ALBERT

Description: 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.

Description: 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. However, the influence of different experimental treatments on those predictions depends on the exact methods and techniques used for data assimilation. Here, we introduce a hierarchical Bayesian DA approach (Data Assimilation of Pine Plantation Ecosystem Research, DAPPER) that uses observations of carbon stocks, carbon fluxes, water fluxes, and vegetation dynamics from loblolly pine plantation ecosystems across the Southeastern U.S. to constrain parameters in a modified version of the 3-PG forest growth model. The observations included major experiments that manipulated atmospheric carbon dioxide (CO2) concentration, water, and nutrients, along with non-experimental studies that spanned environmental gradients across an 8.6 × 105 km2 region. We optimized regionally representative posterior distributions for the most sensitive model parameters, which dependably predicted data from plots withheld from the data assimilation. The posterior distributions of parameters associated with ecosystem responses to CO2, precipitation, and nutrient addition, along with the corresponding regional changes in production associated with nutrient fertilization and drought, depended on how the experimental data were assimilated. In particular, assimilating nutrient addition experiments reduced the predicted sensitivity to nutrient fertilization while assimilated water manipulation experiments increased the sensitivity to drought. Further, it was necessary to assimilate data from the CO2 experimental enrichment site before other studies to constrain the parameters associated with the influence of CO2 on canopy photosynthesis. The ambient CO2 plots were numerous and had a large contribution to the cost function compared to the low number of elevated CO2 plots (289 ambient vs. 5 elevated plots). Overall, we demonstrated how three decades of research in southeastern U.S. planted pine forests can be used to develop data assimilation techniques that use multiple locations, multiple data streams, and multiple ecosystem experiment types to optimize parameters. This approach allows for future predictions to be consistent with a rich history of ecosystem research across a region.