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This project aims to model soil carbon dynamics across diverse ecosystems in Florida to understand soil carbon variability, linkages to processes, and total carbon pools. The study involves resampling historic sites and current soil sampling, with data analysis using statistical methods and machine learning techniques.
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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 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
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.
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)
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)
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.)
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.
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
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
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
Climatic Data – FL Time frame: 1971 – 2000 Data source: PRISM
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
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
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.)
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.)
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.)
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.)
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.)
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
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)
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
http://soils.ifas.ufl.edu/faculty/grunwald sabgru@ufl.edu