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

2015 ◽  
Vol 46 (4) ◽  
pp. 494-506 ◽  
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.

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

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

<p>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.</p><p>Previous GlobSnow SWE products used a constant snow density of 0.24 kg m<sup>-3</sup> 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.</p><p>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<sup>-3</sup> 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.</p>

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

2008 ◽  
Vol 9 (6) ◽  
pp. 1416-1426 ◽  
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.

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

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.

scholarly journals Characteristics of avalanching: Kootenay Pass, British Columbia, Canada

1993 ◽  
Vol 39 (132) ◽  
pp. 316-322 ◽  
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.

scholarly journals Snow cover variability across glaciers in Nordenskiöldland (Svalbard) from point measurements in 2014–2016

2019 ◽  
Author(s):  
Marco Möller ◽  
Rebecca Möller
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
East Coast ◽  
Data Repository ◽  
Snow Density ◽  
Snow Layer ◽  
Density Measurements ◽  
Sampling Locations ◽  
Snow Water ◽  
Terrain Elevation

Abstract. Snow depths and bulk densities of the annual snow layer were measured at 69 different locations on glaciers across Nordenskiöldland, Svalbard, during the spring seasons of the period 2014–2016. Sampling locations lie along nine transects extending over 17 individual glaciers. Several of the locations were visited repeatedly, leading to a total of 109 point measurements, on which we report in this study. Snow water equivalents were calculated for each point measurement. In the dataset, snow depth and density measurements are accompanied by appropriate uncertainties which are rigorously transferred to the calculated snow water equivalents using a straightforward Monte Carlo simulation-style procedure. The final dataset can be downloaded from the Pangaea data repository (https://www.pangaea.de; https://doi.org/10.1594/PANGAEA.896581). Snow cover data indicate a general and statistically significant increase of snow depths and water equivalents with terrain elevation. A significant increase of both quantities with decreasing distance towards the east coast of Nordenskiöldland is also evident, but shows distinct interannual variability. Snow density does not show any characteristic spatial pattern.

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

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.

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

2005 ◽  
Vol 49 ◽  
pp. 301-306 ◽  
Author(s):  
Hirokazu IZUMI ◽  
So KAZAMA ◽  
Takehiro TOTSUKA ◽  
Masaki SAWAMOTO
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snow Density ◽  
Snow Water

scholarly journals Characteristics of avalanching: Kootenay Pass, British Columbia, Canada

1993 ◽  
Vol 39 (132) ◽  
pp. 316-322 ◽  
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.

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

2018 ◽  
Vol 12 (4) ◽  
pp. 1293-1306 ◽  
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.

scholarly journals Using an ensemble of artificial neural networks to convert snow depth to snow water equivalent over Canada

2020 ◽  
Author(s):  
Konstantin Franz Fotios Ntokas ◽  
Jean Odry ◽  
Marie-Amélie Boucher ◽  
Camille Garnaud
Keyword(s):  
Snow Depth ◽  
Regression Models ◽  
Snow Water Equivalent ◽  
Large Data ◽  
Multilayer Perceptrons ◽  
Snow Density ◽  
General Applicability ◽  
Data Set ◽  
Snow Water ◽  
Artificial Neural

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 with 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 favourably 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 non-uniformly distributed sites across Canada. These data cover almost four 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, optimizing MLP parameters separately for each snow climate class further improves the accuracy of SWE estimates. 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 artificial neural network theory helps improve SWE estimation and that using a greater number of MLP parameters could lead to even further improvements.

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