reducing uncertainty in carbon cycle science of north america: a synthesis program across united...
TRANSCRIPT
Reducing uncertainty in carbon cycle science of North America: a synthesis program across United
States and Mexico Rodrigo Vargas
Department of Plant and Soil SciencesUniversity of Delaware
CoPIs: Nathaniel BrunsellUniversity of Kansas
Daniel HayesUniversity of Maine
Contact: [email protected]
Agroclimatology PD meetingDecember 16-18, 2016San Francisco, CA
Interagency Carbon Cycle Science (FY 2014)(2014-67003-22070)
• Synthesize new existing datasets and models across the United States (U.S.) and Mexico in a consistent analysis framework.
…directed towards improving our understanding of forest and soil carbon dynamics, and the validation of terrestrial ecosystem models.
The specific objectives:a) Harmonize available datasets b) Develop the synthesis approaches for scaling informationc) Develop a to identify knowledge gaps.
Objectives
Biederman, et al (2016) Global Change Biology 22:1867–1879. Villarreal, et al (2016) Journal of Geophysical Research-Biogeosciences 121:494-508. Petrie, et al (2016) Journal of Geophysical Research- Biogeosciences 121:280-294. Reimer, et al (2016) Progress in Oceanography 143 (2016) 1–12 McKinney et al (2015) IEEE 11th International Conference on e-Science: 108-117. Programa de Investigación en Cambio Climático (PICC) (2015) Reporte Mexicano de Cambio Climático. (Mexican Report on Climate Change. Group I: Scientific Bases, Models and Modeling).FAO and ITPS (2015) Status of the World’s Soil Resources (SWSR) – Main Report. Vargas , et al (2015) EOS, 96. doi:10.1029/ 2015EO037893 Reimer, et al (2015) PLoS ONE. 10(4):e0125177Cueva, et al (2015) Journal of Geophysical Research-Biogeosciences 120:737-751. King, et al (2015) Biogeosciences 12:399-414 Milne, et al (2015) “Soil Carbon: science, management and policy for multiple benefits”. CABI. 10-25.Banwart, et al (2014) Carbon Management 5:185-19Hengl, et al. SoilGrids250m: global gridded soil information based on Machine Learning (in review) PlosONE
Vargas R, et al. (in review) Enhancing interoperability to facilitate implementation of REDD+: case study of Mexico. Carbon Management
Publications
Soil carbon across North America
- For decades the USA and Mexico have collected soil organic carbon (SOC) information.
- Can we describe the spatial variability of SOC across North America?
- Can we relate observations with biophysical information to predict SOC?
• Digital soil mapping (predictive soil mapping)
- Computer-assisted production of digital maps of soil properties.
- Use of field and laboratory observational (data and methods) with spatial and non-spatial inference systems.
Digital soil mapping
+ many others
United States MexicoInternational Soil Carbon Network Federal agencies
NRCS N=94778 1938-2010
INEGI Legacy Series 1 N=21153 1969-2001
USGS N=5623 1928-2006
INEGI Legacy Series 2 N=2805 1999-2009
Oak Ridge National Lab N=588 1992-2006
INEGI – National land degradation project N=2472 2008-2012
Other institutions (e.g. Universities, Long Term Ecological Research sites) N=2330 1905-2009
CONAFOR – INFyS N=3061 2009-2011
TOTAL=103319 analyzed samples TOTAL=29491 analyzed samples
NRCS = Natural Resource Conservation ServiceUSGS = United States Geological SurveyINEGI = National Institute for Statistics and GeographyCONAFOR = National Forest Commission
SOC databases
SOC databases
0-30cm n=12,360
SOC database for USA (ISCN)
• Randomized sample from INEGI series 1 & 2
0-30cm n=12,997
SOC database for Mexico (INEGI and CONAFOR)
SOC = f(S,C,O,R,P,A,N)+eSoil- soil type mapsClimate, climatic propertiesOrganisms, land cover and natural vegetationRelief, terrain parameters from DEM`sParent material, geological maps Age, the time factorN, space, relative positione, autocorrelated random spatial variation
Dokuchayev 1883->Jenny 1941->McBratney et al., 2003,-> Grunwald et al., 2011
Conceptual model for SOC variability
Model evaluation(e.g. cross valitacion,
AIC, BIC, Cp)
Variable selection(e.g. linear
model)
Prediction to
new data(e.g. random
forestCubist)
Uncertainty Estimation(e.g. different
models, Global/local)
SOC = f (Soils, Climate, Organisms, Parent material, Age, Space) + error
Digital soil mapping
Guevara and Vargas (in preparation)
Hypothesis driven
Machine learning
*Median
Statistical performance(explained variance)
Unexplained variability
Model r raca RMSEraca r inegi RMSE inegi MX (Pg) US (Pg)
linear 5km 0.45 0.94 0.47 0.23 16.6 ± 2.16e-05 23.16 ± 2.96e-05
rf 5km 0.46 0.95 0.33 0.24 17.4 ±2.68-e05 21.53 ± 3.36e-05
SOC stocks across North America (PRELIMINARY)
29.3Pg for 0-30 cm depth (SSURGO; Bliss et al 2014) for CONUS
14.2 Pg for 0-20 cm depth (+- 3.9 Pg; Murray-Tortarolo et al 2015) for Mexico
RaCA = Rapid Assessment of US Soil Carbon (USDA)INEGI = Series 1& 2
RaCA INEGI
Soil carbon density:CONUS = 2.8 Mg km-2
Mexico = 8.5 Mg km-2
Towards a continental map of SOC for North America
SOC next steps
- - This approach represents a regional baseline estimate of SOC (0-30cm) including variability
- - Useful in future soil sampling planning (i.e. for inventory, SOC monitoring networks) aiming to reduce areas dominated by high variability
- -This approach is reproducible (and semi automated) and can be periodically updated with new data and new covariates (Land use time 1, land use time2 and so on)
Conclusions
Vargas et al (in review)
Stakeholder Scientists
Interoperability
Vargas et al (in review)
Interoperability
Interoperability is a collective effort with the ultimate goal of sharing and using information to produce knowledge and apply knowledge gained, by removing conceptual, technological, organizational, and cultural barriers.
Vargas et al (in review)
Interoperability
Interagency Carbon Cycle Science (FY 2014)(2014-67003-22070)
THANK YOU