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International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE (Open Access)

A phenology-driven fire danger index for northern grasslands

Johan Sjöström https://orcid.org/0000-0001-8670-062X A * and Anders Granström https://orcid.org/0000-0003-0723-024X B
+ Author Affiliations
- Author Affiliations

A Department of Fire and Safety, RISE Research Institutes of Sweden, Box 857, 501 15 Borås, Sweden.

B Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, 901 83, Umeå, Sweden. Email: anders.granstrom@slu.se

* Correspondence to: johan.sjostrom@ri.se

International Journal of Wildland Fire 32(9) 1332-1346 https://doi.org/10.1071/WF23013
Submitted: 30 January 2023  Accepted: 8 July 2023  Published: 31 July 2023

© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background

Directly after snowmelt, northern grasslands typically have highly flammable fuel-beds consisting of 100% grass litter. With green-up, the addition of high-moisture foliage leads to progressively decreasing fire hazard.

Aims

Our aim was to create a fire-danger index for northern grasslands that incorporated grass phenology.

Methods

We made use of 25 years of Swedish wildfire data and 56 experimental fires conducted during one full fire-season, merged with established models for moisture content and flame spread rates. Refined data on equilibrium moisture content of grass litter were obtained through laboratory tests.

Key results

The RING (Rate of spread In Northern Grasslands) model uses cumulative air temperature as a proxy for growing season progression. Three independent functions account for impact of wind, moisture content and the damping effect of live grass, respectively. The latter results in exponentially decaying rate of spread (ROS) with the progressing season. Following the field experiments, green grass proportion as low as 10–20% (live/dead dry-mass) resulted in model-ROS so reduced that the grassland fire season could effectively be considered over.

Conclusions

The model, calculated from standard meteorological data only, matches the experimental results and separately performed validation tests, as well as wildfire dispatch data.

Implications

RING has been used in Sweden since 2021 and is likely applicable to other northern regions as well.

Keywords: ecosystems, boreal, fire behaviour, northern grasslands, phenology, propagation, fire danger, fuel, wildland–urban interface.

References

Alexander ME, Lawson BD, Lynham TJ, McAlpine RS, Stocks BJ, Van Wagner CE (1992) ‘Development and structure of the Canadian forest fire behaviour prediction system.’ (Forestry Canada, Science and Sustainable Development Directorate: Ottawa, Canada)

Andréasson J, Gardelin M (2002) ‘Utveckling av en modell för gräsbrandsvarning under våren.’ [Development of a model for grassfires during spring] (Räddningsverket: Karlstad, Sweden) [In Swedish]

Blackmarr WH (1971) Equilibrium moisture content of common fine fuels found in south-eastern forests. Research Paper SE-74. (USDA Forest Service, Southeastern Forest Experiment Station: Asheville, NC, USA)

Brandt M, Eklund A, Westman Y (1999) ‘Snö I Sverige, Snödjup och vatteninnehåll i snön’ [Snow in Sweden, depth and water content in the snow], SMHI – Fakta nr 2:1999. Available at https://www.smhi.se/polopoly_fs/1.6338!/snofakta% 5B1% 5D.pdf [In Swedish]

Cheney NP, Gould JS (1995) Fire growth in grassland fuels. International Journal of Wildland Fire 5, 237-247.
| Crossref | Google Scholar |

Cheney P, Sullivan A (Eds) (2008) ‘Grassfires: fuel, weather and fire behaviour.’ (CSIRO: Canberra, ACT, Australia)

Cheney NP, Gould JS, Hutchings PT (1989) ‘Prediction of fire spread in grassland.’ (CSIRO: Canberra, ACT, Australia)

Cheney NP, Gould JS, Catchpole WR (1998) Prediction of fire spread in grasslands. International Journal of Wildland Fire 8, 1-13.
| Crossref | Google Scholar |

Cruz MG, Gould JS, Kidnie S, Bessell R, Nichols D, Slijepcevic A (2015) Effects of curing on grassfires: II. Effect of grass senescence on the rate of fire spread. International Journal of Wildland Fire 24, 838-848.
| Crossref | Google Scholar |

