer, J., in preparation, [Unnamed M.S. thesis]: University of Southern California, Los Angeles, California. Felix, David, 1969, Origin and Recent History of Newport Submarine Canyon, California Continental Borderland: Technical Report For Office Of Naval Research Contract No. NONR 228 (17) NR 083-144 submitted to Geophysics Branch, Office of Naval Research, Washington, D.C., 53 p. Folk, R.L., 1966, A review of grain-size parameters: Sedimentology, v. 6, p. 73- 93. Gaynor, J.M., 1984, Sources and transport of sand in the littoral zone of Lake Tahoe, California and Nevada: Fourier grain-shape analysis: Unpublished M.S. Thesis, University of Southern California, Los Angeles, California, 112 p. Gorsline, D.S., and Grant, D.J., 1972, Sediment textural patterns on the San Pedro shelf, California (1951-1971): Reworking and transporting by waves and currents, in Swift, D.J.P., Duane, D.B., and Pilkey, O.H., (eds.), Shelf sediment transport: Dowden, Hutchinson & Ross, Inc., Stroudsberg, Pennsylvania, p. 575-600. Hand, B.M., and Emery, K.O., 1964, Turbidites and topography of the north end of the San Diego Trough, California: Journal of Geology, v. 72, p. 526-542. 93 Hudson, C.B., and Ehrilch, R., 1980, Determination of relative provenance contributions in samples of quartz using Qrmode factor analysis of Fourier grain shape data: Journal of Sedimentary Petrology, v. 50, p. 1101 - 1110 . Krumbein, W.C., and Pettijohn, F.J., 1938, Manual of sedim entary petrography: Appleton-Century Crofts, New York, 549 p. Lee, A.C., Osborne, R.H., and Liu, J., 1993, Fourier Grain-Shape Analyses: effects of ocean storms, dredging and bypassing in supplying sand to Oceanside Harbor Area beaches (S. California): Geological Society of America, v. 25, p. 5 (abstract). Lu, Y., 1992, Fourier grain-shape analysis of beach sand samples and associated sedimentary processes, Dockweiler and El Segundo beaches, Santa Monica Bay, Southern California [Unpublished Ph.D. thesis]: University of Southern California, Los Angeles, California, 169 p. Mazzullo, J. M., and Ehrlich, Robert, 1980, A vertical variation in the St. Peter Sandstone - Fourier grain shape analysis: Journal of Sedimentary Petrology, v. 50, p. 63-70. Mazzullo, J. M., and Ehrlich, Robert, 1983, Grain shape variation in the St. Peter Sandstone: A record of eolian and fluvial sedimentation of an early Paleozoic cratonic sheet sand: Journal of Sedimentary Petrology, v. 53, p. 105-119. Mazzullo, J. M., and Magenheimer, Stewart, 1987, The original shapes of quartz sand grains: Journal of Sedimentary Petrology, v. 57, p. 479-487. Mrakovitch, John., Ehrlich, Robert, and Weinberg, Bernhard, 1976, New technique for stratigraphic analysis and correlation - Fourier grain- shape analysis, Louisiana offshore Pliocene: Journal of Sedimentary Petrology, v. 46, p. 226-233. Murillo, J.M., 1992, Characteristics and sources of the Creciente barrier island sediments within Margarita lagoonal complex, Baja California Sur, Mexico [Unpublished M.S. thesis]: University of Southern California, Los Angeles, California, 150 p. Norris, M.N., and Webb, W.W., 1991, Geology of California (2nd edition), John Wiley and Sons, Inc., New York, 541 p. Osborne, R.H., 1985, Coast of California storm and tidal wave study: Littoral zone sediments: Los Angeles District, U.S. Army Corps of Engineers, Technical Report CCSTWS 85-11,148 p. 94 Osborne, R. H., Ahlschwede, K. S., Broadhead, S. D., Cho, K. H., Compton, E . A., and Yeh, C . C., 1990, Fourier grain-shape and mineralogic analysis of coastal and inner shelf sand samples: Dana Point to the United States-Mexico border: Los Angeles District, U.S. Army Corps of Engineers, Technical Report CCSTW S 89-1,116 p. Osborne, R. H., Darigo, N. J., and Scheidemann, R. C., Jr., 1983, Potential offshore sand and gravel resources of the inner continental shelf of southern California: State of California, Department of Boating and Waterways, 302 p. Osborne, R. H., Edelman, M . C., Gaynor, J. M., and Waldron, J. M., 1985, Sedimentology of the littoral zone in Lake Tahoe, Califomia-Nevada: California State Lands Commission, 88 p. Osborne, R.H., and Cho, K.H., 1989, Sedimentology of a composite inner-shelf sand body resulting from the resuspension of nearshore sediment by episodic, storm-generated currents, Proceedings Volume, Coastal Zone ‘89: American Society of Civil Engineers, p. 4391-4405. Osborne, R.H., Liu, J., and Lee, A.C., 1992, Oceanside Harbor experimental sand bypass system: Oceanside Monitoring Program Engineering report, “Evaluation of the littoral sand composition of November, 1991 and February, 1992 Foreshore sample sets: Fourier Grain-shape Analysis: The Applied Geophysics Corporation, 24 p. Osborne, R.H., and Yeh, C.C., 1989, Oceanside Harbor experimental sand bypass system: Oceanside Monitoring Program Engineering report, “Evaluation of the littoral sand composition of May, July, and October, 1989 beach sample sets: Fourier Grain-shape Analysis: The Applied Geophysics Corporation, 24 p. Osborne, R. H., and Yeh, C. C., 1991, Fourier grain-shape analysis of coastal and inner continental-shelf sand samples: Oceanside littoral cell, southern Orange and San Diego Counties, southern California: in, Robert H. Osborne (ed.) in, From Shoreline to Abyss: Contributions in Marine Geology in Honor of Francis Parker Shepard, SEPM (Society for Sedimentary Geology), Special Publication No. 46, p. 51-66. Osborne, R.H., Yeh, C.C., Lu, Y., 1991, Grain-shape analysis of littoral and shelf sands, Southern California in Krause, N.C. ed., Coastal sediments *91: American Society of Civil Engineers, p. 846-859. Pipkin, B.W., 1985, Santa Monica to Dana Point: in, Griggs, G., and Savoy, L (eds.) Living with the California Coast, Duke University Press, Durham, North Carolina, p. 338-340. Pipkin, B.W., 1992, Coastal Erosion in Southern California - An Overview: in, Pipkin, B . and Proctor, R. (eds.) Engineering Geology Practice in Southern California, AEG (Association of Engineering Geologists), Special Publication No. 4, p. 461-483. 95 Porter, G. A., Ehrlich, Robert, Osborne, R. H., and Combellick, R. A., 1979, Sources and nonsources of beach sand along southern Monterey Bay, California - Fourier shape analysis: Journal of Sedimentary Petrology, v. 49, p. 727-732. Reister, D. D„ Shipp, R. C., and Ehrlich, Robert, 1982, Patterns of quartz sand shape variation, Long Island littoral and shelf: Journal of Sedimentary Petrology, v. 52, p. 1307-1314. Robinson, R. A., 1993, Fourier grain-shape analyses of sand samples from the central and eastern regions of the Santa Barbara littoral cell [Unpublished M.S. thesis]: University of Southern California, Los Angeles, California, 151 p. Sharp, R.P., 1972, Geology: Field guide to Southern California: C. Brown Company and Publishers, Dubuque, Iowa, WM„ 181 p. Shaw, Martha, J., 1980, Artificial Sediment Transport