Snow-cover area vs. snowmelt runoff relation and its dependence on geomorphology — A study from the Beas catchment (Himalayas, India)

1982 ◽  
Vol 58 (3-4) ◽  
pp. 325-339 ◽  
Author(s):  
R.P. Gupta ◽  
A.J. Duggal ◽  
S.N. Rao ◽  
G. Sankar ◽  
B.B.S. Singhal
Keyword(s):  
Snow Cover ◽  
Snowmelt Runoff ◽  
Snow Cover Area

scholarly journals ESTIMATION OF SNOW DEPLETION CURVE FOR GANGOTRI BASIN USING MULTI-SOURCE REMOTE SENSING DATA

2021 ◽  
Vol V-3-2021 ◽  
pp. 197-202
Author(s):  
P. Verma ◽  
S. K. Ghosh ◽  
R. Ramsankaran
Keyword(s):  
Snow Cover ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Snowmelt Runoff ◽  
Multiple Sources ◽  
Snow Cover Area ◽  
Depletion Curve ◽  
Normalized Difference Snow Index ◽  
Runoff Model ◽  
Key Parameter

Abstract. Snow Depletion Curve derived from satellite images is a key parameter in Snowmelt Runoff Model. The fixed temporal resolution of a satellite and presence of cloud cover in Himalayas restricts accuracy of generated SDC. This study presents an effective approach of reducing temporal interval between two consecutive dates by integrating normalized Snow Cover Area estimated from multiple sources of satellite data. SCA is extracted by using Normalized Difference Snow Index for six snowmelt seasons from 2013 to 2018 for Gangotri basin situated in Indian Himalayas. This work also explores potential of recently launched Sentinel-3A for estimating SCA. Normalized SCA is utilized to eliminate the effect of difference in spatial resolution of various satellites. The result develops an important linear relation between SDC and time with a decrease in snow cover of 0.005/day that may be further refined by increasing the number of snowmelt seasons. This relationship may help scientific community in understanding hydrological response of glaciers to climate change.

scholarly journals Modis Snowline Elevation Changes During Snowmelt Runoff Events in Europe

2019 ◽  
Vol 67 (1) ◽  
pp. 101-109 ◽  
Author(s):  
Juraj Parajka ◽  
Nejc Bezak ◽  
John Burkhart ◽  
Bjarki Hauksson ◽  
Ladislav Holko ◽  
...  
Keyword(s):  
Snow Cover ◽  
Base Flow ◽  
Snowmelt Runoff ◽  
Regional Variability ◽  
Snow Cover Area ◽  
Cloud Coverage ◽  
Maximum Elevation ◽  
Elevation Changes ◽  
The Mean ◽  
Runoff Events

Abstract This study evaluates MODIS snow cover characteristics for large number of snowmelt runoff events in 145 catchments from 9 countries in Europe. The analysis is based on open discharge daily time series from the Global Runoff Data Center database and daily MODIS snow cover data. Runoff events are identified by a base flow separation approach. The MODIS snow cover characteristics are derived from Terra 500 m observations (MOD10A1 dataset, V005) in the period 2000-2015 and include snow cover area, cloud coverage, regional snowline elevation (RSLE) and its changes during the snowmelt runoff events. The snowmelt events are identified by using estimated RSLE changes during a runoff event. The results indicate that in the majority of catchments there are between 3 and 6 snowmelt runoff events per year. The mean duration between the start and peak of snowmelt runoff events is about 3 days and the proportion of snowmelt events in all runoff events tends to increase with the maximum elevation of catchments. Clouds limit the estimation of snow cover area and RSLE, particularly for dates of runoff peaks. In most of the catchments, the median of cloud coverage during runoff peaks is larger than 80%. The mean minimum RSLE, which represents the conditions at the beginning of snowmelt events, is situated approximately at the mean catchment elevation. It means that snowmelt events do not start only during maximum snow cover conditions, but also after this maximum. The mean RSLE during snowmelt peaks is on average 170 m lower than at the start of the snowmelt events, but there is a large regional variability.

