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

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.

Download Full-text

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

The Cryosphere ◽

10.5194/tc-12-1293-2018 ◽

2018 ◽

Vol 12 (4) ◽

pp. 1293-1306 ◽

Cited By ~ 7

Author(s):

Antonella Senese ◽

Maurizio Maugeri ◽

Eraldo Meraldi ◽

Gian Pietro Verza ◽

Roberto Sergio Azzoni ◽

...

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

High Elevation ◽

Average Density ◽

Snow Density ◽

Alpine Regions ◽

Yearly Average ◽

Snow Water ◽

The Mean ◽

Observation Period

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

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.

Download Full-text

Estimating Snow Water Equivalent Using Snow Depth Data and Climate Classes

Journal of Hydrometeorology ◽

10.1175/2010jhm1202.1 ◽

2010 ◽

Vol 11 (6) ◽

pp. 1380-1394 ◽

Cited By ~ 216

Author(s):

Matthew Sturm ◽

Brian Taras ◽

Glen E. Liston ◽

Chris Derksen ◽

Tobias Jonas ◽

...

Keyword(s):

United States ◽

Bulk Density ◽

Snow Depth ◽

Snow Water Equivalent ◽

Estimation Procedure ◽

The United States ◽

Practical Applications ◽

Continental Scale ◽

Depth Data ◽

Snow Water

Abstract In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

Download Full-text

Snow water equivalents exclusively from snow depths and their temporal changes: the Δsnow model

Hydrology and Earth System Sciences ◽

10.5194/hess-25-1165-2021 ◽

2021 ◽

Vol 25 (3) ◽

pp. 1165-1187

Author(s):

Michael Winkler ◽

Harald Schellander ◽

Stefanie Gruber

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Optimal Parameter ◽

R Package ◽

Model Parameters ◽

Major Advance ◽

Newtonian Viscosity ◽

Climatic Regions ◽

Depth Data ◽

Snow Water

Abstract. Reliable historical manual measurements of snow depths are available for many years, sometimes decades, across the globe, and increasingly snow depth data are also available from automatic stations and remote sensing platforms. In contrast, records of snow water equivalent (SWE) are sparse, which is significant as SWE is commonly the most important snowpack feature for hydrology, climatology, agriculture, natural hazards, and other fields. Existing methods of modeling SWE either rely on detailed meteorological forcing being available or are not intended to simulate individual SWE values, such as seasonal “peak SWE”. Here we present a new semiempirical multilayer model, Δsnow, for simulating SWE and bulk snow density solely from a regular time series of snow depths. The model, which is freely available as an R package, treats snow compaction following the rules of Newtonian viscosity, considers errors in measured snow depth, and treats overburden loads due to new snow as additional unsteady compaction; if snow is melted, the water mass is stepwise distributed from top to bottom in the snowpack. Seven model parameters are subject to calibration. Snow observations of 67 winters from 14 stations, well-distributed over different altitudes and climatic regions of the Alps, are used to find an optimal parameter setting. Data from another 71 independent winters from 15 stations are used for validation. Results are very promising: median bias and root mean square error for SWE are only −3.0 and 30.8 kg m−2, and +0.3 and 36.3 kg m−2 for peak SWE, respectively. This is a major advance compared to snow models relying on empirical regressions, and even sophisticated thermodynamic snow models do not necessarily perform better. As such, the new model offers a means to derive robust SWE estimates from historical snow depth data and, with some modification, to generate distributed SWE from remotely sensed estimates of spatial snow depth distribution.

Download Full-text

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.

Download Full-text

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.

Download Full-text