in Coastal Southern California: SIO Reference Series No. 80-41, University of California, Scripps Institue of Oceanography, La Jolla, California, 109 p. Sherman, L., 1931, A history of Newport Beach: Times Mirror Press. Los Angeles, California p. 58-61. Stevenson, R. E., 1954, The marshlands at Newport Bay, California: Unpublished Ph.D. Dissertation, University of Southern California, Los Angeles, California, 199 p. United States Army Corps of Engineers, 1986, Southern California coastal processes data summary: Unites States Army Corps of Engineers, Coast of California storm and tidal wave study, CCSTW 86-1, Los Angeles, CA,572 p. Van Nieuwenhuise, D. S., Yarns, J. M., Przxygocki, R. S., and Ehrlich, Robert, 1978, Sources of shoaling in Charleston Harbor: Fourier grain shape analysis: Journal of Sedimentary Petrology, v. 48. p. 373-384. Wyman, Major T., Jr., 1939, The Santa Ana River California Flood Control: War Department, U.S. Engineer Office, Los Angeles, California, 134 p. Yeh, C.C., 1991, Grain-shape composition of coastal and inner continental shelf sand samples from 1983 to 1990: Oceanside littoral cell, southern Orange and San Diego Counties, Southern California [Unpublished Ph.D. thesis]: University of Southern California, Los Angeles, California, 250 p. 96 13.0 APPENDICES 97 APPENDIX A Grain-Size Histograms of Potential Sources W EIGHT PERCENT 50 iD ID Histogram of weight percent versus phi size for sample SRFSDF.. 4.5 W E IG H T PERCENT Histogram of weight percent versus phi size for sample SAR. W EIG H T PERCENT Histogram of weight percent versus phi size for sample SAR. W EIG H T PERCENT 60 - 50 1 i 1 i tf> t— L f J O L O t — L O f S j L T J f O L r ) O O r-' (\i (T) PHI SIZE o no Histogram of weight percent versus phi size for sample HC. W EIGHT PERCENT 60 i 40 30 2 0 -i 10 0 PHI SIZE Histogram of weight percent versus phi size for sample PB. o U ) W EIG H T PERCENT 60 50 40 30 i 20 10 0 in 't PHI SIZE Histogram of weight percent versus phi size for sample PD. -1.5 l o O loi — L n c \ J L n r o O o i- ^ rJ PHI SIZE Histogram of weight percent versus phi size for sample CAT. W E IG H T PERCENT 50 40 30 20 10 - o - S p m in o in o m (\i in r\j c o PHI SIZE m co o O ) Histogram of weight percent versus phi size for sample SAS. 4.5 O i r t i — m r s i m c o m ^ O i— <\j co PHI SIZE Histogram of weight percent versus phi size for sample S I . 4.5 W EIG H T PERCENT 60 50 40 30 20 10 in I I in c i O m o m m PHI SIZE o 00 Histogram of weight percent versus phi size for sample S2. W EIG H T PERCENT 60 50 1 40 30 20 10 0 m m o m d o m c\j m m m < - c \] rd m m 't PHI SIZE o id Histogram of weight percent versus phi size for sample S3. WEIGHT P E R C E N T 50 4 0 3 0 20 10 0 " * L D d d m cm PHI S IZ E l o r\j cn L D CO Histogram of weight percent versus phi size for sample S5. o APPENDIX B Grain-Size Histograms of Foreshore Locations W EIGHT PERCENT 60 50 40 30 - l o «— l o O l o i — t r t o J L n r o * * ‘ » O O T — ( \1 I I PHI SIZE j . . . . . . L O T f 0 0 Histogram of weight percent versus phi size for sample A1. 4.5 W EIGHT PERCENT 60 50 40 30 20 10 un I to 0 1 PHI SIZE r o Histogram of weight percent versus phi size for sample A2. O J 4.5 W EIG H T PERCENT 60 - j 1 50 | i ! | 40 - PHI SIZE Histogram of weight percent versus phi size for sample A3. 4.5 W EIGHT PERCENT 50 i 40 30 20 10 0 -1.5 -1 -0.5 0 1 1.5 2 PHI SIZE 2.5 3 3.5 cn Histogram of weight percent versus phi size for sample A4. 