Evaluation of new methods for drought estimation in the Canadian Forest Fire Danger Rating System
Chelene C. Hanes A B * , Mike Wotton A B , Laura Bourgeau-Chavez C , Douglas G. Woolford D , Stéphane Bélair E , David Martell A and Mike D. Flannigan F
+ Author Affiliations
- Author Affiliations
A Institute of Forestry and Conservation, John H. Daniels Faculty of Architecture, Landscape and Design, University of Toronto, 33 Willcocks Street, Toronto, ON M5S 3B3, Canada.
B Great Lakes Forestry Centre, Canadian Forest Service, Natural Resources Canada, 1219 Queen Street East, Sault Ste Marie, ON P6A 2E5, Canada.
C Michigan Tech Research Institute, 3600 Green Crescent Suite 100, Ann Arbor, MI 48105, USA.
D Statistical and Actuarial Sciences, University of Western Ontario, 1151 Richmond Street, London, ON N6A 3K7, Canada.
E Environment and Climate Change Canada, Meteorological Research Division, 2121 Route Transcanadienne, Dorval, QC H9P 1J3, Canada.
F Natural Resource Science, Thompson Rivers University, 805 TRU Way, Kamloops, BC V2C 0C8, Canada.
* Correspondence to: chelene.hanes@nrcan-rncan.gc.ca
International Journal of Wildland Fire 32(6) 836-853 https://doi.org/10.1071/WF22112
Submitted: 1 July 2022 Accepted: 16 March 2023 Published: 6 April 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: Canadian fire management agencies track drought conditions using the Drought Code (DC) in the Canadian Forest Fire Danger Rating System. The DC represents deep organic layer moisture.
Aims: To determine if electronic soil moisture probes and land surface model estimates of soil moisture content can be used to supplement and/or improve our understanding of drought in fire danger rating.
Methods: We carried out field studies in the provinces of Alberta and Ontario. We installed in situ soil moisture probes at two different depths in seven forest plots, from the surface through the organic layers, and in some cases into the mineral soil.
Results: Our results indicated that the simple DC model predicted the moisture content of the deeper organic layers (10–18 cm depths) well, even compared with the more sophisticated land surface model.
Conclusions: Electronic moisture probes can be used to supplement the DC. Land surface model estimates of moisture content consistently underpredicted organic layer moisture content.
Implications: Calibration and validation of the land surface model to organic soils in addition to mineral soils is necessary for future use in fire danger prediction.
Keywords: boreal forest, Drought Code, fuel moisture, in situ, modelling, remote sensing, soil moisture probe, wildfire.
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