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Analytical model of seasonal climate impacts on snow hydrology: Continuous snowpacks

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)1465-1481
Number of pages17
JournalAdvances in Water Resources
Volume32
Issue number10
DOIs
DatePublished - Oct 2009

Abstract

We formulate and solve an analytical model of seasonal snowpack dynamics, by assuming a simple temperature index model for the snowpack, driven by purely seasonal climate forcing. Three dimensionless variables control the modeled system: one to indicate the temperature regime, one for the seasonality of both temperature and precipitation, and one for the mean precipitation rate relative to a characteristic melt rate. The purpose of the model is to provide insight into the relative roles of the mean and seasonality of temperature, the mean and seasonality of precipitation, and the melt factor, in controlling snow climatology.

The model can be used to make broad-scale predictions of the climatology of seasonal snow water storage, and its sensitivity to climate. Particular variables of interest include the maximum seasonal snow Storage, the start and end of the snow accumulation period, and the time of year at which the snowpack is completely melted. The model makes useful uncalibrated predictions at six widely separated sites in the western USA which have a continuous seasonal snowpack.

The connections between the model and a widely-used snow classification of Sturm et al. are briefly explored. Limitations of the model are discussed, extensions to the model are foreshadowed, and an example is given of a global application. If further testing demonstrates that the model gives adequate estimates of seasonal snowpack response for a wider range of locations, we might envisage applications of this approach to (i) definition of hydrological similarity indices in snow-dominated regions (ii) interpretation of output from complex simulation models covering a wide range of environments (iii) screening-level analyses of sensitivity to climate variations (iv) low-dimensional modeling where there are limited data or computation resources or technical expertise. (C) 2009 Elsevier Ltd. All rights reserved.

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