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Greenwood, Wm. Jason.
Mapping porewater salinity with electromagnetic and electrical methods in shallow coastal environments, Terra Ceia, Florida
h [electronic resource] /
by Wm. Jason Greenwood.
[Tampa, Fla.] :
University of South Florida,
Thesis (M.S.)--University of South Florida, 2004.
Includes bibliographical references.
Text (Electronic thesis) in PDF format.
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ABSTRACT: The feasibility of predicting porewater salinity based on calibrated surface electromagnetic methods is discussed in a coastal wetland on the southern banks of Tampa Bay in West-Central Florida. This study utilizes a new method to float commercial land based electromagnetic (EM) instruments in shallow marine waters of less than 1.5 meters. The floating EM-31 (Geonics, Ltd.) effectively sensed the magnitude and lateral extent of high and low salinity porewaters within mangrove lined ditches and ponds. Resistivity and EM geophysical methods are merged with direct sampling data to calibrate layers in electromagnetic models to infer shallow ( < 30m) groundwater salinity patterns. Initial marine resistivity surveys are necessary to discriminate between equivalent EM model solutions for seafloor conductivities beneath shallow (0.1-1.5m) marine (~30 ppt) waters. Using formation factors computed from nearby resistivity surveys, porewater conductivity predictions based on surface EM-31 and EM-34 measurements are successful at distinguishing overall porewater salinity trends. At the Tampa Bay study site, the most distinctive terrain conductivity anomalies are associated with mangroves bordering marine waters. Highly elevated porewater conductivities are found within 5m of the mangrove trunks, falling sharply off within 10m, presumably due to saltwater exclusion by mangrove roots. Modeling indicates the shallow water EM-31 measurements probably lack the resolution necessary to image more subtle porewater conductivity variations, such as those expected in association with diffuse submarine groundwater discharge. However, the technique has potential application for locating high contrast zones of freshwater discharge and other salinity anomalies in shallow and nearshore areas not accessible to conventional marine resistivity or land-based arrays, and hence may be useful for interdisciplinary studies of coastal wetland ecosystems.
Adviser: Kruse, Sarah
mangrove soil salinization.
marine resistivity methods.
marine electromagnetic methods.
submarine groundwater discharge.
t USF Electronic Theses and Dissertations.
Mapping Porewater Salinity with Electro magnetic and Electrical Methods in Shallow Coastal Environments: Terra Ceia, Florida by Wm. Jason Greenwood A thesis submitted in partial fulfillment of the requirements for the degree of Masters of Science Department of Geology College of Arts and Sciences University of South Florida Major Professor: Sarah Kruse, Ph.D. Charles Connor, Ph.D. Mark Stewart, Ph.D. Peter Swarzenski, Ph.D. Date of Approval: April 7, 2004 Keywords: marine electromagnetic met hods, marine resistivity methods, wetland hydrology, mangrove soil salinization, submarine groundwater discharge Copyright 2004, Wm. Jason Greenwood
DEDICATION I dedicate this thes is to my parents: Dr. William R. Greenwood (1938-1992) and Ellen M. Greenwood
ACKNOWLEDGEMENTS I thank my thesis committee of Dr. Charles Connor3, Dr. Sarah Kruse3, Dr. Mark Stewart3, and Dr. Peter Swarzenski4 for their guidance and helpful review of this work. I am grateful to Dr. Sarah Kruse3 for making the time for numerous meetings and manuscript reviews and for facilitating a US Geological Survey research assistantship. I am thankful to the scientists of the US GS Tampa Bay Integrated Science Study for logistical support and guidance dur ing this study. It was a privilege to work for Dr. Terry Edgar4 and Dr. Peter Swarzenski4 at the US Geological Survey. Dr. Stewart Sandberg5 was a helpful resource for both elect romagnetic and resi stivity methods. Many hours of invaluable field assist ance were provided by Joel Bellucci2. Field assistance was also provided by Chandra Dreher4, Arnell Harrison3, Dr. Randy Runnels1, Jennifer Smith2, Yvonne Werzinsky1 and the University of South Florida Fall 2002 Applied Geophysics class, all of whom persev ered in high temperatures and humidity and amongst numerous fire ants, mosquitoes and spiders. 1 Florida Department of Environmen tal Protection, Terra Ceia, Florida. 2 University of South Florid a, College of Marine Scien ce, St. Petersburg, Florida. 3 University of South Florida, Depa rtment of Geology, Tampa, Florida. 4 U.S. Geological Survey, St. Petersburg, Florida 5 Geophysical Solutions, Inc., Albuquerque, New Mexico.
i Table of Contents List of Tables iii List of Figures iv Abstract vi Introduction 1 Electromagnetic Methods 5 Use of EM in Groundwater Studies 5 EM-31 and EM-34 Operation at the TCSA 7 Modeling of EM Data 9 Equivalence in EM inversions 11 Resistivity Methods 13 Use of Resistivity Data in Groundwater Studies 13 Resistivity Data Acquisition at the TCSA 14 Resistivity Data Interpretation 15 Estimation of Porewater Conductivity from Terrain Conductivity 16 Terra Ceia Study Area 18 Surficial Geology 21 Core Samples 21 Climate 25 Hydrology 25 Results 28 EM-34 and EM-31 Data Coverage 28 Shallow Marine EM-34 28 Shallow Marine EM-31 Data 33 Correlation of Terrain Conductivit y and Porewater Conductivity 42 Imaging Submarine Groundwater Discharge 49 Effect of Mangroves on EM Measurements 55 Conclusions 63 References 65 Appendices 73 Appendix 1 Method for Measuri ng Water Conductivity and Depth 74
ii Appendix 2 Geonics, Ltd. EM-34 Instrument Response Curve 75 Appendix 3 EM-34 HMD 10 Meter Coil Spacing Raw Data 76 Appendix 4 EM-34 HMD 20 Meter Coil Spacing Raw Data 77 Appendix 5 EM-34 HMD 40 Meter Coil Spacing Raw Data 78 Appendix 6 EM-34 VMD Sounding Locations 79 Appendix 7 Raw EM-31 Soundings on Land and Over Water 80 Appendix 8 Two-Layer Resistivity Modeling Programs and RMS Error 81 Appendix 9 Location of Wenner Arra y Lines and USGS TC1 Multi-Port Well 82 Appendix 10 RES2DNV 2-D Wenner Array Resistivity Inversions 83 Appendix 11 Potential Salinity Halo Around Ponds 84 Appendix 12 Local Influence of Mosquito Control Ditches 85 Appendix 13 Local Influence of Mosqu ito Control Ditches with Elevation 86
iii List of Tables Table 1 Formation factors determined by porewater and resistivity data 43 Table 2 Predicted and measured porewater conductivity based on EM models, direct samples and local resistivity formation factors 48
iv List of Figures Figure 1 EM-31 mounted in non-conductive canoe. 8 Figure 2 PCLOOP forward model of EM-31 response over a homogenous half-space with infinite depth 11 Figure 3 Floating Schlumberger arra y connected to Terrameter SAS 300C 15 Figure 4 Location of the TCSA within Tampa Bay and Florida 19 Figure 5 The TCSA plotted on a 1999 USGS color infrared orthophoto 20 Figure 6 Schematic of the USGS TC1 multi-port well 23 Figure 7 Split-spoon cores and predominate soil types on the TCSA 24 Figure 8 SWFWMD shallow water table levels near EM collections sites 26 Figure 9 Hydraulic head distributio n at the USGS TC1 multi-port well 27 Figure 10 Hydraulic head distribu tion for 11 wells within the TCSA 27 Figure 11 PCLOOP two-layer forward models of EM-34 VMD response over shallow marine water 31 Figure 12 PCLOOP two-layer forward models of EM-34 HMD response over shallow marine water 32 Figure 13 PCLOOP two-layer forward models of EM-31 VMD response for changing water column thickness and lower layer conductivity 34 Figure 14 Floating Schlumberger Array (A), Floating EM-31 (B), Floating EM-34 (C) and a cross-section of th e floating EM-31 cal ibration model (D) 35 Figure 15 Raw EM-31 VMD readings and water column thickness during a rising tide (A), Correlati on of raw EM-31 VMD readings and water column thickness during a rising tide (B) 37 Figure 16 IX1D two-layer model of fl oating Schlumberger array resistivity data 38
v Figure 17 Upper and lower water co lumn conductivity and temperature beneath the floating EM-31 39 Figure 18 Comparison of predicted to measured apparent conductivity for 8 two layer PCLOOP models 41 Figure 19 RES2DNV inversion profile of Wenner array resistivity data with TC1 multiport well and por ewater conductivities 43 Figure 20 Porewater and geophysical data used to calculate formation factors 47 Figure 21 Predicted vs. measured porewater conductivity based on EM models and local resistivity derived formation factors 48 Figure 22 PCLOOP 2-layer forwar d models of floating EM-31 VMD response at three saline water depths over a SGD anomaly 52 Figure 23 PCLOOP 2-layer forwar d models of floating EM-34 VMD response at three saline water depths over a SGD anomaly 53 Figure 24 PCLOOP 2-layer forwar d models of floating EM-34 HMD response at three saline water depths over a SGD anomaly 54 Figure 25 Porewater conductivity transe ct at 0.3 0.7 m sediment depth leading away from a red mangrove forest going towards Tampa Bay 56 Figure 26 Lower model layer conduc tivity beneath open marine water, mangrove trees and upland vegetation 58 Figure 27 Profile of fl oating EM-31 VMD lower model layers across Moses Hole pond 60 Figure 28 EM-31 survey in mosquito control ditch lined with red mangrove trees. Raw data (A,B), water dept h (C) and lower EMIX model layer and porewater sample (D) 62
vi Mapping Porewater Salinity with Electro magnetic and Electrical Methods in Shallow Coastal Environments: Terra Ceia, Florida Wm. Jason Greenwood ABSTRACT The feasibility of predicting porewater salinity based on calibrated surface electromagnetic methods is discussed in a coastal wetland on the southern banks of Tampa Bay in west-central Florida. This st udy utilizes a new method to float commercial land based electromagnetic (EM) instruments in shallow marine waters of less than 1.5 meters. The floating EM-31 (Geonics, Ltd.) e ffectively sensed the magnitude and lateral extent of high and low salinity porewaters within mangrove lined ditches and ponds. Resistivity and EM geophysical methods are merged with direct sampling data to calibrate layers in electromagnetic models to infer shallow (<30m) groundwater salinity patterns. Initial marine resistivity surv eys are necessary to discriminate between equivalent EM model solutions for seafl oor conductivities ben eath shallow (0.1-1.5m) marine (~30 ppt) waters. Using formation factors computed from nearby resistivity surveys, porewater conductivity predic tions based on surface EM-31 and EM-34 measurements are successful at distinguish ing overall porewater salinity trends. At the Tampa Bay study site, the most di stinctive terrain conductivity anomalies are associated with mangroves bordering mari ne waters. Highly elevated porewater conductivities are found within 5m of the mangrove trunks, falling sharply off within 10m, presumably due to saltwat er exclusion by mangrove roots.
vii Modeling indicates the shallow water EM-31 measurements probably lack the resolution necessary to image more subtle porewater conduct ivity variations, such as those expected in association with diffuse submarine groundwater discharge. However, the technique has potential application for locating high contrast zones of freshwater discharge and other salinity anomalies in sh allow and nearshore areas not accessible to conventional marine resistivity or land-bas ed arrays, and hence may be useful for interdisciplinary studies of coastal wetland ecosystems.
