MODELING FIRE OBSERVATIONS, IGNITION SOURCES, AND NOVEL FUELS TO UNDERSTAND HUMAN IMPACTS ON FIRE REGIMES ACROSS THE U.S.

Fire is a natural, and necessary, component of many ecosystems. However, people are changing the spatial and temporal distribution of wildfires in the U.S. at great economic and ecological costs. My dissertation addresses the impacts of humans on U.S. fires both through the introduction of ignition sources and flammable grasses. Further, I evaluate fire datasets that are widely used to investigate these phenomena over large spatial and temporal scales. Finally, I create an aboveground carbon map that can be used to estimate the potential carbon loss consequences in western U.S. ecosystems most at risk to fire. My work shows that humans ignited more than 77% of fires in seven western U.S. ecoregions, and when modeling human ignited fires, I found that the importance of ignition proxies varied considerably among ecoregions. In 21 ecoregions across the U.S., I found that eight species of non-native invasive grasses increased rates of fire occurrence by 27%-230%, and six species increased rates of fire frequency by 24%-150%. I also quantified differences in commonly used satellite derived and agency recorded fire records and found they were disparate across the U.S., suggesting that great care should be taken when deciding which fire database to use when analyzing human impacts on fire regimes. Finally, the new estimates I provide for aboveground carbon in semi-arid western U.S. ecosystems are roughly double that of previous estimates; indicating that potential carbon losses from fire in these ecosystems are much larger than originally thought. I conclude that fire ignitions from human sources, and the alteration of fuels through the introduction of non-native, invasive grasses, have already dramatically impacted fire regimes across the U.S. These impacts are presently and will continue to be compounded by climate change. My dissertation suggests that we must consider human impacts on ignitions, vegetation, and their interaction with climate to most effectively manage, predict, and live with fire

Quantifying burned area for North American forests: Implications for direct reduction of carbon stocks

A synthesis was carried out to analyze information available to quantify fire activity and burned area across North America, including a comparison of different data sources and an assessment of how variations in burned area estimate impact carbon emissions from fires. Data sets maintained by fire management agencies provide the longest record of burned area information. Canada and Alaska have the most well developed data sets consisting of the perimeters of large fires (\u3e200 ha) going back to 1959 and 1950, respectively. A similar data set back to 1980 exists for the Conterminous U.S., but contains data only from federal land management agencies. During the early half of the 20th century, average burned area across North America ranged between 10 and 20 × 106 ha yr−1, largely because of frequent surface fires in the southeastern U.S. Over the past two decades, an average of 5 × 106 ha yr−1 has burned. Moderate-resolution (500–1000 m) satellite burned area products information products appear to either underestimate burned area (GFED3 and MCD45A1) or significantly overestimate burned area (L3JRC and GLOBCARBON). Of all the satellite data products, the GFED3 data set provides the most consistent source of burned area when compared to fire management data. Because they do not suitably reflect actual fire activity, the L3JRC and GLOBCARBON burned area data sets are not suitable for use in carbon cycle studies in North America. The MCD45A1 data set appears to map a higher fraction of burned area in low biomass areas compared to the GFED3 data set

Improving our understanding of individual wildfires by combining satellite data with fire spread modelling

