Refine your search
Collections
Co-Authors
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kulkarni, A. V.
- Bioefficacy of Flubendiamide 39.35 % SC against Chilli Fruit Borer (Spodoptera litura Fb)
Abstract Views :1793 |
PDF Views:0
Authors
Affiliations
1 Department of Zoology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, M.S., IN
2 DBT-RBC Project, Narp Campus, Paithan Road, Aurangabad, M.S., IN
3 Department of Entomology, Dr. Y.S. Parmar University of Horticulture and forestry, Nauni, Solan, H.P., IN
1 Department of Zoology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, M.S., IN
2 DBT-RBC Project, Narp Campus, Paithan Road, Aurangabad, M.S., IN
3 Department of Entomology, Dr. Y.S. Parmar University of Horticulture and forestry, Nauni, Solan, H.P., IN
Source
Asian Journal of Bio Science, Vol 8, No 2 (2013), Pagination: 241-244Abstract
A field experiment was conducted in Maharashtra, India, during chilli crop season in 2009 and 2010 to evaluate the efficacy of flubendiamide 39.35 per cent SC (Fame) at two concentrations (60 and 48 g a.i./ha), emamectin benzoate 5 per cent SG (10 g.a.i./ha), indoxacarb 14.5 per cent SC (50 g.a.i./ha), spinosad 45 per cent SC, (73 g.a.i./ha ), novaluron 10 per cent EC (33.50 g.a.i./ha) and profenofos 50 per cent EC (750 g.a.i./ha). The results on bioefficacy of aforesaid insecticides showed that maximum reduction in mean larvae per plant as well as lowest fruit damage was recorded in flubendiamide 39.35 per cent SC @ 60 g.a.i./ha followed by flubendiamide 39.35 per cent SC @ 48 g.a.i./ha with more yield at high concentration with cost: benefit ratio 1: 7.12.Keywords
Bioefficacy, Insecticides, Spodoptera litura, ChillReferences
- Ameta, O.P and Ajay, K. (2008). Efficacy of flubendiamide against Helicoverpa armigera (Hubner) and Spodoptera litura (Fabr.) in chilli. Pestology, 32(5): 26-29.
- Ameta, O.P., Sharma, U.S. and Jeengar, K.L. (2011). Efficacy of flubendiamide 480 SC against pod borers, Helicoverpa armigera (Hubner) and Maruca testulalis (L.) in Pigeonpea. Indian J. Entomol., 73(3): 191-195.
- Anonymous (2008). Krushi Dainandini. Marathwada Agricultural University, Parbhani, M.S. (INDIA).
- Anonymous (2012). Department of Agriculture and Cooperation Agricultural Statistics at a glance (Horticulture Division). 176 pp.
- Ashok Kumar, C.T. and Shivaraju, C. (2009). Evaluation of newer insecticide molecules against pod borers of black gram. Karnataka J. Agric. Sci., 22(3): 521-523.
- Berke, T. (2005). Suggested cultural practices for chili pepper. Shanhua, Taiwan: AVRDCThe World Vegetable Center. AVRDC.
- Crop Life Foundation (2012). Insecticides have boosted chili pepper production and farmer incomes in India. International Pesticide Benefits Case Study No. 68.
- Ghosal, A., Chatterjee, M.L. and Manna, D. (2012). Studies on some insecticides with novel mode of action for the management of tomato fruit borer, Helicoverpa armigera Hub. J. Crop & Weed, 8(2): 126-129.
- Gomez, K.A. and Gomez, A.A. (1984). Statistical procedures for agricultural research. John Wiley and Sons, NEWYORK, U.S.A.
- Javaregowda and Naik, L.K. (2005). Bio-efficacy of flubendiamide 20% WDG (RIL -038) against paddy pests and their natural enemies. Pestology, 29: 58-60.
- Lakshminarayana, S. and Rajashri, M. (2006). Flubendiamide 20 %WG a new molecule for management of American bollworm, Heliothis armigera on cotton. Pestology, 30: 16-18.
- Mallikarjunappa, S., Kendappa, G.N. and Ganesh. (2008). Novel insecticide for the control of rice stem borer, Scirpophaga incertulus and leaf folder, Cnaphalocrosis medinalis. In Coleman memorial National Symposium Bhat. Flubendiamide 20 %WG a on Plant Protection. Univeristy of Agriculturral. Science. GK.V.K. Bengaluru (KARNATAKA) INDIA84 p.