1 Introduction Coastal hydrologists, oceanographers, biol ogists and land managers all seek an understanding of the patterns of shallo w groundwater salinity. Salinity strongly influences the health, productivity and species composition of essen tially all coastal life (Morss, 1927; Chapman, 1960; Mitsch and Gosselink, 1993). Knowledge of groundwater salinity patterns improves and ga uges the effects of wetland restoration planning, which is complicated by inaccessible terrain that exhibits large lateral and vertical salinity variations over small distan ces. Increased resolution is afforded when salinity data extends beyond av ailable wells in dual densit y numerical groundwater flow models (Voss, 1984; SUTRA, Souza, 1987; SEAWAT, Guo and Langevin, 2003). Closer to shore, anomalous zones of low sa linity groundwater have been associated with submarine groundwater discharge (SGD), th e upward flux of groundwater across the sediment-water interface (Johannes, 1980; Vanek, 1991; Hoefel and Evans, 2001; Manheim et al., 2001). SGD has significant ecological consequences and may be an important public health risk, as it is a poten tial source of excess nutrients, pollutants and human pathogens into coastal waters (Joha nnes, 1980; Capone and Bautista, 1985; Paul et al., 1997). Effective delineation of salinity patterns in coastal zones, particularly wetlands, often requires numerous wells, which are pr ohibitively expensive in comparison to widely used geophysical methods that are sensitive to the conductivity contrast between
2 fresh and saline saturated te rrain (terrain conductivity) (C ameron et al., 1981; Barker, 1990; McNeill, 1990; Fitterman and Deszcz-Pan, 1999; Stewart, 1999; Hoefel and Evans, 2001; Manheim et al., 2001). In this study, the feasibility of mapping groundwater salinity is assessed in the Terra Ceia Study Area (TCSA), which encompasses 7.8 km2 of primarily tidal marsh interspersed with coastal uplands and freshwater ponds on the southern bank of Tampa Bay, 10 kilometers (k m) north of Palmetto and 15 km west of Parish, in Manatee County, Fl orida (Figures 4 and 5). I nvestigations were conducted cooperatively between the US Geological Su rvey Tampa Bay Integr ated Science Study, the University of South Florida Geology Depart ment and the State of Florida Department of Environmental Protection. Groundwater salinity patterns in the TC SA are strongly influenced by topography, precipitation, evaporation, transpiration, ma ngrove soil salinization, tides, and surface water flow in ditches and ponds. These in fluences are often highly variable. For example, such as the case where groundwater salinity was found to vary from 2 to 27 parts per thousand (ppt) in the uppermost 15 meters (m) of a 50 m2 area of densely vegetated upland and wetland modified by dre dge and fill structures including mosquito control ditches and berms (Figure 5, Area 4) Increased coverage may be possible by aerial electromagnetic methods of this relatively inaccessible terrain, but these methods are expensive and may lack resolution necessary to identify small-scale features (Fitterman and Deszcz-Pan, 1999; Fitterman and Deszcz-Pan, 2001; Stewart et al., 2002). Locating general areas of high and low salinity groundwater is possible based on surface based geophysical methods, however quan tifying these areas requires additional knowledge of the factors that influence terrain conductivit y. These factors include
3 porewater conductivity, temperature, conductive clay content, porosity, pore space shape and connection and degree of saturation (Kel ler and Frishknecht, 1970; McNeill, 1990). Mapping groundwater salinity via geophysical methods requires the following three steps: (1) reconnaissance mapping by geophys ical methods to assess horizontal and vertical variability in terrai n conductivity, (2) direct sampli ng of areas of interest to determine the local relationships between te rrain conductivity and por ewater conductivity and (3) application of the widely recogni zed standard for the relationship between seawater salinity and porewater cond uctivity (IES 80 method in Appendix 1). Results from this study incorporate me thods to measure terrain conductivity, relate terrain conductivity to groundwater conductivity based on local direct samples and the adaptation to shallow wate r (<1.5 m depth) of commerci al electromagnetic (EM) and resistivity (DC) systems. Discussion of resu lts and their relevance to other sites includes the strengths and limitations of EM and DC inst ruments in coastal set tings, influences of mangroves on groundwater salinity and the pot ential imaging of submarine groundwater discharge. Electromagnetic data is typically expresse d in the units of co nductivity or Siemen per meter (S/m); direct current resistivity data is typically reported as resistivity or Ohmmeters (Ohm-m). An ohm-meter is the recipr ocal of a Siemen per meter. Comparison is facilitated in this text by consistently expressing all EM and DC data in units of conductivity in milli-Siemens per meter (mS/m). Development of shallow-water geophysi cal techniques in this study have the potential for imaging submarine groundwat er discharge (SGD) which occurs when groundwater flows upward across the sedime nt-seawater interface into near shore
4 environments when an aquifer is hydraulical ly connected with the sea through permeable bottom sediments and the hydraulic head is above sea level (Johannes, 1980; Hutchinson, 1983). The presence of SGD has been docum ented in most coastal environments, including bays, coves and coral reefs (Lewis 1987; Giblin and Gaines, 1990; Vanek, 1991; Simmons, 1992; Simmons et al., 1992; Schnei der, 2003). In previous studies, SGD has been shown to contribute up to 20% of all freshwater and 20% of the total dissolved nitrogen to Great South Bay, New York (C apone and Bautista, 1985) and 50% of the total dissolved nitrogen input near Perth, Australia (Johanne s, 1980) as well as being a potential vehicle for the disper sal of human pathogens to co astal waters, especially in regions with waste water injection wells (Paul et al., 1995; Paul et al., 1997). A finite difference numerical groundwater model of Tampa Bay estimates SGD as 5% of the total fresh water input (Hutchin son, 1983). This model did not account for dual density water and was run under steady stat e conditions, so this value may rise as high as 10-20%, during peak months, usi ng current groundwater models (Swarzenski pers. comm.). Submarine groundwater discharge may be f ound either in the form of diffusive seeps or more localized springs, both of which have been clearly delineated with marine resistivity and electromagnetic methods (Hoefel and Evans, 2001; Manheim et al., 2001). Locating diffuse SGD from surficial aquifers is more difficult because anomalies are subtle and analytical models, seepage meter data, and tracer studies all indicate that overall flux rates will most likely be greatest close to the shoreline where interference with mangrove soil salinization may occur and shallow depths may limit the use of marine systems (Vanek, 1991; Passioura et al ., 1992; Banks et al., 1996; Corbett et al.,
5 1999; Uchiyama et al., 2000). Wetlands and shal low (<1 m) water depths coincide with a spatial gap between existing land-based and ma rine EM and DC methods. Recirculated seawater pumped by tides and bioturbation mi xes with fresh groundwater to form fresher porewaters near the sediment seawater interface in areas of shallow SGD (Moore, 1999). One focus of this thesis is to present adap tations of commercially available land based electromagnetic and resistivity devices to se nse shallow porewater conductivity that may lead to improved imaging of spatial patte rns of SGD in near shore environments. Electromagnetic Methods Electromagnetic instruments generate alte rnating currents in a transmitting coil at the surface, which induce eddy currents in th e sub-surface. The ratio of the secondary magnetic field induced by the eddy currents to the primary magnetic field is measured by a receiving coil, and can be related to the terrain conductivit y, or bulk electrical conductivity of the materi al beneath the instrument (McNeill, 1980a). Use of EM in Groundwater Studies EM methods are widely used in hydroge ologic studies, exploiting the terrain conductivity variations associated with fres hwater/saltwater interfaces, highly conductive clay confining units, high conductivity cont aminant plumes, and low conductivity aquifer units (McNeill, 1990; Cherkauer et al., 1991; Woldt et al., 1998; Ayotte et al., 1999; Fitterman and Deszcz-Pan, 1999; Fitterman a nd Deszcz-Pan, 2001; e.g., Bendjoudi et al.,
6 2002; Stewart et al., 2002). In coastal envir onments, these methods have also been used to map freshwater lens morphology and seasona l variation on siliciclastic barrier islands (Stewart, 1990; Anthony, 1992; Caballero, 1993 ; Ruppel et al., 2000; Schneider and Kruse, 2001). Use of electromagnetic methods offshore or in lakes has not been extensive. Time domain EM was used in a freshwater lake in order to estimate the depth of a saline body of water underlying the lake bed (Goldm an et al., 1995; Goldman et al., 1998). In other studies, a marine EM transmitter-receiver array, with multiple frequency and coil spacing capability, similar to the Geonics, Ltd. EM-34, was towed along the seabed in order to delineate paleo-channels by changes in associated porosity and to locate prospective zones of submarine groundwater discharge (Evans et al., 2000; Hoefel and Evans, 2001). Nadeau et al. (2003) used a st reaming digital EM-34 with the receiver and transmitter coils mounted in small non-conductive boats. This system was used to map gravel deposits associated with a municipal well field recharge area beneath a freshwater river. A simple numerical correction for the e ffect of the river water was feasible in this relatively low-conductivity environment (M cNeill, 1980a; Nadeau et al., 2003). The TCSA site differs from most sites discussed in the literature in that terrain conductivities in the uppermost few meters are an order of magnitude or more higher. Most previous studies show less complex a nd spatially variable terrain conductivity structures. In addition, the water-born da ta acquisition and interpretation techniques described in preceding studies were not direc tly transferable to the TCSA, where water depths in areas of interest are shallow (< 1.5 m) and surface water conductivities are very
7 high. EM data acquisition methods and interpre tation that are feasible in shallow marine environments is the focus of discussion below. EM-31 and EM-34 Operation at the TCSA The electromagnetic instruments used in this study are the EM-31 and EM-34 of Geonics, Ltd. The EM-34 consists of a pa ir of transmitter and receiver loop type antennas with corresponding control boxes that are connected by coaxial cables. The EM-34 operates at three frequencies designed to work with transmitter and receiver coil separations of 10, 20 and 40m. The two antenna coils can be placed in either the vertical co-planar orientation (horizontal magnetic di pole HMD), or in the horizontal co-planar orientation (vertical magnetic dipole VMD). The HMD mode is significantly more sensitive to near surface materials when compared to the VMD mode (McNeill, 1980a; Kaufman and Keller, 1983; Kaufman and Ho ekstra, 2001). Ideally, all three coil separations and two magnetic dipole orientatio ns may be used over the same location for a total of six unique effective exploration de pths. Practical limitations in the highly conductive environment at the TCSA ar e discussed in the results below. The EM-31 operates at one frequency and has a fixed length boom type antenna with a coil spacing of 3.67 m, so explora tion depth is a function of magnetic dipole orientation and instrument height. The EM31 used in this study had the capability of logging data at timed intervals, allowing th e operator to move the instrument in a streaming mode by carrying the in strument at hip he ight (0.9m) or towing the instrument in a boat (floating 0.1m above the water surf ace). The boat used to hold the EM-31 in this study was constructed of polyethylene and fitted with wooden supports, plastic
8 splash shields and a foam and plastic outrigger (Figure 1). EM-31 data were later merged with global positioning satellite fixes by synchronizing the time of data acquisition. Figure 1 EM-31 mounted in non-conductive canoe. The effective depth of exploration for el ectromagnetic methods has been defined as the depth where 70% of instrument res ponse is from the overlying material, and is controlled by variations in instrument de sign and acquisition parameters such as coil orientation (Stewart, 1982; St ewart and Bretnall, 1986). Effe ctive depth of exploration on the TCSA with the EM-31 and EM-34 ra nges between approximately 1 and 30 m. The coil spacings and frequencies of th e EM-31 and EM-34 are designed such that, where terrain conductivi ties are less than 80-100 mS/m, the ratio of the secondary magnetic field induced by eddy currents to the primary magnetic field is linearly proportional to the terrain conductivity over a homogenous sub-surface (McNeill, 1980a). Thus, EM instrument readings are expressed as apparent conductivit y: the conductivity of a homogenous half-space that will produce the same response as that measured over the
9 real heterogeneous sub-surface when using the same acquisition parameters (Spies and Eggers, 1986). Throughout most of the TCSA terrain conductivities are greater than 100 mS/m; therefore, the raw instrument readi ngs do not represent an apparent conductivity, or equivalent conductivity of a homogene ous subsurface. Nevertheless, following convention, the instrument readout here is re ferred to as the raw apparent conductivity ( raw). To infer terrain conductivity structure ba sed on raw apparent conductivity data in this high-conductivity environment, the raw da ta must be compared to layered models that incorporate EM instrument design and da ta acquisition parameters. In this study, models are restricted to si mple horizontal layers with homogeneous conductivities. The conductivities of individual laye rs in these models are referre d to as terrain conductivities or model layer conductivities ( t, 1, 2, etc). The instrument response predicted from the layer models is designated as predicted apparent conductivity ( p). Modeling of EM Data The forward modeling program PCLOOP was used to calculate predicted apparent conductivity ( p) over layered earth models (Geonics, 1994). PCLOOP calculates instrument response with an algor ithm by Anderson (1979) that incorporates theoretical solutions by (Frishknecht, 1967; Kaufman, 1969; McNeill, 1980a). The testing of instrument sensitivity to various model parameters can be done with forward models. For example, forward models can predict the maximum practical exploration depth and conductivity sensitivity for a particular EM instrument in a given environment. Portions of the data were also interprete d using the EMIX 34 program (Interpex, 1994).
