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Thakur, Praveen K.

Integrated Approach for Snowmelt Run-Off Estimation Using Temperature Index Model, Remote Sensing and GIS

Abstract Views :242 | PDF Views:84

Authors

S. P. Aggarwal ¹, Praveen K. Thakur ¹, Bhaskar R. Nikam ¹, Vaibhav Garg ¹

Affiliations
1 Water Resources Department, Indian Institute of Remote Sensing Dehradun, 4-Kalidas Road, Dehradun 248 001, IN

Source

Current Science, Vol 106, No 3 (2014), Pagination: 397-407

Abstract

The snow and glacier melt run-off is one of the most important sources of freshwater for the perennial Himalayan rivers. The water from these rivers sustains billions of people in South Asia, especially during lean season. The study has been done to integrate temporal snow cover area (SCA) and digital elevation model (DEM) derived from satellite remote sensing data with Geographic Information System (GIS) and finally into temperature index-based snowmelt run-off estimation model. The study area for snowmelt run-off estimation is part of head reach sub-basins of Ganga river, i.e. Alakhnanda and Bhagirathi river basins up to Joshimath and Uttarkashi respectively. The temporal SCA (2002-07 for Bhagirathi river and 2000, 2008 for Alakhnanda river) was derived from remote sensing data and DEM was used to find elevation zones and aspect maps. Snowmelt run-off model (SRM) is a temperature index-based snowmelt run-off simulation model, which has been used in this study for simulating snowmelt run-off. The daily hydro meteorological data from India Meteorological Department and Central Water Commission were used for estimating snowmelt. Overall accuracy of SRM for Alakhnanda river in terms of coefficient of correlation (R²) is 0.84-0.90 for years 2000 and 2008, and 0.74-0.84 in Bhagirathi river for 2002-2007.

Keywords

Remote Sensing, Snowmelt Run-Off, Snow Cover Area, Temperature Index Model.

Full Text

Satellite-Based Mapping and Monitoring of Heavy Snowfall in North Western Himalaya and its Hydrologic Consequences

Abstract Views :201 | PDF Views:79

Authors

Bhaskar R. Nikam ¹, Vaibhav Garg ¹, Prasun K. Gupta ¹, Praveen K. Thakur ¹, A. Senthil Kumar ¹, Arpit Chouksey ¹, S. P. Aggarwal ¹, Pankaj Dhote ¹, Saurabh Purohit ¹

Affiliations
1 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN

Source

Current Science, Vol 113, No 12 (2017), Pagination: 2328-2334

Abstract

Snow cover is one of the most important land surface parameters in global water and energy cycle. Large area of North West Himalaya (NWH) receives precipitation mostly in the form of snow. The major share of discharge in rivers of NWH comes from snow and glacier melt. The hydrological models, used to quantify this runoff contribution, use snow-covered area (SCA) along with hydro-meteorological data as essential inputs. In this context, information about SCA is essential for water resource management in NWH region. Regular mapping and monitoring of snow cover by traditional means is difficult due to scarce snow gauges and inaccessible terrain. Remote sensing has proven its capability of mapping and monitoring snow cover and glacier extents in these area, with high spatial and temporal resolution. In this study, 8-day snow cover products from MODIS, and 15-daily snow cover fraction product from AWiFS were used to generate long-term SCA maps (2000–2017) for entire NWH region. Further, the long term variability of 8-daily SCA and its current status has been analysed. The SCA mapped has been validated using AWiFS derived SCA. The analysis of current status (2016–17) of SCA has indicated that the maximum extent of snow cover in NWH region in last 17 years. In 2nd week of February 2017, around 67% of NWH region was snow covered. The comparison of SCA during the 1st week of March and April in 2016–17 against 2015–16 indicates 7.3% and 6.5%, increased SCA in current year. The difference in SCA during 1st week of March 2017 and 1st week of April 2017 was observed to be 14%, which indicates that the 14% SCA has contributed to the snow melt during this period. The change in snow water equivalent retrieved using SCATSAT-1 data also validates this change in snow volume.

