Snow Water Equivalent
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2021 ◽  
Vol 15 (11) ◽  
pp. 5261-5280
Author(s):  
Yufei Liu ◽  
Yiwen Fang ◽  
Steven A. Margulis

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.


2021 ◽  
Vol 15 (11) ◽  
pp. 5227-5239
Author(s):  
Anton Jitnikovitch ◽  
Philip Marsh ◽  
Branden Walker ◽  
Darin Desilets

Abstract. Grounded in situ, or invasive, cosmic ray neutron sensors (CRNSs) may allow for continuous, unattended measurements of snow water equivalent (SWE) over complete winter seasons and allow for measurements that are representative of spatially variable Arctic snow covers, but few studies have tested these types of sensors or considered their applicability at remote sites in the Arctic. During the winters of 2016/2017 and 2017/2018 we tested a grounded in situ CRNS system at two locations in Canada: a cold, low- to high-SWE environment in the Canadian Arctic and at a warm, low-SWE landscape in southern Ontario that allowed easier access for validation purposes. Five CRNS units were applied in a transect to obtain continuous data for a single significant snow feature; CRNS-moderated neutron counts were compared to manual snow survey SWE values obtained during both winter seasons. The data indicate that grounded in situ CRNS instruments appear able to continuously measure SWE with sufficient accuracy utilizing both a linear regression and nonlinear formulation. These sensors can provide important SWE data for testing snow and hydrological models, water resource management applications, and the validation of remote sensing applications.


Author(s):  
Stephen R. Sobie ◽  
Trevor Q. Murdock

Abstract Information about snow water equivalent in southwestern British Columbia is used for flood management, agriculture, fisheries, and water resource planning. This study evaluates whether a process-based, energy balance snow model supplied with high-resolution statistically downscaled temperature and precipitation data can effectively simulate snow water equivalent (SWE) in the mountainous terrain of this region. Daily values of SWE from 1951 to 2018 are simulated at 1 km resolution and evaluated using a reanalysis SWE product (SNODAS), manual snow survey measurements at 41 sites, and automated snow pillows at six locations in the study region. Simulated SWE matches observed inter-annual variability well (R2 > 0.8 for annual maximum SWE) but peak SWE biases of 20% to 40% occur at some sites in the study domain, and higher biases occur where observed SWE is very low. Modelled SWE displays lower bias compared to SNODAS reanalysis at most manual survey locations. Future projections for the study area are produced using 12 downscaled climate model simulations and used to illustrate the impacts of climate change on SWE at 1°C, 2°C, and 3°C of warming. Model results are used to quantify spring SWE changes at different elevations of the Whistler mountain ski resort, and the sensitivity of annual peak SWE in Metro Vancouver municipal watersheds to moderate temperature increases. The results illustrate both the potential utility of a process-based snow model, and identify areas where the input meteorological variables could be improved.


2021 ◽  
Author(s):  
Donghang Shao ◽  
Hongyi Li ◽  
Jian Wang ◽  
Xiaohua Hao ◽  
Tao Che ◽  
...  

Abstract. Snow water equivalent is an important parameter of the surface hydrological and climate systems, and it has a profound impact on Arctic amplification and climate change. However, there are great differences among existing snow water equivalent products. In the Pan-Arctic region, the existing snow water equivalent products are limited time span and limited spatial coverage, and the spatial resolution is coarse, which greatly limits the application of snow water equivalent data in cryosphere change and climate change studies. In this study, utilizing the ridge regression model (RRM) of a machine learning algorithm, we integrated various existing snow water equivalent (SWE) products to generate a spatiotemporally seamless and high-precision RRM SWE product. The results show that it is feasible to utilize a ridge regression model based on a machine learning algorithm to prepare snow water equivalent products on a global scale. We evaluated the accuracy of the RRM SWE product using Global Historical Climatology Network (GHCN) data and Russian snow survey data. The MAE, RMSE, R, and R2; between the RRM SWE products and observed snow water equivalents are 0.24, 30.29 mm, 0.87, and 0.76, respectively. The accuracy of the RRM SWE dataset is improved by 24 %, 25 %, 32 %, 7 %, and 10 % compared with the original AMSR-E/AMSR2 snow water equivalent dataset, ERA-Interim SWE dataset, Global Land Data Assimilation System (GLDAS) SWE dataset, GlobSnow SWE dataset, and ERA5-land SWE dataset, respectively, and it has a higher spatial resolution. The RRM SWE product production method does not rely too much on an independent snow water equivalent product, it makes full use of the advantages of each snow water equivalent dataset, and it considers the altitude factor. The average MAE of RRM SWE product at different altitude intervals is 0.24 and the average RMSE is 23.55 mm, this method has good stability, it is extremely suitable for the production of snow datasets with large spatial scales, and it can be easily extended to the preparation of other snow datasets. The RRM SWE product is expected to provide more accurate snow water equivalent data for the hydrological model and climate model and provide data support for cryosphere change and climate change studies. The RRM SWE product is available from the ‘A Big Earth Data Platform for Three Poles’ (http://dx.doi.org/10.11888/Snow.tpdc.271556) (Li et al., 2021).