10 The EMIX program can perform forward calcula tions similar to those of PCLOOP using theoretical solutions published by (McNeill, 1980a; Patra and Mallick, 1980). However for this study, the program was used in an inversion mode. Gi ven an initial model conductivity structure, EMIX can invert a se t of raw apparent c onductivity readings to find the best-fitting values of one or more layer conductivities or layer thicknesses (McNeill, 1980a; Patra and Ma llick, 1980; Kaufman and Ho ekstra, 2001) using a ridge regression estimation algorithm (Inman, 1975). Appendices 3 to 7 contain the full set of EM and resistivity data collected in this study. Portions of these data were incorporated in EM mode ls with depths ranging from less than a few meters for shoreline models to a maximum of 15m at upland sites. Model complexity was minimized by representing ground and surface water layers by just two or three conductively uniform and horizont al layers. Upper layer thickness and/or conductivity ( 1) were constrained by other measurements. For example, for measurements made on land, an upper layer c onductivity was set to a value determined by resistivity soundings to a loca l site with similar lithology. For readings over water, the water depth and conductivity were both meas ured; hence the properties of the surface water layer were known and fixed in the model. Because EMIX 34 ta kes into account the instrument height and dipole orientation (as well as coil spacing a nd operating frequency) the program could be used for data collect ed over shallow water with floating coils.
11 Equivalence in EM inversions The very high terrain conductivities present on the TCSA produce a non-linear instrument response in the EM-31 and EM-34 that changes from a positive to a negative slope with increasing terrain conductivity (Figure 2, Appendix 2). Due to this response, there is a non-unique relationship between instrument readings ( raw) and terrain conductivities ( t), even for a simple condition, such as the one layer model consisting of a homogeneous half-space shown in Figure 2. Note that for ( t) of both 400 and 2100 mS/m, a ( raw) of 200 mS/m is produced (black dots in Figure 2). HMD response slope is negative beyond a ( t) of 9000 mS/m, which yields a ( raw) of 1730 mS/m which is beyond the 1000 mS/m range of the EM-31 MK II used in this study. Similar response curves for the EM-34 are in Appendix 2. Terrain Conductivity t (mS/m) 0100020003000400050006000 Apparent conductivity a (mS/m) -1000 -750 -500 -250 0 250 500 750 1000 Linear response VMD HMD Equivalence Figure 2 PCLOOP forward model of EM31 response over a homogenous half-space with infinite depth (Geonics, 1994).
12 In settings such as the example in the preceding paragraph (EM-31 VMD response of 200 mS/m expected for terra in conductivities of either 400 mS/m or 2000 mS/m), particular care must be take n when using the EMIX 34 inversion routine to solve for the terrain conductivity. The i nversion routine requir es that an initial estimate for terrain conductivity be input by th e user. If, for example, the true terrain conductivity is 400 mS/m, then the initial estim ate given to inversion routine must be reasonably close to this true value. If the initial estimate terrain conductivity specified is closer to 2000 mS/m, th e inversion routine will converge on 2000 mS/m rather than 400 mS/m. This need to have a reasonably good idea of which of the equivalently possible terrai n conductivities is valid can be solved by either collection of more detailed EM data afforded by multip le dipole orientations, coil spacings and heights, or with resistivity measurements. Additional EM modes were not practical while using the EM-31 VMD over shallow high conductivity water because they lacked resolution, exceeded the instruments scale or were not possible when the instrument was logging while moving. Limitations of the use of EM in very high conductivity environments are discussed further in the resu lts section below. In these cases, ambiguity was resolved by running resistivity soundings at representative s ites. The terrain conductivities derived from a resistivity sounding were then used as the starting structure for inversions of EM readings in the vicini ty of the resistivity sounding. In this way, local variations in terrain conductivity between sites of re sistivity surveys could be mapped with the more rapid EM methods.
13 Resistivity Methods Resistivity methods use arrays of electrode s that are driven into the ground, towed on a floating streamer or positioned on a stationa ry floating tube. Direct current is then introduced into the ground (or surface water) from a pair of current electrodes and the resulting potential differences at another pair or pairs of electrodes are measured. The circuit that is completed by th ese arrays includes the eart h, groundwater and any surface water as a resistor whose resistance is em pirically related to the source current and measured voltage by Ohms law (Koefoed, 1979) Depth and degree of spatial resolution are controlled by electrode spacing and the conductivity of the s ub-surface (Koefoed, 1979). When compared to EM methods over th e same target, resistivity methods are generally regarded as a more accurate and reliable estimat e of apparent conductivity (Koefoed, 1979; Patra and Mallick, 1980; Kaufman and Keller, 1983; Kaufman and Hoekstra, 2001). Use of Resistivity Data in Groundwater Studies DC resistivity methods have been used extensively to estimate water quality, locate salt/freshwater interfaces, monitor contam inant plumes, and to locate aquifers e.g. (Cameron et al., 1981; Barker, 1990; Griff iths and Barker, 1993; Sharma, 1997; e.g. Aristodemou and Thomas-Betts, 2000; Fetter, 2001). The more cumbersome resistivity
14 methods are often combined with the fast er but less accurate EM methods (McNeill, 1990). Marine resistivity methods have been used to map zones of low terrain conductivity or sea bed conduc tivity that have been associated with submarine groundwater discharge (Vanek, 1991; Hoefel and Evans, 2001; Manheim et al., 2001). Towed dipole-dipole resistivity streamers built by Zonge, Inc. and Advanced Geosciences, Inc., were successful at locating prospective zones of submarine groundwater discharge which were subse quently confirmed by direct sampling (Swarzenski pers. comm., Manheim et al., 2001 ). However, these marine resistivity systems are limited to open water applications compatible with the draft of the boat and the turning radius of the typically long (~100m) towed streamer. Further, these commercial systems typically use a dipoledipole array geometry which has lower vertical resolution than othe r array geometries such as the Wenner or Schlumberger. Resistivity Data Acquisition at the TCSA In this study, land based profiles were run with a 50-electrode Campus Geopulse resistivity system using the Wenner traverse geometry with electrodes spaced between 1 and 6 m. Resulting profiles were 50-300m l ong with effective depths of exploration of 0.2 to 50 m. For resistivity su rveys over shallow (< 1m) water, a novel floating electrode array with Schlumberger geometry was constr ucted at the University of South Florida Geology Department, with electrodes spaced between 0.5 and 4m for an effective depth of exploration of approximately 1.5 m (Fi gure 3 and 14a, Edwards, 1977). Resistivity
15 measurements for the floating electrode array were made manually with a Terrameter SAS 300C resistivity system. Figure 3 Floating Schlumberger array conn ected to Terrameter SAS 300C operated by Arnell Harrison of the USF Ge ology Department Geophysics Lab. Resistivity Data Interpretation Land-based Wenner traverse resistivity surveys were inverted for apparent conductivity using the two-dimensional RES2DINV inversion program (Loke, 2002, Appendix 8,9,10). Marine Schlumberger sound ing data were inverted for apparent conductivity using the one-dimensional 1IXD inversion program (Interpex, 2002, Appendix 8). Both of these programs assign ea ch sub-surface grid node an initial terrain conductivity and then calculate the apparent conduc tivity that would result and iteratively adjusts the model until the RMS error is minimized to less than 5%.
16 Estimation of Porewater Conductivi ty from Terrain Conductivity As discussed above, terrain conductivity is a function of porewater conductivity, temperature, conductive clay content, por osity, pore space shape and connection and degree of saturation (Keller a nd Frishknecht, 1970; McNeill, 1990). This relationship is summarized with Archie's law, used extensively in the oil exploration industry to calculate the porosity of oil reservoirs. Ar chies Law relates the formation conductivity t (equivalent to terrain conductivity) to the po rewater conductivity ( w), in fully saturated media, by t = w m/ a + c. The a and m symbols are empirically determined constants, is the porosity and c is the grain surface conductivity attributed to clay (Archie, 1942; Keller and Frishknecht, 1970; Robinson and Coruh, 1988; McNeill, 1990; Sharma, 1997; Hearst et al., 2000). In a common alternative formulation, the relationship between terrain and water conductivity is described as the formation factor F = w / t (Keller and Frishknecht, 1970; Fitterman a nd Deszcz-Pan, 1999; Hearst et al., 2000; Fitterman and Deszcz-Pan, 2001; Manheim et al., 2001; Stewart et al., 2002). This alternative formulation is commonly used in gr oundwater studies to fi nd the relationship between terrain and porewater conductivity a nd to estimate a formation factor (F) for lithologic units of interest (Fitterman a nd Deszcz-Pan, 1999; Fitterman and Deszcz-Pan, 2001; Manheim et al., 2001; Stewart et al., 2002). Implicit in the alternative formation factor expression are that clay conductivit y effects are small compared to those of
17 porewater conductivities, units are saturated and porosity variations are small within the units defined. It was initially unclear what effect the variability in clay content, porosity and saturation on the TCSA would have on the reliab ility of formation factor calculations for different lithologic units of interest and the subsequent predictions of porewater conductivity from terrain conduc tivity where these units were defined. To determine a formation factor requires measurements of terrain conductivity and porewater conductivity at the same location and depth. The TCSA has 3-D spatial variability in terrain conductivity, so uncerta inties in terrain conductivity estimates are expected when 2D models are used for computing terrain c onductivities from resistivity data and, most importantly, 1D models are used to com pute terrain conductivities from EM data. To minimize uncertainties, formation f actors were only computed where both resistivity surveys were made and water samples were collected. With these data, formation factors were computed in highly porous organic rich ma ngrove soils and at three depths within the clay rich Hawthor n Formation. Once a formation factor was determined using the more reliable resistiv ity methods on a particular lithology, then terrain conductivities derived from the more rapid EM methods were used to extend groundwater conductivity predictions out laterally until new lithologies were encountered. The efficiency of this met hod was then tested by comparing predicted porewater conductivities ag ainst directly measured porewater samples.
