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

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.

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Long-term (1900-2100) Hydrometeorological and Snow Water Equivalent reconstructions in the French Southern Alps (Mercantour Natural Parc).

10.5194/egusphere-egu21-15872 ◽

2021 ◽

Author(s):

Thibault Mathevet ◽

Cyril Thébault ◽

Jérôme Mansons ◽

Matthieu Le Lay ◽

Audrey Valery ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Water Resources ◽

Air Temperature ◽

Snow Water Equivalent ◽

Hydrological Regime ◽

Southern Alps ◽

Snow Line ◽

Snow Water

The aim of this communication is to present a study on climate variability and change on snow water equivalent (SWE) and streamflow over the 1900-2100 period in a mediteranean and moutainuous area. &#160;It is based on SWE and streamflow observations, past reconstructions (1900-2018) and future GIEC scenarii (up to 2100) of some snow courses and hydrological stations situated within the French Southern Alps (Mercantour Natural Parc). This has been conducted by EDF (French hydropower company) and Mercantour Natural Parc.This issue became particularly important since a decade, especially in regions where snow variability had a large impact on water resources availability, poor snow conditions in ski resorts and artificial snow production or impacts on mountainous ecosystems (fauna and flora). As a water resources manager in French mountainuous regions, EDF developed and managed a large hydrometeorological network since 1950. A recent data rescue research allowed to digitize long term SWE manual measurements of a hundred of snow courses within the French Alps. EDF have been operating an automatic SWE sensors network, complementary to historical snow course network. Based on numerous SWE observations time-series and snow modelization (Garavaglia et al., 2017), continuous daily historical SWE time-series have been reconstructed within the 1950-2018 period. These reconstructions have been extented to 1900 using 20 CR (20th century reanalyses by NOAA) reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii (+4.5 W/m&#178; and + 8.5 W/m&#178; hypotheses). In the scope of this study, Mercantour Natural Parc is particularly interested by snow scenarii in the future and its impacts on their local flora and fauna.Considering observations within Durance watershed and Mercantour region, this communication focuses on: (1) long term (1900-2018) analyses of variability and trend of hydrometeorological and snow variables (total precipitation, air temperature, snow water equivalent, snow line altitude, snow season length, streamflow regimes) , (2) long term variability of snow and hydrological regime of snow dominated watersheds and (3) future trends (2020 -2100) using GIEC Climate Change scenarii.Comparing old period (1950-1984) to recent period (1984-2018), quantitative results within these regions roughly shows an increase of air temperature by 1.2 &#176;C, an increase of snow line height by 200m, a reduction of SWE by 200 mm/year and a reduction of snow season duration by 15 days. Characterization of the increase of snow line height and SWE reduction are particularly important at a local and watershed scale. Then, this communication focuses on impacts on long-term time scales (2050, 2100). This long term change of snow dynamics within moutainuous regions both impacts (1) water resources management, (2) snow resorts and artificial snow production developments or (3) ecosystems dynamics.Connected to the evolution of snow seasonality, the impacts on hydrological regime and some streamflow signatures allow to characterize the possible evolution of water resources in this mediteranean and moutianuous region This study allowed to provide some local quantitative scenarii.

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Towards a long term global snow climate data record from satellite data generated within the Snow Climate Change Initiative

10.5194/egusphere-egu2020-19228 ◽

2020 ◽

Author(s):

Gabriele Schwaizer ◽

Lars Keuris ◽

Thomas Nagler ◽

Chris Derksen ◽

Kari Luojus ◽

...

Keyword(s):

Time Series ◽

Satellite Data ◽

Snow Water Equivalent ◽

Climate Model ◽

Process Models ◽

Climate Data ◽

Seasonal Snow ◽

Snow Water

Seasonal snow is an important component of the global climate system. It is highly variable in space and time and sensitive to short term synoptic scale processes and long term climate-induced changes of temperature and precipitation. Current snow products derived from various satellite data applying different algorithms show significant discrepancies in extent and snow mass, a potential source for biases in climate monitoring and modelling. The recently launched ESA CCI+ Programme addresses seasonal snow as one of 9 Essential Climate Variables to be derived from satellite data.In the snow_cci project, scheduled for 2018 to 2021 in its first phase, reliable fully validated processing lines are developed and implemented. These tools are used to generate homogeneous multi-sensor time series for the main parameters of global snow cover focusing on snow extent and snow water equivalent. Using GCOS guidelines, the requirements for these parameters are assessed and consolidated using the outcome of workshops and questionnaires addressing users dealing with different climate applications. Snow extent product generation applies algorithms accounting for fractional snow extent and cloud screening in order to generate consistent daily products for snow on the surface (viewable snow) and snow on the surface corrected for forest masking (snow on ground) with global coverage. Input data are medium resolution optical satellite images (AVHRR-2/3, AATSR, MODIS, VIIRS, SLSTR/OLCI) from 1981 to present. An iterative development cycle is applied including homogenisation of the snow extent products from different sensors by minimizing the bias. Independent validation of the snow products is performed for different seasons and climate zones around the globe from 1985 onwards, using as reference high resolution snow maps from Landsat and Sentinel- 2as well as in-situ snow data following standardized validation protocols.Global time series of daily snow water equivalent (SWE) products are generated from passive microwave data from SMMR, SSM/I, and AMSR from 1978 onwards, combined with in-situ snow depth measurements. Long-term stability and quality of the product is assessed using independent snow survey data and by intercomparison with the snow information from global land process models.The usability of the snow_cci products is ensured through the Climate Research Group, which performs case studies related to long term trends of seasonal snow, performs evaluations of CMIP-6 and other snow-focused climate model experiments, and applies the data for simulation of Arctic hydrological regimes.In this presentation, we summarize the requirements and product specifications for the snow extent and SWE products, with a focus on climate applications. We present an overview of the algorithms and systems for generation of the time series. The 40 years (from 1980 onwards) time series of daily fractional snow extent products from AVHRR with 5 km pixel spacing, and the 20-year time series from MODIS (1 km pixel spacing) as well as the coarse resolution (25 km pixel spacing) of daily SWE products from 1978 onwards will be presented along with first results of the multi-sensor consistency checks and validation activities.

