Current Science

Open Access Open Access  Restricted Access Subscription Access

Capabilities of Satellite-Derived Datasets to Detect Consecutive Indian Monsoon Droughts of 2014 and 2015


Affiliations
1 Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
 

India received anomalously deficit southwest monsoon rainfall during 2014 and 2015, which resulted in consecutive droughts across the country. Reliable detection and monitoring of droughts are crucial for the reduction in drought vulnerability and associated socio-economic impacts. In this study, the potential of multiple high-resolution satellite datasets is examined using distinct drought indices over India for these two successive monsoon seasons. The satellite-derived precipitation, soil moisture and land surface temperature estimates are capable of depicting the anomalous drought conditions with some exceptions. A non-parametric multivariate standardized drought index, based on precipitation and soil moisture estimates is proven to be better in the detection of droughts when compared to conventional standardized drought indices. Overall, remote sensing satellite datasets provide immense opportunity to detect and monitor different kinds of droughts using a composite of indices. However, limited temporal records of these high-resolution satellite datasets restrain their applicability from the climatological perspective.

Keywords

Drought, Multi-Satellite Product, Non-Parametric Multivariate Drought Index, Southwest Monsoon.
User
Notifications
Font Size

  • Dai, A., Increasing drought under global warming in observations and models. Nat. Clim. Change, 2013, 3, 52–58; doi:10.1038/nclimate1633.

  • Mishra, A. and Liu, S. C., Changes in precipitation pattern and risk of drought over India in the context of global warming. J. Geophys. Res. Atmos., 2014, 119, 7833–7841; doi:10.1002/2014JD021471.

  • Mallya, G., Mishra, V., Niyogi, D., Tripathi, S. and Govindaraju, R. S., Trends and variability of droughts over the Indian monsoon region. Weather Clim. Extrem., 2016, 12, 43–68; doi:10.1016/j.wace.2016.01.002.

  • Salvi, K. and Ghosh, S., Projections of extreme dry and wet spells in the 21st century India using stationary and non-stationary standardized precipitation indices. Clim. Change, 2016, 139, 667–681; doi:10.1007/s10584-016-1824-9.

  • Lehner, F., Coasts, S., Stocker, T. F., Pendergrass, A. G., Sanderson, B. M., Raible, C. C. and Smerdon, J. E., Projected drought risk in 1.5°C and 2°C warming climates. Geophys. Res. Lett., 2017, 44, 7419–7428; doi:10.1002/2017GL074117.

  • Panda, D. K., AghaKouchak, A. and Ambast, S. K., Increasing heat waves and warm spells in India, observed from a multi-aspect framework. J. Geophys. Res. Atmos., 2017, 122, 3837–3858; doi: 10.1002/2016JD026292.

  • Jain, V. K., Pandey, R. P. and Jain, M. K., Spatio-temporal assessment of vulnerability to drought. Nat. Hazards, 2015, 76, 443–469; doi:10.1007/s11069-014-1502-z.

  • Patel, S., Prakash, S. and Bhatt, B., An assessment of Kalpana-1 rainfall product for drought monitoring over India at meteorological sub-division scale. Water Int., 2015, 40, 689–702; doi: 10.1080/02508060.2015.1072784.

  • Pai, D. S., Guhathakurta, P., Kulkarni, A. and Rajeevan, M. N., Variability of meteorological droughts over India. In Observed Climate Variability and Change Over the Indian Region (eds Rajeevan, M. N. and Nayak, S.), Springer Geology, 2017, pp. 73–87; doi:10.1007/978-981-2531-0_5.

  • Pai, D. S., Sridhar, L., Guhathakurta, P. and Hatwar, H. R., Districtwide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Nat. Hazards, 2011, 59(3), 1797–1813; doi:10.1007/s11069-011-9867-8.

  • Kiran Kumar, A. S., Tools from the Indian space programme for observing and forecasting extreme weather events – retrospect and prospect. Mausam, 2016, 67(1), 77–92.