18 Terra Ceia Study Area The TCSA is described as a nearly le vel coastal lowland with progressively rolling terrain to the east (Hyde and Huckle 1983). Maximum relief in the study area is approximately 2 m with low-lying ridges and hammocks having slopes generally less than 2% (Hyde and Huckle, 1983; Carter et al., 2003; UF, 2003) Upland areas are comprised of maritime hammocks or fallow agricultural lands overgrown with invasive exotic plants. The lowlands are comprised of mangrove fringe fore sts, interior salt barrens and the following wetlands: freshwater creek, freshwater marsh, karst tidal ponds, karst freshwater ponds, high and low estuarin e marshes, and transitional marshes (Hyde and Huckle, 1983). Almost the entire upland area of the TC SA was cleared and farmed between 1890 and 1967. Numerous dredge and fill structures changed the shallow groundwater salinity (Figure 5). Even though the TCSA has been si gnificantly altered from a natural state, it provides habitat for a wide variety of flor a and fauna, including endangered species and economically important game fish. The State of Florida plans on rest oring the TCSA to a more natural state, which will improve the wetland functions of flood water dampening and denitrification, as well as improve habitat for native species and mitigate invasion by exotic species (Mitsch and Gosse link, 1993; Bendjoudi et al., 2002).
19 Figure 4 Location of the TCSA within Ta mpa Bay and Florida. USGS 1:24K scale shoreline basemap with UTM NAD83 Zone 17 datum.
20 Figure 5 The TCSA plotted on a 1999 USGS color infrared orthophoto. Boxed study locations  TCSA;  Moses Hole;  Ma rine EM calibration;  Fresh and saline transition zone with USGS TC1 multi-port well. (Greenwood et al., 2002; MCMC, 2003)
21 Surficial Geology Exploration depths of th e geophysical methods employed in this study are limited to the upper 50 m, which consist of poorly drained, moderately permeable Pliocene to recent surficial sediment underlain by the Miocene Hawthorn Group phosphatic sand, clay, marl, and intermittent beds of fossilife rous limestone that form the upper confining unit of the Floridan Aquifer (Miller, 1997). This lithology co ntains a high content (2240%) of electrically c onductive clays such as illite, ka olinite, palygorskite sepiolite, and smectite (Hyde and Huckle, 1983; Compton, 1997). Core Samples Hyde and Huckle (1983) mapped virtua lly the entire upland area soil type of the TCSA as Bradenton fine sand with minor occurrences of Wabasso fine sand, both of which formed from the underlying Hawthorn Gr oup (1983). This cl assification scheme is limited to the upper 2m of sediment and was based on shallow hand auger type core samples. The frequently flooded portions of the study area consist of Wulfert-Kesson type soil, which also formed from re worked Hawthorn Group sediment (Hyde and Huckle, 1983). The USGS collected a 15m hydraulic rotary core and installed the TC1 multi-port well (Figure 5 and 6) in February 2002. The State of Florida Department of Environmental Protection took seven 3-m dept h percussion driven split-spoon cores and gave sample splits to the author for this st udy in October of 2002 (Figure 7). Penetration of the split-spoon cores was limited to 3m by a thin limestone layer that was sampled with the 15m USGS rotary core. All core s on the TCSA showed a surficial 0.5 to 1 m
22 layer of organic-rich quartz sand grading into underlying iron-stained clay and marl with the deeper rotary core showing clay and marl with intermittent thin (<10cm) limestone layers at 3, 10 and 15 m. These core samp les resemble descriptions of the Hawthorn Group sediment in Sarasota and Manatee County (Barr, 1996). Visual inspection of grain size, texture and mineralogy from these 8 co res suggests that to a depth of 15m, the upland portions of the TCSA may have a fairly uniform lithology comprised of Hawthorn formation clay and marl with intermittent limestone overlain by Bradenton Fine Sand soil. Three vibra-core samples were taken by th e USGS, one in the center and two in the adjacent mangrove wetlands of Moses Hole pond (Figure 5, Area 2). The center of Moses Hole is characterized by 2 meters of bioturbated phosphatic quartz sand with occasional 1-2 cm clay and mud lenses and small <2cm shell fragments which then terminates in 60cm of cohesive clay with a bundant semi-lithified lim estone clasts that resemble those found in upland cores on th e TCSA. Two adjacent mangrove wetland cores consisted of approximately 60cm of s pongy organic rich mangrove peat mixed with sand grading into 70cm of cohesive clay with mud lenses similar to the center of the pond, but lacking limestone clasts and app earing to be considerably more porous.
23 Figure 6 Schematic of the USGS TC1 mu lti-port well plotted on a RES2DNV (Loke, 2002) inversion profile of Wenner arra y resistivity data (Appendix 9-10).
24 Figure 7 Location of split-spoon cores and predominate soil types on the TCSA (boxed area). Bradenton and Wabasso soils pre dominate the upland areas and Wulfert-Kesson forms the wetlands (Hyde and Huckle, 1983).
25 Climate Manatee County receives an average of approximately 127 cm of rainfall a year, with 66% occurring in the wet season (May to September). The mean temperature is 21.1 degrees Celsius ( C). Tides are 80% semi-diurnal and 20% diurnal and average 82.7 cm (Hyde and Huckle, 1983). Data collect ion on the TCSA began in April of 2001 and continued into Fall of 2002, with both years having greater than normal rainfall of 144 and 164 cm respectively. Hydrology The TCSA is bordered by the saline waters of the Tampa Bay estuary to the north and the fresh to saline Frog Creek to the sout h (Figure 4 and 5). Be fore portions of Frog creek and related wetlands were drained or filled with causeways, the TCSA was an island bounded by wetlands and restricted marine waters. The slow-moving and meandering Frog Creek headwaters begin in a fresh water wetland complex 12.5 km to the east of the TCSA. Salin ity becomes stratified in Fr og Creek as the slow moving, fresher and less dense waters from the east mix with the tidal, mo re saline and dense waters of Tampa Bay to the west. Numerous round ponds dot the landscape of the TCSA, however no research was found that classified these ponds as active karst features or co nduits between surface waters and the Floridan Aquife r (Figure 5). Pond salinity is controlled by marine waters flooding through mosquito control ditches or na tural creeks, rainfall, and mixing with the surficial aquifer. Ponds in lower elevation terrain are more frequently flooded and tend
26 to have higher salinities. Heavy rainfall during the wet season can cause short term salinity stratification in surface water bodies with fresher water temporarily overlying more saline water. Hyde and Huckle (1983) report that if undr ained, the TCSA soil types will have a water table within 20 cm of the surface for 2 to 6 months of the year and a depth of 25 to 100 cm for much of the rest of the year (1983). The TCSA has been extensively modified by dredge and fill structures, which may lower ground and surface water levels. Higher than normal rainfall during 2001 and 2002 probably raised the water table above normal ranges, nevertheless, SWFWMD well data show the water table to be closer to 50 to 100cm during the wet seasons of 2001-2 (Figur e 8). Slow drainage and standing water were observed after precipitation. The USGS TC1 well and 11 SWFWMD wells have hydraulic heads that are above high tide dur ing the wet season (Figure 9,10). Thus, favorable conditions exist for submarine groundw ater discharge into the shallow waters of Tampa Bay. 0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 4/1/015/21/017/10/018/29/0110/18/0112/7/011/26/023/17/025/6/02 DateDepth of water table (m) P1 P2 P3 P4 P7 P8 Figure 8 SWFWMD shallow water table levels near EM collections sites.
27 0 200 400 600 800 1000 1200 1400 -505101520253035404550Hydraulic head (cm)Port depth (cm) Nov-02 Jan-03 May-03 Figure 9 Hydraulic head distribution at the USGS TC1 multi-port well using North American Vertical Datum of 1988. The TC1 well has greate r pressure with depth and positive hydraulic head for all ports accept th e 2 shallow ports during the dry season in May of 2003 when compared to local mean sea level in Tampa Bay. -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 04/01/0105/21/0107/10/0108/29/0110/18/0112/07/0101/26/0203/17/0205/06/02 DateHydraulic head (m) Figure 10 Hydraulic head distribution for 11 SWFWMD wells with in the TCSA during geophysical data collection period using the North American Vertic al Datum of 1988 and the local mean sea level of Tampa Bay.
28 Results EM-34 and EM-31 Data Coverage EM-34 and EM-31 readings over both land and water were acquired in various modes on the TCSA (Appendices 3-7). Both lowland and upland sites contain EM-34 HMD mode data, while the analog EM-34 VM D mode was in most cases limited to upland regions where terrain conductivities were less than 600 mS/m (Appendix 2 and 6). EM-34 readings over water were not compatib le with values predicted from reasonable models. The EM-31 model MK2 was successfully used in VMD and HMD mode at ground level (0 m) and hip height (0.9m) over land, and floating (0.1m) over shallow (<1.5m) marine salinity water (4000 -5000mS/m) in the study area. Shallow Marine EM-34 Nadeau et al. (2003) showed a floati ng EM-34 in VMD mode could be used successfully in freshwater (10 mS/m) to im age lake floor conductivity between 0.3 and 42 mS/m allowing for a minor numerical correc tion for water conductivity and depth. No studies were found, however, for saline envir onments that require the application of models such as PCLOOP and EMIX to correct for water depth. The concept of floating an EM-34 over saline water and attaining useful information on seabed conductivity was tested by a suite of PCLOOP forward tw o-layer models (Fig ure 11,12,14c) with upper layers run using a value of 4550 mS/m (comm on in surface waters on the TCSA) and at
29 water depths from 0.2 to 1.5m. These models incorporated a lower layer seabed conductivities ranging from 10 to 3000 mS/m and spanning the value of ~1000 mS/m expected on the TCSA based on resistivity data. These models show that negative apparent conductivity readings will likely occur in VMD mode unless an unlikely <200 mS/m seabed is encountered, which limits the analog EM-34 available in this study to HMD mode because only positive apparent conductivity readings up to 300 mS/m can be measured (Appendix 2). A gauge replacement or rewiring may solve this probl em (Stewart pers. comm.). Figures 11 and 12 show that VMD and HMD mode could in th eory provide useful information on seabed conductivity in shallow water under a variety of conditions. Both VMD and HMD mode were tested, and as expected, no readings were attained in VMD mode. HMD mode readin gs at 10 and 20 m coil spacings contained noise that was associated with small moveme nts of the floats in waves and wind. The 40 m coil spacing HMD data was the least affected by this surface noise, as expected from response curves (McNeill, 1980b). EMIX twolayer inversion models were created for each of the 27 floating stations (see A ppendix 3,4,5 for locations over water) which included an upper layer set to the surface water depth and conductivity and included HMD apparent conductivity data at combin ed 10, 20 and 40 m coil spacings. EMIX inversions converged at the same solution when using starting lower layer seabed conductivity values of both 200 mS/m and 3000 mS/m. These starting values were chosen based on EM-31 data showing a s eabed conductivity of 3000-5000 mS/m at the edges of Moses Hole pond and between 340 and 1880 mS/m in the middle (discussed in the mangrove salinization section below).
