Relating McArthur fire danger indices to remote sensing derived burned area across Australia
Sami Ullah Shah A B * , Marta Yebra A B , Albert I. J. M. Van Dijk A and Geoffrey J. Cary A
+ Author Affiliations
- Author Affiliations
A Fenner School of Environment & Society, College of Science, The Australian National University, ACT, Canberra, Australia.
B School of Engineering, College of Engineering and Computer Science, The Australian National University, ACT, Canberra, Australia.
* Correspondence to: sami.shah@anu.edu.au
International Journal of Wildland Fire 32(2) 133-148 https://doi.org/10.1071/WF21108
Submitted: 28 July 2021 Accepted: 21 October 2022 Published: 15 November 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of IAWF.
Abstract
The McArthur grassland and forest fire danger indices, widely used in Australia, predict six fire danger classes from ‘Low-Moderate’ to ‘Catastrophic.’ These classes were linked to the rate of fire spread and difficulty of suppression. However, the lack of rate of fire spread data, especially for elevated fire danger classes, has hindered improvement of the McArthur methodology or an alternate approach. We explored the relationship between fire danger classes and burned areas (derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument) within six climate zones during the 2000–2016 Australian fire seasons. A negative binomial linear regression model was used to explore this relationship. The fire danger classes demonstrated a corresponding increase in burned area from ‘Low-Moderate’ to ‘Very High’ classes in Australia’s inland regions. The elevated fire danger classes did not contribute to this trend. In coastal regions, the satellite-derived burned area showed no relationship between fire danger classes and satellite-derived burned area. We used accumulated burned area from the daily MODIS product, which could be subjected to lagged detection as observed in the Kilmore East fire. Thus, the satellite-derived total burned area may not be a suitable metric for informing the McArthur fire danger classes across Australia.
Keywords: Black Saturday fires, burned area, fire danger classes, fire danger index, forest fire danger index, grassland fire danger index, rate of fire spread, remote sensing.
References
Alkharusi H (2012) Categorical variables in regression analysis: A comparison of dummy and effect coding. International Journal of Education 4, 202| Categorical variables in regression analysis: A comparison of dummy and effect coding.Crossref | GoogleScholarGoogle Scholar |
Austin PC, Stryhn H, Leckie G, Merlo J (2018) Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data. Statistics in Medicine 37, 572–589.
| Measures of clustering and heterogeneity in multilevel Poisson regression analyses of rates/count data.Crossref | GoogleScholarGoogle Scholar |
Beck HE, Zimmermann NE, McVicar TR, Vergopolan N, Berg A, Wood EF (2018) Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 5, 180214
| Present and future Köppen-Geiger climate classification maps at 1-km resolution.Crossref | GoogleScholarGoogle Scholar |
Bedia J, Herrera S, Gutiérrez JM, Benali A, Brands S, Mota B, Moreno JM (2015) Global patterns in the sensitivity of burned area to fire-weather: Implications for climate change. Agricultural and Forest Meteorology 214–215, 369–379.
| Global patterns in the sensitivity of burned area to fire-weather: Implications for climate change.Crossref | GoogleScholarGoogle Scholar |
Beer T, Williams A (1995) Estimating Australian forest fire danger under conditions of doubled carbon dioxide concentrations. Climatic Change 29, 169–188.
| Estimating Australian forest fire danger under conditions of doubled carbon dioxide concentrations.Crossref | GoogleScholarGoogle Scholar |
Bowman D, Williamson G, Yebra M, Lizundia-Loiola J, Pettinari ML, Shah S, Bradstock R, Chuvieco E (2020) Wildfires: Australia needs national monitoring agency. Nature 584, 188–191.
| Wildfires: Australia needs national monitoring agency.Crossref | GoogleScholarGoogle Scholar |
Bradshaw LS, Deeming JE, Burgan RE, Cohen JD (1984) ‘The 1978 national fire-danger rating system: technical documentation’. General Technical Report INT-169. 44 p. (US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT)
Bradstock RA, Cohn JS, Gill AM, Bedward M, Lucas C (2009) Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather. International Journal of Wildland Fire 18, 932–943.
