Rain or Snow: Hydrologic Processes, Observations, Prediction, and Research Needs

Harpold, Adrian A.; Kaplan, Michael; Klos, P. Zion; Link, Timothy E.; McNamara, J. P.; Rajagopal, S.; Schumer, R.; Steele, C. M.

doi:10.5194/hess-2016-436

Cited by 32 publications

(57 citation statements)

References 95 publications

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“…In a warm maritime climate such as the Pacific Northwest, generally lower SWE model skills can be attributed to two causes. Greater sensitivity to errors associated with the temperature‐only precipitation partitioning method (Harpold, Kaplan, et al, ; Jennings et al, ; Wayand et al, ). Improvement can be made by incorporating wet bulb temperature, dew point temperature, or relative humidity into the partitioning method (Ding et al, ; Harpold, Rajagopal, et al, ; Jennings et al, ; Marks et al, ). Greater sensitivity to errors in model simulated energy balances (Essery et al, ; Lapo et al, ).…”

Section: Resultsmentioning

confidence: 99%

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

et al. 2019

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In snow‐dominated regions, a key source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, this work demonstrates that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy‐balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature‐only precipitation partitioning approach. Poor model skill in high‐altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large‐domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results suggest that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher‐humidity Pacific Northwest to 4.0 °C in the colder, lower‐humidity Rocky Mountains.

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Section: Resultsmentioning

confidence: 99%

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

et al. 2019

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“…However, there are only a few studies that address the influence of precipitation phase on simulated hydrologic variables (Harder and Pomeroy, ). It is also important to recognize that precipitation phase can have a cascading effect on hydrologic processes such as interception, snow storage, surface runoff, infiltration, evapotranspiration and streamflow generation (Harpold et al, ). Snow storage delays the transfer of precipitation to runoff which affects peak flow magnitude and timing (Wang et al, ), hydrograph recession (Yarnell et al, ), summer baseflow magnitudes (Godsey et al, ; Safeeq et al, ) and spring snow‐melt dynamics (Harder and Pomeroy, ; Mizukami et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Precipitation‐phase prediction methods (PPMs) typically use near‐surface air temperature ( T a ) and can be broadly classified into four techniques: (a) static threshold, (b) linear transition, (c) minimum and maximum temperature, and (d) sigmoidal curve (Harpold et al, ). The static threshold defines a critical threshold for air temperature ( CT a ), above and below which all precipitation is rain and snow respectively.…”

Section: Introductionmentioning

confidence: 99%

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On distinguishing snowfall from rainfall using near‐surface atmospheric information: Comparative analysis, uncertainties and hydrologic importance

Behrangi

Yin²,

Rajagopal

et al. 2018

Quart J Royal Meteoro Soc

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The accurate estimation of precipitation phase has broad applications. In this study, we compared the skill of using various atmospheric variables and their combinations as predictors in accurately identifying surface precipitation phase, determined uncertainties associated with commonly used fixed temperature thresholds, and explored the sensitivity of hydrologic model output to uncertainty in precipitation phase using two case‐studies. The results suggest that among all single predictors, wet‐bulb temperature yields the highest skill score for determining precipitation phase and can reduce uncertainties due to regional differences, especially compared to the commonly used near‐surface air temperature. However, addition of good‐quality near‐surface wind speed measurement to dew‐point temperature and pressure showed slightly higher skill than wet‐bulb temperature. We showed that the scale mismatch between temperature from stations and gridded products can cause large uncertainties in determining precipitation phase, especially in regions with rugged topography. Such uncertainties need to be considered when the relationships developed based on station data are applied to remote‐sensing observations and model‐generated data to separate rain from snowfall. The sensitivity of hydrologic model outputs to uncertainty in precipitation phase delineation was also assessed over two major basins in California by modifying default near‐surface temperatures used in the Variable Infiltration Capacity (VIC) model. It was found that regional and scaling uncertainties in determining temperature thresholds can largely influence the accuracy of simulated downstream runoff and snow water equivalent (SWE) (e.g. up to 40% change in SWE for 2 °C shift in temperature threshold). Therefore, to reduce simulation uncertainties, it is important to improve rain–snow partitioning methods, consider regional variabilities in determining temperature thresholds, and perform the analysis at the highest possible resolutions to mitigate scale‐related uncertainties.

