Thursday, November 9 |
07:00 - 09:00 |
Breakfast (Vistas Dining Room) |
09:00 - 09:30 |
Steve Cumming: Towards a hierarchical model of the joint distribution of annual fire counts and fire sizes. ↓ A number of substantial ecological modelling and conservation planning initiatives now underway in North America use simple landscape fire models to estimate such quantities as: the range of historical variation in indices of landscape pattern; the minimum size for new protected areas; the threshold of human disturbances beyond which populations of woodland caribou can not be sustained. The results of these simulation-based studies have considerable policy and economic implications, so it’s important that they rest on accurate and unbiased estimates of the quantities of interest. These quantities are all variances of some sort, and they all depend on the variance of an underlying driving process, namely Xi, the annual area burned by wildland fires within some focal region. In this talk, I will outline some of the ways that landscape fire models are constructed from empirical data so as to reproduce E[Xi], possibly conditioned on covariates. Because the sampling variance of these models is large, one has tended to assume that it is large enough, but this may not be the case. I outline how one modelling methodology may be extended to more reliably encompass the variance exhibited by the empirical data. The number of fires per year is modelled as a Poisson process with Gamma-distributed rate parameter. This compound process can be parameterised from a fitted negative binomial model of annual counts. Similarly, annual fire sizes may be modeled as a Gamma mixture of exponentials, and the joint process could be modelled using pairs of correlated Gamma random variables. The intended benefit of this formulation is that it can be: a) estimated directly and automatically from fire management records; b) expanded to include time-varying covariates for climate and forest structure; and c) generalised to a hierarchical model that explicitly accounts for the fire detection and fire suppression processes. (TCPL 201) |
09:30 - 10:00 |
Lori Daniels: 2017 Wildfires in British Columbia: Causes, Consequences and Solutions ↓ The 2017 extreme wildfire season of 2017 has proven forests and communities in BC are not resilient to wildfire. A holistic, landscape view of this problem and transformative changes to wildfire and forest management are urgently needed to achieve forest and community resilience to contemporary and future wildfires. My colleagues, Robert Gray and Phil Burton, and I have proposed a four-pronged solution in BC:
1. Increases in human resources for all facets of wildland fire management and immediately reduce fuels in the wildland-urban interface of high-risk communities.
2. Make forest resilience the primary land management objective, achieved by proactive landscape planning to reinforce wildland-urban interface treatments.
3. Science and traditional ecological knowledge must inform forest restoration and management.
4. Invest in new research so transformation of wildfire management and risk reduction planning is evidence-based and tested for efficacy.
To support these points, I will illustrate how research on mixed-severity fire regimes in dry forests of BC provide strong evidence of the causes and consequences of altered fire regimes. This knowledge provides a foundation for ecosystem-specific adaptive management to increase forest and community resilience to wildfire. (TCPL 201) |
10:00 - 10:30 |
Patrick James: The influence of cumulative spruce budworm defoliation on the probability of ignition ↓ Outbreaks of forest insects are a significant agent of disturbance in Canada’s boreal and mixed-wood forests that affect forest landscape structure, including the accumulation of combustible fuels. As a result of repeated defoliation over consecutive years, defoliation by the spruce budworm (Choristoneura fumiferana) creates large patches of dead fir or spruce that have the potential to affect fire activity. Although it is generally believed that forest insects affect fire activity, how they have such an influence, and how this affect varies through time, remains equivocal. In this study, we sought to better understand how historical defoliation by the spruce budworm affects the probability of ignition, while controlling for weather. We modelled the relationship between historical fire ignitions and defoliation in the province of Ontario, using a a series of generalized additive logistic regression models. Using these models we contrasted fire-defoliation relationships between spring and summer fire seasons, as well as between ecoregions in eastern and western Ontario. We found that, in general, spruce budworm activity increases the risk of ignition 8-10 years after defoliation occurred, but decreases this risk immediately following defoliation. (TCPL 201) |
10:30 - 10:45 |
Coffee Break (TCPL Foyer) |
10:45 - 11:30 |
Xianli Wang: Mapping fire risk in Canada: from landscape to national scale ↓ (Authors: Xianli Wang, Marc-André Parisien, Stephen W. Taylor, Diana Stralberg, Sandy Erni, Mike D. Flannigan)
This presentation will focus on what we have learned from modeling burn probability, one of the major components of fire risk, at various spatial and temporal scales. At landscape scale, we explored how fire ignition, fuel dynamics, and climate change affect burn probability; at a regional scale, we explored how ecosystem would respond to the changing fire activities as well as climates. With the accumulated experience learned from modeling burn probability at the landscape and regional scales, we are now working on modeling burn probability across Canada. Outputs from this effort in combination with the wildland-urban-interface products will provide us a rough estimate of fire risk across Canada. (TCPL 201) |
11:30 - 12:00 |
Steve Taylor: Predicting Severe Wildfire Occurrence in Canada ↓ (Authors: S.W. Taylor, K. Nadeem, D.G. Woolford , C.B. Dean)
