Snow processes in mountain forests: Interception modeling for coarse-scale applications

Autor: N. Helbig, D. Moeser, M. Teich, L. Vincent, Y. Lejeune, J.-E. Sicart, J.-M. Monnet
Přispěvatelé: Institut Fédéral de Recherches sur la Forêt, la Neige et le Paysage (WSL), Institut Fédéral de Recherches [Suisse], USGS New Mexico Water Science Center, United States Geological Survey (USGS), Federal Research and Training Centre For Forests, Natural Hazards and Landscape (BFW), Department of Wildland Resources, Utah State University (USU), Centre d'Etudes de la Neige (CEN), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA), Université Grenoble Alpes (UGA), Université de Toulouse (UT), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Laboratoire des EcoSystèmes et des Sociétés en Montagne (UR LESSEM), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Federal Office of the Environment (FOEN) - USGS South Central Climate Adaptation Science Center - Swiss National Science Foundation (SNSF) grant nos. P2EZP2_155606 and P300P2_171236) - Utah Agricultural Experiment Station (UAES, as part of the 'Forests and snow microstructure: key to water supply in the 21st century?' research project) - Utah State University (USU) Department of Wildland Resources - Universite Grenoble Alpes as part of the Labex-OSUG@2020 research (grant no. ANR10 LABX 56) - National Science Foundation Established Program to Stimulate Competitive Research (NSF EPSCoR) cooperative agreement (grant no. IIA 1208732), ANR-10-LABX-0056,OSUG@2020,Innovative strategies for observing and modelling natural systems(2010), European Project: 1208732(2012), Copernicus GmbH, Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées, Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )
Jazyk: angličtina
Rok vydání: 2019
Předmět:
010504 meteorology & atmospheric sciences
0208 environmental biotechnology
02 engineering and technology
mountain forest
[SDU.STU.ME]Sciences of the Universe [physics]/Earth Sciences/Meteorology
Atmospheric sciences
01 natural sciences
lcsh:Technology
Standard deviation
lcsh:TD1-1066
Snow
Physical Sciences and Mathematics
technique and approach
lcsh:Environmental technology. Sanitary engineering
[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
lcsh:Environmental sciences
0105 earth and related environmental sciences
General Environmental Science
lcsh:GE1-350
Tree canopy
model
lcsh:T
Empirical modelling
lcsh:Geography. Anthropology. Recreation
Life Sciences
Albedo
15. Life on land
020801 environmental engineering
Spatial heterogeneity
lcsh:G
Earth Sciences
General Earth and Planetary Sciences
Environmental science
Interception
Scale (map)
Zdroj: Hydrology and Earth System Sciences
Hydrology and Earth System Sciences, 2020, 24 (5), pp.2545-2560. ⟨10.5194/hess-24-2545-2020⟩
Wildland Resources Student Research
Hydrology and Earth System Sciences, Vol 24, Pp 2545-2560 (2020)
T.W. "Doc" Daniel Experimental Forest
Hydrology and Earth System Sciences, European Geosciences Union, 2020, 24 (5), pp.2545-2560. ⟨10.5194/hess-24-2545-2020⟩
ISSN: 1607-7938
1027-5606
DOI: 10.5194/hess-24-2545-2020⟩
Popis: Snow interception by the forest canopy controls the spatial heterogeneity of subcanopy snow accumulation leading to significant differences between forested and nonforested areas at a variety of scales. Snow intercepted by the forest canopy can also drastically change the surface albedo. As such, accurately modeling snow interception is of importance for various model applications such as hydrological, weather, and climate predictions. Due to difficulties in the direct measurements of snow interception, previous empirical snow interception models were developed at just the point scale. The lack of spatially extensive data sets has hindered the validation of snow interception models in different snow climates, forest types, and at various spatial scales and has reduced the accurate representation of snow interception in coarse-scale models. We present two novel empirical models for the spatial mean and one for the standard deviation of snow interception derived from an extensive snow interception data set collected in an evergreen coniferous forest in the Swiss Alps. Besides open-site snowfall, subgrid model input parameters include the standard deviation of the DSM (digital surface model) and/or the sky view factor, both of which can be easily precomputed. Validation of both models was performed with snow interception data sets acquired in geographically different locations under disparate weather conditions. Snow interception data sets from the Rocky Mountains, US, and the French Alps compared well to the modeled snow interception with a normalized root mean square error (NRMSE) for the spatial mean of ≤10 % for both models and NRMSE of the standard deviation of ≤13 %. Compared to a previous model for the spatial mean interception of snow water equivalent, the presented models show improved model performances. Our results indicate that the proposed snow interception models can be applied in coarse land surface model grid cells provided that a sufficiently fine-scale DSM is available to derive subgrid forest parameters.
Databáze: OpenAIRE
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