scholarly journals Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009)

2018 ◽  
Vol 4 (1) ◽  
pp. e1701550 ◽  
Author(s):  
Taylor Smith ◽  
Bodo Bookhagen
Keyword(s):  
Snow Water Equivalent ◽  
High Mountain ◽  
Seasonal Snow ◽  
Snow Water

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.

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

2021 ◽  
Vol 15 (11) ◽  
pp. 5261-5280
Author(s):  
Yufei Liu ◽  
Yiwen Fang ◽  
Steven A. Margulis
Keyword(s):  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Water Cycle ◽  
Remote Sensing Data ◽  
Spatiotemporal Distribution ◽  
High Mountain ◽  
Reanalysis Dataset ◽  
Seasonal Snow ◽  
Snow Storage ◽  
Snow Water

Abstract. Seasonal snowpack is an essential component in the hydrological cycle and plays a significant role in supplying water resources to downstream users. Yet the snow water equivalent (SWE) in seasonal snowpacks, and its space–time variation, remains highly uncertain, especially over mountainous areas with complex terrain and sparse observations, such as in High Mountain Asia (HMA). In this work, we assessed the spatiotemporal distribution of seasonal SWE, obtained from a new 18-year HMA Snow Reanalysis (HMASR) dataset, as part of the recent NASA High Mountain Asia Team (HiMAT) effort. A Bayesian snow reanalysis scheme previously developed to assimilate satellite-derived fractional snow-covered area (fSCA) products from Landsat and MODIS platforms has been applied to develop the HMASR dataset (at a spatial resolution of 16 arcsec (∼500 m) and daily temporal resolution) over the joint Landsat–MODIS period covering water years (WYs) 2000–2017. Based on the results, the HMA-wide total SWE volume is found to be around 163 km3 on average and ranges from 114 km3 (WY2001) to 227 km3 (WY2005) when assessed over 18 WYs. The most abundant snowpacks are found in the northwestern basins (e.g., Indus, Syr Darya and Amu Darya) that are mainly affected by the westerlies, accounting for around 66 % of total seasonal SWE volume. Seasonal snowpack in HMA is depicted by snow accumulating through October to March and April, typically peaking around April and depleting in July–October, with variations across basins and WYs. When examining the elevational distribution over the HMA domain, seasonal SWE volume peaks at mid-elevations (around 3500 m), with over 50 % of the volume stored above 3500 m. Above-average amounts of precipitation causes significant overall increase in SWE volumes across all elevations, while an increase in air temperature (∼1.5 K) from cooler to normal conditions leads to an redistribution in snow storage from lower elevations to mid-elevations. This work brings new insight into understanding the climatology and variability of seasonal snowpack over HMA, with the regional snow reanalysis constrained by remote-sensing data, providing a new reference dataset for future studies of seasonal snow and how it contributes to the water cycle and climate over the HMA region.

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.

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 Measuring changes in snowpack SWE continuously on a landscape scale using lake water pressure 

2021 ◽  
Author(s):  
Hamish Pritchard ◽  
Daniel Farinotti ◽  
Steven Colwell
Keyword(s):  
Lake Water ◽  
Large Scale ◽  
Climate Models ◽  
Snow Water Equivalent ◽  
Water Pressure ◽  
High Mountain ◽  
Alpine Valley ◽  
Weather And Climate ◽  
Forecast Models ◽  
Snow Water

<p>The seasonal snowpack is a globally important water resource that is notoriously difficult to measure. Existing instruments make measurements of falling or accumulating snow water equivalent (SWE) that are susceptible to bias, and most can represent only a point in the landscape. Furthermore the global array of SWE sensors is too sparse and too poorly distributed to be an adequate constraint on snow in weather and climate models. We present a new approach to monitoring snowpack SWE from time series of lake water pressure. We tested our method in the lowland Finnish Arctic and in an alpine valley and high-mountain cirque in Switzerland, and found that we could measure changes in SWE and their uncertainty through snowfalls with little bias and with an uncertainty comparable to or better than that achievable by other instruments. More importantly, our method inherently senses change over the whole lake surface which can be several square kilometres, or hundreds of million of times larger than the aperture of a pluviometer. This large scale makes our measurements directly comparable to the grid cells of weather and climate models. We find, for example, snowfall biases of up to 100% in operational forecast models AROME-Arctic and COSMO-1. Seasonally-frozen lakes are widely distributed at high latitudes and are particularly common in mountain ranges, hence our new method is particularly well suited to the widespread, autonomous monitoring of snow-water resources in remote areas that are largely unmonitored today. This is potentially transformative in reducing uncertainty in regional precipitation and runoff in seasonally-cold climates.</p>

