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Dynamic Data Driven Ensemble for Wildfire Behaviour Assessment: A Case Study

Abstract : Wildfire information has long been collected in Europe, with particular focus on forest fires. The European Forest Fire Information System (EFFIS) of the European Commission complements and harmonises the information collected by member countries and covers the forest fire management cycle. This latter ranges from forest fire preparedness to post-fire impact analysis. However, predicting and simulating fire event dynamics requires the integrated modelling of several sources of uncertainty. Here we present a case study of a novel conceptualization based on a Semantic Array Programming (SemAP) application of the Dynamic Data Driven Application Systems (DDDAS) concept. The case study is based on a new architecture for adaptive and robust modelling of wildfire behaviour. It focuses on the module for simulating wildfire dynamics under fire control scenarios. Rapid assessment of the involved impact due to carbon emission and potential soil erosion is also shown. Uncertainty is assessed by ensembling an array of simulations which consider the uncertainty in meteorology, fuel, software modules. The event under investigation is a major wildfire occurred in 2012, widely reported as one of the worst in the Valencia region, Spain. The inherent data, modelling and software uncertainty are discussed and preliminary results of the robust data-driven ensemble application are presented. The case study suitably illustrates a typical modelling context in many European areas – for which timely collecting accurate local information on vegetation, fuel, humidity, wind fields is not feasible – where robust and flexible approaches may prove as a viable modelling strategy.
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Margherita Leo, Daniele Rigo, Dario Rodriguez-Aseretto, Claudio Bosco, Thomas Petroliagkis, et al.. Dynamic Data Driven Ensemble for Wildfire Behaviour Assessment: A Case Study. 10th International Symposium on Environmental Software Systems (ISESS), Oct 2013, Neusiedl am See, Austria. pp.11-22, ⟨10.1007/978-3-642-41151-9_2⟩. ⟨hal-01457461⟩

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