2021 ◽  
Author(s):  
Leung Tsang ◽  
Michael Durand ◽  
Chris Derksen ◽  
Ana P. Barros ◽  
Do-Hyuk Kang ◽  
...  

Abstract. Seasonal snow cover is the largest single component of the cryosphere in areal extent, covering an average of 46 million square km of Earth's surface (31 % of the land area) each year, and is thus an important expression of and driver of the Earth’s climate. In recent years, Northern Hemisphere spring snow cover has been declining at about the same rate (~ −13 %/decade) as Arctic summer sea ice. More than one-sixth of the world’s population relies on seasonal snowpack and glaciers for a water supply that is likely to decrease this century. Snow is also a critical component of Earth’s cold regions' ecosystems, in which wildlife, vegetation, and snow are strongly interconnected. Snow water equivalent (SWE) describes the quantity of snow stored on the land surface and is of fundamental importance to water, energy, and geochemical cycles. Quality global SWE estimates are lacking. Given the vast seasonal extent combined with the spatially variable nature of snow distribution at regional and local scales, surface observations will not be able to provide sufficient SWE information. Satellite observations presently cannot provide SWE information at the spatial and temporal resolutions required to address science and high socio-economic value applications such as water resource management and streamflow forecasting. In this paper, we review the potential contribution of X- and Ku-Band Synthetic Aperture Radar (SAR) for global monitoring of SWE. We describe radar interactions with snow-covered landscapes, characterization of snowpack properties using radar measurements, and refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. SAR can image the surface during both day and night regardless of cloud cover, allowing high-frequency revisit at high spatial resolution as demonstrated by missions such as Sentinel-1. The physical basis for estimating SWE from X- and Ku-band radar measurements at local scales is volume scattering by millimetre-scale snow grains. Inference of global snow properties from SAR requires an interdisciplinary approach based on field observations of snow microstructure, physical snow modelling, electromagnetic theory, and retrieval strategies over a range of scales. New field measurement capabilities have enabled significant advances in understanding snow microstructure such as grain size, densities, and layering. We describe radar interactions with snow-covered landscapes, the characterization of snowpack properties using radar measurements, and the refinement of retrieval algorithms via synergy with other microwave remote sensing approaches. This review serves to inform the broader snow research, monitoring, and applications communities on progress made in recent decades, and sets the stage for a new era in SWE remote-sensing from SAR measurements.


2021 ◽  
Vol 11 (18) ◽  
pp. 8365
Author(s):  
Liming Gao ◽  
Lele Zhang ◽  
Yongping Shen ◽  
Yaonan Zhang ◽  
Minghao Ai ◽  
...  

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced.


2021 ◽  
Author(s):  
Rebecca Gugerli ◽  
Darin Desilets ◽  
Nadine Salzmann

Abstract. Monitoring the snow water equivalent (SWE) in the harsh environments of high mountain regions is a challenge. Here, we explore the use of muon counts to infer SWE. We deployed a muonic cosmic ray snow gauge (µ-CRSG) on a Swiss glacier during the snow rich winter season 2020/21 (almost 2000 mm w.e.). The µ-CRSG measurements agree well with measurements by a neutronic cosmic ray snow gauge (n-CRSG) and they lie within the uncertainty of manual observations. We conclude that the µ-CRSG is a highly promising method to monitor SWE in remote high mountain environments with several advantages over the n-CRSG.


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