Lightning Sparking More Boreal Forest Fires

Our Research Brief this month covers a new NASA-funded study led by Sander Veraverbeke of Vrije Universiteit  in Amsterdam which found lightning storms to be a main driver of recent large fire seasons in Alaska and Canada.  Results of the study are published in the July, 2017 issue of Nature Climate Change.

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July 2017 Nature Climate Change

MODIS (Moderate-Resolution Imaging Spectroradiometer) satellite images and data from ground-based lightning networks were employed to study fire ignitions. Sander’s analysis found increases of between two and five percent a year in the number of lightning-ignited fires since 1975. Veraverbeke said that the observed trends are consistent with climate change, with higher temperatures linked to both more burning and more thunderstorms.

Study co-author Brendan Rogers at Woods Hole Research Center in Massachusetts says these trends are likely to continue. “We expect an increasing number of thunderstorms, and hence fires, across the high latitudes in the coming decades as a result of climate change.” This is confirmed in the study by different climate model outputs.

Charles Miller of NASA’s Jet Propulsion Laboratory in California, another co-author, said while data from Alaska’s agency lightning networks were critical to this study, it is challenging to use these data to verify trends because of continuing network upgrades. “A spaceborne sensor that provides lightning data that can be linked with fire dynamics would be a major step forward,” he said. Such a sensor exists already– NASA’s spaceborne Optical Transient Detector –but it’s geostationary orbit limits its utility for high latitudes.

The researchers found that the fires are creeping farther north, near the transition from boreal forests to Arctic tundra. “In these high-latitude ecosystems, permafrost soils store large amounts of carbon that become vulnerable after fires pass through,” said co-author James Randerson of the University of California, Irvine. “Exposed mineral soils after tundra fires also provide favorable seedbeds for trees migrating north under a warmer climate.”

The Alaska Fire Science Consortium at the University of Alaska, Fairbanks, also participated in the study, and provides this 2-page Research Brief executive summary.

Citation: Veraverbeke, S., B.M. Rogers, M.L. Goulden, R.R. Jandt, C.E. Miller, E.B. Wiggins and J.T. Randerson. Lightning as a major driver of recent large fire years in North American boreal forests. Nature Climate Change 7: 529–534 (2017). DOI: 10.1038/nclimate3329

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You asked: what happened with IFTDSS? Here’s the answer:

That would be the Interagency Fuels Treatment Decision Support System–you know–that’s been in development and then beta-testing since 2006?  Well, the good news is they’ve officially released it now as a finished tool and it’s free and available to everyone.  See the new official IFTDSS webpage to review the history and capabilities.  For the uninitiated, IFTDSS is a web-based software and data integration framework that organizes fire and fuels software applications to make fuels treatment planning and analysis more efficient.

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Ft. Richardson and BLM personnel conduct a prescribed burn on military training facility in 2006.  (R. Jandt)

We’ve had the beta-test version available for a while but funding availability to maintain the web-based tool has been a subject of debate so it’s nice to see this 2017 roll-out!  If you haven’t checked out IFTDSS, one of it’s strengths is enabling you to complete an analysis using “cloud”-power without loading a lot of disparate pieces of software for project definition, fuel types, fire behavior and spread rate, etc. onto your personal or government computer.  The platform has integrated links to sources of vegetation data (LANDFIRE), topography, etc. making them easy to upload.  The proliferation of different software systems, by different entities, to “help” managers plan fuel treatments was identified as a source of confusion and inefficiency by the national fuels management committee, which spurred the initial development of IFTDSS.  So check it out–they offer both training and a help center, and IFTDSS is now included in the training for Prescribed Fire Planner (aka Burn Boss) RX341 class.

Future Fire Costs in Alaska

April Melvin of EPA’s National Climate Change Division has spent some time in the field in Alaska. In a just-released publication her research team takes a look at how firefighting costs in Alaska are likely to change through the next several decades.

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“Pumpkin” water bladder preparing burnout on the Chicken fire 2004. Photo by Cyle Wold, USFS-PNW.

They use the ALFRESCO  model developed at UAF, which simulates fire ignition and spread (annual timesteps) under different climate projections in 100-km grid cells. Read their paper (citation below) for all the details, but in a nutshell they found:  1) it’s hard to nail down precise fire cost records in the multi-jurisdictional setting!  2) Fire costs go up in the future and the biggest expenditures will be in the Full fire protection option.   3) by 2030, predicted federal fire suppression costs (not including base–support and pre-suppression) will average $27-47M annually under the RCP 4.5 (moderate emissions) climate projection. That compares to about $31M on average from 2002-2013.  Adding in state costs boosts this to about $116M total firefighting cost for Alaska assuming the state costs are still roughly 68% of the total cost.  Again this does not include base operating costs.  The paper provides some good analysis for fire protection agencies to take to the bank.  Or at least to the Legislature!

