scholarly journals Impacts of Forest Thinning on Wildland Fire Behavior

Forests ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 918 ◽  
Author(s):  
Tirtha Banerjee
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Severity ◽  
Fire Behavior ◽  
Fire Intensity ◽  
Fuel Moisture ◽  
Forest Thinning ◽  
Rate Of Spread ◽  
Wide Range ◽  
Actual Fire

Key message: We have explored the impacts of forest thinning on wildland fire behavior using a process based model. Simulating different degrees of thinning, we found out that forest thinning should be conducted cautiously as there could be a wide range of outcomes depending upon the post-thinning states of fuel availability, fuel connectivity, fuel moisture and micrometeorological features such as wind speed. Context: There are conflicting reports in the literature regarding the effectiveness of forest thinning. Some studies have found that thinning reduces fire severity, while some studies have found that thinning might lead to enhanced fire severity. Aims: Our goal was to evaluate if both of these outcomes are possible post thinning operations and what are the limiting conditions for post thinning fire behavior. Methods: We used a process based model to simulate different degrees of thinning systematically, under two different conditions, where the canopy fuel moisture was unchanged and when the canopy fuel moisture was also depleted post thinning. Both of these scenarios are reported in the literature. Results: We found out that a low degree of thinning can indeed increase fire intensity, especially if the canopy fuel moisture is low. A high degree of thinning was effective in reducing fire intensity. However, thinning also increased rate of spread under some conditions. Interestingly, both intensity and rate of spread were dependent on the competing effects of increased wind speed, fuel loading and canopy fuel moisture. Conclusion: We were able to find the limits of fire behavior post thinning and actual fire behavior is likely to be somewhere in the middle of the theoretical extremes explored in this work. The actual fire behavior post thinning should depend on the site specific conditions which would determine the outcome of the interplay among the aforementioned conditions. The work also highlights that policymakers should be careful about fine scale canopy architectural attributes and micrometeorological aspects when planning fuel treatment operations.

A cellular automata model to link surface fires to firebrand lift-off and dispersal

2013 ◽  
Vol 22 (4) ◽  
pp. 428 ◽  
Author(s):  
Holly A. Perryman ◽  
Christopher J. Dugaw ◽  
J. Morgan Varner ◽  
Diane L. Johnson
Keyword(s):  
Wind Speed ◽  
Wildland Fire ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Fuel Loading ◽  
Cellular Automata Model ◽  
Rate Of Spread ◽  
Lift Off ◽  
Fuel Moisture Content ◽  
Spot Fires

In spite of considerable effort to predict wildland fire behaviour, the effects of firebrand lift-off, the ignition of resulting spot fires and their effects on fire spread, remain poorly understood. We developed a cellular automata model integrating key mathematical models governing current fire spread models with a recently developed model that estimates firebrand landing patterns. Using our model we simulated a wildfire in an idealised Pinus ponderosa ecosystem. Varying values of wind speed, surface fuel loading, surface fuel moisture content and canopy base height, we investigated two scenarios: (i) the probability of a spot fire igniting beyond fuelbreaks of various widths and (ii) how spot fires directly affect the overall surface fire’s rate of spread. Results were averages across 2500 stochastic simulations. In both scenarios, canopy base height and surface fuel loading had a greater influence than wind speed and surface fuel moisture content. The expected rate of spread with spot fires occurring approached a constant value over time, which ranged between 6 and 931% higher than the predicted surface fire rate of spread. Incorporation of the role of spot fires in wildland fire spread should be an important thrust of future decision-support technologies.

