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

High-resolution fire danger forecast for Poland based on the Weather Research and Forecasting Model

Alan Mandal A , Grzegorz Nykiel https://orcid.org/0000-0002-6827-0205 A B , Tomasz Strzyzewski A D , Adam Kochanski C , Weronika Wrońska A , Marta Gruszczynska A and Mariusz Figurski https://orcid.org/0000-0001-9602-5007 A
+ Author Affiliations
- Author Affiliations

A Institute of Meteorology and Water Management, National Research Institute, 01-673 Warsaw, Poland.

B Faculty of Civil and Environmental Engineering, Gdansk University of Technology, 80-233 Gdansk, Poland.

C Department of Meteorology and Climate Science, San Jose State University, San Jose, CA 95192-0104, USA.

D Corresponding author. Email: tomasz.strzyzewski@imgw.pl

International Journal of Wildland Fire 31(2) 149-162 https://doi.org/10.1071/WF21106
Submitted: 5 August 2021  Accepted: 10 November 2021   Published: 23 December 2021

Journal Compilation © IAWF 2022 Open Access CC BY-NC-ND

Abstract

Due to climate change and associated longer and more frequent droughts, the risk of forest fires increases. To address this, the Institute of Meteorology and Water Management implemented a system for forecasting fire weather in Poland. The Fire Weather Index (FWI) system, developed in Canada, has been adapted to work with meteorological fields derived from the high-resolution (2.5 km) Weather Research and Forecasting (WRF) model. Forecasts are made with 24- and 48-h lead times. The purpose of this work is to present the validation of the implemented system. First, the results of the WRF model were validated using in situ observations from ~70 synoptic stations. Second, we used the correlation method and Eastaugh’s percentile analysis to assess the quality of the FWI index. The data covered the 2019 fire season and were analysed for the whole forest area in Poland. Based on the presented results, it can be concluded that the FWI index (calculated based on the WRF model) has a very high predictive ability of fire risk. However, the results vary by region, distance from human habitats, and size of fire.

Keywords: fire weather index, FWI, fire danger, forecasting, forest fires, Weather Research and Forecasting Model, WRF, Poland.


References

Alves D, Ribeiro LM, Viegas DX (2018) Calibration of the Canadian FWI system for the territory of Europe. In ‘Advances in Forest Fire Research’. pp. 33–43. (Imprensa da Universidade de Coimbra)
| Crossref |

Barbero R, Abatzoglou JT, Pimont F, Ruffault J, Curt T (2020) Attributing increases in fire weather to anthropogenic climate change over France. Frontiers in Earth Science 8, 104.
Attributing increases in fire weather to anthropogenic climate change over France.Crossref | GoogleScholarGoogle Scholar |

Bowman DMJS, Balch J, Artaxo P, Bond WJ, Cochrane MA, D’Antonio CM, DeFries R, Johnston FH, Keeley JE, Krawchuk MA, Kull CA, Mack M, Moritz MA, Pyne S, Roos CI, Scott AC, Sodhi NS, Swetnam TW (2011) The human dimension of fire regimes on Earth: the human dimension of fire regimes on Earth. Journal of Biogeography 38, 2223–2236.
The human dimension of fire regimes on Earth: the human dimension of fire regimes on Earth.Crossref | GoogleScholarGoogle Scholar |

Brown JK, Smith JK (2000) Wildland fire in ecosystems: effects of fire on flora. US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT.
| Crossref |

Cane D, Ciccarelli N, Gottero F, Francesetti A, Pelfini F, Pelosini R (2008) Fire Weather Index application in north-western Italy. Advances in Science and Research 2, 77–80.
Fire Weather Index application in north-western Italy.Crossref | GoogleScholarGoogle Scholar |

Carvalho A, Flannigan MD, Logan K, Miranda AI, Borrego C (2008) Fire activity in Portugal and its relationship to weather and the Canadian Fire Weather Index System. International Journal of Wildland Fire 17, 328–338.
Fire activity in Portugal and its relationship to weather and the Canadian Fire Weather Index System.Crossref | GoogleScholarGoogle Scholar |

Chas-Amil ML, Touza J, García-Martínez E (2013) Forest fires in the wildland–urban interface: a spatial analysis of forest fragmentation and human impacts. Applied Geography (Sevenoaks, England) 43, 127–137.
Forest fires in the wildland–urban interface: a spatial analysis of forest fragmentation and human impacts.Crossref | GoogleScholarGoogle Scholar |

Chuvieco E, González I, Verdú F, Aguado I, Yebra M (2009) Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem. International Journal of Wildland Fire 18, 430–441.
Prediction of fire occurrence from live fuel moisture content measurements in a Mediterranean ecosystem.Crossref | GoogleScholarGoogle Scholar |

