Snow depth variability in the Northern Hemisphere mountains observed from space

Abstract Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

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Influence of snow water equivalent on droughts and their prediction in the USA

10.5194/hess-2018-420 ◽

2018 ◽

Author(s):

Daniel Abel ◽

Felix Pollinger ◽

Heiko Paeth

Keyword(s):

Sierra Nevada ◽

Large Scale ◽

Snow Water Equivalent ◽

Southern Oscillation ◽

Original Data ◽

The United States ◽

Early Summer ◽

Drought Severity ◽

Southern Us ◽

Snow Water

Abstract. Droughts can result in enormous impacts for environment, societies, and economy. In arid or semiarid regions with bordering high mountains, snow is the major source of water supply due to its role as natural water storage. The goal of this study is to examine the influence of snow water equivalent (SWE) on droughts in the United States and find large-scale climatic predictors for SWE and drought. For this, a Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition, is performed with snow data from the ERA–Interim reanalysis and the self-calibrating Palmer Drought Severity Index (sc–PDSI) as drought index. Furthermore, the relationship of resulting principal components and original data with atmospheric patterns is investigated. The leading mode shows the spatial connection between SWE and drought via downstream water/moisture transport. Especially the Rocky Mountains in Colorado (CR) play a key role for the central and western South, but the Sierra Nevada and even the Appalachian Mountains are relevant, too. The temperature and precipitation based sc–PDSI is able to capture this link because increased soil moisture results in higher evapotranspiration with lower sensible heat and vice versa. A time shifted MCA indicates a prediction skill for drought conditions in spring and early summer for the downstream regions of CR on the basis of SWE in March. Furthermore, the phase of the El Niño–Southern Oscillation is a good predictor for drought in the southern US and SWE around Colorado. The influence of the North Atlantic Oscillation and Pacific North American Pattern is not that clear.

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Spatiotemporal variation of snow depth in the Northern Hemisphere from 1992 to 2016

10.5194/tc-2019-33 ◽

2019 ◽

Author(s):

Xiongxin Xiao ◽

Tingjun Zhang ◽

Xinyue Zhong ◽

Xiaodong Li ◽

Yuxing Li

Keyword(s):

Snow Cover ◽

Northern Hemisphere ◽

Snow Depth ◽

Snow Water Equivalent ◽

Retrieval Algorithm ◽

Support Vector ◽

Snow Water ◽

Microwave Sensors ◽

Global Surface

Abstract. Snow cover is an effective best indicator of climate change due to its effect on regional and global surface energy, water balance, hydrology, climate, and ecosystem function. We developed a long term Northern Hemisphere daily snow depth and snow water equivalent product (NHSnow) by the application of the support vector regression (SVR) snow depth retrieval algorithm to historical passive microwave sensors from 1992 to 2016. The accuracies of the snow depth product were evaluated against observed snow depth at meteorological stations along with the other two snow cover products (GlobSnow and ERA-Interim/Land) across the Northern Hemisphere. The evaluation results showed that NHSnow performs generally well with relatively high accuracy. Further analysis were performed across the Northern Hemisphere during 1992–2016, which used snow depth, total snow water equivalent (snow mass) and, snow cover days as indexes. Analysis showed the total snow water equivalent has a significant declining trends (~ 5794 km3 yr−1, 12.5 % reduction). Although spatial variation pattern of snow depth and snow cover days exhibited slight regional differences, it generally reveals a decreasing trend over most of the Northern Hemisphere. Our work provides evidence that rapid changes in snow depth and total snow water equivalent are occurring beginning at the turn of the 21st century with dramatic, surface-based warming.

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Snow water scarcity induced by record-breaking warm winter in 2020 in Japan

Scientific Reports ◽

10.1038/s41598-020-75440-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Satoshi Watanabe ◽

Shunji Kotsuki ◽

Shinjiro Kanae ◽

Kenji Tanaka ◽

Atsushi Higuchi

Keyword(s):

Water Resources ◽

High Temperatures ◽

Water Scarcity ◽

Land Surface ◽

Snow Water Equivalent ◽

River Basins ◽

Reanalysis Data ◽

Temperature And Precipitation ◽

Snow Water ◽

The Impact

Abstract This study highlights the severity of the low snow water equivalent (SWE) and remarkably high temperatures in 2020 in Japan, where reductions in SWE have significant impacts on society due to its importance for water resources. A continuous 60-year land surface simulation forced by reanalysis data revealed that the low SWE in many river basins in the southern snowy region of mainland Japan are the most severe on record. The impact of the remarkably high temperatures in 2020 on the low SWE was investigated by considering the relationships among SWE, temperature, and precipitation. The main difference between the 2020 case and prior periods of low SWE is the record-breaking high temperatures. Despite the fact that SWE was the lowest in 2020, precipitation was much higher than that in 2019, which was one of the lowest SWE on record pre-2020. The results indicate the possibility that even more serious low-SWE periods will be caused if lower precipitation and higher temperatures occur simultaneously.