Citation: Melvin, A.M., Murray, J., Boehlert, B. et al. 2017. Estimating wildfire response costs in Alaska’s changing climate.   Climate Change:  p 1-13.  doi:10.1007/s10584-017-1923-2.

Fire’s Role in a Broadleaf Future for Alaska?

As climate warming brings more wildfire to the North, scientists and citizens wonder how the landscape will be transformed.  Will forests continue their 2000’s-era trend toward less spruce and more hardwoods, catalyzed by larger fires and more frequent burning?  If so, that might slow down the trend for larger and more intense fires. However, will hotter summers with more effective drying lead to increased fire re-entry into the early successional hardwoods, making them less strategic barriers for fire protection? A research team modeling the former question just unveiled an interactive web tool to model forest changes under various future climate scenarios (Feb. 1 webinar recording available HERE).  With the new web tool, funded by JFSP,  Paul Duffy and Courtney Schultz will be working with fire managers in Alaska to look at fire occurrence and cost in the future.  Try it for yourself at  http://uasnap.shinyapps.io/jfsp-v10/

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Photo by USFS, PNW (2004).

As for the second question–will it be harder for hardwoods to resist fire–a recent paper in Ecosphere (Barrett et al. 2016) is one of the first published studies to look for an answer.  AFSC highlights that work with a Research Brief this month: A Deeper Look at Drivers of Fire Activity, Re-burns, and Unburned Patches in Alaska’s Boreal Forest.  Check out all our Research Briefs in our web Library.

Citation: Barrett, K, T. Loboda, AD McGuire, H. Genet, E. Hoy, and E. Kasischke. 2016. Static and dynamic controls on fire activity at moderate spatial and temporal scales in the Alaskan boreal forest. Ecosphere 7(11):e01572. 10.1002/ecs2.1572

Call for Data: US Post-Fire Tree Mortality

We seek data contributions to a Joint Fire Sciences Program project examining tree mortality due to wildland fire in the U.S. We are interested in U.S. datasets that at minimum include year of fire, county, state, and individual tree records of species, DBH and crown injury (some measure of crown scorch, kill, and/or consumption).

These datasets will be aggregated into an archived database of post-fire tree mortality and used to:

  1. validate existing predictive post-fire mortality models and
  2. examine the influence of pre-fire climate to improve predictions of post-fire tree mortality.

The archived data product will be made publicly available within one year of project completion (approximately 2020). Additional project detail from JFSP »

Contributors will receive authorship of the formally published archived data product and, at minimum, acknowledgement of contribution in published articles.

Please contact C. Alina Cansler via ccansler@fs.fed.us or (406) 829-6980 for additional information or questions. Thank you for your interest.

Can the 2015 Alaska fire season be attributed to warming climate?

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Smoke plume from 2015 Card Street fire (Alaska Division of Forestry).

A collaboration between NOAA, UAF, UAA, NWS, AFSC and AICC Predictive Services has produced a new paper on attribution of extreme fire seasons to climate change. The report appears in the Bulletin of the American Meteorological Society (BAMS), “An Assessment of the Role of Anthropogenic Climate Change in the Alaska Fire Season of 2015,” announced at AGU last week.

Bottom line: Human-induced climate change may have increased the risk of a fire season of 2015 severity by 34%–60%.  (LINK:  Chapter 4 in  https://www.ametsoc.org/ams/index.cfm/publications/bulletin-of-the-american-meteorological-society-bams/explaining-extreme-events-from-a-climate-perspective/)

Climate analogs to see the future today

The subject of a new study (and a recent AFSC webinar by Sean Parks of the USFS Rocky Mountain Research Station) introduces a novel way to look at fire regime changes through time over a landscape using the idea of “climate analogs”.  We’ve all seen maps showing future changes in temperature and precipitation based on climate projection models. Spatial analysis can locate a “future” climate analog for any pixel on a map using projections of variables like temperature, precipitation, or modeled evapotranspiration. Parks et al. 2016 provide a way to “look next door to see the future”, i.e. our pixel or region of interest, may be expected to show a fire return interval, burn severity, etc. similar to that now reflected in its analog which has those climate characteristics today.  If the average annual temperature in Fairbanks was 30⁰F in 2015, for example, we could map the nearest points that may have similar temperatures by 2085—possibly at higher elevations around Fairbanks. If a “path of least resistance” with respect to skirting areas that may have way different temperatures due to topographic features is added, you get a figure kind of like the one below from Yellowstone park (Dobrowski and Parks 2016). The authors have used the method to look at future availability of wildlife habitats, and to hypothesize fire regime characteristics of parks and wilderness areas in the mountain west.  Among their findings were thresholds for climate moisture deficit which seemed to make fire frequency jump up and other areas which seemed to indicate fuel limitations may lead to lower fire severity.  So far, the approach has not been tried in Alaska, but might provide an interesting comparison to vegetation and future fire modeling being done by SNAP.

