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Meta-analysis of leaf area index, canopy height and root depth of three bioenergy crops and their effects on land surface modeling
Agricultural and Forest Meteorology ( IF 5.6 ) Pub Date : 2021-05-13 , DOI: 10.1016/j.agrformet.2021.108444
Johana Paola Forero Urrego , Bo Huang , Jan Sandstad Næss , Xiangping Hu , Francesco Cherubini

The expected large-scale expansion of biofuel production in climate change mitigation scenarios calls for improvements in the representation of bioenergy crops in land surface models. Leaf area index (LAI), canopy height (CH) and root depth (RD) are key parameters that regulate exchanges of heat and moisture between land and atmosphere. This study performs a meta-analysis combining unique data from 34 observational studies and 14 countries to estimate monthly variability in LAI, CH, and RD of three main bioenergy crops: miscanthus (MSC), switchgrass (SWG), and reed canary grass (RCG). Using the Community Land Model v.5.0 and the results from the meta-analysis, we also tested the effects that variability in parameterization of LAI and CH have on key components of the surface energy budget, relative to prescribed values. Results from the meta-analysis show a strong seasonality of LAI and CH, with mean (± one standard error) LAI values at the peak summer month of 6.05 ± 0.84, 5.56 ± 0.75, and 5.39 ± 1.15 m2/m2, and maximum CH of 246 ± 23, 147 ± 16, and 156 ± 10 cm, for MSC, SWG, and RCG, respectively. These values are typically larger than the default parameterizations in CLM. Information on RD was limited and average values are 172 ± 56, 165 ±46, and 193 ± 11 cm, for MSC, SWG, and RCG, respectively. The seasonal cycles of latent heat, sensible heat and surface albedo are primarily affected by the range of LAI values, and less sensitive to variability in CH. Relative to the default values, higher LAI values increase latent heat and decrease sensible heat, with the highest absolute changes in summer. They also decrease surface albedo in winter months due to a larger snow masking effect. Our results offer a basis to compare experimental work and modelling studies, improve parameterization in land surface models, and identify the importance of vegetation structure parameters to evaluate key climate processes in response to bioenergy crops.



中文翻译:

三种生物能源作物的叶面积指数,冠层高度和根深的荟萃分析及其对地表建模的影响

在缓解气候变化的设想中,预期生物燃料生产的大规模扩张将要求改善土地表面模型中生物能源作物的代表性。叶面积指数(LAI),冠层高度(CH)和根深(RD)是调节陆地与大气之间热量和水分交换的关键参数。这项研究进行了荟萃分析,结合了来自14个国家/地区的34个观察性研究的独特数据,以估计三种主要生物能源作物:桂花(MSC),柳枝((SWG)和金丝雀草(RCG)的LAI,CH和RD的月变化。 )。使用社区土地模型v.5.0和荟萃分析的结果,我们还测试了LAI和CH参数化的可变性对表面能预算的关键组成部分(相对于规定值)的影响。2 /米2,对于MSC,SWG和RCG,最大CH分别为246±23、147±16和156±10 cm。这些值通常大于CLM中的默认参数设置。有关RD的信息有限,MSC,SWG和RCG的平均值分别为172±56、165±46和193±11 cm。潜热,显热和表面反照率的季节性周期主要受LAI值范围的影响,并且对CH的变化较不敏感。相对于默认值,较高的LAI值会增加潜热并降低显热,夏季的绝对变化最大。由于更大的积雪掩盖作用,它们在冬季还减少了地表反照率。我们的结果为比较实验工作和建模研究,改善陆地表面模型的参数化,

更新日期:2021-05-13
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