- Masanori, T., Hayani, N. and Fujioka, S. (2005). Flubendiamide a novel insecticide highly effective against lepidopteran insect pests. J. Pesticide Sci., 30: 354-360.
- Reddy, M.R.S. and Reddy, G.S. (1999). An eco friendly method to combat Helicoverpa armigera (Hub.). Insect Environ., 4: 143-144.
- Sreenivas, A.G., Sharanabasappa, Hosamani, A.C., Bheemanna, M., Suresh, B.K., Shivaleela and Patil, B.V. (2008). Efficacy of flubendiamide 480 SC against chilli fruit borers complex. Pesticide Res. J., 20(2): 243-246.
- Tatagar, M.H., Mohan Kumar, H.D., Shivaprasad, M. and Mesta, R.K. (2009). Bioefficacy of flubendiamide 20 WG against chilli fruit borers, Helicoverpa armigera (Hub.) and Spodoptera litura (Fb.). Karnataka J. Agric. Sci., 22(3) : 579-581.
- Snow Depth Estimation in the Indian Himalaya Using Multi-Channel Passive Microwave Radiometer
Abstract Views :346 |
PDF Views:163
Authors
Affiliations
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 National Institute of Technology, Kurukshetra 136 119, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 National Institute of Technology, Kurukshetra 136 119, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
Source
Current Science, Vol 108, No 5 (2015), Pagination: 942-953Abstract
Snow depth is an important parameter for avalanche forecast and hydrological studies. In the Himalaya, manual snow depth data collection is difficult due to remote and rugged terrain and the severe weather conditions. However, microwave-based sensors in various satellites have the capability to estimate snow depth in all weather conditions. In the present study, experiments were performed to establish an algorithm for snow depth estimation using ground-based passive microwave radiometer with 6.9, 18.7 and 37 GHz antenna frequencies at Dhundhi and Patseo, Himachal Pradesh, India. Different layers in the snowpack were identified and layer properties, i.e. thickness, density, moisture content, etc. were measured manually and using a snow fork. Brightness temperature (TB) of the entire snowpack and of the individual snow layers was measured using passive microwave radiometer. It was observed that TB of the snow is affected by various snow properties such as depth, density, physical temperature and wetness. A decrease in TB with increase in snow depth was observed for all types of snow. TB of the snowpack was observed higher at Dhundhi in comparison to Patseo. Based on the measured radiometer data, snow depth algorithms were developed for the Greater Himalaya and Pir-Panjal ranges. These algorithms were validated with ground measurements for snow depth at different observatory locations and a good agreement between the two was observed (absolute error: 7 to 39 cm; correlation: 0.95).Keywords
Brightness Temperature, Microwave Radiometer, Snow Depth Algorithm, Snowpack.- Deployment of Differential Global Positioning System in Regional Gravity Surveys
Abstract Views :365 |
PDF Views:143
Authors
Affiliations
1 Geophysics Division, Geological Survey of India, WR, 15-16, Jhalana Doongri, Jaipur 302 004, IN
2 Geophysics Division, Geological Survey of India, Complex, CR, Seminary Hills, Nagpur 440 006, IN
1 Geophysics Division, Geological Survey of India, WR, 15-16, Jhalana Doongri, Jaipur 302 004, IN
2 Geophysics Division, Geological Survey of India, Complex, CR, Seminary Hills, Nagpur 440 006, IN
Source
Current Science, Vol 111, No 11 (2016), Pagination: 1747-1750Abstract
This note presents the deployment of differential global positioning system in regional gravity surveys. The high-precision three-dimensional (latitude, longitude and elevation) real-time global satellite navigation system data play a key role in the processing of gravity data in the form of 'elevation correction'. A suitable method has been developed to maintain accuracy in the elevation data and subsequently the gravity observations. The data acquired during this survey are used to map the obvious geological province areas in the northwestern part of India and for the preparation of Bouguer gravity contour map of India with 1 mGal interval under National Geophysical Mapping of the Geological Survey of India in a phased manner.- Estimation of Glacier Mass Balance on a Basin Scale:An Approach Based on Satellite-Derived Snowlines and a Temperature Index Model
Abstract Views :359 |
PDF Views:119
Authors
Affiliations
1 Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, IN
2 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
1 Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, IN
2 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
Source
Current Science, Vol 111, No 12 (2016), Pagination: 1977-1989Abstract