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Systematic Patterns of the Inconsistency between Snow Water Equivalent and Accumulated Precipitation as Reported by the Snowpack Telemetry Network

Journal of Hydrometeorology ◽

10.1175/jhm-d-12-066.1 ◽

2012 ◽

Vol 13 (6) ◽

pp. 1970-1976 ◽

Cited By ~ 19

Author(s):

Jonathan D. D. Meyer ◽

Jiming Jin ◽

Shih-Yu Wang

Keyword(s):

Sierra Nevada ◽

Rocky Mountains ◽

Snow Water Equivalent ◽

Systematic Bias ◽

Mountain Ranges ◽

Wind Speeds ◽

Drifting Snow ◽

Snow Water ◽

Interior West ◽

One Year

Abstract The authors investigated the accuracy of snow water equivalent (SWE) observations compiled by 748 Snowpack Telemetry stations and attributed the systematic bias introduced to SWE measurements to drifting snow. Often observed, SWE outpaces accumulated precipitation (AP), which can be statistically and physically explained through 1) precipitation undercatchment and/or 2) drifting snow. Forty-four percent of the 748 stations reported at least one year where the maximum SWE was greater than AP, while 16% of the stations showed this inconsistency for at least 20% of the observed years. Regions with a higher likelihood of inconsistency contained drier snow and are exposed to higher winds speeds, both of which are positively correlated to drifting snow potential as well as gauge undercatch. Differentiating between gauge undercatch and potential drifting scenarios, days when SWE increased but AP remained zero were used. These drift days occurred on an average of 13.3 days per year for all stations, with 31% greater wind speeds at 10 m for such days (using reanalysis winds). Findings suggest marked consistency between SWE and AP throughout the Cascade Mountains and lower elevations of the interior west while indicating notable inconsistency between these two variables throughout the higher elevations of the Rocky Mountains, Utah mountain ranges, and the Sierra Nevada.

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Assessment of a multiresolution snow reanalysis framework: a multidecadal reanalysis case over the upper Yampa River basin, Colorado

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3575-2018 ◽

2018 ◽

Vol 22 (7) ◽

pp. 3575-3587 ◽

Cited By ~ 4

Author(s):

Elisabeth Baldo ◽

Steven A. Margulis

Keyword(s):

Data Assimilation ◽

River Basin ◽

Large Scale ◽

Snow Water Equivalent ◽

Low Complexity ◽

Snow Accumulation ◽

Computationally Efficient ◽

Snow Cover Area ◽

Mountain Ranges ◽

Snow Water

Abstract. A multiresolution (MR) approach was successfully implemented in the context of a data assimilation (DA) framework to efficiently estimate snow water equivalent (SWE) over a large head water catchment in the Colorado River basin (CRB), while decreasing computational constraints by 60 %. A total of 31 years of fractional snow cover area (fSCA) images derived from Landsat TM, ETM+, and OLI sensor measurements were assimilated to generate two SWE reanalysis datasets, a baseline case at a uniform 90 m spatial resolution and another using the MR approach. A comparison of the two showed negligible differences in terms of snow accumulation, melt, and timing for the posterior estimates (in terms of both ensemble median and coefficient of variation). The MR approach underestimated the baseline peak SWE by less than 2 % and underestimated day of peak and duration of the accumulation season by a day on average. The largest differences were, by construct, limited primarily to areas of low complexity, where shallow snowpacks tend to exist. The MR approach should allow for more computationally efficient implementations of snow data assimilation applications over large-scale mountain ranges, with accuracies similar to those that would be obtained using ∼ 100 m simulations. Such uniform resolution applications are generally infeasible due to the computationally expensive nature of ensemble-based DA frameworks.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-16-0188.1 ◽

2017 ◽

Vol 18 (4) ◽

pp. 1021-1031 ◽

Cited By ~ 31

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.

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Estimation of snow water equivalent in a mountain range by using a dynamic regression approach

10.5194/egusphere-egu2020-13112 ◽

2020 ◽

Author(s):

Antonio-Juan Collados-Lara ◽

David Pulido-Velazquez ◽

Eulogio Pardo-Igúzquiza ◽

Esteban Alonso-González ◽

Juan Ignacio López-Moreno

Keyword(s):

