Spatial Patterns in Soil Moisture, Salinity and Topography

Transcription

1 ONLINE SUPPLEMENT TO: Relationship of Salt Marsh Vegetation Zonation to Spatial Patterns in Soil Moisture, Salinity and Topography Kevan B. Moffett 1*, David A. Robinson 2, and Steven M. Gorelick 1 1 Dept. of Environmental Earth System Science, Stanford University, Stanford, California 94305, USA 2 Centre for Ecology & Hydrology, Environment Centre Wales, Bangor, Gwynedd LL57 2UW, UK Contents: I. Complete vegetation maps II. III. IV. EMI signal depth calculation ECa uncertainty determination Soil sampling results and ECa relationship to edaphic conditions V. Archie s Law parameter estimations: f, φ, and σ s VI. VII. Cross-correlation of geographic and edaphic metrics Literature cited in supplement * Corresponding author. moffett@stanford.edu. Fax:

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3 Figure S1. Relative cover fraction of each major plant species at the site: primary cover, secondary cover, tertiary cover, present as minor cover. Clockwise from lower left: Distichlis spicata, Salicornia virginica (S. depressa), Spartina foliosa, Grindelia stricta, Frankenia salina, Jaumea carnosa Complete vegetation maps I

4 II. EMI signal depth calculation Many portable EMI devices, like the DUALEM-1S used in this study, include two pairs of electrodes in orthogonal horizontal and vertical configurations. Due to the difference in the directionality of the induced and received magnetic fields, these two configurations result in different signal penetration depths. In terrestrial environments, 70% of the apparent bulk soil electrical conductivity signal measured with the electrodes in the horizontal configuration (ECa H ) by the DUALEM-1S typically originates above s H = 0.76 m depth; 70% of the vertical signal (ECa V ) originates above s V = 1.59 m (Abdu and Robinson 2007). In the highly conductive salt marsh environment a skin effect due to soil currents travelling more rapidly and with less resistance through highly conductive near-surface sediments reduces the effective signal depths (Callegary and others 2007). We fit logarithmic curves to the data of Callegary and others (2007, after correcting their reversal of their vertical and horizontal data labels) and extrapolated their calculations of the skin effect to the range of bulk conductivities measured in the salt marsh (Eqns. S1 and S2). The asymptotic logarithmic shape of the data by Callegary and others (2007) gave us good confidence that the large extrapolation required was reasonable. The units of these regressions are: depth in m and conductivity in ms/m. ( ECa ) s ln + (R 2 = 0.96) (Eqn. S1) H = H ( ECa ) s ln + (R 2 = 0.98) (Eqn. S2) V = V Using these regressions and ECa H = 1364 ms/m and ECa V = 1453 ms/m, the medians of the temperature-corrected ECa data from this study (dry and wet conditions pooled), we calculate effective support depths of m (s H, shallow) and m (s V, deep). Only the shallow data, more relevant to the root zone (of comparable depth), was presented in the paper. 23 4

5 III. ECa uncertainty determination The stated accuracy of the Electromagnetic Induction (EMI) geophysical instrument used in this study (DUALEM-1S, Dualem, Inc., Milton, ON, Canada) was ds/m. Despite this very high accuracy, we took the conservative approach of determining the in situ measurement uncertainty based on two types of successive field measurements of ECa obtained at multiple test locations. Based on these two types of tests, described presently, we estimated the measurement uncertainty for this study as 0.01 ds/m. The first method we used to estimate ECa measurement uncertainty was to stand in one location and collect a streaming series of 1479 ECa measurements, one measurement every one to two seconds. The 95% confidence level for the mean of these measurements (horizontal electrode configuration) was 0.01 ds/m. The second method we used to roughly confirm this ECa measurement uncertainty was to identify, from the locations of all the measurement points within each survey, pairs of points nearly-overlapping measurement points created either by temporary pauses during the streaming survey or by the intersection of survey traverses in different directions. To identify such pairs, the distances between all points within a given survey were calculated and filtered for points within 15 cm. Among a total of 5859 data pairs (2024 from dry conditions, 3835 from wet conditions), the median ECa difference within the pairs was ds/m. The result changed little for a small increase in the distance filter: a distance filter of 20 cm resulted in a total of pairs (11910 from dry conditions, from wet conditions), and a median difference of ds/m. Although this value was higher than the uncertainty determined using the first method, we also award it lower overall confidence because the points in question were not identical, but rather separated somewhat in space, time, and potentially in instrument orientation. 5

6 47 48 Given this additional uncertainty, we felt this second method sufficiently confirmed the magnitude of the uncertainty estimated by the first method to proceed with our analysis. 49 6

