Bibliographic record
Abstract
Oklahoma State University Agricultural Communications Services. Flickr/Greenfleet Australia Wildfire is known to have a dramatic impact on soil, but do soil conditions also affect wildfire? A new study says yes, and the finding could lead to better predictions of wildfire danger. The research, which appears in the November–December 2015 issue of Soil Science Society of America Journal, aimed to address a simple but understudied question, says Oklahoma State University (OSU) soil scientist and lead author, Erik Krueger: “Is soil moisture related to wildfire?” When the scientists crunched the numbers, they found that 91% of Oklahoma's largest fires during the growing season broke out only when soil moisture dropped below levels that cause plants severe stress. The link between fire and soil moisture may seem obvious, says Krueger, who led the study with SSSA member Tyson Ochsner, an OSU soil physicist. But to the team's knowledge, a direct connection hasn't been made until now because the soil moisture data “just weren't there to do it.” What made this study possible was a comprehensive, soil moisture monitoring network, known as the Oklahoma Mesonet, along with a wildfire dataset compiled by the Oklahoma State Fire Marshal's Office. Now that the relationship has been established, wildfire scientists can test whether soil moisture data improve fire risk assessments in Oklahoma, where thousands of wildfires erupt each year. The new information should be especially valuable during the growing season, when the water held inside living vegetation makes it harder to predict fire danger from weather conditions alone. But Krueger and his colleagues also hope scientists far beyond Oklahoma will take note of the work. And, in fact, the team is already asking how its results may apply in other wildfire-prone regions, such as those dominated by forests. “Wildfire scientists are very conditioned, with good reason, to think about things like wind speed, for example. Low relative humidity also helps dry fuels, and that happens more quickly at higher temperatures,” Krueger says. “So those are important things, no doubt about it. But I think soil moisture should be considered right up there with those variables.” During winter in Oklahoma, when most plants are dead or dormant, wildfire danger can be estimated accurately from relative humidity, wind speed, and other weather conditions. But in spring and summer—when plants are alive, full of water, and less likely to burn—wildfire scientists also emphasize another factor: live fuel moisture, or the moisture inside living plants. Krueger's original job was to estimate live fuel moisture from soil moisture, with the goal of linking soil moisture to wildfire using live fuel moisture as the bridge. But measuring the water levels inside vegetation is time-consuming and laborious, and the researchers grew impatient as they waited for new data to come in. So, they decided to forge ahead with the data they already had and attempt to relate soil moisture directly to wildfire. “The cool thing was there wasn't any lag time to collect the [live fuel moisture] data,” Krueger says. “I could get right to work.” A key point is that he didn't use total, volumetric soil moisture in his analysis, instead calculating the “fraction of available water capacity,” or FAW. That's because plant-available water can differ substantially among soils based on their properties, Krueger explains. Even when a clay soil and a silt loam contain the exact same amount of moisture, for example, less water will be available to plants growing in the clay because clay particles bind water so tightly. The team first calculated plant-available water by tapping a dataset on soil properties that Ochsner and others had collected for the Oklahoma Mesonet. Then they normalized the values to get FAW. “FAW is the plant-available water on a given day for a given soil relative to the maximum possible amount of plant-available water for that soil,” Krueger says. “And what's awesome about it is then we can compare soil moisture not only across our sites but also with other studies.” FAW's range—from 0 to 1.0—is also simple and intuitive, he adds. Zero means no water is available to plants relative to the total amount possible for a soil, while 1.0 indicates plant-available water is at its maximum. When Krueger calculated FAW for Oklahoma's soils and related it to the occurrence and severity of wildfire, he found that during the growing season, 91% of the largest fires (bigger than 121 ha) took place at FAW below 0.5, and 77% happened at FAW below 0.2. A FAW of 0.2 corresponds to extreme drought by other standards while 0.5 signals lesser—but still significant—dry conditions, Krueger explains. “So I think this clearly says that vegetation has to be stressed, and pretty severely stressed, for a large, growing season wildfire to occur.” In a companion study, the team went further, examining the importance of soil moisture and four weather variables (temperature, wind speed, relative humidity, and precipitation) for predicting fire during the growing season. Precipitation and temperature, surprisingly, didn't make the cut; all that was required in the model was relative humidity, wind speed—and soil moisture. Moreover, if wind speed and relative humidity were ripe for a large wildfire, but soil moisture was high, the probability of a big fire ended up being very low, Krueger says. “So each of those variables worked in concert to promote conditions of high wildfire [danger].” The bottom line: “We need to be thinking about soil moisture when we are thinking about wildfires in Oklahoma,” he concludes—and possibly elsewhere, as well. One of the group's next projects will ask how soil moisture connects to wildfire when other types of vegetation are present, such as forests. The findings could have implications for places like the U.S. West Coast and western Canada, where massive fires raged this year. Krueger cautions that much work needs to be done, though, as the relationship between soil moisture and wildfire will almost certainly differ between western forests and Great Plains grasslands. “But, yes, I absolutely think this is applicable to other parts of the country,” he says. “We just need to figure out how.” The interdisciplinary research team also included rangeland ecologists, Dave Engle, Sam Fuhlendorf, and Dirac Twidwell; and meteorologist, J.D. Carlson. The research was funded by the federal Joint Fire Science Program. View the original article in the November–December 2015 issue of the Soil Science Society of America Journal at https://doi.org/10.2136/sssaj2015.01.0041.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".