A Strategy for Global Snow Information for Nature and Society
Abstract
The properties of snow vary dramatically in time and in space. Each year the snowpack transitions from the accumulation phase, when snowfall and snow redistribution are dominant processes; to a ripening phase, when the snow's temperature and energy balance are most critical to determining melt timing; to a melt phase, when the energy balance and snow disappearance best define snow's role on the landscape (Fig. 1). Beyond these temporal changes, snow varies drastically in different regions of the world. Considering two extremes (Fig. 1), dry tundra snow covers 16.5 million km2 but is only 0.3-0.5 m deep, while wet maritime snow covers only 3.6 million km2 but is much deeper (1.5-2.0 m) [Sturm et al. 1995]. These very different snow types, which require very different sensing approaches, contain a similar volume of total snow ( 5000-8000 km3), and both are critical to global energy balance, water supplies, and ecosystems. Here, we review different modeling and observational strategies and offer guidelines for the best approach in different regions of the world and for different applications. For example, snowfall is notoriously hard to measure directly, but atmospheric models are rapidly improving in their representations of precipitation, and monitoring snow accumulation on the ground can help improve both model representations of large-scale snowfall and of fine-scale snow distribution. Where snow is deep and narrow (mountains and maritime regions), geodetic or altimetric snow depth mapping, which can be done by lidar, radar, or photogrammetry, combined with model- or in-situ-based estimates of snow density, appears to be a good option. Where snow is shallow and wide (arctic and plains), measurements that integrate over wide areas and are sensitive to snow microstructure and water equivalent, e.g. radar backscatter and passive radiometry, likely provide a better option, but these must be coupled with models and data assimilation schemes that account for potential complicating factors.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.H22B..04L
- Keywords:
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- 0736 Snow;
- CRYOSPHEREDE: 0740 Snowmelt;
- CRYOSPHEREDE: 1860 Streamflow;
- HYDROLOGYDE: 1863 Snow and ice;
- HYDROLOGY