Evaluating a Model of Snow Cover Area versus Runoff against a Concurrent Flow Correlation Model in the Western Himalayas

1984 ◽  
Vol 15 (2) ◽  
pp. 103-110 ◽  
Author(s):  
B. Dey ◽  
D. C. Goswami
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Snow Cover ◽  
Cross Correlation ◽  
Western Himalayas ◽  
Correlation Model ◽  
Snowmelt Runoff ◽  
Snow Cover Area ◽  
Concurrent Flow ◽  
Operational Forecasting

This study evaluates the estimates of seasonal snowmelt runoff in the Sutlej, Indus, Kabul and Chenab rivers derived from the model of snow cover area vs. runoff against those obtained from cross correlation of concurrent flows in the rivers. The concurrent flow correlation model explains more than 90 percent of the variability in flow of these rivers. Compared to this model, the model of snow-cover area vs. runoff explains less of the variability in flow. However, unlike the snow-cover model, the concurrent flow correlation model cannot be used for operational forecasting procedures. Where the strength of correlation is high, the concurrent flow correlation model has potential for use in retrospective analysis of flow for estimating missing data, extending time series and for evaluating estimates derived from other models. In the Himalayan basins under study and at least for the period under observation, the concurrent flow correlation model provides a set of results with which to compare the estimates from the snow cover model.

Does change in the upstream snowpack properties contribute to desiccation of Lake Urmia?

2021 ◽  
Author(s):  
Ehsan Saeedy ◽  
Somayeh Sima ◽  
Meysam Atakhorrami
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Snow Accumulation ◽  
Hypersaline Lake ◽  
Snowmelt Runoff ◽  
Lake Urmia ◽  
Snow Cover Area ◽  
Lake Inflows ◽  
Main Regulator ◽  
Snowpack Properties

<p>Lake Urmia, the largest hypersaline lake in the Middle East has desiccated significantly during the past two decades. Zarrineh River Basin (ZRB), is the largest upstream basin of Lake Urmia and supplies about half of the lake inflows. We investigated 20-years of snowpack properties of this mountainous basin including snow cover area, albedo, depth, and snow water equivalent using MODIS satellite data. We found that maximum and median snow cover area, albedo, the extent and duration of deep snow have been decreased since 2010, particularly at high altitudes. Furthermore, we observed a shift in the timing of snow accumulation and depletion since 2010. These variations have led to slightly decreased inflows to the Bukan reservoir that is the main regulator of Lake Urmia inflows. However, even in those years that the share of snowmelt runoff was high, reservoir releases have not led to more water supply to Lake Urmia due to irrigation consumption. Finally, we indicated that inflows to the Bukan Reservoir and Lake Urmia have been negatively influenced by snowpack reduction. But human decisions play a more crucial role on how to use the increased snowmelt-runoff in wet years to restore the desiccated lake through improved environmental flow allocations.  </p>

scholarly journals Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

2016 ◽  
Vol XLI-B8 ◽  
pp. 353-360 ◽  
Author(s):  
Shaini Naha ◽  
Praveen K. Thakur ◽  
S. P. Aggarwal
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Land Surface ◽  
Land Surface Model ◽  
Hydrologic Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Surface Model ◽  
Snowmelt Runoff ◽  
Snow Cover Area