7 IV. Soil sampling results and ECa relationship to edaphic conditions Salt marsh soil properties were determined from 23 cores from 0-30 cm depth (Table S1). Empirical relationships have shown ECa to increase with increasing clay content (Domsch and Giebel 2004; Rhoades and others 1999), increasing soil water content (Malicki and Walczak 1999; Rhoades and others 1999), or increasing solution conductivity (Malicki and Walczak 1999, Rhoades and others 1999; Sam and Ridd 1998), though not for as high values of these properties as occur in salt marshes. To establish the specific relationships between ECa and edaphic variables for the salt marsh environment, we conducted linear regression analyses (Figure S2). The relationship between temperature-corrected (Reedy and Scanlon 2003) ECa and soil paste extract conductivity (ECe) for our data was essentially an extrapolation of that reported by Rhoades and others (1999) for a glacial till. The pore water conductivity (ECw) values of our marsh were higher than those reported previously in the EMI literature (Friedman 2005; Rhoades and others 1999) but spanned a smaller range compared to our ECa range, resulting in a shallower ECw/ECa regression slope. Although soil paste extract conductivity (ECe) represents the total soluble soil salinity, related to the osmotic potential in the soil, the dissolved salt incident upon a plant root membrane as part of the transpiration stream is better represented by the pore water solute conductivity (ECw); we defined an interstitial electrical conductivity index (IECI) as the difference between these two parameters to represent the excess soil salinity above the background pore water level. Variance in ECa was more closely related to variance in IECI than to variance in ECw (Table S2), highlighting the importance of considering the effects of interstitial salinity in clayey salt marsh soils. The relationship between ECa and volumetric water content (θ) for our data was roughly a linear extrapolation of that reported by Malicki and others (1999) for a silty loam with high 7

8 solution conductivity (11.7 ds/m). The relationship of ECa to clay content for our data was unique: the clay contents of our soils were beyond the range of those used by Domsch and Giebel (2004) to compare ECa and clay fraction; our soils were likely more similar to the gleysols excluded from their regression analysis than to the soils they included. The fit of the linear relationship between ECa and clay fraction was strongly influenced by a few data points associated with the margin of the marsh closest to the bay, which unsurprisingly contained smaller clay fractions. In summary, variability in ECa values mostly reflected variability in soil water content (θ) and in the total salt content of the soil (ECe). These results establish the overall utility of ECa data as a proxy for these zonation-relevant edaphic variables and confirm the applicability of EMI methods to the salt marsh environment. 83 8

9 84 Table S1. Root Zone Soil Core and Pore Water Characteristics Characteristic Average Statistic Std. Dev. Wet core bulk density (g/cm 3 )* Core water fraction (g H 2 0 / g total)* Volumetric water content [θ] (cm 3 H 2 O/cm 3 total)* Sand fraction* 2.64% 6.95% Silt fraction* 35.54% 5.04% Clay fraction* 61.82% 9.44% Textural ClasS8 Paste extract electrical conductivity [ECe] (ds/m)* Pore water electrical conductivity [ECw] (ds/m)** Interstitial electrical conductivity index [IECI] (ds/m)** * N (0-30 cm depth soil cores) = 23, ** N -30 cm depth) = 17 Table S2. ECa - Soil Properties Correlation Matrix ECa 1 ECa ECe ECw IECI θ Clay Silt Sand ECe 0.674*** 1 ECw 0.526* 0.596* 1 IECI 0.675** 0.891*** θ 0.437* Clay 0.509* 0.513* 0.631* Silt * ** * *** 1 Sand * *** N = 23, N = 17for ECw samples * significant at p < 0.05, ** significant at p < 0.005, *** significant at p < Table S3. Parameters of Linear Relationships between ECa and Salt Marsh Soil Properties (Illustrated in Figure S2), with 95% Confidence Intervals Dependent Variable Slope Intercept Clay Lower 95% Value Upper 95% Lower 95% Value Upper 95% ECe (ds/m) IECI (ds/m) ECw (ds/m) θ (fraction) Clay (fraction) R 2 9

10 Figure S2. Relationships between apparent bulk soil electrical conductivity (ECa) and related soil properties for salt marsh sediment cores 0-30 cm deep and pore water samples from -30 cm