Cruz MG, Kidnie S, Matthews S, Hurley RJ, Slijepcevic A, Nichols D, Gould JS (2016) Evaluation of the predictive capacity of dead fuel moisture models for Eastern Australia grasslands. International Journal of Wildland Fire 25, 995-1001.
| Crossref | Google Scholar |

Cruz MG, Sullivan AL, Gould JS, Hurley RJ, Plucinski MP (2018) Got to burn to learn: the effect of fuel load on grassland fire behaviour and its management implications. International Journal of Wildland Fire 27, 727-741.
| Crossref | Google Scholar |

Cruz MG, Sullivan AL, Gould JS (2021) The effect of fuel bed height in grass fire spread: addressing the findings and recommendations of Moinuddin et al. (2018). International Journal of Wildland Fire 30, 215-220.
| Crossref | Google Scholar |

Cruz MG, Alexander ME, Kilinc M (2022) Wildfire Rates of Spread in Grasslands under Critical Burning Conditions. Fire 5, 55.
| Crossref | Google Scholar |

Fovell RG, Brewer MJ, Garmong RJ (2022) The December 2021 Marshall Fire: Predictability and Gust Forecasts from Operational Models. Atmosphere 13, 765.
| Crossref | Google Scholar |

Garvey M, Millie S (2000) ‘Grassland curing guide.’ (Community Safety Department, Victorian Country Fire Authority: Melbourne, Australia)

Granström A, Berglund L, Hellberg E (2000) ‘Gräsbrand - Uttorkning och brandspridning i relation till brandindex.’ [Grassfires – Drying and rate of flame spread in relation to fire danger indices]. P21-337/00. (Räddningsverket: Karlstad, Sweden) [In Swedish]

Gustavsson A-M, Bonesmo H, Rinne M (2003) Modelling growth and nutritive value of grass. In ‘Proceedings of the International Symposium Early harvested forage in milk and meat production’, 23–24 October 2003, Nannestad. pp. 44–58. (Agricultural university of Norway: Ås, Norway). ISBN 82-7479-016-2.

Holmes JD (2007) ‘Wind loading of structures.’ (CRC Press: London, UK) doi:10.4324/9780203964286

Jin H, Jönsson AM, Olsson C, Lindström J, Jönsson P, Eklundh L (2019) New satellite-based estimates show significant trends in spring phenology and complex sensitivities to temperature and precipitation at northern European latitudes. International Journal of Biometeorology 63, 763-775.
| Crossref | Google Scholar |

Kidnie S, Cruz MG, Gould J, Nichols D, Anderson W, Bessell R (2015) Effects of curing on grassfires: I. Fuel dynamics in a senescing grassland. International Journal of Wildland Fire 24, 828-837.
| Crossref | Google Scholar |

Knapp AK, Briggs JM, Hartnett DC, Collins SL (1998) ‘Grassland dynamics. Long-term ecological research in tallgrass prairie.’ (Oxford University Press: New York, NY, USA)

Krueger ES, Levi MR, Achieng KO, Bolten JD, Carlson JD, Coops NC, Holden ZA, Magi BI, Rigden AJ, Ochsner TE (2023) Using soil moisture information to better understand and predict wildfire danger: a review of recent developments and outstanding questions. International Journal of Wildland Fire 32, 111-132.
| Crossref | Google Scholar |

Landström S (1990) Influence of soil frost and air temperature in spring growth of Timothy in Northern Sweden. Swedish Journal of Agricultural Research 20, 147-152.
| Google Scholar |

Leys BA, Marlon JR, Umbanhowar C, Vannière B (2018) Global fire history of grassland biomes. Ecology and Evolution 8, 8831-8852.
| Crossref | Google Scholar |

Matthews S (2006) A process-based model of fine fuel moisture. International Journal of Wildland Fire 15, 155-168.
| Crossref | Google Scholar |

McArthur AG (1960) ‘Fire danger rating tables for annual grasslands.’ (Forestry and Timber Bureau: Canberra, Australia)

McArthur AG (1977) ‘Grassland Fire Danger Meter Mk V sliderule.’ (Country Fire Authority of Victoria: Melbourne, Australia)

