Abstract 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.

中文翻译：

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亚马逊东部的树冠湿度

摘要 冠层湿度是影响光合作用、溶质的浸出或吸收、冠层的水分状态和能量平衡以及涡度协方差和遥感数据解释的常见条件。虽然经常被视为二元变量，“湿”或“干”，但森林冠层通常是部分湿润的，需要使用对湿度的连续描述。已发现少量降水事件（如露水使部分树冠变湿）有助于亚马逊东部旱季叶面水分的吸收，并且对树冠能量平衡至关重要。然而，很少有研究报告森林生态系统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，我们使用了两个叶湿度传感器的冠层轮廓，结合气象数据，解决亚马逊雨林东部叶片湿度时空变化的基本问题。我们还使用两种模型研究气象塔数据如何预测冠层湿度，一种是经验模型，另一种是基于物理的模型。结果表明，树冠只有 34% 的时间是 100% 干燥的，否则在 5% 到 100% 之间是潮湿的。露水占全年叶片总湿度的 20% 或 43%，旱季占冠层湿度的 36% 或 50%，分别排除或包括与降雨同时发生的露水事件。冠层顶部的湿润持续时间高于冠层底部，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，并且在整个冠层中水平变化。最佳经验模型解释了冠层湿度方差的 55%，而物理模型解释了 48% 的方差。我们讨论了物理模型的未来建模改进，以提高其预测能力。