Hydrometry. Measurement of snow water equivalent using snow mass registration devices

2015 ◽  
Keyword(s):  
Snow Water Equivalent ◽  
Snow Water ◽  
Snow Mass

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 The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

2019 ◽  
Author(s):  
Abbas Fayad ◽  
Simon Gascoin
Keyword(s):  
Snow Cover ◽  
Liquid Water ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Water Percolation ◽  
Snow Water ◽  
Snow Model ◽  
Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

scholarly journals Recent Evidence of Large-Scale Receding Snow Water Equivalents in the European Alps

2017 ◽  
Vol 18 (4) ◽  
pp. 1021-1031 ◽  
Author(s):  
Christoph Marty ◽  
Anna-Maria Tilg ◽  
Tobias Jonas
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Water Cycle ◽  
Critical Role ◽  
European Alps ◽  
Measurement Series ◽  
Snow Water ◽  
Snow Mass

Abstract Snow plays a critical role in the water cycle of many mountain regions and heavily populated areas downstream. In this study, changes of snow water equivalent (SWE) time series from long-term stations in five Alpine countries are analyzed. The sites are located between 500 and 3000 m above mean sea level, and the analysis is mainly based on measurement series from 1 February (winter) and 1 April (spring). The investigation was performed over different time periods, including the last six decades. The large majority of the SWE time series demonstrate a reduction in snow mass, which is more pronounced for spring than for winter. The observed SWE decrease is independent of latitude or longitude, despite the different climate regions in the Alpine domain. In contrast to measurement series from other mountain ranges, even the highest sites revealed a decline in spring SWE. A comparison with a 100-yr mass balance series from a glacier in the central Alps demonstrates that the peak SWEs have been on a record-low level since around the beginning of the twenty-first century at high Alpine sites. In the long term, clearly increasing temperatures and a coincident weak reduction in precipitation are the main drivers for the pronounced snow mass loss in the past.

scholarly journals GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kari Luojus ◽  
Jouni Pulliainen ◽  
Matias Takala ◽  
Juha Lemmetyinen ◽  
Colleen Mortimer ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Microwave Radiometer ◽  
Emission Model ◽  
Annual Maximum ◽  
Current Form ◽  
Continental Scale ◽  
Snow Water ◽  
Bayesian Data Assimilation ◽  
Snow Mass

AbstractWe describe the Northern Hemisphere terrestrial snow water equivalent (SWE) time series covering 1979–2018, containing daily, monthly and monthly bias-corrected SWE estimates. The GlobSnow v3.0 SWE dataset combines satellite-based passive microwave radiometer data (Nimbus-7 SMMR, DMSP SSM/I and DMSP SSMIS) with ground based synoptic snow depth observations using bayesian data assimilation, incorporating the HUT Snow Emission model. The original GlobSnow SWE retrieval methodology has been further developed and is presented in its current form in this publication. The described GlobSnow v3.0 monthly bias-corrected dataset was applied to provide continental scale estimates on the annual maximum snow mass and its trend during the period 1980 to 2018.

Development of Long-Term Satellite-Based Snow Mass Records in the ESA Snow CCI Project

2020 ◽  
Author(s):  
Kari Luojus ◽  
Matias Takala ◽  
Jouni Pulliainen ◽  
Juha Lemmetyinen ◽  
Mikko Moisander ◽  
...  
Keyword(s):  
Snow Cover ◽  
Northern Hemisphere ◽  
Snow Water Equivalent ◽  
Reliable Information ◽  
The Arctic ◽  
Climate Research ◽  
Data Record ◽  
Snow Water ◽  
Snow Mass

<p>Reliable information on snow cover across the Northern Hemisphere and Arctic and sub-Arctic regions is needed for climate monitoring, for understanding the Arctic climate system, and for the evaluation of the role of snow cover and its feedback in climate models. In addition to being of significant interest for climatological investigations, reliable information on snow cover is of high value for the purpose of hydrological forecasting and numerical weather prediction. Terrestrial snow covers up to 50 million km² of the Northern Hemisphere in winter and is characterized by high spatial and temporal variability making satellite observations the only means for providing timely and complete observations of the global snow cover. The ESA Snow CCI project was initiated in 2018 to improve methodologies for snow cover extent (SE) and snow water equivalent (SWE) retrieval [1] using satellite data and construct long term data records of terrestrial snow cover for climate research purposes.</p><p>The first new long term SWE data record from the ESA Snow CCI project, spanning 1979 to 2018 has been constructed and assessed in terms of retrieval performance, homogeneity and temporal stability. The initial results show that the new SWE dataset is more robust, more accurate and more consistent over the 40-year time series, compared to the earlier ESA GlobSnow SWE v1.0 and v2.0 data records [1].</p><p>The improved SWE retrieval methodology incorporates a new emission model (within the retrieval scheme), an improved synoptic weather station snow depth data record (applied to support SWE retrieval), extension of the SWE retrieval to cover the whole Northern Hemisphere.</p><p>The new Snow CCI SWE data record has been used to assess changes in the long term hemispherical snow conditions and climatological trends in Northern Hemisphere, Eurasia and North America. The general finding is that the peak hemispherical snow mass during the satellite era has not yet decreased significantly but has remained relatively stable, with changes to lower and higher SWE conditions in different geographical regions.</p><p> </p><p>References:</p><p>[1] Takala, M, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J.-P. Kärnä, J. Koskinen, B. Bojkov. 2011. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sensing of Environment, 115, 12, 3517-3529, doi:10.1016/j.rse.2011.08.014.</p>

scholarly journals The role of liquid water percolation representation in estimating snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

