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Bayesian analysis for uncertainty estimation of a canopy transpiration model
Author(s) -
Samanta S.,
Mackay D. S.,
Clayton M. K.,
Kruger E. L.,
Ewers B. E.
Publication year - 2007
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2006wr005028
Subject(s) - transpiration , markov chain monte carlo , monte carlo method , canopy conductance , bayesian probability , penman–monteith equation , mathematics , statistics , leaf area index , econometrics , environmental science , computer science , evapotranspiration , ecology , vapour pressure deficit , botany , photosynthesis , biology
A Bayesian approach was used to fit a conceptual transpiration model to half‐hourly transpiration rates for a sugar maple ( Acer saccharum ) stand collected over a 5‐month period and probabilistically estimate its parameter and prediction uncertainties. The model used the Penman‐Monteith equation with the Jarvis model for canopy conductance. This deterministic model was extended by adding a normally distributed error term. This extension enabled using Markov chain Monte Carlo simulations to sample the posterior parameter distributions. The residuals revealed approximate conformance to the assumption of normally distributed errors. However, minor systematic structures in the residuals at fine timescales suggested model changes that would potentially improve the modeling of transpiration. Results also indicated considerable uncertainties in the parameter and transpiration estimates. This simple methodology of uncertainty analysis would facilitate the deductive step during the development cycle of deterministic conceptual models by accounting for these uncertainties while drawing inferences from data.