De Groot WJD (1987) Interpreting the Canadian Forest Fire Weather Index (FWI) System. In ‘Proceedings: Fourth Central Regional Fire Weather Committee Scientific and Technical Seminar,’ 2 April 1987, Winnipeg, Manitoba. Canadian Forestry Service, Northern Forestry Centre, Edmonton, Alberta, pp. 3–13.

de Jong MC, Wooster MJ, Kitchen K, Manley C, Gazzard R, McCall FF (2016) Calibration and evaluation of the Canadian Forest Fire Weather Index (FWI) System for improved wildland fire danger rating in the United Kingdom. Natural Hazards and Earth System Sciences 16, 1217–1237.
Calibration and evaluation of the Canadian Forest Fire Weather Index (FWI) System for improved wildland fire danger rating in the United Kingdom.Crossref | GoogleScholarGoogle Scholar |

de Rigo D, Libertà G, Houston Durrant T, Artés Vivancos T, San-Miguel-Ayanz J (2017) Forest fire danger extremes in Europe under climate change: variability and uncertainty. Publications Office of the European Union: Luxembourg.
| Crossref |

Di Giuseppe F, Pappenberger F, Wetterhall F, Krzeminski B, Camia A, Libertá G, San Miguel J (2016) The potential predictability of fire danger provided by numerical weather prediction. Journal of Applied Meteorology and Climatology 55, 2469–2491.
The potential predictability of fire danger provided by numerical weather prediction.Crossref | GoogleScholarGoogle Scholar |

Di Giuseppe F, Vitolo C, Krzeminski B, San-Miguel J (2020) Fire weather index: the skill provided by ECMWF ensemble prediction system. Other hazards (e.g., glacial and snow hazards, karst, wildfires hazards, and medical geo-hazards), preprint.
| Crossref |

Eastaugh CS, Arpaci A, Vacik H (2012) A cautionary note regarding comparisons of fire danger indices. Natural Hazards and Earth System Sciences 12, 927–934.
A cautionary note regarding comparisons of fire danger indices.Crossref | GoogleScholarGoogle Scholar |

Flannigan MD, Wotton BM (2001) Climate, weather, and area burned. In ‘Forest Fires’ pp. 351–373 (Academic Press, New York, USA)

Francos M, Úbeda X (2021) Prescribed fire management. Current Opinion in Environmental Science & Health 21, 100250.
Prescribed fire management.Crossref | GoogleScholarGoogle Scholar |

González-Cabán A (2013) The economic dimension of wildland fires. In ‘Vegetation Fires and Global Change – Challenges for Concerted International Action. A white paper directed to the United Nations and international organizations’ pp. 229–237. (Kassel Publishing House, Germany)

Grajewski S (2017) Effectiveness of forest fire security systems in Poland. Infrastructure and Ecology of Rural Areas IV, 1563–1576.
Effectiveness of forest fire security systems in Poland.Crossref | GoogleScholarGoogle Scholar |

Grell GA, Freitas SR (2014) A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling. Atmospheric Chemistry and Physics 14, 5233–5250.
A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling.Crossref | GoogleScholarGoogle Scholar |

Hantson S, Pueyo S, Chuvieco E (2015) Global fire size distribution is driven by human impact and climate: spatial trends in global fire size distribution. Global Ecology and Biogeography 24, 77–86.
Global fire size distribution is driven by human impact and climate: spatial trends in global fire size distribution.Crossref | GoogleScholarGoogle Scholar |

Horel JD, Ziel R, Galli C, Pechmann J, Dong X (2014) An evaluation of fire danger and behaviour indices in the Great Lakes Region calculated from station and gridded weather information. International Journal of Wildland Fire 23, 202–214.
An evaluation of fire danger and behaviour indices in the Great Lakes Region calculated from station and gridded weather information.Crossref | GoogleScholarGoogle Scholar |

Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD (2008) Radiative forcing by long–lived greenhouse gases: calculations with the AER radiative transfer models. Journal of Geophysical Research 113, D13103.
Radiative forcing by long–lived greenhouse gases: calculations with the AER radiative transfer models.Crossref | GoogleScholarGoogle Scholar |

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 | 26172867PubMed |

Lavorel S, Flannigan MD, Lambin EF, Scholes MC (2006) Vulnerability of land systems to fire: interactions among humans, climate, the atmosphere, and ecosystems. Mitigation and Adaptation Strategies for Global Change 12, 33–53.
Vulnerability of land systems to fire: interactions among humans, climate, the atmosphere, and ecosystems.Crossref | GoogleScholarGoogle Scholar |

Lawson BD, Armitage OB (2008) Weather Guide for the Canadian Forest Fire Danger Rating System. Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, Edmonton, AB, Canada.

