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<https://discovery.ucl.ac.uk/id/eprint/10116897> <http://purl.org/dc/terms/title> "Canopy wetness in the Eastern Amazon"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10116897> <http://purl.org/ontology/bibo/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.\r\n\r\nIn 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.\r\n\r\nThe 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."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/10116897> <http://purl.org/dc/terms/date> "2020-11-24" .
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