Quantifying the human influence on fire ignition across the western USA

The timing of fireworks-caused wildfire ignitions during the 4th of July holiday season

Although anthropogenic climate change is causing increased wildfire activity in the United States (US), humans are also an important ignition source. Humans cause a surge in wildfire ignitions every 4th of July (Independence Day in the US) through the use of fireworks. We examine the 4th of July peak in fireworks-caused wildfire ignitions and show that their spatial distribution varies but has been heavily concentrated in the west and north central US and predominantly on tribal lands. Further, we show that the weekly timing of the 4th of July influences both the number and weekly distribution structure of fireworks-caused ignitions. We interpret these weekly and daily-scale distribution patterns of fireworks-caused ignitions to reflect the influences of human behavioral variations, culture, and fireworks regulations. For example, our analysis suggests that weekends and religious days of rest (e.g., Saturday, Sunday) have a dampening effect on the number on wildfire ignitions due to fireworks, and that weekends and the timing of work holidays likely impact the weekly distribution of fireworks-caused ignitions. Additionally, comparisons of fireworks-caused ignitions before and after the 4th of July at the daily and weekly scale likely reflect the efficacy of firework sales regulations and human behavioral tendencies towards pre-holiday impulsiveness. Given the predictability of the fireworks-caused ignitions and rising costs of wildfire mitigation, these results have several important management and policy implications.

Impacts of Heavy and Persistent Precipitation on Railroad Infrastructure in July 2021: A Case Study from the Ahr Valley, Rhineland-Palatinate, Germany

In contrast to river floods, the enormous erosion potential in catchments contributes significantly to the extent of damage to infrastructure in valleys. This paper investigates the impact of the heavy precipitation event of 14–15 July 2021 on the railroad in the Ahr valley in Rhineland-Palatinate, Germany. In a first step, a detailed overview of the climatological and hydrological drivers using spatially high-resolved precipitation distribution and peak discharge modeling is provided, and the event is placed in a broader context by comparing it to past flash flood events from 1910 and 2016. In a second step, a detailed mapping of damages along the railroad line is performed using aerial photographs. The mapping revealed that bridges are the weakest point during a flood event and that they contribute to an increase and modification of the flood wave through backwater effects. Since flood events are expected to increase in the future, there is an urgent need to increase the resilience of transportation to this hazard and to answer the question of what magnitudes and return periods of events should be used in future sizing of rail infrastructure.

Machine learning-based observation-constrained projections reveal elevated global socioeconomic risks from wildfire

Reliable projections of wildfire and associated socioeconomic risks are crucial for the development of efficient and effective adaptation and mitigation strategies. The lack of or limited observational constraints for modeling outputs impairs the credibility of wildfire projections. Here, we present a machine learning framework to constrain the future fire carbon emissions simulated by 13 Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6), using historical, observed joint states of fire-relevant variables. During the twenty-first century, the observation-constrained ensemble indicates a weaker increase in global fire carbon emissions but higher increase in global wildfire exposure in population, gross domestic production, and agricultural area, compared with the default ensemble. Such elevated socioeconomic risks are primarily caused by the compound regional enhancement of future wildfire activity and socioeconomic development in the western and central African countries, necessitating an emergent strategic preparedness to wildfires in these countries.

A new study develops a machine learning framework to observationally constrain CMIP6-simulated fire carbon emissions, finding a weaker increase in 21st-century global fires but higher increase in their socioeconomic risks than previously thought.

Combining Participatory Mapping and Geospatial Analysis Techniques to Assess Wildfire Risk in Rural North Vietnam

Participatory mapping (PM) is a valuable research tool for assessing fire risk, especially in regions where data are difficult to collect or inconsistent; in such areas, the integration between crowdsourced data and geospatial techniques plays a fundamental role in gathering more consistent and reliable information. This study combines a participatory (community-based) mapping approach with geospatial techniques to assess fire risk in Van Chan district, northern Vietnam, an area where the economy relies mainly on forestry activities. Local stakeholders designed a map of wildfires, which was modelled as a function of a set of physical and socio-economic variables. A fire-probability map of the district was obtained and compared with MODIS data (2000-2020). The results suggest that higher fire probability occurs in areas with lower human pressure, and they provide information on related socio-economic drivers that affect this phenomenon. This study highlights the importance of combining participatory approaches and geospatial techniques to assess fire dynamics and prevent wildfires in terms of understanding and predicting the risks. The involvement of local communities is fundamental to this innovative participatory approach with regard to better supporting decision-making and prevention actions and to developing fire control management guidelines.

