Estimating the snow water equivalent on a glacierized high elevation site (Forni Glacier, Italy)

Abstract. We present and compare 11 years of snow data (snow depth and snow water equivalent, SWE) measured by an automatic weather station (AWS) and corroborated by data from field campaigns on the Forni Glacier in Italy. The aim of the analysis is to estimate the SWE of new snowfall and the annual SWE peak based on the average density of the new snow at the site (corresponding to the snowfall during the standard observation period of 24 h) and automated snow depth measurements. The results indicate that the daily SR50 sonic ranger measurements and the available snow pit data can be used to estimate the mean new snow density value at the site, with an error of ±6 kg m−3. Once the new snow density is known, the sonic ranger makes it possible to derive SWE values with an RMSE of 45 mm water equivalent (if compared with snow pillow measurements), which turns out to be about 8 % of the total SWE yearly average. Therefore, the methodology we present is interesting for remote locations such as glaciers or high alpine regions, as it makes it possible to estimate the total SWE using a relatively inexpensive, low-power, low-maintenance, and reliable instrument such as the sonic ranger.

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Snow data intercomparison on remote and glacierized high elevation areas (Forni Glacier, Italy)

10.5194/tc-2017-124 ◽

2017 ◽

Author(s):

Antonella Senese ◽

Maurizio Maugeri ◽

Eraldo Meraldi ◽

Giampietro Verza ◽

Roberto Sergio Azzoni ◽

...

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

High Elevation ◽

Mean Value ◽

Snow Density ◽

Depth Data ◽

Fresh Snow ◽

Measuring Site ◽

Snow Water ◽

The Mean

Abstract. We present and compare 11 years of snow data (snowfall, snow depth and snow water equivalent (SWE)) measured by an Automatic Weather Station and by some field campaigns on the Forni Glacier. The data have been acquired by means of (i) a Campbell SR50 sonic ranger from October 2005 (snow depth data), (ii) manual snow pits from January 2006 (snow depth and SWE data), (iii) a Sommer USH8 sonic ranger from May 2014 (snow depth data), (iv) a Park Mechanical SS-6048 snow pillow from May 2014 (SWE data), (v) a manual snow weighting tube (Enel-Valtecne©) from May 2014 (snow depth and SWE data). The aim of the analyses is to assess the mean value of fresh snow density and the most appropriate method to evaluate SWE for this measuring site. The results indicate that the daily SR50 sonic ranger measures allow a rather good estimation of the SWE, and the provided snow pit data are available for defining the site mean value of fresh snow density. For the Forni Glacier measuring site, this value turned out to be 140 kg m−3. The SWE derived from sonic ranger data is rather sensitive to this value: a change in fresh snow density of 20 kg m−3 causes a mean variation in SWE of ±0.093 m w.e. for each hydrological year, ranging from ±0.050 m w.e. to ±0.115 m w.e.

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Improved Northern Hemisphere Snow Water Equivalent product from passive microwave remote sensing and in situ data

10.5194/egusphere-egu21-1325 ◽

2021 ◽

Author(s):

Colleen Mortimer ◽

Lawrence Mudryk ◽

Chris Derksen ◽

Kari Luojus ◽

Pinja Venalainen ◽

...

Keyword(s):