  • Navalgund, R. R. and Singh, R. P., Climate change studies using space based observation. J. Indian Soc. Remote Sensing, 2011, 39(3), 281–295; doi:10.1007/s12524-011-0092-4.

  • Kogan, F. N., Global drought watch from space. Bull. Am. Meteorol. Soc., 1997, 78, 621–636.

  • Mishra, A. K. and Singh, V. P., A review of drought concepts. J. Hydrol., 2010, 391, 202–216; doi:10.1016/j.jhydrol.2010.07.012.

  • AghaKouchak, A., Farahmand, A., Teixeira, J., Wardlow, B. D., Melton, F. S., Anderson, M. C. and Hain, C. R., Remote sensing of drought: progress, challenges and opportunities. Rev. Geophys., 2015, 53, 452–480; doi:10.1002/2014RG000456.

  • Farahmand, A. and AghaKouchak, A., A generalized framework for deriving nonparametric standardized drought indicators. Adv. Water Resour., 2015, 76, 140–145; doi:10.1016/j.advwatres.2014.11.012.

  • Hao, Z. and AghaKouchak, A., A nonparametric multivariate multi-index drought monitoring framework. J. Hydrometeorol., 2014, 15, 89–101; doi:10.1175/JHM-D-12-0160.1.

  • Zhao, M., Geruo, A., Velicogna, I. and Kimball, J. S., A global gridded dataset of GRACE drought severity index for 2002–14: comparison with PDSI and SPEI and a case study of the Australia millennium drought. J. Hydrometeorol., 2017, 18(8), 2117–2129; doi:10.1175/JHM-D-16-0182.1.

  • Zhang, L., Jiao, W., Zhang, H., Huang, C. and Tong, Q., Studying drought phenomena in the Continental United States in 2011 and 2012 using various drought indices. Remote Sens. Environ., 2017, 190, 96–106; doi:10.1016/j.rse.2016.12.010.

  • Gairola, R. M., Prakash, S., Bushair, M. T. and Pal, P. K., Rainfall estimation from Kalpana-1 satellite data over Indian land and oceanic regions. Curr. Sci., 2014, 107(8), 1275–1282.

  • Mahesh, C., Prakash, S., Gairola, R. M., Shah, S. and Pal, P. K., Meteorological sub-divisional scale rainfall monitoring using Kalpana1 VHRR measurements. Geogr. Res., 2014, 52(3), 328–336; doi:10.1111/1745-5871.12068.

  • Aadhar, S. and Mishra, V., High-resolution near real-time drought monitoring in South Asia. Sci. Data, 2017, 4, 170145; doi:10.1038/sdata.2017.145.

  • Ray, S. S., Sesha Sai, M. V. R. and Chattopadhyay, N., Agricultural drought assessment: operational approaches in India with special emphasis on 2012. In High-Impact Weather Events over the SAARC Region (eds Ray, K. et al.), Springer, Cham, 2015, pp. 349–364; doi:10.1007/978-3-319-10217-7_24.

  • Sesha Sai, M. V. R., Murthy, C. S., Chandrasekar, K., Jeyaseelan, A. T., Diwakar, P. G. and Dadhwal, V. K., Agricultural drought: assessment and monitoring. Mausam, 2016, 67(1), 131–142.

  • Kulkarni, A., Gadgil, S. and Patwardhan, S., Monsoon variability, the 2015 Marathwada drought and rainfed agriculture. Curr. Sci., 2016, 111, 1182–1193; doi:10.18520/cs/v111/i7/1182-1193.

  • Mishra, V., Aadhar, S., Asoka, A., Pai, S. and Kumar, R., On the frequency of the 2015 monsoon season drought in the Indo-Gangetic Plain. Geophys. Res. Lett., 2016, 43, 12102–12112; doi:10.1002/2016GL071407.

  • Pai, D. S., Sridhar, L., Rajeevan, M., Sreejith, O. P., Satbhai, N. S. and Mukhopadhyay, B., Development of a new high spatial resolution (0.25° x 0.25°) long period (1901–2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region. Mausam, 2014, 65, 1–18.

