Rain or snow: hydrologic processes, observations, prediction, and research needs

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

doi:10.5194/hess-21-1-2017

Cited by 159 publications

(125 citation statements)

References 126 publications

(172 reference statements)

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“…We used a simple linear transition precipitation partitioning scheme. Harpold et al (2017) outlines a range of more sophisticated methods and these could be employed in future work. Additional uncertainties are the neglect of processes other than melt.…”

Section: Precipitation Pattern and Amountmentioning

confidence: 99%

Modeling Winter Precipitation Over the Juneau Icefield, Alaska, Using a Linear Model of Orographic Precipitation

et al. 2018

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Assessing and modeling precipitation in mountainous areas remains a major challenge in glacier mass balance modeling. Observations are typically scarce and reanalysis data and similar climate products are too coarse to accurately capture orographic effects. Here we use the linear theory of orographic precipitation model (LT model) to downscale winter precipitation from the Weather Research and Forecasting Model (WRF) over the Juneau Icefield, one of the largest ice masses in North America (>4,000 km 2 ), for the period 1979-2013. The LT model is physically-based yet computationally efficient, combining airflow dynamics and simple cloud microphysics. The resulting 1 km resolution precipitation fields show substantially reduced precipitation on the northeastern portion of the icefield compared to the southwestern side, a pattern that is not well captured in the coarse resolution (20 km) WRF data. Net snow accumulation derived from the LT model precipitation agrees well with point observations across the icefield. To investigate the robustness of the LT model results, we perform a series of sensitivity experiments varying hydrometeor fall speeds, the horizontal resolution of the underlying grid, and the source of the meteorological forcing data. The resulting normalized spatial precipitation pattern is similar for all sensitivity experiments, but local precipitation amounts vary strongly, with greatest sensitivity to variations in snow fall speed. Results indicate that the LT model has great potential to provide improved spatial patterns of winter precipitation for glacier mass balance modeling purposes in complex terrain, but ground observations are necessary to constrain model parameters to match total amounts.

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Section: Precipitation Pattern and Amountmentioning

confidence: 99%

Modeling Winter Precipitation Over the Juneau Icefield, Alaska, Using a Linear Model of Orographic Precipitation

et al. 2018

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“…For example, precipitation shifts from snow to rain are anticipated to occur across several regions, such as the North American Rockies, where snowpacks have traditionally dominated water resources (Bales et al 2006;Barnett, Adam, and Lettenmaier 2005). Harpold et al (2017) recently highlighted how these changes in precipitation phases could alter streamflow timing and amounts (Berghuijs, Woods, and Hrachowitz 2014;Jepsen et al 2016;Luce and Holden 2009), increase rain-on-snow flooding (McCabe, Clark, and Hay 2007), and impact the ability to accurately forecast water supplies (Milly et al 2008). Our understanding of how shifts in precipitation phases and how variability in frozen-ground patterns (Campbell et al 2010) impact not only streamflow amounts but also stream-water chemistry and its sources is, unfortunately, still rather limited .…”

Section: Introductionmentioning

confidence: 99%

Lessons learned from monitoring the stable water isotopic variability in precipitation and streamflow across a snow-dominated subarctic catchment

Lyon

Ploum

Velde³

et al. 2018

Arctic, Antarctic, and Alpine Research

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Lessons learned from monitoring the stable water isotopic variability in precipitation and streamflow across a snow-dominated subarctic catchment Arctic, Antarctic and Alpine research, 50(1): e1454778 https://doi.org/10. 1080/15230430.2018.1454778 Access to the published version may require subscription. N.B. When citing this work, cite the original published paper. This empirical study explores shifts in stable water isotopic composition for a subarctic catchment located in northern Sweden as it transitions from spring freshet to summer low flows. Relative changes in the isotopic composition of streamflow across the main catchment and fifteen nested subcatchments are characterized in relation to the isotopic composition of precipitation. With our sampling campaign, we explore the variability in stream-water isotopic composition that originates from precipitation as the input shifts from snow to rain and as landscape flow pathways change across scales. The isotopic similarity of high-elevation snowpack water and early season rainfall water seen through our sampling scheme made it difficult to truly isolate the impact of seasonal precipitation phase change on stream-water isotopic response. This highlights the need to explicitly consider the complexity of arctic and alpine landscapes when designing sampling strategies to characterize hydrological variability via stable water isotopes. Results show a potential influence of evaporation and source water mixing both spatially (variations with elevation) and temporally (variations from post-freshet to summer flows) on the composition of stream water across Miellajokka. As such, the data collected in this empirical study allow for initial conceptualization of the relative importance of, for example, hydrological connectivity within this mountainous, subarctic landscape. ARTICLE HISTORY

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“…For this study, we set z 0 = 0.01 for the time period between December and March and z 0 = 0.03 for the rest. Different approaches for precipitation phase determination have been summarized in Harpold et al (2017). Most common methods rely on the use of mean air temperature (T a ) thresholds (e.g., Marks et al, 2013).…”

Section: A Fayad Et Al: Snow Observations In Mount Lebanonmentioning

confidence: 99%

Snow observations in Mount Lebanon (2011–2016)

Fayad

Gascoin

Faour

et al. 2017

Earth Syst. Sci. Data

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Abstract. We present a unique meteorological and snow observational dataset in Mount Lebanon, a mountainous region with a Mediterranean climate, where snowmelt is an essential water resource. The study region covers the recharge area of three karstic river basins (total area of 1092 km 2 and an elevation up to 3088 m). The dataset consists of (1) continuous meteorological and snow height observations, (2) snowpack field measurements, and (3) medium-resolution satellite snow cover data. The continuous meteorological measurements at three automatic weather stations (MZA, 2296 m; LAQ, 1840 m; and CED, 2834 m a.s.l.) include surface air temperature and humidity, precipitation, wind speed and direction, incoming and reflected shortwave irradiance, and snow height, at 30 min intervals for the snow seasons (November-June) between 2011 and 2016 for MZA and between 2014 and 2016 for CED and LAQ. Precipitation data were filtered and corrected for Geonor undercatch. Observations of snow height (HS), snow water equivalent, and snow density were collected at 30 snow courses located at elevations between 1300 and 2900 m a.s.l. during the two snow seasons of 2014-2016 with an average revisit time of 11 days. Daily gap-free snow cover extent (SCA) and snow cover duration (SCD) maps derived from MODIS snow products are provided for the same period (2011)(2012)(2013)(2014)(2015)(2016). We used the dataset to characterize mean snow height, snow water equivalent (SWE), and density for the first time in Mount Lebanon. Snow seasonal variability was characterized with high HS and SWE variance and a relatively high snow density mean equal to 467 kg m −3 . We find that the relationship between snow depth and snow density is specific to the Mediterranean climate. The current model explained 34 % of the variability in the entire dataset (all regions between 1300 and 2900 m a.s.l.) and 62 % for high mountain regions (elevation 2200-2900 m a.s.l.). The dataset is suitable for the investigation of snow dynamics and for the forcing and validation of energy balance models. Therefore, this dataset bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the eastern Mediterranean.

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Rain or snow: hydrologic processes, observations, prediction, and research needs

Cited by 159 publications

References 126 publications

Modeling Winter Precipitation Over the Juneau Icefield, Alaska, Using a Linear Model of Orographic Precipitation

Modeling Winter Precipitation Over the Juneau Icefield, Alaska, Using a Linear Model of Orographic Precipitation

Lessons learned from monitoring the stable water isotopic variability in precipitation and streamflow across a snow-dominated subarctic catchment

Snow observations in Mount Lebanon (2011–2016)

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