Mass balance is an important metric to assess the growth or decline of water stored in a glacier. The Accumulation Area Ratio (AAR) method where mass balance is proportional to AAR has been used to estimate glacier mass balance by several studies in the past. Since field estimates of AAR are not feasible on every glacier, it is usually estimated by identifying the snowline at the end of ablation season as a proxy of Equilibrium Line Altitude (ELA) on satellite images. However, locating ELA on satellite images is challenging due to temporal gaps, cloud cover and fresh snowfall on glaciers. Hence, the highest observed snowline has been traditionally used to estimate AAR, which usually leads to an underestimate of mass loss. To rectify this problem we propose a method to estimate the position of ELA by combining satellite images with in situ meteorological observations and a snowmelt model. The main advantage of this method is that it can be used to estimate the mass balance of individual glaciers and basins. Application of the method to eight glaciers in the Chandra basin, Western Himalaya is found to reduce the bias in mass balance estimates compared to the traditional AAR technique and the modelled estimates are in good agreement with the geodetic method. When applied to 12 selected glaciers in the Chandra basin, the modelled cumulative mass balance is -1.67 Β± 0.72 Gt (-0.79 Β± 0.34 m w.e. a-1) during 1999/2000-2008/09. This method can also be used to estimate the future deviations in mass balance using climate change projections of temperature and precipitation.Keywords
Accumulation Area Ratio, Equilibrium Line Altitude, Glacier Mass Balance, Temperature Index Model, Transient Snowline.- Estimation of Snow Accumulation on Samudra Tapu Glacier, Western Himalaya Using Airborne Ground Penetrating Radar
Abstract Views :365 |
PDF Views:132
Authors
K. K. Singh
1,
H. S. Negi
1,
A. Kumar
2,
A. V. Kulkarni
3,
S. K. Dewali
1,
P. Datt
1,
A. Ganju
1,
S. Kumar
1
Affiliations
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 National Institute of Technology, Kurukshetra 136 119, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 National Institute of Technology, Kurukshetra 136 119, IN
3 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
Source
Current Science, Vol 112, No 06 (2017), Pagination: 1208-1218Abstract
In this study an airborne ground penetrating radar (GPR) is used to estimate spatial distribution of snow accumulation in the Samudra Tapu glacier (the Great Himalayan Range), Western Himalaya, India. An impulse radar system with 350 MHz antenna was mounted on a helicopter for the estimation of snow depth. The dielectric properties of snow were measured at a representative site (Patseo Observatory) using a snow fork to calibrate GPR data. The snow depths estimated from GPR signal were found to be in good agreement with those measured on ground with an absolute error of 0.04 m. The GPR survey was conducted over Samudra Tapu glacier in March 2009 and 2010. A kriging-based geostatistical interpolation method was used to generate a spatial snow accumulation map of the glacier with the GPR-collected data. The average accumulated snow depth and snow water equivalent (SWE) for a part of the glacier were found to be 2.23 m and 0.624 m for 2009 and 2.06 m and 0.496 m for 2010 respectively. Further, the snow accumulation data were analysed with various topographical parameters such as altitude, aspect and slope. The accumulated snow depth showed good correlation with altitude, having correlation coefficient varying between 0.57 and 0.84 for different parts of the glacier. Higher snow accumulation was observed in the north- and east-facing regions, and decrease in snow accumulation was found with an increase in the slope of the glacier. Thus, in this study we generate snow accumulation/SWE information using airborne GPR in the Himalayan terrain.Keywords
Glacier, Ground Penetrating Radar, Snow Accumulation, Snow Water Equivalent.References
- Bolch, T. et al., The state and fate of Himalayan glaciers. Science, 2012, 336, 310β314; doi: 10.1126/science.1215828
- Kulkarni, A. V., Bahuguna, I. M., Rathore, B. P., Singh, S. K., Randhawa, S. S., Sood, R. K. and Dhar, S., Glacial retreat in Himalayas using Indian remote sensing satellite data. Curr. Sci., 2007, 92, 69β74.
- Lau, W. K. M., Kim, M. K., Kim, K. M. and Lee, W. S., Enhanced surface warming and accelerated snow melt in the Himalayas and Tibetan Plateau induced by absorbing aerosols. Environ. Res. Lett., 2010, 5; doi:10.1088/1748-9326/5/2/025204
- Cogley, J. G., Present and future states of Himalaya and Karakoram glaciers. Ann. Glaciol., 2011, 52, 69β73.
