Evaluation and comparison of simple empirical models for dead fuel moisture content

Jason J. Sharples; P. Jyoteeshkumar Reddy; Victor Resco de Dios; Rachael H. Nolan; Matthias M. Boer; Ross A. Bradstock

doi:10.1071/WF23120

RESEARCH ARTICLE (Open Access)

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Evaluation and comparison of simple empirical models for dead fuel moisture content

Jason J. Sharples

^A ^B ^C ^* , P. Jyoteeshkumar Reddy

^D , Victor Resco de Dios ^E ^F , Rachael H. Nolan

^C ^G , Matthias M. Boer ^C ^G and Ross A. Bradstock ^C ^H

+ Author Affiliations

- Author Affiliations

^A School of Science, University of New South Wales, Canberra, ACT, Australia.

^B ARC Centre of Excellence for Climate Extremes, UNSW Canberra, ACT, Australia.

^C NSW Bushfire and Natural Hazards Research Centre, NSW, Australia.

^D CSIRO Environment, Hobart, Tas., Australia.

^E JRU CTFC-AGROTECNIO-CERCA, Lleida, Spain.

^F Department of Forest and Agricultural Science and Engineering, University of Lleida, Lleida, Spain.

^G Hawkesbury Institute for the Environment, Western Sydney University, Richmond, NSW, Australia.

^H Department of Planning and Environment, Parramatta, NSW, Australia.

^* Correspondence to: j.sharples@unsw.edu.au

International Journal of Wildland Fire 33, WF23120 https://doi.org/10.1071/WF23120

Submitted: 20 July 2023 Accepted: 19 May 2024 Published: 13 June 2024

© 2024 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

The moisture content of litter and woody debris is a key determinant of fire potential and fire behaviour. Obtaining reliable estimates of the moisture content of dead fine fuels (i.e. 1-h and 10-h fuels) is therefore a critical requirement for effective fire management.

Aims

We evaluated and compared the performance of five simple models for fuel moisture content. The models belong to two separate classes: (1) exponential functions of the vapour pressure deficit; and (2) affine functions of the (weighted) difference between air temperature and relative humidity.

Methods

Model performance is assessed using error and correlation statistics, calculated using cross validation, over four empirical datasets.

Key results

Overall, the best performing models were the relaxed and generalised models based on the weighted difference between temperature and relative humidity.

Conclusions

Simple functions of the difference between air temperature and relative humidity can perform as well as, if not better than exponential functions of vapour pressure deficit. However, it is important to note the limitations of all these models when applied to fuels with moisture contents <10%.

Implications

The moisture content of fine dead fuels and woody debris can be reliably estimated using simple models that are amenable to easy application.

Keywords: 10-h fuels, empirical modelling, fine dead fuels, fuel moisture content, fuel moisture index, relative humidity, temperature, vapour pressure deficit.

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