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1. Downscaled subseasonal fire danger forecast skill across the contiguous United States

   https://doi.org/10.1002/asl.1165  

   Abatzoglou, John T.; McEvoy, Daniel J.; Nauslar, Nicholas J.; Hegewisch, Katherine C.; Huntington, Justin L. (March 2023, Atmospheric Science Letters)

   Abstract The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead‐times of several weeks can help in wildland fire resource allocation as well as complement short‐term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble‐based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly‐to‐monthly fire business decision making for suppression‐based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season. 

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2. The changing risk and burden of wildfire in the United States

   https://doi.org/10.1073/pnas.2011048118  

   Burke, Marshall; Driscoll, Anne; Heft-Neal, Sam; Xue, Jiani; Burney, Jennifer; Wara, Michael (January 2021, Proceedings of the National Academy of Sciences)

   Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from wildfire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the United States. We estimate that nearly 50 million homes are currently in the wildland–urban interface in the United States, a number increasing by 1 million houses every 3 y. To illustrate how changes in wildfire activity might affect air pollution and related health outcomes, and how these linkages might guide future science and policy, we develop a statistical model that relates satellite-based fire and smoke data to information from pollution monitoring stations. Using the model, we estimate that wildfires have accounted for up to 25% of PM 2.5 (particulate matter with diameter <2.5 μm) in recent years across the United States, and up to half in some Western regions, with spatial patterns in ambient smoke exposure that do not follow traditional socioeconomic pollution exposure gradients. We combine the model with stylized scenarios to show that fuel management interventions could have large health benefits and that future health impacts from climate-change–induced wildfire smoke could approach projected overall increases in temperature-related mortality from climate change—but that both estimates remain uncertain. We use model results to highlight important areas for future research and to draw lessons for policy. 

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3. Multimodal Wildland Fire Smoke Detection

   https://doi.org/10.3390/rs15112790  

   Bhamra, Jaspreet Kaur; Anantha_Ramaprasad, Shreyas; Baldota, Siddhant; Luna, Shane; Zen, Eugene; Ramachandra, Ravi; Kim, Harrison; Schmidt, Chris; Arends, Chris; Block, Jessica; et al (June 2023, Remote Sensing)

   Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. 

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4. Post-fire erosion potential of clayey sand soils and slopes – A laboratory study

   Tabassum, Tanzila (October 2021, 10th International Conference on Soil Erosion and Scour)

   One of the principal side effects of wildland fire extreme events is mass soil erosion event of the soil and slopes denuded by the fire. These soil erosion events may be devastating and extreme in their own rights, damaging critical infrastructure downslope or downstream of the fire burn scars. While there are many variables influencing the severity of post fire erosion, the amounts of soil erosion are largely dependent on the water content of the soil at the time of the fire coupled with the fire intensity. Fires that are lower intensity or soils that are “wet” at time of burning have significantly less damage to root structures of grasses and other plants while showing lessened soil erosion potential. Fires that are higher intensity or on dry soils have higher damages to root structures and increased soil erosion potential. In this laboratory study, a single clayey sand soil material common to the ground surface across the Black Hills of South Dakota and Wyoming is studied for erosion potential after burning in a controlled container burn. This material is varied by initial water content and burned at a soil surface temperature of 800 Celsius for 75 minutes, a temperature-time continuum consistent with severe wildland fire. Burning is used rather than kilns to preserve the same atmospheric conditions as in the field fire event. A laboratory soil-erosion device is then used to measure soil erosion potential across a range of fluid velocities and soil slopes. The results of this study show that the initial water content of the soil at the time of fire is a key parameter in understanding soil erosion potential post-fire. While not a complete study on time-temperature-water content across many soil types, this pilot research shows promise for future models and mapping tools. These future tools will enable planners to target resources for post wildfire erosion mitigation based on surficial soil water content at the time of the fire. 

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5. A Comparison of Fire Weather Indices with MODIS Fire Days for the Natural Regions of Alaska

   https://doi.org/10.3390/f11050516  

   Ziel, Robert H.; Bieniek, Peter A.; Bhatt, Uma S.; Strader, Heidi; Rupp, T. Scott; York, Alison (May 2020, Forests)

   Research Highlights: Flammability of wildland fuels is a key factor influencing risk-based decisions related to preparedness, response, and safety in Alaska. However, without effective measures of current and expected flammability, the expected likelihood of active and problematic wildfires in the future is difficult to assess and prepare for. This study evaluates the effectiveness of diverse indices to capture high-risk fires. Indicators of drought and atmospheric drivers are assessed along with the operational Canadian Forest Fire Danger Rating System (CFFDRS). Background and Objectives: In this study, 13 different indicators of atmospheric conditions, fuel moisture, and flammability are compared to determine how effective each is at identifying thresholds and trends for significant wildfire activity. Materials and Methods: Flammability indices are compared with remote sensing characterizations that identify where and when fire activity has occurred. Results: Among these flammability indicators, conventional tools calibrated to wildfire thresholds (Duff Moisture Code (DMC) and Buildup Index (BUI)), as well as measures of atmospheric forcing (Vapor Pressure Deficit (VPD)), performed best at representing the conditions favoring initiation and size of significant wildfire events. Conventional assessments of seasonal severity and overall landscape flammability using DMC and BUI can be continued with confidence. Fire models that incorporate BUI in overall fire potential and fire behavior assessments are likely to produce effective results throughout boreal landscapes in Alaska. One novel result is the effectiveness of VPD throughout the state, making it a potential alternative to FFMC among the short-lag/1-day indices. Conclusions: This study demonstrates the societal value of research that joins new academic research results with operational needs. Developing the framework to do this more effectively will bring science to action with a shorter lag time, which is critical as we face growing challenges from a changing climate. 

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