11 V. Archie s Law parameter estimations: f, φ, and σ s Archie s Law is an empirical model that relates temperature-corrected apparent bulk soil electrical conductivity data (ECa) to the pore water fluid conductivity (ECw), a formation factor (f = 1/F = φ m /a, with porosity φ), soil saturation (S), and the mineral surface conductivity (σ s ) (Kirsch 2006) ECa ϕ a m n n = ECw f S + σ s = ECw S + σ s (Eqn. S3) Surface conductivity is important in soils with large clay fractions, such as our salt marsh, but had not been tabulated for saline, clay, salt marsh soils. The parameters σ s and f (and φ) are difficult to measure but can be estimated in multiple ways. We compare three methods here and test the uncertainty in the salt marsh clay soil parameter estimates. Method 1 Rhoades and others (1989) proposed an empirical relationship between σ s (ds/m) and the volumetric clay content (VCC) of a soil. σ = s 2.31 VCC (Eqn. S4) We calculated σ s for each of 23 cores taken from cm depth based on their clay mass fraction and a clay bulk density of 1.05 g/cm 3. We used cores from this depth interval because they were below the water table and so were saturated, permitting the assignment of S = 1. Using these values of σ s, a = 1, m = 2, n = 2, and S = 1, we solved Eqn. S3 for φ for each core. 2 ( ECa ) ( ECw S ) ϕ = a σ s (Eqn. S5) The resulting averages were σ s = ± ds/m (µ ± 1σ) and φ = ± 0.036, which are reasonable for clay soils. The corresponding average formation factor value was f = ± Use of a different m value yielded no difference in the saturation changes estimated by 11

12 the differential EMI calculation because the values of σ s and f used in evaluating equation E1 were not affected by (insensitive to) the value of m. Method 2 Pore water samples were collected from below the water table (60 cm depth) at 16 locations concurrently with coring and ECa measurements. A simple linear regression between the ECw (ds/m) of these samples and the coincident temperature-corrected ECa (ds/m) yielded an equation in the form of Eqn. S3 (for S = 1). The slope of the regression was f = (φ = forϕ = f ) with intercept σ s = ds/m. These parameter estimates were uncertain because single point-samples of pore water were related to ECa measurements of an overlying volume in each case, inherently assuming sediment and solution homogeneity. The simplicity of the method enhanced its general utility, however. Method 3 Sen and others (1988) proposed a complex formulation of Archie s Law describing ECa (S/m) for clay-bearing sandstones in terms of ECw (S/m), the soil cation exchange capacity (CEC, meq/g), grain density (ρ g = 2.65 g/ml), a formation factor (f = 1/F = φ m, with porosity φ), and empirical parameters m, A = 1.93*m, C = 0.7, and E ~ 0. [Note that our study uses standard units of ds/m and cmolc/kg; we provide the conversion from the units used by Sen and others (1988) where applicable.] m A Q ECa = ECw ϕ 1+ + E Q (Eqn. S6a) ECw + C Q ( ) ϕ Q = CEC ρ 1 ϕ (Eqn. S6b) g The parameter Q represents volume-normalized CEC. For unsaturated conditions, one would 136 replace Q with Q/S and f with n f S (Kirsch 2006). A parameter for mineral surface 12

13 conductivity (σ s ), analogous to that in equation E1, can be algebraically extracted from Eqn. S6b (for EQ = 0). A Q σ s = ECw f (Eqn. S7) ECw + C Q Because φ, m, and CEC were unknown, there was one excess degree of freedom in Eqns. S6a and S6b. We addressed this uncertainty by parameter estimation and sensitivity analysis. Because measurements of CEC for the salt marsh soils were unavailable, we solved equations S7 and S6b for CEC using the values for f, φ, and σ s found in the above empirical regression and an observed average value of ECw = S/m (52.85 ds/m) for the marsh pore water. We tested the sensitivity of the result to multiple m values (Table S4). The CEC estimates were very close to the cmolc/kg CEC of Stege Marsh, a San Francisco Bay salt marsh similar to our study area and located 53 km to the north (Córdova-Kreylos and Scow 2007). Table S4. Sensitivity of CEC Estimates to m Values Parameter Test 1 Test 2 f same φ same σ s S/m (2.479 ds/m) same ECw S/m (52.85 ds/m) same m CEC meq/g (10.11 cmolc/kg) meq/g (10.68 cmolc/kg) With a CEC estimate, Eqn. S6 can be solved in terms of an φ polynomial and the resulting value of φ used to calculate f and σ s. 151 For a ρ = ρ : = A CEC and g c C CEC g m+ ϕ 1 m ( ECw c a) + ϕ ( c + a) + ϕ (( c ECa ECw) ECa) ( c ECa ECw) 0 = (Eqn. S8) We tested the sensitivity of this method to uncertainty in CEC and m (Table S5) using the ECa and ECw data for the same subset of cores as above. Halving CEC from 0.1 meq/g to