30 Unfortunately, EMIX models of floating EM -34 data, including individual runs of the relatively noise free 40 m coil spacing data produced implausibly high or low seabed layer conductivities. Simple 2-layer PCLOOP forward models with seabed conductivities between 100 and 5000 mS/m also fa iled to fit the observations. The misfit between the EM-34 observations and any reas onable 2-layer model may be linked to the following factors: three-dimensional conductiv ity variation at the scales imaged, water depth error, surface water conductivity variation not accounted for, EM coil misalignment or movement, or instrument ca libration. Further study would be needed to determine the importance of the various factors mentioned above. In summary, forward models predict that the floating EM-34 VMD and HMD modes have potential for measuring useful information on seabed conductivity. The field experiments on the TCSA, however, were unsuc cessful at reproduci ng these theoretical results. Given the space needed for EM-34 measurements (10-40 meters between coils), instrument development efforts targeting these settings may be better focused on short marine resistivity streamers (~10-50m).
31 -600 -500 -400 -300 -200 -100 0 100 0.20.30.40.188.8.131.52.91.01.11.21.31.41.5Water column (m)EM-34 10 meter VMD Apparent conductivity (mS/m) Lower layer 2 (mS/m)Upper layer1 = 4550 mS/m -700 -600 -500 -400 -300 -200 -100 0 100 200 300 0.20.30.40.184.108.40.206.91.01.11.21.31.41.5Water column (m)EM-34 20 meter VMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4550 mS/m -650 -550 -450 -350 -250 -150 -50 50 150 250 350 0.20.30.40.220.127.116.11.91.01.11.21.31.41.5Water column (m)EM-34 40 meter VMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4550 mS/m Figure 11 PCLOOP two-layer forward mode ls of EM-34 VMD response over shallow marine water.
32 180 230 280 330 380 430 0.20.30.40.18.104.22.168.91.01.11.21.31.41.5Water column (m)EM-34 10 meter HMD Apparent conductivity (mS/m) Lower layer 2 (mS/m)Upper layer1 = 4550 mS/m 90 140 190 240 290 340 390 440 0.20.30.40.22.214.171.124.91.01.11.21.31.41.5Water column (m)EM-34 20 meter HMD Apparent conductivity (mS/m) Lower layer 2 (mS/m)Upper layer1 = 4550 mS/m 30 80 130 180 230 280 330 380 0.20.30.40.126.96.36.199.91.01.11.21.31.41.5Water column (m)EM-34 40 meter HMD Apparent conductivity (mS/m) Lower layer 2 (mS/m)Upper layer1 = 4550 mS/m Figure 12 PCLOOP two-layer forward mode ls of EM-34 HMD response over shallow marine water.
33 Shallow Marine EM-31 Data The relatively small size of the EM-31, re lative to resi stivity streamers or the space needed between EM-34 coils, may offer the possibility of pr ofiling in otherwise inaccessible coastal terrains. Application of the floa ting EM-31 method to discriminating seafloor conductivities and the conditions under which it might be successful are discussed next. HMD mode data were not used because two-layer PCLOOP models predict that saline water depths as shallow as 0.1m, even when combined with seabed conductivities as low as 200 mS/m, produced out of range readings (>1000mS/m). Field trials proved (1) the HMD mode has signi ficant noise problems associated with sensitivity to near surface materials and m ovement of the floating coils (McNeill, 1980a) and (2) rotating the instrument coils between VMD and HMD mode while streaming data was impractical. Floating EM-31 in VMD mode shows gr eater promise, with limitations (Figure 13). Conclusions from analysis of PCLOOP two-layer forward models of EM-31 VMD data (Figure 13) include (1) at depths great er than 0.70 m, seafl oor conductivities less than ~1000 mS/m are distinguishable from one another and (2) equivalence issues exist at shallower water depths, where low a t (~100 mS/m) and a high t (~2000 mS/m) may yield similar data.
34 -250 -150 -50 50 150 250 350 0.20.30.40.188.8.131.52.91.01.11.21.31.41.5Water column (m)EM-31 VMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4550 mS/m Figure 13 PCLOOP two-layer forward mode ls of EM-31 VMD response for changing water column thickness and lower layer conductiv ity. Note the relative lack of sensitivity to 100 mS/m changes in lower layer terrain conductivity at water columns greater than 0.75 m. Also note the approximately equivalent readings for lower layers of 10 to 1000 mS/m at a water column of 0.75m. To test whether the EM-31 actually performs as predic ted by these models, an experiment was conducted floating the EM-31 in shallow seawater at the location shown in Figure 5, Area 3. Stationary time series EM-31 VMD readings were taken during a rising tide, with the assumption that change s in subseafloor terr ain conductivity during this period were small. Data were compar ed against PCLOOP forward models, with the expectation that all readi ngs should be compatible with approximately the same subseafloor conductivity.
35 Figure 14 Floating Schlumberger Array (A), Floating EM-31 (B), Floating EM-34 (C) and a cross-section of the floating EM-31 calibration model (D).
36 A non-conductive canoe held the EM-31 instrument 0.1 m above the water surface and was laterally fixed, but allowed to ri se with the tide along plastic poles driven into the sediment. The EM-31 was program med to log readings every 3 minutes for 14.75 hours over one half of a Tampa Bay tidal cycle (Figure 14b and 15a). The VMD mode was chosen in order to limit the eff ect of the highly conductive surface layer of seawater and because rotating the EM-31 to HMD mode inside the canoe while logging was not practical. A site sh ielded from wind and waves was chosen in Bishop Harbor (Figure 5, Area 3). A Van Essen conductivity, temperature, depth sensor (CTD) logged readings every 10 minutes at the sediment s eawater interface directly beneath the EM-31 while manual readings of the upper water co lumn were measured with a YSI-30 probe (Figure 15 and 17). Field trials found no conductivity effect from placing the small stainless steel CTD (2 cm diameter by 26 cm length) directly beneath the EM-31 (the in phase component of the EM-31 signal may have been able to detect the CTD, but was out of range in this high co nductivity environment). For interpretation of the EM results, a resistivity sounding was run and porewater samples were then collected at the site of the EM-31 experiment. The resistivity sounding was conducted with the floating Schlum berger array and inverted for terrain conductivity using a two-layer IX1D model with the upper layer fixed to the water column measurements of 4702 mS/m and 0.87m. A lower layer conductivity of 1170 mS/m provides the best fit to the observati ons, with an RMS error of 7.8% (Figure 16).
37 Time 07:00 09:00 11:00 13:00 15:00 17:00 19:00 21:00 Water column [blue] (cm) 50 55 60 65 70 75 80 85 90 95 100 105 110 115 Raw apparent conductivity [green] (mS/m) 32 40 48 56 64 72 80 88 96 104 112 120 128 136 Water column Raw apparent conductivity (A) (B) Water column (cm) 50556065707580859095100105110115 Raw apparent conductivity (mS/m) 32 40 48 56 64 72 80 88 96 104 112 120 128 136 Figure 15 (A) Raw EM-31 VMD readings for over 14 hr of a rising tide. Note the inverse response of water column thickness to raw apparent conductivity and the raw apparent conductivity shifts between 09:00 a nd 13:00hrs. (B) Correlation of raw EM-31 VMD and water column thickness readi ngs for over 14 hr of a rising tide.
38 0.1 1 10 1000 10000 Apparent conductivity (mS/m)Electrode spacing AB/2 ~ Depth (m) Raw data Model best fit 2-layer model Sediment seawater interface Model RMS error = 7.8% Layer 1 = 4702 mS/m Layer 2 = 1170 mS/m Figure 16 IX1D two-layer model of floating Schlumberger array resistivity data over 0.87 m of 4702 mS/m marine salin ity (28.7 ppt) water with a theo retical best fitting lower layer (sea-bed) conductivity of 1170 mS/m.
39 Time (24hr) 06:00 10:00 14:00 18:00 22:00 Temperature (C) 22 23 24 25 26 27 28 Lower Temperature Upper Temperature Time (hr) 06:00 10:00 14:00 18:00 22:00 Water conductivity (mS/m) 4200 4400 4600 4800 5000 Lower Conductivity Upper Conductivity Conductivity noise from clay or porewater stirred by walking near CTD? Figure 17 Upper and lower water column conductivity and temp erature beneath the floating EM-31(water sampling method in Appendix 1). Noise centered around 07:00hr may be due to the author walking near the CTD on the sea floor, which may have stirred up conductive clays or released more saline porewater.
40 A porewater sample was obtained from 1 meter beneath the sediment seawater interface beneath the canoe (Appendix 1) a nd was slightly lower in conductivity ( w = 4270) than the overlying surface water (Figure 17 ). Combining this porewater value with the resistivity sounding result yields a formation factor F = w / t = 3.65. A formation factor of 3.65 at a depth of 1 m below the sedi ment seawater interface is consistent with data from resistivity probes of core samp les in Hawthorne Group cl ays in other shallow marine sites in Tampa Bay (Manheim, pers. comm.), thus increasing confidence in the floating Schlumberger array resistiv ity-derived terrain conductivity. To determine whether the EM readings are in agreement with the resistivityderived subseafloor conductiv ity of 1170 mS/m, a set of tw o-layer forward EM models were run using this lower layer value. Th e upper layer thickness wa s set to the water column measurement at the corresponding time (blue dots in Figure 15). For each model, the upper layer (water column) was set to a uniform conductivity equal to the average of the upper and lower water conduc tivities measured at that time (Figure 17). The orange triangles in Figure 18 show the forward mode l results simulating eight different times during the experiment. Lower layers of 10 and 2000 mS/m were run for comparison purposes (Figure 18). Clearly, predicted apparent conductivities calculated wi th lower model layers of 10 and 2000 mS/m do not match measured values as well as the 1170 mS/m lower model layer (Figure 18). Readings between 9:15 and 13:00 hrs sh ow the poorest fit in the 1170 mS/m model, which corresponds with a time window that begins and ends with shifts in
41 raw apparent conductivity that seem unrelated to water co lumn measurements (Figure 15, 16 and 18) Time (hr) 07:00 09:00 11:00 13:00 15:00 17:00 19:00 21:00 Apparent conductivity (mS/m) -100 -75 -50 -25 0 25 50 75 100 125 150 Raw apparent conductivity raw Predicted a with layer 2=1170 mS/m Predicted a with layer 2=100 mS/m Predicted a with layer 2=2000 mS/m Figure 18 Comparison of predicted to meas ured apparent conductivity for 8 two layer PCLOOP models with upper layers fixed to surface water data and lower layers set at 100, 1170 and 2000 mS/m (Geonics, 1994). The strengths and limitations of this us e of the EM-31 are highlighted in the experimental and model results in Figure 18. The primary limitation is equivalent solutions, which are most severe for water depths of 0.65-0.75 meters (for surface water of ~4550 mS/m), as seen in the model suite in Figure 13. At this depth range, all lower layer conductiv ities of 1000 mS/m yield equivalent pred icted apparent conductivity readings. In practice, simila r equivalent results occur duri ng the 9:15-11:00 hr range in Figure 18 when the 1170 mS/m and 100 mS/m model predictions and observed raw
42 apparent conductivities converge. It is clearly difficult at th ese water depths to resolve the lower layer conductivity based on models of raw apparent conductivity readings. The growing and abruptly terminati ng discrepancies between the 1170 mS/m model and the readings between 8:00 and 10:00 hr further illustrate the uncertainties in this method and the need for good calibration against other data. The cause of this discrepancy is unresolved as it co incides only with a slowing in the rate of water rise into the bay and not with detectab le changes in the water column or any changes to the instrument set-up. At the shallowest and deepest water dept hs encountered (< 50 cm and > 1.0 m) there is remarkably good agreement between the resistivity results and the EM-readings. Within these depth ranges, EMIX inversi ons of the EM readin gs for lower layer conductivity would yield values close to the observed resistivity value (Figure 18). Further tests of the EM-31 in shallow coastal waters are described below in the context of comparing observed and EM-predi cted porewater conductivities. Correlation of Terrain Conductivity and Porewater Conductivity Formation factors in Table 1 were calculat ed from resistivity surveys coincident with porewater sampling (Figures 19 and 20) Formation factors are lower in the Hawthorn Group (2.5-2.9) than in the mangrove soils (3.65) which is expected as there are conductive clays present in the Hawthorn Group (see lithologic descriptions in the Introduction). These values are similar to results obtained for sediment resembling the Hawthorn Group 50 km to the north in Tampa Bay (Manheim pers. comm.) Results of porewater conductivity predictions using th is formulation are discussed below.
