Modelling sorption processes of 10-h dead Pinus pinaster branches
Sérgio Lopes
A B C * , Sandra Santos A , Nuno Rodrigues C , Paulo Pinho B C and Domingos Xavier Viegas A
A CEIF/ADAI/LAETA, Association for the Development of Industrial Aerodynamics, Rua Pedro Hispano, 12, 3031-289 Coimbra, Portugal.
B CISeD, Research Centre in Digital Services, Polytechnic Institute of Viseu, Campus Politécnico, 3504-510 Viseu, Portugal.
C Polytechnic Institute of Viseu, Campus Politécnico, 3504-510 Viseu, Portugal.
International Journal of Wildland Fire 32(6) 903-912 https://doi.org/10.1071/WF22127
Submitted: 1 July 2022 Accepted: 3 May 2023 Published: 26 May 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: Forest fuel moisture content (FMC) is an important parameter that determines wildfire risk; therefore, its accurate prediction is of great importance. In the absence of rainfall, dead FMC changes mainly by water vapour sorption processes.
Aims: In the present work, sorption processes of 10-h dead Pinus pinaster branches (PPBs) were studied in order to develop a moisture content prediction model for this fuel type.
Methods: Laboratory tests were used to determine sorption curves, timelag and equilibrium moisture content (EMC) for different environmental conditions. Sorption curves and EMC were modelled with existing sorption models. Dead PPBs moisture content was determined in field tests carried out in central Portugal to validate the sorption models.
Key results: Sorption curves were not pure exponential functions, but had different timelag values until equilibrium was reached. EMC values allowed us to obtain a sigmoid curve and hysteresis effect.
Conclusions: Comparing predicted and observed FMCs of PPB, the Modified Henderson and Pabis models for sorption curves and the Van Wagner model for EMC show high prediction ability.
Implications: The model can be applied in early fire risk assessment, in particular in the methods that use other fuels besides fine forest fuels.
Keywords: moisture content, dead forest fuel, 10-h fuels, EMC, timelag, sorption processes, fire risk, fire management.
References
Adab H, Devi Kanniah K, Beringer J (2016) Estimating and up-scaling fuel moisture and leaf dry matter content of a temperate humid forest using multiresolution remote sensing data. Remote Sensing 8, 961| Estimating and up-scaling fuel moisture and leaf dry matter content of a temperate humid forest using multiresolution remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Aguado I, Chuvieco E, Borén R, Nieto H (2007) Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment. International Journal of Wildland Fire 16, 390–397.
| Estimation of dead fuel moisture content from meteorological data in Mediterranean areas. Applications in fire danger assessment.Crossref | GoogleScholarGoogle Scholar |
Bakšić N, Bakšić D (2022) Predicting the fine fuel moisture content in Dalmatian black pine needle litter. International Journal of Wildland Fire 31, 708–719.
| Predicting the fine fuel moisture content in Dalmatian black pine needle litter.Crossref | GoogleScholarGoogle Scholar |
Bovill W, Hawthorne S, Radic J, Baillie C, Ashton A, Noske P, Lane P, Sheridan G (2015) Effectiveness of automated fuelsticks for predicting the moisture content of dead fuels in Eucalyptus forests. In ‘MODSIM2015, 21st International Congress on Modelling and Simulation’. (Eds T Weber, MJ McPhee, RS Anderssen) pp. 201–207. (Modelling and Simulation Society of Australia and New Zealand)
| Crossref |
Bradshaw LS, Deeming JE, Burgan RE, Cohen JD (1983) The 1978 National Fire-Danger Rating System: technical documentation. General Technical Report INT-169. 44 p. (USDA Forest Service, Intermountain Research Station: Ogden, Utah)
Byram GM (1963) An analysis of the drying process in forest fuel material. Report. (USDA Forest Service, Fire Sciences Laboratory, Rocky Mountain Research Station: Missoula, MT)
Catchpole EA, Catchpole WR, Viney NR, McCaw WL, Marsden-Smedley JB (2001) Estimating fuel response time and predicting fuel moisture content from field data. International Journal of Wildland Fire 10, 215–222.
| Estimating fuel response time and predicting fuel moisture content from field data.Crossref | GoogleScholarGoogle Scholar |
Cawson JG, Nyman P, Schunk C, Sheridan GJ, Duff TJ, Gibos K, Bovill WD, Conedera M, Pezzatti GB, Menzel A (2020) Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide. International Journal of Wildland Fire 29, 548–559.
| Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide.Crossref | GoogleScholarGoogle Scholar |
Chen CC (1990) Modification of Oswin EMC/ERH equation. Chinese Agricultural Research 39, 367–376.
Henderson SM (1974) Progress in developing the thin layer drying equation. Transactions of the ASAE 17, 1167–1168.
| Progress in developing the thin layer drying equation.Crossref | GoogleScholarGoogle Scholar |
Henderson SM, Pabis S (1961) Grain drying theory, I. Temperature effect on drying coefficient. Journal of Agricultural Engineering Research 6, 169–174.