Empirical modelling of surface fire behaviour in maritime pine stands

2009 ◽  
Vol 18 (6) ◽  
pp. 698 ◽  
Author(s):  
Paulo M. Fernandes ◽  
Hermínio S. Botelho ◽  
Francisco C. Rego ◽  
Carlos Loureiro
Keyword(s):  
Wind Speed ◽  
Moisture Content ◽  
Fire Spread ◽  
Fire Intensity ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Spread Rate ◽  
Surface Fire ◽  
Rate Of Spread ◽  
Fuel Moisture Content

An experimental burning program took place in maritime pine (Pinus pinaster Ait.) stands in Portugal to increase the understanding of surface fire behaviour under mild weather. The spread rate and flame geometry of the forward and backward sections of a line-ignited fire front were measured in 94 plots 10–15 m wide. Measured head fire rate of spread, flame length and Byram’s fire intensity varied respectively in the intervals of 0.3–13.9 m min–1, 0.1–4.2 m and 30–3527 kW m–1. Fire behaviour was modelled through an empirical approach. Rate of forward fire spread was described as a function of surface wind speed, terrain slope, moisture content of fine dead surface fuel, and fuel height, while back fire spread rate was correlated with fuel moisture content and cover of understorey vegetation. Flame dimensions were related to Byram’s fire intensity but relationships with rate of spread and fine dead surface fuel load and moisture are preferred, particularly for the head fire. The equations are expected to be more reliable when wind speed and slope are less than 8 km h–1 and 15°, and when fuel moisture content is higher than 12%. The results offer a quantitative basis for prescribed fire management.

Fire Weather and Smoke Management

2000 ◽  
Author(s):  
C. David Whiteman
Keyword(s):  
Forest Canopy ◽  
Wildland Fire ◽  
Fire Suppression ◽  
Fire Behavior ◽  
Plant Succession ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Resource Managers ◽  
Insect Infestations ◽  
Ecological Objectives

Wildland fires consume large areas of forest and grasslands every year. Fires are described in terms of fire behavior, which includes rate of spread and fire intensity. A fire that spreads rapidly burns less of the available fuel per square unit of area than a fire that moves slowly and allows the flaming front a longer residence time. A fire with flames that reach only two feet above the ground produces less heat and is less destructive than an intense fire that crowns, that is, has long flames and burns at the top (i.e., crown) of the forest canopy (figure 13.1). Fire suppression activities are initiated when a wildfire threatens people, property, or natural areas that need protection. These activities include dropping water or chemicals on a fire and establishing a fire line around the fire. A fire line is a zone along a fire’s edge where there is little or no fuel available to the fire. Roads, cliffs, rivers, and lakes can be part of a fire line, or land can be cleared by firefighters. Backfires may be set within the fire line to burn toward the fire, widening the fire line and reducing the likelihood of the fire spreading beyond it (figures 13.2 and 13.3). Fires can cross a fire line if the intensity is high or if spotting occurs, that is, if the wind carries burning material (firebrands) beyond the fire and across the fire line (figure 13.4). A wildland fire can be very destructive, but it can also be beneficial and may be used by land resource managers to accomplish specific ecological objectives. For example, smaller fires can reduce the danger of a large catastrophic fire by burning off underbrush. Fire can also be used to prepare land for planting, to control the spread of disease or insect infestations, to benefit plant species that are dependent on fire, to influence plant succession, or to alter the nutrients in the soil. When a fire is used to manage land resources, it is called a prescribed fire.

An Evaluation of NDFD Weather Forecasts for Wildland Fire Behavior Prediction

2018 ◽  
Vol 33 (1) ◽  
pp. 301-315 ◽  
Author(s):  
Wesley G. Page ◽  
Natalie S. Wagenbrenner ◽  
Bret W. Butler ◽  
Jason M. Forthofer ◽  
Chris Gibson
Keyword(s):  
United States ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Wildland Fire ◽  
Fire Behavior ◽  
The United States ◽  
Mean Bias Error ◽  
Bias Error ◽  
Behavior Prediction

