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.

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

2017 ◽  
Vol 24 (3) ◽  
pp. 553-567 ◽  
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.

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

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.

scholarly journals Modelling LAI at a regional scale with ISBA-A-gs: comparison with satellite-derived LAI over southwestern France

2009 ◽  
Vol 6 (8) ◽  
pp. 1389-1404 ◽  
Author(s):  
A. Brut ◽  
C. Rüdiger ◽  
S. Lafont ◽  
J.-L. Roujean ◽  
J.-C. Calvet ◽  
...  
Keyword(s):  
Land Surface ◽  
Regional Scale ◽  
Land Surface Model ◽  
Temperature Response ◽  
Climatic Conditions ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Satellite Signal

Abstract. A CO2-responsive land surface model (the ISBA-A-gs model of Météo-France) is used to simulate photosynthesis and Leaf Area Index (LAI) in southwestern France for a 3-year period (2001–2003). A domain of about 170 000 km2 is covered at a spatial resolution of 8 km. The capability of ISBA-A-gs to reproduce the seasonal and the interannual variability of LAI at a regional scale, is assessed with satellite-derived LAI products. One originates from the CYCLOPES programme using SPOT/VEGETATION data, and two products are based on MODIS data. The comparison reveals discrepancies between the satellite LAI estimates and between satellite and simulated LAI values, both in their intensity and in the timing of the leaf onset. The model simulates higher LAI values for the C3 crops than the satellite observations, which may be due to a saturation effect within the satellite signal or to uncertainties in model parameters. The simulated leaf onset presents a significant delay for C3 crops and mountainous grasslands. In-situ observations at a mid-altitude grassland site show that the generic temperature response of photosynthesis used in the model is not appropriate for plants adapted to the cold climatic conditions of the mountainous areas. This study demonstrates the potential of LAI remote sensing products for identifying and locating models' shortcomings at a regional scale.

Data assimilation in lake Geneva using the SPUX framework

2020 ◽  
Author(s):  
Artur Safin ◽  
Damien Bouffard ◽  
James Runnalls ◽  
Fotis Georgatos ◽  
Eric Bouillet ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Hydrodynamic Model ◽  
Particle Filtering ◽  
Variance Reduction ◽  
Natural State ◽  
Model Parameters ◽  
In Situ Observation ◽  
Lake Geneva

<p>Lakes form an integral component of ecosystems and our communities. Aside from being a source of drinking water, lakes provide additional benefits such as recreation, heat management and fishing. At the same time, human activity can significantly disrupt the natural state of the aquatic ecology. In the past, limited understanding of the hydrological and biochemical processes in aquatic systems has led to significant eutrophication in certain cases. To mitigate further risk, monitoring programs have been implemented. Recently new instrumentation, such as in situ observation platforms, remote sensing and computational resources enable comprehensive monitoring of the temporal evolution of the environment’s spatial heterogeneity.</p><p>A major focus of the DATALAKES project is to use the multiple sources of observational measurements for data assimilation and forecasting purposes. The aim is to infer the entire state of the lake as accurately as possible using high-resolution three-dimensional hydrodynamic models. Uncertainty quantification using Bayesian inference and modern Markov Chain Monte Carlo methods is implemented using the SPUX package, with the stochasticity provided by an ensemble of weather forecasts. To obtain predictions in a reasonable time, we parallelize both the particle filtering and the hydrodynamic model on the CSCS cluster. The data assimilation component will combine multiple in-situ sources with remote sensing measurements of lake water surface temperature and incorporate the respective uncertainties in measurement into the error model. To enable the use of multi-level variance reduction schemes, we perform calibration of essential hydrodynamic model parameters for a hierarchy of discretisations. The results show that the framework is capable of inferring the state of lake Geneva from observational measurements.</p>

Sign in / Sign up

Export Citation Format

Share Document