43 Figure 19 RES2DNV inversion profile of We nner array resistivity data with TC1 multiport well and porewater condu ctivities (location in Figu re 5 and Appendix 9 and 10) Table 1 Formation factors determined by porewater and resistivity data. Depth (m) w (mS/m) a (mS/m)Model RMS Error Formation Factor (w/a) Lithology Resistivity Array 1.0 4270 1170 7.80 3.65 Mangrove soil (sand/mud) Schlumberger 3.4 1128 441 1.94 2.56 Hawthorn Form ation (Sandy clay) Wenner 5.7 1550 633 1.94 2.45 Hawthorn Form ation (Sandy clay) Wenner 14.3 590 203 1.94 2.90 Hawthorn Form ation (Sandy clay) Wenner Resistivity-derived formation factors were applied to 12 unique EM models from 8 sites with directly measured porewater samp les (yellow dots in Figure 20 and Table 2). A reasonable degree of correlation exists betw een the measured and predicted porewater conductivity for 12 samples (Figure 21 and Table 2). A similar correlation is plotted for aerial electromagnetic data ove r relatively clay free sedime nt in a study area ~330 km
44 south in the Everglades National Park, Flor ida (Fitterman and Deszcz-Pan, 2001). This study used three types of EMIX models to predict porewater conductivity. (1) Seven two-layer models used EM-31 data over si ngle port wells. The upper model layer, designed to represent the unsaturated zone, wa s fixed to the shallow conductivity derived from a nearby resistivity line (200mS/m). This upper layer was set to the thickness of the unsaturated zone based on water level in the well. Inversions were run with an initial lower model layer conductivity set to values based on nearby resistivity data (blue circles in Figure 21). (2) Two two-layer models for EM-31 data over water incorporated upper layers with direct measurements of surf ace water depth and conductivity. Initial model lower layer conductivity was set to values based on nearby resistivity data (red points in Figure 21). (3) Three models had three-laye rs that used EM-34 VMD and HMD data at three coil spacings with bottom of the model la yers set to the mid-point of the screened intervals of the TC1 well and starting values based on resistivity da ta (green points in Figure 21). Water levels and associated unsat urated zone effects were not accounted for in these three models. EM-31 data at seven locations over land (blu e circles) plot closer to the one-toone line (black line) than the three EM-34 da ta points (green points), which may be due to the following factors. (1) The unsaturat ed zone accounted for by resistivity and well data in the EM-31 models has a significan t effect on terrain conductivity not accounted for with the EM-34 models. Expanding the models for the EM-34 soundings to include an unsaturated zone layer may improve thei r predictive capabilities. (2) The EM-31 samples a smaller and thus probably more conductively homogenous volume relative to the EM-34. In addition, the general case wh ere porewater predictions that are too low
45 (below the one-to-one line) may be caused by poor estimates of the unsaturated zone (determined at a nearby resistivity survey), wh ere-as predictions that are too high may be caused by an increase in clay content at the EM site. The misfit between observed and predicted porewater conductivities over larg er depth ranges derived from three-layer models of EM-34 data suggests that this method at best dist inguishes the ge neral range of salinity trends (freshwater, brackish, saline, or hypersaline). EM31 readings targeting shallow porewaters, however, may be useful at distinguishing sa linity trends within smaller areas. Very small error is expected in the m easured porewater conductivity measured by a calibrated YSI-30 probe rela tive to the predicted porewat er conductivity (instrument specifications in Appendix 1). Predicted por ewater conductivity error bars were not feasible in this study because they comprise an unknown combination of formation factor error caused by variations in clay conten t, saturation, EMIX, RE S2DNV and 1IXD model error as well as other errors associated w ith EM and DC data acquisition, such as coil misalignment and instrument calibration. While the uncertainties in estimating por ewater conductivity from calibrated EM data may be considerable, this method appears adequate to establish trends of porewater conductivity on the TCSA. The following five factors probably influenced the relative success of using surface geophysical methods to sense porewaters at depth on the TCSA: 1) Relatively flat and consistent lithol ogy in the upper 30 m of exploration depth, 2) predominately saturated formations overlai n by a thin unsaturated zone, 3) large conductivity contrasts between ta rgets (freshwater, saline a nd hypersaline water saturated formations), 4) predominately high salin ity porewaters dominated the apparent
46 conductivity signal and limited the effects intr oduced by conductive clays, and 5) a lack of power transmission lines and conductive an thropogenic materials that interfere with EM soundings.
47 Figure 20 Porewater and geophysical data used to calculate formation factors. Multiple depths were available from the TC1 well w ith Wenner array RES2DNV inversion profile, EM-34 and EM-31 data. Single depth por ewater data was available from the Schlumberger array location (Figure 16). The remaining stations have single depth wells and EM-31 data.
48 0 1000 2000 3000 4000 5000 6000 7000 8000 010002000300040005000600070008000 Measured w (mS/m)Predicted w (mS/m) A ll Data: R2 = 0.95 Figure 21 Predicted vs. measured porewate r conductivity based on EM models and local resistivity derived formation factors. EM -31 over land (blue circles), EM-34 (green points) and EM-31 over water (red points). A one-to-one correlation would fall on the black line. Error and formation factors are discussed in this chapter. Table 2 Predicted and measured porewater conductivity based on EM models, direct samples and local resistivity formation factors from Table 1. Porewater Depth (m) Surface Water (m) Measured w (mS/m) Predicted w (mS/m) FF ( w/ a)Device Coil spacing (m) Coil height (m) EM mode EMIX Model RMS % Error 5.50 0.0 1550 929 2.56 EM-34 10,20,400 VMD/HMD 4.0 2.75 0.0 1128 1699 2.56 EM-34 10,20,400 VMD/HMD 4.0 14.00 0.0 590 1707 2.90 EM-34 10,20,400 VMD/HMD 4.0 1.00 0.6 4270 4449 3.65 EM-313.67 0.1 VMD 1.0E-01 0.61 0.1 7155 6617 3.65 EM-313.67 0.8 VMD 19 2.26 0.0 189 120 2.56 EM-313.67 0.9 VMD 0.7 2.26 0.0 190 118 2.56 EM-313.67 0.9 VMD 0.1 2.39 0.0 263 215 2.56 EM-313.67 0.9 VMD 0.1 2.39 0.0 380 333 2.56 EM-313.67 0.9 VMD 3.0E-02 2.65 0.0 1738 1352 2.56 EM-313.67 0.9 VMD 0.0 2.42 0.0 3209 2647 2.56 EM-313.67 0.9 VMD 3.0E-03 2.53 0.0 189 197 2.56 EM-313.67 0.9 VMD 0.2
49 Imaging Submarine Groundwater Discharge Discerning freshwater, seawater, and hype rsaline porewaters was successful using the method discussed above. Locating zones of submarine groundwater discharge (SGD), however, typically requires th e identification of more sub tle conductivity anomalies that occur when an upward flux of fresher groundw ater mixes with more saline surface water at a few meters below the sediment seaw ater interface. The magnitude of EM raw anomalies expected in associati on with SGD are examined next. While the water table data on the TCSA suggests SGD may occur (Figures 9 and 10), as of the date of this publication, it has only been pred icted in groundwater models and has not been directly measured here or elsewhere in Tampa Bay using seepage meters, piezometers or geochemical tracers (Swarzenski pers. comm.). Thus no sites were available within the TCSA for directly examining potential c onductivity effects of SGD. Further investigations beyond the scope of this study are needed to determine if the TCSA or other sites within Ta mpa Bay have significant SGD. For the purposes of estimating EM instrument response to SGD conductivity anomalies in a setting such as Tampa Bay, we can use the results from a low-conductivity anomaly recently identified from a Tampa Bay marine resistivity survey located 29 km north of the TCSA, in 3.7m of water and 1.1 km from shore. Porewaters squeezed from a vibracore at the site of the resistivity anom aly revealed salinity that was 6.1 ppt fresher than the surface water at 5.0 m below the sedime nt seawater interface (unpublished data).
50 This fresher porewater may indicate that an upward flux of fresher groundwater has mixed with saline surface water at 5m below the sediment seawater interface. Using the pressure and temperature from the vibraco re site (see method in Appendix 1) and a formation factor of 3.7 that was measured w ithin the TCSA and si milarly within Tampa Bay (this study and Manheim pers. comm.), a porewater salinity low anomaly of 6.1 ppt would theoretically lower the bulk seabed conductivity by 270 mS/m. Floating EM methods used in this study are not suited to the water depth of the sample described above. For testing purposes the existence of a similar anomaly in seafloor sediments beneath shallower water de pths is assumed in a suite of PCLOOP 2layer forward models with a water column conductivity common on in the TCSA of 4600 mS/m and surface water depths of 0.3, 0.7 and 1m. Lower model layers were set at 1200 mS/m (based on a resistivity measurement at the TCSA) and then lowered to 930 mS/m to simulate the hypothetical SGD anomaly. The EM-31 models predict an apparent conductivity change of between 7.0 and 22 mS/m in VMD mode with HMD response falling outside the instruments range (Figur e 22). A change of 7 mS/m in EM-31 readings is detectable when the instrument is held stationary, but would be within noise levels if the instrument were towed ra pidly or run in any but calm conditions. Identical EM-34 models were run in VMD and HMD mode at 10, 20 and 40 meter coil spacings (Figures 23 and 24) with an apparent conductivity change of between -34 and -79 mS/m, which is detectable using the float system in this study. The EM-34 HMD apparent conductivity response to this anomaly was on the order of 1-20 mS/m (Figure 24), which is most likely with in noise levels and not detectable.
