Articles | Volume 14, issue 14
Biogeosciences, 14, 3525–3547, 2017
Biogeosciences, 14, 3525–3547, 2017

Research article 26 Jul 2017

Research article | 26 Jul 2017

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

R. Quinn Thomas1, Evan B. Brooks1, Annika L. Jersild1, Eric J. Ward2, Randolph H. Wynne1, Timothy J. Albaugh1, Heather Dinon-Aldridge3, Harold E. Burkhart1, Jean-Christophe Domec4,5, Thomas R. Fox1, Carlos A. Gonzalez-Benecke6, Timothy A. Martin7, Asko Noormets8,a, David A. Sampson9, and Robert O. Teskey10 R. Quinn Thomas et al.
  • 1Department of Forest Resources and Environmental Conservation, Virginia Tech, Blacksburg, VA, USA
  • 2Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
  • 3State Climate Office of North Carolina, North Carolina State University, Raleigh, NC, USA
  • 4Bordeaux Sciences Agro, UMR 1391 INRA-ISPA, Gradignan CEDEX, France
  • 5Nicholas School of the Environment, Duke University, Durham, NC, USA
  • 6Department of Forest Engineering, Resources and Management, Oregon State University, Corvallis, OR, USA
  • 7School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
  • 8Department of Forestry and Environmental Resources, North Carolina State University, Raleigh, NC, USA
  • 9Decision Center for a Desert City, Arizona State University, Tempe, AZ, USA
  • 10Warnell School of Forestry and Natural Resources, University of Georgia, Athens, Athens, GA, USA
  • acurrent address: Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, USA

Abstract. Predicting how forest carbon cycling will change in response to climate change and management depends on the collective knowledge from measurements across environmental gradients, ecosystem manipulations of global change factors, and mathematical models. Formally integrating these sources of knowledge through data assimilation, or model–data fusion, allows the use of past observations to constrain model parameters and estimate prediction uncertainty. Data assimilation (DA) focused on the regional scale has the opportunity to integrate data from both environmental gradients and experimental studies to constrain model parameters. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation to Predict Productivity for Ecosystems and Regions, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the southeastern US to constrain parameters in a modified version of the Physiological Principles Predicting Growth (3-PG) forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with nonexperimental surveys that spanned environmental gradients across an 8.6  ×  105 km2 region. We optimized regionally representative posterior distributions for model parameters, which dependably predicted data from plots withheld from the data assimilation. While the mean bias in predictions of nutrient fertilization experiments, irrigation experiments, and CO2 enrichment experiments was low, future work needs to focus modifications to model structures that decrease the bias in predictions of drought experiments. Predictions of how growth responded to elevated CO2 strongly depended on whether ecosystem experiments were assimilated and whether the assimilated field plots in the CO2 study were allowed to have different mortality parameters than the other field plots in the region. We present predictions of stem biomass productivity under elevated CO2, decreased precipitation, and increased nutrient availability that include estimates of uncertainty for the southeastern US. Overall, we (1) demonstrated how three decades of research in southeastern US planted pine forests can be used to develop DA techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters and (2) developed a tool for the development of future predictions of forest productivity for natural resource managers that leverage a rich dataset of integrated ecosystem observations across a region.

Short summary
To improve predictions of future forest productivity, we introduce an analytical approach that uses data from numerous research experiments that have occurred across the southeastern US to calibrate a mathematical forest model and estimate uncertainty in predictions. As a result, predictions using the model are consistent with a rich history of forest research in a region that supplies a large fraction of wood products to the world.
Final-revised paper