Concurrent and antecedent soil moisture relate positively or negatively to probability of large wildfires depending on season
Erik S. Krueger A E , Tyson E. Ochsner A , J. D. Carlson B , David M. Engle C , Dirac Twidwell C D and Samuel D. Fuhlendorf C
+ Author Affiliations
- Author Affiliations
A Department of Plant and Soil Sciences, 371 Agricultural Hall, Oklahoma State University, Stillwater, OK 74078, USA.
B Department of Biosystems and Agricultural Engineering, 111 Agricultural Hall, Oklahoma State University, Stillwater, OK 74078, USA.
C Department of Natural Resource Ecology and Management, 011 Agricultural Hall, Oklahoma State University, Stillwater, OK 74078, USA.
D Department of Agronomy and Horticulture, 202 Keim Hall, University of Nebraska-Lincoln, Lincoln, NE 68588, USA.
E Corresponding author. Email: erik.krueger@okstate.edu
International Journal of Wildland Fire 25(6) 657-668 https://doi.org/10.1071/WF15104
Submitted: 28 May 2015 Accepted: 3 February 2016 Published: 27 April 2016
Abstract
Measured soil moisture data may improve wildfire probability assessments because soil moisture is physically linked to fuel production and live fuel moisture, yet models characterising soil moisture–wildfire relationships have not been developed. We therefore described the relationships between measured soil moisture (concurrent and antecedent), as fraction of available water capacity (FAW), and large (≥405 ha) wildfire occurrence during the growing (May–October) and dormant (November–April) seasons from 2000 to 2012 in Oklahoma, USA. Wildfires were predominantly grass and brush fires but occurred across multiple fuel types including forests. Below-average FAW coincided with high wildfire occurrence each season. Wildfire probability during the growing season was 0.18 when concurrent FAW was 0.5 (a threshold for plant water stress) but was 0.60 when concurrent FAW was 0.2 (extreme drought). Dormant season wildfire probability was influenced not only by concurrent but also by antecedent FAW. Dormant season wildfire probability was 0.29 and 0.09 when FAW during the previous growing season was 0.9 (near ideal for plant growth) and 0.2, respectively. Therefore, although a wet growing season coincided with reduced wildfire probability that season, it also coincided with increased wildfire probability the following dormant season, suggesting that the mechanisms by which soil moisture influences wildfire probability are seasonally dependent.
References
Allen RG, Pereira LS, Raes D, Smith M 1998. Crop evapotranspiration: guidelines for computing crop water requirements, FAO Irrigation and Drainage Paper No. 56, Rome, Italy.Bradshaw LS, Deeming JE, Burgan RE, Cohen JD 1983. 1978 National Fire-Danger Rating System, USDA Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-169. (Ogden, UT)
Caccamo G, Chisholm LA, Bradstock RA, Puotinen ML, Pippen BG (2012) Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data. International Journal of Wildland Fire 21, 257–269.
| Monitoring live fuel moisture content of heathland, shrubland and sclerophyll forest in south-eastern Australia using MODIS data.Crossref | GoogleScholarGoogle Scholar |
Carlson JD 2010. OK–FIRE: a weather-based decision support for wildland fire managers in Oklahoma. Final report. Joint Fire Science Program, Project Number 05–2-1–81. Available at https://www.firescience.gov/projects/05-2-1-81/project/05-2-1-81_jfsp_final_report_05-2-1-81_ok-fire.pdf [Verified 6 April 2016]
Carlson JD, Bradshaw LS, Nelson RM, Bensch RR, Jabrzemski R (2007) Application of the Nelson model to four timelag fuel classes using Oklahoma field observations: model evaluation and comparison with National Fire Danger Rating System algorithms. International Journal of Wildland Fire 16, 204–216.
| Application of the Nelson model to four timelag fuel classes using Oklahoma field observations: model evaluation and comparison with National Fire Danger Rating System algorithms.Crossref | GoogleScholarGoogle Scholar |
Castro FX, Tudela A, Sebastia MT (2003) Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain). Agricultural and Forest Meteorology 116, 49–59.
| Modeling moisture content in shrubs to predict fire risk in Catalonia (Spain).Crossref | GoogleScholarGoogle Scholar |
Catry FX, Rego FC, Bação FL, Moreira F (2009) Modeling and mapping wildfire ignition risk in Portugal. International Journal of Wildland Fire 18, 921–931.