Spatial Resolution ◽

Snow Depth ◽

Snow Water Equivalent ◽

Microwave Remote Sensing ◽

Passive Microwave ◽

Snow Density ◽

Snow Season ◽

Snow Water ◽

Snow Mass

The European Space Agency Snow CCI+ project provides global homogenized long time series of daily snow extent and snow water equivalent (SWE). The Snow CCI SWE product is built on the Finish Meteorological Institute's GlobSnow algorithm, which combines passive microwave data with in situ snow depth information to estimate SWE. The CCI SWE product improves upon previous versions of GlobSnow through targeted changes to the spatial resolution, ancillary data, and snow density parameterization.Previous GlobSnow SWE products used a constant snow density of 0.24 kg m-3 to convert snow depth to SWE. The CCI SWE product applies spatially and temporally varying density fields, derived by krigging in situ snow density information from historical snow transects to correct biases in estimated SWE. Grid spacing was improved from 25 km to 12.5 km by applying an enhanced spatial resolution microwave brightness temperature dataset. We assess step-wise how each of these targeted changes acts to improve or worsen the product by evaluating with snow transect measurements and comparing hemispheric snow mass and trend differences.Together, when compared to GlobSnow v3, these changes improved RMSE by ~5 cm and correlation by ~0.1 against a suite of snow transect measurements from Canada, Finland, and Russia. Although the hemispheric snow mass anomalies of CCI SWE and GlobSnow v3 are similar, there are sizeable differences in the climatological SWE, most notably a one month delay in the timing of peak SWE and lower SWE during the accumulation season. These shifts were expected because the variable snow density is lower than the former fixed value of 0.24 kg m-3 early in the snow season, but then increases over the course of the snow season. We also examine intermediate products to determine the relative improvements attributable solely to the increased spatial resolution versus changes due to the snow density parameterizations. Such systematic evaluations are critical to directing future product development.

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Spatiotemporal Characteristics of Snowpack Density in the Mountainous Regions of the Western United States

Journal of Hydrometeorology ◽

10.1175/2008jhm981.1 ◽

2008 ◽

Vol 9 (6) ◽

pp. 1416-1426 ◽

Cited By ~ 45

Author(s):

Naoki Mizukami ◽

Sanja Perica

Keyword(s):

United States ◽

Snow Depth ◽

Snow Water Equivalent ◽

Winter Season ◽

Western United States ◽

Densification Rate ◽

Snow Density ◽

Mountainous Regions ◽

Spatiotemporal Characteristics ◽

Snow Water

Abstract Snow density is calculated as a ratio of snow water equivalent to snow depth. Until the late 1990s, there were no continuous simultaneous measurements of snow water equivalent and snow depth covering large areas. Because of that, spatiotemporal characteristics of snowpack density could not be well described. Since then, the Natural Resources Conservation Service (NRCS) has been collecting both types of data daily throughout the winter season at snowpack telemetry (SNOTEL) sites located in the mountainous areas of the western United States. This new dataset provided an opportunity to examine the spatiotemporal characteristics of snowpack density. The analysis of approximately seven years of data showed that at a given location and throughout the winter season, year-to-year snowpack density changes are significantly smaller than corresponding snow depth and snow water equivalent changes. As a result, reliable climatological estimates of snow density could be obtained from relatively short records. Snow density magnitudes and densification rates (i.e., rates at which snow densities change in time) were found to be location dependent. During early and midwinter, the densification rate is correlated with density. Starting in early or mid-March, however, snowpack density increases by approximately 2.0 kg m−3 day−1 regardless of location. Cluster analysis was used to obtain qualitative information on spatial patterns of snowpack density and densification rates. Four clusters were identified, each with a distinct density magnitude and densification rate. The most significant physiographic factor that discriminates between clusters was proximity to a large water body. Within individual mountain ranges, snowpack density characteristics were primarily dependent on elevation.

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What drives basin scale spatial variability of snow water equivalent during two extreme years?

The Cryosphere Discussions ◽

10.5194/tcd-7-2943-2013 ◽

2013 ◽

Vol 7 (3) ◽

pp. 2943-2977

Author(s):

G. A. Sexstone ◽

S. R. Fassnacht

Keyword(s):

