Importance of model structure and irradiance input to modeling snow surface temperature and snowmelt in complex terrain
Abstract
The initiation and rate of snowmelt depend primarily on the surface energy balance. This is represented quite differently across snow models. Differences arise from choices in the input longwave irradiance, shortwave irradiance, albedo parameterizations, and model structure. Examinations of only one of these processes can lead to incorrect conclusions with regards to the best modeling approach because multiple compensating errors are possible. Observations of snow surface temperature, Tss, provide us with an additional constraint to guide model choices. Here, we examine these compensating errors using the 2-layer Distributed Soil Vegetation Model, DHSVM in the upper Tuolumne River watershed, California, as an example. We consider multiple incoming longwave datasets, including direct observations, NASA's Clouds and the Earth's Radiant Energy System (CERES), and empirical formulations. We represent variations in model structure by varying the model's top layer depth, TLD, from thicknesses of 0.025 - 0.125 m SWE, and we test the albedo parameterization by changing the fresh snow albedo. The impacts of these choices on snow evolution are assessed by comparing with in situ observations of Tss and streamflow. The layer thickness at the snow-air interface is crucial for simulating the timing and magnitude of the Tss diel swings, and consequently snowmelt. Thicker top layers have dampened diel variations of Tss and lower warming rates, melting the snow slower than the thinner top layers. Lower albedo melts the snow faster than higher albedo, with higher rates for thinner top layer. Choices in any one of these model decisions can offset effects of incorrect choices in another area, indicating that for snowmelt modeling, DHSVM model layer architecture is as important as the energy budget representation. Tss is a key variable to investigate snow energy dynamics, and we recommend including it in future model assessments.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.C42B..06C
- Keywords:
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- 0736 Snow;
- CRYOSPHEREDE: 0772 Distribution;
- CRYOSPHEREDE: 0794 Instruments and techniques;
- CRYOSPHEREDE: 1863 Snow and ice;
- HYDROLOGY