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Long-Term Records of Snow Cover Water Equivalent in the Swiss Alps

Hydrology Research ◽

10.2166/nh.1994.0019 ◽

1994 ◽

Vol 25 (1-2) ◽

pp. 53-64 ◽

Cited By ~ 26

Author(s):

M. B. Rohrer ◽

L. N. Braun ◽

H. Lang

Keyword(s):

Snow Cover ◽

Snow Water Equivalent ◽

Water Cycle ◽

Swiss Alps ◽

Special Investigation ◽

Quality Aspects ◽

Snow Water ◽

Ranking Order ◽

Seasonal Discharge

The snow-water equivalent (SWE) of the seasonal snow cover is an important component of the water cycle in the Swiss Alps. It is used for predicting seasonal discharge, for short-range discharge forecasts and also for assessing water quality aspects. The SWE has been measured every two weeks at about 50 stations located between 860 and 2,540 m a.s.l. for more than 30 years. In addition there are special investigation areas with stations located between 600 m and 2,900 m a.s.l. where SWE is measured once per winter. The main characteristics of temporal and spatial SWE distributions are analyzed. The variations of SWE values depend in ranking order on elevation, on the year-to-year variations, on the region and on the exposition. The standardized SWE-values depend mostly on the year-to-year variations and on the region.

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

10.5194/egusphere-egu2020-14123 ◽

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

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 km&#178; 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.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].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.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.&#160;References:[1] Takala, M, K. Luojus, J. Pulliainen, C. Derksen, J. Lemmetyinen, J.-P. K&#228;rn&#228;, 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.

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Evaluation of MRMS Snowfall Products over the Western United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0266.1 ◽

2017 ◽

Vol 18 (6) ◽

pp. 1707-1713 ◽

Cited By ~ 6

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.

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

10.5194/tc-2021-139 ◽

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

10.3403/30206740u ◽

2015 ◽

Keyword(s):

Snow Water Equivalent ◽

Snow Water ◽

Snow Mass

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Mercury trends and cycling in northern Wisconsin related to atmospheric and hydrologic processes

Canadian Journal of Fisheries and Aquatic Sciences ◽

10.1139/cjfas-2018-0157 ◽

2019 ◽

Vol 76 (5) ◽

pp. 831-846 ◽

Cited By ~ 5

Author(s):

C.J. Watras ◽

D. Grande ◽

A.W. Latzka ◽

L.S. Tate

Keyword(s):

Time Series ◽

Water Cycle ◽

Water Levels ◽

Hydrologic Processes ◽

Littoral Sediments ◽

Long Term Trends ◽

Acid Base Status ◽

Explanatory Mechanism ◽

Regional Water

Atmospheric deposition is the principal source of mercury (Hg) to remote northern landscapes, but its fate depends on multiple factors and internal feedbacks. Here we document long-term trends and cycles of Hg in the air, precipitation, surface water, and fish of northern Wisconsin that span the past three decades, and we investigate relationships to atmospheric processes and other variables, especially the regional water cycle. Consistent with declining emission inventories, there was evidence of declining trends in these time series, but the time series for Hg in some lakes and most fish were dominated by a near-decadal oscillation that tracked the regional oscillation of water levels. Concentrations of important solutes (SO4, dissolved organic carbon) and the acid–base status of lake water also tracked water levels in ways that cannot be attributed to simple dilution or concentration. The explanatory mechanism is analogous to the “reservoir effect” wherein littoral sediments are periodically exposed and reflooded, altering the internal cycles of sulfur, carbon, and mercury. These climatically driven, near-decadal oscillations confound short or sparse time series and complicate relationships among Hg emissions, deposition, and bioaccumulation.

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Snowpack variability and trends at long-term stations in northern Colorado, USA

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-371-131-2015 ◽

2015 ◽

Vol 371 ◽

pp. 131-136 ◽

Cited By ~ 10

Author(s):

S. R. Fassnacht ◽

M. Hultstrand

Keyword(s):

Snow Depth ◽

Snow Water Equivalent ◽

Entire Period ◽

Northern Colorado ◽

Snow Water ◽

Highly Correlated ◽

The Individual

Abstract. The individual measurements from snowcourse stations were digitized for six stations across northern Colorado that had up to 79 years of record (1936 to 2014). These manual measurements are collected at the first of the month from February through May, with additional measurements in January and June. This dataset was used to evaluate the variability in snow depth and snow water equivalent (SWE) across a snowcourse, as well as trends in snowpack patterns across the entire period of record and over two halves of the record (up to 1975 and from 1976). Snowpack variability is correlated to depth and SWE. The snow depth variability is shown to be highly correlated with average April snow depth and day of year. Depth and SWE were found to be significantly decreasing over the entire period of record at two stations, while at another station the significant trends were an increase over the first half of the record and a decrease over the second half. Variability tended to decrease with time, when significant.

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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.

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