Project Q12: Contribution of wind and topography on wildfire hazard

Timescale: Oct. 2021 – Sept. 2024

Supervisors:

Dr. Kirsten Thonicke, PIK Potsdam

Prof. Henning Rust, FU Berlin

Description

Recent wildfire events (e.g. Greece 2007, 2018, Portugal 2017, and California 2018) have shown that wind and topography are important contributing factors that can change a wildfire occurring during a climate extreme event to a fire extreme, thus hazard. Wildfire risk is increased during prolonged or extreme drought which desiccates vegetation and dead biomass which then form dry fuel conditions that strongly influences wildfire behaviour. Fire spread increase with wind speed and in mountainous areas. However, recent fire extreme events have shown that extremely high wind speeds accelerated fire spread in mountainous areas that has led to severe impact in forest ecosystems and caused high number of casualties. Vegetation structure, especially dense forests with often high fuel loading, and wildland-urban interfaces contributed to the increased hazard from this extreme event. However, current process-based vegetation-fire models such as SPITFIRE embedded in LPJmL4.0 (Thonicke et al. 2010; Schaphoff et al. 2018), do not consider extreme wind conditions and ignore topographic conditions for fire spread so far, which is important if we want to understand the occurrence and impact of such extreme fire events under future climate change. Since drought conditions are predicted to increase for the Mediterranean area, vegetation growth can become increasingly limited in the future, which would limit fire spread due to limited fuel availability. Thus process-based modelling of vegetation-fire interactions is an important pre-requisite to improve the quantification of future fire hazard.

The project Q12 comprises the following tasks:

• investigate wind speed data and topographical indices with regard to their contribution to high fire spread rates in very short time of recent fire extreme events;
• investigate the connection to heat waves as a pre-condition for extremely dry dead and live fuel (amount of fuel in different fuel types, fuel composition, flammability of vegetation types, forest structure);
• develop and incorporate respective modelling function in process-based fire model (SPITFIRE embedded in LPJmL4.0) and compare with flexible-trait model LPJmL-FIT (Sakschewski et al. 2015);
• apply improved vegetation-fire models to climate and land-use scenarios to quantify future risk of fire extreme events and the resulting extreme impact on ecosystem (and infrastructure).

The following results are expected:

• improved vegetation-fire model with wind-speed and topographic function incorporated in the SPITFIRE model;
• quantification of future hazard and risk from extreme fire events in Southern Europe under climate change;
• analysis of changes in the interaction of vegetation, climate (drought, wind) and fire during possible future climate extreme events.

Dedicated Regional Clusters: Central Europe and the Mediterranean region

Responsibilities: Based on observations of meteorological conditions, specifically wind speed, vegetation status (fuel composition and dryness), and on topographical information, the successful candidate will investigate the environmental conditions that led to recent fire extremes in Europe using exploratory data analysis. In a second step, the successful candidate will use this new knowledge to develop and incorporate a respective modeling function into the established, process-based fire model SPITFIRE which is embedded in the dynamic global vegetation model LPJmL. The improved model should then be used to simulate changes in fire interacting with vegetation to analyse future risk of fire extremes that lead to extreme ecosystem impact under climate change.

Requirements: We are seeking applications from highly motivated individuals with an excellent Master’s degree in meteorology, physics, or a related discipline in environmental sciences with strong modeling background. The candidates are expected to have data analysis (statistical methods) and advanced programming skills (R, Julia, Python, or similar for data analysis), as well as a solid background in environmental physics and meteorology. Experiences in working with Linux, C and version control software, in setting up extended simulation experiments and in analysing atmospheric model outputs are desirable. The PhD-project will be carried out in an interdisciplinary research team. Fluency in the English language (speaking and writing) is mandatory. We expect willingness to travel for work to present scientific results in workshops and scientific conferences.