Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

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.

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Snowfall and snow accumulation processes during the MOSAiC winter and spring season

10.5194/tc-2021-126 ◽

2021 ◽

Author(s):

David N. Wagner ◽

Matthew D. Shupe ◽

Ola G. Persson ◽

Taneil Uttal ◽

Markus M. Frey ◽

...

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Snow Water Equivalent ◽

Temporal Dynamics ◽

Snow Accumulation ◽

Blowing Snow ◽

Drifting Snow ◽

Snow Water ◽

Abstract Data ◽

Good Agreement

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation, and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth (HS) and density retrievals from a SnowMicroPen (SMP) and approximately weekly-measured snow depths along fixed transect paths. Hence, the computed SWE considers surface heterogeneities over an average path length of 1469 m. We used the SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were compared with ERA5 reanalysis snowfall rates for the drift track. Our study shows that the simple fitted HS-SWE function can well be used to compute SWE along a transect path based on SMP SWE retrievals and snow-depth measurements. We found an accumulated snow mass of 34 mm SWE until 26 April 2020. Further, we found that the Vaisala Present Weather Detector 22 (PWD22), installed on a railing on the top deck of research vessel Polarstern was least affected by blowing snow and showed good agreements with SWE retrievals along the transect, however, it also systematically underestimated snowfall. The OTT Pluvio2 and the OTT Parsivel2 were largely affected by wind and blowing snow, leading to higher measured precipitation rates, but when eliminating drifting snow periods, especially the OTT Pluvio2 shows good agreements with ground measurements. A comparison with ERA5 snowfall data reveals a good timing of the snowfall events and good agreement with ground measurements but also a tendency towards overestimation. Retrieved snowfall from the ship-based Ka-band ARM Zenith Radar (KAZR) shows good agreements with SWE of the snow cover and comparable differences as ERA5. Assuming the KAZR derived snowfall as an upper limit and PWD22 as a lower limit of a cumulative snowfall range, we estimate 72 to 107 mm measured between 31 October 2019 and 26 April 2020. For the same period, we estimate the precipitation mass loss along the transect due to erosion and sublimation as between 53 and 68 %. Until 7 May 2020, we suggest a cumulative snowfall of 98–114 mm.

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Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment

The Cryosphere ◽

10.5194/tc-11-1647-2017 ◽

2017 ◽

Vol 11 (4) ◽

pp. 1647-1664 ◽

Cited By ~ 29

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.

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Development of a Snow Load Alert System, “YukioroSignal” for Aiding Roof Snow Removal Decisions in Snowy Areas in Japan

Journal of Disaster Research ◽

10.20965/jdr.2020.p0688 ◽

2020 ◽

Vol 15 (6) ◽

pp. 688-697

Author(s):

Hiroyuki Hirashima ◽

Tsutomu Iyobe ◽

Katsuhisa Kawashima ◽

Hiroaki Sano ◽

◽

...

Keyword(s):

Snow Depth ◽

Load Distribution ◽

Snow Water Equivalent ◽

Snow Accumulation ◽

Risk Level ◽

Residential Areas ◽

Alert System ◽

Snow Removal ◽

Snow Load ◽

Snow Water

This study developed a snow load alert system, known as the “YukioroSignal”; this system aims to provide a widespread area for assessing snow load distribution and the information necessary for aiding house roof snow removal decisions in snowy areas of Japan. The system was released in January 2018 in Niigata Prefecture, Japan, and later, it was expanded to Yamagata and Toyama prefectures in January 2019. The YukioroSignal contains two elements: the “Quasi-Real-Time Snow Depth Monitoring System,” which collects snow depth data, and the numerical model known as SNOWPACK, which can calculate the snow water equivalent (SWE). The snow load per unit area is estimated to be equivalent to SWE. Based on the house damage risk level, snow load distribution was indicated by colors following the ISO 22324. The system can also calculate post-snow removal snow loads. The calculated snow load was validated by using the data collected through snow pillows. The simulated snow load had a root mean square error (RMSE) of 21.3%, which was relative to the observed snow load. With regard to residential areas during the snow accumulation period, the RMSE was 13.2%. YukioroSignal received more than 56,000 pageviews in the snowheavy 2018 period and 26,000 pageviews in the less snow-heavy 2019 period.

