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At least 613 records · Page 34Linked to original sources
USGS2021

Forecasting the frequency and magnitude of postfire debris flows across southern California

Southern California has a long history of damaging debris flows after wildfire. Despite recurrent loss, forecasts of the frequency and magnitude of postfire debris flows are not available for the region like they are for earthquakes. Instead, debris flow hazards are typically assessed in a reactive manner after wildfires. Such assessments are crucial for evaluating debris flow risk by postfire emergency response teams; however, time between the fire and first rainstorm is often insufficient to fully develop and implement effective emergency response plans like those in place for earthquakes. Here, we use both historical distributions of fire and precipitation frequency and empirical models of postfire debris flow likelihood and volume to map the expected frequency and magnitude of postfire debris flows across southern California. We find that at least small debris flows can be expected almost every year, while major debris flows capable of damaging 40 or more structures have a recurrence interval between 10 and 13 years, a return interval that is comparable to a magnitude 6.7 earthquake. A sensitivity analysis to possible future changes in current fire and precipitation regimes indicates that debris flow activity in southern California is more sensitive to increases in precipitation intensity than increases in fire frequency and severity. Projected increases in rainfall intensity of 18% result in an overall 110% increase in the probability of major debris flows. Our results, in combination with an assessment of exposure, can be used to prioritize watersheds for further analysis and possible prefire mitigation.

California↗
USGS2022

Rapid and gradual permafrost thaw: A tale of two sites

Warming temperatures and increasing disturbance by wildfire and extreme weather events is driving permafrost change across northern latitudes. The state of permafrost varies widely in space and time, depending on landscape, climate, hydrologic, and ecological factors. Despite its importance, few approaches commonly measure and monitor the changes in deep (>1 m) permafrost conditions with high spatial resolution. Here, we use electrical resistivity tomography surveys along two transects in interior Alaska previously disturbed by wildfire and more recently by warming temperatures and extreme precipitation. Long-term point observations of permafrost depth, temperature, and water content inform geophysical measurements which, in turn, are used to extrapolate interpretations over larger areas and with high spatial fidelity. We contrast gradual loss of recently formed permafrost driven by warmer temperatures and increased snowfall, with rapid permafrost loss driven by changes in air temperature, snow depth, and extreme summer precipitation in 2014.

Alaska↗
USGS2010

Recent acceleration of biomass burning and carbon losses in Alaskan forests and peatlands

Climate change has increased the area affected by forest fires each year in boreal North America. Increases in burned area and fire frequency are expected to stimulate boreal carbon losses. However, the impact of wildfires on carbon emissions is also affected by the severity of burning. How climate change influences the severity of biomass burning has proved difficult to assess. Here, we examined the depth of ground-layer combustion in 178 sites dominated by black spruce in Alaska, using data collected from 31 fire events between 1983 and 2005. We show that the depth of burning increased as the fire season progressed when the annual area burned was small. However, deep burning occurred throughout the fire season when the annual area burned was large. Depth of burning increased late in the fire season in upland forests, but not in peatland and permafrost sites. Simulations of wildfire-induced carbon losses from Alaskan black spruce stands over the past 60 years suggest that ground-layer combustion has accelerated regional carbon losses over the past decade, owing to increases in burn area and late-season burning. As a result, soils in these black spruce stands have become a net source of carbon to the atmosphere, with carbon emissions far exceeding decadal uptake.

