Date of this Version
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.
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 (rc) data for different vegetation surfaces. rc could be estimated by inversion of the PM model if the actual evapotranspiration (ETa) rate is known, but this approach has its own set of issues. Instead, an empirical method of estimating rc is suggested in this study. We investigated the relationships between primary micrometeorological parameters and rc and developed seven models to estimate rc for a nonstressed maize canopy on an hourly time step using a generalized‐linear modeling approach. The most complex rc model uses net radiation (Rn), air temperature (Ta), vapor pressure deficit (VPD), relative humidity (RH), wind speed at 3 m (u3), aerodynamic resistance (ra), leaf area index (LAI), and solar zenith angle (Q). The simplest model requires Rn, Ta, and RH. We present the practical implementation of all models via experimental validation using scaled up rc data obtained from the dynamic diffusion porometer‐measured leaf stomatal resistance through an extensive field campaign in 2006. For further validation, we estimated ETa by solving the PM model using the modeled rc from all seven models and compared the PM ETa estimates with the Bowen ratio energy balance system (BREBS)‐measured ETa for an independent data set in 2005. The relationships between hourly rc versus Ta, RH, VPD, Rn, incoming shortwave radiation (Rs), u3, wind direction, LAI, Q, and ra were presented and discussed. We demonstrated the negative impact of exclusion of LAI when modeling rc, whereas exclusion of ra and Q did not impact the performance of the rc 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 rc 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 rc. 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 ETa using modeled rc. These findings can aid in the selection of a suitable model based on the availability and quality of the input data to predict rc for one‐step application of the PM model to estimate ETa for a nonstressed maize canopy.