| Modeling and mapping wildfire ignition risk in Portugal.Crossref | GoogleScholarGoogle Scholar |
Chuvieco E, Riaño D, Aguado I, Cocero D (2002) Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment. International Journal of Remote Sensing 23, 2145–2162.
| Estimation of fuel moisture content from multitemporal analysis of Landsat Thematic Mapper reflectance data: applications in fire danger assessment.Crossref | GoogleScholarGoogle Scholar |
Collins BM, Omi PN, Chapman PL (2006) Regional relationships between climate and wildfire-burned area in the Interior West, USA. Canadian Journal of Forest Research 36, 699–709.
| Regional relationships between climate and wildfire-burned area in the Interior West, USA.Crossref | GoogleScholarGoogle Scholar |
Cramer JS (1999) Predictive performance of the binary logit model in unbalanced samples. The Statistician 48, 85–94.
| Predictive performance of the binary logit model in unbalanced samples.Crossref | GoogleScholarGoogle Scholar |
Crimmins MA, Comrie AC (2004) Interactions between antecedent climate and wildfire variability across south-eastern Arizona. International Journal of Wildland Fire 13, 455–466.
| Interactions between antecedent climate and wildfire variability across south-eastern Arizona.Crossref | GoogleScholarGoogle Scholar |
De Lannoy GJM, Verhoest NEC, Houser PR, Gish TJ, Van Meirvenne M (2006) Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3). Journal of Hydrology 331, 719–730.
| Spatial and temporal characteristics of soil moisture in an intensively monitored agricultural field (OPE3).Crossref | GoogleScholarGoogle Scholar |
Dennison PE, Moritz MA, Taylor RS (2008) Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California. International Journal of Wildland Fire 17, 18–27.
| Evaluating predictive models of critical live fuel moisture in the Santa Monica Mountains, California.Crossref | GoogleScholarGoogle Scholar |
Dente L, Vekerdy Z, de Jeu R, Su Z (2013) Seasonality and autocorrelation of satellite-derived soil moisture products. International Journal of Remote Sensing 34, 3231–3247.
| Seasonality and autocorrelation of satellite-derived soil moisture products.Crossref | GoogleScholarGoogle Scholar |
Dimitrakopoulos AP, Bemmerzouk AM (2003) Predicting live herbaceous moisture content from a seasonal drought index. International Journal of Biometeorology 47, 73–79.
Engle DM, Stritzke JF, Claypool PL (1987) Herbage standing crop around eastern red cedar trees. Journal of Range Management 40, 237–239.
| Herbage standing crop around eastern red cedar trees.Crossref | GoogleScholarGoogle Scholar |
Entekhabi D, Njoku EG, O’Neill PE, Kellogg KH, Crow WT, Edelstein WN, Entin JK, Goodman SD, Jackson TJ, Johnson J, Kimball J, Piepmeier JR, Koster RD, Martin N, McDonald KC, Moghaddam M, Moran S, Reichle R, Shi JC, Spencer MW, Thurman SW, Tsang L, Van Zyl J (2010) The soil moisture active passive (SMAP) Mission. Proceedings of the IEEE 98, 704–716.
| The soil moisture active passive (SMAP) Mission.Crossref | GoogleScholarGoogle Scholar |
Forestry Canada Fire Danger Group 1992. Development and structure of the Canadian Forest Fire Behavior Prediction System. Forestry Canada, Science and Sustainable Development Directorate, Information Report ST-X-3. (Ottawa, Canada)
Hillel D 1998. ‘Environmental Soil Physics’. (Academic Press: San Diego, CA)
Homer CG, Dewitz JA, Yang L, Jin S, Danielson P, Xian G, Coulston J, Herold ND, Wickham JD, Megown K (2015) Completion of the 2011 National Land Cover Database for the conterminous United States – representing a decade of land cover change information. Photogrammetric Engineering and Remote Sensing 81, 345–354.
Illston BG, Basara JB, Fiebrich CA, Crawford KC, Hunt E, Fisher DK, Elliott R, Humes K (2008) Mesoscale monitoring of soil moisture across a statewide network. Journal of Atmospheric and Oceanic Technology 25, 167–182.
| Mesoscale monitoring of soil moisture across a statewide network.Crossref | GoogleScholarGoogle Scholar |
Jackson RB, Canadell J, Ehleringer JR, Mooney HA, Sala OE, Schulze ED (1996) A global analysis of root distributions for terrestrial biomes. Oecologia 108, 389–411.
| A global analysis of root distributions for terrestrial biomes.Crossref | GoogleScholarGoogle Scholar |
JFSP 2011. OK-FIRE: Weather-based decision support for wildland fire management. Joint Fire Sceince Program Fire Science Brief, Issue 127, February 2011.
