11:00 〜 13:00
[ACG39-P03] Siberian wildfires estimation with improved fire module on Spatially Explicit Individual Based (SEIB) DGVM
キーワード：Siberian, Wildfires, Fire module, SEIB-DGVM
Fire is one of the major ecological and environmental disturbances that has a great impact on global climate, via altering biogeochemical cycle and vegetation distribution. Globally total burned area including small fires approximately increased by 35%, from 345 to 464 Mha yr-1 and the biomass burning carbon emissions increased by 35% from 1.9 to 2.5 Pg C yr-1, during 2001 – 2010. For the year 2021, Siberia experienced the large wildfire over 16,06 Mha, amounted to 21% of total global burned area, and the carbon emissions reached to 0.4 Gt C or three times the 1997 – 2020 average according to GFED4s. Therefore, inclusion of the fire disturbance in vegetation model is absolutely important to simulate vegetation dynamics correctly.
The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) can simulate the competition among individual trees within a spatially explicit virtual forest and is able to analyze land energy and water budget, plant physiological, and plant dynamic processes. Original SEIB-DGVM incorporate the fire module, Glob-FIRM (Global Fire Model), which has several limitations such as: Glob-FIRM derives fractional area burnt in a grid cell from the simulated length of fire season and minimal fuel load annually but does not specify ignition sources and assumes a constant fire-induced mortality rate for each plant functional type (PFT), and human-changed fire regimes.
The aim of this study is to estimate burned area, CO2 and aerosol emissions from wildfire in Siberia using SEIB-DGVM. To do it accurately, we improve the current fire module: Glob-FIRM as well as install new fire module: SPITFIRE, which has been incorporated in several terrestrial ecosystem model. There are several improvements inside default fire module: Glob-FIRM, such as parameter optimization of fuel load, fraction of litter moisture, fraction of trunk and its litter lost by fire, and added new wind as disturbance that affect fire spread. The SPITFIRE is the improved Glob-FIRM that includes explicit representation of ignition events by lightning-caused and human-caused, physical properties, and processes determining fire spread and intensity.
Based on the simulation results, we expect to get the output of probability of fire occurrence, fractional area burnt, length of the fire season, and the Fire Return Interval (FRI), Fire Danger Index (FDI), number of fires, burned area, CO2 and aerosol emissions, we validate all of the output with the real condition dataset such as: Global Fire Emissions Database (GFED) and Moderate-resolution Imaging Spectroradiometer (MODIS) Active Fire Data. After validating the output, we compare the results from Glob-FIRM and the SPITFIRE, then predicting the future condition.
The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) can simulate the competition among individual trees within a spatially explicit virtual forest and is able to analyze land energy and water budget, plant physiological, and plant dynamic processes. Original SEIB-DGVM incorporate the fire module, Glob-FIRM (Global Fire Model), which has several limitations such as: Glob-FIRM derives fractional area burnt in a grid cell from the simulated length of fire season and minimal fuel load annually but does not specify ignition sources and assumes a constant fire-induced mortality rate for each plant functional type (PFT), and human-changed fire regimes.
The aim of this study is to estimate burned area, CO2 and aerosol emissions from wildfire in Siberia using SEIB-DGVM. To do it accurately, we improve the current fire module: Glob-FIRM as well as install new fire module: SPITFIRE, which has been incorporated in several terrestrial ecosystem model. There are several improvements inside default fire module: Glob-FIRM, such as parameter optimization of fuel load, fraction of litter moisture, fraction of trunk and its litter lost by fire, and added new wind as disturbance that affect fire spread. The SPITFIRE is the improved Glob-FIRM that includes explicit representation of ignition events by lightning-caused and human-caused, physical properties, and processes determining fire spread and intensity.
Based on the simulation results, we expect to get the output of probability of fire occurrence, fractional area burnt, length of the fire season, and the Fire Return Interval (FRI), Fire Danger Index (FDI), number of fires, burned area, CO2 and aerosol emissions, we validate all of the output with the real condition dataset such as: Global Fire Emissions Database (GFED) and Moderate-resolution Imaging Spectroradiometer (MODIS) Active Fire Data. After validating the output, we compare the results from Glob-FIRM and the SPITFIRE, then predicting the future condition.