A seasonal algorithm of the snow-covered area fraction for mountainous terrain
The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seaso...
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| Veröffentlicht in: | The cryosphere 2021-09, Vol.15 (9), p.4607-4624 |
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| creator | Helbig, Nora Schirmer, Michael Magnusson, Jan Mäder, Flavia van Herwijnen, Alec Quéno, Louis Bühler, Yves Deems, Jeff S Gascoin, Simon |
| description | The snow cover spatial variability in mountainous terrain changes considerably over the course of a snow season. In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application. |
| doi_str_mv | 10.5194/tc-15-4607-2021 |
| format | Article |
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In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. Overall, the results suggest that our seasonal fSCA algorithm can be applied in other geographic regions by any snow model application.</description><identifier>ISSN: 1994-0424</identifier><identifier>ISSN: 1994-0416</identifier><identifier>EISSN: 1994-0424</identifier><identifier>EISSN: 1994-0416</identifier><identifier>DOI: 10.5194/tc-15-4607-2021</identifier><language>eng</language><publisher>Katlenburg-Lindau: Copernicus GmbH</publisher><subject>Ablation ; Algorithms ; Analysis ; Continental interfaces, environment ; Data acquisition ; Datasets ; Digital Elevation Models ; Energy balance ; Imagery ; Mountains ; Multilayers ; Parameterization ; Parameters ; Precipitation ; Satellite imagery ; Scale models ; Sciences of the Universe ; Seasons ; Snow ; Snow accumulation ; Snow cover ; Snow depth ; Snow-water equivalent ; Spatial variability ; Spatial variations ; Standard deviation ; Terrain ; Topography ; Tracking ; Trends ; Water depth</subject><ispartof>The cryosphere, 2021-09, Vol.15 (9), p.4607-4624</ispartof><rights>COPYRIGHT 2021 Copernicus GmbH</rights><rights>2021. 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In this context, fractional snow-covered area (fSCA) is an essential model parameter characterizing how much ground surface in a grid cell is currently covered by snow. We present a seasonal fSCA algorithm using a recent scale-independent fSCA parameterization. For the seasonal implementation, we track snow depth (HS) and snow water equivalent (SWE) and account for several alternating accumulation–ablation phases. Besides tracking HS and SWE, the seasonal fSCA algorithm only requires subgrid terrain parameters from a fine-scale summer digital elevation model. We implemented the new algorithm in a multilayer energy balance snow cover model. To evaluate the spatiotemporal changes in modeled fSCA, we compiled three independent fSCA data sets derived from airborne-acquired fine-scale HS data and from satellite and terrestrial imagery. Overall, modeled daily 1 km fSCA values had normalized root mean square errors of 7 %, 12 % and 21 % for the three data sets, and some seasonal trends were identified. Comparing our algorithm performances to the performances of the CLM5.0 fSCA algorithm implemented in the multilayer snow cover model demonstrated that our full seasonal fSCA algorithm better represented seasonal trends. 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| ispartof | The cryosphere, 2021-09, Vol.15 (9), p.4607-4624 |
| issn | 1994-0424 1994-0416 1994-0424 1994-0416 |
| language | eng |
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| source | DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals |
| subjects | Ablation Algorithms Analysis Continental interfaces, environment Data acquisition Datasets Digital Elevation Models Energy balance Imagery Mountains Multilayers Parameterization Parameters Precipitation Satellite imagery Scale models Sciences of the Universe Seasons Snow Snow accumulation Snow cover Snow depth Snow-water equivalent Spatial variability Spatial variations Standard deviation Terrain Topography Tracking Trends Water depth |
| title | A seasonal algorithm of the snow-covered area fraction for mountainous terrain |
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