Plant-water sensitivity regulates wildfire vulnerability

Extreme wildfires extensively impact human health and the environment. Increasing vapour pressure deficit (VPD) has led to a chronic increase in wildfire area in the western United States, yet some regions have been more affected than others. Here we show that for the same increase in VPD, burned area increases more in regions where vegetation moisture shows greater sensitivity to water limitation (plant-water sensitivity; R² = 0.71). This has led to rapid increases in human exposure to wildfire risk, both because the population living in areas with high plant-water sensitivity grew 50% faster during 1990–2010 than in other wildland–urban interfaces and because VPD has risen most rapidly in these vulnerable areas. As plant-water sensitivity is strongly linked to wildfire vulnerability, accounting for ecophysiological controls should improve wildfire forecasts. If recent trends in VPD and demographic shifts continue, human wildfire risk will probably continue to increase.

The authors show that an ecosystem’s sensitivity to drought, measured as the amount of change in vegetation moisture content for a given change in background moisture, predicts the fire hazard in that location.

Relationships of climate, human activity, and fire history to spatiotemporal variation in annual fire probability across California

In the face of recent wildfires across the Western United States, it is essential that we understand both the dynamics that drive the spatial distribution of wildfire, and the major obstacles to modeling the probability of wildfire over space and time. However, it is well documented that the precise relationships of local vegetation, climate, and ignitions, and how they influence fire dynamics, may vary over space and among local climate, vegetation, and land use regimes. This raises questions not only as to the nature of the potentially nonlinear relationships between local conditions and the fire, but also the possibility that the scale at which such models are developed may be critical to their predictive power and to the apparent relationship of local conditions to wildfire. In this study we demonstrate that both local climate-through limitations posed by fuel dryness (CWD) and availability (AET)-and human activity-through housing density, roads, electrical infrastructure, and agriculture, play important roles in determining the annual probabilities of fire throughout California. We also document the importance of previous burn events as potential barriers to fire in some environments, until enough time has passed for vegetation to regenerate sufficiently to sustain subsequent wildfires. We also demonstrate that long-term and short-term climate variations exhibit different effects on annual fire probability, with short-term climate variations primarily impacting fire probability during periods of extreme climate anomaly. Further, we show that, when using nonlinear modeling techniques, broad-scale fire probability models can outperform localized models at predicting annual fire probability. Finally, this study represents a powerful tool for mapping local fire probability across the state of California under a variety of historical climate regimes, which is essential to avoided emissions modeling, carbon accounting, and hazard severity mapping for the application of fire-resistant building codes across the state of California.

In the Line of Fire: Consequences of Human-Ignited Wildfires to Homes in the U.S. (1992–2015)

With climate-driven increases in wildfires in the western U.S., it is imperative to understand how the risk to homes is also changing nationwide. Here, we quantify the number of homes threatened, suppression costs, and ignition sources for 1.6 million wildfires in the United States (U.S.; 1992–2015). Human-caused wildfires accounted for 97% of the residential homes threatened (within 1 km of a wildfire) and nearly a third of suppression costs. This study illustrates how the wildland-urban interface (WUI), which accounts for only a small portion of U.S. land area (10%), acts as a major source of fires, almost exclusively human-started. Cumulatively (1992–2015), just over one million homes were within human-caused wildfire perimeters in the WUI, where communities are built within flammable vegetation. An additional 58.8 million homes were within one kilometer across the 24-year record. On an annual basis in the WUI (1999–2014), an average of 2.5 million homes (2.2–2.8 million, 95% confidence interval) were threatened by human-started wildfires (within the perimeter and up to 1-km away). The number of residential homes in the WUI grew by 32 million from 1990–2015. The convergence of warmer, drier conditions and greater development into flammable landscapes is leaving many communities vulnerable to human-caused wildfires. These areas are a high priority for policy and management efforts that aim to reduce human ignitions and promote resilience to future fires, particularly as the number of residential homes in the WUI grew across this record and are expected to continue to grow in coming years.

Predicting forest fire kernel density at multiple scales with geographically weighted regression in Mexico

Identifying the relative importance of human and environmental drivers on fire occurrence in different regions and scales is critical for a sound fire management. Nevertheless, studies analyzing fire occurrence spatial patterns at multiple scales, covering the regional to national levels at multiple spatial resolutions, both in the fire occurrence drivers and in fire density, are very scarce. Furthermore, there is a scarcity of studies that analyze the spatial stationarity in the relationships of fire occurrence and its drivers at multiple scales. The current study aimed at predicting the spatial patterns of fire occurrence at regional and national levels in Mexico, utilizing geographically weighted regression (GWR) to predict fire density, calculated with two different approaches -regular grid density and kernel density - at spatial resolutions from 5 to 50 km, both in the dependent and in the independent human and environmental candidate variables. A better performance of GWR, both in goodness of fit and residual correlation reduction, was observed for prediction of kernel density as opposed to regular grid density. Our study is, to our best knowledge, the first study utilizing GWR to predict fire kernel density, and the first study to utilize GWR considering multiple scales, both in the dependent and independent variables. GWR models goodness of fit increased with fire kernel density search radius (bandwidths), but saturation in predictive capacity was apparent at 15-20 km for most regions. This suggests that this scale has a good potential for operational use in fire prevention and suppression decision-making as a compromise between predictive capability and spatial detail in fire occurrence predictions. This result might be a consequence of the specific spatial patterns of fire occurrence in Mexico and should be analyzed in future studies replicating this methodology elsewhere.