- Gurung, D. R., Kulkarni, A. V., Giriraj, A., Aung, K. S. and Shrestha, B., Monitoring of seasonal snow cover in Bhutan using remote sensing technique. Curr. Sci., 2011, 101(10), 1364β1370.
- Vincent, C. et al., Balanced conditions or slight mass gain of glaciers in the Lahaul and Spiti region (northern India, Himalaya) during the nineties preceded recent mass loss. Cryosphere, 2013, 7, 569β582.
- IPCC, Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (eds Edenhofer, O. et al.), Cambridge University Press, Cambridge, United Kingdom, 2014.
- Lozej, A. and Tabacco, I., Radio echo sounding on Strandline Glacier, Terra Nova Bay (Antarctica). Boll. Geofis. Teor. Appl., 1993, 35, 231β244.
- Holmund, P., Radar measurement of annual snow accumulation rates. Z. Gletscherkd. Glazialgeol., 1996, 32, 193β196.
- Marshall, H. P. and Koh, G., FMCW radars for snow research. Cold Reg. Sci. Technol., 2008, 52, 118β131.
- Peduzzi, P., Herold, C. and Silverio, W., Assessing high altitude glacier thickness, volume and area changes using field, GIS and remote sensing techniques: the case of Nevado Coropuna (Peru). Cryosphere, 2010, 4, 313β323.
- Mitterer, C., Heilig, A., Schweizer, J. and Eisen, O., Upwardlooking ground-penetrating radar for measuring wet-snow properties. Cold Reg. Sci. Technol., 2011, 69, 129β138.
- Williams, R. M., Ray, L. E., Lever, J. H. and Burzynski, A. M., Crevasse detection in ice sheets using ground penetrating radar and machine learning. IEEE J. Sel. Top. Appl. Earth Obs., 2014, 7, 4836β4848.
- Schmid, L., Heilig, A., Mitterer, C., Schweizer, J., Maurer, H., Okorn, R. and Eisen, O., Continuous snowpack monitoring using upward-looking ground-penetrating radar technology. J. Glaciol., 2014, 60, 509β525; doi:10.3189/2014JoG13J084
- Van Pelt, W. J. J., Pettersson, R., Pohjola, V. A., Marchenko, S., Claremar, B. and Oerlemans, J., Inverse estimation of snow accumulation along a radar transect on NordenskiΓΆldbreen, Svalbard. J. Geophys. Res.: Earth Surf., 2014, 119, 816β835; doi:10.1002/2013JF003040
- Singh, K. K., Datt, P., Sharma, V., Ganju, A., Mishra, V. D., Parashar, A. and Chauhan, R., Snow depth and snow layer interface estimation using GPR. Curr. Sci., 2011, 100, 1532β1539.
- Loveson, V. J., Khare, R., Mayappan, S. and Gujar, A. R., Remote-sensing perspective and GPR subsurface perception on the growth of a recently emerged spit at Talashil coast, west coast of India. GISci. Remote Sensing, 2014, 51, 644β661.
- Forte, E., Dossi, M., Colucci, R. R. and Pipan, M., A new fast methodology to estimate the density of frozen materials by means of common offset GPR data. J. Appl. Geophys., 2013, 99, 135β145; doi:10.1016/j.jappgeo.2013.08.013
- Forte, E., Dossi, M., Pipan, M. and Colucci, R. R., Velocity analysis from common offset GPR data inversion: theory and application on synthetic and real data. Geophys. J. Int., 2014, 197(3), 1471β1483; doi:10.1093/gji/ggu103
- Colucci, R. R., Forte, E., Boccali, C., Dossi, M., Lanza, L., Pipan, M. and Guglielmin, M., Evaluation of internal structure, volume and mass of glacial bodies by integrated LiDAR and ground penetrating radar (GPR) surveys: the case study of Canin Eastern Glacieret (Julian Alps, Italy). Surv. Geophys., 2015, 36, 231β525; doi:10.1007/s10712-014-9311-1
- Machguth, H., Eisen, O., Paul, F. and Hoelzle, M., Strong spatial variability of snow accumulation observed with helicopter-borne GPR on two adjacent Alpine glaciers. Geophys. Res. Lett., 2006, 33, L13503; doi:10.1029/2006GL026576
- Sold, L., Huss, M., Hoelzle, M., Andereggen, H., Joerg, P. C. and Zemp, M., Methodological approaches to infer end-of-winter snow distribution on alpine glaciers. J. Glaciol., 2013, 59, 1047β1059; doi:10.3189/2013JoG13J015
- Conway, H., Smith, B., Vaswani, P., Matsuoka, K., Rignot, E. and Claus, P., A low frequency ice-penetrating radar system adopted for use from an airplane: test results from Bering and Malaspina glacier, Alaska, USA. Ann. Glaciol., 2009, 51, 93β104.