Nature Geoscience↗
USGS2015

Relations between soil hydraulic properties and burn severity

Wildfire can affect soil hydraulic properties, often resulting in reduced infiltration. The magnitude of change in infiltration varies depending on the burn severity. Quantitative approaches to link burn severity with changes in infiltration are lacking. This study uses controlled laboratory measurements to determine relations between a remotely sensed burn severity metric ( dNBR , change in normalised burn ratio) and soil hydraulic properties (SHPs). SHPs were measured on soil cores collected from an area burned by the 2013 Black Forest fire in Colorado, USA. Six sites with the same soil type were selected across a range of burn severities, and 10 random soil cores were collected from each site within a 30-m diameter circle. Cumulative infiltration measurements were made in the laboratory using a tension infiltrometer to determine field-saturated hydraulic conductivity, K fs , and sorptivity, S . These measurements were correlated with dNBR for values ranging from 124 (low severity) to 886 (high severity). SHPs were related to dNBR by inverse functions for specific conditions of water repellency (at the time of sampling) and soil texture. Both functions had a threshold value for dNBR between 124 and 420, where K fs and S were unchanged and equal to values for soil unaffected by fire. For dNBR s >~420, the K fs was an exponentially decreasing function of dNBR and S was a linearly decreasing function of dNBR . These initial quantitative empirical relations provide a first step to link SHPs to burn severity, and can be used in quantitative infiltration models to predict post-wildfire infiltration and resulting runoff.

International Journal of Wildland Fire↗
USGS2019

Evaluating the factors responsible for post-fire water quality response in forests of the western USA

Wildfires commonly increase nutrient, carbon, sediment, and metal inputs to streams yet the factors responsible for the type, magnitude and duration of water quality effects are poorly understood. Prior work by the current authors found increased nitrogen, phosphorus and cation exports were common the first five post-fire years from a synthesis of 159 wildfires across the western United States. In the current study, an analysis is undertaken to determine factors that best explain post-fire streamwater responses observed in those watersheds. Increased post-fire total nitrogen and phosphorus loading were proportional to the catchment extent of moderate and high burn severity. Post-fire dissolved metal concentrations increased in catchments with < 2% pre-fire soil organic matter. In contrast, in catchments with > 2% soil organic matter, post-fire dissolved metal concentrations decreased compared to pre-fire conditions. Where post-fire normalized difference vegetation index (NDVI), a remote sensing indicator of live green vegetation, was low, total metal concentrations increased by 25% on average and by > 100% in some cases. When pre-fire soil field capacity exceeded 17%, there was a 750% median increase in total metals export to streams. Overall, the current analysis identified burn severity, post-fire vegetation cover, and several soil properties as the key variables explaining extended post-fire water quality response across a broad range of conditions found in the western US.

Arizona, California, Colorado, Idaho, Montana, New↗
USGS2020

Mapping fire regime ecoregions in California

The fire regime is a central framing concept in wildfire science and ecology and describes how a range of wildfire characteristics vary geographically over time. Understanding and mapping fire regimes is important for guiding appropriate management and risk reduction strategies and for informing research on drivers of global change and altered fire patterns. Most efforts to spatially delineate fire regimes have been conducted by identifying natural groupings of fire parameters based on available historical fire data. This can result in classes with similar fire characteristics but wide differences in ecosystem types. We took a different approach and defined fire regime ecoregions for California to better align with ecosystem types, without using fire as part of the definition. We used an unsupervised classification algorithm to segregate the state into spatial clusters based on distinctive biophysical and anthropogenic attributes that drive fire regimes – and then used historical fire data to evaluate the ecoregions. The fire regime ecoregion map corresponded well with the major land cover types of the state and provided clear separation of historical patterns in fire frequency and size, with lower variability in fire severity. This methodology could be used for mapping fire regimes in other regions with limited historical fire data or forecasting future fire regimes based on expected changes in biophysical characteristics.

California↗
USGS2024

Climate and weather drivers in southern California Santa Ana Wind and non-Santa Wind fires

Background. Autumn and winter Santa Ana Winds (SAW) are responsible for the largest and most destructive wildfires in southern California. Aims. 1) To contrast fires ignited on SAW days vs non-SAW days, 2) evaluate the predictive ability of the Canadian Fire Weather Index (CFWI) for these two fire types, and 3) determine climate and weather factors responsible for the largest wildfires. Methods. Cal Fire FRAP fire data were coupled with hourly climate data from 4 stations, and with regional indices of SAW wind speed, and with seasonal drought from the Palmer Drought Severity Index. Key results. Fires on non-SAW days were more numerous and burned more area, and were significant May to October. CFWI indices were tied to fire occurrence and size for both non-SAW and SAW days, and in the days following ignition. Multiple regression models for months with greatest area burned explained up to a quarter of variation in area burned. Conclusions. The drivers of fire size differ between non-SAW and SAW fires. The best predictor of fire size for non-SAW fires was drought during the prior five years, followed by current-year vapor pressure deficit. For SAW fires, wind speed followed by drought were most important.

