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

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

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p>

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.

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

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

<p>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.  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.</p><p>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 (20<sup>th</sup> century reanalyses by NOAA) reanalyses (ANATEM method, Kuentz et al., 2015) and up to 2100 using GIEC Climate Change scenarii (+4.5 W/m² and + 8.5 W/m² 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.</p><p>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.</p><p>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 °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.</p>

Evaluation and Intercomparison of Multiple Snow Water Equivalent Products over the Tibetan Plateau

2019 ◽  
Vol 20 (10) ◽  
pp. 2043-2055 ◽  
Author(s):  
Qingyun Bian ◽  
Zhongfeng Xu ◽  
Long Zhao ◽  
Yong-Fei Zhang ◽  
Hui Zheng ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Asian Summer Monsoon ◽  
Accurate Estimation ◽  
The Tibetan Plateau ◽  
Seasonal Forecasts ◽  
Snow Condition ◽  
Snow Water

Abstract Snow cover affects the thermal conditions of the Tibetan Plateau through snow–albedo feedback and snowmelt, which, in turn, modulates the Asian summer monsoon climate. An accurate estimation of the snow condition on the Tibetan Plateau is therefore of great importance in both seasonal forecasts and climate studies. Estimation of snow water equivalent (SWE) over the Tibetan Plateau is challenging due to the high altitude, complex terrain, and insufficient in situ observations. Multiple SWE products derived from satellite estimates, reanalyses, regional climate model simulations, and land data assimilations are intercompared in terms of daily, seasonal, and annual variations and are then evaluated against in situ SWE observations. The results show a relatively consistent seasonal to interannual variability of the SWE estimates among the products. The discrepancies in magnitude are large, however, especially in winter and spring. Evaluation against in situ SWE observations indicates that none of these products is capable of accurately characterizing both the spatial pattern and temporal variations.

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 A regional climate model hindcast for Siberia: analysis of snow water equivalent

2013 ◽  
Vol 7 (4) ◽  
pp. 1017-1034 ◽  
Author(s):  
K. Klehmet ◽  
B. Geyer ◽  
B. Rockel
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Snow Water Equivalent ◽  
Climate Model ◽  
Regional Climate ◽  
Added Value ◽  
Temporal Consistency ◽  
Climate Data ◽  
Snow Water ◽  
Good Agreement

Abstract. This study analyzes the added value of a regional climate model hindcast with respect to snow water equivalent (SWE) for Siberia when compared to SWE estimates from forcing NCEP-R. In addition, we examine the discrepancies of simulated SWE to several recent reanalysis products (NCEP-R2 NCEP-CFSR, ERA-Interim). We apply the regional climate model COSMO-CLM (CCLM) to a 50 km grid spacing using NCEP-R1 as driving force to obtain a 63 yr (1948 to 2010) gridded dataset of historical SWE. Simulated regional climate data is necessary because of the absence of station data in that region. To perform large-scale assessments we use the satellite-derived daily SWE product of ESA DUE GlobSnow from 1987 to 2010. Russian station SWE data is used for cross-checking the findings. In January (mid-winter), the SWE hindcast is in good agreement with GlobSnow, whereas it overestimates SWE during the melting season. CCLM shows a clear added value in providing realistic SWE information compared to the driving reanalysis. The temporal consistency of CCLM is higher than that presented by ERA-Interim and NCEP-R2.

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 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).

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