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On the dynamics of canopy resistance: Generalized linear estimation and relationships with primary micrometeorological variables
Author(s) -
Irmak Suat,
Mutiibwa Denis
Publication year - 2010
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 - 10.1029/2009wr008484
Subject(s) - evapotranspiration , vapour pressure deficit , wind speed , energy balance , atmospheric sciences , bowen ratio , canopy , penman–monteith equation , shortwave radiation , vapour pressure of water , mathematics , environmental science , leaf area index , shortwave , relative humidity , meteorology , water vapor , sensible heat , physics , radiative transfer , radiation , transpiration , geography , botany , thermodynamics , ecology , photosynthesis , archaeology , quantum mechanics , biology
The 1‐D and single layer combination‐based energy balance Penman‐Monteith (PM) model has limitations in practical application due to the lack of canopy resistance ( r c ) data for different vegetation surfaces. r c could be estimated by inversion of the PM model if the actual evapotranspiration ( E Ta ) rate is known, but this approach has its own set of issues. Instead, an empirical method of estimating r c is suggested in this study. We investigated the relationships between primary micrometeorological parameters and r c and developed seven models to estimate r c for a nonstressed maize canopy on an hourly time step using a generalized‐linear modeling approach. The most complex r c model uses net radiation ( R n ), air temperature ( T a ), vapor pressure deficit (VPD), relative humidity (RH), wind speed at 3 m ( u 3 ), aerodynamic resistance ( r a ), leaf area index (LAI), and solar zenith angle (Θ). The simplest model requires R n , T a , and RH. We present the practical implementation of all models via experimental validation using scaled up r c data obtained from the dynamic diffusion porometer‐measured leaf stomatal resistance through an extensive field campaign in 2006. For further validation, we estimated E Ta by solving the PM model using the modeled r c from all seven models and compared the PM E Ta estimates with the Bowen ratio energy balance system (BREBS)‐measured E Ta for an independent data set in 2005. The relationships between hourly r c versus T a , RH, VPD, R n , incoming shortwave radiation ( R s ), u 3 , wind direction, LAI, Θ, and r a were presented and discussed. We demonstrated the negative impact of exclusion of LAI when modeling r c , whereas exclusion of r a and Θ did not impact the performance of the r c models. Compared to the calibration results, the validation root mean square difference between observed and modeled r c increased by 5 s m −1 for all r c models developed, ranging from 9.9 s m −1 for the most complex model to 22.8 s m −1 for the simplest model, as compared with the observed r c . The validation r 2 values were close to 0.70 for all models, and the modeling efficiency ranged from 0.61 for the most complex model to −1.09 for the simplest model. There was a strong agreement between the BREBS‐measured and the PM‐estimated E Ta using modeled r c . These findings can aid in the selection of a suitable model based on the availability and quality of the input data to predict r c for one‐step application of the PM model to estimate E Ta for a nonstressed maize canopy.