California↗
USGS2025

Canopy and surface fuels measurement using terrestrial lidar single-scan approach in the Mogollon highlands of Arizona

Background Fuel monitoring data are essential to evaluate wildfire risk, plan management activities and evaluate fuel treatment effects. Terrestrial light detection and ranging (lidar) is a field-based 3D scanning technology with great potential to reduce labor-intensive field measurements and provide new depths of vegetation structure data. Aims To facilitate the integration of terrestrial lidar into fuel monitoring programs, we developed a model, training process, and Python program that produces canopy fuel, surface fuel and terrain metrics commonly used in fire behavior and fire risk modeling. Methods We estimated canopy and surface fuel metrics from terrestrial lidar using a semi-empirical model incorporating physically based modeling of leaf area density and occlusion and a non-destructive model calibration process leveraging Bayesian regression. We compared lidar-derived fuel estimates with conventional fuel estimates across diverse conditions in semi-arid shrubland, woodland and forest in Arizona. We also compared estimates using single- and multiple-scan modes. Key results In single-scan mode, our lidar-derived fuel estimates were significantly related to conventional estimates of total canopy fuel load, maximum canopy bulk density, downed surface fuel load and standing surface fuel load. Implications Our methods provide opportunities to increase the scalability of fuel monitoring to better understand wildfire risk and treatment effectiveness.

Arizona↗
USGS2017

Human presence diminishes the importance of climate in driving fire activity across the United States

Growing human and ecological costs due to increasing wildfire are an urgent concern in policy and management, particularly given projections of worsening fire conditions under climate change. Thus, understanding the relationship between climatic variation and fire activity is a critically important scientific question. Different factors limit fire behavior in different places and times, but most fire-climate analyses are conducted across broad spatial extents that mask geographical variation. This could result in overly broad or inappropriate management and policy decisions that neglect to account for regionally specific or other important factors driving fire activity. We developed statistical models relating seasonal temperature and precipitation variables to historical annual fire activity for 37 different regions across the continental United States and asked whether and how fire-climate relationships vary geographically, and why climate is more important in some regions than in others. Climatic variation played a significant role in explaining annual fire activity in some regions, but the relative importance of seasonal temperature or precipitation, in addition to the overall importance of climate, varied substantially depending on geographical context. Human presence was the primary reason that climate explained less fire activity in some regions than in others. That is, where human presence was more prominent, climate was less important. This means that humans may not only influence fire regimes but their presence can actually override, or swamp out, the effect of climate. Thus, geographical context as well as human influence should be considered alongside climate in national wildfire policy and management.

PNAS↗
USGS2012

The past as prelude to the future for understanding 21st-century climate effects on Rocky Mountain Trout

Bioclimatic models predict large reductions in native trout across the Rocky Mountains in the 21st century but lack details about how changes will occur. Through five case histories across the region, we explore how a changing climate has been affecting streams and the potential consequences for trout. Monitoring records show trends in temperature and hydrographs consistent with a warming climate in recent decades. Biological implications include upstream shifts in thermal habitats, risk of egg scour, increased wildfire disturbances, and declining summer habitat volumes. The importance of these factors depends on the context, but temperature increases are most relevant where population boundaries are mediated by thermal constraints. Summer flow declines and wildfires will be important where trout populations are fragmented and constrained to small refugia. A critical information gap is evidence documenting how populations are adjusting to long-term habitat trends, so biological monitoring is a priority. Biological, temperature, and discharge data from monitoring networks could be used to develop accurate vulnerability assessments that provide information regarding where conservation actions would best improve population resilience. Even with better information, future uncertainties will remain large due to unknowns regarding Earth's ultimate warming trajectory and how effects translate across scales. Maintaining or increasing the size of habitats could provide a buffer against these uncertainties.

