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Rapid Assessment and Trajectory Modeling of Soil Carbon Across a Southeastern Landscape

Soil & Water Science Department, University of Florida. GIS Research Lab. Rapid Assessment and Trajectory Modeling of Soil Carbon Across a Southeastern Landscape . Sabine Grunwald. Project Goals : Modeling of soil carbon along pedo -climatic trajectories across diverse ecosystems

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Rapid Assessment and Trajectory Modeling of Soil Carbon Across a Southeastern Landscape

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  1. Soil & Water Science Department, University of Florida GIS Research Lab Rapid Assessment and Trajectory Modeling of Soil Carbon Across a Southeastern Landscape Sabine Grunwald

  2. Project Goals: Modeling of soil carbon along pedo -climatic trajectories across diverse ecosystems in Florida PD: S. Grunwald Co-PIs: W.G. Harris, N.B. Comerford and G.L. Bruland Post-Docs: D.B. Myers and D. Sarkhot Graduate students: G.M. Vasques, X. Xiong and W.C. Ross Field and lab staff: A. Stoppe, L. Stanley, A. Comerford and S. Moustafa Core Project of the North American Carbon Program Funding source: National Research Initiative Competitive Grant no. 2007-35107-18368 USDA NIFA - AFRI

  3. Rationale and Significance Global issues & priorities Global estimates of terrestrial carbon stocks UNEP-WCMC. http://www.carbon-biodiversity.net/GlobalScale/Map Scharlemann et al. (2009): Harmonized World Soil Database (2009)-SOC values up to 1 m depth (1 km spatial resolution) & Ruesch and Gibbs (2008): Biomass carbon map using IPCC Tier 1 methodology and GLC 2000 land cover data. • Lack in understanding of soil • carbon (C) variability • Assessments rely on historic/ • legacy soil C data • Soil C – a sink or source ? • Soil C – linkages to processes ? • Total soil C – C pools ? Crutzen, 2002. Nature; Steffen et al., 2005. Global Change and the Earth System; Rockström et al., 2009. Nature; Grunwald et al., 2011. Soil Sci. Soc. Am. J.

  4. SOC Observations (FL) • Resampling of 453 historic sites (out of 1,288 historic pedons – FL Soil Database); 1965-1996 (Soil and Water Science Dept., UF & NRCS) • In 2008/2009 soil sampling at 1014 sites (0-20 cm) based on stratified-random sampling design (land use – soil suborder strata): • TC • SOC • IC • HC • RC • BD • TN and TP Historic and current within ≤ 30m Historic and current within ≤ 300m Current (2008/2009)

  5. Modeling of Historic SOC (1 m) – FL SSURGO-Soil Data Mart (NRCS) 1:24,000 STATSGO2-Soil Data Mart (NRCS) 1:250,000 < 5 5 – 10 10 – 15 15 – 20 20 – 50 > 50 Not mapped Block Kriging Block size: 250 x 250 m Target: Ln-SOC kg m-2 Nugget: 0.61 Sill: 0.86 Range: 101,088 m ME: -0.0040 ln[kg m-2] (~ 0.10 kg m-2) Class Pedo-transfer function (PTF) SOC = f {LU, order} N: 1,099 Data source: Florida Soil Characterization Database (FSCD) Vasques G.M. and S. Grunwald. 201_. Global Env. Change J. (in prep.) Presented at the World Congress of Soil Sciences (2010)

  6. Estimates of SOC stocks to 1 m in Florida based on different data/methods was 4.110 ± 1.01 Pg (mean ± std. error) Map unit Florida Vasques G.M. and S. Grunwald. 201_. Global Env. Change J. (in prep.)

  7. Conceptual Modeling Framework: STEP-AWBH (“STEP-UP”) • Predicts the spatially-explicit evolution and behavior of Soil Pixels / Voxels • Explicitly incorporates anthropogenic forcings • Incorporates bio-, topo-, litho-, pedo- and hydrosphere • Provides temporal context to account for ecosystem processes and forcings • Fuses empirical and process-based knowledge Soil pixel (SA): Grunwald S., J.A. Thompson & J.L.Boettinger. 2011. SSSAJ. In press.