4 4.5 W EIGHT PERCENT 60 ■ ; I 50 40 30 20 10 0 L I") i — L T ) o L O * 7 ' o o in c\j in ro m tJ- lo >— c\i ro PHI SIZE Histogram of weight percent versus phi size for sample A5. W EIGHT PERCENT 50 Histogram of weight percent versus phi size for sample A6. 4.5 W E IG H T PERCENT 60 50 I 40 - j 30 - 20 10 0 in f H in o O in O m CM in ro m c v i c n in PHI SIZE Histogram of weight percent versus phi size for sample A7. c o W EIG H T PERCENT 60 50 40 30 20 10 0 in r* I L T ) 0 1 L O PHI SIZE Histogram of weight percent versus phi size for sample A8. W EIG H T PERCENT 60 r 40 - l O r - L O O L O f - i O M m c o > — o o i — c\i I I PHI SIZE L O 't r\J o Histogram of weight percent versus phi size for sample A9. 4.5 W EIGHT PERCENT 60 50 40 30 20 10 0 j — in i in o in o in c \j m evi c n + - in c o PHI SIZE Histogram of weight percent versus phi size for sample A10. 4.5 W E IG H T PERCENT 60 50 i 40 30 20 10 0 L D m o to o o I ----- I . to cm lo ro lo ^ to 1 CM C O M " PHI SIZE Histogram of weight percent versus phi size for sample All. ro I S ) W EIGHT PERCENT 50 40 -j PHI SIZE Histogram of weight percent versus phi size for sample A12. ro co 60 50 40 - i n t y i / i o u ' ) * — L O t M i n r o L D T f L n *7 O O i - csj r o tj- ' PHI SIZE Histogram of weight percent versus phi size for sample A13. ro ■ t * W EIG H T PERCENT 5 0 ro cn Histogram of weight percent versus phi size for sample B1. W EIG H T PERCENT 60 50 40 30 20 10 I in in o m in c m m ro m '3- in ■ — c m r o M " PHI SIZE Histogram of weight percent versus phi size for sample B2. r \> c n W EIGHT PERCENT 60 50 40 30 20 10 o 4- in in o m o d in c\i in ro m 'i* m « - * c v i ro PHI SIZE Histogram of weight percent versus phi size for sample B3. r\) - v j 60 50 40 I — Z L U (J 0 £ UJ 30 £ S3 L U 5 20 10 0 in i in 0 1 m o m co m <\i c o in co m PHI SIZE Histogram of weight percent versus phi size for sample B4. ro c o W EIGHT PERCENT 60 50 40 30 20 10 0 4“ L O r i n 0 1 m o LO M LO c\i ro LO ro io 't PHI SIZE ro L O Histogram of weight percent versus phi size for sample B5. W E IG H T PERCENT 50 40 30 20 10 m to d in o lo cm LO C M C O to CO LO PH I SIZE U ) O Histogram of weight percent versus phi size for sample B6. W EIG H T PERCENT 50 - U I ' - j - L f l O l O r — L O C V J l / l r O L O * 7 O O t- oj ro PHI SIZE Histogram of weight percent versus phi size for sample B7. W EIG H T PERCENT t o r o Histogram of weight percent versus phi size for sample B8. W EIG H T PERCENT 50 4 0 -I 30 20 10 0 L O I L O o i LO o LO <\J PHI SIZE LO CO LO csi co O J Histogram of weight percent versus phi size for sample 69. W E IG H T PERCENT 5 0 -j I I I 4 0 i lO rj - LO O tr jr -L O C V JU O C O LO ' ^ - * 7 o o • “ t\i cn PHI SIZE LO Histogram of weight percent versus phi size for sample B10. 4.5 PHI SIZE Histogram of weight percent versus phi size for sample B11 W EIGHT PERCENT 60 L 0 ' - p L O O u - ) r - L O f \ j L n r O PHI SIZE Histogram of weight percent versus phi size for sample B12 L O Tt C O U J < T > 4.5 i PHI SIZE ^ Histogram of weight percent versus phi size for sample B13. CO -N j 138 20 10 0 4----- in i m O d in o m cm in c\i PHI SIZE Histogram of weight percent versus phi size for sample C l. 60 50 40 fc UJ u c c UJ 30 < D UJ 20 10 m m o m 6 o i -j------ in c m in co in «t in i — rsj ro \j* PHI SIZE Histogram of weight percent versus phi size for sample C2. CO C D W EIG H T PERCENT 60 50 1 40 30 - i O i — L n O i O r - i r t c M L n r O L o ^ * O O > — c\j cri PHI SIZE -th o Histogram of weight percent versus phi size for sample C3. 