Miller EA (2018) Moisture sorption models for fuel beds of standing dead grass in Alaska. Fire 2, 2.
| Crossref | Google Scholar |

Moinuddin KAM, Sutherland D, Mell W (2018) Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread. International Journal of Wildland Fire 27, 800-814.
| Crossref | Google Scholar |

Mouillot F, Field CB (2005) Fire history and the global carbon budget: a 1° × 1° fire history reconstruction for the 20th century. Global Change Biology 11, 398-420.
| Crossref | Google Scholar |

Nilsson D, Hansson P-A (2001) Influence of various machinery combinations, fuel proportions and storage capacities on costs for co-handling of straw and reed canary grass to district heating plants. Biomass and Bioenergy 20, 247-60.
| Crossref | Google Scholar |

Noble IR, Gill AM, Bary GAV (1980) McArthur’s fire-danger meters expressed as equations. Australian Journal of Ecology 5, 201-203.
| Crossref | Google Scholar |

Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomon AM (1992) Special Paper: A Global Biome Model Based on Plant Physiology and Dominance, Soil Properties and Climate. Journal of Biogeography 19, 117-134.
| Crossref | Google Scholar |

Prishchepov AV, Schierhorn F, Löw F (2021) Unraveling the diversity of trajectories and drivers of global agricultural land abandonment. Land 10, 97.
| Crossref | Google Scholar |

Rosén E, Borgegård S-O (1999) The open cultural landscape. In ‘Swedish plant geography. Vol. 84’. (Eds H Rydin, P Snoeijs, M Diekmann) pp. 113–134. (Acta Phytogeographica Suecica: Uppsala, Sweden)

Sjöström J, Granström A (2023) Human activity and demographics drive the fire regime in a highly developed European boreal region. Fire Safety Journal 136, 103743.
| Crossref | Google Scholar |

Sjöström J, Granström A, Jansson A, Böhlin J (2021) ‘En ny modell för gräsbrandsfara i Sverige’. (Myndigheten för samhällsskydd och beredskap: Karlstad, Sweden) ISBN: 978-91-7927-121-3. [In Swedish]

SMHI (2022) ‘Snödjup’ [Snow depth]. Available at https://www.smhi.se/en/weather/observations/snow-depth/ [visited 29 July 2022]

Sutherland D, Sharples JJ, Mell W, Moinuddin KAM (2021) A response to comments of Cruz et al. on: ‘Simulation study of grass fire using a physics-based model: striving towards numerical rigour and the effect of grass height on the rate of spread’. International Journal of Wildland Fire 30, 221-223.
| Crossref | Google Scholar |

Taylor SW, Pike RG, Alexander ME (1997) ‘Field guide to the Canadian Forest Fire Behavior Prediction (FBP) System.’ (Canadian Forest Service: Edmonton, Canada)

Van Wagner CE (1972) ‘Equilibrium moisture contents of some fine forest fuels in Eastern Canada.’ (Canadian Forestry Service, Petawawa Forest Experiment Station: Chalk River, Canada)

Van Wagner CE (1977) ‘Method for computing fine fuel moisture content throughout the diurnal cycle.’ (Canadian Forestry Service, Petawawa Forest Experiment Station: Chalk River, Canada)

Wern L (2013) ‘Luftfuktighet: Variationer i Sverige’. [Humidity: Variations in Sweden]. METEOROLOGI Nr. 154. (Swedish Meteorological and Hydrological Institute: Norrköping, Sweden). urn:nbn:se:smhi:diva-2790

Wern L (2015) ‘Snödjup i Sverige 1904/05–2013/14.’ (Swedish Meteorological and Hydrological Institute: Norrköping, Sweden)

Wilson AAG (1988) Width of firebreak that is necessary to stop grass fires: some field experiments. Canadian Journal of Forest Research 18, 682-687.
| Crossref | Google Scholar |

Wotton B (2009) A grass moisture model for the Canadian Forest Fire Danger Rating System. In ‘Eighth Symposium on Fire and Forest Meteorology’, 13–15 October 2009, Kalispell, MT. (Eds BE Potter, TJ Brown) (American Meteorological Society: Boston, MA)