Sierra Nevada ◽

Snow Depth ◽

Snow Water Equivalent ◽

Coefficient Of Determination ◽

Mountain Range ◽

Snow Cover Area ◽

Explanatory Variables ◽

Regression Approach ◽

Snow Water

<p>The snow dynamics in alpine systems governs the hydrological cycle in these regions. However, snow data are usually limited due to poor accessibility and limited funds. On the other hand, the majority of scientific studies about snow resources are carried out at mountain slope or basin scale. The main goal of this work is to propose a parsimonious methodology to estimate snow water equivalent (SWE) at mountain range scale. A regression model that includes non-steady explanatory variables is proposed to assess snow depth dynamic based on the information coming from snow depth point observations, a digital elevation model, snow cover area from satellite and a precipitation index representative of the area. The main advantages of the method are its applicability in cases with limited information and in mountain ranges scales. In the proposed methodology different regression model structures with different degrees of complexity are calibrated combining steady and non-steady explanatory variables (elevation, slope, longitude, latitude, eastness, northness, maximum upwind slope, radiation, curvature, accumulated snow cover area and precipitation in a temporal window) and four basic mathematical transformations of these variables (square, root square, inverse and logarithm). In the case of the temporal variables different time windows to define the accumulated values of the explanatory indices have been tested too. We have applied the methodology in a case study, the Sierra Nevada mountain range (Southern Spain), where the calibration has been performed by using the snow depth data observation provided by the ERHIN program which have a very low temporal frequency (2 or 3 measurement per year). When only non-steady explanatory variables are considered, the coefficient of determination of the global spatial estimation model is 0.55. When we also include non-steady variables we obtain an approach with a coefficient of determination of 0.62. We have also calibrated a new regression approach by using, in addition to the ERHIN program information, data coming from a detailed temporal series of snow depth in a new specific location, which has allow to obtain models with R&#178; of 0.59 (for steady explanatory variables) and 0.64 (including also non-steady explanatory variables). The dynamic of the snow density in the mountain range has been estimated by means of a physically based simulation driven by WRF data. Combining the snow depth and the density approaches we have estimated the final SWE in Sierra Nevada.&#160;</p><p>This research has been partially supported by the SIGLO-AN project (RTI2018-101397-B-I00) from the Spanish Ministry of Science, Innovation and Universities (Programa Estatal de I+D+I orientada a los Retos de la Sociedad).</p>

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Exploring the Origins of Snow Drought in the Northern Sierra Nevada, California

Earth Interactions ◽

10.1175/ei-d-17-0027.1 ◽

2018 ◽

Vol 22 (2) ◽

pp. 1-13 ◽

Cited By ~ 12

Author(s):

Benjamin J. Hatchett ◽

Daniel J. McEvoy

Keyword(s):

Water Resources ◽

Sierra Nevada ◽

Time Scales ◽

Snow Water Equivalent ◽

Mountain Watersheds ◽

Snow Water ◽

Peak Runoff ◽

Widespread Interest ◽

Runoff Events ◽

Low Precipitation

Abstract The concept of snow drought is gaining widespread interest as the climate of snow-dominated mountain watersheds continues to change. Warm snow drought is defined as above- or near-average accumulated precipitation coinciding with below-average snow water equivalent at a point in time. Dry snow drought is defined as below-average accumulated precipitation and snow water equivalent at a point in time. This study contends that such point-in-time definitions might miss important components of how snow droughts originate, persist, and terminate. Using these simple definitions and a variety of observations at monthly, daily, and hourly time scales, the authors explore the hydrometeorological origins of potential snow droughts in the northern Sierra Nevada from water years 1951 to 2017. This study finds that snow droughts can result from extreme early season precipitation, frequent rain-on-snow events, and low precipitation years. Late-season snow droughts can follow persistent warm and dry periods with effects that depend upon elevation. Many snow droughts were characterized by lower snow fractions and midwinter peak runoff events. These findings can guide improved evaluations of historical and potential future snow droughts, particularly with regards to how impacts on water resources and mountain ecosystems may vary depending on how snow droughts originate and evolve in time.

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Evaluating Snow in EURO-CORDEX Regional Climate Models with Observations for the European Alps: Biases and Their Relationship to Orography, Temperature, and Precipitation Mismatches

Atmosphere ◽

10.3390/atmos11010046 ◽

2019 ◽

Vol 11 (1) ◽

pp. 46

Author(s):

Michael Matiu ◽

Marcello Petitta ◽

Claudia Notarnicola ◽

Marc Zebisch

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Large Scale ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Horizontal Resolution ◽

Winter Period ◽

European Alps ◽

Temperature And Precipitation

Climate models are important tools to assess current and future climate. While they have been extensively used for studying temperature and precipitation, only recently regional climate models (RCMs) arrived at horizontal resolutions that allow studies of snow in complex mountain terrain. Here, we present an evaluation of the snow variables in the World Climate Research Program Coordinated Regional Downscaling Experiment (EURO-CORDEX) RCMs with gridded observations of snow cover (from MODIS remote sensing) and temperature and precipitation (E-OBS), as well as with point (station) observations of snow depth and temperature for the European Alps. Large scale snow cover dynamics were reproduced well with some over- and under-estimations depending on month and RCM. The orography, temperature, and precipitation mismatches could on average explain 31% of the variability in snow cover bias across grid-cells, and even more than 50% in the winter period November–April. Biases in average monthly snow depth were remarkably low for reanalysis driven RCMs (<approx. 30 cm), and large for the GCM driven ones (up to 200 cm), when averaged over all stations within 400 m of altitude difference with RCM orography. Some RCMs indicated low snow cover biases and at the same time high snow depth biases, and vice versa. In summary, RCMs showed good skills in reproducing alpine snow cover conditions with regard to their limited horizontal resolution. Detected shortcomings in the models depended on the considered snow variable, season and individual RCM.

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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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