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Chouksey, Arpit

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.

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References

IMD, India Meteorological Department: AWS Lab Pune; http://www.imdaws.com/ViewAwsData.aspx (accessed on 10 April 2017).

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.

Dozier, J., Snow reflectance from Landast-4 thematic mapper. IEEE Trans. Geosci. Remote Sens., 1984, 22, 323–328.

Dozier, J. and Marks, D., Snow mapping and classification from Landsat Thematic Mapper (TM) data. Ann. Glaciol., 1987, 9, 97–103.

Dozier, J., Spectral signature of alpine snow covers from the Landsat thematic mapper. Remote Sens. Environ., 1989, 28, 9–22.

Dozier, J. and Frew, J., Computational provenance in hydrologic science: a snow mapping example. Philos. Trans. R. Soc. London, Ser. A., 2009, 367, 1021–1033.

Vogel, S. W., Usage of high-resolution Landsat-7 band-8 for single band snow cover classification. Ann. Glaciol., 2002, 34, 53–57.

Dorothy, K. H. et al., Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms, NASA Goddard Space Flight Center, Greenbelt, Maryland, 2001; https://modis-snow-ice.gsfc.nasa.gov (accessed on 13 February 2017).

Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo. N. E. and Bayr, K. J., MODIS snow cover products. Remote Sens. Environ., 2002, 83, 181–194.

Hall, D. K., Riggs, G. A. and Roman, M. O., VIIRS Snow Cover Algorithm Theoretical Basis Document (ATBD), Version 1.0, NASA Goddard Space Flight Center, Greenbelt, Maryland, 2015, p. 38.

NSIDC, National Snow and Ice Data Centre website: Snow Cover Products; https://nsidc.org/data/MOD10A2 (accessed on 13 February 2017).

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.

Jain, S. K., Goswami, A. and Saraf, A. K., Accuracy assessment of MODIS, NOAA and IRS data in snow cover mapping under Himalayan conditions. Int. J. Remote Sens., 2008, 29(20), 5863–5878.

Sharma, V., Mishra, V. D. and Joshi, P. K., Topographic controls on spatio-temporal snow cover distribution in Northwest Himalaya. Int. J. Remote Sens., 2014, 35(9), 3036–3056.

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).

CWC, Central Water Commission: Flood Forecast Portal; http://www.india-water.gov.in/ffs/hydrograph/ (accessed on 10 April 2017).

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

Vaughan, D. G. et al., Observations: cryosphere. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Stocker, T. F. et al.), Cambridge University Press, Cambridge, UK, 2013, pp. 317– 382.

Lieser, J. L. et al., Position Analysis: Antarctic Sea Ice and Climate Change 2014, Antarctic Climate & Ecosystems Cooperative Research Centre, Hobart, Tasmania, Australia, 2013, ISBN 978-0646-91260-8.

Rivas, M. B. and Stoffelen, A., New Bayesian algorithm for seaice detection with QuikSCAT. IEEE Trans. Geosci. Remote Sensing, 2011, 49(6), 1894–1901; doi:10.1109/TGRS.2010.2101608.

Comiso, J. C., Gersten, R. A., Stock, L. V., Turner, J., Perez, G. J. and Cho, K., Positive trend in the Antarctic sea-ice cover and associated changes in surface temperature. J. Climate, 2017, 30, 2251–2267; doi:10.1175/JCLI-D-16-0408.1.

Yuan, N., Ding, M., Ludescher, J. and Bunde, A., Increase of the Antarctic sea-ice extent is highly significant only in the Ross Sea. Sci. Rep., 2017, 7, 41096; doi:10.1038/srep41096.

Santis, A. D., Maier, E., Gomez, R. and Gonzalez, I., Antarctica, 1979–2016 sea-ice extent: total versus regional trends, anomalies and correlation with climatological variables. Int. J. Remote Sensing, 2017, 38, 7566–7584.

Turner, J. et al., Antarctic climate change during the last 50 years. Int. J. Climatol., 2005, 25, 279–294.

Trusel, L. D., Frey, K. E. and Das, S. B., Antarctic surface melting dynamics: enhanced perspectives from radar scatterometer data. J. Geophys. Res., 2012, 117, F02023; doi:10.1029/2011JF002126.

Oza, S. R., Spatial-temporal patterns of surface melting observed over Antarctic ice shelves using scatterometer data. Antarct. Sci., 2015, 27, 403–410.

Bothale, R. V., Rao, P. V. N., Dutt, C. B. S., Dadhwal, V. K. and Maurya, D., Spatio-temporal dynamics of surface melting over Antarctica using OSCAT and QuikSCAT scatterometer data (2001–2014). Curr. Sci., 2015, 109, 733–744.