scholarly journals Recent advances in remote sensing of seasonal snow

2010 ◽  
Vol 56 (200) ◽  
pp. 1141-1150 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Analysis Techniques ◽  
Seasonal Snow ◽  
The Past ◽  
Snow Cover Extent ◽  
Snow Water ◽  
Instruments And Techniques

AbstractRemote sensing offers local, regional and global observations of seasonal snow, providing key information on snowpack processes. This brief review highlights advancements in instrumentation and analysis techniques that have been developed over the past decade. Areas of advancement include improved algorithms for mapping snow-cover extent, snow albedo, snow grain size, snow water equivalent, melt detection and snow depth, as well as new uses of instruments such as multiangular spectroradiometers, scatterometry and lidar. Limitations and synergies of the instruments and techniques are discussed, and remaining challenges such as multisensor mapping, scaling issues, vegetation correction and data assimilation are identified.

scholarly journals Continuous and autonomous snow water equivalent measurements by a cosmic ray sensor on an alpine glacier

2019 ◽  
Vol 13 (12) ◽  
pp. 3413-3434 ◽  
Author(s):  
Rebecca Gugerli ◽  
Nadine Salzmann ◽  
Matthias Huss ◽  
Darin Desilets
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Cosmic Ray ◽  
Snow Accumulation ◽  
Scaling Factor ◽  
High Temporal Resolution ◽  
High Mountain ◽  
Alpine Glacier ◽  
Snow Drift ◽  
Snow Water

Abstract. Snow water equivalent (SWE) measurements of seasonal snowpack are crucial in many research fields. Yet accurate measurements at a high temporal resolution are difficult to obtain in high mountain regions. With a cosmic ray sensor (CRS), SWE can be inferred from neutron counts. We present the analyses of temporally continuous SWE measurements by a CRS on an alpine glacier in Switzerland (Glacier de la Plaine Morte) over two winter seasons (2016/17 and 2017/18), which differed markedly in the amount and timing of snow accumulation. By combining SWE with snow depth measurements, we calculate the daily mean density of the snowpack. Compared to manual field observations from snow pits, the autonomous measurements overestimate SWE by +2 % ± 13 %. Snow depth and the bulk snow density deviate from the manual measurements by ±6 % and ±9 %, respectively. The CRS measured with high reliability over two winter seasons and is thus considered a promising method to observe SWE at remote alpine sites. We use the daily observations to classify winter season days into those dominated by accumulation (solid precipitation, snow drift), ablation (snow drift, snowmelt) or snow densification. For each of these process-dominated days the prevailing meteorological conditions are distinct. The continuous SWE measurements were also used to define a scaling factor for precipitation amounts from nearby meteorological stations. With this analysis, we show that a best-possible constant scaling factor results in cumulative precipitation amounts that differ by a mean absolute error of less than 80 mm w.e. from snow accumulation at this site.

scholarly journals Estimated seasonal snow cover and snowfall in Japan

1993 ◽  
Vol 18 ◽  
pp. 179-184
Author(s):  
Tsutomu Nakamura ◽  
Osamu Abe
Keyword(s):  
Snow Cover ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Heavy Snowfall ◽  
Seasonal Snow ◽  
Snow Covered Area ◽  
Covered Area ◽  
Prone Area ◽  
Snow Water ◽  
Seasonal Snow Cover

The average amounts of seasonal snow cover and snowfall in Japan were calculated as 7.9 × 1013kg and 1.2 × 1014kg, respectively. The mass of seasonal snow cover of a heavy-snowfall winter, 1980–81 (56-Gosetsu), was calculated as 1.3 × 1014kg. The amount of 7.9 × 1013kg was converted to water equivalent of 230 mm on the whole snow-covered area, including snow-prone area. A mean of 370 mm in snow water equivalent was calculated for the snow area where mean snow depth on the ground was more than 10 cm.

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