Anthropogenic effects on global mean fire size

2015 ◽  
Vol 24 (5) ◽  
pp. 589 ◽  
Author(s):  
Stijn Hantson ◽  
Gitta Lasslop ◽  
Silvia Kloster ◽  
Emilio Chuvieco
Keyword(s):  
Population Density ◽  
Burned Area ◽  
Intermediate Step ◽  
Net Income ◽  
Wildland Fires ◽  
Fire Growth ◽  
Fire Size ◽  
Fire Models ◽  
Disturbance Factor ◽  
Earth’S System

Wildland fires are an important agent in the earth’s system. Multiple efforts are currently in progress to better represent wildland fires in earth system models. Although wildland fires are a natural disturbance factor, humans have an important effect on fire occurrence by directly igniting and suppressing fires and indirectly influencing fire behaviour by changing land cover and landscape structure. Although these factors are recognised, their quantitative effect on fire growth and burned area are not well understood and therefore only partly taken into account in current process-based fire models. Here we analyse the influence of humans on mean fire size globally. The mean fire size was extracted from the global Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product MCD45. We found a linear decreasing trend between population density and observed mean fire size over the globe, as well as a negative effect of cropland cover and net income. We implemented the effect of population density on fire growth in a global vegetation model including a process-based fire model (SPITFIRE–JSBACH). When including this demographic control, spatial trends in modelled fraction of burned area generally improved when compared with satellite-derived burned area data. More process-based solutions to limit fire spread are needed in the future, but the empirical relations described here serve as an intermediate step to improve current fire models.

An Inverse Fire Modeling Methodology for the Determination of Fire Size, Fire Location, and Soot Deposition in a Compartment

2012 ◽  
Author(s):  
Kristopher J. Overholt ◽  
Ofodike A. Ezekoye
Keyword(s):  
Gas Phase ◽  
Solid Phase ◽  
Fire Modeling ◽  
Fire Intensity ◽  
Intermediate Step ◽  
Fire Size ◽  
Fire Models ◽  
Input Parameters ◽  
In Fire ◽  
Fire Location

Fire models are routinely used in life safety design projects and are being used more often in fire and arson investigations as well as reconstructions of firefighter line-of-duty deaths (LODDs) and injuries. In all of these applications, the fire heat release rate (HRR), location of a fire in a compartment, gas-phase soot concentration, and solid-phase soot accumulation are important parameters that govern the evolution of thermal conditions within the fire compartment. These input parameters can be a large source of uncertainty in fire models, especially in scenarios in which experimental data or detailed information on fire behavior are not available, such as fire investigations and LODD reconstructions. Various methods have been reported in literature to determine the size and location of a fire in a compartment using ceiling-mounted detectors [1–4]. A previous study by the authors developed an inverse fire modeling technique to determine the time-varying HRR of fire in a compartment using measured thermocouple data [5]. The work presented in this paper extends the inverse HRR methodology by developing a technique to determine the location of a fire using wall-mounted heat flux sensors or a surrogate such as degradation characteristics of enclosure boundaries that can be collected during post-fire assessments. Additionally, the presence of soot modifies the radiative transfer field in the hot gas layer (gas phase) as well as radiative heat transfer to surfaces (condensed phase). As a detailed history of compartment conditions becomes less available, there is a need for an inversion methodology to accurately recover governing input parameters such as fire size, fire location, and fire burning properties while maintaining an adequate level of accuracy. As an intermediate step using measured fire test data, we can begin to construct an approach to use rich data to invert for fire intensity, fire location, and fire properties such as the amount of soot produced by the fire.

scholarly journals Response of simulated burned area to historical changes in environmental and anthropogenic factors: a comparison of seven fire models

2019 ◽  
Vol 16 (19) ◽  
pp. 3883-3910 ◽  
Author(s):  
Lina Teckentrup ◽  
Sandy P. Harrison ◽  
Stijn Hantson ◽  
Angelika Heil ◽  
Joe R. Melton ◽  
...  
Keyword(s):  
Land Use ◽  
Co2 Concentration ◽  
Fire Regimes ◽  
Atmospheric Co2 ◽  
Anthropogenic Factors ◽  
Burned Area ◽  
Earth System ◽  
Atmospheric Co2 Concentration ◽  
Fire Models ◽  
The Impact

