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Agroclimatology

Evaluation of SHAW Model in Simulating Energy Balance, Leaf Temperature, and Micrometeorological Variables within a Maize Canopy

^a Dep. of Environmental Sciences, Nanjing Univ. of Information Science & Technology, Nanjing 210044, China
^b Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
^c USDA-ARS, Northwest Watershed Research Center, 800 Park Blvd., Suite 105, Boise, ID 83712

^* Corresponding author (gflerchi{at}nwrc.ars.usda.gov)

Received for publication May 2, 2005. Understanding and simulating plant canopy conditions can assist in better acknowledgment of plant microclimate characteristics, its effect on plant processes, and the influence of management and climate scenarios. The ability of the Simultaneous Heat and Water (SHAW) model to simulate the surface energy balance and profiles of leaf temperature and micrometeorological variables within a maize canopy and the underlying soil temperatures was tested using data collected during 1999 and 2003 at Yucheng, in the North China Plain. The SHAW model simulates the near-surface heat and water movement driven by input meteorological variables and observed plant characteristic (leaf area index [LAI], height, and rooting depth). For 1999, the model accurately simulated air temperature and relative humidity in the upper one-third of the canopy, but overpredicted midday temperature in the lower canopy. For 2003, although the surface energy balance was simulated quite well, radiometric canopy surface temperature and midday leaf temperature in the upper portion of the canopy were overpredicted, by approximately 5°C. Model efficiency (the fraction of variation in observed values explained by the model) for leaf temperature in the lower two-thirds of the canopy ranged from 0.82 to 0.90, but fell to 0.38 for the uppermost canopy layer. Weaknesses in the model were identified and potentially include: the use of K-theory to simulate turbulent transfer within the canopy; and simplifying assumptions with regard to long-wave radiation transfer within the canopy. Model modifications are planned to address these weaknesses.

Abbreviations: IRGA, infrared gas analyzer • IRT, infrared temperature sensor • IRTS, infrared thermocouple sensor • LAI, leaf area index • MBE, mean bias error • ME, model efficiency • NCP, North China Plain • RMSD, root mean square deviation • SHAW, Simultaneous Heat and Water