Abstract

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

Highlights

  • One of the most prominent impacts of climate warming has been a shift from snow to rain in temperate and cold regions across the globe (e.g., Knowles et al, 2006; Trenberth, 2011), a trend that is expected to continue with further increases in air temperature (Bintanja and Andry, 2017; Klos et al, 2014; O’Gorman, 2014; Safeeq et al, 2015)

  • In terms of mean bias, the binary regression models, RegBi and RegTri, as well as the Ta1 threshold provided the best performance, with average values between 3.1 and 9.1 mm compared to observed snow water equivalent (SWE) and between 4.9 and 14.5 mm compared to observed snow depth

  • In this work we simulated seasonal snow cover evolution using the SNOWPACK model forced with different permutations of five precipitation phase methods at 11 study stations spanning a climatic gradient from warm maritime to cold continental

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Summary

Introduction

One of the most prominent impacts of climate warming has been a shift from snow to rain in temperate and cold regions across the globe (e.g., Knowles et al, 2006; Trenberth, 2011), a trend that is expected to continue with further increases in air temperature (Bintanja and Andry, 2017; Klos et al, 2014; O’Gorman, 2014; Safeeq et al, 2015). P. Molotch: Sensitivity of snow model output to precipitation phase uniform air temperature threshold or a range between two thresholds with a linear mix of liquid and solid precipitation in between. Recent work has called this simplistic treatment of precipitation phase into question (Feiccabrino et al, 2015; Harpold et al, 2017b) because of the pronounced spatial variability in rain–snow partitioning (Jennings et al, 2018b; Ye et al, 2013). Complicating matters is the fact that precipitation phase is rarely observed in mountain regions on a continuous basis over long timescales

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