Sabine Grunwald; James O Sickman; Nicholas B Comerford Assessment of Dynamic Soil Carbon Pools at the Watershed Scale Using Regression Kriging Gustavo M Vasques; Sabine Grunwald; James O Sickman; Nicholas B Comerford Funded by: Cooperative Ecosystem Studies Unit – Natural Resources Conservation Service – USDA

Rationale Florida has the highest amount of soil organic carbon per area compared to other States in the U.S. (Guo et al., 2006) Florida experienced the second highest increase in CO2 emissions from 1990-2004 compared to other States (Cassady, 2007) Guo et al. 2006. Quantity and Spatial Variability of Soil Carbon in the Conterminous United States. Soil Sci. Soc. Am. J. 70:590–600 Cassady. 2007. The Carbon Boom: State and National Trends in Carbon Dioxide Emissions Since 1990. Environment Florida Research & Policy Center, Tallahassee, FL Florida has highest amount of soil organic carbon per area in the U.S. and the second highest total amount when considering the state area Soil is a potential reservoir to sequester atmospheric CO2 and mitigate global warming There is need for rapid and accurate techniques to synthesize soil information to improve land management Sustainable land management requires understanding the dynamics of soil carbon and how it relates to landscape factors 2

Objectives To model the distribution of total soil carbon (TC) across the Santa Fe River Watershed (SFRW) at four depths down to 180 cm To model the distribution of surface soil carbon (SC) fractions in the SFRW 1) Soil is a potential reservoir to sequester atmospheric CO2 and mitigate global warming 2) Study of soil carbon fractions helps to elucidate carbon dynamics on the landscape; Also, they indicate to what scale SC can be sequestered in the soil (RC) and to what scale it can be lost (HC, DOC, and MC) 3) There is need for rapid and accurate techniques to synthesize soil information to improve land management 4) To understand how changes in environmental properties (esp. land use shifts) could affect changes in SC content To compare different methods to upscale TC and SC fractions To identify environmental variables that exert major control on SC

Sand-rich marine sediments - 90% Karst terrain - 10% Subtropical climate: Precipitation – 1,334 mm/yr Temperature – 20.4 oC Diverse land uses: Pinelands - 30% Wetlands - 14% Upland forests - 13% Pasture - 13% Rangelands - 13% Urban areas - 11% Crops - 5% Influenced by wildfires and hurricanes High precipitation rates foster vertical leaching of OM in the soil profile Elevation: 1.5-91 m AMSL Geology: Sand-rich marine sediments - 90% Karst terrain - 10% 4

Stratified random design along soil-land use trajectories 141 sites and 4 depths 0-30 cm TC - Total carbon (combustion) HC - Hydrolysable carbon (6N HCl) RC - Recalcitrant carbon (TC - HC) DOC - Dissolved organic carbon (using hot water extraction) MC - Mineralizable carbon (from 15th until 29th day of incubation) 30-60 cm TC Stratified random sampling along soil-land use trajectories to capture the variability of SC across soil type and land use >99% of TC is organic carbon 60-120 cm TC 120-180 cm TC 5

(Environmental Correlation) Upscaling Methods All SC properties were log-transformed Samples were split at each depth into training (2/3) and validation (1/3) Methods Trend Model (Environmental Correlation) Residuals (1) LK Lognormal Kriging (2) RK/SMLR Stepwise Multiple Linear Regression (3) RK/RT Regression Tree Explain more thoroughly that RK was done in two different modes: RT and SMLR 59 environmental landscape variables were use in the global trend models RK = Regression Kriging

Results – Descriptive Statistics Property (Depth in cm) Number of Obs. Mean (kg m-2) Median (kg m-2) Std. Dev. (kg m-2) Range (kg m-2) HC (0-30) 141 1.59 1.28 1.33 0.02 - 12.7 RC (0-30) 4.67 3.30 6.91 0.51 - 54.5 DOC (0-30) 0.34 0.29 0.27 0.09 - 2.7 MC (0-30) 0.05 0.04 0.01 - 0.31 TC (0-30) 6.26 4.57 8.04 1.17 - 63.9 TC (30-60) 3.73 1.67 12.02 0.43 - 129.9 TC (60-120) 139 3.59 1.71 11.54 0.36 - 112.7 TC (120-180) 133 1.61 1.08 2.05 0.16 – 18.5 TC (0-100) 11.79 7.49 25.08 1.84 - 268.9 RC represented ~75% of the soil TC content, while HC represented ~25% DOC represented ~5% of TC MC represented <1% of TC, and ~15% of DOC 7