Abstract Wildland fire managers in the United States currently utilize the gridded forecasts from the National Digital Forecast Database (NDFD) to make fire behavior predictions across complex landscapes during large wildfires. However, little is known about the NDFDs performance in remote locations with complex topography for weather variables important for fire behavior prediction, including air temperature, relative humidity, and wind speed. In this study NDFD forecasts for calendar year 2015 were evaluated in fire-prone locations across the conterminous United States during periods with the potential for active fire spread using the model performance statistics of root-mean-square error (RMSE), mean fractional bias (MFB), and mean bias error (MBE). Results indicated that NDFD forecasts of air temperature and relative humidity performed well with RMSEs of about 2°C and 10%–11%, respectively. However, wind speed was increasingly underpredicted when observed wind speeds exceeded about 4 m s−1, with MFB and MBE values of approximately −15% and −0.5 m s−1, respectively. The importance of accurate wind speed forecasts in terms of fire behavior prediction was confirmed, and the forecast accuracies needed to achieve “good” surface head fire rate-of-spread predictions were estimated as ±20%–30% of the observed wind speed. Weather station location, the specific forecast office, and terrain complexity had the largest impacts on wind speed forecast error, although the relatively low variance explained by the model (~37%) suggests that other variables are likely to be important. Based on these results it is suggested that wildland fire managers should use caution when utilizing the NDFD wind speed forecasts if high wind speed events are anticipated.

Fire intensity, fire severity and burn severity: a brief review and suggested usage

2009 ◽  
Vol 18 (1) ◽  
pp. 116 ◽  
Author(s):  
Jon E. Keeley
Keyword(s):  
Fire Severity ◽  
Empirical Studies ◽  
Fire Behavior ◽  
Radiant Energy ◽  
Energy Output ◽  
Burn Severity ◽  
Fire Intensity ◽  
Ecosystem Responses ◽  
Single Measure ◽  
Ecosystem Impacts

Several recent papers have suggested replacing the terminology of fire intensity and fire severity. Part of the problem with fire intensity is that it is sometimes used incorrectly to describe fire effects, when in fact it is justifiably restricted to measures of energy output. Increasingly, the term has created confusion because some authors have restricted its usage to a single measure of energy output referred to as fireline intensity. This metric is most useful in understanding fire behavior in forests, but is too narrow to fully capture the multitude of ways fire energy affects ecosystems. Fire intensity represents the energy released during various phases of a fire, and different metrics such as reaction intensity, fireline intensity, temperature, heating duration and radiant energy are useful for different purposes. Fire severity, and the related term burn severity, have created considerable confusion because of recent changes in their usage. Some authors have justified this by contending that fire severity is defined broadly as ecosystem impacts from fire and thus is open to individual interpretation. However, empirical studies have defined fire severity operationally as the loss of or change in organic matter aboveground and belowground, although the precise metric varies with management needs. Confusion arises because fire or burn severity is sometimes defined so that it also includes ecosystem responses. Ecosystem responses include soil erosion, vegetation regeneration, restoration of community structure, faunal recolonization, and a plethora of related response variables. Although some ecosystem responses are correlated with measures of fire or burn severity, many important ecosystem processes have either not been demonstrated to be predicted by severity indices or have been shown in some vegetation types to be unrelated to severity. This is a critical issue because fire or burn severity are readily measurable parameters, both on the ground and with remote sensing, yet ecosystem responses are of most interest to resource managers.

scholarly journals Effects of canopy midstory management and fuel moisture on wildfire behavior

2019 ◽  
Author(s):  
Tirtha Banerjee ◽  
Warren Heilman ◽  
Scott Goodrick ◽  
Kevin Hiers ◽  
Rodman Linn
Keyword(s):  
Fire Behavior ◽  
Fire Risk ◽  
Fire Intensity ◽  
Fuel Moisture ◽  
Fine Scale ◽  
Mean Flow ◽  
Prescribed Burns ◽  
Fuel Heterogeneity ◽  
Fire Exclusion ◽  
Intensity Spread