Abstract. Understanding how fire regimes change over time is of major importance for understanding their future impact on the Earth system, including society. Large differences in simulated burned area between fire models show that there is substantial uncertainty associated with modelling global change impacts on fire regimes. We draw here on sensitivity simulations made by seven global dynamic vegetation models participating in the Fire Model Intercomparison Project (FireMIP) to understand how differences in models translate into differences in fire regime projections. The sensitivity experiments isolate the impact of the individual drivers on simulated burned area, which are prescribed in the simulations. Specifically these drivers are atmospheric CO2 concentration, population density, land-use change, lightning and climate. The seven models capture spatial patterns in burned area. However, they show considerable differences in the burned area trends since 1921. We analyse the trajectories of differences between the sensitivity and reference simulation to improve our understanding of what drives the global trends in burned area. Where it is possible, we link the inter-model differences to model assumptions. Overall, these analyses reveal that the largest uncertainties in simulating global historical burned area are related to the representation of anthropogenic ignitions and suppression and effects of land use on vegetation and fire. In line with previous studies this highlights the need to improve our understanding and model representation of the relationship between human activities and fire to improve our abilities to model fire within Earth system model applications. Only two models show a strong response to atmospheric CO2 concentration. The effects of changes in atmospheric CO2 concentration on fire are complex and quantitative information of how fuel loads and how flammability changes due to this factor is missing. The response to lightning on global scale is low. The response of burned area to climate is spatially heterogeneous and has a strong inter-annual variation. Climate is therefore likely more important than the other factors for short-term variations and extremes in burned area. This study provides a basis to understand the uncertainties in global fire modelling. Both improvements in process understanding and observational constraints reduce uncertainties in modelling burned area trends.

Spatial patterns of fire patch size and shape complexity in the Brazilian savanna

2021 ◽  
Author(s):  
Joana Nogueira ◽  
Julia Rodrigues ◽  
Jan Lehmann ◽  
Hanna Meyer ◽  
Renata Libonati
Keyword(s):  
Land Use ◽  
Shape Index ◽  
Fire Spread ◽  
Core Area ◽  
Validation Dataset ◽  
Burned Area ◽  
Fire Size ◽  
Size And Shape ◽  
The North ◽  
Shape Complexity

<p>Fire events on a landscape scale are a widespread global phenomenon that influences the interactions between atmosphere and biosphere. Global burned area (BA) products derived from satellite images are used in dynamic vegetation fire modules to estimate greenhouse gas emissions, available fuel biomass and anthropic factors driving fire spread. Fire size and shape complexity from individual fire events can provide better estimates of fuel consumption, fire intensity, post fire vegetation recovery and their effects on landscape changes to better understand regional fire dynamics. Especially in the Brazilian savannas (Cerrado), a mosaic of heterogeneous vegetation where has prevailed an official “zero-fire” policy for decades leading to an increase in large wildfires, intensified also by rapid changes of land use using fire to land clearing in agriculture and livestock purposes. In this way, we aim to assess the fire size and shape patterns in Cerrado from 2013 to 2015, identifying each fire patch event from Landsat BA product and calculating its fire features with landscape metrics. We calculated its surface area to evaluate fire size and the metrics of shape index, core area and eccentricity from an ellipse fitting from burned pixels to estimate the fire shape complexity. The study focused on 48 Landsat path/row scenes and the analysis final compared the fire features of overlapped patches between the years. The total number of coincident fire patches is higher between the years 2013 and 2015 than 2013-2014 and 2014-2015. Large fires are found in the north and east regions for all comparisons. In this region, high core area values are consistent for having large areas of burnt patches and low shape index values and more elongated patches revealed a low fire shape complexity. These results demonstrate a greater burned area in the north, where the remaining native vegetation and less fragmented landscapes allow the fire to spread, when associated with favorable meteorological conditions. However, with the implementation of a new agricultural frontier in 2015, this region is under greater anthropic pressure with positive trends to land use. In the south, the fire shapes are already more complex and smaller because they are from agricultural areas historically developed, and consequently the landscape is more fragmented. Our results demonstrate a distinct spatial pattern of fire shape and size in Cerrado related to fragmentation of landscape and fire use to land cleaning. This information can help the modelling estimates of fire spread processes driven by topography, orientation of watersheds or dominant winds at local level, contributing to understanding the feedback with land cover/use, climate and biophysical characteristics at regional level to develop strategies for fire management.</p><p><strong>Acknowledges:</strong> J.N is funded by the 'Women in Research'-fellowship program (WWU Münster) and within the context of BIOBRAS Project “Research-based learning in neglected biodiverse ecosystems of Brazil”; funding by DAAD (number 57393735); validation dataset was performed under the Andurá project (number 441971/2018–0) funding by CNPq</p>

scholarly journals Identifying required model structures to predict global fire activity from satellite and climate data

