Pastor, E.; Oiveras M., I.; Urquiaga-Flores, E.; Quintano, J.; Manta, M.I.; Planas, E. International journal of wildland fire Vol. 26, num. 12, p. 1040-1052 DOI: 10.1071/WF17033 Data de publicació: 2017-12-08 Article en revista
Smouldering ground fires have severe environmental implications. Their main effects are the release of large amounts of carbon to the atmosphere with loses of organic soil and its biota. Quantitative data on the behaviour of smouldering wildfires are very scarce and are needed to understand its ecological effects, to validate fuel consumption and smouldering propagation models and to develop danger-rating systems. We present, for the first time, a methodology for conducting smouldering experiments in field conditions. This method provides key data to investigate smouldering combustion dynamics, acquire fire behaviour metrics and obtain indicators for ecological effects of smouldering fires. It is to be applied in all types of undisturbed soils. The experimental protocol is based on a non-electric ignition source and the monitoring system relies on combining both point and surface specific temperature measurements. The methodology has been developed and applied by means of large series of replicate experiments in highly organic soils at the forest–grassland treeline of the Peruvian Andes. The soil tested exhibited weak ignition conditions. However, transition to oxidation phase was observed, with smouldering combustion during 9¿h at 15-cm depth and residence times at temperatures above dehydration of ~22¿h.
A wildfire forecasting tool capable of estimating the fire perimeter position sufficiently in advance of the actual fire arrival will assist firefighting operations and optimise available resources. However, owing to limited knowledge of fire event characteristics (e.g. fuel distribution and characteristics, weather variability) and the short time available to deliver a forecast, most of the current models only provide a rough approximation of the forthcoming fire positions and dynamics. The problem can be tackled by coupling data assimilation and inverse modelling techniques. We present an inverse modelling-based algorithm that uses infrared airborne images to forecast short-term wildfire dynamics with a positive lead time. The algorithm is applied to two real-scale mallee-heath shrubland fire experiments, of 9 and 25 ha, successfully forecasting the fire perimeter shape and position in the short term. Forecast dependency on the assimilation windows is explored to prepare the system to meet real scenario constraints. It is envisaged the system will be applied at larger time and space scales.
Aircraft are often used to drop suppressants and retardants to assist wildfire containment. Drop effectiveness has rarely been measured due to the difficulties in collecting data from wildfires and running field experiments and the absence of definitions and measures. This paper presents a set of criteria and methodologies for evaluating the effectiveness of aerial suppression drops. These consider drop placement, coverage and effect on fire behaviour. This paper also details drop site and delivery conditions that are required for determining causal factors that influence drop effectiveness and allow drops to be compared. Examples of drop impact evaluations made during experimental fires are used to demonstrate these methodologies. The main methods proposed are based on the analysis of orthorectified airborne infrared imagery of drops, which can be used to measure drop dimensions, proximity to fire perimeter and their effect on fire spread. These evaluations can be used to compare tactics, suppressants and delivery systems and to inform cost–benefit analyses of aerial suppression.