14 meq/g yielded a change in the average saturation difference ( S) calculated by the Q-DEMI methodology of 0.6%, that is, only 10% of the 6.2% average saturation change estimated using CEC = 0.1 meq/g. Changing m from 2 to 2.34 yielded parameter estimates that were unrealistic for clay sediments, so we elected to use m = 2 in the remaining calculations. Table S5. Sensitivity of φ, f, and to CEC and m Values Parameter Test 1 Test 2 Test 3 ECa and ECw from core data same same ρ g 2.65 g/ml (2605 kg/m 3 ) same same m CEC 0.10 meq/g (10 cmolc/kg) 0.05 meq/g (5 cmolc/kg) 0.05 meq/g (5 cmolc/kg) φ (µ ± 1σ) ± ± ± f (µ ± 1σ) ± ± ± σ s (µ ± 1σ) ± S/m (2.307 ± ds/m) ± S/m (1.211 ± ds/m) ± S/m (0.041 ± ds/m) 160 Summary The three methods, described above, for estimating the parameters in Archie s Law are compared in Table S6 (Method 3 using CEC = 0.1 meq/ml and m = 2). The results are comparable from all three methods. This study used the parameters from Method 2 because the simplicity of Method 2 lends it the greatest potential to be widely applied in future studies. Table S6. Comparison of Archie s Law Parameter Estimates Parameter Method 1 (using VCC) Method 2 (linear regression) Method 3 (using CEC) average st. dev. average st. dev φ f r 2 = 0.20 σ s (ds/m)

15 VI. Cross-correlation of geographic and edaphic metrics One aspect of the study was the assessment of the spatial correlation between each of the six metrics used to represent abiotic salt marsh ecosystem structures. Testing the correlation between the metrics for all 2256 marsh locations yielded the correlation structure of Table S7. Some of these correlation coefficients, when pertinent to the main scientific discussion, were included in the paper; all are provided here for reference. Notably, elevation was poorly correlated with any of the other metrics. ECa values were slightly more strongly correlated with the distance to a main tidal channel than to the nearest channel of any size. The magnitude and spatial distribution of ECa values from dry and wet conditions were similar (strongly and significantly correlated). The difference in the sign of the correlation between ECa and dry or wet ECa values was due to the dual nature of the edaphic change between these two conditions, involving both water and salt dynamics in the root zone (see Q-DEMI methodology description in the paper). Table S7. Geographic and Edaphic Metrics Correlation Matrix (N = 2256) Elevation Dist. to main channel Dist. to any channel ECa dry ECa wet ECa Elevation 1 Dist. to main channel Dist. to any channel * ** 0.428** 1 ECa dry ** 0.440** 1 ECa wet 0.061* 0.405** 0.357** 0.831*** 1 ECa ** 0.217** 0.134** 0.272** ** * significant at p < 0.005, ** significant at p < , *** significant at p <<

16 VII. Literature cited in supplement Abdu H, Robinson DA, Jones SB Comparing bulk soil electrical conductivity determination using the DUALEM-1S and EM38-DD electromagnetic induction instruments. Soil Science Society of America Journal 71: Callegary JB, Ferré TPA, Groom RW Vertical spatial sensitivity and exploration depth of low-induction-number electromagnetic-induction instruments. Vadose Zone Journal 6: Córdova-Kreylos AL, Scow KM Effects of ciprofloxacin on salt marsh sediment microbial communities. International Society for Microbial Ecology Journal 1: Domsch H, Giebel A Estimation of soil textural features from soil electrical conductivity recorded using the EM38. Precision Agriculture 5: Friedman SP Soil properties influencing apparent electrical conductivity: a review. Computers and Electronics in Agriculture 46: Kirsch R Petrophysical properties of permeable and low-permeable rocks. Kirsch R, editor. Groundwater Geophysics. New York: Springer. Malicki MA, Walczak RT Evaluating soil salinity status from bulk electrical conductivity and permittivity. European Journal of Soil Science 50: Reedy RC, Scanlon BR Soil water content monitoring using electromagnetic induction. Journal of Geotechnical and Geoenvironmental Engineering 129: Rhoades JD, Chanduvi F, Lesch S Soil salinity assessment: methods and interpretation of electrical conductivity measurements. Irrigation and Drainage Paper 57, FAO, Rome, Italy. 16

17 Rhoades JD, Manteghi NA, Shouse PJ, Alves WJ Soil electrical conductivity and soil salinity: new formulations and calibrations. Soil Science Society of America Journal 53: Sam R, Ridd P Spatial variations of groundwater salinity in a mangrove-salt flat system, Cocoa Creek, Australia. Mangroves and Salt Marshes 2: Sen PN, Goode PA, Sibbit A Electrical conduction in clay bearing sandstones at low and high salinities. Journal of Applied Physics 63: