Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics.

The ensemble Kalman filter (EnKF) has been used in weather forecasting to assimilate observations into weather models. In this study, we examine how effectively forecasts of a forest carbon cycle can be improved by assimilating observations with the EnKF. We used the EnKF to assimilate into the terrestrial ecosystem (TECO) model eight data sets collected at the Duke Forest between 1996 and 2004 (foliage biomass, fine root biomass, woody biomass, litterfall, microbial biomass, forest floor carbon, soil carbon, and soil respiration). We then used the trained model to forecast changes in carbon pools from 2004 to 2012. Our daily analysis of parameters indicated that all the exit rates were well constrained by the EnKF, with the exception of the exit rates controlling the loss of metabolic litter and passive soil organic matter. The poor constraint of these two parameters resulted from the low sensitivity of TECO predictions to their values and the poor correlation between these parameters and the observed variables. Using the estimated parameters, the model predictions and observations were in agreement. Model forecasts indicate 15 380-15 660 g C/ m2 stored in Duke Forest by 2012 (a 27% increase since 2004). Parameter uncertainties decreased as data were sequentially assimilated into the model using the EnKF. Uncertainties in forecast carbon sinks increased over time for the long-term carbon pools (woody biomass, structure litter, slow and passive SOM) but remained constant over time for the short-term carbon pools (foliage, fine root, metabolic litter, and microbial carbon). Overall, EnKF can effectively assimilate multiple data sets into an ecosystem model to constrain parameters, forecast dynamics of state variables, and evaluate uncertainty.

[1]  Alicia Karspeck,et al.  Experimental Implementation of an Ensemble Adjustment Filter for an Intermediate ENSO Model , 2007 .

[2]  Shenfeng Fei,et al.  Ecological forecasting and data assimilation in a data-rich era. , 2011, Ecological applications : a publication of the Ecological Society of America.

[3]  Scott V. Ollinger,et al.  Environmental variation is directly responsible for short‐ but not long‐term variation in forest‐atmosphere carbon exchange , 2007 .

[4]  S. Roxburgh,et al.  OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models , 2007 .

[5]  Clifford H. Dey,et al.  Observing-Systems Simulation Experiments: Past, Present, and Future , 1986 .

[6]  W. Schlesinger,et al.  SOIL CARBON SEQUESTRATION AND TURNOVER IN A PINE FOREST AFTER SIX YEARS OF ATMOSPHERIC CO2 ENRICHMENT , 2005 .

[7]  R. B. Jackson,et al.  Fine root dynamics in a loblolly pine forest are influenced by free‐air‐CO2‐enrichment: a six‐year‐minirhizotron study , 2008 .

[8]  Yiqi Luo,et al.  Spatial patterns of ecosystem carbon residence time and NPP‐driven carbon uptake in the conterminous United States , 2008 .

[9]  Christopher Umans.  Group-theoretic algorithms for matrix multiplication , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

[10]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[11]  Dean S. Oliver,et al.  The Ensemble Kalman Filter for Continuous Updating of Reservoir Simulation Models , 2006 .

[12]  W. Knorr,et al.  Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling , 2005 .

[13]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[14]  Li Zhang,et al.  Parameter identifiability, constraint, and equifinality in data assimilation with ecosystem models. , 2009, Ecological applications : a publication of the Ecological Society of America.

[15]  Using sensitivity analysis to assist parameter zonation in ground water flow model , 1996 .

[16]  Qianlai Zhuang,et al.  A global sensitivity analysis and Bayesian inference framework for improving the parameter estimation and prediction of a process-based Terrestrial Ecosystem Model , 2009 .

[17]  B. Law,et al.  An improved analysis of forest carbon dynamics using data assimilation , 2005 .

[18]  D. Dee.  On-line Estimation of Error Covariance Parameters for Atmospheric Data Assimilation , 1995 .

[19]  Christopher Umans,et al.  Group-theoretic Algorithms for Matrix Multiplication , 2005, FOCS.

[20]  L. White,et al.  Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction , 2006 .

[21]  W. Schlesinger,et al.  EFFECTS OF FREE-AIR CO2 ENRICHMENT (FACE) ON BELOWGROUND PROCESSES IN A PINUS TAEDA FOREST , 2000 .

[22]  N. Jarvis,et al.  Modeling macropore flow effects on pesticide leaching: inverse parameter estimation using microlysimeters. , 2003, Journal of environmental quality.

[23]  Yiqi Luo,et al.  Relative information contributions of model vs. data to short- and long-term forecasts of forest carbon dynamics. , 2011, Ecological applications : a publication of the Ecological Society of America.

[24]  T. A. Black,et al.  Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an ensemble Kalman filter , 2008 .

[25]  Steven G. McNulty,et al.  Aboveground biomass and nitrogen allocation of ten deciduous southern Appalachian tree species , 1998 .

[26]  Yiqi Luo,et al.  Partitioning interannual variability in net ecosystem exchange between climatic variability and functional change. , 2003, Tree physiology.

[27]  G. Evensen,et al.  Assimilation of Geosat altimeter data for the Agulhas current using the ensemble Kalman filter with , 1996 .

[28]  Stefano Tarantola,et al.  Sensitivity Analysis in Practice: A Guide to Assessing Scientific Models , 2004 .

[29]  S. Naidu,et al.  Contrasting patterns of biomass allocation in dominant and suppressed loblolly pine , 1998 .

[30]  Yuqiong Liu,et al.  Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .

[31]  M. G. Ryan,et al.  Evaluating different soil and plant hydraulic constraints on tree function using a model and sap flow data from ponderosa pine , 2001 .

[32]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[33]  James S. Clark,et al.  Tree growth inference and prediction from diameter censuses and ring widths. , 2007, Ecological applications : a publication of the Ecological Society of America.

[34]  Josep G. Canadell,et al.  Sustainability of terrestrial carbon sequestration: A case study in Duke Forest with inversion approach , 2003 .

[35]  D. Ellsworth,et al.  Forest litter production, chemistry and decomposition following two years of Free-Air CO2 Enrichment , 2001 .

[36]  W. Schlesinger,et al.  Forest carbon balance under elevated CO2 , 2002, Oecologia.

[37]  Yiqi Luo.  Terrestrial Carbon-Cycle Feedback to Climate Warming , 2007 .

[38]  C. Körner.  F1000Prime recommendation of SOIL CARBON SEQUESTRATION AND TURNOVER IN A PINE FOREST AFTER SIX YEARS OF ATMOSPHERIC co 2 ENRICHMENT. , 2006 .

[39]  B O B B,et al.  Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations , 2005 .

[40]  Jarrett J. Barber,et al.  Long-term Effects of Free Air CO2 Enrichment (FACE) on Soil Respiration , 2006 .

[41]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[42]  Ian G. Enting,et al.  A review of applications of model–data fusion to studies of terrestrial carbon fluxes at different scales , 2009 .

[43]  R. B. Jackson,et al.  Progressive nitrogen limitation of ecosystem processes under elevated CO2 in a warm-temperate forest. , 2006, Ecology.

[44]  Jonathan D. Beezley,et al.  Morphing ensemble Kalman filters , 2007, ArXiv.

[45]  W. Schlesinger,et al.  Effects of elevated atmospheric CO2 on fine root production and activity in an intact temperate forest ecosystem , 2000 .