Sensitivity and Uncertainty Analyses of Flux-based Ecosystem Model towards Improvement of Forest GPP Simulation

Hanqing Ma,Wenping Yuan,Xin Li,Chunfeng Ma,Zhengjia Liu,Gaofeng Zhu

doi:10.3390/su12072584

Abstract

An ecosystem model serves as an important tool to understand the carbon cycle in the forest ecosystem. However, the sensitivities of parameters and uncertainties of the model outputs are not clearly understood. Parameter sensitivity analysis (SA) and uncertainty analysis (UA) play a crucial role in the improvement of forest gross primary productivity GPP simulation. This study presents a global SA based on an extended Fourier amplitude sensitivity test (EFAST) method to quantify the sensitivities of 16 parameters in the Flux-based ecosystem model (FBEM). To systematically evaluate the parameters’ sensitivities, various parameter ranges, different model outputs, temporal variations of parameters sensitivity index (SI) were comprehensively explored via three experiments. Based on the numerical experiments of SA, the UA experiments were designed and performed for parameter estimation based on a Markov chain Monte Carlo (MCMC) method. The ratio of internal CO2 to air CO2 ( f C i ) , canopy quantum efficiency of photon conversion ( α q ) , maximum carboxylation rate at 25 ° C ( V m 25 ) were the most sensitive parameters for the GPP. It was also indicated that α q , E V m and Q 10 were influenced by temperature throughout the entire growth stage. The result of parameter estimation of only using four sensitive parameters (RMSE = 1.657) is very close to that using all the parameters (RMSE = 1.496). The results of SA suggest that sensitive parameters, such as f c i , α q , E V m , V m 25 strongly influence on the forest GPP simulation, and the temporal characteristics of the parameters’ SI on GPP and NEE were changed in different growth. The sensitive parameters were a major source of uncertainty and parameter estimation based on the parameter SA could lead to desirable results without introducing too great uncertainties.

Highlights

Ecosystem models are valuable tools that describe and explain the processes and variable dynamics of photosynthesis and respiration in a forest ecosystem [1,2]
The objective of this study is to evaluate the sensitivity index (SI) of all the parameters on the gross primary productivity (GPP) of Flux-based ecosystem model (FBEM) using the extended Fourier amplitude sensitivity test (EFAST) method and to improve the parameter estimation process based on the results of the sensitivity analysis (SA) for quantizing uncertainty
According to Bayes theorem, the posterior probability density functions (PDFs) of the model parameters (p) can be calculated from prior knowledge and information generated by comparing the model with the observed values

Summary

Introduction

Ecosystem models are valuable tools that describe and explain the processes and variable dynamics of photosynthesis and respiration in a forest ecosystem [1,2]. The more complicated the models are, the more parameters they may have This may introduce larger uncertainty caused by the parameter sensitivity, it is indispensable to quantify the uncertainty and sensitivity of the parameters in forest ecosystem models. Under such background, various sensitivity analyses (SAs) were proposed and applied to ascertain the corresponding responses in the output variables when input parameters alter within their valid ranges [11,12]. Performing an SA is a feasible way to characterize and reduce uncertainties in ecosystem models, and to improve their performance [19,20]

Objectives

Methods

Results

Conclusion