2016 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Satellite Observations ◽  
Burned Area ◽  
Human Society ◽  
Fire Emissions ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation ◽  
Model Structures

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

scholarly journals Assessing the accuracy of remotely-sensed fire datasets across the Southwestern Mediterranean basin

2020 ◽  
Author(s):  
Luiz Felipe Galizia ◽  
Thomas Curt ◽  
Renaud Barbero ◽  
Marcos Rodrigues
Keyword(s):  
Mediterranean Basin ◽  
Quantitative Estimate ◽  
State Of The Art ◽  
Warm Season ◽  
Burned Area ◽  
Fire Size ◽  
Northern Iberian Peninsula ◽  
Fire Atlas ◽  
Highly Correlated ◽  
Size Threshold

Abstract. Recently, many remote-sensing (RS) based datasets providing features of individual fire events from gridded global burned area products have been released. Although very promising, these datasets still lack a quantitative estimate of their accuracy with respect to historical ground-based fire databases. Here, we compared three state-of-the-art RS datasets (Fire Atlas, FRY and GlobFire) with high-quality ground databases compiled by regional fire agencies (AG) across the Southwestern Mediterranean basin (2005–2015). We assessed the spatial and temporal accuracy in estimated RS burned area (BA) and number of fires (NF) aggregated at monthly and 0.25° resolutions, considering different individual fire size thresholds ranging from 1 to 500 ha. Our results show that RS datasets were highly correlated with AG in terms of monthly BA and NF but severely underestimated both (by 38 % and 96 %, respectively) when considering all fires > 1 ha. Stronger agreement was found when increasing the fire size threshold, with fires > 100 ha denoting higher correlation and much lower error (BA 10 %; NF 35%). The agreement between RS and AG was also the highest during the warm season (May to October) in particular across the regions with greater fire activity such as the Northern Iberian Peninsula. The Fire Atlas displayed a slightly better performance, with a lower relative error, although uncertainty in gridded BA product largely outpaced uncertainties across the RS datasets. Overall, our findings suggest a reasonable agreement between RS and ground-based datasets for fires larger than 100 ha, but care is needed when examining smaller fires at regional scales.

scholarly journals Cajander larch (<i>Larix cajanderi</i>) biomass distribution, fire regime and post-fire recovery in northeastern Siberia

2012 ◽  
Vol 9 (10) ◽  
pp. 3943-3959 ◽  
Author(s):  
L. T. Berner ◽  
P. S. A. Beck ◽  
M. M. Loranty ◽  
H. D. Alexander ◽  
M. C. Mack ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Aboveground Biomass ◽  
Forest Biomass ◽  
Burned Area ◽  
Forest Disturbances ◽  
Biomass Distribution ◽  
Fire Size ◽  
Northeastern Siberia ◽  
Fire Activity ◽  
Larix Cajanderi

Abstract. Climate change and land-use activities are increasing fire activity across much of the Siberian boreal forest, yet the climate feedbacks from forest disturbances remain difficult to quantify due to limited information on forest biomass distribution, disturbance regimes and post-disturbance ecosystem recovery. Our primary objective here was to analyse post-fire accumulation of Cajander larch (Larix cajanderi Mayr.) aboveground biomass for a 100 000 km2 area of open forest in far northeastern Siberia. In addition to examining effects of fire size and topography on post-fire larch aboveground biomass, we assessed regional fire rotation and density, as well as performance of burned area maps generated from MODIS satellite imagery. Using Landsat imagery, we mapped 116 fire scar perimeters that dated c. 1966–2007. We then mapped larch aboveground biomass by linking field biomass measurements to tree shadows mapped synergistically from WorldView-1 and Landsat 5 satellite imagery. Larch aboveground biomass tended to be low during early succession (≤ 25 yr, 271 ± 26 g m−2, n = 66 [mean ± SE]) and decreased with increasing elevation and northwardly aspect. Larch aboveground biomass tended to be higher during mid-succession (33–38 yr, 746 ± 100 g m−2, n = 32), though was highly variable. The high variability was not associated with topography and potentially reflected differences in post-fire density of tree regrowth. Neither fire size nor latitude were significant predictors of post-fire larch aboveground biomass. Fire activity was considerably higher in the Kolyma Mountains (fire rotation = 110 yr, fire density = 1.0 ± 1.0 fires yr−1 × 104 km−2) than along the forest-tundra border (fire rotation = 792 yr, fire density = 0.3 ± 0.3 fires yr−1 × 104 km−2). The MODIS burned area maps underestimated the total area burned in this region from 2000–2007 by 40%. Tree shadows mapped jointly using high and medium resolution satellite imagery were strongly associated (r2 &amp;approx; 0.9) with field measurements of forest structure, which permitted spatial extrapolation of aboveground biomass to a regional extent. Better understanding of forest biomass distribution, disturbances and post-disturbance recovery is needed to improve predictions of the net climatic feedbacks associated with landscape-scale forest disturbances in northern Eurasia.

