scholarly journals GNSS signal-based snow water equivalent determination for different snowpack conditions along a steep elevation gradient

2021 ◽  
Author(s):  
Achille Capelli ◽  
Franziska Koch ◽  
Patrick Henkel ◽  
Markus Lamm ◽  
Florian Appel ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Low Cost ◽  
Elevation Gradient ◽  
Liquid Water Content ◽  
Climatic Conditions ◽  
Swiss Alps ◽  
Snow Density ◽  
Validation Data ◽  
Snow Water ◽  
Snow Conditions

Abstract. Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and derives additionally liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS-sensors under a broad range of climatic conditions and LWC and HS are valuable add-ons.

Assessment of operational monitoring of snow water equivalent measurements with low-cost GNSS sensors

2020 ◽  
Author(s):  
Achille Capelli ◽  
Franziska Koch ◽  
Patrik Henkel ◽  
Markus Lamm ◽  
Christoph Marty ◽  
...  
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Low Cost ◽  
Elevation Gradient ◽  
Swiss Alps ◽  
Mountain Areas ◽  
Promising Alternative ◽  
Wet Snow ◽  
Snow Water

<p>The water stored in the snowpack is a crucial contribution to the hydrological cycle in mountain areas. Estimating the spatial distribution and temporal evolution of snow water equivalent (SWE) in mountain regions is, therefore, a key question in snow hydrology research. For this reason, direct measurements of SWE are still essential, but they are often scarce, not easy to install and maintain, mostly non-continuous or rather expensive. A promising alternative to conventional SWE in-situ measurement methods is a newly developed method based on signals of the freely available Global Navigation Satellite System (GNSS), which can be received with standard low-cost sensors. In general, this measurement technique is based on signal differences between one GNSS antenna buried below the snowpack and one reference antenna above the snow cover. The signal differences reflect the GNSS carrier phase time delay and the GNSS signals strength attenuation within the snowpack, which can be translated into SWE and the snow liquid water content (LWC). So far, this method showed excellent results over several years at the high-alpine test and validation site Weissfluhjoch (Eastern Swiss Alps, 2540 m asl.). Currently, our aim is to assess whether this method is suitable for deriving SWE continuously with reasonable accuracy also at other locations with different characteristics. Therefore, we set up further GNSS sensors at different elevations, where the snow characteristics can vary considerably. At lower elevations the snow cover is normally shallower and is more frequently subject to melt-freeze cycles leading to faster snow aging and different snow densities. Moreover, rapid transition from dry- to wet-snow conditions as well as steep valley sites can be seen as a challenge. In total, we were operating for two season four GNSS stations along a steep elevation gradient (820 m, 1185 m, 1510 m, and 2540 m asl.) within only a few kilometres in the Eastern Swiss Alps. For validation purposes, we monitored SWE and snow height manually and with additional automatic sensors at all locations. We analysed the GNSS SWE derivation accuracy in general and in detail for different meteorological conditions as snowfall, snow settlement, rain on snow and dry or wet snow periods. Eventually, we compared the GNSS results with results from numerical snow cover models.</p>

scholarly journals In Situ Determination of Dry and Wet Snow Permittivity: Improving Equations for Low Frequency Radar Applications

2021 ◽  
Vol 13 (22) ◽  
pp. 4617
Author(s):  
Ryan W. Webb ◽  
Adrian Marziliano ◽  
Daniel McGrath ◽  
Randall Bonnell ◽  
Tate G. Meehan ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Low Frequency ◽  
Liquid Water Content ◽  
Seasonal Snow ◽  
Wet Snow ◽  
Primary Driver ◽  
Snow Water ◽  
Snow Conditions ◽  
Ground Penetrating

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

scholarly journals Estimating snow water equivalent over long mountain transects using snowmobile-mounted ground-penetrating radar

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. WA183-WA193 ◽  
Author(s):  
W. Steven Holbrook ◽  
Scott N. Miller ◽  
Matthew A. Provart
Keyword(s):  
Ground Penetrating Radar ◽  
Snow Water Equivalent ◽  
Ground Surface ◽  
Snow Accumulation ◽  
Accurate Estimation ◽  
Snow Density ◽  
Snow Stratigraphy ◽  
Flood Estimation ◽  
Snow Water ◽  
Ground Penetrating

The water balance in alpine watersheds is dominated by snowmelt, which provides infiltration, recharges aquifers, controls peak runoff, and is responsible for most of the annual water flow downstream. Accurate estimation of snow water equivalent (SWE) is necessary for runoff and flood estimation, but acquiring enough measurements is challenging due to the variability of snow accumulation, ablation, and redistribution at a range of scales in mountainous terrain. We have developed a method for imaging snow stratigraphy and estimating SWE over large distances from a ground-penetrating radar (GPR) system mounted on a snowmobile. We mounted commercial GPR systems (500 and 800 MHz) to the front of the snowmobile to provide maximum mobility and ensure that measurements were taken on pristine snow. Images showed detailed snow stratigraphy down to the ground surface over snow depths up to at least 8 m, enabling the elucidation of snow accumulation and redistribution processes. We estimated snow density (and thus SWE, assuming no liquid water) by measuring radar velocity of the snowpack through migration focusing analysis. Results from the Medicine Bow Mountains of southeast Wyoming showed that estimates of snow density from GPR ([Formula: see text]) were in good agreement with those from coincident snow cores ([Formula: see text]). Using this method, snow thickness, snow density, and SWE can be measured over large areas solely from rapidly acquired common-offset GPR profiles, without the need for common-midpoint acquisition or snow cores.

scholarly journals Sodankylä manual snow survey program

2016 ◽  
Vol 5 (1) ◽  
pp. 163-179 ◽  
Author(s):  
Leena Leppänen ◽  
Anna Kontu ◽  
Henna-Reetta Hannula ◽  
Heidi Sjöblom ◽  
Jouni Pulliainen
Keyword(s):  
Snow Water Equivalent ◽  
Measurement Techniques ◽  
Liquid Water Content ◽  
The Arctic ◽  
Research Centre ◽  
Future Research ◽  
Forest Zone ◽  
Finnish Meteorological Institute ◽  
Snow Properties ◽  
Snow Water

Abstract. The manual snow survey program of the Arctic Research Centre of the Finnish Meteorological Institute (FMI-ARC) consists of numerous observations of natural seasonal taiga snowpack in Sodankylä, northern Finland. The easily accessible measurement areas represent the typical forest and soil types in the boreal forest zone. Systematic snow measurements began in 1909 with snow depth (HS) and snow water equivalent (SWE). In 2006 the manual snow survey program expanded to cover snow macro- and microstructure from regular snow pits at several sites using both traditional and novel measurement techniques. Present-day snow pit measurements include observations of HS, SWE, temperature, density, stratigraphy, grain size, specific surface area (SSA) and liquid water content (LWC). Regular snow pit measurements are performed weekly during the snow season. Extensive time series of manual snow measurements are important for the monitoring of temporal and spatial changes in seasonal snowpack. This snow survey program is an excellent base for the future research of snow properties.

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 Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

2019 ◽  
Vol 55 (5) ◽  
pp. 4465-4487 ◽  
Author(s):  
Franziska Koch ◽  
Patrick Henkel ◽  
Florian Appel ◽  
Lino Schmid ◽  
Heike Bach ◽  
...  
Keyword(s):  
Time Delay ◽  
Water Content ◽  
Liquid Water ◽  
Snow Water Equivalent ◽  
Liquid Water Content ◽  
Signal Attenuation ◽  
Wet Snow ◽  
Snow Water

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.

A Process-Based Energy Balance Snowmelt Model II: Application

2013 ◽  
Vol 726-731 ◽  
pp. 3346-3352
Author(s):  
Gao Jie
Keyword(s):  
Energy Balance ◽  
Snow Water Equivalent ◽  
Sensible Heat Flux ◽  
Colorado Front Range ◽  
Wave Radiation ◽  
Snow Pack ◽  
Front Range ◽  
Snow Density ◽  
Column Model ◽  
Snow Water

Based on the process-based energy balance snow melting model Snow Column Model, several scenarios are set to study the response of snow pack to climate change according to site-based data in snowpit 006, Niwot Ridge, Colorado, Front Range of Rocky Mountains. Based on an introduction and validation of Snow Column Model by data of 1996, a further validation is made on data during 1997 and 1999. Scenarios are set based on observations of solar radiation, long-wave radiation, air temperature, latent and sensible heat flux during 1996 and 1999. The responses of snow pack to an average temperature fluctuation within 6.2°C are analyzed. The results illustrate that snow density and snow water equivalent accelerated decreases while the variance in snow density does not increase monotonically over time.

scholarly journals A virtual network for estimating daily new snow water equivalent and snow depth in the Swiss Alps

2010 ◽  
Vol 51 (54) ◽  
pp. 32-38 ◽  
Author(s):  
Luca Egli ◽  
Tobias Jonas ◽  
Jean-Marie Bettems
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Weather Prediction ◽  
Practical Importance ◽  
Cost Effective ◽  
Numerical Weather Prediction Model ◽  
Swiss Alps ◽  
Virtual Network ◽  
Systematic Bias ◽  
Snow Water

AbstractDaily new snow water equivalent (HNW) and snow depth (HS) are of significant practical importance in cryospheric sciences such as snow hydrology and avalanche formation. In this study we present a virtual network (VN) for estimating HNW and HS on a regular mesh over Switzerland with a grid size of 7 km. The method is based on the HNW output data of the numerical weather prediction model COSMO-7, driving an external accumulation/melting routine. The verification of the VN shows that, on average, HNW can be estimated with a mean systematic bias close to 0 and an averaged absolute accuracy of 4.01 mm. The results are equivalent to the performance observed when comparing different automatic HNW point estimations with manual reference measurements. However, at the local scale, HS derived by the VN may significantly deviate from corresponding point measurements. We argue that the VN presented here may introduce promising cost-effective options as input for spatially distributed snow hydrological and avalanche risk management applications in the Swiss Alps.

Stubble management effects on canola performance across different climatic regions of western Canada

2015 ◽  
Vol 95 (1) ◽  
pp. 149-159 ◽  
Author(s):  
Michael J. Cardillo ◽  
Paul Bullock ◽  
Rob Gulden ◽  
Aaron Glenn ◽  
Herb Cutforth
Keyword(s):  
Snow Water Equivalent ◽  
Growing Season ◽  
Moisture Stress ◽  
Climatic Conditions ◽  
Western Canada ◽  
Spring Precipitation ◽  
Canadian Prairies ◽  
Climatic Regions ◽  
Snow Water ◽  
Management Effects

Cardillo, M. J., Bullock, P., Gulden, R., Glenn, A. and Cutforth, H. 2015. Stubble management effects on canola performance across different climatic regions of western Canada. Can. J. Plant Sci. 95: 149–159. Previous research in the most arid region of the Canadian prairies has shown that wheat stubble cut tall the previous year can improve performance of the following canola crop. This study aimed to determine if tall stubble could benefit canola across the climatic conditions typically experienced in western Canada. Tall stubble impacts on canola were monitored over 11 site-years located throughout the prairies. At each site, tall stubble (50 cm) was compared with short stubble (20 cm). At some sites the stubble lodged allowing an unintended comparison between stubble that remained intact and stubble that was flattened. The comparison of snow water equivalent showed tall stubble caught more snow than short stubble but the benefit of additional spring soil moisture was masked by heavy spring precipitation in both 2011 and 2012. Canola biomass and yield were significantly lower in damaged versus intact stubble, either short or tall. In both years, wet spring conditions were followed by hotter and drier weather in the mid to late growing season. Soil under the damaged stubble (short or tall) likely warmed and dried more slowly in the spring, limiting early-season growth, biomass and yield. At sites where both tall and short stubble remained intact, there was a significant yield advantage with tall stubble. The intact tall stubble may have slowed evaporation and soil drying compared with intact short stubble, which reduced moisture stress later in the growing season, imparting a yield advantage.

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