The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity) is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH) have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006). Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I) and Ensemble Kalman Filter (EnKF) that uses observations of snow covered area (SCA) to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU), rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD) data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed data from BBMB (Bhakra Beas Management Board) and coefficient of Correlation(R<sup>2</sup>) measured for (2003-2006) was 0.67 and 0.61 for the year 2006.But as VIC does not consider snowmelt runoff as a part of the total discharge, snowmelt runoff has been estimated for the simulation both with and without D.A. The snow fluxes as generated from VIC gives basin average estimates of Snow Cover, SWE, Snow Depth and Snow melt. It has been observed to be overestimated when model predicted snow cover is compared with MODIS SCA of 500 m resolution from MOD10A2 for each year. So MODIS 8-day snow cover area has been assimilated directly into the model state as well as by using EnKF after every 8 days for the year 2006.D.I Technique performed well as compared to EnKF. R<sup>2</sup> between Model SCA and MODIS SCA is estimated as 0.73 after D.I with Root Mean Square Error (RMSE) of +0.19. After direct Insertion of D.A, SCA has been reduced comparatively which resulted in 7% reduction of annual snowmelt contribution to total discharge.The assimilation of MODIS SCA data hence improved the snow cover area (SCA) fraction and finally updated other snow components.

scholarly journals Hydrological Modelling and data assimilation of Satellite Snow Cover Area using a Land Surface Model, VIC

2016 ◽  
Vol XLI-B8 ◽  
pp. 353-360
Author(s):  
Shaini Naha ◽  
Praveen K. Thakur ◽  
S. P. Aggarwal
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Land Surface ◽  
Land Surface Model ◽  
Hydrologic Model ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Surface Model ◽  
Snowmelt Runoff ◽  
Snow Cover Area

The snow cover plays an important role in Himalayan region as it contributes a useful amount to the river discharge. So, besides estimating rainfall runoff, proper assessment of snowmelt runoff for efficient management and water resources planning is also required. A Land Surface Model, VIC (Variable Infiltration Capacity) is used at a high resolution grid size of 1 km. Beas river basin up to Thalot in North West Himalayas (NWH) have been selected as the study area. At first model setup is done and VIC has been run in its energy balance mode. The fluxes obtained from VIC has been routed to simulate the discharge for the time period of (2003-2006). Data Assimilation is done for the year 2006 and the techniques of Data Assimilation considered in this study are Direct Insertion (D.I) and Ensemble Kalman Filter (EnKF) that uses observations of snow covered area (SCA) to update hydrologic model states. The meteorological forcings were taken from 0.5 deg. resolution VIC global forcing data from 1979-2006 with daily maximum temperature, minimum temperature from Climate Research unit (CRU), rainfall from daily variability of NCEP and wind speed from NCEP-NCAR analysis as main inputs and Indian Meteorological Department (IMD) data of 0.25 °. NBSSLUP soil map and land use land cover map of ISRO-GBP project for year 2014 were used for generating the soil parameters and vegetation parameters respectively. The threshold temperature i.e. the minimum rain temperature is -0.5°C and maximum snow temperature is about +0.5°C at which VIC can generate snow fluxes. Hydrological simulations were done using both NCEP and IMD based meteorological Forcing datasets, but very few snow fluxes were obtained using IMD data met forcing, whereas NCEP based met forcing has given significantly better snow fluxes throughout the simulation years as the temperature resolution as given by IMD data is 0.5°C and rainfall resolution of 0.25°C. The simulated discharge has been validated using observed data from BBMB (Bhakra Beas Management Board) and coefficient of Correlation(R<sup>2</sup>) measured for (2003-2006) was 0.67 and 0.61 for the year 2006.But as VIC does not consider snowmelt runoff as a part of the total discharge, snowmelt runoff has been estimated for the simulation both with and without D.A. The snow fluxes as generated from VIC gives basin average estimates of Snow Cover, SWE, Snow Depth and Snow melt. It has been observed to be overestimated when model predicted snow cover is compared with MODIS SCA of 500 m resolution from MOD10A2 for each year. So MODIS 8-day snow cover area has been assimilated directly into the model state as well as by using EnKF after every 8 days for the year 2006.D.I Technique performed well as compared to EnKF. R<sup>2</sup> between Model SCA and MODIS SCA is estimated as 0.73 after D.I with Root Mean Square Error (RMSE) of +0.19. After direct Insertion of D.A, SCA has been reduced comparatively which resulted in 7% reduction of annual snowmelt contribution to total discharge.The assimilation of MODIS SCA data hence improved the snow cover area (SCA) fraction and finally updated other snow components.