Linear Regression ◽

Spatial Variability ◽

Multiple Linear Regression ◽

Snow Depth ◽

Snow Water Equivalent ◽

Density Model ◽

Snow Density ◽

Depth Measurement ◽

Basin Scale ◽

Snow Water

Abstract. This study uses a combination of field measurements and Natural Resource Conservation Service (NRCS) operational snow data to understand the drivers of snow water equivalent (SWE) spatial variability at the basin scale. Historic snow course snowpack density observations were analyzed within a multiple linear regression snow density model to estimate SWE directly from snow depth measurements. Snow surveys were completed on or about 1 April 2011 and 2012 and combined with NRCS operational measurements to investigate the spatial variability of SWE. Bivariate relations and multiple linear regression models were developed to understand the relation of SWE with terrain and canopy variables (derived using a geographic information system (GIS)). Calculation of SWE directly from snow depth measurement using the snow density model has strong statistical performance and model validation suggests the model is transferable to independent data within the bounds of the original dataset. This pathway of estimating SWE directly from snow depth measurement is useful when evaluating snowpack properties at the basin scale, where many time consuming measurements of SWE are often not feasible. During both water year (WY) 2011 and 2012, elevation and location (UTM Easting and UTM Northing) were the most important model variables, suggesting that orographic precipitation and storm track patterns are likely consistent drivers of basin scale SWE variability. Terrain characteristics, such as slope, aspect, and curvature, were also shown to be important variables, but to a lesser extent at the scale of interest.

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Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0007.1 ◽

2018 ◽

Vol 19 (11) ◽

pp. 1777-1791 ◽

Cited By ~ 12

Author(s):

Nicholas Dawson ◽

Patrick Broxton ◽

Xubin Zeng

Keyword(s):

United States ◽

Remote Sensing ◽

Snow Cover ◽

Snow Depth ◽

Snow Water Equivalent ◽

Remote Sensing Data ◽

Sensing Data ◽

Snow Cover Extent ◽

Snow Water ◽

The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

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Characteristics of avalanching: Kootenay Pass, British Columbia, Canada

Journal of Glaciology ◽

10.1017/s0022143000015975 ◽

1993 ◽

Vol 39 (132) ◽

pp. 316-322 ◽

Cited By ~ 20

Author(s):

D. M. McClung ◽

John Tweedy

Keyword(s):

British Columbia ◽

Wind Speed ◽

Correlation Analysis ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snow Density ◽

Precipitation Total ◽

Snow Water ◽

Individual Variables

AbstractIndividual variables found to be significant from a correlation analysis are analyzed as a function of probability of avalanching for data from Kootenay Pass, British Columbia. The analysis is compared with a similar study for data from Alta, Utah, U.S.A. The results show that the variable significance is very similar for the two areas. Primary variables include: snowfall rate, weight of new snow, water equivalent of new precipitation, total storm snow and new snow depth. Secondary variables include wind speed and direction, and new-snow density.

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Weather SDM: estimating snow density with high precision using snow depth and local climate

Hydrology Research ◽

10.2166/nh.2015.059 ◽

2015 ◽

Vol 46 (4) ◽

pp. 494-506 ◽

Cited By ~ 3

Author(s):

Oddbjørn Bruland ◽

Åshild Færevåg ◽

Ingelin Steinsland ◽

Glen E. Liston ◽

Knut Sand

Keyword(s):

High Precision ◽

Snow Depth ◽

Prediction Error ◽

Snow Water Equivalent ◽

Snow Density ◽

Final Model ◽

Local Climate ◽

Density Measurements ◽

Snow Water ◽

Seasonal Weather

Snow density is an important measure in hydrology used to convert snow depth to the snow water equivalent (SWE). A model developed by Sturm, Tara and Liston predicts the snow density by using snow depth, the snow age and a snow class defined by the location. In this work this model is extended to include location and seasonal weather-specific variables. The model is named Weather Snow Density Model (Weather SDM). A Bayesian framework is chosen, and the model is fitted to and tested for 4,040 Norwegian snow depth and densities measurements between 1998 and 2011. The final model improved the snow density predictions for the Norwegian data compared to the model of Sturm by up to 50%. Further, the Weather SDM is extended to utilize local year-specific snow density observations (Weather&ObsDensity SDM). This reduced the prediction error an additional 16%, indicating a significant improvement when utilizing information provided by annual snow density measurements.

