Submitted as: development and technical paper
18 May 2022
Submitted as: development and technical paper | 18 May 2022
Status: this preprint is currently under review for the journal GMD.

Spatial parameter optimization of a terrestrial biosphere model for improving estimation of carbon fluxes for deciduous forests in the eastern United States: an efficient model-data fusion method

Rui Ma1, Jingfeng Xiao2, Shunlin Liang3, Han Ma1, Tao He1, Da Guo4, Xiaobang Liu1, and Haibo Lu5 Rui Ma et al.
  • 1School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • 2Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
  • 3Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
  • 4College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
  • 5Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China

Abstract. Inaccurate parameter estimation is a significant source of uncertainty in complex terrestrial biosphere models. Model parameters may have large spatial variability, even within a vegetation type. Model uncertainty from parameter uncertainty can be significantly reduced by model-data fusion (MDF), which, however, is difficult to implement over a large region with traditional methods due to the high computational cost. This study proposed a hybrid modeling approach that couples a terrestrial biosphere model with a data-driven machine learning method, which uses satellite information and considers the physical mechanisms. We developed a two-step framework to estimate the essential parameters of the revised Integrated Biosphere Simulator (IBIS) pixel by pixel using the satellite-derived leaf area index (LAI) and gross primary productivity (GPP) products as “true values.” The first step was to estimate the optimal parameters for each sample using a modified adaptive surrogate modeling algorithm (MASM). We applied the Gaussian Process Regression algorithm (GPR) as a surrogate model to learn the relationship between model parameters and errors. In our second step, we built an eXtreme Gradient Boosting (XGBoost) model between the optimized parameters and local environmental variables. The trained XGBoost model was then used to predict optimal parameters spatially across the deciduous forests in the eastern United States. The results showed that the parameters were highly variable spatially and quite different from the default values over forests, and the simulation errors of the GPP and LAI could be markedly reduced with the optimized parameters. The effectiveness of the optimized model in estimating GPP, ecosystem respiration (ER) and net ecosystem exchange (NEE) were also tested through site validation. The optimized model reduced the root-mean-squared-error (RMSE) from 7.03 to 6.22 gC m-2 8 d-1 for GPP, 2.65 to 2.11 gC m-2 8 d-1 for ER and 4.45 to 4.38 gC m-2 8 d-1 for NEE. The mean annual GPP, ER and NEE of the region from 2000 to 2019 were 5.79, 4.60 and -1.19 Pg year-1, respectively. The strategy used in this study requires only a few hundred model runs to calibrate regional parameters and is readily applicable to other complex terrestrial biosphere models with different spatial resolutions. Our study also emphasizes the necessity of pixel-level parameter calibration and the value of remote sensing products for per-pixel parameter optimization.

Rui Ma et al.

Status: open (until 13 Jul 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-96', Anonymous Referee #1, 01 Jun 2022 reply
    • AC1: 'Reply on RC1', Rui Ma, 08 Jun 2022 reply
  • RC2: 'Comment on gmd-2022-96', Anonymous Referee #2, 05 Jun 2022 reply
  • CEC1: 'Comment on gmd-2022-96', Juan Antonio Añel, 15 Jun 2022 reply

Rui Ma et al.


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Short summary
Parameter optimization can improve the accuracy of the modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters, and we also improved the spatial pattern of carbon fluxes.