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ARS Home » Pacific West Area » Boise, Idaho » Northwest Watershed Research Center » Research » Publications at this Location » Publication #183206

Title: Evaluation of SHAW model in simulating energy balance, leaf temperature and micrometeorological variables within a maize canopy

Author
item XIAO, WEI - NANJING UNIV
item YU, QIANG - CHINESE ACAD SCI
item Flerchinger, Gerald
item ZHENG, YOUFEI - NANJING UNIV

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/20/2006
Publication Date: 1/20/2006
Citation: Xiao, W., Yu, Q., Flerchinger, G., and Zheng Y. 2006. Evaluation of SHAW Model in Simulating Energy Balance, Leaf Temperature, and Micrometeorological Variables within a Maize Canopy. Agronomy Journal, 98:722-729 (2006).

Interpretive Summary: The near-surface microclimate controls vital plant biological processes such as photosynthesis, respiration, transpiration and damage from extreme temperatures to crops. The ability to predict microclimatic conditions within the soil-plant-atmosphere system enhances our ability to predict plant response to microclimatic conditions and to evaluate management and climate scenarios. The ability of the Simultaneous Heat and Water (SHAW) model, a detailed model of near-surface heat and water movement, 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 at Yucheng, in the North China Plain. Based on simulation results, the SHAW model can reasonably simulate the surface energy balance, but weaknesses in the model were identified and point toward areas for future model improvements. Model modifications are planned to address these weaknesses identified in the model. An improved model will lead to a more reliable model for evaluation of management and climate scenario influences on plant microclimate and plant response.

Technical Abstract: Meteorological factors such as air temperature, humidity and wind speed are the ambient conditions that affect plant biological processes. Canopy and leaf temperatures reflect overall plant health. Understanding and simulating canopy conditions can assist in better acknowledgement 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, a detailed model of near-surface heat and water movement, 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 at Yucheng, in the North China Plain. Minor model modification was made to better simulate wind speed profiles measured in 1999; the model was then further tested using data from 2003. For 1999, the model accurately simulated air temperature and relative humidity in the upper 1/3 of the canopy, but overpredicted midday temperature in the lower canopy. For 2003, the model simulated the diurnal variation in the surface energy balance well. The model mimicked measured net radiation values with model efficiency (ME, the fraction of variation in observed values explained by the model) equaling 0.97. Latent and sensible heat fluxes were simulated well with model efficiencies around 0.80. Although the surface energy balance was simulated reasonably well, radiometric canopy surface temperature, and midday leaf temperature in upper portion of the canopy were overpredicted, by approximately 5 C. Model efficiency for leaf temperature in the lower 2/3 of the maize 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 point toward areas for future model improvements. Weaknesses 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 identified in the model.