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“…Uncertainty in the rainsnow transition principally arises from predicting climate forcing and in particular temperature. However, the underlying phase prediction method and related model decisions and climate forcing data can also be important for the quality of precipitation 10 phase prediction (Harpold et al, 2017). Further complicating rain-snow transition mechanisms is storage or drainage of liquid water in the snowpacks (Lundquist et al, 2008;Marks et al, 2001).…”

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confidence: 99%

Now You See It Now You Don’t: A Case Study of Ephemeral Snowpacks in the Great Basin U.S.A.

Petersky

Harpold

2018

Preprint

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Ephemeral snowpacks, or those that routinely experience accumulation and ablation at the same time and persist for <60 days, are challenging to observe and model. Using 328 site years from the Great Basin, we show that ephemeral snowmelt delivers water earlier than seasonal snowmelt. For example, we found that day of peak soil moisture preceded day of last snowmelt in the Great Basin by 79 days for shallow soil moisture in ephemeral snowmelt compared to 5 days for seasonal snowmelt. To 5 understand Great Basin snow distribution, we used moderate resolution imaging spectroradiometer (MODIS) and Snow Data Assimilation System (SNODAS) data from water years 2005-2014 to map snow extent. During this time period snowpack was highly variable. The maximum seasonal snow cover was 64 % in 2010 and the minimum was 24 % in 2014. We found that elevation had a strong control on snow ephemerality, and nearly all snowpacks over 2500 m were seasonal. Snowpacks were more likely to be ephemeral on south facing slopes than north facing slopes at elevations above 2500 m. Additionally, we 10 used SNODAS-derived estimates of solid and liquid precipitation, melt, sublimation, and blowing snow sublimation to define snow ephemerality mechanisms. In warm years, the Great Basin shifts to ephemerally dominant as the rain-snow transition increases in elevation. Given that snow ephemerality is expected to increase as a consequence of climate change, we put forward several challenges and recommendations to bolster physics based modeling of ephemeral snow such as better metrics for snow ephemerality and more ground-based observations. 15 IntroductionSeasonal snowmelt supplies water to one-sixth of the world's population, which supports one-fourth of the global economy (Barnett et al., 2005;Sturm et al., 2017). Seasonal snowpack provides predictable melt timing and volumes in the spring, which influences streamflow timing, surface water and groundwater availability (Berghuijs et al., 2014;Jasechko et al., 2014;Stewart et al., 2005). Reliable spring snowmelt also provides a strong control on vegetation phenology and productivity in many 20 ecosystems (Parida and Buermann, 2014;Trujillo et al., 2012). Despite the importance of seasonal snow to water supplies, much of the world's snow is ephemeral, which means it melts and sublimates throughout the snow cover season instead of having one consistent period of snowmelt. Even small shifts from seasonal to ephemeral snowpack due to regional warming could disrupt 1 Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-749 Manuscript under review for journal Hydrol. Earth Syst. Sci. Discussion started: 8 January 2018 c Author(s) 2018. CC BY 4.0 License. snowmelt timing in ways that could alter summer productivity, soil temperature, and soil moisture regimes Harpold and Molotch, 2015;Jefferson, 2011;Parida and Buermann, 2014;Regonda et al., 2005;Stielstra et al., 2015;Trujillo et al., 2012). A shift from seasonal to ephemeral snowpacks will also have negative implications for the wi...

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Rain or Snow: Hydrologic Processes, Observations, Prediction, and Research Needs

Cited by 32 publications

References 95 publications

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

Regional Snow Parameters Estimation for Large‐Domain Hydrological Applications in the Western United States

On distinguishing snowfall from rainfall using near‐surface atmospheric information: Comparative analysis, uncertainties and hydrologic importance

Now You See It Now You Don’t: A Case Study of Ephemeral Snowpacks in the Great Basin U.S.A.

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