| Prediction of the probability of large fires in the Sydney region of south-eastern Australia using fire weather.Crossref | GoogleScholarGoogle Scholar |
Cameron AC, Trivedi PK (1999) Essentials of Count Data Regression. In ‘A Companion to Theoretical Econometrics’. (Ed. BH Baltagi) (Blackwell Publishing Ltd: Malden, MA)
| Crossref |
Cameron AC, Trivedi PK (2013) ‘Regression analysis of count data.’ (Cambridge university press: New York)
Canadell JG, Meyer CP, Cook GD, Dowdy A, Briggs PR, Knauer J, Pepler A, Haverd V (2021) Multi-decadal increase of forest burned area in Australia is linked to climate change. Nature Communications 12, 6921
| Multi-decadal increase of forest burned area in Australia is linked to climate change.Crossref | GoogleScholarGoogle Scholar |
Chang L-Y (2005) Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network. Safety Science 43, 541–557.
| Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network.Crossref | GoogleScholarGoogle Scholar |
Cheney NP (1968) Predicting fire behaviour with fire danger tables. Australian Forestry 32, 71–79.
| Predicting fire behaviour with fire danger tables.Crossref | GoogleScholarGoogle Scholar |
Cheney P, Sullivan A (2008) ‘Grassfires: fuel, weather and fire behaviour.’ (CSIRO Publishing: Melbourne)
Clarke H, Lucas C, Smith P (2013) Changes in Australian fire weather between 1973 and 2010. International Journal of Climatology 33, 931–944.
| Changes in Australian fire weather between 1973 and 2010.Crossref | GoogleScholarGoogle Scholar |
Collins KM, Price OF, Penman TD (2015) Spatial patterns of wildfire ignitions in south-eastern Australia. International Journal of Wildland Fire 24, 1098
| Spatial patterns of wildfire ignitions in south-eastern Australia.Crossref | GoogleScholarGoogle Scholar |
Collins KM, Penman TD, Price OF (2016) Some wildfire ignition causes pose more risk of destroying houses than others. PLoS One 11, e0162083
| Some wildfire ignition causes pose more risk of destroying houses than others.Crossref | GoogleScholarGoogle Scholar |
Cruz MG, Gould J (2009) Field-based fire behaviour research: past and future roles. In ‘Proceedings of the 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation, 13–17 July, Cairns Australia.’ (Eds RS Anderssen, RD Braddock, LTH Newham) pp. 247-253 (Modelling and Simulation Society of Australia and New Zealand and International Association for Mathematics and Computers in Simulation) Available at https://mssanz.org.au/modsim09/A4/cruz.pdf
Cruz MG, Alexander ME, Fernandes PAM (2008) Development of a model system to predict wildfire behaviour in pine plantations. Australian Forestry 71, 113–121.
| Development of a model system to predict wildfire behaviour in pine plantations.Crossref | GoogleScholarGoogle Scholar |
Cruz MG, Sullivan AL, Gould JS, Sims NC, Bannister AJ, Hollis JJ, Hurley RJ (2012) Anatomy of a catastrophic wildfire: the Black Saturday Kilmore East fire in Victoria, Australia. Forest Ecology and Management 284, 269–285.
| Anatomy of a catastrophic wildfire: the Black Saturday Kilmore East fire in Victoria, Australia.Crossref | GoogleScholarGoogle Scholar |
Cruz MG, Gould JS, Alexander ME, Sullivan AL, McCaw WL, Matthews S (2015) ‘A guide to rate of fire spread models for Australian vegetation.’ (CSIRO Land and Water Flagship: Canberra, ACT, AFAC: Melbourne, Vic.)