Calculation of fire spread rates across random landscapes

2003 ◽  
Vol 12 (2) ◽  
pp. 167 ◽  
Author(s):  
Mark A. Finney
Keyword(s):  
Travel Time ◽  
Growth Rates ◽  
Fire Spread ◽  
Harmonic Mean ◽  
Fire Growth ◽  
Fire Size ◽  
Spread Rate ◽  
Fuel Treatments ◽  
Fuel Mixtures

An approach is presented for approximating the expected spread rate of fires that burn across 2-dimensional landscapes with random fuel patterns. The method calculates a harmonic mean spread rate across a small 2-dimensional grid that allows the fire to move forward and laterally. Within this sample grid, all possible spatial fuel arrangements are enumerated and the spread rate of an elliptical fire moving through the cells is found by searching for the minimum travel time. More columns in the sample grid are required for accurately calculating expected spread rates where very slow-burning fuels are present, because the fire must be allowed to move farther laterally around slow patches. This calculation can be used to estimate fire spread rates across spatial fuel mixtures provided that the fire shape was determined from wind and slope. Results suggest that fire spread rates on random landscapes should increase with fire size and that random locations of fuel treatments would be inefficient in changing overall fire growth rates.

scholarly journals A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

2017 ◽  
Vol 10 (12) ◽  
pp. 4443-4476 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Observations ◽  
Data Driven ◽  
Predictor Variables ◽  
Burned Area ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Factors influencing large wildland fire suppression expenditures

2008 ◽  
Vol 17 (5) ◽  
pp. 650 ◽  
Author(s):  
Jingjing Liang ◽  
Dave E. Calkin ◽  
Krista M. Gebert ◽  
Tyron J. Venn ◽  
Robin P. Silverstein
Keyword(s):  
Wildland Fire ◽  
Fire Suppression ◽  
Burned Area ◽  
Private Land ◽  
Fire Size ◽  
Department Of Agriculture ◽  
Factors Influencing ◽  
The Us ◽  
Large Fires ◽  
Parsimonious Model

There is an urgent and immediate need to address the excessive cost of large fires. Here, we studied large wildland fire suppression expenditures by the US Department of Agriculture Forest Service. Among 16 potential non-managerial factors, which represented fire size and shape, private properties, public land attributes, forest and fuel conditions, and geographic settings, we found only fire size and private land had a strong effect on suppression expenditures. When both were accounted for, all the other variables had no significant effect. A parsimonious model to predict suppression expenditures was suggested, in which fire size and private land explained 58% of variation in expenditures. Other things being equal, suppression expenditures monotonically increased with fire size. For the average fire size, expenditures first increased with the percentage of private land within burned area, but as the percentage exceeded 20%, expenditures slowly declined until they stabilised when private land reached 50% of burned area. The results suggested that efforts to contain federal suppression expenditures need to focus on the highly complex, politically sensitive topic of wildfires on private land.

scholarly journals Relationships between Human Population Density and Burned Area at Continental and Global Scales

PLoS ONE ◽  
2013 ◽  
Vol 8 (12) ◽  
pp. e81188 ◽  
Author(s):  
Ioannis Bistinas ◽  
Duarte Oom ◽  
Ana C. L. Sá ◽  
Sandy P. Harrison ◽  
I. Colin Prentice ◽  
...  
Keyword(s):  
Population Density ◽  
Human Population ◽  
Burned Area ◽  
Human Population Density
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