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Experimental measurements for improved understanding and simulation of snowmelt events in the Western Tatra Mountains

Journal of Hydrology and Hydromechanics ◽

10.1515/johh-2016-0038 ◽

2016 ◽

Vol 64 (4) ◽

pp. 316-328 ◽

Cited By ~ 5

Author(s):

Pavel Krajčí ◽

Michal Danko ◽

Jozef Hlavčo ◽

Zdeněk Kostka ◽

Ladislav Holko

Keyword(s):

Spatial Variability ◽

Snow Cover ◽

Air Temperature ◽

Snow Water Equivalent ◽

Flood Forecasting ◽

Snow Accumulation ◽

Small Scale ◽

Climatic Data ◽

Tatra Mountains ◽

Snow Water

AbstractSnow accumulation and melt are highly variable. Therefore, correct modeling of spatial variability of the snowmelt, timing and magnitude of catchment runoff still represents a challenge in mountain catchments for flood forecasting. The article presents the setup and results of detailed field measurements of snow related characteristics in a mountain microcatchment (area 59 000 m2, mean altitude 1509 m a. s. l.) in the Western Tatra Mountains, Slovakia obtained in winter 2015. Snow water equivalent (SWE) measurements at 27 points documented a very large spatial variability through the entire winter. For instance, range of the SWE values exceeded 500 mm at the end of the accumulation period (March 2015). Simple snow lysimeters indicated that variability of snowmelt and discharge measured at the catchment outlet corresponded well with the rise of air temperature above 0°C. Temperature measurements at soil surface were used to identify the snow cover duration at particular points. Snow melt duration was related to spatial distribution of snow cover and spatial patterns of snow radiation. Obtained data together with standard climatic data (precipitation and air temperature) were used to calibrate and validate the spatially distributed hydrological model MIKE-SHE. The spatial redistribution of input precipitation seems to be important for modeling even on such a small scale. Acceptable simulation of snow water equivalents and snow duration does not guarantee correct simulation of peakflow at short-time (hourly) scale required for example in flood forecasting. Temporal variability of the stream discharge during the snowmelt period was simulated correctly, but the simulated discharge was overestimated.

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Changes in Snow Storage in the Upper Hron River Basin (Slovakia)

Acta Silvatica et Lignaria Hungarica ◽

10.2478/aslh-2014-0011 ◽

2014 ◽

Vol 10 (2) ◽

pp. 145-160

Author(s):

Katarína Kotríková ◽

Kamila Hlavčová ◽

Róbert Fencík

Keyword(s):

Snow Cover ◽

Snow Depth ◽

Satellite Images ◽

Snow Water Equivalent ◽

Winter Season ◽

Kendall Test ◽

Measured Data ◽

Time Step ◽

Snow Storage ◽

Snow Water

Abstract An evaluation of changes in the snow cover in mountainous basins in Slovakia and a validation of MODIS satellite images are provided in this paper. An analysis of the changes in snow cover was given by evaluating changes in the snow depth, the duration of the snow cover, and the simulated snow water equivalent in a daily time step using a conceptual hydrological rainfall-runoff model with lumped parameters. These values were compared with the available measured data at climate stations. The changes in the snow cover and the simulated snow water equivalent were estimated by trend analysis; its significance was tested using the Mann-Kendall test. Also, the satellite images were compared with the available measured data. From the results, it is possible to see a decrease in the snow depth and the snow water equivalent from 1961-2010 in all the months of the winter season, and significant decreasing trends were indicated in the months of December, January and February

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The role of liquid water percolation representation to estimate snow water equivalent in a Mediterranean mountain region (Mount Lebanon)

10.5194/hess-2019-514 ◽

2019 ◽

Author(s):

Abbas Fayad ◽

Simon Gascoin

Keyword(s):