4.5 W EIG H T PERCENT 50 40 - 30 - PHI SIZE Histogram of weight percent versus phi size for sample C4. 4.5 W EIG H T PERCENT 60 50 40 30 20 10 0 L O L O C O L O PHI SIZE -u IN) Histogram of weight percent versus phi size for sample C5. 20 10 0 -1------- L O L O o L O o LO c\J LO CO t\i LO C O PHI SIZE -p> w Histogram of weight percent versus phi size for sample C6. W EIG H T PERCENT 50 - 40 - 30 - t r t r j - L O O l O i — L n ( \ l L O r O L f J ' a - L O < 7 o O r - ^ < \ j P O PHI SIZE Histogram of weight percent versus phi size for sample C7. ■ f* W EIG H T PERCENT 50 40 - 30 20 - 10 to PHI SIZE Histogram of weight percent versus phi size for sample C8. to W EIG H T PERCENT 60 50 40 30 20 10 0 in I --4....--4 ------- f LO O 0 1 m O L O (\J m co cvj to 00 PHI SIZE 4 * 0 1 Histogram of weight percent versus phi size for sample C9. W EIGHT PERCENT 50 - 40 - 30 - ty O d ^ rvi ro PHI SIZE Histogram of weight percent versus phi size for sample C10. 4 * 4.5 W E IG H T PERCENT 60 50 40 30 -! 20 10 0 L O ,_r L O o ■ L O o LO C\J PHI SIZE LO oJ ------ C O LO C O L O 4 * 0 0 Histogram of weight percent versus phi size for sample Cl 1. W EIG H T PERCENT 50 40 30 - 20 10 - 0 LO PHI SIZE Histogram of weight percent versus phi size for sample Cl 2. C O 4.5 W EIGHT PERCENT 40 - i 30 20 10 0 _ | ..... m i- m o m d o m c\i m c\i co PHI SIZE O l O Histogram of weight percent versus phi size for sample Cl 3 |-------- 4------- lo n j- ro 4.5 APPENDIX C Grain-Size Histograms of Berm Locations ^ r j - i n o i n i - L n c s j i n r o L o t t L n t o 7 o o r-: csi tn ^ PHI SIZE Histogram of weight percent versus phi size for sample BM4. cn PO W EIG H T PERCENT 60 50 1 4 0 -I 30 - 10 I 0 _ t---------- h L O LO o L O o LO C M LO C O C M L O CO PHI SIZE cn U J Histogram of weight percent versus phi size for sample BM5. W E IG H T PERCENT 5 0 4 0 - 30 H 20 10 0 in I in o i m d m < \i in rv j m m on PHI SIZE Histogram of weight percent versus phi size for sample BM 6 i n -0.5 PH I SIZ E Histogram of weight percent versus phi size for sample BM10. WEIGHT P E R C E N T 50 - 40 H 3 0 - L O f N j L n r O L n ' t *7 o o r- evi co PHI SIZE Ln a Histogram of weight percent versus phi size for sample BM11. 4.5 W E IG H T PERCENT PH) SIZE cn *N l Histogram of weight percent versus phi size for sample BM15. W EIGHT PERCENT 60 - - - j i 50 - j j j i 40 - ! Ln * y Lf^ O i O i — L o c v j i o r o L o ^ L o u n *7 O O r - C V J CO xt PHI SIZE Histogram of weight percent versus phi size for sample BM16. i n CO I J I J 50 - 40 - PHI SIZE Histogram of weight percent versus phi size for sample BM19. U 1 C0 60 50 - 40 £ U J (J oc U J °* 30 £ O UJ 5 20 10 o 4- -• I- io I m o O LO o LO C\J LO CO lO ( \ j C O L O LO PHI SIZE Histogram of weight percent versus phi size for sample BM20. <r> O W EIG H T PERCENT 50 40 - LO r p Ln o i n r - L o c \ i L n c ' O L O ' 3 - T ? O « — c\i co PHI SIZE ^ Histogram of weight percent versus phi size for sample BMZ4. c r > 4.5 W EIGHT PERCENT 50 c r > IM Histogram of weight percent versus phi size for sample BM25. APPENDIX D Grain-Size Histograms of Berm Locations W EIG H T PERCENT 60 50 40 30 - 20 10 0 -j----- m ■ r - in o o i in o m cm PHI SIZE m C M cn in rrj 0 1 Histogram of weight percent versus phi size for sample BS1. W EIGHT PERCENT 60 50 - 40 - 