Doutoramento em Engenharia Florestal e dos Recursos Naturais - Instituto Superior de AgronomiaWildfires pose real threats to life and property. In Portugal, the recent year of 2017 had the largest burnt area extent and number of casualties. A knowledge gap still exists in wildfire research related with better understanding individual wildfires, which has important implications for fire suppression, management, and policies. Wildfire spread models have been used to study individual wildfires, however, associated uncertainties and the lack of systematic evaluation methods hamper their capability for accurately predicting their spread. Understanding how fire spread predictions can be improved is a critical research task, as they will only be deemed useful if they can provide accurate and reliable information to fire managers. The present Thesis proposes to contribute to improve fire spread predictions by: i) Developing a methodology to systematically evaluate fire spread predictions ii) Thoroughly characterizing input data uncertainty and its impact on predictions; iii) Improving predictions using data-driven model calibration. The spread of large historical wildfires were studied by combining satellite data and models. The major findings of the present Thesis were: i) Satellite data accurately contributed to provide accurate fire dates and ignition information for large wildfires. ii) The evaluation metrics were very useful in identifying areas and periods of low/high spatio-temporal agreement, highlighting the strong underprediction bias and poor accuracy of the predictions. iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions iii) Uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy. iv) Predictions were improved by ‘learning’ from past wildfires, significantly reducing the impact of data uncertainty on the accuracy of fire spread predictions Overall, the work contributed to advance the body of knowledge regarding individual wildfires and identified future research steps towards a reliable operational fire system capable of supporting more effective and safer fire management decisions with the aim of reducing the dramatic impacts of wildfiresN/

On the role of continuing currents in lightning-induced fire ignition

Lightning flashes are an important source of wildfires worldwide, contributing to the emission of trace gases to the atmosphere. Based on experiments and field observation, continuing cur rents in lightning have since a long time been proposed to play a significant role in the ignition of wildfires. However, simultaneous detections of optical and radio signals from fire-igniting lightning confirming the role of continuing currents in igniting wildfires are rare. In this work, we first analyze the optical signal of the lightning-ignited wildfires reported by the Geostationary Lightning Mapper over the Contiguous United States (CONUS) during the summer of 2018, and we then analyze the optical and the Extremely Low Frequency signal of a confirmed fire-igniting lightning flash in the Swiss Alps. Despite data uncertainties, we found that the probability of ignition of a lightning flash with Continuing Current (CC) lasting more than 10 ms is higher than that of cloud-to-ground lightning in CONUS. Finally, we confirm the existence of a long continuing current (lasting about 400 ms) associated with a long-lasting optical signal (lasting between 2 and 4 s) of a video-recorded fire-igniting lightning flash

Planning for an unknown future: incorporating meteorological uncertainty into predictions of the impact of fires and dust on US particulate matter

2019 Summer.Includes bibliographical references.Exposure to particulate matter (PM) pollution has well documented health impacts and is regulated by the United States (U.S.) Environmental Protection Agency (EPA). In the U.S. wildfire smoke and wind-blown dust are significant natural sources of PM pollution. This dissertation shows how the environmental conditions that drive wildfires and wind-blown dust are likely to change in the future and what these changes imply for future PM concentrations. The first component of this dissertation shows how human ignitions and environmental conditions influence U.S. wildfire activity. Using wildfire burn area and ignition data, I find that in both the western and southeastern U.S., annual lightning- and human-ignited wildfire burn area have similar relationships with key environmental conditions (temperature, relative humidity, and precipitation). These results suggest that burn area for human- and lightning-ignited wildfires will be similarly impacted by climate change. Next, I quantify how the environmental conditions that drive wildfire activity are likely to change in the future under different climate scenarios. Coupled Model Intercomparison Project phase 5 (CMIP5) models agree that western U.S. temperatures will increase in the 21st century for representative concentration pathways (RCPs) 4.5 and 8.5. I find that averaged over seasonal and regional scales, other environmental variables demonstrated to be relevant to fuel flammability and aridity, such as precipitation, evaporation, relative humidity, root zone soil moisture, and wind speed, can be used to explain historical variability in wildfire burn area as well or better than temperature. My work demonstrates that when objectively selecting environmental predictors using Lasso regression, temperature is not always selected, but that this varies by western U.S. ecoregion. When temperature is not selected, the sign and magnitude of future changes in burn area become less certain, highlighting that predicted changes in burn area are sensitive to the environmental predictors chosen to predict burn area. Smaller increases in future wildfire burn area are estimated whenever and wherever the importance of temperature as a predictor is reduced. The second component of this dissertation examines how environmental conditions that drive fine dust emissions and concentrations in the southwestern U.S. change in the future. I examine environmental conditions that influence dust emissions including, temperature, vapor pressure deficit, relative humidity, precipitation, soil moisture, wind speed, and leaf area index (LAI). My work quantifies fine dust concentrations in the U.S. southwest dust season, March through July, using fine iron as a dust proxy, quantified with measurements from the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network between 1995 and 2015. I show that the largest contribution to the spread in future dust concentration estimates comes from the choice of environmental predictor used to explain observed variability. The spread between different environmental predictor estimates can be larger than the spread between climate scenarios or intermodel spread. Based on linear estimates of how dust concentrations respond to changes in LAI, CMIP5 estimated increases in LAI would result in reduced dust concentrations in the future. However, when I objectively select environmental predictors of dust concentrations using Lasso regression, LAI is not selected in favor of other variables. When using a linear combination of objectively selected environmental variables, I estimate that future southwest dust season mean concentrations will increase by 0.24 μg m−3 (12%) by the end of the 21st century for RCP 8.5. This estimated increase in fine dust concentration is driven by decreases in relative humidity, precipitation, soil moisture, and buffered by decreased wind speeds