Wildfires burning more and more areas in North America can partly be attributed to fire exclusion activities in the past few decades which led to higher fuel accumulation. Mechanical thinning and prescribed burns are effective techniques to manage fuel loads and to establish a higher degree of control over future fire risk as well as to restore fire prone landscapes to their natural states of succession. However, given the complexity of interactions between fine scale fuel heterogeneity and wind, it is difficult to assess the success of thinning operations and prescribed burns. The present work addresses this issue systematically by simulating a fire starting from a simple fire line and moving through a vegetative environment where the midstory has been cleared in different degrees, leading to a canopy with almost no midstory, another with a sparse midstory and another with a thick midstory. The simulations are conducted for these three canopies under two different conditions, where the fuel moisture is high and where it is low. These six sets of simulations show widely different fire behavior, in terms of fire intensity, spread rate and consumption. To understand the physical mechanisms that lead to these differences, detailed analyses are conducted to look at wind patterns, mean flow and turbulent fluxes of momentum and energy. The analyses also lead to improved understanding of processes leading to high intensity crowning behavior in presence of a dense midstory. Moreover, this work highlights the importance of considering fine scale fuel heterogeneity, seasonality, wind effects and the associated fire-canopy-atmosphere interactions while considering prescribed burns and forest management operations.

scholarly journals Evaluating Model Predictions of Fire Induced Tree Mortality Using Wildfire-Affected Forest Inventory Measurements

Forests ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 958 ◽  
Author(s):  
Jason S. Barker ◽  
Jeremy S. Fried ◽  
Andrew N. Gray
Keyword(s):  
Wind Speed ◽  
Forest Inventory ◽  
Tree Mortality ◽  
Forest Type ◽  
Flame Length ◽  
Weather Data ◽  
Fuel Moisture ◽  
Forest Types ◽  
Mean Error ◽  
Wide Range

Forest land managers rely on predictions of tree mortality generated from fire behavior models to identify stands for post-fire salvage and to design fuel reduction treatments that reduce mortality. A key challenge in improving the accuracy of these predictions is selecting appropriate wind and fuel moisture inputs. Our objective was to evaluate postfire mortality predictions using the Forest Vegetation Simulator Fire and Fuels Extension (FVS-FFE) to determine if using representative fire-weather data would improve prediction accuracy over two default weather scenarios. We used pre- and post-fire measurements from 342 stands on forest inventory plots, representing a wide range of vegetation types affected by wildfire in California, Oregon, and Washington. Our representative weather scenarios were created by using data from local weather stations for the time each stand was believed to have burned. The accuracy of predicted mortality (percent basal area) with different weather scenarios was evaluated for all stands, by forest type group, and by major tree species using mean error, mean absolute error (MAE), and root mean square error (RMSE). One of the representative weather scenarios, Mean Wind, had the lowest mean error (4%) in predicted mortality, but performed poorly in some forest types, which contributed to a relatively high RMSE of 48% across all stands. Driven in large part by over-prediction of modelled flame length on steeper slopes, the greatest over-prediction mortality errors arose in the scenarios with higher winds and lower fuel moisture. Our results also indicated that fuel moisture was a stronger influence on post-fire mortality than wind speed. Our results suggest that using representative weather can improve accuracy of mortality predictions when attempting to model over a wide range of forest types. Focusing simulations exclusively on extreme conditions, especially with regard to wind speed, may lead to over-prediction of tree mortality from fire.

Exploring fire response to high wind speeds: fire rate of spread, energy release and flame residence time from fires burned in pine needle beds under winds up to 27 ms−1

2020 ◽  
Vol 29 (1) ◽  
pp. 81
Author(s):  
Bret Butler ◽  
Steve Quarles ◽  
Christine Standohar-Alfano ◽  
Murray Morrison ◽  
Daniel Jimenez ◽  
...  
Keyword(s):  
Wind Speed ◽  
Energy Release ◽  
Residence Time ◽  
Wildland Fire ◽  
Ground Level ◽  
Fire Spread ◽  
Convective Heating ◽  
Atmospheric Conditions ◽  
Wind Speeds ◽  
Rate Of Spread