Wang, X, Wang, C. and Li, B., Spatiotemporal analysis of Antarctic snowmelt changes using microwave radiometer data (1978– 2015). Fresesius Environ. Bull., 2018, 3028–3034.

Hock, R., Rees, G., Williams, M. W. and Ramirez, E., Preface – contribution from glaciers and snow cover to runoff from mountains in different climates. Hydrol. Process., 2006, 20, 2089–2090.

Takala, M. et al., Estimating northern hemisphere snow water equivalent for climate research through assimilation of spaceborne radiometer data and ground- based measurements, Remote Sensing Environ., 2011, 115, 3517–3529.

Sturm, M. and Liston, G. E., The snow cover on lakes of the Arctic coastal plain of Alaska, USA. J. Glaciol., 2003, 49, 370–380.

Ridley, J., Surface melting on Antarctic Peninsula ice shelves detected by passive microwave sensors. Geophys. Res. Lett., 1993, 23, 2639–2642; doi:10.1029/93GL02611.

Kunz, L. B. and Long, D. G., Melt detection in Antarctic ice shelves using scatterometers and microwave radiometers. IEEE Trans. Geosci. Remote Sensing, 2006, 44, 2461–2469.

Liu, H., Wang, L. and Jezek, K. C., Spatiotemporal variations of snowmelt in Antarctica derived from satellite scanning multichannel microwave radiometer and Special Sensor Microwave Imager data (1978–2004). J. Geophys. Res., 2006, 111, F01003; doi:10.1029/2005JF000318.

Picard, C. and Fily, M., Surface melting observations in Antarctica by microwave radiometers: correcting 26-year time series from changes in acquisition hours. Remote Sensing Environ., 2006, 104, 325–336.

Tedesco, M., Assessment and development of snowmelt retrieval algorithms over Antarctica from K-band spaceborne brightness temperature (1979–2008). Remote Sensing Environ., 2009, 113, 979–997.

Oza, S. R., Singh, R. K. K., Vyas, N. K., Gohil, B. S. and Sarkar, A., Spatio-temporal coherence based technique for near-real time sea-ice identification from scatterometer data. J. Indian Soc. Remote Sensing, 2010, 39, 169–176.

Bothale, R. V., Rao, P. V. N., Dutta, C. B. S. and Dadhwal, V. K., Dynamics of surface melting over Amery and Ross ice shelf in Antarctic using OCAT data. Int. Arch. Photogramm., Remote Sensing Spat. Inf. Sci., 2014, XL-8, 505–509; doi:10.5194/ isprsarchives-XL-8-505-2014.

Cavalieri, D. J., Gloersen, P. and Campbell, W. J., Determination of sea-ice parameters with the Nimbus 7 SMMR. J. Geophys. Res., 1984, 89, 5355–5369.

Cavalieri, D. J., Crawford, J., Drinkwater, M. R., Eppler, D. T., Farmer, L. D., Jentz, R. R. and Wackerman, C., Aircraft active and passive microwave validations of sea-ice concentrations from the DMSP SSM/I. J. Geophys. Res., 1991, 96, 21989–22008.

Zwally, H. J., Comiso, J. C., Parkinson, C. L., Campbell, W. J., Carsey, F. D. and Gloersen, P., Antarctic sea-ice, 1973–1976: satellite passive microwave observations, National Aeronautics and Space Administration, Washington, DC (NASA SP 459), 1983.

Parkinson, C. L., Comiso, J. C., Zwally, H. J., Cavalieri, D. J., Gloersen, P. and Campbell, W. J., Arctic sea ice 1973–1976 from satellite passive microwave observations. NASA Special Publication No. 489, 1987.

Comiso, J. C., Cavalieri, D. J., Parkinson, C. L. and Gloersen, P., Passive microwave algorithms for sea-ice concentration: a comparison of two techniques. Remote Sensing Environ., 1997, 60, 357–384.

Vyas, N. K. and Dash, M. K., Oceansat-MSMR observes interesting features on the frozen continent and surrounding sea. J. Indian Soc. Remote Sensing, 2000, 28, 67.

Vyas, N. K., Dash, M. K., Bhandari, S. M., Khare, N., Mitra, A. and Pandey, P. C., Large-scale Antarctic features captured by Multi-Frequency Scanning Microwave Radiometer onboard Oceansat-1. Curr. Sci., 2001, 80, 1319–1322.

Dash, M. K., Bhandari, S. M., Vyas, N. K., Khare, N., Mitra, A. and Pandey, P. C., Oceansat-MSMR imaging of the Antarctic and the Southern Polar Ocean. Int. J. Remote Sensing, 2001, 22, 3253– 3259.