2020 ◽  
Vol 24 (3) ◽  
pp. 1527-1542
Author(s):  
Abbas Fayad ◽  
Simon Gascoin
Keyword(s):  
Snow Cover ◽  
Liquid Water ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Mountain Regions ◽  
Snow Cover Area ◽  
Water Percolation ◽  
Snow Water ◽  
Snow Model ◽  
Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the SWE and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 and 3000 m a.s.l. The results show that the new percolation scheme yields better performance, especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments, with values ranging between 73 and 286 mm w.e. (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snowmelt events during the accumulation season, and the high variability of snow depth patterns at the subpixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in nonmonitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

scholarly journals Evaluation of MRMS Snowfall Products over the Western United States

2017 ◽  
Vol 18 (6) ◽  
pp. 1707-1713 ◽  
Author(s):  
Yixin Wen ◽  
Pierre Kirstetter ◽  
J. J. Gourley ◽  
Yang Hong ◽  
Ali Behrangi ◽  
...  
Keyword(s):  
Water Resources ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Western United States ◽  
Quantitative Estimates ◽  
Snow Water ◽  
Flux Increase ◽  
Beam Height ◽  
Snow Mass

Abstract Snow is important to water resources and is of critical importance to society. Ground-weather-radar-based snowfall observations have been highly desirable for large-scale weather monitoring and water resources applications. This study conducts an evaluation of the Multi-Radar Multi-Sensor (MRMS) quantitative estimates of snow rate using the Snowpack Telemetry (SNOTEL) daily snow water equivalent (SWE) datasets. A detectability evaluation shows that MRMS is limited in detecting very light snow (daily snow accumulation <5 mm) because of the quality control module in MRMS filtering out weak signals (<5 dBZ). For daily snow accumulation greater than 10 mm, MRMS has good detectability. The quantitative comparisons reveal a bias of −77.37% between MRMS and SNOTEL. A majority of the underestimation bias occurs in relatively warm conditions with surface temperatures ranging from −10° to 0°C. A constant reflectivity–SWE intensity relationship does not capture the snow mass flux increase associated with denser snow particles at these relatively warm temperatures. There is no clear dependence of the bias on radar beam height. The findings in this study indicate that further improvement in radar snowfall products might occur by deriving appropriate reflectivity–SWE relationships considering the degree of riming and snowflake size.

scholarly journals Inter-Sensor Calibration between HY-2B and AMSR2 Passive Microwave Data in Land Surface and First Result for Snow Water Equivalent Retrieval

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5023
Author(s):  
Shuo Gao ◽  
Zhen Li ◽  
Quan Chen ◽  
Wu Zhou ◽  
Mingsen Lin ◽  
...  
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Microwave Radiometer ◽  
Sensor Calibration ◽  
Marine Resources ◽  
Internal Calibration ◽  
Snow Water ◽  
Development And Utilization ◽  
Synchronous Orbit ◽  
Snow Mass

The self-designed HaiYang-2B (HY-2B) satellite was launched on 24 October 2018 in China at 22:57 UT in a 99.34° inclination sun-synchronous orbit. The Scanning Microwave Radiometer (SMR) on the core observatory has the capability to provide near-real-time multi-channel brightness temperature (Tb) observations, which are designed mainly for improving the level of marine forecasting and monitoring, serving the development and utilization of marine resources. After internal calibration and ocean calibration, the first effort to retrieve land surface snow parameters was performed in this study, which obtained extremely low accuracy both in snow extent and snow mass. Accordingly, land inter-sensor calibration was carried out between SMR and the Advanced Microwave Scanning Radiometer 2 (AMSR2) in order to broaden the research and application of SMR data on the Earth’s land surface. Finally, we evaluated the consistency of the snow extent and snow mass derived from the initial and land-calibrated SMR data. The results indicated that a systematic SMR cold deviation whose magnitude depends on the channel is present for all the compared channels. After intercalibration, the conformity of the snow extent and snow mass were substantially improved compared to before; the relative bias of the snow extent and snow mass decreased from −49.97% to 2.97% and from −51.71% to 3.01%, respectively.

scholarly journals Spatiotemporal distribution of seasonal snow water equivalent in High-Mountain Asia from an 18-year Landsat-MODIS era snow reanalysis dataset

2021 ◽  
Author(s):  
Yufei Liu ◽  
Yiwen Fang ◽  
Steven A. Margulis
Keyword(s):  
Snow Water Equivalent ◽  
Water Cycle ◽  
Regional Climate ◽  
Spatiotemporal Distribution ◽  
High Mountain ◽  
Reanalysis Dataset ◽  
Seasonal Snow ◽  
Snow Water ◽  
Regional Water ◽  
Snow Mass

Abstract. Seasonal snowpack is a key water resource and plays an important role in regional climate. However, how seasonal snow mass is distributed over space and time is not fully understood. This is due to the difficulties in estimation from remote sensing or ground measurements, especially over mountainous areas, such as High-Mountain Asia (HMA). In this paper we examined the spatiotemporal distribution of seasonal snow water equivalent (SWE) over HMA using a newly developed snow reanalysis dataset. The dataset was derived using a data assimilation method constrained by satellite observed snow data, spanning across 18 water years (2000–2017), at a high spatial (~500 m) and temporal (daily) resolution. Based on the results, the climatology of seasonal SWE volume is quantified as ~163 km3 over the entire HMA region, with 66 % of that in the northwestern watersheds (e.g. Indus, Amu Darya and Syr Darya). An elevational analysis shows that seasonal SWE volume peaks at mid-elevations (~3500 m). This work should help better understanding the snowpack climatology and variability over HMA, providing insights for future studies in assessing seasonal snow and its contribution to the regional water cycle and climate.

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