Sensible heat and latent heat flux estimates in a tall and dense forest canopy under unstable conditions
A method to estimate the sensible heat flux (H) for unstable atmospheric condition requiring measurements taken in half-hourly basis as input and involving the land surface temperature (LST), HLST, was tested over a tall and dense aspen stand. The method avoids the need to estimate the zero-plane di...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat de Lleida (UdL) |
| Repositorio: | Repositori Obert UdL |
| OAI Identifier: | oai:repositori.udl.cat:10459.1/83173 |
| Acceso en línea: | https://doi.org/10.3390/atmos13020264 http://hdl.handle.net/10459.1/83173 |
| Access Level: | acceso abierto |
| Palabra clave: | Sensible heat flux Latent heat flux In situ sensing Aspen forest Boscos i silvicultura Àlbers |
| Sumario: | A method to estimate the sensible heat flux (H) for unstable atmospheric condition requiring measurements taken in half-hourly basis as input and involving the land surface temperature (LST), HLST, was tested over a tall and dense aspen stand. The method avoids the need to estimate the zero-plane displacement and the roughness length for momentum. The net radiation (Rn) and the latent heat flux (λE) dominated the surface energy balance (SEB). Therefore, λE was estimated applying the residual method using HLST as input, λER-LST. The sum of H and λE determined with the eddy covariance (EC) method led to a surface energy imbalance of 20% Rn. Thus, the reference taken for the comparisons were determined forcing the SEB using the EC Bowen ratio (BREB method). For clear sky days, HLST performed close to HBREB. Therefore, it showed potential in the framework of remote sensing because the input requirements are similar to current methods widely used. For cloudy days, HLST scattered HBREB and nearly matched the accumulated sensible hear flux. Regardless of the time basis and cloudiness, λER-LST was close to λEBREB. For all the data, both HLST and λER-LST were not biased and showed, respectively, a mean absolute relative error of 24.5% and 12.5% and an index of agreement of 68.5% and 80%. |
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