AIMS AND CONTENT

LEARNING OUTCOMES

L’insegnamento si propone di fornire agli studenti le nozioni di base di quantificazione e gestione del rischio di incendi boschivi. Il modulo fornirà le conoscenze necessarie per comprendere i processi e le condizioni al suolo e meteorologiche legate al verificarsi degli incendi nell'ambiente agro-forestale, con particolare attenzione alle dinamiche degli estremi locali del pericolo incendi. Scopo dell’insegnamento è quello di avere una panoramica del problema degli incendi boschivi a livello globale concentrandosi sui diversi aspetti coinvolti in questo tipo di rischio, compresi gli effetti del cambiamento climatico. Al termine dell’insegnamento, lo studente sarà in grado di utilizzare ed interpretare i più comuni sistemi di quantificazione del pericolo da incendi boschivi; interpretare mappe suscettività, di hazard e rischio incendi boschivi, ed individuare le fonti di dati e le tecniche necessarie alla costruzione di suddette mappe; lo studente sarà altresì in grado di conoscere i principali modelli di propagazione di incendio ed il loro ruolo nelle azioni di contrasto agli incendi e di generazione scenari.

AIMS AND LEARNING OUTCOMES

Aims of the course is to transfer knowledge on the main processes related with the occurrence and the behavior of forest fires considering all the aspects involved, including cause of ignition, vegetation, topography, meteorology and climate, coping capacity. Tools and methods for wildfire risk assessment will be presented both considering static and dynamic mapping. The students at the end of the course will be able to:

• understand the phenomena: causes, regimes, impacts
• create wildfire risk maps
• predict fire danger
• suggest prevention activities
• simulate the potential behavior of wildfires
• support decisions in firefighting activities
• evaluate the potential impacts of wildfires
• analyze wildfire risk scenarios

TEACHING METHODS

Theoretical aspects will be presented through frontal lectures. Practical aspects of data analysis and wildfire risk mapping will be approached through laboratory activities. Python language and Jupyter environment will be adopted. The students are encouraged to work in small groups, with particular attention to the final project that will be evaluated during the exam. It is suggested to attend all the classes and the exercises in laboratory. Field trips and seminars will be organized. 

SYLLABUS/CONTENT

• Introduction to the course
• Overview of wildfire regimes and impacts
• The phases of wildfire risk management: prevention & preparedness, response, restoration & recovery
• Static wildfire risk assessment: susceptibility, hazard, vulnerability and risk
• Fire spread simulators: physical and empirical approaches
• Dynamic wildfire danger assessment and early warning systems

RECOMMENDED READING/BIBLIOGRAPHY

Teaching materials will be provided to the students during the lessons, including slides, scientific papers, reports, etc.

LESSONS

LESSONS START

https://courses.unige.it/10553/p/students-timetable

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam consists in an oral session where it is strongly suggested to present the results of the project assigned during the laboratory activities. Theoretical questions concerning the whole set of topics presented during the course will complete the evaluation of the students.

Students with learning disorders ("disturbi specifici di apprendimento", DSA) will be allowed to use specific modalities and supports that will be determined on a case-by-case basis in agreement with the delegate of the Engineering courses in the Committee for the Inclusion of Students with Disabilities.

ASSESSMENT METHODS

The oral exam will assess the following capacities:

• understanding the main drivers of wildfire
• producing wildfire risk maps
• knowledge on different approaches to predict fire danger
• suggesting effective prevention activities
• evaluating the potential impacts of wildfires
• analyzing wildfire risk scenarios
• implementing in Python environment routines for risk mapping and data analysis