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 dormancy 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 tree LAI, gross primary production (GPP), and above ground biomass. Most remarkably, the spatial distribution of tree LAI was estimated separately from undergrowth LAI because the SEIB-DGVM simulated the vertical structure of forest explicitly, and because satellite-observed LAI provided information on the onset and the end of the leaf season of tree and undergrowth, respectively. The DA system also provided the spatial distribution of the model parameters for tree separately from those of undergrowth. DA experiments started dormancy of trees more than a month earlier than the default phenology model and resulted in a decrease of the GPP.

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

Progress in Earth and Planetary Science ◽

10.1186/s40645-021-00443-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hazuki Arakida ◽

Shunji Kotsuki ◽

Shigenori Otsuka ◽

Yohei Sawada ◽

Takemasa Miyoshi

Keyword(s):

Data Assimilation ◽

Regional Scale ◽

Spatially Explicit ◽

Model Parameters ◽

In Situ Observation ◽

Vegetation Model ◽

Dynamic Global Vegetation Model ◽

Area Index ◽

Global Vegetation

AbstractThis 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.

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Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

Nonlinear Processes in Geophysics ◽

10.5194/npg-24-553-2017 ◽

2017 ◽

Vol 24 (3) ◽

pp. 553-567 ◽

Cited By ~ 2

Author(s):

Hazuki Arakida ◽

Takemasa Miyoshi ◽

Takeshi Ise ◽

Shin-ichiro Shima ◽

Shunji Kotsuki

Keyword(s):

Data Assimilation ◽

Leaf Area Index ◽

Leaf Area ◽

Model Parameters ◽

Vegetation Model ◽

Dynamic Global Vegetation Model ◽

Area Index ◽

The Real ◽

Global Vegetation ◽

The Impact

Abstract. We developed a data assimilation system based on a particle filter approach with the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, simulating the satellite-based LAI. Although we assimilated only LAI as a whole, the tree and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data and obtained promising results.

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Non-Gaussian data assimilation of satellite-based Leaf Area Index observations with an individual-based dynamic global vegetation model

10.5194/npg-2016-30 ◽

2016 ◽

Author(s):

Hazuki Arakida ◽

Takemasa Miyoshi ◽

Takeshi Ise ◽

Shin-ichiro Shima ◽

Shunji Kotsuki

Keyword(s):

Data Assimilation ◽

Leaf Area Index ◽

Leaf Area ◽

Model Parameters ◽

Vegetation Model ◽

Dynamic Global Vegetation Model ◽

Area Index ◽

The Real ◽

Global Vegetation ◽

The Impact

Abstract. We newly developed a data assimilation system based on a particle filter approach with the Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, assuming the satellite-based LAI. Although we assimilated only LAI as a whole, the forest and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data, and obtained promising results.

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Review of Non-Gaussian data assimilation of satellite-based Leaf Area Index observations with an individual-based dynamic global vegetation model

10.5194/npg-2016-30-rc1 ◽

2016 ◽

Author(s):

Matthias Morzfeld

Keyword(s):

Data Assimilation ◽

Leaf Area Index ◽

Leaf Area ◽

Vegetation Model ◽

Dynamic Global Vegetation Model ◽

Area Index ◽

Non Gaussian ◽

Global Vegetation ◽

Gaussian Data

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Effect of plant dynamic processes on African vegetation responses to climate change: Analysis using the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM)

Journal of Geophysical Research Atmospheres ◽

10.1029/2012jg002056 ◽

2012 ◽

Vol 117 (G3) ◽

pp. n/a-n/a ◽

Cited By ~ 24

Author(s):

Hisashi Sato ◽

Takeshi Ise

Keyword(s):

Climate Change ◽

Dynamic Processes ◽

Spatially Explicit ◽

Vegetation Model ◽

Change Analysis ◽

Dynamic Global Vegetation Model ◽

Climate Change Analysis ◽

Vegetation Responses ◽

Global Vegetation

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SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach

Ecological Modelling ◽

10.1016/j.ecolmodel.2006.09.006 ◽

2007 ◽

Vol 200 (3-4) ◽

pp. 279-307 ◽

Cited By ~ 236

Author(s):

Hisashi Sato ◽

Akihiko Itoh ◽

Takashi Kohyama

Keyword(s):

Spatially Explicit ◽

Vegetation Model ◽

Dynamic Global Vegetation Model ◽

Global Vegetation

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Accounting for forest age in the tile-based dynamic global vegetation model JSBACH4 (4.20p7; git feature/forests) – a land surface model for the ICON-ESM

10.5194/gmd-2019-68 ◽

2019 ◽

Author(s):

Julia E. M. S. Nabel ◽

Kim Naudts ◽

Julia Pongratz

Keyword(s):

Forest Management ◽

Land Surface ◽

Gross Primary Production ◽

Surface Model ◽

Forest Age ◽

Age Classes ◽

Dynamic Global Vegetation Model ◽

Area Index ◽

Global Vegetation ◽

Age Structures

Abstract. Natural and anthropogenic disturbances, in particular forest management, affect forest age-structures all around the globe. Forest age-structures in turn influence biophysical and biogeochemical interactions of the vegetation with the atmosphere. Yet, many dynamic global vegetation models (DGVMs), including those used as land surface models (LSMs) in Earth system models (ESMs), do not account for subgrid forest age structures, despite being used to investigate land-use effects on the global carbon budget or simulating land–atmosphere interactions. In this paper we present a new scheme to introduce forest age-classes in hierarchical tile-based DGVMs combining benefits of recently applied approaches. Our scheme combines a computationally efficient age-dependent simulation of all relevant processes, such as photosynthesis and respiration, without loosing the information about the exact forest age, which is a prerequisite for the implementation of age-based forest management. This combination is achieved by using the hierarchy to track the area fraction for each age on an aggregated plant functional type level, whilst simulating the relevant processes for a set of age-classes. We describe how we implemented this scheme in JSBACH4, the LSM of the ICON-ESM. Subsequently, we compare simulation output against global observation-based products for gross primary production, leaf area index and above-ground biomass to assess the ability of simulations with and without age-classes to reproduce the annual cycle and large-scale spatial patterns of these variables. The comparisons show differences exponentially decreasing with the number of distinguished age-classes and linearly increasing computation costs. The results demonstrate the benefit of the introduction of age-classes, with the optimal number of age-classes being a compromise between computation costs and accuracy.

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