scholarly journals A Response of Snow Cover to the Climate in the Northwest Himalaya (NWH) Using Satellite Products

2021 ◽  
Vol 13 (4) ◽  
pp. 655
Author(s):  
Animesh Choudhury ◽  
Avinash Chand Yadav ◽  
Stefania Bonafoni
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Significant Negative Correlation ◽  
Shuttle Radar Topography Mission ◽  
Vital Role ◽  
Snow Cover Area ◽  
Study Region ◽  
Northwest Himalaya ◽  
The Past ◽  
Average Annual Temperature

The Himalayan region is one of the most crucial mountain systems across the globe, which has significant importance in terms of the largest depository of snow and glaciers for fresh water supply, river runoff, hydropower, rich biodiversity, climate, and many more socioeconomic developments. This region directly or indirectly affects millions of lives and their livelihoods but has been considered one of the most climatically sensitive parts of the world. This study investigates the spatiotemporal variation in maximum extent of snow cover area (SCA) and its response to temperature, precipitation, and elevation over the northwest Himalaya (NWH) during 2000–2019. The analysis uses Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day composite snow Cover product (MOD10A2), MODIS/Terra/V6 daily land surface temperature product (MOD11A1), Climate Hazards Infrared Precipitation with Station data (CHIRPS) precipitation product, and Shuttle Radar Topography Mission (SRTM) DEM product for the investigation. Modified Mann-Kendall (mMK) test and Spearman’s correlation methods were employed to examine the trends and the interrelationships between SCA and climatic parameters. Results indicate a significant increasing trend in annual mean SCA (663.88 km2/year) between 2000 and 2019. The seasonal and monthly analyses were also carried out for the study region. The Zone-wise analysis showed that the lower Himalaya (184.5 km2/year) and the middle Himalaya (232.1 km2/year) revealed significant increasing mean annual SCA trends. In contrast, the upper Himalaya showed no trend during the study period over the NWH region. Statistically significant negative correlation (−0.81) was observed between annual SCA and temperature, whereas a nonsignificant positive correlation (0.47) existed between annual SCA and precipitation in the past 20 years. It was also noticed that the SCA variability over the past 20 years has mainly been driven by temperature, whereas the influence of precipitation has been limited. A decline in average annual temperature (−0.039 °C/year) and a rise in precipitation (24.56 mm/year) was detected over the region. The results indicate that climate plays a vital role in controlling the SCA over the NWH region. The maximum and minimum snow cover frequency (SCF) was observed during the winter (74.42%) and monsoon (46.01%) season, respectively, while the average SCF was recorded to be 59.11% during the study period. Of the SCA, 54.81% had a SCF above 60% and could be considered as the perennial snow. The elevation-based analysis showed that 84% of the upper Himalaya (UH) experienced perennial snow, while the seasonal snow mostly dominated over the lower Himalaya (LH) and the middle Himalaya (MH).

scholarly journals Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers

Geosciences ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 130
Author(s):  
Sebastian Rößler ◽  
Marius S. Witt ◽  
Jaakko Ikonen ◽  
Ian A. Brown ◽  
Andreas J. Dietz
Keyword(s):  
Remote Sensing ◽  
Snow Cover ◽  
Air Temperature ◽  
Kendall Test ◽  
The Arctic ◽  
Boreal Winter ◽  
Snowmelt Runoff ◽  
River Catchment ◽  
Catchment Areas ◽  
Runoff Model