Jolly WM, Cochrane MA, Freeborn PH, Holden ZA, Brown TJ, Williamson GJ, Bowman DMJS (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nature Communications 6, 7537
| Climate-induced variations in global wildfire danger from 1979 to 2013.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhtlCjsb7P&md5=59fc53be9e40d61307f172a0571e3afdCAS | 26172867PubMed |
Jurdao S, Chuvieco E, Arevalillo JM (2012) Modelling fire ignition probability from satellite estimates of live fuel moisture content. Fire Ecology 8, 77–97.
| Modelling fire ignition probability from satellite estimates of live fuel moisture content.Crossref | GoogleScholarGoogle Scholar |
Knapp PA (1998) Spatio-temporal patterns of large grassland fires in the Intermountain West, U.S.A. Global Ecology and Biogeography Letters 7, 259–272.
| Spatio-temporal patterns of large grassland fires in the Intermountain West, U.S.A.Crossref | GoogleScholarGoogle Scholar |
Krueger ES, Ochsner TE, Engle DM, Carlson JD, Twidwell D, Fuhlendorf SD (2015) Soil moisture affects growing-season wildfire size in the Southern Great Plains. Soil Science Society of America Journal 79, 1567–1576.
| Soil moisture affects growing-season wildfire size in the Southern Great Plains.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XivVakt7o%3D&md5=370049837a7bdd13db3102f8cb1325b3CAS |
Littell JS, McKenzie D, Peterson DL, Westerling AL (2009) Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003. Ecological Applications 19, 1003–1021.
| Climate and wildfire area burned in western U.S. ecoprovinces, 1916–2003.Crossref | GoogleScholarGoogle Scholar | 19544740PubMed |
Magnussen S, Taylor SW (2012) Prediction of daily lightning- and human-caused fires in British Columbia. International Journal of Wildland Fire 21, 342–356.
| Prediction of daily lightning- and human-caused fires in British Columbia.Crossref | GoogleScholarGoogle Scholar |
Martínez J, Vega-Garcia C, Chuvieco E (2009) Human-caused wildfire risk rating for prevention planning in Spain. Journal of Environmental Management 90, 1241–1252.
| Human-caused wildfire risk rating for prevention planning in Spain.Crossref | GoogleScholarGoogle Scholar | 18723267PubMed |
Matthews S (2014) Dead fuel moisture research: 1991–2012. International Journal of Wildland Fire 23, 78–92.
| Dead fuel moisture research: 1991–2012.Crossref | GoogleScholarGoogle Scholar |
McFadden DL 1979. Quantitative methods for analyzing travel behaviour of individuals: some recent developments. In ‘Behavioural Travel Modelling’. (Eds DA Hensher, PR Stopher) p. 307. (Croom Helm: London)
McPherson RA, Fiebrich CA, Crawford KC, Kilby JR, Grimsley DL, Martinez JE, Basara JB, Illston BG, Morris DA, Kloesel KA, Melvin AD, Shrivastava H, Wolfinbarger JM, Bostic JP, Demko DB, Elliott RL, Stadler SJ, Carlson JD, Sutherland AJ (2007) Statewide monitoring of the mesoscale environment: a technical update on the Oklahoma Mesonet. Journal of Atmospheric and Oceanic Technology 24, 301–321.
| Statewide monitoring of the mesoscale environment: a technical update on the Oklahoma Mesonet.Crossref | GoogleScholarGoogle Scholar |
Menard S 2001. ‘Applied Logistic Regression Analysis’, 2nd edn, Sage University paper series on quantitative applications in the social sciences, 07–106. (Sage: Thousand Oaks, CA)
Mermoz M, Kitzberger T, Veblen TT (2005) Landscape influences on occurrence and spread of wildfires in Patagonian forests and shrublands. Ecology 86, 2705–2715.
| Landscape influences on occurrence and spread of wildfires in Patagonian forests and shrublands.Crossref | GoogleScholarGoogle Scholar |
Mondal N, Sukumar R (2014) Characterising weather patterns associated with fire in a seasonally dry tropical forest in southern India. International Journal of Wildland Fire 23, 196–201.
| Characterising weather patterns associated with fire in a seasonally dry tropical forest in southern India.Crossref | GoogleScholarGoogle Scholar |
Nelson RM (2000) Prediction of diurnal change in 10-h fuel stick moisture content. Canadian Journal of Forest Research 30, 1071–1087.