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From Dobrowski and Parks, 2016, Fig. 1: Climate trajectories are defined by a source pixel (start) with a given temperature under current conditions (1981–2010) and a destination pixel (end) with a similar temperature under future conditions (2071–2100). Curved path (2) minimizes traversing pixels with large differences in temperature.

Citation: Parks, S.A.; Miller, C.; Abatzoglou, J.T.; Holsinger, L.M.; Parisien, M-A.; Dobrowski, S.Z. How will climate change affect wildland fire severity in the western US? Environ. Res. Lett. 201611, 035002. http://dx.doi.org/10.1088/1748-9326/11/3/035002

More fire–fewer firefighters?

Today’s science topic highlights a fire management conundrum:  While the number of acres burned in Alaska and most of the West is increasing, the number of wildland
firefighters available to suppress them is doing the opposite.  Conscoldtrail-sider the data published in Wildfire Today’s article last year  (Gabbert 2015).  The number of employees in the 5 major federal land management agencies who manage fires have all shrunk–by 6% at FWS to 18% at BLM to 33% at BIA.   Although these numbers are national, Alaska’s agencies have mirrored some of these reductions (and recall that large fire incidents tap the national pool of firefighters).  By some estimates the number of federally-employed firefighters is down by about 20% from 2011.

Boundary Fire study relates burn severity to permafrost degradation

The most important ecological effects of fire may not be evident for many years after burning.  Take permafrost, for example:   just-published research is revealing extensive thawing and drying of soils in the aftermath of the Boundary Fire in interior Alaska.  Brown et al. 2016 found almost all the severely burned plots in their study had thawed by 10 years after the 2004 fire.  Without permafrost the burned areas were better drained, leading to drier soils, and influencing vegetation succession.

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Typical burn appearance after 3 years (R. Jandt)

Another interesting facet of their study was the array of remotely-sensed data that Brown and colleagues employed, including optical and infrared spectra (Landsat 7 & 8), radar (L-band Synthetic Aperture Radar, or ALOS-PALSAR), and topographic (Light Detection and Ranging–LiDAR) datasets. Infrared indices used in the study were strongly correlated with soil moisture–allowing researchers to map the distribution of permafrost and compare it to burn severity maps.

Citation:
Brown, D.R.N., Jorgenson, M.T., Kielland, Knut, Verbyla, D.L., Prakash, Anupma and J.C. Koch. 2016. Landscape effects of wildfire on permafrost distribution in interior Alaska derived from remote sensing.  Remote Sensing 8 (8): 654, doi:10.3390/rs8080654.

 

C.A.R.V.E. and the Carbon Detectives

How do you know whether forest fires or factories and diesel generators are responsible for Black Carbon or CO2 in the air or deposited in icefields?  An experiment called CARVE (Carbon in Arctic Reservoirs Vulnerability Experiment) led by Chip Miller of the NASA Jet Propulsion Laboratory was conducted in Alaska’s airspace and some results just published explain how the source can be identified.  The combustion of woody biomass (or more importantly in Alaska–layers of compacted dead moss and organic soil) liberates primarily carbon deposited since World War II into CO2.  That modern post-bomb carbon contains traces of radioactive  carbon (Δ14C) in contrast to fossil fuels, deposited in prehistoric times, which have none.

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CARVE:  Sherpa aircraft flew sensors over fires in Alaska in 2013 to measure atmospheric concentrations of gases.

 

 

 

 

During the CARVE experiment, Sherpa aircraft flew sensors to measure atmospheric concentrations of CH4, CO2, and CO and parameters that control gas emissions (i.e. soil moisture, freeze/thaw state, surface temperature). They directly flew over some fires (including fires near Fairbanks and Delta) to measure the “fingerprint” concentrations of isotopes released by typical boreal burning.  Mouteva et al. (2015) published findings that showed most of the C in the summer skies over Alaska in 2013 was indeed attributable to forest fires and the age of the biomass converted to black carbon averaged about 20 years (range 11-47 yrs).  The authors also explore using the carbon isotope “fingerprint” of fires to estimate the average depth of consumption–since Δ14C increases with depth from the surface moss to the mesic horizon.  Pooled results of radioactive isotope fractions yielded an average depth of burn of about 8 inches for the 2013 Alaska fires–a result that may vary depending on fuel conditions.  Burn severity, expressed as depth of consumption, is a hot topic among agencies and land managers because it drives ecological response to burning as well as vegetation changes which may come with the hypothesized climate-driven increased boreal burning.

Citation:  Mouteva, G. O., et al. (2015), Black carbon aerosol dynamics and isotopic composition in Alaska linked with boreal fire emissions and depth of burn in organic soils, Global Biogeochem. Cycles: 29, doi:10.1002/2015GB005247.