Keywords

AWiFS, MOD10A2, North Western Himalaya, Snow Cover Area, SCATSAT-1.

Full Text

References

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Aggarwal, S. P., Thakur, P. K., Nikam, B. R. and Garg, V., Integrated approach for snowmelt run-off estimation using temperature index model, remote sensing and GIS. Curr. Sci., 2014, 106(3), 397–407.

Kulkarni, A. V., Singh, S. K., Mathur, P. and Mishra, V. D., Algorithm to monitor snow cover using AWiFS data of Resourcesat-1 for the Himalayan region. Int. J. Remote Sens., 2006, 27, 2449–2457.

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Hall, D. K. and Riggs, G. A., MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2016; doi: http://dx.doi.org/10.5067/MODIS/MOD-10A2.006 (accessed on 13 February 2017).

Nikam, B. R., Garg, V., Gupta, P. K., Thakur, P. K., Aggarwal, S. P. and Kumar, A. S., A Preliminary Assessment Report on Assessment of Long-Term and Current Status (2016-17) of Snow Cover Area in North Western Himalayan River Basins using Remote Sensing. Technical Report, IIRS/WRD/Technical Report/ 2017/212, IIRS Dehradun, 2017, p. 26.

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ISRO, Indian Space Research Organisation; http://www.isro.gov.in/Spacecraft/scatsat-1 (accessed on 10 April 2017).

Yueh, S., Cline, D. and Elder, K., POLSCAT Ku-band Radar Remote Sensing of Terrestrial Snow Cover. IEEE Proc. International Geoscience and Remote Sensing Symposium-2008, Boston, MA, USA, 7–11 July 2008 (doi:10.1109/IGARSS.2008.4779276).

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Thakur, P. K., Aggarwal, S. P., Arun, G., Sood, S., Kumar, A. S., Snehmani and Dobhal, D. P., Estimation of snow cover area, snow physical properties and glacier classification in parts of Western Himalayas using C-band SAR data. J. Indian Soc. Remote Sens., 2016; doi:10.1007/s12524-016-0609-y.

Thakur, P. K., Garg, P. K., Aggarwal, S. P., Garg, R. D. and Snehmani, Snow cover area mapping using synthetic aperture radar in Manali watershed of Beas River in the Northwest Himalayas. J. Indian Soc. Remote Sens., 2013; doi:10.1007/s12524-012-0236-1.

Assessment of Cryospheric Parameters Over the Himalaya and Antarctic Regions using SCATSAT-1 Enhanced Resolution Data

Abstract Views :245 | PDF Views:78

Authors

Sandip R. Oza ¹, Rajashree V. Bothale ², D. Ram Rajak ¹, P. Jayaprasad ¹, Saroj Maity ¹, Praveen K. Thakur ³, Naveen Tripathi ¹, Arpit Chouksey ³, I. M. Bahuguna ¹

Affiliations
1 Space Applications Centre, ISRO, Ahmedabad 380 015, IN
2 National Remote Sensing Centre, ISRO, Hyderabad 500 037, IN
3 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN

