On the dynamics of canopy resistance: Generalized linear estimation and relationships with primary micrometeorological variables

Authors

Suat Irmak, University of Nebraska-LincolnFollow
Denis Mutiibwa, University of Nebraska-Lincoln

Date of this Version

2010

Citation

Irmak, S., and D. Mutiibwa (2010), On the dynamics of canopy resistance: Generalized linear estimation and relationships with primary micrometeorological variables, Water Resour. Res., 46, W08526, doi:10.1029/2009WR008484.

Comments

Copyright 2010 by the American Geophysical Union.

Abstract

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 rc 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 (u₃), aerodynamic resistance (r_a), leaf area index (LAI), and solar zenith angle (Q). 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 rc versus T_a, RH, VPD, R_n, incoming shortwave radiation (R_s), u₃, wind direction, LAI, Q, 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 Q 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 rc 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 r² 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 BREBSmeasured 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.