Wildfire Hazard and Risk Assessment

Wildfire risk can be perceived as the combination of wildfire hazards (often described by likelihood and intensity) with the susceptibility of people, property, or other valued resources to that hazard. Reflecting the seriousness of wildfire risk to communities around the world, substantial resources are devoted to assessing wildfire hazards and risks. Wildfire hazard and risk assessments are conducted at a wide range of scales, from localized to nationwide, and are often intended to communicate and support decision making about risks, including the prioritization of scarce resources. Improvements in the underlying science of wildfire hazard and risk assessment and in the development, communication, and application of these assessments support effective decisions made on all aspects of societal adaptations to wildfire, including decisions about the prevention, mitigation, and suppression of wildfire risks. To support such efforts, this Special Issue of the journal Fire compiles articles on the understanding, modeling, and addressing of wildfire risks to homes, water resources, firefighters, and landscapes

A multi-analysis approach for estimating regional health impacts from the 2017 Northern California wildfires

Smoke impacts from large wildfires are mounting, and the projection is for more such events in the future as the one experienced October 2017 in Northern California, and subsequently in 2018 and 2020. Further, the evidence is growing about the health impacts from these events which are also difficult to simulate. Therefore, we simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling with WRF-CMAQ, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses. To demonstrate these analyses, we estimated the health impacts from smoke impacts during wildfires in October 8–20, 2017, in Northern California, when over 7 million people were exposed to Unhealthy to Very Unhealthy air quality conditions. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ system. Interestingly, this approach did not necessarily improve overall results, however it was key to simulating the initial 12-hr explosive fire activity and smoke impacts. To improve these results, we applied one data fusion and three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across all cases, the best Pearson correlation was 0.5. Overall, WRF-CMAQ simulations were biased high and the geostatistical methods were biased low. Finally, we applied the optimized PM2.5 exposure estimate in an exposure-response function. Estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% CI: 0, 196) with 47% attributable to wildland fire smoke. Implications: Large wildfires in the United States and in particular California are becoming increasingly common. Associated with these large wildfires are air quality and health impact to millions of people from the smoke. We simulated air quality conditions using a suite of remotely-sensed data, surface observational data, chemical transport modeling, one data fusion, and three machine learning methods to arrive at datasets useful to air quality and health impact analyses from the October 2017 Northern California wildfires. Temporary monitors deployed for the wildfires provided an important model evaluation dataset. Total estimated regional mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to the wildland fire smoke. This illustrates the profound effect that even a 12-day exposure to wildland fire smoke can have on human health