Fisheries↗
USGS2010

Monitoring and characterizing natural hazards with satellite InSAR imagery

Interferometric synthetic aperture radar (InSAR) provides an all-weather imaging capability for measuring ground-surface deformation and inferring changes in land surface characteristics. InSAR enables scientists to monitor and characterize hazards posed by volcanic, seismic, and hydrogeologic processes, by landslides and wildfires, and by human activities such as mining and fluid extraction or injection. Measuring how a volcano's surface deforms before, during, and after eruptions provides essential information about magma dynamics and a basis for mitigating volcanic hazards. Measuring spatial and temporal patterns of surface deformation in seismically active regions is extraordinarily useful for understanding rupture dynamics and estimating seismic risks. Measuring how landslides develop and activate is a prerequisite to minimizing associated hazards. Mapping surface subsidence or uplift related to extraction or injection of fluids during exploitation of groundwater aquifers or petroleum reservoirs provides fundamental data on aquifer or reservoir properties and improves our ability to mitigate undesired consequences. Monitoring dynamic water-level changes in wetlands improves hydrological modeling predictions and the assessment of future flood impacts. In addition, InSAR imagery can provide near-real-time estimates of fire scar extents and fire severity for wildfire management and control. All-weather satellite radar imagery is critical for studying various natural processes and is playing an increasingly important role in understanding and forecasting natural hazards.

Annals of GIS↗
USGS2022

Simulating burn severity maps at 30 meters in two forested regions in California

Climate change is altering wildfire behavior and vegetation regimes in California’s forested ecosystems. Present day fires are seeing an increase in high burn severity area and high severity patch size. The ability to predict future burn severity patterns would support better policy and land management decisions. Here we demonstrate a methodology to first, statistically estimate individual burn severity classes at 30 meters and second, cluster and smooth high severity patches onto a landscape. Our goal here was not to exactly replicate observed burn severity maps, but rather to utilize observed maps as one realization of a random process dependent on climate, topography, fire weather, and fuels, to inform creation of additional realizations through our simulation technique. We developed two sets of empirical models with two different vegetation datasets to test if coarse vegetation could accurately model for burn severity. While visual acuity can be used to assess the performance of our simulation process, we also employ the Ripley’s K function to compare spatial point processes at different scales to test if the simulation is capturing an appropriate amount of clustering. We utilize FRAGSTATS to obtain high severity patch metrics to test the contiguity of our high severity simulation. Ripley’s K function helped identify the number of clustering iterations and FRAGSTATS showed how different focal window sizes affected our ability to cluster high severity patches. High severity patch simulations was comparable between the coarse and fine resolution vegetation datasets. Improving our ability to simulate burn severity will help advance our understanding of the potential influence of land and fuels management on ecosystem-level response variables that are important for decision-makers. Simulated burn severity maps can support managing habitat and estimating risks of habitat loss, protecting infrastructure and homes, improving future wildfire emissions projections, and better mapping and planning for fuels treatment scenarios.