- Das, I. et al., Influence of persistent wind-scour on the surface mass balance of Antarctica. Nature Geosci., 2013, 6, 367β371.
- Bell, R. E. et al., Widespread persistent thickening of the East Antarctic Ice Sheet by freezing from the base. Science, 2011, 331, 1592β1595.
- Arcone, S. A., Jacobel, R. and Hamilton, G., Unconformable stratigraphy in East Antarctica: Part 1. Large firncosets, recrystallized growth, and model evidence for intensified accumulation. J. Glaciol., 2012, 58, 240β252.
- Gergen, J. T., Dobhal, D. P. and Kaushik, R., Ground penetrating radar ice thickness measurements of Dokrianibamak (glacier), Garhwal Himalaya. Curr. Sci., 1999, 77, 169β173.
- Singh, K. K., Kulkarni, A. V. and Mishra, V. D., Estimation of glacier depth and moraine cover study using ground penetrating radar (GPR) in the Himalayan region. J. Indian Soc. Remote Sensing, 2010, 38, 1β9.
- Azam, M. F. et al., From balance to imbalance: a shift in the dynamic behavior of Chhota Shigri glacier, western Himalaya, India. J. Glaciol., 2012, 58, 315β324.
- Singh, S. K., Rathore, B. P., Bahuguna, I. M., Ramnathan, A. L. and Ajai, Estimation of glacier ice thickness using ground penetrating radar in the Himalayan region. Curr. Sci., 2012, 103, 68β73.
- Negi, H. S., Mishra, V. D., Singh, K. K. and Mathur, P., Application of ground penetrating radar for snow, ice and glacier studies. In Proceedings of the International Symposium on Snow Monitoring and Avalanches, Snow and Avalanche Study Establishment, Manali, 12β16 April 2004.
- Negi, H. S., Snehmani, Thakur, N. K. and Sharma, J. K., Estimation of snow depth and detection of buried objects using airborne ground penetrating radar in Indian Himalaya. Curr. Sci., 2008, 94, 865β870.
- Gusain, H. S., Singh, A., Ganju, A. and Singh, D., Characteristics of the seasonal snow cover of Pir Panjal and Great Himalayan ranges in Indian Himalaya. In Proceedings of the International Symposium on Snow Monitoring and Avalanches, Manali, 12β16 April 2004.
- Gusain, H. S., Chand, D., Thakur, N. K., Singh, A. and Ganju, A., Snow avalanche climatology of Indian Western Himalaya. In Proceedings of the International Symposium on Snow and Avalanches, SASE Manali, 6β10 April 2009.
- Sharma, S. S. and Ganju, A., Complexities of avalanche forecasting in Western Himalaya β an overview. Cold Reg. Sci. Technol., 2000, 31, 95β102.
- Jaedicke, C., Snow mass quantification and avalanche victim search by ground penetrating radar. Surv. Geophys., 2003, 24, 431β445.
- Daniels, D., Ground Penetrating Radar β 2nd Edition, The Institution of Electrical Engineers, London, 2004.
- Sihvola, A. and Tiuri, M., Snow fork for field determination of the density and wetness profiles of a snowpack. IEEE Trans. Geosci. Remote Sensing, 1986, 24, 717β721.
- User/Technical Manual of Snow Fork, the Portable Snow Properties Measuring Instrument, Ins. Toimisto Toikka Oy, Hannuntie 18,02360 Espoo, Finland, 2010.
- Hengl, T., A Practical Guide to Geostatistical Mapping (2nd extended edn). 2009; http://spatial-analyst.net/book/system/files/Hengl_2009_GEOSTATe2c1w.pdf
- Heilig, A., Schneebeli, M. and Eisen, O., Upward looking ground penetrating radar for monitoring snowpack starigraphy. Cold Reg. Sci. Technol., 2009, 59, 152β162.