Martell DL (2001) Chapter 15 – Forest Fire Management. In ‘Forest Fires’ (Eds EA Johnson, K Miyanishi) pp. 527–583. (Academic Press: San Diego, CA, USA)
| Crossref |

Mölders N (2008) Suitability of the Weather Research and Forecasting (WRF) Model to predict the June 2005 fire weather for Interior Alaska. Weather and Forecasting 23, 953–973.
Suitability of the Weather Research and Forecasting (WRF) Model to predict the June 2005 fire weather for Interior Alaska.Crossref | GoogleScholarGoogle Scholar |

Nakanishi M, Niino H (2006) An improved Mellor–Yamada level 3 model: its numerical stability and application to a regional prediction of advecting fog. Boundary-Layer Meteorology 119, 397–407.
An improved Mellor–Yamada level 3 model: its numerical stability and application to a regional prediction of advecting fog.Crossref | GoogleScholarGoogle Scholar |

Nakanishi M, Niino H (2009) Development of an improved turbulence closure model for the atmospheric boundary layer. Journal of the Meteorological Society of Japan 87, 912.
Development of an improved turbulence closure model for the atmospheric boundary layer.Crossref | GoogleScholarGoogle Scholar |

Narayanaraj G, Wimberly MC (2012) Influences of forest roads on the spatial patterns of human- and lightning-caused wildfire ignitions. Applied Geography (Sevenoaks, England) 32, 878–888.
Influences of forest roads on the spatial patterns of human- and lightning-caused wildfire ignitions.Crossref | GoogleScholarGoogle Scholar |

Neary DG, Ryan KC, DeBano LF (2005) Wildland fire in ecosystems: effects of fire on soils and water. USDA Forest Service, Rocky Mountain Research Station, Ogden, UT.
| Crossref |

Nykiel G, Figurski M (2020) Fire Weather Index data for Poland (March–September 2019) based on high-resolution Weather Research and Forecasting Model [Dataset]. Gdańsk University of Technology.
| Crossref |

Papagiannaki K, Giannaros TM, Lykoudis S, Kotroni V, Lagouvardos K (2020) Weather-related thresholds for wildfire danger in a Mediterranean Region: the case of Greece. Agricultural and Forest Meteorology 291, 108076.
Weather-related thresholds for wildfire danger in a Mediterranean Region: the case of Greece.Crossref | GoogleScholarGoogle Scholar |

Peckham SE, Smirnova TG, Benjamin SG, Brown JM, Kenyon JS (2016) Implementation of a Digital Filter Initialization in the WRF Model and its application in the rapid refresh Monthly Weather Review 144, 99–106.
Implementation of a Digital Filter Initialization in the WRF Model and its application in the rapid refreshCrossref | GoogleScholarGoogle Scholar |

Potter BE, Goodrick S, Brown T (2003) Development of a statistical validation methodology of fire weather indices. In ‘Proceedings of 2nd International Wildland Fire Ecology and Fire Management Congress, 5th Symposium on Fire and Forest Meteorology’, 16–20 November 2003, Orlando, FL. (American Meteorological Society: Boston, MA)

Rodríguez H, Lighezzolo A, Martina A, Zigarán G, Viscardi DA, Rodriguez A, Baudo F, Scavuzzo C, Bellis L, Arganaraz J (2018) Towards the operational implementation of the Fire Weather Index FWI based on the High-Resolution WRF Model. In ‘IEEE Biennial Congress of Argentina (ARGENCON)’, pp. 1–6. (IEEE)
| Crossref |

Romero R, Mestre A, Botey R (2014) A new calibration for Fire Weather Index in Spain (AEMET). In ‘Advances in Forest Fire Research’. pp. 1044–1053. (Imprensa da Universidade de Coimbra)
| Crossref |

Rozkrut D (2019) Statistical Yearbook of Forestry. (Statistics Poland: Warsaw). Available at https://stat.gov.pl/en/topics/statistical-yearbooks/statistical-yearbooks/statistical-yearbook-of-forestry-2019,12,2.html

San-Miguel-Ayanz J (2002) Methodologies for the evaluation of forest fire risk: from long-term (static) to dynamic indices. In ‘Forest Fires: Ecology and Control’. (Eds T Anfodillo, V Carraro) pp. 117–132. (University degli Studi di Padova)

San-Miguel-Ayanz J, Durrant T, Boca R, Maianti P, Liberta G, Artes Vivancos T, Jacome Felix Oom D, Branco A, De Rigo D, Ferrari D, et al. (2019) Forest Fires in Europe, Middle East and North Africa 2018, Joint Research Centre Technical Report, JRC117883. Publications Office of the European Union, Luxembourg
| Crossref |