Results - Upscaling Property (Depth in cm) RMSE of Training (kg m-2) RMSE of Validation Total Stock (Mt) LK RK/ SMLR RT HC (0-30) 1.14 1.94 1.59 0.73 2.49 1.73 6.21 9.88 9.81 RC (0-30) 4.74 8.32 6.30 2.85 6.36 2.57 17.3 20.21 21.77 DOC (0-30) 0.10 1.43 1.16 0.14 10.88 1.19 1.30 45.72 5.34 MC (0-30) 0.02 1.70 1.31 11.43 1.22 0.18 47.98 4.49 TC (0-30) 5.16 9.65 11.23 3.34 6.96 9.99 23.0 25.36 27.74 TC (30-60) 2.41 11.72 10.96 12.6 20.27 6.20 10.5 65.30 11.65 TC (60-120) 8.28 27.30 11.98 2.02 19.32 3.42 10.6 37.29 20.57 TC (120-180) 0.44 2.94 2.75 3.46 5.28 8.98 12.38 TC (0-100) 12.7 29.39 26.35 7.21 16.67 15.82 39.3 82.59 90.13 OK performed best in training mode for all properties because it is an exact interpolator, so estimated and observed values should match at the sampling sites Reduced clay content (average = 15%) can explain why correlations are low between SC and environmental properties When OK performed better, this is an indicative that the spatial dependency structure is more important a determinant than the environmental factors Best methods are in bold 8

SC Fractions - Variograms logRC (0-30) logDOC (0-30) Semivariance Distance (m) logHC (0-30) logMC (0-30) Variograms of the log-transformed properties, not the residuals Semivariograms show a similar range for the different SC fraction 9

SC Fractions - Best Methods SC fractions were a function of/highly correlated with TC, so the distribution of SC fractions follows the distribution of TC Correlations with environmental variables were not very strong Selected variables: RC(0-30) – Depth to water table DOC (0-30) – Soil hydrologic group 10

Total Carbon - Variograms logTC (30-60) logTC (120-180) Distance (m) Semivariance logTC (0-30) logTC (60-120) Semivariograms show a similar range for TC at different depths 11

Total Carbon - Best Methods Florida soils are atypical: They do not follow the typical decreasing pattern of SC along the profile because of spodosols The widespread presence of wetlands promotes the accumulation of SC due to slow decomposition Selected variables: TC(30-60) – Elevation (mouth of the river), Landsat 7 Band 2 TC(120-180) – Spodosols, Entisols, Slope, Clay percent, Agriculture, Env Geology = Medium fine sand and silt 12

Total Carbon - Best Methods TC (0-30) is controlled mainly by the land use, especially by the presence of wetlands TC (30-60) is high close to the mouth of the river, and low in upland forests and agricultural fields TC (60-120) and TC (120-180) are influenced by the presence of Spodosols 13

Total Carbon – TC (0-100) High values – close to wetlands or Spodosols Medium values – areas of pine plantation, agriculture, and improved pasture Low values (SE) – due to the presence of Entisols in areas of high infiltration rate and low productivity; usually areas of upland forest Low values (NW) – Ultisols 14

Conclusions TC and SC fractions behaved similarly across the SFRW SC in the SFRW was highest in the surface, in saturated horizons, and in deep spodic horizons This is also true for similar landscapes in the SE US TC and SC fractions behaved similarly across the SFRW Overall, major controlling factors of SC storage were soil type, land use and hydrologic properties 15

Outlook Land management to enhance SC in Florida should focus on two strategies: Protect/improve SC-rich areas (e.g. areas of Spodosols, wetlands) Identify strategies to conserve SC Identify strategies to conserve SC Land restoration Control of urban expansionA Adoption of more sustainable management practices in productive areas Identify strategies to improve SC storage in productive areas (agriculture, forestry, cattle ranching) No-till Improved pasture Land management should aim at promoting soil aggregation Enhance SC storage in areas with low SC (e.g. areas of excessive drainage) Identify strategies to improve SC storage 16

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