Cruz MG, Gould JS, Hollis JJ, McCaw W (2018) A Hierarchical classification of wildland fire fuels for Australian vegetation types. Fire 1, 13
| A Hierarchical classification of wildland fire fuels for Australian vegetation types.Crossref | GoogleScholarGoogle Scholar |
Dowdy AJ (2018) Climatological Variability of Fire Weather in Australia. Journal of Applied Meteorology and Climatology 57, 221–234.
| Climatological Variability of Fire Weather in Australia.Crossref | GoogleScholarGoogle Scholar |
Dowdy AJ, Mills GA, Finkele K, de Groot W (2009) ‘Australian fire weather as represented by the McArthur forest fire danger index and the Canadian forest fire weather index.’ (Centre for Australian Weather and Climate Research: Melbourne, Australia)
Edwards GP, Allan GE, Brock C, Duguid A, Gabrys K, Vaarzon-Morel P (2008) Fire and its management in central Australia. The Rangeland Journal 30, 109–121.
| Fire and its management in central Australia.Crossref | GoogleScholarGoogle Scholar |
Fang L, Yang J, Zu J, Li G, Zhang J (2015) Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape. Forest Ecology and Management 356, 2–12.
| Quantifying influences and relative importance of fire weather, topography, and vegetation on fire size and fire severity in a Chinese boreal forest landscape.Crossref | GoogleScholarGoogle Scholar |
Finkele K, Graham AMl, Grant B, David AJ (2006) National daily gridded soil moisture deficit and drought factors for use in prediction of Forest Fire Danger Index in Australia. Rep No. 119. (Bureau of Meteorology Research Centre: Australia)
Freeborn PH, Cochrane MA, Jolly WM (2015) Relationships between fire danger and the daily number and daily growth of active incidents burning in the northern Rocky Mountains, USA. International Journal of Wildland Fire 24, 900–910.
| Relationships between fire danger and the daily number and daily growth of active incidents burning in the northern Rocky Mountains, USA.Crossref | GoogleScholarGoogle Scholar |
Friedl MA, McIver DK, Hodges JCF, Zhang XY, Muchoney D, Strahler AH, Woodcock CE, Gopal S, Schneider A, Cooper A, Baccini A, Gao F, Schaaf C (2002) Global land cover mapping from MODIS: algorithms and early results. Remote Sensing of Environment 83, 287–302.
| Global land cover mapping from MODIS: algorithms and early results.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Loboda T, Roy DP, Quayle B, Justice CO (2009) An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment 113, 408–420.
| An active-fire based burned area mapping algorithm for the MODIS sensor.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Boschetti L, Roy D, Hoffmann AA, Humber M, Hall JV (2018a) ‘Collection 6 MODIS Burned Area Product User’s Guide Version 1.2.’ Available at https://lpdaac.usgs.gov/documents/1006/MCD64_User_Guide_V61.pdf
Giglio L, Boschetti L, Roy DP, Humber ML, Justice CO (2018b) The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment 217, 72–85.
| The Collection 6 MODIS burned area mapping algorithm and product.Crossref | GoogleScholarGoogle Scholar |
Giglio L, Schroeder W, Hall J, Justice C (2021) ‘MODIS Collection 6 Active Fire Product User’s Guide Revision C.’ Available at https://lpdaac.usgs.gov/documents/876/MOD14_User_Guide_v6.pdf
Gill AM, Christian KR, Moore PHR, Forrester RI (1987) Bushfire incidence, fire hazard and fuel reduction burning. Australian Journal of Ecology 12, 299–306.
| Bushfire incidence, fire hazard and fuel reduction burning.Crossref | GoogleScholarGoogle Scholar |
Griffiths D (1999) Improved formula for the drought factor in McArthur’s Forest Fire Danger Meter. Australian Forestry 62, 202–206.
| Improved formula for the drought factor in McArthur’s Forest Fire Danger Meter.Crossref | GoogleScholarGoogle Scholar |
Hadi MA, Aruldhas J, Chow L-F, Wattleworth JA (1995) ‘Estimating safety effects of cross-section design for various highway types using negative binomial regression.’ (Transportation Research Center, University of Florida) Available at https://onlinepubs.trb.org/Onlinepubs/trr/1995/1500/1500-021.pdf
Hall JV, Loboda TV, Giglio L, McCarty GW (2016) A MODIS-based burned area assessment for Russian croplands: Mapping requirements and challenges. Remote Sensing of Environment 184, 506–521.