  8. Predict soil- • environmental • properties: • TC • SOC • SOC seq. • Carbon pools • TN, TP • … and more Model development: • PLSR • CART • Ensemble • regression • trees • … and others Model validation: Uncertainty assessment Spatially & temporally explicit environmental matrix (FL): ~2 TB of data N: 200+ variables ….. • STEP variables: • Soil • Topographic • Ecological / geographic • Parent material + • AWBH variables: • Atmosphere / climate • Water • Biota: LU/LC • H(uman) + Soil observations

  9. Net Primary Productivity – FL Soil Taxonomic Classes – FL Histosol Spodosol Data source: NRCS-USDA, Soil Geographic Database / Soil Data Mart. Time period: 2000 – 2005; data source: MODIS satellite data

  10. July January March February September April August December June November October May Climatic Data – FL Avg. Monthly Precipitation (mm) [1971-2000] 35 – 55 33 – 75 75 – 55 55 – 75 75 – 95 95 – 115 115 – 135 135 – 155 155 – 175 175 – 195 195 – 215 215 – 235 Data source: PRISM

  11. Climatic Data – FL Time frame: 1971 – 2000 Data source: PRISM

  12. Land Use Change (1970 – 2003) Based on Satellite Data 2003 1995 1990 ? 1970 1970 to 2003: ↑ Urbanization (5.4% - 12.1% - 11.0%) ↓ Agriculture (21.9% - 7.4% - 8.6%) ↓ ↑ Rangeland (8.8% - 4.7% - 8.2%) ↓ ↑Forest (29.9% - 23.2% - 26.2%) ↓ Wetland (21.7% - 4.4% - 5.8%) Data sources: Land use / land cover 1970: USGS; 1990 and 1995: Water Management Districts & FL Department of Transportation 2003: Florida Fish and Wildlife Conservation Commission

  13. Modeling of Current SOC (0-20 cm) – FL Methods: Ensemble regression trees (RT) and other data mining methods Inputs (predictor variables): STEP-AWBH environmental variables Predict SOC stocks

  14. Modeling of Current (2009) SOC Stocks (0-20 cm) – FL Validation results – STEP-AWBH Modeling (kg C m-2) Total N: 1,014; Randomized 70/30 calibration/validation split of dataset Myers D.B., S. Grunwald et al. 201_. Global Change Biology J. (in prep.)

  15. Modeling of Current (2009) SOC Stocks (kg m-2) (0-20 cm) – FL • Predictor variables of importance: • Available water capacity 50 cm 1.0 • Soil Great Group 0.85 • Land cover / land use (NLCD) 0.83 • Land cover / land use (FFWC, 2003) 0.74 • Ecologic region 0.50 • Soil Order 0.25 • Soil Suborder 0.22 • … and more Method: Random Forest Independent validation (N: 304) Myers D.B., S. Grunwald et al. 201_. Global Change Biology J. (in prep.)

  16. Modeling of Current (2009) SOC Stocks (20 cm) – FL SOC (kg m-2) Spatial resolution: 30 m Myers D.B., S. Grunwald et al. 201_. Global Change Biology J. (in prep.)

  17. SOC Sequestration in Florida (1965 – 2009) • SOC sequestration (g C m-2 yr-1) • Mean: 11.6; Median: 17.7 • STDev: 93.3 • Max: 511.3 • Time frame of sequestration (yrs) • Mean: 30.3; Median: 29.6 • STDev: 5.3 • Max: 43.5 Historic & current sites ≤ 30 m (N: 194) SOC sequestration (g C m-2 yr-1) Grunwald et al., 201_. Front Ecol. Env. J. (in prep.)

  18. Modeling of SOC Sequestration Rates (g C m-2 yr-1) (0-20 cm) –FL Predictor variables of importance: • Surficial geology 100 • Land use 1995 75.4 • Long-term max. temp. May 75.4 • Long-term max. temp. March 62.9 • Long-term max. temp. April 35.9 • Soil Great Group 27.3 • Land use 1970 25.9 • MODIS EVI (day 137) 22.8 • MODIS EVI (day 169) 22.7 • Landsat Bd. 3 20.6 • Forest canopy cover 17.5 • …. and more STEP-AWBH model evaluation (g C m-2 yr-1): MSE = 85.93 MAD = 47.61 Methods: Ensemble trees (bagging mode) 10% V-fold cross-validation Grunwald et al., 201_. Front Ecol. Env. J. (in prep.)

  19. Significance of research: • Predict high-resolution soil C pixels across large landscapes • Reduce the uncertainty of soil C assessment • Model spatial variability of soil C (C pools and nutrients) along climate and land use trajectories • Model soil change in dependence of anthropogenic induced stressors

  20. Rapid and cost-effective sensing of Soil C and Pools using visible/near-infrared (VNIR) diffuse reflectance spectroscopy Spectral soil C modeling Soil attributes = f (VNIR) Soil attributes = f (VNIR; MIR)

  21. Research Results VNIR & MIR

  22. Follow-up Research Project (NRCS, Grunwald – UF & McBratney – U Sydney) • Rapid soil C assessment across the U.S. • Soil C ↔ Land use/land cover, ecoregion, climate, … • Soil C ↔ VNIR Apply research methodology tested in FL to U.S. FL

  23. http://soils.ifas.ufl.edu/faculty/grunwald sabgru@ufl.edu

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