The area burned by wildfire in the southwestern US has increased by over 400% since the 1980s. While many of the forests in the southwest are adapted to deal with frequent-fire, we’ve had a long fire-free period because of fire suppression. This has allowed forests to grow dense and fuels to increase. As a result, we are now seeing increased area burned by large, hot, tree-killing fire, which is uncharacteristic for many southwestern forest types. This is problematic for a number of reasons, but becomes especially problematic when a large, hot wildfire burns through a watershed that serves a community.
The Santa Fe Fireshed is approximately 111,000 acres and encompasses the City of Santa Fe’s municipal watershed. The Santa Fe Fireshed Coalition is a collaborative group working to develop and implement management strategies to reduce the chance that a large, hot fire impacts the municipal watershed.
In a recent study led by Dan Krofcheck, we ran simulations to quantify the effects of different management options on fire severity and carbon dynamics in the Fireshed under future climate and future fire weather. We used the Fireshed Coalition’s implemented, planned, and proposed treatments to develop and simulate a treatment scenario we called the prioritized scenario. We also ran a series of simulations without management (No-Management) to identify the areas with the greatest risk of tree-killing fire. We used these scenarios to develop the optimized scenario. The optimized scenario differs from the prioritized scenario by only thinning the areas with the largest chance of burning at high-severity and increasing the area that is only treated with prescribed fire.
We then ran simulations using future climate from different climate models, ending with a total of 6250 simulation years of data for each management scenario (No-Management, Prioritized, Optimized). We compiled all of the data for each scenario to determine if the optimized scenario was as effective as the prioritized scenario at reducing high-severity fire. We found that it was actually a bit more effective because the area that was treated with prescribed burning was expanded to include dry mixed-conifer forest.
We also looked at the effects of these different scenarios on carbon, because forests are important for helping to regulate the climate. Since thinning treatments reduce the amount of carbon stored in the forest and prescribed burning causes emissions of carbon to the atmosphere, we expected both treatment scenarios to cause the amount of carbon stored in the forest to initially decrease, relative to the no-management scenario. However, because the management scenarios decrease the amount of tree-killing fire, we expected that over time, the carbon stored on the landscape would increase relative to the no-management scenario. We found the while both management scenarios ended up storing more carbon by 2050, the optimized scenario carbon storage surpassed the no-management scenario in approximately 25 years. In fact, the optimized scenario ended up storing approximately 0.3 teragrams more carbon than the no-management scenario. That is equivalent to the annual carbon emissions from 15,000 average Americans.
The reason that the optimized scenario carbon storage surpassed the no-management scenario twenty years earlier than the prioritized scenario is that we thinned less area in the optimized scenario. By only thinning areas with the largest chance of burning at high-severity, we reduced the thinned area by 54%. It is important to note that the only reason the optimized scenario was as effective as the prioritized scenario was because the area burned with prescribed fire increased by 27%. Our results suggest that in this southwestern landscape, restoring regular surface fire will provide more climate benefit than leaving the forests dense and running the risk that they will burn at high-severity.
We are seeking a motivated and independent postdoc to advance the state of the art in remote sensing and geospatial data integration in the field of ecosystem ecology. The successful candidate will work with the Landsat and Sentinel archive in conjunction with very high resolution drone acquired imagery to investigate how vegetation and topography govern microclimatic variability in post-wildfire landscapes. The objective of this project is to quantify influences on post-disturbance microclimatic variability and its effects on tree seedling survival. The Earth Systems Ecology Lab (www.hurteaulab.org) is an interdisciplinary group of ecosystem ecologists in the Department of Biology at the University of New Mexico. We work collaboratively to tackle a range of question related to global change and forest ecosystems.
We are seeking an individual with a quantitative ecosystem ecology or remote sensing background that is fluent in R or Python, has extensive geospatial analytic experience using any GIS, and experience with model-data integration. Familiarity with UAS data acquisition and processing using Agisoft and with geodetics (GNSS, RTKLIB) are a plus. Starting salary is $48,000 plus benefits. The position is initially for one year with the potential for extension. Preferred start date is fall 2019. To apply please send your CV, two-page statement of research interests, and list of three references to Matthew Hurteau (firstname.lastname@example.org) and Dan Krofcheck (email@example.com). We will begin reviewing applications 23 September.
Across the western US, the area burned by wildfire has been increasing as a result of higher temperatures and earlier spring snowmelt. When temperature goes up, ecosystems dry out and become more available to burn. This relationship has formed the basis for projecting future area burned as a result of climate change. With the projected increase in area burned, the expectation is that wildfire emissions will also increase. In a prior study, we estimated a 19-101% increase in emissions from wildfires burning in California by the end of this century. However, the majority of fire projections, including our future work makes the assumption that there will be vegetation available to burn if a fire occurs. The problem with that assumption is that fire is a self-limiting process, meaning for some period of time after a wildfire occurs, there is not enough vegetation available to support another fire. Further, even if enough vegetation is present to support a second fire, the amount of vegetation may be lower than the first fire and result in fewer wildfire emissions. To determine the effects of prior wildfires on future wildfires, we modeled this vegetation-fire feedback by simulating forest growth and wildfire under future climate across three transects in the Sierra Nevada (Figure 1). We re-estimated wildfire size distributions at each decade from 2010-2100 to account for the effect of prior wildfires on future fire size. We used the area burned and the type of vegetation that it burned to estimate the emissions from wildfire.
We found that when we accounted for the vegetation-fire feedback, the cumulative area burned was 9.8-21.8% lower than our simulations that only accounted for the effect of climate on wildfire (Figure 2). Scaled to the entire mountain range, this equals a 14.3% reducing in cumulative area burned through 2100.
Figure 2: Cumulative area burned for the three transects in the Sierra Nevada under projected climate. The dynamics simulations (solid lines) account for the effects of prior fires limiting future fires and projected climate. The statics simulations (dashed lines) only account for projected climate.
The largest wildfires are typically the most impactful to both society and ecosystems because large wildfires typically occur under extreme weather conditions, which cause them to spread rapidly. When we accounted for the effects of prior wildfires on fire size (dynamic), we found that by mid-century the largest wildfires were significantly smaller than the largest wildfires in the climate-only (static) simulations (Figure 3). However, by late-century, vegetation recovered in the previously burned areas and the largest wildfire sizes were no longer statistically different.
The data in Figure 3 are plotted on a log-scale to meet the assumptions of the statistical test we used to compare them. By mid-century, the dynamic simulations had a median fire size of 24,053 acres and the static simulations had a median value of 53,389 acres. The biggest fire we simulated in the dynamic scenario occurred during early-century and was 210,452 acres. Whereas, in the static scenario, the biggest fire we simulated occurred in late-century and was 440,754 acres. For comparison, the 2013 Rim Fire burned 257,314 acres in the Sierra Nevada and estimated to have emitted the equivalent of 12 Tg of carbon dioxide, equivalent to 2.7% of California’s total emissions. When we calculated the emissions that our simulated wildfires would emit, we found that even when we account for the effects of previous wildfires limiting future wildfire size, total emissions in the Sierra Nevada are equivalent to one Rim Fire occurring every 3.8 years. This could have significant implications for air quality in California.