Concurrent and antecedent soil moisture relate positively or negatively to probability of large wildfires depending on season

Krueger, Erik S.; Ochsner, Tyson; Carlson, J. D.; Engle, David M.; Fuhlendorf, Samuel D.

doi:10.1071/wf15104

Cited by 53 publications

(62 citation statements)

References 49 publications

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“…Fuel moisture is a critical property for evaluating fire danger, but there is limited accessible data. It has been known that soil moisture is strongly correlated with fuel moisture, thus soil moisture can be approximated as an indicator of fuel moisture (Krueger et al, 2016). Here, we used the monthly surface soil moisture (0-10 cm) to represent the influence of fuel moisture.…”

Section: Predictor Variablesmentioning

confidence: 99%

Predicting wildfire burned area in South Central US using integrated machine learning techniques

Wang

2019

Preprint

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<p><strong>Abstract.</strong> Occurrences of devastating wildfires have been on the rise in the United States for the past decades. While the environmental controls, including weather, climate, and fuels, are known to play important roles in controlling wildfires, the interrelationships between fires and the environmental controls are highly complex and may not be well represented by traditional parametric regressions. Here we develop a model integrating multiple machine learning algorithms to predict gridded monthly wildfire burned area during 2002&#8211;2015 over the South Central United States and identify the relative importance of the environmental drivers on the burned area for both the winter-spring and summer fire seasons of that region. The developed model is able to alleviate the issue of unevenly-distributed burned area data and achieve a cross-validation (CV) R2 value of 0.42 and 0.40 for the two fire seasons. For the total burned area over the study domain, the model can explain 50&#8201;% and 79&#8201;% of interannual total burned area for the winter-spring and summer fire season, respectively. The prediction model ranks relative humidity (RH) anomalies and preceding months&#8217; drought severity as the top two most important predictors on the gridded burned area for both fire seasons. Sensitivity experiments with the model show that the effect of climate change represented by a group of climate-anomaly variables contributes the most to the burned area for both fire seasons. Antecedent fuel amount and conditions are found to outweigh weather effects for the burned area in the winter-spring fire season, while the current-month fire weather is more important for the summer fire season likely due to the controlling effect of weather on fuel moisture in this season. This developed model allows us to predict gridded burned area and to access specific fire management strategies for different fire mechanisms in the two seasons.</p>

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Section: Predictor Variablesmentioning

confidence: 99%

Predicting wildfire burned area in South Central US using integrated machine learning techniques

Wang

2019

Preprint

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“…Thus VOD might be a valuable predictor variable for the biomass-driven variability in fire activity. Satellite datasets of surface soil moisture might be valuable proxies for the moisture of surface fuels in empirical fire models (Krueger et al, 2015(Krueger et al, , 2016 because they represent the top ∼ 3 cm of the soil (Dorigo et al, 2015). Such datasets might potentially provide useful information for empirical fire models to represent fuel loads, fuel moisture, or fire weather conditions.…”

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confidence: 99%

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

et al. 2017

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Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within processoriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and processoriented global fire models, we introduce a new flexible datadriven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model-data integration approaches can guide the future development of global process-oriented vegetation-fire models.

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“…In both cases, a stochastic approach would allow for the possibility of zero fire under high flammability conditions. A second component of future work could be to evaluate the role of antecedent soil moisture, similar to past studies that have been completed using biomes [22,64], on fire activity on all land-use and land-cover types.…”

Section: Discussionmentioning

confidence: 99%

Land-Cover Dependent Relationships between Fire and Soil Moisture

Schaefer

Magi

2019

Fire

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For this study, we characterized the dependence of fire counts (FCs) on soil moisture (SM) at global and sub-global scales using 15 years of remote sensing data. We argue that this mathematical relationship serves as an effective way to predict fire because it is a proxy for the semi-quantitative fire–productivity relationship that describes the tradeoff between fuel availability and climate as constraints on fire activity. We partitioned the globe into land-use and land-cover (LULC) categories of forest, grass, cropland, and pasture to investigate how the fire–soil moisture (fire–SM) behavior varies as a function of LULC. We also partitioned the globe into four broadly defined biomes (Boreal, Grassland-Savanna, Temperate, and Tropical) to study the dependence of fire–SM behavior on LULC across those biomes. The forest and grass LULC fire–SM curves are qualitatively similar to the fire–productivity relationship with a peak in fire activity at intermediate SM, a steep decline in fire activity at low SM (productivity constraint), and gradual decline as SM increases (climate constraint), but our analysis highlights how forests and grasses differ across biomes as well. Pasture and cropland LULC are a distinctly human use of the landscape, and fires detected on those LULC types include intentional fires. Cropland fire–SM curves are similar to those for grass LULC, but pasture fires are evident at higher SM values than other LULC. This suggests a departure from the expected climate constraint when burning is happening at non-optimal flammability conditions. Using over a decade of remote sensing data, our results show that quantifying fires relative to a single physical climate variable (soil moisture) is possible on both cultivated and uncultivated landscapes. Linking fire to observable soil moisture conditions for different land-cover types has important applications in fire management and fire modeling.

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Concurrent and antecedent soil moisture relate positively or negatively to probability of large wildfires depending on season

Cited by 53 publications

References 49 publications

Predicting wildfire burned area in South Central US using integrated machine learning techniques

Predicting wildfire burned area in South Central US using integrated machine learning techniques

A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

Land-Cover Dependent Relationships between Fire and Soil Moisture

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