| A MODIS-based burned area assessment for Russian croplands: Mapping requirements and challenges.Crossref | GoogleScholarGoogle Scholar |
Hardy MA (1993) ‘Regression with dummy variables.’ (SAGE Publications, Inc.: Newbury Park, CA, USA)
Hilbe JM (2011) ‘Negative binomial regression.’ (Cambridge University Press: NY, USA)
Jones DA, Wang W, Fawcett R (2009) High-quality spatial climate data-sets for Australia. Australian Meteorological and Oceanographic Journal 58, 233
| High-quality spatial climate data-sets for Australia.Crossref | GoogleScholarGoogle Scholar |
Justice CO, Giglio L, Korontzi S, Owens J, Morisette JT, Roy D, Descloitres J, Alleaume S, Petitcolin F, Kaufman Y (2002) The MODIS fire products. Remote Sensing of Environment 83, 244–262.
| The MODIS fire products.Crossref | GoogleScholarGoogle Scholar |
Keeley JE, Fotheringham CJ, Morais M (1999) Reexamining fire suppression impacts on brushland fire regimes. Science 284, 1829–1832.
| Reexamining fire suppression impacts on brushland fire regimes.Crossref | GoogleScholarGoogle Scholar |
Keetch JJ, Byram GM (1968) ‘A drought index for forest fire control. Vol. 35’. Research Paper SE-38. p. 38. (US Department of Agriculture, Forest Service, Southeastern Forest Experiment Station: Asheville, NC)
Kenny B, Matthews S, Grootemaat S, Hollis J, Sauvage S, Fox-Hughes P (2019) Australian Fire Danger Rating System Research Prototype: National fuel map. In ‘Proceedings for the 6th International Fire Behavior and Fuels Conference. Sydney, Australia’. (International Association of Wildland Fire: Missoula, MT, USA). Available at https://www.researchgate.net/publication/332901037_Proceedings_for_the_6th_International_Fire_Behavior_and_Fuels_Conference_Australian_Fire_Danger_Rating_System_Research_Prototype_National_fuel_map
Kuleshov Y, Mackerras D, Darveniza M (2006) Spatial distribution and frequency of lightning activity and lightning flash density maps for Australia. Journal of Geophysical Research: Atmospheres 111, D19105
| Spatial distribution and frequency of lightning activity and lightning flash density maps for Australia.Crossref | GoogleScholarGoogle Scholar |
Luke R, McArthur A (1986) ‘Bushfires in Australia’, 2nd edn. (Australian Government Publishing Service: Canberra)
Maffei C, Menenti M (2019) Predicting forest fires burned area and rate of spread from pre-fire multispectral satellite measurements. ISPRS Journal of Photogrammetry and Remote Sensing 158, 263–278.
| Predicting forest fires burned area and rate of spread from pre-fire multispectral satellite measurements.Crossref | GoogleScholarGoogle Scholar |
Martin D, Chen T, Nichols D, Bessell R, Kidnie S, Alexander J (2015) Integrating ground and satellite-based observations to determine the degree of grassland curing. International Journal of Wildland Fire 24, 329
| Integrating ground and satellite-based observations to determine the degree of grassland curing.Crossref | GoogleScholarGoogle Scholar |
Matthews S (2009) A comparison of fire danger rating systems for use in forests. Australian Meteorological and Oceanographic Journal 58, 41
| A comparison of fire danger rating systems for use in forests.Crossref | GoogleScholarGoogle Scholar |
Matthews S, Fox-Hughes P, Grootemaat S, Hollis J, Kenny B, Sauvage S (2019) ‘Australian Fire Danger Rating System; Research Prototype.’ (NSW Rural Fire Serrvice: Lindcombe, NSW)
McArthur AG (1967) ‘Fire behaviour in eucalypt forests.’ (Forestry and Timber Bureau: Canberra, Australia)
McArthur A (1973) ‘Grassland fire danger meter Mark 4.’ (Commonwealth of Australia, Forestry and Timber Bureau: Canberra, ACT)
McCarthy G (2003) ‘Drought Factor (fine fuel consumption) prediction from field measurement of Fine Fuel Moisture Content.’ (Department. of Sustainability and Environment, Forest Science Center: Melbourne, Vic.)
Noble IR, Gill AM, Bary GAV (1980) McArthur’s fire‐danger meters expressed as equations. Australian Journal of Ecology 5, 201–203.
| McArthur’s fire‐danger meters expressed as equations.Crossref | GoogleScholarGoogle Scholar |
Nolan RH, Resco de Dios V, Boer MM, Caccamo G, Goulden ML, Bradstock RA (2016) Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data. Remote Sensing of Environment 174, 100–108.
| Predicting dead fine fuel moisture at regional scales using vapour pressure deficit from MODIS and gridded weather data.Crossref | GoogleScholarGoogle Scholar |
Parisien M-A, Parks SA, Krawchuk MA, Flannigan MD, Bowman LM, Moritz MA (2011) Scale‐dependent controls on the area burned in the boreal forest of Canada, 1980–2005. Ecological Applications 21, 789–805.
| Scale‐dependent controls on the area burned in the boreal forest of Canada, 1980–2005.Crossref | GoogleScholarGoogle Scholar |
Plucinski MP, Sullivan AL, McCaw WL (2020) Comparing the performance of daily forest fire danger summary metrics for estimating fire activity in southern Australian forests. International Journal of Wildland Fire 29, 926–938.
| Comparing the performance of daily forest fire danger summary metrics for estimating fire activity in southern Australian forests.Crossref | GoogleScholarGoogle Scholar |
Preece N (2002) Aboriginal fires in monsoonal Australia from historical accounts. Journal of Biogeography 29, 321–336.
| Aboriginal fires in monsoonal Australia from historical accounts.Crossref | GoogleScholarGoogle Scholar |
Preisler HK, Chen S-C, Fujioka F, Benoit JW, Westerling AL (2008) Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices. International Journal of Wildland Fire 17, 305–316.
| Wildland fire probabilities estimated from weather model-deduced monthly mean fire danger indices.Crossref | GoogleScholarGoogle Scholar |
Raupach M, Briggs P, Haverd V, King E, Paget M, Trudinger C (2012) ‘Australian water availability project.’ (CSIRO Marine and Atmospheric Research: Canberra) Available at http://www.cmar.csiro.au/e-print/open/raupachmr_2006d.pdf
Riley KL, Abatzoglou JT, Grenfell IC, Klene AE, Heinsch FA (2013) The relationship of large fire occurrence with drought and fire danger indices in the western USA, 1984–2008: the role of temporal scale. International Journal of Wildland Fire 22, 894–909.
| The relationship of large fire occurrence with drought and fire danger indices in the western USA, 1984–2008: the role of temporal scale.Crossref | GoogleScholarGoogle Scholar |
Russell-Smith J, Edwards AC (2006) Seasonality and fire severity in savanna landscapes of monsoonal northern Australia. International Journal of Wildland Fire 15, 541–550.
| Seasonality and fire severity in savanna landscapes of monsoonal northern Australia.Crossref | GoogleScholarGoogle Scholar |
Russell-Smith J, Yates C, Edwards A, Allan GE, Cook GD, Cooke P, Craig R, Heath B, Smith R (2003) Contemporary fire regimes of northern Australia, 1997–2001: change since Aboriginal occupancy, challenges for sustainable management. International Journal of Wildland Fire 12, 283–297.
| Contemporary fire regimes of northern Australia, 1997–2001: change since Aboriginal occupancy, challenges for sustainable management.Crossref | GoogleScholarGoogle Scholar |
Russell-Smith J, Yates CP, Whitehead PJ, Smith R, Craig R, Allan GE, Thackway R, Frakes I, Cridland S, Meyer MCP, Gill AM (2007) Bushfires ‘down under’: patterns and implications of contemporary Australian landscape burning. International Journal of Wildland Fire 16, 361–377.
