A multi-scale approach on snow cover observations and models (SnowCover)
This is chapter 11 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). Data on snow properties such as cover fraction, depth, water equivalents, and melt date are important per se , but also as input in various models, and to verify model res...
| Main Authors: | , , , |
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| Format: | Report |
| Language: | English |
| Published: |
Zenodo
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.4294091 https://zenodo.org/record/4294091 |
| Summary: | This is chapter 11 of the State of Environmental Science in Svalbard (SESS) report 2020 (https://sios-svalbard.org/SESS_Issue3). Data on snow properties such as cover fraction, depth, water equivalents, and melt date are important per se , but also as input in various models, and to verify model results. Earth observation (EO) gathers information on these parameters. Different EO methods for snow have different strengths. Manual measurements and locally deployed sensors give precise data, but only at individual sites. Satellite-based methods give huge amounts of data covering vast areas, but at lower resolution, and only when the satellite passes over relevant sites. Three SIOS projects attempt to bridge the spatial and temporal gaps between remote sensing data and point measurements of snow cover. PASSES gathers information about time-lapse cameras already deployed around Svalbard for research or other purposes. Most of them show snow-cover extent on an intermediate scale (10 m 2 to 10 km 2 ), with good temporal resolution. Some have been in place for 20 years, providing a valuable historic record. SATMODSNOW finds that discrepancies between satellite data and model results arise from weaknesses in how the models handle precipitation and temperature. Since snow cover disappears in similar patterns every year, with a time shift depending on precipitation and temperature, close examination of satellite observations offers a way to refine hydrological snow models. SvalSCESIA compares satellite data on both sea ice area and snow cover against ground-based monitoring data and snow model output. They find major shifts in the duration of summer snow-free periods, especially in valleys and lowlands. Snow cover also correlates with the ice cover in adjacent seas, indicating a strong effect of energy exchange between land and sea. Integration and intercomparison of EO data obtained with different methods and on different scales will likely improve snow models. |
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