Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

Abstract

Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m 2 /month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m 2 /month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m 2 /month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003-2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.

A Bayesian ensemble data assimilation to constrain model parameters and land-use carbon emissions

“…However, the partitioning of this land-atmosphere flux to effects from humaninduced land-use and land-cover change (LULCC) and the transient change in the residual terrestrial sink remains highly debated (Schimel et al, 2015). It is estimated that approximately a third of the cumulative anthropogenic CO 2 emissions in the industrial period stem from the effects of LULCC (Arneth et al, 2017;Brovkin et al, 2013;Houghton and Nassikas, 2017;McGuire et al, 2001;Mahowald et al, 2017;Pongratz and Caldeira, 2012;Sitch et al, 2015;Strassmann et al, 2008;Stocker et al, 2017Stocker et al, , 2014Peng et al, 2017). A better understanding of the mechanisms of the historical terrestrial carbon cycle is vital for more accurate future projections of the global carbon cycle and climate.…”

The Global Land Surface Satellite (GLASS) Product Suite

“…The GLASS LAI products have also been used to drive various process-oriented models, such as the Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM), to achieve a better agreement between the estimated and observed GPP (Ma et al 2017c).…”

A Bayesian Ensemble Data Assimilation to Constrain Model Parameters and Land Use Carbon Emissions

“…Various approaches to incorporate constraining data exist, such as variational approaches minimizing a cost function using the adjoint of the model (Houweling et al, 1999;10 Kato et al, 2013;Kaminski et al, 2013) or the use of ensemble Kalman filters (Houweling et al, 1999;Lorenc, 2003;Gerber and Joos, 2013;Stöckli et al, 2011;Ma et al, 2017). However since these methods operate sequentially, the metric and relative weighting of observational products have to be chosen beforehand, which can prove difficult if multiple observational data sets are to be assimilated simultaneously.…”