California↗
USGS2024

Propagating observation errors to enable scalable and rigorous enumeration of plant population abundance with aerial imagery

Estimating and monitoring plant population size is fundamental for ecological research, as well as conservation and restoration programs. High-resolution imagery has potential to facilitate such estimation and monitoring. However, remotely sensed estimates typically have higher uncertainty than field measurements, risking biased inference on population status. We present a model that accounts for false negative (missed plants) and false positive (misclassified or double-counted plants) error in counts from high-resolution imagery via integration with ground data. We apply it to estimate the abundance of a foundational shrub species in post-wildfire landscapes in the western United States. In these landscapes, plant recruitment is crucial for ecological recovery but locally patchy, motivating the use of spatially extensive measurements from unoccupied aerial systems (UAS). Integrating >16 ha of UAS imagery with >700 georeferenced field plots, we fit our model to generate insights into the prevalence and drivers of observation errors associated with classification algorithms used to distinguish individual plants, relationships between abundance and landscape context, and to generate spatially explicit maps of shrub abundance. Raw counts of plant abundance in high-resolution imagery resulted in substantial false negative and false positive observation errors. The probability of detecting ( p ) adult plants (≥0.25 m tall) varied between sites within 0.52 < p̂adult < 0.82, whereas the detection of smaller plants (<0.25 m) was lower, 0.03 < p̂small < 0.3. On average, we estimate that 19% of all detected plants were false positive errors, which varied spatially in relation to topographic predictors. Abundance declined toward the interior of previous wildfires and was positively associated with terrain roughness. Our study demonstrates that integrated models accounting for imperfect detection improve estimates of plant population abundance derived from inherently imperfect UAS imagery. We believe such models will further improve inference on plant population dynamics—relevant to restoration, wildlife habitat and related objectives—and echo previous calls for remote sensing applications to better differentiate between ecological and observational processes.

Methods in Ecology and Evolution↗
USGS2023

Satellite-derived prefire vegetation predicts variation in field-based invasive annual grass cover after fire

Aims Invasion by annual grasses (IAGs) and concomitant increases in wildfire are impacting many drylands globally, and an understanding of factors that contribute to or detract from community resistance to IAGs is needed to inform postfire restoration interventions. Prefire vegetation condition is often unknown in rangelands but it likely affects variation in postfire invasion resistance across large burned scars. Whether satellite-derived products like the Rangeland Analysis Platform (RAP) can fulfill prefire information needs and be used to parametrize models of fire recovery to inform postfire management of IAGs is a key question. Methods We used random forests to ask how IAG abundances in 669 field plots measured in the 2-3 years following megafires in sagebrush steppe rangelands of western USA responded to RAP estimates of annual:perennial prefire vegetation cover, the effects of elevation, heat load, postfire treatments, soil moisture–temperature regimes, and land-agency ratings of ecosystem resistance to invasion and resilience to disturbance. Results Postfire IAG cover measured in the field was 22¯% and RAP-estimated prefire annual herbaceous cover was 15.7¯%. The random forest model had an R 2 of 0.36 and a root-mean-squared error (RMSE) of 4.41. Elevation, postfire herbicide treatment, and prefire estimates from RAP for the ratio of annual:perennial and shrub cover were the most important predictors of postfire IAG cover. Threshold-like relationships between postfire IAG cover and the predictors indicate that maintaining annual:perennial cover below 0.4 and shrub cover below <10% prior to wildfire would decrease invasion, at low elevations below 1400 m above sea level. Conclusion Despite known differences between RAP and field-based estimates of vegetation cover, RAP was still a useful predictor of variation in IAG abundances after fire. IAG management is oftentimes reactive, but our findings indicate impactful roles for more inclusively addressing the exotic annual community, and focusing on prefire maintenance of annual:perennial herbaceous and shrub cover at low elevations.

Applied Vegetation Science↗
USGS2021

Protecting restoration investments from the cheatgrass-fire cycle in sagebrush steppe

The US federal government has recently committed to the difficult task of slowing and managing the invasive grass-fire cycle in sagebrush steppe, where property, livelihoods, and entire ecosystems are at risk. To safely manage this crisis, the government recently proposed to construct about 17,700 km of fuel breaks and millions of hectares of fuel reduction treatments in six western states. A challenge for resource managers will be the strategic placement of these land treatments. We investigated the need for this massive effort from the perspective of protecting previous rehabilitation and restoration seeding investments, including over 3,400 seedings implemented from 1990 – 2019 covering about 24,540 km2. We found that portions of over 26% of these seedings have since burned representing nearly 17% of this seeded area. Locations that had recurrent wildfire had repeat treatments and thus multiple investments in the same location. We concluded that management actions protecting remaining sagebrush and investments are warranted, especially in areas where the invasive grass-fire cycle is most pervasive. Given the decades required for most sagebrush to recover after wildfire, the urgency of this management intervention is evident. The specific details, placement, and effectiveness of these interventions could influence outcomes and potential unintended consequences.

