“…8b, c). This confirms transition from flaming to smouldering, which is the process in which the flaming duff layer causes the smouldering hotspot in peat layer below (Purnomo et al 2021).…”
Section: Surface Treatment and Smouldering Spreadsupporting
Peat wildfires can burn over large areas of peatland, releasing ancient carbon and toxic gases into the atmosphere over prolonged periods. These emissions cause haze episodes of pollution and accelerate climate change. Peat wildfires are characterised by smouldering -the flameless, most persistent type of combustion. Mitigation strategies are needed in arctic, boreal, and tropical areas but are hindered by incomplete scientific understanding of smouldering. Here, we present GAMBUT, the largest and longest to-date field experiment of peat wildfires, conducted in a degraded peatland of Sumatra. Temperature, emission and spread of peat fire were continuously measured over 4-10 days and nights, and three major rainfalls. Measurements of temperature in the soil provide field experimental evidence of lethal fire severity to the biological system of the peat up to 30 cm depth. We report that the temperature of the deep smouldering is ~13% hotter than shallow layer during daytime. During night-time, both deep and shallow smouldering had the same level of temperature. The experiment was terminated by suppression with water. Comparison of rainfall with suppression confirms the existence of a critical water column height below which extinction is not possible. GAMBUT provides a unique understanding of peat wildfires at field conditions that can contribute to mitigation strategies.
“…8b, c). This confirms transition from flaming to smouldering, which is the process in which the flaming duff layer causes the smouldering hotspot in peat layer below (Purnomo et al 2021).…”
Section: Surface Treatment and Smouldering Spreadsupporting
Peat wildfires can burn over large areas of peatland, releasing ancient carbon and toxic gases into the atmosphere over prolonged periods. These emissions cause haze episodes of pollution and accelerate climate change. Peat wildfires are characterised by smouldering -the flameless, most persistent type of combustion. Mitigation strategies are needed in arctic, boreal, and tropical areas but are hindered by incomplete scientific understanding of smouldering. Here, we present GAMBUT, the largest and longest to-date field experiment of peat wildfires, conducted in a degraded peatland of Sumatra. Temperature, emission and spread of peat fire were continuously measured over 4-10 days and nights, and three major rainfalls. Measurements of temperature in the soil provide field experimental evidence of lethal fire severity to the biological system of the peat up to 30 cm depth. We report that the temperature of the deep smouldering is ~13% hotter than shallow layer during daytime. During night-time, both deep and shallow smouldering had the same level of temperature. The experiment was terminated by suppression with water. Comparison of rainfall with suppression confirms the existence of a critical water column height below which extinction is not possible. GAMBUT provides a unique understanding of peat wildfires at field conditions that can contribute to mitigation strategies.
“…Recent developments in physical modelling are improving the understanding of the ignition and extinction limits of smouldering peat [ 30 , 31∗ , 32 ]. Cellular Automata modelling has been applied recently to simulate peat fires at the field scale [ 29 , 33 ] ( Figure 2 a). In the near future, a peat fire simulator (similar to the flaming simulator FARSITE [ 34 ]) should be developed for smouldering, combining fuel, water table and wind information based on GIS.…”
Section: Large Peat Firesmentioning
confidence: 99%
“…Unlike simulating flaming wildfires, the peat fire simulator should include the additional dimension of depth to visualise the 3D spread processes in the ground. Figure 2 ( a ) Simulation of smouldering peat fire of 573 ha in Indonesia triggered by the prescribed fire [ 29 ] and ( b ) fighting and control the peat wildfire (Gunstone, CC-BY). …”
Section: Large Peat Firesmentioning
confidence: 99%
“… ( a ) Simulation of smouldering peat fire of 573 ha in Indonesia triggered by the prescribed fire [ 29 ] and ( b ) fighting and control the peat wildfire (Gunstone, CC-BY). …”
Wildfires can be divided into two types, flaming or smouldering, depending on the dominant combustion processes. Both types are present in most wildfires, and despite being fundamentally different in chemical and physical terms, one transitions to the other. Traditionally, science has focused on flames, while smouldering is often misinterpreted. But smouldering wildfires are emerging as a global concern because they cause extensive air pollution, emit very large amounts of carbon, are difficult to detect and suppress, and could accelerate climate change. Central to the topic are smouldering peat fires that lead to the largest fires on Earth. Smouldering also dominates the residual burning after flames have died out and firebrand ignition. Finally, smouldering is an important part of Arctic wildfires, which are increasing in frequency. Here, we present a scientific overview of smouldering wildfires, the associated environmental and health issues, including climate change, and the challenges in prevention and mitigation.
“…In addition, many types of data are required to simulate fires in farsite, and some of these data are difficult to obtain. CA [16][17][18][19] has been applied in many disciplines because it is easy to use on computers [20]. CA [21,22] has better simulated performance in complex forest environments because fires do not usually spread in elliptical patterns.…”
The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. The simulation of CA is driven by the rate of fire spread (ROS), which is hard to estimate, because some input parameters of the current ROS model cannot be provided with a high precision, so the CA approach has not been well applied yet in the forest fire management system to date. The forest fire spread simulation model LSTM-CA using CA with LSTM is proposed in this paper. Based on the interaction between wind and fire, S-LSTM is proposed, which takes full advantage of the time dependency of the ROS. The ROS estimated by the S-LSTM is satisfactory, even though the input parameters are not perfect. Fifteen kinds of ROS models with the same structure are trained for different cases of slope direction and wind direction, and the model with the closest case is selected to drive the transmission between the adjacent cells. In order to simulate the actual spread of forest fire, the LSTM-based models are trained based on the data captured, and three correction rules are added to the CA model. Finally, the prediction accuracy of forest fire spread is verified though the KAPPA coefficient, Hausdorff distance, and horizontal comparison experiments based on remote sensing images of wildfires. The LSTM-CA model has good practicality in simulating the spread of forest fires.
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