About 8000 wildfires occur in the protected area of Canada each year. Approximately 2% of these fires exceed 100+ ha in size, but account for most of the suppression costs and are the greatest threat to our communities. Although statistical approaches to fire occurrence Prediction (FOP) have evolved over the past 40 years and been implemented in Ontario and BC, FOP is not yet implemented operationally a national scale in Canada. We develop a big data based statistical modeling approach, applying Lasso logistic regression and supervised machine learning methods to a set of spatially gridded meteorological, topographic and demographic covariates to predict person, lightning and large wildfire occurrences in Canada one an two weeks ahead. Case control sampling was used to tackle the zero-inflation problem inherent to rare events problems. Both LASSO logistic and random forest methods allowed for the inclusion and selection of a large number of covariates, and the selection and fitting of models with useful skill. We anticipate that the implemented models will better facilitate agency preparedness as well as tactical decisions regarding resource allocation and sharing between fire management agencies in Canada. However, predicting surges in ignitions following large lightning storms remains challenging, and an area for future focus (TCPL 201) |
12:15 - 13:30 |
Lunch (Vistas Dining Room) |
13:30 - 14:15 |
Geoff Cary: An Australian and international modelling perspective on quantifying mitigation of wildfire risk ↓ The devastating ‘Black Saturday’ bushfires that burned in the state of Victoria in southern Australia tragically caused 173 fatalities and destroyed well over 2,000 houses on the 7th of February, 2009. Marked changes in fire management occurred as a result of these fires, including a revision of the Forest Fire Danger Rating System and highly modified policies in that state for bushfire fuel treatment at landscape scales. An initial recommendation of prescribed burning an annual rolling target of 5% minimum of public land was eventually replaced by a risk-based fuel treatment strategy, although a recent parliamentary enquiry has recommended the risk-based approach be combined with a minimum hectare target of at least 5% for prescribed burning. This presentation reports on modelling experiments designed to quantify the effectiveness of fuel treatment in fire-prone ecosystems in Australia and around the world. Early simulations of mesic landscapes in Tasmania, Australia, demonstrate the importance of strategically-located fuel treatments for protecting fire-intolerant temperate rainforest and alpine vegetation from wildfires. Comparative modelling, including cases from Canada, USA, Europe and Australia, indicates a greater relative effectiveness of ignition management effort and importance of inter-annual variation in weather, compared with fuel treatment effort, for determining total area burned by simulated wildfires. A recent study with a subset of these models shows that similar relationships hold for area of moderate-to-high intensity fire. Taken together, these modelled results are similar to empirical findings concerning fuel treatment and house loss in the 2009 Black Saturday bushfires. Overall, landscape-scale modelling of wildfire risk mitigation suggests at least three key principles: (i) fuel treatment effects are most meaningful when expressed as relative effects in relation to other factors like ignition management and weather; (ii) proximity matters, with strategically-located fuel treatment generally being most effective; and (iii) greatest insights into fuel treatment effectiveness result from multiple lines of evidence, including multiple-model comparisons. (TCPL 201) |
14:15 - 15:00 |
Ellen Whitman: Landscape patterns of burn severity in the boreal forest ↓ Burn severity is overstory, understory, and surface combustion due to wildfire and associated ecological impacts. Burn severity is relevant to forest and fire management as it affects immediate post-fire erosion risk, recruitment and regeneration of forests, and is an important consideration when planning salvage logging or prescribed burns. We measured four field metrics of severity, one year post-fire in six large, natural wildfires, and found that burn severity was correlated with prefire vegetation communities. We fitted models relating field observations of severity to remotely sensed multispectral imagery and mapped landscape burn severity. High-severity patches, which made up most of the landscape, were large and aggregated, and low-severity patches were numerous, small, and complex. These trends reflect the high-intensity stand-replacing boreal fire regime; however, there was substantial variability in burn severity across the landscape. This variability in burn severity was explained by prefire stand structure and fire weather at the time of burning. Landscape patterns of burn severity are often reflected for subsequent years in the species composition and stand structure of regenerated stands, making burn severity a potential driver of future fuels. Where landscape patterns of burn severity and fire occurrence fall outside of characteristic ranges, shifts in ecological outcomes of fire may occur. (TCPL 201) |
15:00 - 15:30 |
Coffee Break (TCPL Foyer) |
15:30 - 16:00 |
Frederic Schoenberg: Did your model account for earthworms? ↓ In modeling wildfire occurrences, missing variables are inevitable. Nevertheless, in some circumstances the estimates of parameters in point process models may have nice properties, even when confounding variables have been omitted from the model. Results are reviewed detailing under what conditions consistent estimates may be obtained by maximum likelihood when variables are omitted. (TCPL 201) |
16:00 - 16:30 |
Brett Moore: Probabilistic Prometheus Fire Growth Simulation Using Ensemble Weather Forecasts ↓ To date, fire growth simulation models have been deterministic and provide a single perimeter projection. While these outputs are informative, they provide little opportunity for contingency planning. Using Environment Canada’s Regional Ensemble Prediction System forecasts we can generate 20 outputs (one for each ensemble member) and combine them to generate a probabilistic projection. The utility of this output can be enhanced by validating the simulation against the actual perimeter. Comparisons between probability contours and final perimeters provides a predictive tool that may increase awareness of potential wildfire spread over the lifetime of a wildfire. The current comparison is a typical meteorological method, but does not take perimeter timing into account. The weather forecast covers 2.75 days (64 hours). Many of these wildfires were on the landscape for multiple weeks, however they typically only spread for roughly 3 days (based on progression reported by field staff). The fires chosen were all greater than 5000 hectares to better represent the models assumption that fire is free burning (unimpeded by suppression activity). Overall, the probabilistic critical success metric is significantly greater (p < 0.05) than the deterministic model for probability contours of 30% or lower in fires over 5000 hectares. (TCPL 201) |
16:30 - 17:00 |
Discussion (TCPL 201) |
17:30 - 19:30 |
Dinner (Vistas Dining Room) |