Iglesias HA, Chirife J (1976) Prediction of the effect of temperature on water sorption isotherms of food materials. Journal of Food Technology 11, 109–116.
| Prediction of the effect of temperature on water sorption isotherms of food materials.Crossref | GoogleScholarGoogle Scholar |
Karathanos VT (1999) Determination of water content of dried fruits by drying kinetics. Journal of Food Engineering 39, 337–344.
| Determination of water content of dried fruits by drying kinetics.Crossref | GoogleScholarGoogle Scholar |
Lee H, Won M, Yoon S, Jang K (2020) Estimation of 10-hour fuel moisture content using meteorological data: A model inter-comparison study. Forests 11, 982
| Estimation of 10-hour fuel moisture content using meteorological data: A model inter-comparison study.Crossref | GoogleScholarGoogle Scholar |
Lopes S, Viegas DX, Teixeira de Lemos L, Viegas MT (2014) Equilibrium moisture content and timelag of dead Pinus pinaster needles. International Journal of Wildland Fire 23, 721–732.
| Equilibrium moisture content and timelag of dead Pinus pinaster needles.Crossref | GoogleScholarGoogle Scholar |
Matthews S (2014) Dead fuel moisture research: 1991–2012. International Journal of Wildland Fire 23, 78–92.
| Dead fuel moisture research: 1991–2012.Crossref | GoogleScholarGoogle Scholar |
Matthews S, Gould J, McCaw L (2010) Simple models for predicting dead fuel moisture in eucalyptus forests. International Journal of Wildland Fire 19, 459–467.
| Simple models for predicting dead fuel moisture in eucalyptus forests.Crossref | GoogleScholarGoogle Scholar |
Nelson Jr RM (1984) A method for describing equilibrium moisture content of forest fuels. Canadian Journal of Forest Research 14, 597–600.
| A method for describing equilibrium moisture content of forest fuels.Crossref | GoogleScholarGoogle Scholar |
Page GE (1949) Factors influencing the maximum rate of air drying shelled corn in thin-layers. MSc thesis, Purdue University, West Lafayette, IN, USA.
Pfost HB, Maurer SG, Chung DS, Milliken GA (1976) Summarizing and reporting equilibrium moisture data for grains, ASAE paper number 76–3520. (American Society of Agricultural Engineers (ASAE): St Joseph, MI)
Resco de Dios V, Fellows AW, Nolan RH, Boer MM, Bradstock RA, Domingo F, Goulden ML (2015) A semi-mechanistic model for predicting the moisture content of fine litter. Agricultural and Forest Meteorology 203, 64–73.
| A semi-mechanistic model for predicting the moisture content of fine litter.Crossref | GoogleScholarGoogle Scholar |
Rossa CG (2017) The effect of fuel moisture content on the spread rate of forest fires in the absence of wind or slope. International Journal of Wildland Fire 26, 24–31.
| The effect of fuel moisture content on the spread rate of forest fires in the absence of wind or slope.Crossref | GoogleScholarGoogle Scholar |
Rothermel RC (1983) How to predict the spread and intensity of forest and range fires. General Technical Report INT-143. 161 pp. (USDA Forest Service, Intermountain Forest and Range Experiment Station: Ogden, UT)
Sharples JJ, McRae RHD (2011) Evaluation of a very simple model for predicting the moisture content of eucalypt litter. International Journal of Wildland Fire 20, 1000–1005.
| Evaluation of a very simple model for predicting the moisture content of eucalypt litter.Crossref | GoogleScholarGoogle Scholar |
Simard AJ (1968) The moisture content of forest fuels – I: a review of the basic concepts. Information Report. FF-X-14. (Canadian Department of Forest and Rural Development, Forest Fire Research Institute: Ottawa, ON)
Van Wagner CE (1977) Conditions for the start and spread of crown fire. Canadian Journal of Forest Research 7, 23–34.
| Conditions for the start and spread of crown fire.Crossref | GoogleScholarGoogle Scholar |
Van Wagner, CE (1987) Development and Structure of the Canadian Forest Fire Weather Index System. Forestry Technical Report 35 (Canadian Forestry Service, Petawawa National Forestry Institute: Chalk River, ON)
Viegas DX, Viegas MT, Ferreira AD (1992) Moisture content of fine forest fuels and fire occurrence in central Portugal. International Journal of Wildland Fire 2, 69–86.
| Moisture content of fine forest fuels and fire occurrence in central Portugal.Crossref | GoogleScholarGoogle Scholar |
Viney NR, Catchpole EA (1991) Estimating fuel moisture response times from field observations. International Journal of Wildland Fire 1, 211–214.
| Estimating fuel moisture response times from field observations.Crossref | GoogleScholarGoogle Scholar |