- Ambach, W. and Denoth, A., The dielectric behavior of snow: a study versus liquid water content. In NASA Workshop on Microwave Remote Sensing of Snowpack Properties (ed. Rango, A.), NASA Conference Publication, 1980, NASA CP-2153, pp. 59β62.
- Johnson, G. L. and Hanson, C. L., Topographic and atmospheric influences on precipitation variability over a mountainous watershed. J. Appl. Meteorol., 1995, 34, 68β87; doi:10.1175/15200450-34.1.68
- Roe, G. H. and Baker, M. B., Microphysical and geometrical controls on the pattern of orographic precipitation. J. Atmos. Sci., 2006, 63, 861β880; doi:10.1175/jas3619.1
- Farinotti, D., Magnusson, J., Huss, M. and Bauder, A., Snow accumulation distribution inferred from time-lapse photography and simple modelling. Hydrol. Process., 2010, 24, 2087β2097; doi:10.1002/hyp.7629
- Asaoka, Y. and Kominami, Y., Spatial snowfall distribution in mountainous areas estimated with a snow model and satellite remote sensing. Hydrol. Res. Lett., 2012, 6, 1β6.
- GrΓΌnewald, T., BΓΌhler, Y. and Lehning, M., Elevation dependency of mountain snow depth. Cryosphere, 2014, 8, 2381β2394, doi:10.5194/tc-8-2381-2014
- Kirchner, P. B., Bales, R. C., Molotch, N. P., Flanagan, J. and Guo, Q., LiDAR measurement of seasonal snow accumulation along an elevation gradient in the southern Sierra Nevada, California. Hydrol. Earth Syst. Sci., 2014, 18, 4261β4275.
- 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 Sensing, 2008, 29, 5863β5878.
- SASE Annual Technical Reports, 2009 and 2010, Snow and Avalanche Study Establishment, Manali.
- Temporal Change and Flow Velocity Estimation of Patseo Glacier, Western Himalaya, India
Abstract Views :403 |
PDF Views:124
Authors
K. K. Singh
1,
D. K. Singh
1,
H. S. Negi
1,
A. V. Kulkarni
2,
H. S. Gusain
1,
A. Ganju
1,
K. Babu Govindha Raj
3
Affiliations
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
3 Indian Space Research Organization, Head Quarters, New BEL Road, Bengaluru 560 231, IN
1 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
2 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, IN
3 Indian Space Research Organization, Head Quarters, New BEL Road, Bengaluru 560 231, IN
Source
Current Science, Vol 114, No 04 (2018), Pagination: 776-784Abstract
In the present study we estimate the velocity and thickness of the Patseo glacier, Himachal Pradesh, India. The average velocity of the glacier was estimated as ~5.47 m/year using co-registration of optically sensed images and correlation (COSI-Corr) method. The glacier thickness was found to vary between 12 and 278 m, with an average value 59 m. The total glacier ice volume was estimated as ~15.8 Γ 107 m3, with equivalent water reservoir of ~14.5 Γ 107 m3. Ground penetrating radar (GPR) surveys were conducted during 2004 and 2013 for validation of the estimated glacier thickness. The glacier thickness estimated using COSI-Corr method was found to be in agreement with GPR-retrieved glacier thickness (RMSE = 4.75 m; MAE = 3.74 m). The GPR profiles collected along the same geographic locations on the glacier during 2004 and 2013 showed a reduction in ice thickness of ~1.89 m, and thus resulting in an annual ice thickness decrease of ~0.21 m. The glacier area was estimated for 2004 and 2013 using LISS IV satellite data and found to be ~2.52 and ~2.30 sq. km respectively. This shows an annual reduction of ~0.024 sq. km in glacier area. The total annual loss in glacier ice volume was estimated as ~4.55 Γ 105 m3. This loss in the glacier ice volume of the Patseo glacier is supported by the snow and meteorological observations collected at a nearby field observatory of Snow and Avalanche Study Establishment (SASE). The climate data collected at SASE meteorological observatory at Patseo (3800 m), between 1993β94 and 2014β15 showed an increasing trend in the mean annual temperature and a decreasing trend in winter precipitation.Keywords
Glaciers, Ground Penetrating Radar Surveys, Velocity and Thickness Estimation, Winter Precipitation.References
- Oerlemans, J., Extracting climate signals from 169 glacier records. Science, 2005, 308, 675β677.
