Regional-scale data assimilation with the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM) over Siberia

Abstract

This study examined the regional performance of a data assimilation (DA) system that couples the particle filter and the Spatially Explicit Individual-based Dynamic Global Vegetation Model (SEIB-DGVM). This DA system optimizes model parameters of defoliation and photosynthetic rate, which are sensitive to phenology in the SEIB-DGVM, by assimilating satellite-observed leaf area index (LAI). The experiments without DA overestimated LAIs over Siberia relative to the satellite-observed LAI, whereas the DA system successfully reduced the error. DA provided improved analyses for the LAI and other model variables consistently, with better match with satellite observed LAI and with previous studies for spatial distributions of the estimated overstory LAI, gross primary production (GPP), and aboveground biomass. However, three main issues still exist: (1) the estimated start date of defoliation for overstory was about 40 days earlier than the in situ observation, (2) the estimated LAI for understory was about half of the in situ observation, and (3) the estimated overstory LAI and the total GPP were overestimated compared to the previous studies. Further DA and modeling studies are needed to address these issues.

Future prediction of Siberian wildfire and aerosol emissions via the improved fire module of the spatially explicit individual-based dynamic global vegetation model

“…This parameter setting is also in line with the simulation settings in other SEIB-DGVM studies: Sato et al (2007) performed a 1000-year spin-up and combined it with all of the simulation phases to extract general trends of postfire succession. Another study by Arakida et al (2021) also confirmed that a spin-up period of 100 years was enough for the saturation of the LAI, aboveground biomass, and GPP at all of the study sites in Siberia.…”

Drought Sensitivity and Vulnerability of Rubber Plantation GPP—Insights from Flux Site-Based Simulation

“…Based on calibrated SEIB-DGVM, used to simulate the response of rubber forest GPP in multiple drought scenarios, the sensitivity and vulnerability characteristics of natural rubber plantation ecosystems in response to different drought characteristics were quantified and clarified. In the existing studies, SEIB-DGVM has exhibited good performance in simulations at the site, intercontinental, and global scales [40], and there have been studies that have improved the simulation performance of the model through parameter optimization [41]. Moreover, by enabling flexible selection of vegetation life and phenological characteristics and allowing the activation or deactivation of plant function types (PFTs), the model has demonstrated the ability to generate simulations covering various ecosystems such as the Malaysian tropical rainforest and eastern Siberian larch forest [42].…”