An Architecture for Adaptive Robust Modelling of Wildfire Behaviour under Deep Uncertainty

Abstract

Abstract. Wildfires in Europe -especially in the Mediterranean region -are one of the major treats at landscape scale. While their immediate impact ranges from endangering human life to the destruction of economic assets, other damages exceed the spatio-temporal scale of a fire event. Wildfires involving forest resources are associated with intense carbon emissions and alteration of surrounding ecosystems. The induced land cover degradation has also a potential role in exacerbating soil erosion and shallow landslides. A component of the complexity in assessing fire impacts resides in the difference between uncontrolled wildfires and those for which a control strategy is applied. Robust modelling of wildfire behaviour requires dynamic simulations under an array of multiple fuel models, meteorological disturbances and control strategies for mitigating fire damages. Uncertainty is associated to meteorological forecast and fuel model estimation. Software uncertainty also derives from the datatransformation models needed for predicting the wildfire behaviour and its consequences. The complex and dynamic interactions of these factors define a context of deep uncertainty. Here an architecture for adaptive and robust modelling of wildfire behaviour is proposed, following the semantic array programming paradigm. The mathematical conceptualisation focuses on the dynamic exploitation of updated meteorological information and the design flexibility in adapting to the heterogeneous European conditions. Also, the modelling architecture proposes a multicriteria approach for assessing the potential impact with qualitative rapid assessment methods and more accurate a-posteriori assessment.

Continental-Scale Living Forest Biomass and Carbon Stock: A Robust Fuzzy Ensemble of IPCC Tier 1 Maps for Europe

“…The exemplified fuzzy ensemble modelling strategywhose D-TM detailed computational description [32] is reproducible [33][34][35] and entirely based on free software [36] and data [37,38] -lies within a family of semantically-enhanced ensemble techniques [39][40][41][42] which might also serve as reference for merging multiple partially independent estimations of biomass and carbon stock [43] and deriving new improved maps. Several factors, neglected in Fig.…”

“…The effect of fires and other disturbances also impacts on the canopy cover and thus on the non-living component of the carbon stock -here not modelled -by means of soil erosion [54][55][56]. Recent advances in European soil data [57], soil erosion [58,59] and its relationship with wildfires might help to complement the analysis of changes in living and non-living forest carbon [41,42].…”

Multi-scale Robust Modelling of Landslide Susceptibility: Regional Rapid Assessment and Catchment Robust Fuzzy Ensemble

“…A new approach based on a robust modelling ensemble at catchment scale is here proposed for improving landslide susceptibility estimation. A preliminary regional-scale rapid assessment is also introduced as a possible support in real-time operations such as wildfire emergency management [19,20].…”

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

“…However, the aim for the described fire information system to support the variety of conditions at continental scale leads the modelling architecture [4] to cover several transdisciplinary aspects [2] aside the direct life and health risks [5]. Fire-induced geomorphic and ecological transformations [6][7][8] may cause severe carbon emissions and alter biodiversity [9,10] even due to habitat fragmentation [11][12][13].…”

“…The case study is based on a new architecture for adaptive and robust modelling of wildfire behaviour, proposed in [4]. We focus on the architecture modules for simulating wildfire dynamics under fire control scenarios.…”