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      Machine learning to predict final fire size at the time of ignition

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          Abstract

          Fires in boreal forests of Alaska are changing, threatening human health and ecosystems. Given expected increases in fire activity with climate warming, insight into the controls on fire size from the time of ignition is necessary. Such insight may be increasingly useful for fire management, especially in cases where many ignitions occur in a short time period. Here we investigated the controls and predictability of final fire size at the time of ignition. Using decision trees, we show that ignitions can be classified as leading to small, medium or large fires with 50.4 ± 5.2% accuracy. This was accomplished using two variables: vapour pressure deficit and the fraction of spruce cover near the ignition point. The model predicted that 40% of ignitions would lead to large fires, and those ultimately accounted for 75% of the total burned area. Other machine learning classification algorithms, including random forests and multi-layer perceptrons, were tested but did not outperform the simpler decision tree model. Applying the model to areas with intensive human management resulted in overprediction of large fires, as expected. This type of simple classification system could offer insight into optimal resource allocation, helping to maintain a historical fire regime and protect Alaskan ecosystems.

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          Warming and earlier spring increase western U.S. forest wildfire activity.

          Western United States forest wildfire activity is widely thought to have increased in recent decades, yet neither the extent of recent changes nor the degree to which climate may be driving regional changes in wildfire has been systematically documented. Much of the public and scientific discussion of changes in western United States wildfire has focused instead on the effects of 19th- and 20th-century land-use history. We compiled a comprehensive database of large wildfires in western United States forests since 1970 and compared it with hydroclimatic and land-surface data. Here, we show that large wildfire activity increased suddenly and markedly in the mid-1980s, with higher large-wildfire frequency, longer wildfire durations, and longer wildfire seasons. The greatest increases occurred in mid-elevation, Northern Rockies forests, where land-use histories have relatively little effect on fire risks and are strongly associated with increased spring and summer temperatures and an earlier spring snowmelt.
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            Global fire emissions estimates during 1997–2016

            Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED) and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the Carnegie–Ames–Stanford Approach (CASA) biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1) new burned area estimates with contributions from small fires, (2) a revised fuel consumption parameterization optimized using field observations, (3) modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4) fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25°) and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were 2.2  ×  10 15  grams of carbon per year (Pg C yr −1 ) during 1997–2016, with a maximum in 1997 (3.0 Pg C yr −1 ) and minimum in 2013 (1.8 Pg C yr −1 ). These estimates were 11 % higher than our previous estimates (GFED3) during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37 %), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (−19 %) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emission factors. If small fire burned area was excluded (GFED4 without the s for small fires), average emissions were 1.5 Pg C yr −1 . The addition of small fires had the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. GFED data are available from http://www.globalfiredata.org .
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              Present-day climate forcing and response from black carbon in snow

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                Author and article information

                Journal
                101699224
                46119
                Int J Wildland Fire
                Int J Wildland Fire
                International journal of wildland fire
                1049-8001
                1448-5516
                11 May 2021
                17 September 2019
                17 September 2019
                26 May 2021
                : 28
                : 11
                : 861-873
                Affiliations
                [A ]Department of Earth System Science, Croul Hall, University of California, Irvine, CA 92697, USA.
                [B ]Department of Computer Science, Donald Bren Hall, University of California, Irvine, CA 92697, USA.
                [C ]Department of Civil and Environmental Engineering, Engineering Hall 5400, University of California, Irvine, CA 92697, USA.
                Author notes
                [D ]Corresponding author. scoffiel@ 123456uci.edu
                Author information
                http://orcid.org/0000-0002-0550-5126
                http://orcid.org/0000-0002-2284-7363
                http://orcid.org/0000-0002-0993-7081
                http://orcid.org/0000-0003-1078-231X
                http://orcid.org/0000-0001-6559-7387
                Article
                NASAPA1701731
                10.1071/wf19023
                8152111
                34045840
                a28d23d6-0897-459a-8b3a-984f7a9dde9b

                Open Access CC BY-NC-ND http://creativecommons.org/licenses/by/4.0/

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                boreal forests,decision trees,fire management,random forests,vapour pressure deficit

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