New Report: Regional effectiveness of wildfire mitigation treatments in the US

The Joint Fire Sciences has just released the final report on its study “Fuel Treatment Effectiveness in the United States,” and it confirms what many of us have been stating on this blog: in most forested areas of North America, the effects and extent of wildfire can be significantly reduced with a combination of stand thinnings and prescribed fire. Other benefits, of course, include fuels and construction materials for people, stable and productive environments for wildlife, and aesthetically pleasing locations for recreation and employment.
The “Key Points” of the report are listed below. Here is a link to the complete report: https://www.firescience.gov/projects/06-3-3-11/project/06-3-3-11_final_report.pdf
Here is a link to the article, published in the International Journal of Wildland Firehttps://www.firescience.gov/projects/06-3-3-11/project/06-3-3-11_06-3-3-11_cochrane_etal_IJWF_2012.pdf
K E Y   P O I N T S

Fuel treatments can be effective for reducing both local severity and fire extent. However, the effectiveness varies by ecoprovince, treatment type, and treatment age.
NORTHERN ROCKIES -  prescribed fires as a stand-alone treatment do not mimic wildfire and is ineffective for dependable fire severity reduction and may offset the effects of thinning for the 6-10 year interval if the treatments are combined.  Stand-alone thinning treatments were the most reliably effective and effectiveness lasted longer (>10 years) than wildfire (> 5 years).

SOUTHERN ROCKIESprescribed burning with or without thinning reduces fire severity for > 10 years after treatment. Prescribed burns were more reliable and longer lasting in their effectiveness when applied as stand-alone treatments without previous thinning. Thinning alone is ineffective for reducing fire severity and should be discouraged as a fuel treatment.

PACIFIC NORTHWEST - wildfires only reduce subsequent fire severity for <10 years after the initial fire. Prescribed fire was ineffective unless combined with thinning treatments. Thinning as a stand-alone treatment was the most consistent treatment for reducing fire severity with treatment effectiveness lasting longer than 10 years after implementation.

NORTHERN CALIFORNIA prescribed burning provides similar reductions in fire severity as wildfire; neither shows significant value beyond 10 years. Thinning reduces fire severity for more than 10 years but only after >5 years since implementation. The combination of thinning and burning may capture the short- and long-term effects, but this requires further study. Mastication/site prep is ineffective and possibly detrimental for reducing fire severity in the short term (2-5 years).

SOUTHERN CALIFORNIA previous wildfires reduce subsequent fire severity for < 10 years while prescribed burning is most reliably effective at > 10 years after implementation and highly variable in effectiveness for the first 10 years. Both thinning and mastication are apparently effective for at least 5 years, with thinning having somewhat greater effects, but more study is needed to verify this finding.

SOUTHEAST previous wildfires have little impact on fire severity of subsequent wildfires. In contrast, prescribed burning, although somewhat variable in its impacts, is the most effective treatment for reducing subsequent fire severity, with greater and more reliable effectiveness >5 years after implementation.Mastication/site prep is ineffective for fire severity reduction after the first 5 years, with enhanced fire severity apparent 10 years after treatment.

INTERIOR BROADLEAF ecoprovince -  prescribed burning is uniquely reliable and effective at reducing fire severity for at least 10 years after implementation. All other fuels treatments appear to be ineffective or detrimental to reducing wildfire severity. The combination of thinning and burning has particularly lethal results. However, more study of fuel treatment effectiveness in needed in these systems.

SEMI-DESERT ecoprovince - prescribed burning appears ineffective until >10 years, but may have greater effectiveness than wildfire after that point. However, few fuel treatments were available for study in this province.

GREAT LAKES ecoprovinces - prescribed fire may be effective, but more study is needed to confirm this finding.

5 Comments

  1. BobZ

    Thank you very much for this broad brush summary – Sure shows that one size doesn’t fit all. Sure shows the value of site specific sound forest management if you want to keep forests as forests instead of former forests.

  2. hi Bob, thanks for posting this, a couple questions… Are these all the correct links? I’m trying to do a word or phrase search for the material in your “key points” in the articles, and not having much luck, but it’s quite possible that I’m doing something wrong.

    Another point perhaps worth mentioning: this was a simulation modeling study, right? “To investigate landscape-level influences of the treatments, we used the FARSITE modelling system (Finney 2004) to simulate the observed wildfire progression and spread rates..” -from the methods section. Not that a simulation study is necessarily a bad thing, but I seem to recall some very strenuous criticism on this forum of models as research tools, perhaps even from some of the same folks who now applaud some conclusions of this study.

    You correctly noted that these (simulated) results varied by location, but perhaps it’s also worth emphasizing the study’s results that “In terms of net efficiency, fuels treatments ranged between preventing 4 ha of burning (West) for every hectare treated, to causing 3 ha of burning for every treated hectare (Kelsay),” which is somewhat less optimistic than the key points above.

    One final point in the paper worth noting, indeed it’s their concluding sentence, is that “It should be stressed, however, that the millions of hectares of fuels treatments, and the concomitant changes in fire effects when they burn, represent novel disturbance regimes that will have unknown effects on ecosystem composition, structure and processes, even if they do serve to mitigate fires of uncharacteristic size or severity.”

    • GuyK

      Re: “Another point perhaps worth mentioning: this was a simulation modeling study, right? “To investigate landscape-level influences of the treatments, we used the FARSITE modelling system (Finney 2004) to simulate the observed wildfire progression and spread rates..” -from the methods section. Not that a simulation study is necessarily a bad thing, but I seem to recall some very strenuous criticism on this forum of models as research tools, perhaps even from some of the same folks who now applaud some conclusions of this study.”

      –> I don’t consider a model to be true research. So, yes, I am opposed to using models in place of research as was done for the NSO recovery plan where there was an extreme dearth of research as the daddy of the NSO, Eric Forsman, admits (Old post here on NCFP which I can dig up if anyone wants to contest this).

      –> So I don’t see the results of various model runs (scenarios) as being the conclusions of a study except as a study of the model.

      –> What I see here is that various scenarios modeled by a particular model don’t contradict 80 years of established science. While I might question some of the conclusions such as the omission of the value of thinning in the South, I am not going to nit pick the findings since we haven’t the details or time to critique this particular model. Since I am pleased that the model doesn’t have any obvious gross shortcomings, I see this as partial validation of the model. The model is not validating established science over the last 80 years. Consistency with established science is how a model is evaluated. A model can not gen up established science.

      –> So the value of the model is definitely not that it is a substitute for research. The value of this partial validation of this model is that it can be used as an aid in prioritizing the need for future research which would be based on statistically sound experimental designs to be implemented in the field and in the lab. Then as that research is completed the model can be further refined for problems resulting from its extrapolation to scenarios outside of its data set or bad data or bad modeling or bad computer programming. And then the cycle repeats and as it does so, we learn the model’s limits, refine it again and begin to trust it more or less with each iteration.

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