Canopy wetness in the Eastern Amazon
Canopy wetness in the Eastern Amazon
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DOI：
10.1016/j.agrformet.2020.108250
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发表时间：
2021-01-15
影响因子：
6.2
通讯作者：
Meir, Patrick
中科院分区：
文献类型：
--
作者：
Binks, Oliver;Finnigan, John;Meir, Patrick
冠层湿度是影响光合作用、溶质淋滤或吸收、冠层水分状态和能量平衡以及涡动相关和遥感数据解释的常见条件。虽然通常被视为二元变量，“湿”或“干”，但森林冠层通常是部分湿的，需要使用连续的湿度描述。在亚马逊东部地区，像露珠这样的小降水事件会使部分冠层湿润，从而促进干季叶片水分的吸收，并且对冠层能量平衡至关重要。然而，很少有研究报道森林生态系统的冠层湿度的时空分布，或露水对叶片湿度的相对贡献。在这项研究中，我们使用两个叶片湿度传感器的冠层剖面，结合气象数据，解决了关于东亚马逊雨林叶片湿度时空变化的基本问题。我们还研究了气象塔数据如何使用两个模型预测冠层湿度，一个是经验模型，另一个是基于物理模型。结果表明，冠层只有34%的时间是100%干燥的，其他时间在5%到100%潮湿之间。在旱季，露水分别占年叶片总湿度的20%或43%，占冠层总湿度的36%或50%，其中不包括与雨同时发生的露水事件。湿润持续时间在冠层顶部高于冠层底部，这主要是由于降雨事件，而露水的形成强烈依赖于局部冠层结构，并在冠层内水平变化。最佳经验模型对冠层湿度方差的贡献率为55%，而物理模型对冠层湿度方差的贡献率为48%。我们讨论了未来物理模型的建模改进，以提高其预测能力。
Canopy wetness is a common condition that influences photosynthesis, the leaching or uptake of solutes, the water status and energy balance of canopies, and the interpretation of eddy covariance and remote sensing data. While often treated as a binary variable, 'wet' or `dry', forest canopies are often partially wet, requiring the use of a continuous description of wetness. Minor precipitation events such as dew, that wet a fraction of the canopy, have been found to contribute to dry season foliar water uptake in the Eastern Amazon, and are fundamentally important to the canopy energy balance. However, few studies have reported the spatial and temporal distribution of canopy wetness, or the relative contribution of dew to leaf wetness, for forest ecosystems.In this study, we use two canopy profiles of leaf wetness sensors, coupled with meteorological data, to address fundamental questions about spatial and temporal variation of leaf wetness in an Eastern Amazonian rainforest. We also investigate how well meteorological tower data can predict canopy wetness using two models, one empirical and one that is physically-based.The results show that the canopy is 100% dry only for 34% of the time, otherwise being between 5% and 100% wet. Dew accounts for 20% or 43% of total annual leaf wetness, and 36% or 50% of canopy wetness in dry season, excluding or including dew events that co-occur with rain, respectively. Wetness duration was higher at the top than bottom of the canopy, mainly because of rain events, whilst dew formation was strongly dependent on the local canopy structure and varied horizontally through the canopy. The best empirical model accounted for 55% of the variance in canopy wetness, while the physical model accounted for 48% of the variance. We discuss future modelling improvements of the physical model to increase its predictive capacity.