51 At 0.3 to 1.0 m water depth, the SGD an omaly discussed above thus appears detectable with the EM-34 in VMD mode and at or below the detection limit of the EM31 and EM-34 HMD. Thus although EM met hods offer access to te rrain inaccessible to marine resistivity methods, they lack the resolution needed for identifying zones of diffuse SGD. Clearly these techniques w ill have greater success in identifying SGD anomalies that have a higher por ewater conductivity contrast. The shallow exploration depths and resolu tion of the EM methods used in this study preclude estimations of the 3-D volume of SGD anoma lies, although estimates of aerial extent and concentration are feasible. Calculating SGD flux would be feasible with dual density numerical groundwat er flow models based on hydraulic head distribution and information on the aerial extent and concentration of SGD zones (Voss, 1984; SUTRA, Souza, 1987; SEAWAT Guo and Langevin, 2003). Other processes in tropical and sub-tropi cal climates, such as Tampa Bay, can further complicate locating SGD anomalies, regardless of the geophysical or direct sampling techniques used. For example, the subtle anomaly discussed above was observed in open water 1.1 km from shore, but if it were closer to shore, it may have been reduced or completely masked due to the mangrove soil salinization process discussed next.
52 -25 -5 15 35 55 75 95 115 135 155 175 195 215 0.20.30.40.184.108.40.206.91.01.11.2Water column (m)EM-31 VMD Raw apparent conductivity (mS/m) Lower layer 2 (mS/m)Upper layer 1 = 4600 mS/m Figure 22 PCLOOP 2-layer forward models of floating EM-31 VMD response at three saline water depths over a SGD anomaly. A background seabed conductivity (blue circles) and a lower SGD influenced seab ed conductivity (black squares) are shown.
53 -450 -400 -350 -300 -250 -200 -150 -100 -50 0 0.20.30.40.220.127.116.11.91.01.1 Water column (m)EM-34 10 meter VMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4600 mS/m -400 -350 -300 -250 -200 -150 -100 -50 0 0.20.30.40.18.104.22.168.91.01.1Water column (m)EM-34 20 meter VMD Apparent conductivity (mS/m ) Lower layer 2 (mS/m) Upper layer1 = 4600 mS/m -300 -250 -200 -150 -100 -50 0 0.20.30.40.22.214.171.124.91.01.1 Water column (m)EM-34 40 meter VMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4600 mS/m Figure 23 PCLOOP 2-layer forward models of floating EM-34 VMD response at three saline water depths over a SGD anomaly. A background seabed conductivity (blue circles) and a lower SGD influenced seab ed conductivity (black squares) are shown.
54 340 350 360 370 380 390 0.20.30.40.126.96.36.199.91.01.1 Water column (m)EM-34 10 meter HMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4600 mS/m 330 340 350 360 370 380 390 400 0.20.30.40.188.8.131.52.91.01.1 Water column (m)EM-34 20 meter HMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4600 mS/m 320 330 340 350 360 370 380 0.20.30.40.184.108.40.206.91.01.1 Water column (m)EM-34 40 meter HMD Apparent conductivity (mS/m) Lower layer 2 (mS/m) Upper layer1 = 4600 mS/m Figure 24 PCLOOP 2-layer forward models of floating EM-34 HMD response at three saline water depths over a SGD anomaly. A background seabed conductivity (blue circles) and a lower SGD influenced seab ed conductivity (black squares) are shown.
55 Effect of Mangroves on EM Measurements At the TCSA, the very highe st terrain conductivities ar e found at shallow depths near mangroves (Figure 25, 26D, Appendix 7). Previous studies indicate that mangrove roots can uptake saline water a nd exclude 90-99% of all salt; therefore, leaving behind a concentrated solution in the soil (Scholander, 1968; Passi oura et al., 1992; Tomlinson, 1994). This process raises so il porewater salin ities until a quasi-stea dy state is reached, in which the flow of salt in the soil by convect ion in the seawater traveling to the roots is equaled by diffusion of the concentrated solu tion of salt water back to the soil surface (Passioura et al., 1992). A model of this process took 20 days to double the salinity of the porewaters within the uppe r 40 cm of intertidal mud (Passioura et al., 1992), which would correspond to a porewa ter conductivity of 8000-12,000 mS/m in the TCSA (Figure 27). As porewater salinity concentrati on by mangroves may dominate nearshore salinity patterns, and have not been widely described in hydrogeologic contexts, we sought to investigate their ex tent and associated electromagnetic anomalies. Mangrove root depth and extent are c ontrolled by mangrove species, tran spiration levels, porewater salinity, soil flushing and bi oturbation (Passioura et al ., 1992). Shallow hand auger samples in the TCSA show that the Rizophora mangle (red mangrove species) trees that typically line the shores of Tampa Bay and the saline and brackish water ponds in the TCSA extend a dense shallow network of feed ing and drinking roots up to approximately
56 5 m from shore and to a depth of 10-15 cm be low the sediment seawater interface. This root distribution is typi cal of this species of mangrove (Passioura et al., 1992; Tomlinson, 1994). On a transect running seaward from a red mangrove forest into Tampa Bay, extremely high shallow porewa ter conductivities of two to three times surface water levels were found near (~2m) and at the same depth (0.3-0.7m) as this network of roots and then fell off to background surface water conductivities within 5-10 m (Figure 5 Area 2, Figure 27). 4250 5250 6250 7250 8250 9250 10250 11250 12250 01234567891011 Distance (m)Porewater conductivity (mS/m) Figure 25 Porewater conductivity transect at 0.3 0.7 m sediment depth leading away from a red mangrove forest going towards Ta mpa Bay. Note a high contrast with the surface water conductivity ~ 4220 mS/m near ma ngroves. Distances are in meters from the nearest red mangrove tree trunk (location in Figure 5, Area 2). The dramatic mangrove conductivity effect s are easily detectable with EM-31 surveys. Anomalous EM readings are expe cted to extend a few meters beyond the zone of elevated porewater conduc tivities, as mangrove root so il salinization may have a similar EM lateral detection limit as shallow buried metallic targets such as buried steel drums, unexploded ordnance and steel pipe. Studies of 0.5-1.0m depth metallic targets
57 reported the first detection of anomalies at a distance of 1-5 m using the EM-31 in VMD mode at 0.9m height (McNeill, 1980a; Westphalen and Rice, 1992; Vogelsand, 1995; Bailey and Sauck, 2000; Barrow et al., 2000). Two EM transects perpendicular to mangrove zones and a more detailed 3-D grid are discussed below. The first EM mangrove transect was a combined marine and land profile shown in Figure 26 that travels from a grass covered upland area through a 10m wide line of red mangroves( Rhizophora mangle ) and then out across approximately 1 m deep saline water. Both VMD and HMD readings were ta ken at a constant instrument height of 0.1m. The EM-31 was oriented parallel to th e shoreline in order to maximize resolution of potential anomalies smaller than the coil spacing of the instrument (Geonics, 1995). A suite of two-layer EMIX mode ls that incorporated water column measurements in the upper layer and starting values based on resi stivity at Bishop Har bor (Figure 16) was used for locations over water. For locations over land, a suite of one -layer EMIX models was run with starting values based on a resi stivity measurement at a similar upland site (Figure 19). The lower mode l layer terrain conductivity anomaly associated with the mangroves (Figure 26) extends approximately 1 m to either side of the expected mangrove root zone (see shallow auger sample s discussed above), which agrees with the porewater profile extending into Tampa Bay shown in Figure 25 and with the lower end (~1m) of lateral detection limits for case st udies of shallow high conductivity targets.
58 0 200 400 600 800 1000 1200 -60-50-40-30-20-1001020304050607080 MetersLower model layer conductivity (mS/m)Open Marine Water (Bishop Harbor) Upland Vegetation Mangroves Figure 26 Lower model layer conductivity be neath open marine water, mangrove trees (centered at 0m) and upland vegetation. Lo wer model layer terrain conductivity was calculated from EMIX two-layer models at a constant instrument height over water and one-layer EMIX models over land (location in Figure 5, Area 3). Porewater sample of 4270 mS/m from 1m depth is located at +8m. Surface water = 4140 mS/m. One porewater sample was available near this profile at Bishop Harbor (Figure 26) from a sediment depth of 1m and located at +8m from the nearest mangrove trunk. Porewater conductivity from this sample (4270 mS /m) is very close to that of the surface water conductivity (4140 mS/m), suggesting that porewater fl ushing is sufficient at 8m from the nearest mangrove trunk to dilute to a background level close to that of surface water. This porewater sample is in accor dance with the EM profile (Figure 26), which shows terrain conductiv ities close to open marine wa ter values 8m from mangrove trunks. Thus the Bishop Harbor land-mar ine profile (Figure 26) and the porewater conductivity transect extending into Tampa Bay (Figure 25) show a similar scale (~5-10 m) for the extent of the zone of hype rsaline porewaters surrounding mangroves. A second EM transect perpendicular to mangrove zones indicates considerably broader zones of hypersaline waters. This seco nd EM transect consists of floating EM-31 VMD data across Moses Hole pond (Figure 27) Raw apparent conductivities were
59 interpreted with two-layer EMIX models w ith water column data comprising the upper layer and the lower layer initially set based on resistivity data. The best fitting lower model layer (seabed) conductivity is plotted as a function of distance (easting) across Moses Hole pond in Figure 27. These data s how decreasing seabed conductivities with distance from the mangrove shorelines of over 50 m from the western shore and over 125 meters from the eastern shore. The ex tended distances of the anomalously high conductivities present in Figure 27 (50-125m) are an order of magnitude larger than those seen on the transects extending into Tampa Ba y and Bishop Harbor (Figures 25 and 26). One possible explanation for the apparently different scales of mangrove effects is that flushing of Moses Hole Pond is much mo re restricted than the movement of Tampa Bay surface waters (Smith and Swarzenski pe rs. comm.). The Moses Hole Pond is only indirectly connected to Tampa Bay by mos quito ditches and a tid al creek (Smith and Swarzenski pers. comm.). More rapid flus hing in Tampa Bay and Bishop Harbor may reduce the extent of the zone of mangrove salinization and associated EM anomalies relative to that preserved in Moses Hole.
