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Extreme-Value Statistics for Snowpack Water Equivalent in the Northeastern United States Using the Cooperative Observer Network

Daniel S. WilksDepartment of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York

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Megan McKayDepartment of Soil, Crop and Atmospheric Sciences, Cornell University, Ithaca, New York

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Abstract

A procedure is developed to estimate extreme-value statistics for snowpack water equivalent (SWE) using historical snow depth measurements at cooperative observer stations in the northeastern United States. The method specifies “pseudodensities” that allow transformation of the statistical distribution of the deepest snow-packs to the distribution of extreme SWE values at a location. These pseudodensities vary according to characteristics of the local snow climatology, the geographic location, and the values of the snow depth data themselves. The performance of a suite of theoretical probability distributions for representing the resulting distributions of extreme SWE is also investigated, and it is concluded that five-parameter Wakeby distributions provide the best representations for the region overall. The results suggest that previous estimates for extreme SWE values may underestimate the wettest snowpacks at northern and/or higher-elevation locations, but may yield overestimates in warmer portions of the northeastern United States.

Abstract

A procedure is developed to estimate extreme-value statistics for snowpack water equivalent (SWE) using historical snow depth measurements at cooperative observer stations in the northeastern United States. The method specifies “pseudodensities” that allow transformation of the statistical distribution of the deepest snow-packs to the distribution of extreme SWE values at a location. These pseudodensities vary according to characteristics of the local snow climatology, the geographic location, and the values of the snow depth data themselves. The performance of a suite of theoretical probability distributions for representing the resulting distributions of extreme SWE is also investigated, and it is concluded that five-parameter Wakeby distributions provide the best representations for the region overall. The results suggest that previous estimates for extreme SWE values may underestimate the wettest snowpacks at northern and/or higher-elevation locations, but may yield overestimates in warmer portions of the northeastern United States.

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