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Fire is a key disturbance process that shapes the structure and function of montane temperate rainforest in the Pacific Northwest (PNW). Recent research is revealing more frequent historical fire activity in the western central Cascades than expected by conventional theory. Indigenous peoples have lived in the PNW for millennia. However, Indigenous people's roles in shaping vegetation mosaics in montane temperate forests of the PNW has been overlooked, despite archaeological evidence of long-term, continuous human use of these landscapes. In this paper, we present a generalizable research framework for overcoming biases often inherent in historical fire research. The framework centers Indigenous perspectives and ethnohistory, leveraging theory in human ecology and archaeology to interpret fire histories. We apply this framework to place-based, empirical evidence of Indigenous land use and dendroecological fire history. Our framework leads us to conclude that the most parsimonious explanation for the occurrence of historical high fire frequency in the western Cascades is Indigenous fire stewardship. Further, our case study makes apparent that scholars can no longer ignore the role of Indigenous people in driving montane forest dynamics in the PNW. 

Abstract Background. Wildfire smoke events are increasing in frequency and intensity due to climate change. Children are especially vulnerable to health effects even at moderate smoke levels. However, it is unclear how parents respond to Air Quality Indices (AQIs) frequently used by agencies to communicate air pollution health risks.Methods. In an experiment (3 × 2 × 2 factorial design), 2,100 parents were randomly assigned to view one of twelve adapted AQI infographics that varied by visual (table, line, gauge), index type (AQI [0-500], AQHI [1-11+]), and risk level (moderate, high). Participants were told to imagine encountering the infographic in a short-term exposure scenario. They reported worry about wildfire smoke, intentions to take risk-mitigating actions (e.g., air purifier use), and support for various exposure reduction policies. Subsequently, participants were told to imagine encountering the same infographic daily during a school week in a long-term exposure scenario and again reported worry, action intentions, and policy support.Results. Parents’ responses significantly differentiated between risk levels that both pose a threat to children’s health; worry and action intentions were much higher in the high-risk group than the moderate-risk group in both short-exposure (F = 748.68 p<.001; F = 411.59, p<.001) and long-exposure scenarios (F = 470.51, p<.001; F = 212.01, p<.001). However, in the short-exposure scenario, when shown the AQHI [1-11+] with either the line or gauge visuals, parents’ action intentions were more similar between moderate- and high-risk level groups (3-way interaction, F = 6.03, p = .002).Conclusions. These results suggest some index formats such as the AQHI—rather than the AQI—may better attune parents to moderate levels of wildfire smoke being dangerous to children’s health. Our research offers insights for agencies and officials seeking to improve current public education efforts during wildfire smoke events and speaks to the critical need to educate parents and help them act short-term and long-term to protect children’s health. 

Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change ( dB H / dt ) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind. 

Abstract Prescribed fire has been increasingly promoted to reduce wildfire risk and restore fire‐adapted ecosystems. Yet, the complexities of forest ecosystem dynamics in response to disturbances, climate change, and drought stress, combined with myriad social and policy barriers, have inhibited widespread implementation. Using the forest succession model LANDIS‐II, we investigated the likely impacts of increasing prescribed fire frequency and extent on wildfire severity and forest carbon storage at local and landscape scales. Specifically, we ask how much prescribed fire is required to maintain carbon storage and reduce the severity and extent of wildfires under divergent climate change scenarios? We simulated four prescribed fire scenarios (no prescribed fire, business‐as‐usual, moderate increase, and large increase) in the Siskiyou Mountains of northwest California and southwest Oregon. At the local site scale, prescribed fires lowered the severity of projected wildfires and maintained approximately the same level of ecosystem carbon storage when reapplied at a ~15‐year return interval for 50‐year simulations. Increased frequency and extent of prescribed fire decreased the likelihood of aboveground carbon combustion during wildfire events. However, at the landscape scale, prescribed fire did not decrease the projected severity and extent of wildfire, even when large increases (up to 10× the current levels) of prescribed fire were simulated. Prescribed fire was most effective at reducing wildfire severity under a climate change scenario with increased temperature and precipitation and on sites with north‐facing aspects and slopes greater than 30°. Our findings suggest that placement matters more than frequency and extent to estimate the effects of prescribed fire, and that prescribed fire alone would not be sufficient to reduce the risk of wildfire and promote carbon sequestration at regional scales in the Siskiyou Mountains. To improve feasibility, we propose targeting areas of high concern or value to decrease the risk of high‐severity fire and contribute to meeting climate mitigation and adaptation goals. Our results support strategic and targeted landscape prioritization of fire treatments to reduce wildfire severity and increase the pace and scale of forest restoration in areas of social and ecological importance, highlighting the challenges of using prescribed fire to lower wildfire risk. 

Abstract Detailed information about the historical range of variability in wildfire activity informs adaptation to future climate and disturbance regimes. Here, we describe one of the first annually resolved reconstructions of historical (1500–1900 ce) fire occurrence in coast Douglas‐fir dominated forests of the west slope of the Cascade Range in western Oregon. Mean fire return intervals (MFRIs) across 16 sites within our study area ranged from 6 to 165 years. Variability in MFRIs was strongly associated with average maximum summer vapor pressure deficit. Fire occurred infrequently in Douglas‐fir forest stands seral to mountain hemlock or silver fir, but fire frequency was much shorter than predicted by theory in other forest types. MFRIs within Douglas‐fir stands seral to western hemlock or grand fir ranged from 19 to 45 years, and MFRIs in stands seral to Douglas‐fir ranged from 6 to 11 years. There was little synchrony in fire occurrence or tree establishment across 16 sites separated by 4 km. The lack of synchrony in fire suggests that large, wind‐driven fire events that are often considered to be characteristic of coast Douglas‐fir forests were not an important driver of succession in our study area during the last ~400–500 years. Climate was more arid than normal during fire years in most forest types, but historical fire in stands seral to Douglas‐fir was strongly associated with antecedent moisture and less strongly associated with drought. We interpret the extraordinary tempo of fire we observed in stands seral to Douglas‐fir and the unique climate pattern associated with fire in these stands to be indicative of Indigenous fire stewardship. This study provides evidence of far more frequent historical fire in coast Douglas‐fir forests than assumed by managers or scientists—including some of the most frequent fire return intervals documented in the Pacific Northwest. We recommend additional research across the western Cascades to create a comprehensive account of historical fire in highly productive forests with significant cultural, economic, and ecological importance. 

Abstract The prediction of large fluctuations in the ground magnetic field (dB/dt) is essential for preventing damage from Geomagnetically Induced Currents. Directly forecasting these fluctuations has proven difficult, but accurately determining the risk of extreme events can allow for the worst of the damage to be prevented. Here we trained Convolutional Neural Network models for eight mid‐latitude magnetometers to predict the probability thatdB/dtwill exceed the 99th percentile threshold 30–60 min in the future. Two model frameworks were compared, a model trained using solar wind data from the Advanced Composition Explorer (ACE) satellite, and another model trained on both ACE and SuperMAG ground magnetometer data. The models were compared to examine if the addition of current ground magnetometer data significantly improved the forecasts ofdB/dtin the future prediction window. A bootstrapping method was employed using a random split of the training and validation data to provide a measure of uncertainty in model predictions. The models were evaluated on the ground truth data during eight geomagnetic storms and a suite of evaluation metrics are presented. The models were also compared to a persistence model to ensure that the model using both datasets did not over‐rely ondB/dtvalues in making its predictions. Overall, we find that the models using both the solar wind and ground magnetometer data had better metric scores than the solar wind only and persistence models, and was able to capture more spatially localized variations in thedB/dtthreshold crossings.