Invasive grasses increase fire occurrence and frequency across US ecoregions

Fire-prone invasive grasses create novel ecosystem threats by increasing fine-fuel loads and continuity, which can alter fire regimes. While the existence of an invasive grass-fire cycle is well known, evidence of altered fire regimes is typically based on local-scale studies or expert knowledge. Here, we quantify the effects of 12 nonnative, invasive grasses on fire occurrence, size, and frequency across 29 US ecoregions encompassing more than one third of the conterminous United States. These 12 grass species promote fire locally and have extensive spatial records of abundant infestations. We combined agency and satellite fire data with records of abundant grass invasion to test for differences in fire regimes between invaded and nearby "uninvaded" habitat. Additionally, we assessed whether invasive grass presence is a significant predictor of altered fire by modeling fire occurrence, size, and frequency as a function of grass invasion, in addition to anthropogenic and ecological covariates relevant to fire. Eight species showed significantly higher fire-occurrence rates, which more than tripled for Schismus barbatus and Pennisetum ciliare. Six species demonstrated significantly higher mean fire frequency, which more than doubled for Neyraudia reynaudiana and Pennisetum ciliare Grass invasion was significant in fire occurrence and frequency models, but not in fire-size models. The significant differences in fire regimes, coupled with the importance of grass invasion in modeling these differences, suggest that invasive grasses alter US fire regimes at regional scales. As concern about US wildfires grows, accounting for fire-promoting invasive grasses will be imperative for effectively managing ecosystems.

Environmental Conditions, Ignition Type, and Air Quality Impacts of Wildfires in the Southeastern and Western United States

This research contrasts the environmental conditions, meteorological drivers, and air quality impacts of human- and lightning-ignited wildfires in the southeastern and western United States, the two continental U.S. regions with the most wildfire burn area. We use the Fire Program Analysis Wildfire Occurrence Data (FPA FOD) to determine wildfire abundance and ignition sources between 1992 and 2015. We investigate specific ecoregions within these two U.S. regions and find that in the majority of ecoregions, annual lightning- and human-ignited wildfire burn area have similar relationships with key meteorological parameters. We investigate the fuel moisture values where wildfires occur segregated by ignition type and show that within a given ecoregion, the differences in median fuel moisture between ignition types are generally smaller than the differences between ecoregions. Our results suggest that annual wildfire burn area for human- and lightning-ignited wildfires within a given ecoregion are modulated by environmental conditions, and climate change may similarly impact wildfires of both ignition types. Finally, we estimate fine particulate matter emissions for Fire Program Analysis Wildfire Occurrence Data wildfires using the Fire INventory from NCAR model framework. We show that emissions of fine particulate matter from human-ignited wildfires is significant and of a similar total magnitude between the west and southeastern United States. Additionally, the west and southeast have a similar number of wildfires associated with National Weather Service air quality smoke forecasts.

Novel disturbance interactions between fire and an emerging disease impact survival and growth of resprouting trees

Human-altered ecological disturbances may challenge system resilience and disrupt biological legacies maintaining ecosystem recovery. Yet, the extent to which novel regimes challenge these legacies varies. This may be partially explained by differences in the vulnerability of life history strategies to disturbance characteristics. In the fire-prone, resprouter-dominated coast redwood forests of California, the introduced disease sudden oak death (SOD) alters fuel profiles, fire behavior, and aboveground tree mortality; however, this system is dominated by resprouting trees that are well-adapted to aboveground damage, and belowground survival of individuals may represent the principal biological legacy connecting pre- and post-fire communities. Much of the research exploring altered disturbances and forest recovery has focused on legacies determined by seed dispersal and aboveground survival of adults. In this work, we use pre- and post-fire data from a long-term monitoring network to assess the impacts of novel disturbance interactions between wildfire and SOD on the belowground survival and vegetative reproduction of resprouters. We found that increasing accumulation of coarse woody surface fuels from SOD-killed hosts decreased the likelihood of belowground survival for resprouting tanoak trees, but not for redwoods. Tanoaks' belowground survival was negatively related to substrate burn severity, which increased with the volume of surface fuels from hosts, suggesting heat damage as a possible mechanism influencing altered patterns of resprouter mortality. These impacts increased with decreasing tree size. By contrast, redwood and tanoak trees that survived both disturbances resprouted more vigorously, regardless of post-fire infection by P. ramorum, and generated similar recruitment at the stand level. Our results demonstrate that disease-fire interactions can narrow recruitment filters for resprouters, which could impact long-term population and demographic structure; yet, compounded disturbance may also reduce stand density and disease pressure, allowing competitive release of survivors. Resprouters displayed vulnerabilities to altered disturbance, but our research suggests that legacies maintained by resprouting may be more resilient to certain compounded disturbances, compared to seed-obligate species, because of high rates of individual survival under increasingly severe events. These trends have important implications for conservation of declining tree species in SOD-impacted forests, as well as predictions of human impacts in other disturbance-prone systems where resprouters are present.

