scholarly journals Multi-sensor analysis of monthly gridded snow precipitation on alpine glaciers

2021 ◽  
Vol 18 ◽  
pp. 7-20
Author(s):  
Rebecca Gugerli ◽  
Matteo Guidicelli ◽  
Marco Gabella ◽  
Matthias Huss ◽  
Nadine Salzmann
Keyword(s):  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Remotely Sensed ◽  
High Elevation ◽  
High Mountain ◽  
Data Set ◽  
Novel Approach ◽  
Spatially Distributed ◽  
Precipitation Estimates ◽  
Snow Water

Abstract. Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.

scholarly journals Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

2019 ◽  
Vol 13 (12) ◽  
pp. 3413-3434 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
High Mountain ◽  
Alpine Glacier ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

scholarly journals Investigating the Variability of High-Elevation Seasonal Orographic Snowfall Enhancement and Its Drivers across Sierra Nevada, California

2018 ◽  
Vol 19 (1) ◽  
pp. 47-67 ◽  
Author(s):  
Laurie S. Huning ◽  
Steven A. Margulis
Keyword(s):  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Mountain Range ◽  
Distributed Data ◽  
Mountainous Regions ◽  
Average Maximum ◽  
Spatially Distributed ◽  
Orographic Enhancement ◽  
Snow Water

Abstract While orographically driven snowfall is known to be important in mountainous regions, a complete understanding of orographic enhancement from the basin to the mountain range scale is often inhibited by limited distributed data and spatial and/or temporal resolutions. A novel, 90-m spatially distributed snow water equivalent (SWE) reanalysis was used to overcome these limitations. Leveraging this SWE information, the interannual variability of orographic gradients in cumulative snowfall (CS) was investigated over 14 windward (western) basins in the Sierra Nevada in California from water years 1985 to 2015. Previous studies have not provided a detailed multidecadal climatology of orographic CS gradients or compared wet-year and dry-year orographic CS patterns, distributions, and gradients across an entire mountain range. The magnitude of seasonal CS gradients range from over 15 cm SWE per 100-m elevation to under 1 cm per 100 m with a 31-yr average of 6.1 cm per 100 m below ~2500 m in the western basins. The 31-yr average CS gradients generally decrease in higher elevation zones across the western basins and become negative at the highest elevations. On average, integrated vapor transport and zonal winds at 700 hPa are larger during wet years, leading to higher orographically driven CS gradients across the Sierra Nevada than in dry years. Below ~2500 m, wet years yield greater enhancement (relative to dry years) by factors of approximately 2 and 3 in the northwestern and southwestern basins, respectively. Overall, the western Sierra Nevada experiences about twice as much orographic enhancement during wet years as in dry years below the elevation corresponding to the 31-yr average maximum CS.

scholarly journals Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

2017 ◽  
Author(s):  
Edward H. Bair ◽  
Andre Abreu Calfa ◽  
Karl Rittger ◽  
Jeff Dozier
Keyword(s):  
Machine Learning ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Machine Learning Techniques ◽  
Microwave Brightness Temperature ◽  
Time Estimates ◽  
Covered Area ◽  
Spatially Distributed ◽  
Snow Water ◽  
Feed Forward Neural Networks

Abstract. In many mountains, snowmelt provides most of the runoff. In Afghanistan, few ground-based measurements of the snow resource exist. Operational estimates use imagery from optical and passive microwave sensors, but with their limitations. An accurate approach reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance, but reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE early in the snowmelt season, we consider physiographic and remotely-sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that the methods can accurately estimate SWE during the snow season in remote mountains.

scholarly journals Snow observations in Mount-Lebanon (2011–2016)

2017 ◽  
Author(s):  
Abbas Fayad ◽  
Simon Gascoin ◽  
Ghaleb Faour ◽  
Pascal Fanise ◽  
Laurent Drapeau ◽  
...  
Keyword(s):  
Snow Cover ◽  
Mediterranean Climate ◽  
Snow Water Equivalent ◽  
Current Model ◽  
High Mountain ◽  
Snow Density ◽  
Data Set ◽  
Study Region ◽  
Snow Cover Extent ◽  
Snow Water

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 km2 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 minute intervals for the snow seasons (November–June) between 2011 and 2016 for MZA and 2014–2016 for CED and LAQ. Precipitation data was 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 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 (2011–2016). 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 data set bears the potential to greatly improve the quantification of snowmelt and mountain hydrometeorological processes in this data-scarce region of the Eastern Mediterranean. The doi for the data is: 10.5281/zenodo.291405.

scholarly journals Brief communication: Application of a muonic cosmic ray snow gauge to monitor the snow water equivalent on alpine glaciers

2021 ◽  
Author(s):  
Rebecca Gugerli ◽  
Darin Desilets ◽  
Nadine Salzmann
Keyword(s):  
Snow Water Equivalent ◽  
Winter Season ◽  
Cosmic Ray ◽  
Promising Method ◽  
Harsh Environments ◽  
High Mountain ◽  
Mountain Regions ◽  
Mountain Environments ◽  
Alpine Glaciers ◽  
Snow Water

Abstract. Monitoring the snow water equivalent (SWE) in the harsh environments of high mountain regions is a challenge. Here, we explore the use of muon counts to infer SWE. We deployed a muonic cosmic ray snow gauge (µ-CRSG) on a Swiss glacier during the snow rich winter season 2020/21 (almost 2000 mm w.e.). The µ-CRSG measurements agree well with measurements by a neutronic cosmic ray snow gauge (n-CRSG) and they lie within the uncertainty of manual observations. We conclude that the µ-CRSG is a highly promising method to monitor SWE in remote high mountain environments with several advantages over the n-CRSG.

scholarly journals Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

2018 ◽  
Vol 12 (5) ◽  
pp. 1579-1594 ◽  
Author(s):  
Edward H. Bair ◽  
Andre Abreu Calfa ◽  
Karl Rittger ◽  
Jeff Dozier
Keyword(s):  
Machine Learning ◽  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Machine Learning Techniques ◽  
Microwave Brightness Temperature ◽  
Time Estimates ◽  
Covered Area ◽  
Spatially Distributed ◽  
Snow Water

Abstract. In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques – bagged regression trees and feed-forward neural networks – produced similar mean results, with 0–14 % bias and 46–48 mm RMSE on average. Nash–Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

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