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 Funding source: National Research Initiative Competitive Grant no USDA NIFA - AFRI Core Project of the North American Carbon Program 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

Rationale and Significance Crutzen, Nature; Steffen et al., Global Change and the Earth System; Rockström et al., Nature; Grunwald et al., Soil Sci. Soc. Am. J. Global issues & prioritiesGlobal estimates of terrestrial carbon stocks UNEP-WCMC. 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 ?

Historic and current within ≤ 30m Historic and current within ≤ 300m Current (2008/2009) Resampling of 453 historic sites (out of 1,288 historic pedons – FL Soil Database); (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 SOC Observations (FL)

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

SOC statistic (depth to 1 m) SSURGOSTATSGO2FSCD obser- vations FSCD block kriging FSCD PTF Map unit655,155 map units 2,823 map units 1,099 points2,282, m cells 7 soil orders Minimum (kg m -2 ) Maximum (kg m -2 ) Median (kg m -2 ) Mean (kg m -2 ) Std. dev. (kg m -2 ) Total mapped area (km 2 ) 128,788142,681N/A142,678142,626 Total stock (Pg) N/A Mean stock (kg m -2 ) N/A Map unit Florida Estimates of SOC stocks to 1 m in Florida based on different data/methods was ± 1.01 Pg (mean ± std. error) Vasques G.M. and S. Grunwald. 201_. Global Env. Change J. (in prep.)

Grunwald S., J.A. Thompson & J.L.Boettinger SSSAJ. In press. 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 Conceptual Modeling Framework: STEP- AWBH (“STEP-UP”) Soil pixel (SA):

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

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

January February March Data source: PRISM 35 – – – – – – – – – – – – 235 Avg. Monthly Precipitation (mm) [ ] April May June July August September October November December Climatic Data – FL

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

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 to 2003: ↑ Urbanization (5.4% % %) ↓ Agriculture (21.9% - 7.4% - 8.6%) ↓ ↑ Rangeland (8.8% - 4.7% - 8.2%) ↓ ↑ Forest (29.9% % %) ↓ Wetland (21.7% - 4.4% - 5.8%) Land Use Change (1970 – 2003) Based on Satellite Data ?

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

Total N: 1,014; Randomized 70/30 calibration/validation split of dataset R2R2 RMSERPD Regression trees (RT) Bootstrapped RT Boosted RT Random Forest Support Vector Machine Modeling of Current (2009) SOC Stocks (0-20 cm) – FL Validation results – STEP-AWBH Modeling (kg C m -2 ) 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 region0.50 Soil Order0.25 Soil Suborder0.22 … 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 Myers D.B., S. Grunwald et al. 201_. Global Change Biology J. (in prep.) SOC (kg m -2 ) Spatial resolution: 30 m

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

Predictor variables of importance: Surficial geology 100 Land use Long-term max. temp. May75.4 Long-term max. temp. March 62.9 Long-term max. temp. April 35.9 Soil Great Group 27.3 Land use MODIS EVI (day 137) 22.8 MODIS EVI (day 169) 22.7 Landsat Bd Forest canopy cover 17.5 …. and more Modeling of SOC Sequestration Rates (g C m -2 yr -1 ) (0-20 cm) – FL Methods: Ensemble trees (bagging mode) 10% V-fold cross-validation Grunwald et al., 201_. Front Ecol. Env. J. (in prep.) STEP-AWBH model evaluation (g C m -2 yr -1 ): MSE = MAD = 47.61

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

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

AuthorsSpectra Type AreaNPropertiesR 2 Cal.R 2 Val. Vasques et al Geoderma VNIRSFRW554TC Vasques et al SSSAJ (Ahn et al., Ecosystems) VNIRSFRW102TC RC SC HC MC Vasques et al JEQ VNIRFL (hist.) 7120SOC Myers et al in prep. VNIRFL (2009) 1014SOC (RC, HC) McDowell et al in prep. VNIR & MIR Hawaii306SOC0.93 (VNIR) 0.97 (MIR) V-fold cross- validation Sarkhot et al., Geoderma VNIRTX514TC HC SOC IC Research Results 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