Characterization of Large Escaped Spring Wildfires in Alberta

This project will characterize seventy-four wildfires in May from 1990 to 2016 that were both initial attack (IA) escapes and being held (BH) escapes, and exceeded 500 ha in size.

Inversion Breakdown Analysis

Different wind models will be assessed with respect to their added value for fire behavior prediction. These models will be applied to the 2016 Horse River Wildfire. The ability to accurately predict when an inversion breaks down is important for reliable fire behaviour forecasts. This project will assess why and when spring inversions occur and break down.

Development  of Thresholds and Triggers to Establish a Provincial ‘Code Red’ State of Preparedness

This project will investigate various input metrics such as the current and forecasted fire load, fire size, and fire perimeter to build a new state of preparedness system for Alberta.

The Influence of Fire Legacy and Severity on Post-Fire Carbon Cycling 

This study will evaluate the post-fire carbon cycling under alternative fire history and fire severity to better understand the response of vegetation to fire. 

Development of an Atmospheric Instability Index for Use in Alberta

Create a fire behaviour danger index based on atmospheric instability for use in Alberta. The relationship between the vertical structure of the atmosphere and wildfire activity, and in particular large wildfire growth is well known. Atmospheric instability can have a strong influence on the rate of spread and intensity of wildfires. It supports column development and circulation which contributes to increasing intensities. We propose to investigate various thermodynamic and severity indices (e.g. Haines Index, Lifted Index, George’s K) for their potential application as a decision support tool for wildfire management in Alberta.

Modelling Initial Attack Containment Success: a Tool to Support Initial Attack Dispatching Decisions

This project focuses on dispatching, in particular, building machine learning models to predict IA success. Previous studies have used historical fire report data for the province of Alberta to model IA containment in relation to a range of variables including IA response time, fire load, fuel conditions (time-since-fire, linear features), and fire behaviour conditions (Arienti et al. 2006, Beverly 2017). A key innovation of this project is the development of a model to predict IA success that incorporates information about the type and the amount of dispatched resources, in addition to fuel and weather conditions.

Exploring the Health Impacts of Occupational Exposure to Wildland Fires for Wildland Firefighters and Related Personnel: a Scoping Review

The aim of this work is to explore the impact of occupational exposure to wildland fires on health outcomes including physical, mental, and social wellbeing and identify mitigation strategies or policies implemented to reduce negative health impacts as reported by current literature and reports. Previous literature combined with stakeholder input will be used to support the development of a collaborative strategic research plan for the BC Wildfire Service.

Psychological Health and Safety in Wildland Firefighting

The current project aims to guide the BC Wildfire Service to accomplish three major objectives relating to psychological health and safety: 
•    Leverage the collective knowledge and expertise pertaining to both psychological health and safety and the process of wildland fire intervention program development to mobilize existing knowledge and best practices from neighbouring jurisdictions and associated industries; 
•    Audit the psychosocial climate, including assessment of employee experiences and availability and evaluation of existing resources, supports and structures; and,
•    Work with BCWS Organizational Development, Safety and Wellbeing, and Research and Innovation staff to determine how to best implement the findings of this project in the BCWS.

Bulkley Valley Research Centre (BVRC) Fire Research and Extension Program Wildfire Fuels and Fire Regimes in B.C.

This ‘fire research and extension program’ consists of two projects that are interconnected with and in support of the ‘wildfire fuels and fire regimes in B.C.’ work being carried out by UBC. These projects focus on providing information on how the implications of past forest management practices relate to the resiliency of current landscapes and on providing data on historical disturbance regimes in the Lakes Timber Supply Area of northern B.C. 

This initiative consists of six separate but interconnected projects and is being carried out in concert with the ‘Bulkley Valley Research Centre fire research and extension program.’ The projects address the topics of fuel loading and fire severity, fuel management efficacy, disrupted fire regimes, fire suppression and fuel loads, and the effects of climate change and disrupted fire regimes on future wildfires.

Kamloops UAV-based Hyperspectral / LiDAR Data Acquisition

The goal of this project is to acquire a unique, advanced high-dimensional dataset of forest canopy cover using a remotely piloted aircraft system (RPAS)-borne Hyperspectral / LiDAR sensor.  This data set will then be analysed with contemporary machine learning methods to classify forest land-cover types of interest to BCWS practitioners.  The Eagle Hills field site selected for data acquisition was recently surveyed for biogeoclimactic ecosystem classifications by the Kamloops office of the Province of British Columbia’s ministry of Forest, Land, Natural Resource Operations, and Rural Development (FLNRORD).  FLNRORD has shared these ground reference data with the project team for validation of the team’s results.  

A Multi-Modal, Life Cycle Approach to Risk and Crisis Communication

Models for risk and crisis communication have evolved over recent decades with a shift from command and-control, top-down, linear approaches to holistic, cyclic models that strive for greater transparency. These newer models are people-centred, include a wider range of stakeholders, and they are designed to (re)build trust. A challenge with these models is that they fail to consider the life cycle of a disaster, or how to effectively use different models and approaches at various points in this life cycle. Risk and crisis communication needs are different at pre-disaster, disaster, and post-disaster stages. Moreover, it is important to determine thresholds, indicators, and transition points for shifting from one model to the next. Standard risk and crisis communication models – even the more evolved and holistic ones - tend to apply a one-size-fits-all approach which may be ineffective during natural disasters. Existing models also fail to distinguish between internal and external communication, and poorly explain how best to incorporate multiple stakeholders from various levels of government including First Nations, first responders, healthcare providers, media, and others. This knowledge synthesis will focus on reviewing existing models, and move towards developing a multi-modal, life cycle approach. Consultation with key stakeholders will inform this research, and an initial emphasis will be placed on wildfire risk in the context of British Columbia.

Stochastic Frontier Analysis of Wildfire Suppression in Alberta: Identifying Sources of Suppression Efficiency

Alberta's priority research question 7 asks “how can wildland fire operations be made more efficient and effective?” This project addresses this question by utilizing historical daily Alberta Wildfire operations data to assess factors affecting operational efficiency of large fire suppression. The efficacy of active wildland fire suppression is dependent on the Incident Management Team's (IMT) daily decisions including: the method of attack, how much and what type of equipment to deploy, and fire crew requirements given dynamic fire behaviour. The growth of wildfires can be variable and often unpredictable given changing weather, topography, vegetation (fuel), presence of lakes and rivers, man-made features such (roads or structures), time of year, and prior forest harvest or burn areas present on the active fire perimeter.

Experiments, Machine Learning and Regression Analysis on Wildfire Suppression Expenditures in Alberta

The project aims to analyze how wildfire suppression operation expenditures can be explained by various factors, including: environmental variables, the location of values-at-risk, operational aspects, and incident commanders’ risk aversion. The experiment measures incident commanders’ risk aversion using laboratory experiments that are recognized in experimental economics literature. These experiments are ongoing and we expect to be able to run preliminary analyses in May 2021.