| Bushfires ‘down under’: patterns and implications of contemporary Australian landscape burning.Crossref | GoogleScholarGoogle Scholar |
Smith R, Adams M, Maier S, Craig R, Kristina A, Maling I (2007) Estimating the area of stubble burning from the number of active fires detected by satellite. Remote Sensing of Environment 109, 95–106.
| Estimating the area of stubble burning from the number of active fires detected by satellite.Crossref | GoogleScholarGoogle Scholar |
Stocks BJ, Mason JA, Todd JB, Bosch EM, Wotton BM, Amiro BD, Flannigan MD, Hirsch KG, Logan KA, Martell DL, Skinner WR (2003) Large forest fires in Canada, 1959–1997. Journal of Geophysical Research 107, 8149
| Large forest fires in Canada, 1959–1997.Crossref | GoogleScholarGoogle Scholar |
Storey MA, Bedward M, Price OF, Bradstock RA, Sharples JJ (2021) Derivation of a Bayesian fire spread model using large-scale wildfire observations. Environmental Modelling & Software 144, 105127
| Derivation of a Bayesian fire spread model using large-scale wildfire observations.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL (2009) Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. International Journal of Wildland Fire 18, 387–403.
| Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models.Crossref | GoogleScholarGoogle Scholar |
Sullivan AL, McCaw WL, Cruz MG, Matthews S, Ellis PF (2012) Fuel, fire weather and fire behaviour in Australian ecosystems. In ‘Flammable Australia: Fire regimes, Biodiversity and Ecosystems in a Changing World’. (Eds RJ Williams, AM Gill, RA Bradstock) pp. 51–77. (CSIRO Publishing: Collingwood, Vic.)
Sykes AO (1993) An introduction to regression analysis. (Coase-Sandor Institute for Law & Economics Working Paper No. 20, 1993). Available at https://chicagounbound.uchicago.edu/cgi/viewcontent.cgi?article=1050&context=law_and_economics
Tolhurst, K (2010) Report on fire danger ratings and public warning. Department of Forest and Ecosystem Science, University of Melbourne, Creswick, Victoria.
Van Wagner CE (1974) ‘Structure of the Canadian forest fire weather index.’ (Department of The Environment, Canadian Forestry Service: Ottawa, Canada)
Vermote EF, El Saleous NZ, Justice CO (2002) Atmospheric correction of MODIS data in the visible to middle infrared: first results. Remote Sensing of Environment 83, 97–111.
| Atmospheric correction of MODIS data in the visible to middle infrared: first results.Crossref | GoogleScholarGoogle Scholar |
Walding NG, Williams HTP, McGarvie S, Belcher CM (2018) A comparison of the US National Fire Danger Rating System (NFDRS) with recorded fire occurrence and final fire size. International Journal of Wildland Fire 27, 99–113.
| A comparison of the US National Fire Danger Rating System (NFDRS) with recorded fire occurrence and final fire size.Crossref | GoogleScholarGoogle Scholar |
Wei F, Lovegrove G (2013) An empirical tool to evaluate the safety of cyclists: Community based, macro-level collision prediction models using negative binomial regression. Accident Analysis & Prevention 61, 129–137.
| An empirical tool to evaluate the safety of cyclists: Community based, macro-level collision prediction models using negative binomial regression.Crossref | GoogleScholarGoogle Scholar |
Williamson GJ, Prior LD, Jolly WM, Cochrane MA, Murphy BP, Bowman DMJS (2016) Measurement of inter- and intra-annual variability of landscape fire activity at a continental scale: the Australian case. Environmental Research Letters 11, 035003
| Measurement of inter- and intra-annual variability of landscape fire activity at a continental scale: the Australian case.Crossref | GoogleScholarGoogle Scholar |
Yates C, Russell-Smith J (2002) ‘An assessment of the accuracy of DOLA’s Northern Australia NOAA-AVHRR Fire Affected Area (FAA) map products.’ (Department of the Environment and Heritage: Canberra)
Yeo, CS, Jeffrey DK, Robin H (2014) ‘Fire Danger Indices: Current Limitations and a Pathway to Better Indices.’ (Bushfire and Natural Hazards CRC: Australia)
