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Exploring snow model parameter sensitivity using Sobol' variance decomposition
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2017
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Source: Environmental Modelling & Software, 89, 144-158
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Journal Title:Environmental Modelling & Software
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Description:This study advances model diagnostics for snowmelt-based hydrological systems using Sobol’ sensitivity analysis, illuminating parameter sensitivities and contrasting model structural differences. We consider several distinct snow-dominated locations in the western United States, running both SNOW-17, a conceptual degree-day model, and the Variable Infiltration Capacity (VIC) snow model, a physically-based model. Model performance is rigorously evaluated through global sensitivity analysis and a temperature warming analysis is conducted to explore how model parameterizations affect portrayals of climate change. Both VIC and SNOW-17 produce comparable results with SNOW-17 performing slightly better for shallower snowpacks and VIC performing better for deeper snowpacks. However, the lack of sensitivity of SNOW-17 to climate warming suggests that it may not be as reliable as a more sensitive model like VIC. Inter-model differences presented here offer insights into physical features with greatest uncertainty and may inform future model development and planning activities.
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Source:Environmental Modelling & Software, 89, 144-158
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DOI:
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ISSN:1364-8152
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Rights Information:Accepted Manuscript
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