60 Easting (m) 0255075100125150175200225250275300325350 Raw apparent conductivity (mS/m) 0 100 200 300 400 500 Model lower layer conductivity (mS/m) 0 1000 2000 3000 4000 5000 6000 Water column (m) 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 Raw data Model lower layer Water column Figure 27 Profile of floating EM-31 VMD lo wer model layers across Moses Hole pond (Figure 5, Area 2). Data processed using twolayer EMIX models. Aerial view of raw data visible as East-West running transect in Appendix 7. Surface water ~ 4500 mS/m. An EM-31 grid within a mangrove forest on the TCSA furthe r indicates that terrain conductivities within and around mangrove vege tation zones are not uniform. Figure 28 shows a grid of EM-31 VMD and HMD soundings over very shallow water within the banks of a mosquito control di tch leading into Moses Hole pond (Figure 5, Area 2). At this site, direct sampling by a drive point piezometer in the ditch produced hyper-saline 6125 and 7155 mS/m po rewater conductivities at 31 and 61 cm respectively (circle with cross on Figure 28D). Su rface water conductivities were 4500 mS/m 80 and water depths ranged between 0.1 and 0.23 m. The elevation of the ditch banks was consistent at ~1m above the water level and straight, which allowed for the rectangular sampling grid shown in Figure 28. All EM-31 readings over the ditch were taken at a constant height above the water of (0.8m) by carrying the instrument with the antennae
61 oriented parallel to the ditc h in order to minimize the e ffect of conductive anomalies smaller than the coil spacing (McNeill, 1980a). The HMD mode raw apparent conductivities and water depth clearly correlate (Figure 28, A and C) which is expected from this modes sensitivity to near surface ma terials (McNeill, 1980a). To interpret the raw apparent conductivities (Figure 28 A a nd B), at each point a two-layer model was created in EMIX using surface water information in a fixed upper layer and a nearby floating Schlumberger resistivity model as a starting point for the unknown lower layer. These models converged with a mean RMS error of <1%. The lower model layer conductivity (Figure 28D) not onl y shows differences laterall y across the ditch, but also significant variability along the length of the ditch. In part icular, extremely high terrain conductivities are derived for a portion of the eastern shore just south of the porewater sampling site. This high conductivity anom aly in Figure 28D is associated with mangroves that appear sickly and smaller th an surrounding trees, which may be due to stress from hypersaline porew aters (Smith pers. comm). Using the resistivity derived formation f actor of 3.65 from a nearby site on the TCSA, the predicted porewater value shown w ith the circle with cross symbol on Figure 28D is remarkably consistent with the measur ed value. The predic ted porewater value is of 6620 mS/m, which differs from the measur ed value of 7155 mS/m by only ~8% (Table 2). Observations at the TCSA suggest that the EM-31 is a useful tool for measuring variability in porewater salinity within as well as adjacent to mangroves. Porewater salinity extremes may be asso ciated with poor mangrove he alth; however the causes and consequences of this relationship are beyond the scope of this paper.
62 Figure 28 EM-31 survey in mosquito control ditch lined with red mangrove trees. Raw data (A,B), water depth (C) and lower EM IX model layer and porewater sample (D).
63 CONCLUSIONS At the TCSA study site, discerning between freshwater, seawater, and hypersaline saturated formations by EM observations with resistivity-derived formation factors was reasonably successful. Porewater conductivi ties estimated from 12 unique EM models from 8 sites were compared against directly measured porewater samples. A reasonable degree of correlation exists between the meas ured and predicted porewater conductivity for these 12 samples. Forward models predict that the fl oating EM-34 VMD and HMD modes have potential for measuring useful information on seabed conductivity. Field experiments, however, were unsuccessful at reproducing th ese theoretical results. Given the space needed for EM-34 measurements, instrument de velopment efforts targeting these settings may be better focused on short marine resistivity streamers. The small size of the floating EM-31, rela tive to resistivity streamers or the space needed between EM-34 coils, proved useful for profiling in otherwise inaccessible terrain. Results from floating EM-31 VM D experiments suggest that conductivity readings interpreted with two-layer models that incorporate calibration information from pore and surface water measurements and DC soundings can be used in areas of extremely high conductivity porewaters ne ar mangroves to predict porewater conductivity, which may be useful for near shore SGD studies and multi-disciplinary studies in wetlands. It is im portant to note that in such very high conductiv ity terrains as the TCSA, without resistivity surveys for calibration, inversi ons of EM data alone were
64 inherently ambiguous. There is still considerably utility in the EM methods, however, as they are faster than the DC methods and can be used in shallower water and less accessible terrain, such a mangrove shorelines. No sites were available at the TCSA for directly examining potential conductivity effects of SGD. A prospective diffuse S GD anomaly located by resistivity methods in deeper water in Tampa Bay was used to assess the capabilities of the EM methods used in this study. EM response models predict the floating EM-31 lacks the necessary resolution to identify diffuse SGD. However, the overall success of predicting porewater salinity distribution within the TCSA suggests that the floating EM-31 method can delineate more concentrated zones of SGD with higher porewater conductivity contrast. The process of mangrove soil saliniza tion was found to significantly effect apparent conductivity readings within 5m of the mangrove trunk and falling sharply off within 10m at the edge of Tampa Bay. Restri ctions in surface water flow and associated slower porewater flushing in some ponds a nd ditches were associated with higher conductivities in general and an extension of this effect to 50125m from the nearest mangrove trunk.
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73 APPENDICES The following appendices provide background on the methods used and the locations of measurements taken for this thesis. Appendi ces 11-13 show examples of data provided to the USGS Tampa Bay Integrated Science Project.
74 Appendix 1 Method for Measuring Water Conductivity and Depth Instruments: Yellow Springs Instruments YSI-3 0 conductivity and temperature meter (accuracy 21mS/m). Van Essen model DI-219 conductivity, temperature and depth data logger (accuracy 50 mS/m and 3 cm depth). The less accurate DI-219 was periodically corrected with the YS I-30 during time series logging. Calibration: Instrument calibrated to using KC l solution; 12.85 mS/cm 0.35% at 25C. This standard is traceable to Sta ndard Reference Material 3193 produced by the National Institute of Standards and Technology in Gaithersburg, MD USA. Depth Measurements: Water depth was measured with a barometrically compensated Van Essen DE-219 for time series data or with a weighted measuring tape for individual data points. Porewater Sampling: Porewater samples were collected in the field using a peristaltic pump and stored in Nalgene HDPE bottles. Samples were collected from the USGS TC1 multi-port well (Figure 2, Appendix 3), a perc ussion hammer drive point piezometer, 0.5 cm hand pushed stainless steel piezometer, single port wells and directly from core samples using a Manheim porewater hydraulic press. Filtering: Particulate clay in porewater samples on the TCSA, especially when total dissolved solids are low, may introduce a conductivity error. The high cation exchange capacity of the clays found on the TCSA may increase pore water conductivity measured by the YSI-30 probe and similar devices (Hyde and Huckle, 1983; Ca ldwell et al., 1986; McNeill, 1990). Marine salinity (4000-5000 mS/m) water on the TCSA probably had a negligible clay conductivity effect because the charge of clay particles is reduced in high TDS waters (McNeill, 1990). The clay conten t of porewater samples varied based on the sampling method used and was independent of TDS measured after f iltration; therefore, all porewater samples were centrifuged and decanted or passed through a 4 m filter in order to remove any variances in troduced by clay particulates. Salinity Calculation and Units: Conversion between salinity and conductivity was computed using the International Equation of State (IES 80) method (Lewis and Perkin, 1978; Lewis, 1980; Fofonoff, 1985). The a pparent conductivity that EM and DC methods measure is in large part a f unction of the porewater and surface water conductivity, which is a functi on of temperature; therefor e, calculations involving pore and surface waters and EM and DC readi ngs did not use specific conductance (Cs), which is referenced to a common temperature, but instead used absolute conductivity (C) which is a function of the temperature at the time of sampling (McNeill, 1990).
75 Appendix 2: Geonics, Ltd. EM -34 Instrument Response Curve -1000 -800 -600 -400 -200 0 200 400 600 800 1000 0100020003000400050006000 Terrain conductivity (mS/m)Apparent conductivity (mS/m) LINEAR RESPONSE EM-34 VMD EM-34 HMD Lower Analog Limit Upper Analog Limit PCLOOP forward model of EM-34 response ove r a homogenous half-space with infinite depth (Geonics, 1994). VMD and HMD respons e slope is negative beyond a terrain conductivity of 600 mS/m and 2000 mS/m re spectively. VMD and HMD 10,20 and 40 m coil spacing response is identical and the di fference between HMD coil spacing data is too small to plot on this graph. Note the limits for readings with the analog EM-34.
76 Appendix 3: EM-34 HMD 10 Meter Coil Spacing Raw Data
77 Appendix 4: EM-34 HMD 20 Meter Coil Spacing Raw Data
78 Appendix 5: EM-34 HMD 40 Meter Coil Spacing Raw Data
79 Appendix 6: EM-34 VMD Sounding Locations
80 Appendix 7: Raw EM-31 Soundings on Land and Over Water
81 Appendix 8: Two-Layer Resistivit y Modeling Programs and RMS Error. Apparent Conductivity Calculat ion for Schlumberger Array: l V I Lr r c2 ; 12 with x = 0 and L >10 l Apparent Conductivity Calculation for Wenner Array: ) / ( 2 ; 1 I V ar r c Apparent resistivity (r), apparent conductivity (c), voltage (V), ampere (I), potential electrode spacing ( l ), current electrode spacing ( L ), offset distance between the current electrode spread ( x ), array spacing ( a ) (Koefoed, 1979; Sharma, 1997) Modeling Programs: The IX1D inverse and forward modeling program by Interpex, Ltd. was used to create the two-layer floa ting Schlumberger array models and fit the measured apparent conductivity to within 8.5% of the same models run in VES and DCEL as an error check (Cooper, 2000; Interpex, 2002; Weller, 2003). Land based Wenner traverse resistivity surveys were inve rted for apparent c onductivity using the two dimensional RES2DINV resistivity inversi on program (Loke, 2002) The inversion process used in these programs assigns each sub-surface grid node an initial terrain conductivity and then calculat es the apparent conductiv ity that would result and iteratively adjusts the model layers until the RMS error is minimized. Definition of RMS (Root Mean Square) Model Error: RMS error is used as an indication of the fit between the theoretical data generated from the model and the measured data. RMS error is calculated by summing the squares of the difference in the log of the data values (apparent conductivity) and then dividing by the number of data points and taking the square root of the re sult. The antilog of this result minus one multiplied by 100 gives the percent RMS error. This method of calculating model error ensures that high data values do not dominate the calculated error and leave large errors in the low data values (Interpex, 2002). References: Cooper, G.R., 2000, VES Schlumberger fo rward modeling and inversion program. Professional Geophysical Software Johannesburg, South Africa. Interpex, 2002, IX1D resistivity inversion program. Interpex, Ltd., Golden, Colorado. Loke, M.H., 2002. RES2DINV. Geotom o Software, Penang, Malaysia. Weller, A., 2003, DCEL resistivity inversi on program, Institut fuer Geophysik der TU Clausthal, Germany.
82Appendix 9: Location of Wenner Arr ay Lines and USGS TC1 Multi-Port Well Note: Location of this area is also visible in Figure 5, Area 4.
83Appendix 10: RES2DNV 2-D Wenner Array Resistivity Inversions
84Appendix 11: Potential Salinity Halo Around Ponds 70 90 110 130 150 170 190 210 230 0102030405060708090 Distance (m) from saline pond edge to upland hamock areaApparent conductivity (mS/m) hmd_10 hmd_20 hmd_40 Road bed pond salinity = 47ppt Upland Hammock 170 180 190 200 210 220 230 240 05101520253035404550 Distance between two ponds (m)Apparent conductivity (mS/m) hmd_10 hmd_20 road bed pond salinity = 23 ppt pond salinity = 35 ppt Pond salinity, road bed location and dist ance with EM-34 HMD raw apparent conductivity data at different coil spacings.
85Appendix 12: Local Influence of Mosquito Control Ditches.
86Appendix 13: Local Influence of Mosqui to Control Ditches with Elevation. Elevation data from a University of Florid a airborne laser swath mapping survey (UF, 2003).