| Prediction of diurnal change in 10-h fuel stick moisture content.Crossref | GoogleScholarGoogle Scholar |
NIFC 2013. Wildland Fire Statistics. National Interagency Fire Center. Boise, ID. Available at http://www.nifc.gov/fireInfo/fireInfo_statistics.html [Verified 4 June 2013]
NWS 2015. Storm Prediction Center. National Centers for Environmental Prediction Storm Prediction Center. Norman, OK. Available at http://www.spc.noaa.gov/. [Verified 21 March 2015]
O’brien R (2007) A caution regarding rules of thumb for variance inflation factors. Quality & Quantity 41, 673–690.
| A caution regarding rules of thumb for variance inflation factors.Crossref | GoogleScholarGoogle Scholar |
Ochsner TE, Cosh MH, Cuenca RH, Dorigo WA, Draper CS, Hagimoto Y, Kerr YH, Larson KM, Njoku EG, Small EE, Zreda M (2013) State of the art in large-scale soil moisture monitoring. Soil Science Society of America Journal 77, 1888–1919.
| State of the art in large-scale soil moisture monitoring.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXhvVyhu73I&md5=56663bf4cee85004b1b19aad423ab729CAS |
OCS 2014. Climate of Oklahoma. Oklahoma Climatological Survey. Norman, OK. Available at http://climate.ok.gov/index.php/site/page/climate_of_oklahoma [Verified 11 February 2014]
Pellizzaro G, Cesaraccio C, Duce P, Ventura A, Zara P (2007) Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species. International Journal of Wildland Fire 16, 232–241.
| Relationships between seasonal patterns of live fuel moisture and meteorological drought indices for Mediterranean shrubland species.Crossref | GoogleScholarGoogle Scholar |
Peng C-YJ, Lee KL, Ingersoll GM (2002) An introduction to logistic regression analysis and reporting. The Journal of Educational Research 96, 3–14.
| An introduction to logistic regression analysis and reporting.Crossref | GoogleScholarGoogle Scholar |
Preisler HK, Brillinger DR, Burgan RE, Benoit JW (2004) Probability based models for estimation of wildfire risk. International Journal of Wildland Fire 13, 133–142.
| Probability based models for estimation of wildfire risk.Crossref | GoogleScholarGoogle Scholar |
Qi Y, Dennison P, Spencer J, Riaño D (2012) Monitoring live fuel moisture using soil moisture and remote sensing proxies. Fire Ecology 8, 71–87.
| Monitoring live fuel moisture using soil moisture and remote sensing proxies.Crossref | GoogleScholarGoogle Scholar |
Reid AM, Fuhlendorf SD, Weir JR (2010) Weather variables affecting Oklahoma wildfires. Rangeland Ecology and Management 63, 599–603.
| Weather variables affecting Oklahoma wildfires.Crossref | GoogleScholarGoogle Scholar |
Riley KL, Stonesifer C, Preisler P, Calkin D 2014. Predicting wildfire ignitions, escapes, and large fire activity using Predictive Service’s 7-Day Fire Potential Outlook in the western USA. In ‘Advances in Forest Fire Research’. (Ed. DX Viegas) pp. 1239–1248. (Coimbra University Press: Coimbra, Portugal).
Schlosser CA, Milly PCD (2002) A model-based investigation of soil moisture predictability and associated climate predictability. Journal of Hydrometeorology 3, 483–501.
| A model-based investigation of soil moisture predictability and associated climate predictability.Crossref | GoogleScholarGoogle Scholar |
SCIPP 2014. Average monthly temperature and rainfall, Oklahoma. Southern Climate Impacts Planning Program. Norman, OK. Available at http://www.southernclimate.org/products/temp_precip.php [Verified 11 February 2014]
Scott BL, Ochsner T, Illston BG, Fiebrich CA, Basara JB, Sutherland AJ (2013) New soil property database improves Oklahoma Mesonet soil moisture estimates. Journal of Atmospheric and Oceanic Technology 30, 2585–2595.
| New soil property database improves Oklahoma Mesonet soil moisture estimates.Crossref | GoogleScholarGoogle Scholar |
Senay GB, Elliott RL (2000) Combining AVHRR-NDVI and landuse data to describe temporal and spatial dynamics of vegetation. Forest Ecology and Management 128, 83–91.
| Combining AVHRR-NDVI and landuse data to describe temporal and spatial dynamics of vegetation.Crossref | GoogleScholarGoogle Scholar |
Short KC (2014a) A spatial database of wildfires in the United States, 1992–2011. Earth System Science Data 6, 1–27.
| A spatial database of wildfires in the United States, 1992–2011.Crossref | GoogleScholarGoogle Scholar |
Short KC 2014b. Spatial wildfire occurrence data for the United States, 1992–2012 [FPA_FOD_20140428], 2nd edn. Forest Service Research Data Archive (Fort Collins, CO).