- Wagnon, P. et al., Four years of mass balance on Chhota Shigri Glacier, Himachal Pradesh, India, a new benchmark glacier in the western Himalaya. J. Glaciol., 2007, 53, 603β611.
- Kaab, A., Chiarle, M., Raup, B. and Schneider, C., Climate change impacts on mountain glaciers and permafrost. Global Planet. Change, 2007, 56, viiβix.
- Tawde, S. A., Kulkarni, A. V. and Bala, G., Estimation of glacier mass balance on a basin scale: an approach based on satellitederived snowlines and a temperature index model. Curr. Sci., 2016, 111(12), 1077β1989.
- Joughin, I., Ice sheet velocity mapping: a combined interferometric and speckle-tracking approach. Ann. Glaciol., 2002, 34, 195β201.
- Strozzi, T., Luckman, A., Maurray, T., Wegmuller, U. and Wener, C. I., Glacier motion estimation using SAR offset-tracking procedure. IEEE Trans. Geosci. Remote Sensing, 2002, 40(11), 2384β2391.
- Kimura, H., Kanamori, T., Wakabayashi, H. and Nishio, F., Ice sheet motion in inland Antarctica from JERS-1 SAR interferometry. IEEE Int. Geosci. Remote Sensing, 2004, 1β7, 3018β3020.
- Scambos, T. A. et al., Application of image cross-correlation to the measurement of glacier velocity using satellite image data. Remote Sensing Environ., 1992, 42(3), 177β186.
- Rolstad, C. et al., Visible and near-infrared digital images for determination of ice velocities and surface elevation during a surge on Osbornebreen, a tidewater glacier in Svalbard. Ann. Glaciol., 1997, 24, 255β261.
- Herman, F., Anderson, B. and Leprince, S., Mountain glacier velocity variation during a retreat/advance cycle quantified using sub-pixel analysis of ASTER images. J. Glaciol., 2011, 57(202), 197β207.
- Leprince, S. et al., Monitoring earth surface dynamics with optical imagery. EOS Trans., 2008, 89(1), 1β2.
- Tiwari, R. K., Gupta, R. P. and Arora, M. K., Estimation of surface ice velocity of Chhota-Shigri glacier using sub-pixel ASTER image correlation. Curr. Sci., 2014, 106(6), 853β859.
- Gantayat, P., Kulkarni, A. V. and Srinivasan, J., Estimation of ice thickness using surface velocities and slope: case study at Gangotri Glacier, India. J. Glaciol., 2014, 60(220), 277β282.
- Negi, H. S., Saravana, G., Rout, R. and Snehmani, Monitoring of great Himalayan glaciers in Patsio region, India using remote sensing and climatic observations. Curr. Sci., 2013, 105(10), 1383β1392.
- Leprince, S., Barbot, S., Ayoub, F. and Avouac, J. P., Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements. IEEE Trans. Geosci. Remote Sensing, 2007, 45(6), 1529β1558.
- Farinotti, D., Huss, M., Bauder, A., Funk, M. and Truffer, M., A method to estimate ice volume and ice-thickness distribution of alpine glaciers. J. Glaciol., 2009, 55(191), 422β430.
- Haeberli, W. and Hoelzle, M., Application of inventory data for estimating characteristics of and regional climate-change effects on mountain glaciers: a pilot study with the European Alps. Ann. Glaciol., 1995, 21, 206β212.
- Kamb, B. and Echelmeyer, K. A., Stress-gradient coupling in glacier flow: I. Longitudinal averaging of the influence of ice thickness and surface slope. J. Glaciol., 1986, 32(111), 267β284.
- Cuffey, K. M. and Paterson, W. S. B., The Physics of Glaciers, Butterworth-Heinemann, Oxford, 2010, 4th edn.
- Jiracek, G. R. and Bentley, C. R., Velocity of electromagnetic waves in Antarctic ice. Antarct. Res. Ser., 1971, 16, 199β208.
- Robin, G. D. E. Q., Velocity of radio waves in ice by means of a bore-hole interferometric technique. J. Glaciol., 1975, 15(73), 151β159.
- Basnett, S., Kulkarni, A. V. and Bolch, T., The influence of debris cover and glacial lakes on the recession of glaciers in Sikkim Himalaya, India. J. Glaciol., 2013, 59(218), 1β12.
- Ulaby, F. T., More, R. K. and Fung, A. K., Microwave Remote Sensing: Active and Passive. Volume III from Theory to Applications, Artech House, Inc, USA, 1986.