Parameterizing snow interception over forest canopy
| Autor: | Helbig, N., Moeser, D., Teich, M., Vincent, L., Lejeune, Y., Lafaysse, M., Sicart, J. E., Jean-Matthieu Monnet |
|---|---|
| Přispěvatelé: | WSL SLF DAVOS DORF CHE, Partenaires IRSTEA, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), United States Geological Survey (USGS), Austrian Research Centre for Forests (BFW), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement (IGE), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Laboratoire des EcoSystèmes et des Sociétés en Montagne (UR LESSEM), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Geophysical Research Abstracts EGU General Assembly 2019 EGU General Assembly 2019, Apr 2019, Vienne, Austria. pp.1 HAL |
| Popis: | International audience; Snow interception drives spatial heterogeneity of snow under forest canopies and displays significant differencesbetween forested, open and alpine areas at a variety of scales. Beyond giving a first order control on snow accu-mulation, interception by canopy drives other processes. A prime example is canopy albedo, as large differencesare readily visible between forest canopy albedo with and without intercepted snow. As such a correct parameteri-zation of interception is necessary as it drives many physical processes important for snow hydrology, climatologyand meteorology studies. However, current parameterizations of snow interception have not always beenable to preserve the large variance of snow beneath canopies at all scales. Various snow interception parameter-izations are applied in land surface models but are generally not validated for different snow climates and/or scales.Here, we developed parameterizations for spatial mean and standard deviation of interception over horizon-tal scales of 50 m. They were developed from a comparison of (1) computed forest structure metrics (sky viewfactor and standard deviation) from a high-resolution Lidar derived digital terrain model and (2) an existingdataset of several thousand interception measurements collected after nine storm events in a coniferous forest inthe Eastern Swiss Alps. By scaling open area snow precipitation with the calculated forest structure metrics, wecomputed spatial mean and standard deviation of forest canopy interception. We obtained similar performancestatistics compared to previously suggested parameterizations, i.e. a RMSE of 1.3 cm (1 mm SWE) for spatialmean and 0.6 cm (0.4 mm SWE) for the standard deviation of canopy interception.Furthermore, we then validated both new interception parameterizations with data from two different geo-graphic regions and snow climates, namely from a study site in Utah, U.S. and one in the French Alps. Thiscomparison suggests that our sub-grid parameterizations for snow interception are applicable in models to describesnow depth heterogeneities for different snow climates and mountain forest environments |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|