30 - i o * y L n O t n i — l o t M L o r o t o ^ - • 7 o o r-1 < \J ro PHI SIZE C D cn Histogram of weight percent versus phi size for sample BS2. W E IG H T PERCENT 50 - 40 30 20 10 0 PHI SIZE o r» <n Histogram of weight percent versus phi size for sample BS3. W EIG H T PERCENT 60 50 - 4 0 - 30 20 - 10 0 + L O PHI SIZE Histogram of weight percent versus phi size for sample BS7. ■ V l W EIG H T PERCENT 60 50 40 30 20 10 0 in in 0 1 PHI SIZE Histogram of weight percent versus phi size for sample BS8. o 00 4.5 W EIG H T PERCENT 50 40 30 20 - 10 0 L O m o in o r - in c m m cn C M m in CO M- PHI SIZE cn to Histogram of weight percent versus phi size for sample BS9. W E IG H T PERCENT 60 50 40 30 20 10 - ~ \--------------- ■ in T “ I L O d IT) o in m PHI SIZE in oJ no in co in in -v ! O Histogram of weight percent versus phi size for sample BS12. W EIG H T PERCENT 60 Histogram of weight percent versus phi size for sample BS13. W EIGHT PERCENT 50 40 30 20 - 10 0 -5 f f in PHI SIZE Histogram of weight percent versus phi size for sample BS14. -N l ro W E IG H T PERCENT 60 - r - 50 - 40 30 20 10 0 to to o d to o to < \j to pj C O to CO to 't to PHI SIZE "J 00 Histogram of weight percent versus phi size for sample BS17. W EIG H T PERCENT 60 50 40 30 20 - 10 0 un -t- i- lo L n PHI SIZE Histogram of weight percent versus phi size for sample BS18. W EIG H T PERCENT 60 50 - 40 - 30 - l o * — i x j O L O f — L o c v i L o m t n ' f *7 1 O d t- oj co PHI SIZE ' s i cn Histogram of weight percent versus phi size for sample BS21. 4 .5 W EIGHT PERCENT 50 4 0 - 30 - i n r j - L q o t O r - m c v i L o r o i / ) *7 o O r- (\j ro PHI SIZE Histogram of weight percent versus phi size for sample BS22. < x > W EIG H T PERCENT 50 - "4 - 4 Histogram of weight percent versus phi size for sample BS23. W EIG H T PERCENT 60 50 - PHI SIZE Histogram of weight percent versus phi size for sample OZ6. -j 00 INFORMATION TO USERS This manuscript has been reproduced from the microfilm m aster. UMI films the text directly from the original or copy submitted. Thus, some thesis and dissertation copies are in typewriter face, while others may be from any type of computer printer. The quality of this reproduction is dependent upon the quality of the copy snbmitted. Broken or indistinct print, colored or poor quality illustrations and photographs, print bleedthrough, substandard margins, and improper alignment can adversely affect reproduction. In the unlikely event that the author did not send UMI a complete manuscript and there are missing pages, these will be noted. Also, if unauthorized copyright material had to be removed, a note will indicate the deletion. Oversize materials (e.g., maps, drawings, charts) are reproduced by sectioning the original, beginning at the upper left-hand comer and continuing from left to right in equal sections with small overlaps. Each original is also photographed in one exposure and is included in reduced form at the back of the book. Photographs included in the original manuscript have been reproduced xerographically in this copy. Higher quality 6" x 9 " black and white photographic prints are available for any photographs or illustrations appearing in this copy for an additional charge. Contact UMI directly to order. A Bell & Howell Information Company 300 North Zeeb Road. Ann Arbor. M l 48106-1346 USA 313/761-4700 800/521-0600 OMI Number: 1376480 Copyright 1996 by Magnusen, Craig Ellsworth All rights reserved. UMI Microform 1376480 Copyright 1995, by UMI Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. UMI 300 North Zeeb Road Ann Arbor, MI 48103