Shtatland E, Cain E, Barton M 2001. The perils of stepwise logistic regression and how to escape them using information criteria and the Output Delivery System. In ‘Proceedings of the 26th Annual SAS Users Group International Conference’, 22 April 2001, Long Beach, CA. pp. 222–226.
Sridhar V, Hubbard KG, You J, Hunt ED (2008) Development of the soil moisture index to quantify agricultural drought and its ‘user friendliness’ in severity-area-duration assessment. Journal of Hydrometeorology 9, 660–676.
| Development of the soil moisture index to quantify agricultural drought and its ‘user friendliness’ in severity-area-duration assessment.Crossref | GoogleScholarGoogle Scholar |
Stockton CW, Meko DM (1983) Drought recurrence in the Great Plains as reconstructed from long-term tree-ring records. Journal of Climate and Applied Meteorology 22, 17–29.
| Drought recurrence in the Great Plains as reconstructed from long-term tree-ring records.Crossref | GoogleScholarGoogle Scholar |
Sutley N 2014. Introducing the National Drought Resilience Partnership. Available at https://www.whitehouse.gov/blog/2013/11/15/introducing-national-drought-resilience-partnership [Verified 21 March 2015]
Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240, 1285–1293.
| Measuring the accuracy of diagnostic systems.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DyaL1c3jsF2jtQ%3D%3D&md5=09c1b82b0e4079793530be251f7be8afCAS | 3287615PubMed |
Turner JA, Lawson BD 1978. Weather in the Canadian Forest Fire Danger Rating system: a user guide to national standards and practices. Environment Canada, Canadian Forest Service, Pacific Forest Research Centre, Information Report BC-X-177. (Victoria, BC)
Ursino N, Rulli MC (2011) Hydrological minimal model for fire regime assessment in a Mediterranean ecosystem. Water Resources Research 47, W11526
| Hydrological minimal model for fire regime assessment in a Mediterranean ecosystem.Crossref | GoogleScholarGoogle Scholar |
Viegas DX, Pinol J, Viegas MT, Ogaya R (2001) Estimating live fine fuels moisture content using meteorologically-based indices. International Journal of Wildland Fire 10, 223–240.
| Estimating live fine fuels moisture content using meteorologically-based indices.Crossref | GoogleScholarGoogle Scholar |
Walsh JE, Shapiro I, Shy TL (2005) On the variability and predictability of daily temperatures in the Arctic. Atmosphere-ocean 43, 213–230.
| On the variability and predictability of daily temperatures in the Arctic.Crossref | GoogleScholarGoogle Scholar |
Weir JR, Scasta JD (2014) Ignition and fire behaviour of Juniperus virginiana in response to live fuel moisture and fire temperature in the southern Great Plains. International Journal of Wildland Fire 23, 839–844.
| Ignition and fire behaviour of Juniperus virginiana in response to live fuel moisture and fire temperature in the southern Great Plains.Crossref | GoogleScholarGoogle Scholar |
Westerling AL, Gershunov A, Brown TJ, Cayan DR, Dettinger MD (2003) Climate and wildfire in the western United States. Bulletin of the American Meteorological Society 84, 595–604.
| Climate and wildfire in the western United States.Crossref | GoogleScholarGoogle Scholar |
Wittich K-P (2011) Phenological observations of grass curing in Germany. International Journal of Biometeorology 55, 313–318.
| Phenological observations of grass curing in Germany.Crossref | GoogleScholarGoogle Scholar | 20574670PubMed |
WMO 2011. Guide to climatological practices. WMO No. 100. (World Meteorological Organization: Geneva, Switzerland)
Yebra M, Dennison PE, Chuvieco E, Riano D, Zylstra P, Hunt ER, Danson FM, Qi Y, Jurdao S (2013) A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products. Remote Sensing of Environment 136, 455–468.
| A global review of remote sensing of live fuel moisture content for fire danger assessment: moving towards operational products.Crossref | GoogleScholarGoogle Scholar |