UNDERSTANDING ENVIRONMENTAL FACTORS DRIVING WILDLAND FIRE IGNITIONS IN ALASKAN TUNDRA

Wildland fire is a dominant disturbance agent that drives ecosystem change, climate forcing, and carbon cycle in the boreal forest and tundra ecosystems of the High Northern Latitudes (HNL). Tundra fires can exert a considerable influence on the local ecosystem functioning and contribute to climate change through biogeochemical and biogeophysical effects. However, the drivers and mechanisms of tundra fires are still poorly understood. Research on modeling contemporary fire occurrence in the tundra is also lacking. This dissertation addresses the overarching scientific question of “What environmental factors and mechanisms drive wildfire ignition in Alaskan tundra?” Environmental factors from multiple aspects are considered including fuel type and state, fire weather, topography, and ignition source. First, to understand the spatial distribution of fuel types in the tundra, multi- year satellite observations and field data were used to develop the first fractional coverage product of major fuel type components across the entire Alaskan tundra at 30 m resolution. Second, to account for the primary ignition source of fires in the HNL, an empirical-dynamical modeling framework was developed to predict the probability of cloud-to-ground (CG) lightning across Alaskan tundra, through the integration of Weather Research and Forecast (WRF) model and machine learning algorithm. Finally, environmental factors including fuel type distribution, fuel moisture state, WRF simulated ignition source and fire weather, and topographical features, were combined with empirical modeling methods to understand their roles in driving wildland fire ignitions across Alaskan tundra from 2001 to 2019. This work demonstrates the strong capability for accurate prediction of CG lightning and wildland fire probabilities, by incorporating dynamic weather models, empirical methods, and satellite observations in data-scarce regions like the HNL. The developed models present a novel component of fire danger modeling that can considerably strengthen the current capability to forecast fire occurrence and support operational fire management agencies in the HNL. In addition, the insights gained from this research will allow for more accurate representation of wildfire ignition probabilities in studies focused on assessing the impact of the projected climate change in HNL tundra which has largely absent in previous modeling efforts

Deciphering the impact of uncertainty on the accuracy of large wildfire spread simulations

Predicting wildfire spread is a challenging task fraught with uncertainties. ‘Perfect’ predictions are unfeasible since uncertainties will always be present. Improving fire spread predictions is important to reduce its negative environmental impacts. Here, we propose to understand, characterize, and quantify the impact of uncertainty in the accuracy of fire spread predictions for very large wildfires. We frame this work from the perspective of the major problems commonly faced by fire model users, namely the necessity of accounting for uncertainty in input data to produce reliable and useful fire spread predictions. Uncertainty in input variables was propagated throughout the modeling framework and its impact was evaluated by estimating the spatial discrepancy between simulated and satellite-observed fire progression data, for eight very large wildfires in Portugal. Results showed that uncertainties in wind speed and direction, fuel model assignment and typology, location and timing of ignitions, had a major impact on prediction accuracy.We argue that uncertainties in these variables should be integrated in future fire spread simulation approaches, and provide the necessary data for any firemodel user to do soinfo:eu-repo/semantics/publishedVersio

Multi-year MODIS Active Fire Type Classification Over the Brazilian Tropical Moist Forest Biome

The Brazilian Tropical Moist Forest Biome (BTMFB) spans almost 4 million km2 and is subject to extensive annual fires that have been categorized into deforestation, maintenance, and forest fire types. Information on fire types is important as they have different atmospheric emissions and ecological impacts. A supervised classification methodology is presented to classify the fire type of MODerate resolution Imaging Spectroradiometer (MODIS) active fire detections using training data defined by consideration of Brazilian government forest monitoring program annual land cover maps, and using predictor variables concerned with fuel flammability, fuel load, fire behavior, fire seasonality, fire annual frequency, proximity to surface transportation, and local temperature. The fire seasonality, local temperature, and fuel flammability were the most influential on the classification. Classified fire type results for all 1.6 million MODIS Terra and Aqua BTMFB active fire detections over eight years (2003–2010) are presented with an overall fire type classification accuracy of 90.9% (kappa 0.824). The fire type user’s and producer’s classification accuracies were respectively 92.4% and 94.4% (maintenance fires), 88.4% and 87.5% (forest fires), and, 88.7% and 75.0% (deforestation fires). The spatial and temporal distribution of the classified fire types are presente