Sandberg DV, Ottmar RD, Peterson JL (2002) Wildland fire in ecosystems: effects of fire on air. Ogden, UT: USDA Forest Service, Rocky Mountain Research Station.
| Crossref |

Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association 63, 1379–1389.
Estimates of the regression coefficient based on Kendall’s tau.Crossref | GoogleScholarGoogle Scholar |

Simpson CC, Pearce HG, Sturman AP, Zawar-Reza P (2014) Verification of WRF modelled fire weather in the 2009–10 New Zealand fire season International Journal of Wildland Fire 23, 34–45.
Verification of WRF modelled fire weather in the 2009–10 New Zealand fire seasonCrossref | GoogleScholarGoogle Scholar |

Skamarock WC, Klemp JB, Dudhia J, Gill DO, Liu Z, Berner J, Wang W, Powers JG, Duda MG, Barker D, Huang XY (2019) A Description of the Advanced Research WRF Model Version 4. NCAR Technical Notes No. NCAR/TN-556+STR. National Center for Atmospheric Research, Boulder, CO.
| Crossref |

Smith JK (2000) Wildland fire in ecosystems: effects of fire on fauna. USDA Forest Service, Rocky Mountain Research Station, Ogden, UT.
| Crossref |

Stacey R (2012) European Glossary for Wildfires and Forest Fires, European Union-INTERREG IVC.

Stocks BJ, Lawson BD, Alexander ME, Van Wagner CE, McAlpine RS, Lynham TJ, Dubé DE (1989) The Canadian Forest Fire Danger Rating System: an overview Forestry Chronicle 65, 450–457.
The Canadian Forest Fire Danger Rating System: an overviewCrossref | GoogleScholarGoogle Scholar |

Syphard AD, Radeloff VC, Keeley JE, Hawbaker TJ, Clayton MK, Stewart SI, Hammer RB (2007) Human influence on California fire regimes. Ecological Applications 17, 1388–1402.
Human influence on California fire regimes.Crossref | GoogleScholarGoogle Scholar | 17708216PubMed |

Szczygieł R, Kwiatkowski M (2020) Dynamic forest fire risk evaluation in Poland. Folia Forestalia Polonica 62, 139–144.
Dynamic forest fire risk evaluation in Poland.Crossref | GoogleScholarGoogle Scholar |

Taylor SW, Alexander ME (2006) Science, technology, and human factors in fire danger rating: the Canadian experience. International Journal of Wildland Fire 15, 121–135.
Science, technology, and human factors in fire danger rating: the Canadian experience.Crossref | GoogleScholarGoogle Scholar |

Theil H (1950) A rank-invariant method of linear and polynomial regression analysis. Indagationes Mathematicae 12, 85–91.

Tian X, Zhao F, Shu L, Wang M (2014) Changes in forest fire danger for south-western China in the 21st Century. International Journal of Wildland Fire 23, 185–195.
Changes in forest fire danger for south-western China in the 21st Century.Crossref | GoogleScholarGoogle Scholar |

Varela V, Sfetsos A, Vlachogiannis D, Gounaris N (2015) Fire Weather Index (FWI) classification for fire danger assessment applied in Greece. Tethys, Journal of Weather and Climate of the Western Mediterrania 15, 31–40.
Fire Weather Index (FWI) classification for fire danger assessment applied in Greece.Crossref | GoogleScholarGoogle Scholar |

Wagner CE (1987) Development and Structure of the Canadian Forest Fire Weather Index System. Forestry technical report, Canada Communication Group Publ: Ottawa, ON, Canada.

Wotton BM (2009) Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications. Environmental and Ecological Statistics 16, 107–131.
Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications.Crossref | GoogleScholarGoogle Scholar |

Xu R, Yu P, Abramson MJ, Johnston FH, Samet JM, Bell ML, Haines A, Ebi KL, Li S, Guo Y (2020) Wildfires, global climate change, and human health. The New England Journal of Medicine 383, 2173–2181.
Wildfires, global climate change, and human health.Crossref | GoogleScholarGoogle Scholar | 33034960PubMed |

Zaidi SM, Gisen JIA (2018) Evaluation of Weather Research and Forecasting (WRF) microphysics single moment class-3 and class-6 in precipitation forecast. MATEC Web of Conferences 150, 03007.
Evaluation of Weather Research and Forecasting (WRF) microphysics single moment class-3 and class-6 in precipitation forecast.Crossref | GoogleScholarGoogle Scholar |

Zielony R, Kliczkowska A (2012) Natural-forest regionalization of Poland 2010. Centrum Informacyjne Lasów Państwowych. Warsaw, Poland [in Polish].