The relationship between wildland fire spread rate and wind has been a topic of study for over a century, but few laboratory studies report measurements in controlled winds exceeding 5ms−1. In this study, measurements of fire rate of spread, flame residence time and energy release are reported for fires burning under controlled atmospheric conditions in shallow beds of pine needles subject to winds ranging from 0 to 27ms−1 (measured 5m above ground level). The data suggested that under constant flow conditions when winds are less than 10ms−1, fire rate of spread increases linearly at a rate of ~3% of the wind speed, which generally agrees with other laboratory-based models. When wind speed exceeds 10ms−1, the fire rate of spread response to wind remains linear but with a much stronger dependence, spreading at a rate of ~13% of the wind speed. Radiative and convective heating correlated directly to wind speed, with radiant heating increasing approximately three-fold as much as convective heating over the range of winds explored. The data suggested that residence time is inversely related to wind speed and appeared to approach a lower limit of ~20s as wind exceeded 15ms−1. Average flame residence time over the range of wind speeds was nominally 26s.

scholarly journals Remote sensing techniques to assess active fire characteristics and post-fire effects

2006 ◽  
Vol 15 (3) ◽  
pp. 319 ◽  
Author(s):  
Leigh B. Lentile ◽  
Zachary A. Holden ◽  
Alistair M. S. Smith ◽  
Michael J. Falkowski ◽  
Andrew T. Hudak ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fire Severity ◽  
Fire Behavior ◽  
Fire Effects ◽  
Ecological Effects ◽  
Fire Intensity ◽  
Active Fire ◽  
Post Fire Effects ◽  
Remote Sensing Techniques ◽  
Fire Characteristics

Space and airborne sensors have been used to map area burned, assess characteristics of active fires, and characterize post-fire ecological effects. Confusion about fire intensity, fire severity, burn severity, and related terms can result in the potential misuse of the inferred information by land managers and remote sensing practitioners who require unambiguous remote sensing products for fire management. The objective of the present paper is to provide a comprehensive review of current and potential remote sensing methods used to assess fire behavior and effects and ecological responses to fire. We clarify the terminology to facilitate development and interpretation of comprehensible and defensible remote sensing products, present the potential and limitations of a variety of approaches for remotely measuring active fires and their post-fire ecological effects, and discuss challenges and future directions of fire-related remote sensing research.

Estimation of Byram’s Fire Intensity and Rate of Spread from Spaceborne Remote Sensing Data in a Savanna Landscape

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 65
Author(s):  
Gernot Ruecker ◽  
David Leimbach ◽  
Joachim Tiemann
Keyword(s):  
Remote Sensing ◽  
Fuel Consumption ◽  
Spatial Resolution ◽  
Fire Behavior ◽  
Remote Sensing Data ◽  
Fire Intensity ◽  
Rate Of Spread ◽  
Radiative Power ◽  
Sentinel 2 ◽  
Fire Radiative Power

Fire behavior is well described by a fire’s direction, rate of spread, and its energy release rate. Fire intensity as defined by Byram (1959) is the most commonly used term describing fire behavior in the wildfire community. It is, however, difficult to observe from space. Here, we assess fire spread and fire radiative power using infrared sensors with different spatial, spectral and temporal resolutions. The sensors used offer either high spatial resolution (Sentinel-2) for fire detection, but a low temporal resolution, moderate spatial resolution and daily observations (VIIRS), and high temporal resolution with low spatial resolution and fire radiative power retrievals (Meteosat SEVIRI). We extracted fire fronts from Sentinel-2 (using the shortwave infrared bands) and use the available fire products for S-NPP VIIRS and Meteosat SEVIRI. Rate of spread was analyzed by measuring the displacement of fire fronts between the mid-morning Sentinel-2 overpasses and the early afternoon VIIRS overpasses. We retrieved FRP from 15-min Meteosat SEVIRI observations and estimated total fire radiative energy release over the observed fire fronts. This was then converted to total fuel consumption, and, by making use of Sentinel-2-derived burned area, to fuel consumption per unit area. Using rate of spread and fuel consumption per unit area, Byram’s fire intensity could be derived. We tested this approach on a small number of fires in a frequently burning West African savanna landscape. Comparison to field experiments in the area showed similar numbers between field observations and remote-sensing-derived estimates. To the authors’ knowledge, this is the first direct estimate of Byram’s fire intensity from spaceborne remote sensing data. Shortcomings of the presented approach, foundations of an error budget, and potential further development, also considering upcoming sensor systems, are discussed.

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