California, Idaho, Nevada, Oregon, Utah, Washingto↗
USGS2018

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km 2 ) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.

Alaska↗
USGS2018

Adapting management to a changing world: Warm temperatures, dry soil, and interannual variability limit restoration success of a dominant woody shrub in temperate drylands

Restoration and rehabilitation of native vegetation in dryland ecosystems, which encompass over 40% of terrestrial ecosystems, is a common challenge that continues to grow as wildfire and biological invasions transform dryland plant communities. The difficulty in part stems from low and variable precipitation, combined with limited understanding about how weather conditions influence restoration outcomes, and increasing recognition that one‐time seeding approaches can fail if they do not occur during appropriate plant establishment conditions. The sagebrush biome, which once covered over 620,000 km 2 of western North America, is a prime example of a pressing dryland restoration challenge for which restoration success has been variable. We analyzed field data on Artemisia tridentata (big sagebrush) restoration collected at 771 plots in 177 wildfire sites across its western range, and used process‐based ecohydrological modeling to identify factors leading to its establishment. Our results indicate big sagebrush occurrence is most strongly associated with relatively cool temperatures and wet soils in the first spring after seeding. In particular, the amount of winter snowpack, but not total precipitation, helped explain the availability of spring soil moisture and restoration success. We also find considerable interannual variability in the probability of sagebrush establishment. Adaptive management strategies that target seeding during cool, wet years or mitigate effects of variability through repeated seeding may improve the likelihood of successful restoration in dryland ecosystems. Given consistent projections of increasing temperatures, declining snowpack, and increasing weather variability throughout midlatitude drylands, weather‐centric adaptive management approaches to restoration will be increasingly important for dryland restoration success.

Great Basin↗
USGS2024

Trajectories and tipping points of piñon–juniper woodlands after fire and thinning

Piñon–juniper (PJ) woodlands are a dominant community type across the Intermountain West, comprising over a million acres and experiencing critical effects from increasing wildfire. Large PJ mortality and regeneration failure after catastrophic wildfire have elevated concerns about the long-term viability of PJ woodlands. Thinning is increasingly used to safeguard forests from fire and in an attempt to increase climate resilience. We have only a limited understanding of how fire and thinning will affect the structure and function of PJ ecosystems. Here, we examined vegetation structure, microclimate conditions, and PJ regeneration dynamics following ~20 years post-fire and thinning treatments. We found that burned areas had undergone a state shift that did not show signs of returning to their previous state. This shift was characterized by (1) distinct plant community composition dominated by grasses; (2) a lack of PJ recruitment; (3) a decrease in the sizes of interspaces in between plants; (4) lower abundance of late successional biological soil crusts; (5) lower mean and minimum daily soil moisture values; (6) lower minimum daily vapor pressure deficit; and (7) higher photosynthetically active radiation. Thinning created distinct plant communities and served as an intermediate between intact and burned communities. More intensive thinning decreased PJ recruitment and late successional biocrust cover. Our results indicate that fire has the potential to create drier and more stressful microsite conditions, and that, in the absence of active management following fire, there may be shifts to persistent ecological states dominated by grasses. Additionally, more intensive thinning had a larger impact on community structure and recruitment than less intensive thinning, suggesting that careful consideration of goals could help avoid unintended consequences. While our results indicate the vulnerability of PJ ecosystems to fire, they also highlight management actions that could be adapted to create conditions that promote PJ re-establishment.

Colorado↗