A new three-dimensional energy balance model for complex plant canopy geometries: Model development and improved validation strategies

被引:40
作者
Bailey, Brian N. [1 ]
Stoll, Rob [1 ]
Pardyjak, Eric R. [1 ]
Miller, Nathan E. [1 ]
机构
[1] Univ Utah, Dept Mech Engn, Salt Lake City, UT 84112 USA
基金
美国国家科学基金会;
关键词
Canopy microclimate; Canopy radiation; Heterogeneous canopies; Leaf energy balance; Plant thermal imaging; SAP FLOW; TEMPERATURE; PHOTOSYNTHESIS; TRANSPIRATION; HEAT; CONDUCTANCE; LEAVES; PARAMETERIZATION; EVAPORATION; SIMULATION;
D O I
10.1016/j.agrformet.2015.11.021
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Canopy microclimate is a critical component of most biophysical processes in plants. Understanding the role of microclimate across scales in canopies with complex, heterogeneous architectures is challenging, as it is difficult to represent the relevant range of scales. In this study, a model was developed and validated to accurately predict the three-dimensional distribution of microclimate-related quantities (e.g., net radiation, surface temperature, evapotranspiration, flux partitioning) in complex canopy geometries. The modeling strategy was to aggregate individual leaves into isothermal sub-volumes, as resolving all leaves over whole-canopy scales is unfeasible. The model takes leaf-level models for convection, evapotranspiration, and radiative absorption/emission and integrates them over a discrete volume using the leaf angle probability distribution function. The model is built on a framework designed to utilize graphics processing units (GPUs) in order to offset the expense associated with model complexity, which can allow for the simulation of canopy-scale problems at sub-tree resolution. Additional cost-saving strategies are also suggested. Three-dimensional canopy energy transfer models have traditionally been difficult to validate. Two validation experiments were designed to simultaneously measure virtually all model outputs, often using multiple methods at many spatial locations. The model was able to reproduce point, 3D distributed, and bulk measurements at high accuracy, with average model errors within expected measurement errors. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码:146 / 160
页数:15
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