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Enregistrement W2560519643 · doi:10.2134/csa2015-60-12-2

Is Soil Moisture Related to Wildfire?

2015· article· en· W2560519643 sur OpenAlexaboutno aff
Madeline Fisher

Notice bibliographique

RevueCSA News · 2015
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueFire effects on ecosystems
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésEnvironmental scienceWater contentMoistureSoil scienceHydrology (agriculture)GeographyMeteorologyGeologyGeotechnical engineering

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,072
Score d'incertitude au seuil0,978

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,023

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,012
Tête enseignante GPT0,230
Écart entre enseignants0,218 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2015
Routes d'admission1
Résumé présentoui

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