Source

Current Science, Vol 117, No 6 (2019), Pagination: 1002-1013

Abstract

Antarctica is the focus of scientific studies considering the largest reservoir of terrestrial water in the form of ice and doubling of ice area during winter due to sea-ice growth. The third pole – Himalaya is equally important due to the large extent of snow and ice cover outside the polar regions, which is a major source of water for the Asian countries. At present, the Ku-band scatterometer observing global cryosphere is the SCATSAT-1 launched by India. This article describes the study carried out on different cryospheric parameters using high-resolution (~2.2 km) scatterometer data in the Antarctica and Himalaya. Impact of seasonal variations in snow/ice and ice calving on the backscatter over Antarctica is discussed in detail. A procedure developed for the estimation of sea-ice extent, which yielded overall accuracy of 89%, has been presented and successfully applied for daily monitoring of the Antarctic ice extent for 2017. Surface melting using backscatter and brightness temperature data has been discussed and the contrast between large-sized and small-sized Antarctic ice shelves during the austral summer period of summer 2017–18 is highlighted. The higher average surface melt observed around majority of east Antarctic ice shelves, particularly near the Indian station ‘Maitri’, is of particular interest. Typical surface melting patterns observed over the third largest Antarctic ice shelf, Amery, are discussed in detail. Over northwest Himalaya, derived changes in snow water equivalent (ΔSWE) shows a good correlation between observed and calculated SWE variations. The present study demonstrates that simultaneous availability of high-resolution brightness temperature and backscatter data from SCATSAT-1 provides a unique opportunity to study the polar and mountain cryosphere.

Keywords

Calving, Scatterometer, Sea-ice, Snow Water Equivalent, Surface Melt.

Full Text

References

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Characterization and Retrieval of Snow and Urban Land Cover Parameters using Hyperspectral Imaging

Abstract Views :208 | PDF Views:91

Authors

S. K. Singh ¹, Gaurav Jain ¹, Asfa Siddiqui ², Smruti Naik ¹, B. P. Rathore ¹, Vaibhav Garg ², Snehmani ³, Vinay Kumar ², I. M. Bahuguna ¹, S. A. Sharma ¹, Chander Shekhar ³, Praveen K. Thakur ², Kavach Mishra ², Pramod Kumar ², T. H. Painter ⁴, J. Dozier ⁵

Affiliations
1 Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, IN
2 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
3 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
4 University of California, Los Angeles, CA, US
5 University of California, Santa Barbara, CA, US

Source

Current Science, Vol 116, No 7 (2019), Pagination: 1182-1195

Abstract

Snow and urban land cover are important due to their role in hydrological management and utility, climate response, social aspects and economic viability, along with influencing the Earth’s environment at local, regional and global scale. Hyperspectral data enable identification, characterization and retrieval of these land-cover features based on physical and chemical properties of compositional materials. AVIRISNG hyperspectral airborne data, with synchronous ground observations using field spectroradiometer and collateral instruments, were collected over two widely varied land-cover types, viz. a relatively homogenous area covered by snow in the extreme cold environment of the Himalaya (Bhaga sub-basin, Himachal Pradesh), and a completely heterogeneous urban area of a metropolitan city (Ahmedabad, Gujarat).

AVIRIS-NG airborne data were analysed to understand the effect of terrain parameters such as slope and aspect on snow reflectance. Snow grain index using visible and near-infrared (VNIR) bands and absorption peak in the near-infrared (NIR) were used to retrieve grain size in parts of the Himalayan region. A radiative transfer model was used to understand the grain size variability and its effect on absorption peak in NIR. Continuum removal was performed for snow spectral observations obtained from airborne, modelled and field platforms to estimate band depth at 1030 nm. Grain size was observed to vary with altitude from 100 to 500 μm using AVIRIS-NG image. In the urban area, the data also separated pervious and impervious surface cover using spectral unmixing technique, identified several urban features over multispectral data such as buildings with red tiled roofs, metallic surfaces and tarpaulin sheets using the material spectral profiles. Two single-frame superresolution methods namely sparse regression and natural prior (SRP), and gradient profile prior (GPP) were applied on AVIRIS-NG data for the mixed environment around Kankaria Lake in the city of Ahmedabad, which revealed that SRP method was better than GPP, and affirmed by eight indices. Preliminary analysis of AVIRIS-NG imaging over snow-covered areas and densely populated cities indicated utility of future spaceborne hyperspectral missions, particularly for hydrological and climatological applications in such diverse environments.

Keywords

AVIRIS-NG, Hyperspectral Imaging, Snow Reflectance, Super-Resolution Method, Terrain Parameters, Urban Land Cover.

Full Text

References

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