Evaluation of snow models in terrestrial biosphere models using ground observation and satellite data: impact on terrestrial ecosystem processes

Authors

  • Kazuhito Ichii,

    Corresponding author
    1. San Jose State University and Ecosystem Science and Technology Branch, NASA Ames Research Center, Mail Stop 242-4, Moffett Field, CA 94035, USA
    • Faculty of Symbiotic Systems Science, Fukushima University, Kanayagawa 1, Fukushima, 960-1296, Japan.
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  • Michael A. White,

    1. Department of Aquatic, Watershed, and Earth Resources, Utah State University, 5210 Old Main Hill, Logan, UT 84322-5210, USA
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  • Petr Votava,

    1. California State University at Monterey Bay and Ecosystem Science and Technology Branch, NASA Ames Research Center, Mail Stop 242-4, Moffett Field, CA 94035, USA
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  • Andrew Michaelis,

    1. California State University at Monterey Bay and Ecosystem Science and Technology Branch, NASA Ames Research Center, Mail Stop 242-4, Moffett Field, CA 94035, USA
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  • Ramakrishna R. Nemani

    1. Ecosystem Science and Technology Branch, NASA Ames Research Center, Mail Stop 242-4, Moffett Field, CA 94035, USA
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Abstract

Snow is important for water management, and an important component of the terrestrial biosphere and climate system. In this study, the snow models included in the Biome-BGC and Terrestrial Observation and Prediction System (TOPS) terrestrial biosphere models are compared against ground and satellite observations over the Columbia River Basin in the US and Canada and the impacts of differences in snow models on simulated terrestrial ecosystem processes are analysed. First, a point-based comparison of ground observations against model and satellite estimates of snow dynamics are conducted. Next, model and satellite snow estimates for the entire Columbia River Basin are compared. Then, using two different TOPS simulations, the default TOPS model (TOPS with TOPS snow model) and the TOPS model with the Biome-BGC snow model, the impacts of snow model selection on runoff and gross primary production (GPP) are investigated. TOPS snow model predictions were consistent with ground and satellite estimates of seasonal and interannual variations in snow cover, snow water equivalent, and snow season length; however, in the Biome-BGC snow model, the snow pack melted too early, leading to extensive underpredictions of snow season length and snow covered area. These biases led to earlier simulated peak runoff and reductions in summer GPP, underscoring the need for accurate snow models within terrestrial ecosystem models. Copyright © 2007 John Wiley & Sons, Ltd.

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