WILDLAND SURFACE FIRE BEHAVIOUR: A SPATIAL SIMULATION MODEL FOR OPERATIONAL EMERGENCY MANAGEMENT
Tesi di Dottorato
Data di Pubblicazione:
2021
Citazione:
WILDLAND SURFACE FIRE BEHAVIOUR: A SPATIAL SIMULATION MODEL FOR OPERATIONAL EMERGENCY MANAGEMENT / D. Voltolina ; tutor: T. Apuani, S. Sterlacchini, G. Cappellini ; coordinatore: F. Camara Artigas. Dipartimento di Scienze della Terra Ardito Desio, 2021 Jun 07. 33. ciclo, Anno Accademico 2020.
Abstract:
Wildfires affect vegetation dynamics, geomorphological processes, biogeochemical cycles, atmospheric chemistry, and climate, posing a severe threat to human lives and activities interacting with the natural system. As both fire activity and wildland-urban interface exposure are expected to increase under future climate projections, the improvement of our ability to promptly predict wildland fire behaviour, in terms of expected intensity and geographic patterns, has become a tangible need.
The general purpose of this research is to investigate on wildland surface fire behaviour simulation models and to support disaster managers in optimising decision making processes in wildfire risk management in a Mediterranean-type climate region, namely Sardinia, Italy. This project is intended to pursue two major objectives: (i) develop and validate a predictive spatially distributed wildland surface fire behaviour simulation model intended for operational use; (ii) design and implement a geospatial decision support system to provide decision makers with appropriate strategies and tools for an integrated wildland fire risk management.
Predicting wildland surface fire behaviour requires a deep understanding of the influence of environmental parameters that act as drivers of the fire spread, including geomorphometrical variables, meteorological conditions, and fuel characteristics, on fire descriptors, such as the rate and direction of the maximum fire spread, the eccentricity of the ellipse approximating the fire shape, the intensity of the fire front, and the flame length. The Rothermel’s mathematical model for predicting surface fire spread in wildland fuels is currently the most extensively used method to estimate fire descriptors, especially for operational purposes. The application of the Rothermel’s model for simulating the behaviour of ongoing wildfires calls for the need of a technique for continuous monitoring of the spatiotemporal variability of weather conditions and fuel characteristics, such as fuel height, loading, and moisture content, in the pre-fire environment. Firstly, freely available data sources and remote sensing products and datasets have been investigated to define a pre-processing methodology for the near real-time estimation of the drivers of fire spread. Secondly, the need for flexibility in handling the equations of the Rothermel’s and associated models, together with the necessity of integrating corrections and updates, have led to an original implementation of a computer algorithm that evaluates the fire descriptors as defined by the extended Rothermel’s mathematical model. Then, a proxy model of this implementation has been developed using a machine learning ensemble method in order to analyse the interdependence of the drivers and to understand their relative importance in predicting fire descriptors. Furthermore, the proxy model for predicting fire spread across heterogeneous landscapes has been integrated into an agent-based simulation model developed to predict the surface fire behaviour and growth with the aim of providing fire management authorities with timely information on the expected progress of the fire front. Finally, the developed simulation model has been applied to and validated against historical wildfire events recorded in Sardinia, Italy, to evaluate its performance in terms of predictive capacity. The effects of fire suppression activities have also been simulated according to the availability of accurate information on timing and location of interventions that effectively extinguished the fire’s spread.
As a whole, the developed wildland surface fire behaviour simulation model, together with the pre-processing methodology, have resulted in a satisfying accuracy in terms of quanti
Tipologia IRIS:
Tesi di dottorato
Keywords:
wildfire behaviour; remote sensing; machine learning; agent-based model; decision support system
Elenco autori:
D. Voltolina
Link alla scheda completa: