scholarly journals In Situ Determination of Dry and Wet Snow Permittivity: Snow Water Equivalent Algorithm Development for Low Frequency Radar Applications

2021 ◽  
Author(s):  
Ryan Webb ◽  
Adrian Marziliano ◽  
Randall Bonnell ◽  
Daniel McGrath ◽  
Tate Meehan ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Low Frequency ◽  
Algorithm Development ◽  
Wet Snow ◽  
Snow Water ◽  
Radar Applications

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 Assessment of MERRA-2 and ERA5 to Model the Snow Water Equivalent in the High Atlas (1981–2019)

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 890
Author(s):  
Mohamed Wassim Baba ◽  
Abdelghani Boudhar ◽  
Simon Gascoin ◽  
Lahoucine Hanich ◽  
Ahmed Marchane ◽  
...  
Keyword(s):  
River Discharge ◽  
Snow Water Equivalent ◽  
High Elevation ◽  
Water Runoff ◽  
High Atlas ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Snow Water

Melt water runoff from seasonal snow in the High Atlas range is an essential water resource in Morocco. However, there are only few meteorological stations in the high elevation areas and therefore it is challenging to estimate the distribution of snow water equivalent (SWE) based only on in situ measurements. In this work we assessed the performance of ERA5 and MERRA-2 climate reanalysis to compute the spatial distribution of SWE in the High Atlas. We forced a distributed snowpack evolution model (SnowModel) with downscaled ERA5 and MERRA-2 data at 200 m spatial resolution. The model was run over the period 1981 to 2019 (37 water years). Model outputs were assessed using observations of river discharge, snow height and MODIS snow-covered area. The results show a good performance for both MERRA-2 and ERA5 in terms of reproducing the snowpack state for the majority of water years, with a lower bias using ERA5 forcing.

scholarly journals Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

2017 ◽  
Vol 11 (4) ◽  
pp. 1647-1664 ◽  
Author(s):  
Emmy E. Stigter ◽  
Niko Wanders ◽  
Tuomo M. Saloranta ◽  
Joseph M. Shea ◽  
Marc F. P. Bierkens ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Snow Cover ◽  
Climate Sensitivity ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Model

Abstract. Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However, snow cover data provide no direct information on the actual amount of water stored in a snowpack, i.e., the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ observations and a modified version of the seNorge snow model to estimate (climate sensitivity of) SWE and snowmelt runoff in the Langtang catchment in Nepal. Snow cover data from Landsat 8 and the MOD10A2 snow cover product were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an ensemble Kalman filter (EnKF). Independent validations of simulated snow depth and snow cover with observations show improvement after data assimilation compared to simulations without data assimilation. The approach of modeling snow depth in a Kalman filter framework allows for data-constrained estimation of snow depth rather than snow cover alone, and this has great potential for future studies in complex terrain, especially in the Himalayas. Climate sensitivity tests with the optimized snow model revealed that snowmelt runoff increases in winter and the early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature. At high elevation a decrease in SWE due to higher air temperature is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature.

scholarly journals Canadian historical Snow Water Equivalent dataset (CanSWE, 1928–2020)

2021 ◽  
Author(s):  
Vincent Vionnet ◽  
Colleen Mortimer ◽  
Mike Brady ◽  
Louise Arnal ◽  
Ross Brown
Keyword(s):  
Newfoundland And Labrador ◽  
Land Surface ◽  
Snow Water Equivalent ◽  
Water Security ◽  
Monitoring And Evaluation ◽  
Surface Models ◽  
Snow Water ◽  
The Government ◽  
Different Sources

Abstract. In situ measurements of snow water equivalent (SWE) – the depth of water that would be produced if all the snow melted – are used in many applications including water management, flood forecasting, climate monitoring, and evaluation of hydrological and land surface models. The Canadian historical SWE dataset (CanSWE) combines manual and automated pan-Canadian SWE observations collected by national, provincial and territorial agencies as well as hydropower companies. Snow depth and derived bulk snow density are also included when available. This new dataset supersedes the previous Canadian Historical Snow Survey (CHSSD) dataset published by Brown et al. (2019), and this paper describes the efforts made to correct metadata, remove duplicate observations, and quality control records. The CanSWE dataset was compiled from 15 different sources and includes SWE information for all provinces and territories that measure SWE. Data were updated to July 2020 and new historical data from the Government of Northwest Territories, Government of Newfoundland and Labrador, Saskatchewan Water Security Agency, and Hydro Quebec were included. CanSWE includes over one million SWE measurements from 2607 different locations across Canada over the period 1928–2020. It is publicly available at https://doi.org/10.5281/zenodo.4734372 (Vionnet et al., 2021).

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

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 On The Estimation of Temporal Changes of Snow Water Equivalent by Spaceborne Sar Interferometry: A New Application for the Sentinel-1 Mission

2019 ◽  
Vol 67 (1) ◽  
pp. 93-100 ◽  
Author(s):  
Vasco Conde ◽  
Giovanni Nico ◽  
Pedro Mateus ◽  
João Catalão ◽  
Anna Kontu ◽  
...  
Keyword(s):  
Measurement Accuracy ◽  
Snow Water Equivalent ◽  
Temporal Variations ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Snow Layer ◽  
Illumination Condition ◽  
Interferometric Phase ◽  
Snow Water

Abstract In this work we present a methodology for the mapping of Snow Water Equivalent (SWE) temporal variations based on the Synthetic Aperture Radar (SAR) Interferometry technique and Sentinel-1 data. The shift in the interferometric phase caused by the refraction of the microwave signal penetrating the snow layer is isolated and exploited to generate maps of temporal variation of SWE from coherent SAR interferograms. The main advantage of the proposed methodology with respect to those based on the inversion of microwave SAR backscattering models is its simplicity and the reduced number of required in-situ SWE measurements. The maps, updated up to every 6 days, can attain a spatial resolution up to 20 m with sub-centimetre ΔSWE measurement accuracy in any weather and sun illumination condition. We present results obtained using the proposed methodology over a study area in Finland. These results are compared with in-situ measurements of ΔSWE, showing a reasonable match with a mean accuracy of about 6 mm.

scholarly journals Determination of snow water equivalent by the atmospheric beta-background

2019 ◽  
Author(s):  
Irina Belyaeva ◽  
Grigorii Yakovlev ◽  
Aleksey Zelinskiy ◽  
Valentina Yakovleva
Keyword(s):  
Snow Water Equivalent ◽  
Snow Water
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