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ESTIMATING THE DISTRIBUTION OF SNOW WATER EQUIVALENT, SNOW DEPTH AND SNOW DENSITY IN JAPAN

PROCEEDINGS OF HYDRAULIC ENGINEERING ◽

10.2208/prohe.49.301 ◽

2005 ◽

Vol 49 ◽

pp. 301-306 ◽

Cited By ~ 1

Author(s):

Hirokazu IZUMI ◽

So KAZAMA ◽

Takehiro TOTSUKA ◽

Masaki SAWAMOTO

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Snow Density ◽

Snow Water

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Investigating ANN architectures and training to estimate snow water equivalent from snow depth

Hydrology and Earth System Sciences ◽

10.5194/hess-25-3017-2021 ◽

2021 ◽

Vol 25 (6) ◽

pp. 3017-3040

Author(s):

Konstantin F. F. Ntokas ◽

Jean Odry ◽

Marie-Amélie Boucher ◽

Camille Garnaud

Keyword(s):

Snow Depth ◽

Regression Models ◽

Snow Water Equivalent ◽

Structural Characteristics ◽

Large Data ◽

Multilayer Perceptrons ◽

Snow Density ◽

General Applicability ◽

Data Set ◽

Snow Water

Abstract. Canada's water cycle is driven mainly by snowmelt. Snow water equivalent (SWE) is the snow-related variable that is most commonly used in hydrology, as it expresses the total quantity of water (solid and liquid) stored in the snowpack. Measurements of SWE are, however, expensive and not continuously accessible in real time. This motivates a search for alternative ways of estimating SWE from measurements that are more widely available and continuous over time. SWE can be calculated by multiplying snow depth by the bulk density of the snowpack. Regression models proposed in the literature first estimate snow density and then calculate SWE. More recently, a novel approach to this problem has been developed and is based on an ensemble of multilayer perceptrons (MLPs). Although this approach compared favorably with existing regression models, snow density values at the lower and higher ends of the range remained inaccurate. Here, we improve upon this recent method for determining SWE from snow depth. We show the general applicability of the method through the use of a large data set of 234 779 snow depth–density–SWE records from 2878 nonuniformly distributed sites across Canada. These data cover almost 4 decades of snowfall. First, it is shown that the direct estimation of SWE produces better results than the estimation of snow density, followed by the calculation of SWE. Second, testing several artificial neural network (ANN) structural characteristics improves estimates of SWE. Optimizing MLP parameters separately for each snow climate class gives a greater representation of the geophysical diversity of snow. Furthermore, the uncertainty of snow depth measurements is included for a more realistic estimation. A comparison with commonly used regression models reveals that the ensemble of MLPs proposed here leads to noticeably more accurate estimates of SWE. This study thus shows that delving deeper into ANN theory helps improve SWE estimation.

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Snowpack Changes in the Hindu Kush–Karakoram–Himalaya from CMIP5 Global Climate Models

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0196.1 ◽

2014 ◽

Vol 15 (6) ◽

pp. 2293-2313 ◽

Cited By ~ 22

Author(s):

Silvia Terzago ◽

Jost von Hardenberg ◽

Elisa Palazzi ◽

Antonello Provenzale

Keyword(s):

Snow Depth ◽

Climate Models ◽

Snow Water Equivalent ◽

Global Climate ◽

Control Period ◽

High Elevation ◽

Global Climate Models ◽

Spatial Average ◽

Hindu Kush ◽

Snow Water

Abstract The Hindu Kush, Karakoram, and Himalaya (HKKH) mountain ranges feed the most important Asian river systems, providing water to about 1.5 billion people. As a consequence, changes in snow dynamics in this area could severely impact water availability for downstream populations. Despite their importance, the amount, spatial distribution, and seasonality of snow in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high-elevation area. This work considers global climate models (GCM) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and analyzes how they represent current and future snowpack in the HKKH region in terms of snow depth and snow water equivalent. It is found that models with high spatial resolution (up to 1.25°) simulate a spatial pattern of the winter snowpack in greater agreement with each other, with observations, with reanalysis datasets, and with the orographic features of the region, compared to most lower-resolution models. The seasonal cycle of snow depth displays a unimodal regime, with a maximum in February–March and almost complete melting in summer. The models generally indicate thicker [in Hindu Kush–Karakoram (HKK)] or comparable (in the Himalayas) snow depth and higher snow water equivalent compared to the reanalyses for the control period 1980–2005. Future projections, evaluated in terms of the ensemble mean of GCM simulations, indicate a significant reduction in the spatial average of snow depth over the HKK and an even stronger decrease in the Himalayas, where a reduction between 25% and 50% is expected by the end of the twenty-first century.

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