The boreal winter 2019/2020 was very irregular in Europe. While there was very little snow in Central Europe, the opposite was the case in northern Fenno-Scandia, particularly in the Arctic. The snow cover was more persistent here and its rapid melting led to flooding in many places. Since the last severe spring floods occurred in the region in 2018, this raises the question of whether more frequent occurrences can be expected in the future. To assess the variability of snowmelt related flooding we used snow cover maps (derived from the DLR’s Global SnowPack MODIS snow product) and freely available data on runoff, precipitation, and air temperature in eight unregulated river catchment areas. A trend analysis (Mann-Kendall test) was carried out to assess the development of the parameters, and the interdependencies of the parameters were examined with a correlation analysis. Finally, a simple snowmelt runoff model was tested for its applicability to this region. We noticed an extraordinary variability in the duration of snow cover. If this extends well into spring, rapid air temperature increases leads to enhanced thawing. According to the last flood years 2005, 2010, 2018, and 2020, we were able to differentiate between four synoptic flood types based on their special hydrometeorological and snow situation and simulate them with the snowmelt runoff model (SRM).

Assessing the quality of a real-time Snow Cover Area product for hydrological applications

2012 ◽  
Vol 127 ◽  
pp. 271-287 ◽  
Author(s):  
G. Thirel ◽  
C. Notarnicola ◽  
M. Kalas ◽  
M. Zebisch ◽  
T. Schellenberger ◽  
...  
Keyword(s):  
Snow Cover ◽  
Real Time ◽  
Snow Cover Area ◽  
Area Product ◽  
Hydrological Applications

The optical behaviour of snow during a melting season at Ny Ålesund (Svalbard, Norway)

2021 ◽  
Author(s):  
Roberto Salzano ◽  
Christian Lanconelli ◽  
Giulio Esposito ◽  
Marco Giusto ◽  
Mauro Montagnoli ◽  
...  
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Full Range ◽  
Time Lapse ◽  
Test Site ◽  
Remotely Sensed ◽  
Optical Behaviour ◽  
Remotely Sensed Data ◽  
Snow Cover Area ◽  
Significant Information

&lt;p&gt;&lt;span&gt;Polar areas are the most sensitive targets of &lt;/span&gt;&lt;span&gt;the &lt;/span&gt;&lt;span&gt;climate change and the continuous monitoring of the cryosphere represents a critical issue. The satellite remote sensing can fill this gap but further integration between remotely-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behaviour of snow, at different wavelengths, provides significant information about the micro-physical characteristics of the surface and this allow to discriminate different snow/ice covers. The aim of this work is to present an approach based on combining unmanned observations on spectral albedo and on the analysis of time-lapse images of sky and ground conditions in a&lt;/span&gt;&lt;span&gt;n &lt;/span&gt;&lt;span&gt;Ar&lt;/span&gt;&lt;span&gt;c&lt;/span&gt;&lt;span&gt;tic &lt;/span&gt;&lt;span&gt;test-site &lt;/span&gt;&lt;span&gt;(Svalbard, Norway). Terrestrial photography can provide, in fact, important information about the cloud cover and support the discrimination between white-sky or clear-sky illuminating conditions. Similarly, time-lapse cameras can provide a detailed description of the snow cover, estimating the fractional snow cover area. The spectral albedo was obtained by a narrow band device that was compared to a full-range commercial system and to remotely sensed data acquired during the 2015 spring/summer period at the &lt;/span&gt;&lt;span&gt;Amundsen - Nobile&lt;/span&gt;&lt;span&gt; Climate Change Tower (Ny &lt;/span&gt;&lt;span&gt;&amp;#197;&lt;/span&gt;&lt;span&gt;lesund). The results confirmed the possibility to have continuous observations of the snow surface (microphisical) characteristics and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. It was possible, &lt;/span&gt;&lt;span&gt;therefore,&lt;/span&gt;&lt;span&gt; to estimate spectral indexes, such as NDSI and SWIR albedo, and to found interesting links between both features and air/ground temperatures, wind-speed and precipitations. Different melting phases were detected and different processes were associated with the observed spectral variations.&lt;/span&gt;&lt;/p&gt;

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