scholarly journals 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)

2012 ◽  
Vol 117 (G3) ◽  
pp. n/a-n/a ◽  
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

scholarly journals Simulating vegetation response to climate change in the Blue Mountains with MC2 dynamic global vegetation model

2018 ◽  
Vol 10 ◽  
pp. 20-32 ◽  
Author(s):  
John B. Kim ◽  
Becky K. Kerns ◽  
Raymond J. Drapek ◽  
G. Stephen Pitts ◽  
Jessica E. Halofsky
Keyword(s):  
Climate Change ◽  
Vegetation Response ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Blue Mountains ◽  
Global Vegetation

scholarly journals LPJmL4 – a dynamic global vegetation model with managed land: Part I – Model description

2017 ◽  
Author(s):  
Sibyll Schaphoff ◽  
Werner von Bloh ◽  
Anja Rammig ◽  
Kirsten Thonicke ◽  
Hester Biemans ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Change Impacts ◽  
Companion Paper ◽  
Future Climate Change ◽  
Model Description ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Reference Simulation ◽  
Managed Ecosystems ◽  
Global Vegetation

Abstract. This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates – internally consistently – the growth and productivity of both natural and agricultural vegetation in direct coupling with water and carbon fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within, and impacts upon, the terrestrial biosphere as increasingly shaped by human activities such as climate change and land-use change. Here we describe the core model structure including recently developed modules now unified in LPJmL4. Thereby we also summarize LPJmL model developments and evaluations (based on 34 earlier publications focused e.g. on improved representations of crop types, human and ecological water demand, and permafrost) and model applications (82 papers, e.g. on historical and future climate change impacts) since its first description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at a Gitlab server, we hope to stimulate the application and further development of LPJmL4 across scientific communities, not least in support of major activities such as the IPCC and SDG process.

scholarly journals Regional-Scale Data Assimilation with The Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM) Over Siberia

2020 ◽  
Author(s):  
Hazuki Arakida ◽  
Shunji Kotsuki ◽  
Shigenori Otsuka ◽  
Yohei Sawada ◽  
Takemasa Miyoshi
Keyword(s):  
Spatial Distribution ◽  
Data Assimilation ◽  
Gross Primary Production ◽  
Regional Scale ◽  
Spatially Explicit ◽  
Model Parameters ◽  
Vegetation Model ◽  
Dynamic Global Vegetation Model ◽  
Area Index ◽  
Global Vegetation

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.

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

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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