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Abstract Extreme floods and landslides in high‐latitude watersheds have been associated with rain‐on‐snow (ROS) events. Yet, the risks of changing precipitation phases on a declining snowpack under a warming climate remain unclear. Normalizing the total annual duration of ROS with that of the seasonal snowpack, the ERA5 data (1941–2023) show that the frequency of high‐runoff ROS events is a characteristic feature of high‐latitude coastal zones, particularly over the coasts of south‐central Alaska and southern Newfoundland. Total rainfall accumulation per seasonal snowpack duration has increased across western mountain ranges, with the Olympic Mountains experiencing more than 40 mm of additional rainfall over the snowpack in the past eight decades, followed by the Sierra Nevada. These trends could drive an 8% increase in rainfall extremes (e.g., more than 10 mm for 6 hr storm with a 15‐year return period), highlighting the need for resilient flood control systems in high‐latitude coastal cities.
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The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient
Abstract. A critical component of hydrologic modeling in cold andtemperate regions is partitioning precipitation into snow and rain, yetlittle is known about how uncertainty in precipitation phase propagates intovariability in simulated snow accumulation and melt. Given the wide varietyof methods for distinguishing between snow and rain, it is imperative toevaluate the sensitivity of snowpack model output to precipitation phasedetermination methods, especially considering the potential of snow-to-rainshifts associated with climate warming to fundamentally change the hydrologyof snow-dominated areas. To address these needs we quantified thesensitivity of simulated snow accumulation and melt to rain–snowpartitioning methods at sites in the western United States using theSNOWPACK model without the canopy module activated. The methods in thisstudy included different permutations of air, wet bulb and dew pointtemperature thresholds, air temperature ranges, and binary logisticregression models. Compared to observations of snow depth and snow water equivalent (SWE), thebinary logistic regression models produced the lowest mean biases, whilehigh and low air temperature thresholds tended to overpredict andunderpredict snow accumulation, respectively. Relative differences betweenthe minimum and maximum annual snowfall fractions predicted by the differentmethods sometimes exceeded 100 % at elevations less than 2000 m in theOregon Cascades and California's Sierra Nevada. This led to rangesin annual peak SWE typically greater than 200 mm,exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelttiming 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 insensitiveto the choice of a precipitation phase method, with average ranges in annualsnowfall fraction, peak SWE, snowmelt timing, and snow cover duration of lessthan 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmeltrate were typically less than 4 mm d−1 and exhibited a smallrelationship to seasonal climate. Overall, sites with a greater proportionof precipitation falling at air temperatures between 0 and4 ∘C exhibited the greatest sensitivity to method selection,suggesting that the identification and use of an optimal precipitation phasemethod is most important at the warmer fringes of the seasonal snow zone.
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- Award ID(s):
- 1637686
- PAR ID:
- 10126821
- Date Published:
- Journal Name:
- Hydrology and Earth System Sciences
- Volume:
- 23
- Issue:
- 9
- ISSN:
- 1607-7938
- Page Range / eLocation ID:
- 3765 to 3786
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/hess-23-3765-2019