Linked assets
University of Southern California Dissertations and Theses
Conceptually similar
PDF
Lateral variability in predation and taphonomic characteristics of turritelline gastropod assemblages from Middle Eocene - Lower Oligocene strata of the Gulf Coastal Plain, United States
PDF
Grain-size and Fourier grain-shape sorting of ooids from the Lee Stocking Island area, Exuma Cays, Bahamas
PDF
Quartz Grain-Shape Variation Within An Individual Pluton: Granite Mountain, San Diego County, California
PDF
Fourier grain-shape analysis of quartz sand from the Santa Monica Bay Littoral Cell, Southern California
PDF
Preservation Of Fossil Fish In The Miocene Monterey Formation Of Southern California
PDF
The study of temporal variation of coda Q⁻¹ and scaling law of seismic spectrum associated with the 1992 Landers Earthquake sequence
PDF
The Hall Canyon pluton: implications for pluton emplacement and for the Mesozoic history of the west-central Panamint Mountains
PDF
A study of the solution crystallization of poly(ether ether ketone) using dynamic light scattering
PDF
Dye laser characterization of two films of baceriorhodopsin
PDF
An analysis of nonresponse in a sample of Americans 70 years of age and older in the longitudinal study on aging 1984-1990
PDF
Helicoplacoid echinoderms: Paleoecology of Cambrian soft substrate immobile suspension feeders
PDF
The relationship of stress to strain in the damage regime for a brittle solid under compression
PDF
Distribution And Transport Of Suspended Matter, Santa Barbara Channel, California
PDF
The relationship between fatty acid composition of subcutaneous adipose tissue and the risk of proliferateive benign breast disease and breast cancer
PDF
Characterization of geochemical and lithologic variations in Milankovitch cycles: Green River Formation, Wyoming
PDF
Effects of age and gender on speed and accuracy of hand movements: and the refinements they suggest for Fitt's Law
PDF
Complementarity problems over matrix cones in systems and control theory
PDF
A kinetic model of AMPA and NMDA receptors
PDF
A physiologic model of granulopoiesis
PDF
Comparison of evacuation and compression for cough assist
Asset Metadata
Creator
Magnusen, Craig Ellsworth
(author)
Core Title
The characterization of Huntington Beach and Newport Beach through Fourier grain-shape, grain-size, and longshore current analyses
School
Graduate School
Degree
Master of Science
Degree Program
Geological Sciences
Degree Conferral Date
1995-08
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
applied mechanics,geology,OAI-PMH Harvest,physical oceanography
Language
English
Contributor
Digitized by ProQuest
(provenance)
Advisor
Gorsline, Donn S. (
committee chair
), Bottjer, David J. (
committee member
), Pipkin, Bernard W. (
committee member
)
Permanent Link (DOI)
https://doi.org/10.25549/usctheses-c18-2788
Unique identifier
UC11357880
Identifier
1376480.pdf (filename),usctheses-c18-2788 (legacy record id)
Legacy Identifier
1376480-0.pdf
Dmrecord
2788
Document Type
Thesis
Rights
Magnusen, Craig Ellsworth
Type
texts
Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the au...
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus, Los Angeles, California 90089, USA
Tags
applied mechanics
geology
physical oceanography