Comparing the Penman-Monteith equation and a modified Jarvis-Stewart model with an artificial neural network to estimate stand-scale transpiration and canopy conductance

Whitley, R; Medlyn, B; Zeppel, M; Macinnis-Ng, C; Eamus, D

Comparing the Penman-Monteith equation and a modified Jarvis-Stewart model with an artificial neural network to estimate stand-scale transpiration and canopy conductance

Whitley, R Medlyn, B Zeppel, M

Macinnis-Ng, C Eamus, D

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Publication Type:: Journal Article
Citation:: Journal of Hydrology, 2009, 373 (1-2), pp. 256 - 266
Issue Date:: 2009-06-30

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Field	Value	Language
dc.contributor.author	Whitley, R	en_US
dc.contributor.author	Medlyn, B	en_US
dc.contributor.author	Zeppel, M https://orcid.org/0000-0002-5510-0936	en_US
dc.contributor.author	Macinnis-Ng, C	en_US
dc.contributor.author	Eamus, D https://orcid.org/0000-0003-2765-8040	en_US
dc.date.issued	2009-06-30	en_US
dc.identifier.citation	Journal of Hydrology, 2009, 373 (1-2), pp. 256 - 266	en_US
dc.identifier.issn	0022-1694	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8883
dc.description.abstract	The responses of canopy conductance to variation in solar radiation, vapour pressure deficit and soil moisture have been extensively modelled using a Jarvis-Stewart (JS) model. Modelled canopy conductance has then often been used to predict transpiration using the Penman-Monteith (PM) model. We previously suggested an alternative approach in which the JS model is modified to directly estimate transpiration rather than canopy conductance. In the present study we used this alternative approach to model tree water fluxes from an Australian native forest over an annual cycle. For comparative purposes we also modelled canopy conductance and estimated transpiration via the PM model. Finally we applied an artificial neural network as a statistical benchmark to compare the performance of both models. Both the PM and modified JS models were parameterised using solar radiation, vapour pressure deficit and soil moisture as inputs with results that compare well with previous studies. Both models performed comparably well during the summer period. However, during winter the PM model was found to fail during periods of high rates of transpiration. In contrast, the modified JS model was able to replicate observed sapflow measurements throughout the year although it too tended to underestimate rates of transpiration in winter under conditions of high rates of transpiration. Both approaches to modelling transpiration gave good agreement with hourly, daily and total sums of sapflow measurements with the modified JS and PM models explaining 87% and 86% of the variance, respectively. We conclude that these three approaches have merit at different time-scales. © 2009 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Journal of Hydrology	en_US
dc.relation.isbasedon	10.1016/j.jhydrol.2009.04.036	en_US
dc.subject.classification	Environmental Engineering	en_US
dc.title	Comparing the Penman-Monteith equation and a modified Jarvis-Stewart model with an artificial neural network to estimate stand-scale transpiration and canopy conductance	en_US
dc.type	Journal Article
utslib.citation.volume	1-2	en_US
utslib.citation.volume	373	en_US
utslib.for	0705 Forestry Sciences	en_US
dc.location.activity	ISI:000268074900023	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access
pubs.issue	1-2	en_US
pubs.publication-status	Published	en_US
pubs.volume	373	en_US

Abstract:

The responses of canopy conductance to variation in solar radiation, vapour pressure deficit and soil moisture have been extensively modelled using a Jarvis-Stewart (JS) model. Modelled canopy conductance has then often been used to predict transpiration using the Penman-Monteith (PM) model. We previously suggested an alternative approach in which the JS model is modified to directly estimate transpiration rather than canopy conductance. In the present study we used this alternative approach to model tree water fluxes from an Australian native forest over an annual cycle. For comparative purposes we also modelled canopy conductance and estimated transpiration via the PM model. Finally we applied an artificial neural network as a statistical benchmark to compare the performance of both models. Both the PM and modified JS models were parameterised using solar radiation, vapour pressure deficit and soil moisture as inputs with results that compare well with previous studies. Both models performed comparably well during the summer period. However, during winter the PM model was found to fail during periods of high rates of transpiration. In contrast, the modified JS model was able to replicate observed sapflow measurements throughout the year although it too tended to underestimate rates of transpiration in winter under conditions of high rates of transpiration. Both approaches to modelling transpiration gave good agreement with hourly, daily and total sums of sapflow measurements with the modified JS and PM models explaining 87% and 86% of the variance, respectively. We conclude that these three approaches have merit at different time-scales. © 2009 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/8883