Bhandari, S. M., Dash, M. K., Vyas, N. K., Khare, N. and Pandey, P. C., Microwave remote sensing of sea-ice in the Antarctic region from Oceansat-1 MSMR. In Advances in Marine and Polar Science (eds Sahoo, D. and Pandey, P. C.), A.P.H. Publishing Corporation, New Delhi, 2002.

Bhandari, S. M., Vyas, N. K., Dash, M., Shrama, N. and Pandey, P. C., Simultaneous MSMR and SSM/I observations and analysis of sea-ice characteristics over the Antarctic region. Int. J. Remote Sensing, 2005, 26, 3123–3136.

Haarpaintner, J., Tonboe, R. T. and Long, D. G., Automatic detection and validity of the sea-ice edge: an application of enhancedresolution QuikSCAT/SeaWinds data. IEEE Trans. Geosci. Remote Sensing, 2004, 42(7), 1433–1443; doi:10.1109/TGRS.2004.828195.

Oza, S. R., Singh, R. K. K., Vyas, N. K. and Sarkar, A., Recent trends of Arctic and Antarctic summer sea-ice cover observed from space-borne scatterometer. J. Indian Soc. Remote Sensing, 2010, 38, 611–616.

Rivas, M. B., Otosaka, I., Stoffelen, A. and Verhoef, A., A scatterometer record of sea-ice extents and backscatter: 1992–2016. Cryosphere Discuss, 2018, doi:10.5194/tc-2018-68.

Li, M., Zhao, C., Zhao, Y., Wang, Z. and Shi, L., Polar sea-ice monitoring using HY-2A scatterometer measurements. Remote Sensing, 2016, 8, doi:10.3390/rs8080688.

Singh, R. K., Singh, K. N., Maisnam, M., Jayaprasad, P. and Maity, S., Antarctic sea-ice extent from ISROs SCATSAT-1 using PCA and an unsupervised classification. In Proceedings of the 2nd International Electronic Conference on Remote Sensing (ECRS2), 2018, 2, doi:10.3390/ecrs-2-05153.

Lindell, D. B. and Long, D. G., Multiyear Arctic ice classification using ASCAT and SSMIS. Remote Sensing, 2016, 8, doi:10.3390/rs8040294.

Rajak, D. R., Singh, R. K. K., Jayaprasad, P., Oza, S. R., Sharma, R. and Raj Kumar, Sea ice occurrence probability data and its applications over the Antarctic. J. Geomat., 2015, 9, 193–197.

Ulaby, F. T., Moore, R. K. and Fung, A. K., Microwave Remote Sensing – Active and Passive, Vol. III: From Theory to Applications, Artech House, Dedham, MA, USA, 1986.

Wismann, V., Monitoring of seasonal snowmelt on Greenland with ERS scatterometer data. IEEE Trans. Geosci. Remote Sensing, 2000, 38, 1821–1826.

Smith, L. C., Sheng, Y., Forster, R. R., Steffen, K., Frey, K. E. and Alsdorf, D. E., Melting of small Arctic caps observed from ERS scatterometer time series. Geophys. Res. Lett., 2003, 30, doi:10.1029/2003GL017641.

Oza, S. R., Singh, R. K. K, Vyas, N. K. and Sarkar, A., Study of inter-annual variations in surface melting over Amery Ice Shelf, East Antarctica using space-borne scatterometer data. J. Earth Syst. Sci., 2011, 120, 329–336.

Yueh, S., Cline, D. and Elder, K., POLSCAT Ku-band radar remote sensing of terrestrial snow cover. In IGARSS 2008 – 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008; doi:10.1109/IGARSS.2008.4779276.

Liang, X., Lettenmaier, D. P., Wood, E. F. and Burges, S. J., A simple hydrologically based model of land surface water and energy fluxes for GSMs. J. Geophys. Res., 1994, 99(D7), 14415– 14428.

Aggarwal, S. P., Garg, V., Gupta, P. K., Nikam, B. R., Thakur, P. K. and Roy, P. S., Runoff potential assessment over Indian landmass: a macro-scale hydrological modeling approach. Curr. Sci., 2013, 104(7), 950–959.

Spreen, G., Kaleschke, L. and Heygster, G., Sea ice remote sensing using AMSR-E 89 GHz channels. J. Geophys. Res., 2008, 113, C02S03; doi:10.1029/2005JC003384.

Simone, S., Oza, S. R., Shah, R. D., Rathore, B. P. and Bahuguna, I. M., Rift assessment and potential calving zone of Amery Ice Shelf, East Antarctica. Curr. Sci., 2018, 115(9), 1799–1804.

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