Synoptic-Scale Atmospheric Circulation and Boreal Canada Summer Drought Variability of the Past Three Centuries

Five independent multicentury reconstructions of the July Canadian Drought Code and one reconstruction of the mean July-August temperature were developed using a network of 120 well-replicated tree-ring chronologies covering the area of the eastern Boreal Plains to the eastern Boreal Shield of Canada. The reconstructions were performed using 54 time-varying reconstruction submodels that explained up to 50% of the regional drought variance during the period of 1919-84. Spatial correlation fields on the six reconstructions revealed that the meridional component of the climate system from central to eastern Canada increased since the mid-nineteenth century. The most obvious change was observed in the decadal scale of variability. Using 500-hPa geopotential height and wind composites, this zonal to meridional transition was interpreted as a response to an amplification of long waves flowing over the eastern North Pacific into boreal Canada, from approximately 1851 to 1940. Composites with NOAA Extended Reconstructed SSTs indicated a coupling between the meridional component and tropical and North Pacific SST for a period covering at least the past 150 yr, supporting previous findings of a summertime global ocean-atmospherel-and surface coupling. This change in the global atmospheric circulation could be a key element toward understanding the observed temporal changes in the Canadian boreal forest fire regimes over the past 150 yr

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

A Global Index for Mapping the Exposure of Water Resources to Wildfire

Wildfires are keystone components of natural disturbance regimes that maintain ecosystem structure and functions, such as the hydrological cycle, in many parts of the world. Consequently, critical surface freshwater resources can be exposed to post-fire effects disrupting their quantity, quality and regularity. Although well studied at the local scale, the potential extent of these effects has not been examined at the global scale. We take the first step toward a global assessment of the wildfire water risk (WWR) by presenting a spatially explicit index of exposure. Several variables related to fire activity and water availability were identified and normalized for use as exposure indicators. Additive aggregation of those indicators was then carried out according to their individual weight. The resulting index shows the greatest exposure risk in the tropical wet and dry forests. Intermediate exposure is indicated in mountain ranges and dry shrublands, whereas the lowest index scores are mostly associated with high latitudes. We believe that such an approach can provide important insights for water security by guiding global freshwater resource preservation.Keywords: wildfire hazard, global index, water security, water resources exposure, wildfire water ris

Scientists' warning on extreme wildfire risks to water supply

2020 is the year of wildfire records. California experienced its three largest fires early in its fire season. The Pantanal, the largest wetland on the planet, burned over 20% of its surface. More than 18 million hectares of forest and bushland burned during the 2019–2020 fire season in Australia, killing 33 people, destroying nearly 2500 homes, and endangering many endemic species. The direct cost of damages is being counted in dozens of billion dollars, but the indirect costs on water‐related ecosystem services and benefits could be equally expensive, with impacts lasting for decades. In Australia, the extreme precipitation (“200 mm day −1 in several location”) that interrupted the catastrophic wildfire season triggered a series of watershed effects from headwaters to areas downstream. The increased runoff and erosion from burned areas disrupted water supplies in several locations. These post‐fire watershed hazards via source water contamination, flash floods, and mudslides can represent substantial, systemic long‐term risks to drinking water production, aquatic life, and socio‐economic activity. Scenarios similar to the recent event in Australia are now predicted to unfold in the Western USA. This is a new reality that societies will have to live with as uncharted fire activity, water crises, and widespread human footprint collide all‐around of the world. Therefore, we advocate for a more proactive approach to wildfire‐watershed risk governance in an effort to advance and protect water security. We also argue that there is no easy solution to reducing this risk and that investments in both green (i.e., natural) and grey (i.e., built) infrastructure will be necessary. Further, we propose strategies to combine modern data analytics with existing tools for use by water and land managers worldwide to leverage several decades worth of data and knowledge on post‐fire hydrology

Can forest management based on natural disturbances maintain ecological resilience?

Given the increasingly global stresses on forests, many ecologists argue that managers must maintain ecological resilience: the capacity of ecosystems to absorb disturbances without undergoing fundamental change. In this review we ask: Can the emerging paradigm of natural-disturbance-based management (NDBM) maintain ecological resilience in managed forests? Applying resilience theory requires careful articulation of the ecosystem state under consideration, the disturbances and stresses that affect the persistence of possible alternative states, and the spatial and temporal scales of management relevance. Implementing NDBM while maintaining resilience means recognizing that (i) biodiversity is important for long-term ecosystem persistence, (ii) natural disturbances play a critical role as a generator of structural and compositional heterogeneity at multiple scales, and (iii) traditional management tends to produce forests more homogeneous than those disturbed naturally and increases the likelihood of unexpected catastrophic change by constraining variation of key environmental processes. NDBM may maintain resilience if silvicultural strategies retain the structures and processes that perpetuate desired states while reducing those that enhance resilience of undesirable states. Such strategies require an understanding of harvesting impacts on slow ecosystem processes, such as seed-bank or nutrient dynamics, which in the long term can lead to ecological surprises by altering the forest's capacity to reorganize after disturbance

One extreme fire weather event determines the extent and frequency of wildland fires

Understanding climate change impacts on wildland fire activity has been constrained by the high uncertainty embedded in the prediction of fire size (FS), annual number of fires (ANF), and annual area burned (AAB). While there has been a sustained effort to make connections between fire weather and fire activity, most studies have focused on individual parameters instead of treating them as a connected group. This study explores the intrinsic relationships among the major parameters of fire activity and how they relate to fire-conducive weather conditions to determine optimal prediction models. We found maximum number of fire spread days and maximum FS best predict ANF and AAB, respectively. Assessing the robustness of these relationships across Canada’s ecozones showed they are stronger in the Cordillera than in the Shields and Plains and more universal for AAB than for ANF. We also found skewness of FS distributions may play an important role in relationship strength. These relationships provide a unique way to model future fire activities under changing climate conditions

Temporal Patterns of Wildfire Activity in Areas of Contrasting Human Influence in the Canadian Boreal Forest

The influence of humans on the boreal forest has altered the temporal and spatial patterns of wildfire activity through modification of the physical environment and through fire management for the protection of human and economic values. Wildfires are actively suppressed in areas with higher human influence, but, paradoxically, these areas have more numerous ignitions than low-impact ones because of the high rates of human-ignited fires, especially during the springtime. The aim of this study is to evaluate how humans have altered the temporal patterns of wildfire activity in the Canadian boreal forest by comparing two adjacent areas of low and high human influence, respectively: Wood Buffalo National Park (WBNP) and the Lower Athabasca Plains (LAP). We carried out Singular Spectrum Analysis to identify trends and cycles in wildfires from 1970 to 2015 for the two areas and examined their association with climate conditions. We found human influence to be reflected in wildfire activity in multiple ways: (1) by dampening (i.e., for area burned)—and even reversing (i.e., for the number of fires)—the increasing trends of fire activity usually associated with drier and warmer conditions; (2) by shifting the peak of fire activity from the summer to the spring; (3) by altering the fire-climate association; and (4) by exhibiting more recurrent (<8 year periodicities) cyclical patterns of fire activity than WBNP (>9 years)