Snow Cover ◽

Liquid Water ◽

Snow Depth ◽

Snow Water Equivalent ◽

Mountain Regions ◽

Snow Cover Area ◽

Water Percolation ◽

Snow Water ◽

Snow Model ◽

Snow Mass

Abstract. In many Mediterranean mountain regions, the seasonal snowpack is an essential yet poorly known water resource. Here, we examine, for the first time, the spatial distribution and evolution of the snow water equivalent (SWE) during three snow seasons (2013–2016) in the coastal mountains of Lebanon. We run SnowModel (Liston and Elder, 2006a), a spatially-distributed, process-based snow model, at 100 m resolution forced by new automatic weather station (AWS) data in three snow-dominated basins of Mount Lebanon. We evaluate a recent upgrade of the liquid water percolation scheme in SnowModel, which was introduced to improve the simulation of the snow water equivalent (SWE) and runoff in warm maritime regions. The model is evaluated against continuous snow depth and snow albedo observations at the AWS, manual SWE measurements, and MODIS snow cover area between 1200 m and 3000 m a.s.l.. The results show that the new percolation scheme yields better performance especially in terms of SWE but also in snow depth and snow cover area. Over the simulation period between 2013 and 2016, the maximum snow mass was reached between December and March. Peak mean SWE (above 1200 m a.s.l.) changed significantly from year to year in the three study catchments with values ranging between 73 mm and 286 mm we (RMSE between 160 and 260 mm w.e.). We suggest that the major sources of uncertainty in simulating the SWE, in this warm Mediterranean climate, can be attributed to forcing error but also to our limited understanding of the separation between rain and snow at lower-elevations, the transient snow melt events during the accumulation season, and the high-variability of snow depth patterns at the sub-pixel scale due to the wind-driven blown-snow redistribution into karstic features and sinkholes. Yet, the use of a process-based snow model with minimal requirements for parameter estimation provides a basis to simulate snow mass SWE in non-monitored catchments and characterize the contribution of snowmelt to the karstic groundwater recharge in Lebanon. While this research focused on three basins in the Mount Lebanon, it serves as a case study to highlight the importance of wet snow processes to estimate SWE in Mediterranean mountain regions.

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Climate sensitivity of snow water equivalent and snowmelt runoff in a Himalayan catchment

10.5194/tc-2016-216 ◽

2016 ◽

Cited By ~ 1

Author(s):

Emmy E. Stigter ◽

Niko Wanders ◽

Tuomo M. Saloranta ◽

Joseph M. Shea ◽

Marc F.P. Bierkens ◽

...

Keyword(s):

Kalman Filter ◽

Snow Cover ◽

Climate Sensitivity ◽

Snow Depth ◽

Snow Water Equivalent ◽

Snowmelt Runoff ◽

Snow Water ◽

Snow Model ◽

Increasing Temperature

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 this provides no 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 meteorological 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. Landsat 8 and MOD10A2 snow cover maps 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. The approach of modelling snow depth in a Kalman filter framework allows for data-constrained estimation of SWE rather than snow cover alone and this has great potential for future studies in the Himalayas. Climate sensitivity tests with the optimized snow model show a strong decrease in SWE in the valley with increasing temperature. However, at high elevation a decrease in SWE 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. Finally the climate sensitivity study revealed that snowmelt runoff increases in winter and 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.

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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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Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0007.1 ◽

2018 ◽

Vol 19 (11) ◽

pp. 1777-1791 ◽

Cited By ~ 12

Author(s):

Nicholas Dawson ◽

Patrick Broxton ◽

Xubin Zeng

Keyword(s):

United States ◽

Remote Sensing ◽

Snow Cover ◽

Snow Depth ◽

Snow Water Equivalent ◽

Remote Sensing Data ◽

Sensing Data ◽

Snow Cover Extent ◽

Snow Water ◽

The Mean

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.

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Spatial and temporal variability of snow depth and ablation rates in a small mountain catchment

The Cryosphere ◽

10.5194/tc-4-215-2010 ◽

2010 ◽

Vol 4 (2) ◽

pp. 215-225 ◽

Cited By ~ 160

Author(s):

T. Grünewald ◽

M. Schirmer ◽

R. Mott ◽

M. Lehning

Keyword(s):

Snow Cover ◽

Temporal Variability ◽

Snow Depth ◽

Energy Transport ◽

Snow Water Equivalent ◽

Laser Scanner ◽

Ablation Rate ◽

Spatial And Temporal Variability ◽

M Cell ◽

Snow Water

Abstract. The spatio-temporal variability of the mountain snow cover determines the avalanche danger, snow water storage, permafrost distribution and the local distribution of fauna and flora. Using a new type of terrestrial laser scanner, which is particularly suited for measurements of snow covered surfaces, snow depth was monitored in a high alpine catchment during an ablation period. From these measurements snow water equivalents and ablation rates were calculated. This allowed us for the first time to obtain a high resolution (2.5 m cell size) picture of spatial variability of the snow cover and its temporal development. A very high variability of the snow cover with snow depths between 0–9 m at the end of the accumulation season was observed. This variability decreased during the ablation phase, while the dominant snow deposition features remained intact. The average daily ablation rate was between 15 mm/d snow water equivalent at the beginning of the ablation period and 30 mm/d at the end. The spatial variation of ablation rates increased during the ablation season and could not be explained in a simple manner by geographical or meteorological parameters, which suggests significant lateral energy fluxes contributing to observed melt. It is qualitatively shown that the effect of the lateral energy transport must increase as the fraction of snow free surfaces increases during the ablation period.

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