  • Huffman, G. J., Adler, R. F., Bolvin, D. T. and Nelkin, E. J., The TRMM multi-satellite precipitation analysis (TMPA). In Satellite Applications for Surface Hydrology (eds Hossain, F. and Gebremichael, M.), Springer, 2010, pp. 3–22.

  • Prakash, S., Mitra, A. K., Momin, I. M., Gairola, R. M., Pai, D. S., Rajagopal, E. N. and Basu, S., A review of recent evaluations of TRMM multisatellite precipitation analysis (TMPA) research products against ground-based observations over Indian land and oceanic regions. Mausam, 2015, 66, 355–366.

  • Prakash, S., Mitra, A. K., Gairola, R. M., Norouzi, H. and Pai, D. S., Status of high-resolution multi-satellite precipitation products across India. In Remote Sensing of Aerosols, Clouds, and Precipitation (eds Islam, T. et al.), Elsevier, 2018, pp. 301–314; doi:10.1016/B978-0-12-810437-8.00014-1.

  • Prakash, S., Mitra, A. K., Pai, D. S. and AghaKouchak, A., From TRMM to GPM: how well can heavy rainfall be detected from space? Adv. Water Resour., 2016, 88, 1–7; doi:10.1016/j.advwatres.2015.11.008.

  • Dorigo, W. et al., ESA CCI soil moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing Environ., 2017, 203, 185–215; doi:10.1016/j.rse.2017.07.001.

  • Reichle, R. H., Koster, R. D., De Lannoy, G. J. M., Forman, B. A., Liu, Q., Mahanama, S. P. P. and Toure, A., Assessment and enhancement of MERRA land surface hydrology estimates. J. Clim., 2011, 24, 6322–6338; doi:10.1175/JCLI-D-10-05033.1.

  • Wan, Z., MYD11C3 MODIS/aqua land surface temperature/ emissivity monthly L3 global 0.05deg CMG V006, NASA EOSDIS Land Processes DAAC, 2015; doi:10.5067/modis/myd11c3.006.

  • Didan, K., MYD13C2 MODIS/Aqua vegetation indices monthly L3 global 0.05deg CMG V006. NASA EOSDIS Land Processes DAAC, 2015; doi:10.5067/modis/myd13c2.006.

  • Gadgil, S. and Srinivasan, J., Monsoon prediction: are dynamical models getting better than statistical models? Curr. Sci., 2012, 103(3), 257–259.

  • Mitra, A. K. et al., Prediction of monsoon using a seamless coupled modelling system. Curr. Sci., 2013, 104(10), 1369–1379.


Abstract Views: 276

PDF Views: 81




  • Capabilities of Satellite-Derived Datasets to Detect Consecutive Indian Monsoon Droughts of 2014 and 2015

Abstract Views: 276  |  PDF Views: 81

Authors

Satya Prakash
Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India

Abstract


India received anomalously deficit southwest monsoon rainfall during 2014 and 2015, which resulted in consecutive droughts across the country. Reliable detection and monitoring of droughts are crucial for the reduction in drought vulnerability and associated socio-economic impacts. In this study, the potential of multiple high-resolution satellite datasets is examined using distinct drought indices over India for these two successive monsoon seasons. The satellite-derived precipitation, soil moisture and land surface temperature estimates are capable of depicting the anomalous drought conditions with some exceptions. A non-parametric multivariate standardized drought index, based on precipitation and soil moisture estimates is proven to be better in the detection of droughts when compared to conventional standardized drought indices. Overall, remote sensing satellite datasets provide immense opportunity to detect and monitor different kinds of droughts using a composite of indices. However, limited temporal records of these high-resolution satellite datasets restrain their applicability from the climatological perspective.

Keywords


Drought, Multi-Satellite Product, Non-Parametric Multivariate Drought Index, Southwest Monsoon.

References





DOI: https://doi.org/10.18520/cs%2Fv114%2Fi11%2F2362-2368