A stochastic model for time series prediction of the number of post-earthquake fire ignitions in buildings based on the ignition record for the 2011 Tohoku Earthquake

This article presents the development of a stochastic model for time series prediction of the number of post-earthquake fire ignitions in buildings for use in post-earthquake fire risk assessment. Two kinds of Poisson regression models with an explanatory variable of JMA instrumental seismic intensity were applied to 126 ignitions affected by ground motion, which were extracted from the ignition record for the 2011 Tohoku Earthquake: (1) a time-dependent occurrence model for the ignitions from electricity-related sources, which is coupled with a statistical model for electrical supply rate after an earthquake, and (2) a time-independent occurrence model for the ignitions from gas-related sources, oil-related sources, and others. In order to verify the models, time series prediction of the number of ignitions in the 2011 Tohoku Earthquake was conducted using Monte Carlo simulation. From the calculated results, we concluded that the models could reasonably explain the occurrence tendency of ignitions in the 2011 Tohoku Earthquake.

Modeling anthropogenic and natural fire ignitions in an inner-alpine valley

Modeling and assessing the factors that drive forest fire ignitions is critical for fire prevention and sustainable ecosystem management. In southern Europe, the anthropogenic component of wildland fire ignitions is especially relevant. In the Alps, however, the role of fire as a component of disturbance regimes in forest and grassland ecosystems is poorly known. The aim of this work is to model the probability of fire ignition for an Alpine region in Italy using a regional wildfire archive (1995–2009) and MaxEnt modeling. We analyzed separately (i) winter forest fires, (ii) winter fires on grasslands and fallow land, and (iii) summer fires. Predictors were related to morphology, climate, and land use; distance from infrastructures, number of farms, and number of grazing animals were used as proxies for the anthropogenic component. Collinearity among predictors was reduced by a principal component analysis. Regarding ignitions, 30 % occurred in agricultural areas and 24 % in forests. Ignitions peaked in the late winter–early spring. Negligence from agrosilvicultural activities was the main cause of ignition (64 %); lightning accounted for 9 % of causes across the study time frame, but increased from 6 to 10 % between the first and second period of analysis. Models for all groups of fire had a high goodness of fit (AUC 0.90–0.95). Temperature was proportional to the probability of ignition, and precipitation was inversely proportional. Proximity from infrastructures had an effect only on winter fires, while the density of grazing animals had a remarkably different effect on summer (positive correlation) and winter (negative) fires. Implications are discussed regarding climate change, fire regime changes, and silvicultural prevention. Such a spatially explicit approach allows us to carry out spatially targeted fire management strategies and may assist in developing better fire management plans.

Modeling anthropogenic and natural fire ignitions in an inner-alpine valley

Modelling and assessing the factors that drive forest fire ignitions is critical for fire prevention and sustainable ecosystem management. In southern Europe, the anthropogenic component of wildland fire ignitions is especially relevant. In the Alps, however, the role of fire as a component of disturbance regimes in forest and grassland ecosystems is poorly known. The aim of this work is to model the probability of fire ignition for an alpine region in Italy using a regional wildfire archive (1995–2009) and MaxEnt modeling. We analyzed separately: i) winter forest fires; ii) winter fires on grasslands and fallow land; iii) summer fires. Predictors were related to morphology, climate, and land use; distance from infrastructures, number of farms, and number of grazing animals were used as proxies for the anthropogenic component; collinearity among predictors was reduced by a Principal Component Analysis. 30 % of ignitions occurred in agricultural areas, 24 % in forests. Ignitions peaked in the late winter–early spring. Negligence from agro-silvicultural activities was the main cause of ignition (64 %); lightning accounted for 9 % of causes across the study timeframe, but increased from 6 % to 10 % between the first and second period of analysis. Models for all groups of fire had a high goodness of-fit (AUC 0.90–0.95). Temperature was proportional to the probability of ignition, and precipitation inverse proportional. Proximity from infrastructures had an effect only on winter fires, while the density of grazing animals had a remarkably different on summer (positive correlation) and winter (negative) fires. Implications are discussed regarding climate change, fire regime changes, and silvicultural prevention. Such spatially explicit approach allows to carry out spatially targeted fire management strategies, and may assist in developing better fire management plans.

Optimal Number and Location of Watchtowers for Immediate Detection of Forest Fires in a Small Island

A crucial factor for prevention and immediate confrontation of destructive fires and their socioeconomic and environmental consequences constitutes the early detection and spatial localization of fire ignitions, so that the firefighting forces to be activated and act within the critical time of response. Thus, principal objective of the paper constitutes the spatial optimization of the most effective locations of watchtowers developing a constructive network for the immediate and early detection of forest fires. This optimization involves the exploration of the fewest locations for watchtowers with the maximum visible area and reduced degree of overlapping. The results highlighted 4 groups of watchtowers (20 observers in total) determining the optimum locations. The total visibility amounted to 70% of the island, while the visibility percentages per land cover are variable, since they are depended on the spatial structure of them. Definitely, the final selection of the final number and the spatial structure of the watchtowers purely constitute decisions of political nature and will.

Contribution of human and biophysical factors to the spatial distribution of forest fire ignitions and large wildfires in a French Mediterranean region

Identifying the factors that drive the spatial distribution of fires is one of the most challenging issues facing fire science in a changing world. We investigated the relative influence of humans, land cover and weather on the regional distribution of fires in a Mediterranean region using boosted regression trees and a set of seven explanatory variables. The spatial pattern of fire weather, which is seldom accounted for in regional models, was estimated using a semi-mechanistic approach and expressed as the length of the fire weather season. We found that the drivers of the spatial distribution of fires followed a fire size-dependent pattern in which human activities and settlements mainly determined the distribution of all fires whereas the continuity and type of fuels mainly controlled the location of the largest fires. The spatial structure of fire weather was estimated to be responsible for an average of 25% of the spatial patterns of fires, suggesting that climate change may directly affect the spatial patterns of fire hazard in the near future. These results enhance our understanding of long-term controls of the spatial distribution of wildfires and predictive maps of fire hazard provide useful information for fire management actions.