Future Fire Impacts on Smoke Concentrations, Visibility, and Health in the Contiguous United States

Fine particulate matter (PM2.5) from U.S. anthropogenic sources is decreasing. However, previous studies have predicted that PM2.5 emissions from wildfires will increase in the midcentury to next century, potentially offsetting improvements gained by continued reductions in anthropogenic emissions. Therefore, some regions could experience worse air quality, degraded visibility, and increases in population-level exposure. We use global climate model simulations to estimate the impacts of changing fire emissions on air quality, visibility, and premature deaths in the middle and late 21st century. We find that PM2.5 concentrations will decrease overall in the contiguous United States (CONUS) due to decreasing anthropogenic emissions (total PM2.5 decreases by 3% in Representative Concentration Pathway [RCP] 8.5 and 34% in RCP4.5 by 2100), but increasing fire-related PM2.5 (fire-related PM2.5 increases by 55% in RCP4.5 and 190% in RCP8.5 by 2100) offsets these benefits and causes increases in total PM2.5 in some regions. We predict that the average visibility will improve across the CONUS, but fire-related PM2.5 will reduce visibility on the worst days in western and southeastern U.S. regions. We estimate that the number of deaths attributable to total PM2.5 will decrease in both the RCP4.5 and RCP8.5 scenarios (from 6% to 4-5%), but the absolute number of premature deaths attributable to fire-related PM2.5 will double compared to early 21st century. We provide the first estimates of future smoke health and visibility impacts using a prognostic land-fire model. Our results suggest the importance of using realistic fire emissions in future air quality projections.

Human-Related Ignitions Increase the Number of Large Wildfires across U.S. Ecoregions

Large fires account for the majority of burned area and are an important focus of fire management. However, ‘large’ is typically defined by a fire size threshold, minimizing the importance of proportionally large fires in less fire-prone ecoregions. Here, we defined ‘large fires’ as the largest 10% of wildfires by ecoregion (n = 175,222 wildfires from 1992 to 2015) across the United States (U.S.). Across ecoregions, we compared fire size, seasonality, and environmental conditions (e.g., wind speed, fuel moisture, biomass, vegetation type) of large human- and lighting-started fires that required a suppression response. Mean large fire size varied by three orders of magnitude: from 1 to 10 ha in the Northeast vs. >1000 ha in the West. Humans ignited four times as many large fires as lightning, and were the dominant source of large fires in the eastern and western U.S. (starting 92% and 65% of fires, respectively). Humans started 80,896 large fires in seasons when lightning-ignited fires were rare. Large human-started fires occurred in locations and months of significantly higher fuel moisture and wind speed than large lightning-started fires. National-scale fire policy should consider risks to ecosystems and economies by these proportionally large fires and include human drivers in large fire risk assessment.

Human-started wildfires expand the fire niche across the United States

The economic and ecological costs of wildfire in the United States have risen substantially in recent decades. Although climate change has likely enabled a portion of the increase in wildfire activity, the direct role of people in increasing wildfire activity has been largely overlooked. We evaluate over 1.5 million government records of wildfires that had to be extinguished or managed by state or federal agencies from 1992 to 2012, and examined geographic and seasonal extents of human-ignited wildfires relative to lightning-ignited wildfires. Humans have vastly expanded the spatial and seasonal "fire niche" in the coterminous United States, accounting for 84% of all wildfires and 44% of total area burned. During the 21-y time period, the human-caused fire season was three times longer than the lightning-caused fire season and added an average of 40,000 wildfires per year across the United States. Human-started wildfires disproportionally occurred where fuel moisture was higher than lightning-started fires, thereby helping expand the geographic and seasonal niche of wildfire. Human-started wildfires were dominant (>80% of ignitions) in over 5.1 million km2, the vast majority of the United States, whereas lightning-started fires were dominant in only 0.7 million km2, primarily in sparsely populated areas of the mountainous western United States. Ignitions caused by human activities are a substantial driver of overall fire risk to ecosystems and economies. Actions to raise awareness and increase management in regions prone to human-started wildfires should be a focus of United States policy to reduce fire risk and associated hazards.