Skip to main content

Advertisement

SpringerLink
Log in

Drought assessment in the Dhar and Mewat Districts of India using meteorological, hydrological and remote-sensing derived indices

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

Abstract

Several drought indices have been developed during the past decades for monitoring the onset, duration and intensity of drought in different agro-climatic regions. The present study attempts to monitor drought in two underprivileged districts, i.e., Mewat of Haryana and Dhar of Madhya Pradesh state of India, using the remote-sensing-derived Vegetation Condition Index (VCI), meteorological-based Standardized Precipitation Index (SPI) and hydrological-based Standardized Water Level Index (SWI). The time series SPOT VGT NDVI data of the rain-fed crop season (kharif) were used for a 13-year period (1998–2010) to assess the long-term vegetation conditions and compare with the meteorological and hydrological based drought indices. It was observed that the NDVI profile of the crop-growing season was remarkably shifted and shortened during drought years, indicating a delay in crop sowing. A detailed spatiotemporal analysis of drought dynamics was carried out using the VCI, which revealed the occurrence of a severe drought in Mewat and Dhar during the year 2002 and 2008, respectively. The correlation coefficient obtained between the VCI and SPI in Dhar (r = 0.55) and Mewat (r = 0.74) shows good agreement between satellite-derived and meteorological drought indices. However, it is also noteworthy that the correlation coefficient between the VCI and SPI is mainly region specific and varies with timescale. In spite of good agreement between these two indices during severe drought years, the drought estimates were found non-analogous during the years with moderate drought. The study also shows that hydrological drought may not correspond with agricultural drought in every year. There is an increasing tendency in both the pre- and post-monsoon SWI indicating a gradual increase in aquifer stress in the region. Although the years with distinct agricultural drought agree with the meteorological drought years, they do not correspond with hydrological drought in most of the years. However, the VCI based on real-time satellite data can be used for drought early warning, and thus it can be helpful for policy makers to reduce the adverse impact of drought.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  • Agarwal S, Joshi PK, Shukla Y, Roy PS (2003) SPOT vegetation multi temporal data for classifying vegetation in south central Asia. Curr Sci 84(11):1440–1448

    Google Scholar 

  • Agnew CT (2000) Using the SPI to identify drought. Drought Netw News 12(1):1–8

    Google Scholar 

  • Bhuiyan C (2004) Various drought indices for monitoring drought condition in Aravalli terrain of India. In: Proceedings of the XXth ISPRS Conference. International Society Photogrammetry Remote Sensing, Istanbul. http://www.isprs.org/proceedings/xxxv/congress/comm7/papers/243.pdf Accessed 15 April 2012

  • Bhuiyan C, Kogan FN (2010) Monsoon variation and vegetative drought patterns in the Luni Basin in the rain-shadow zone. Int J Remote Sens 31(12):3223–3242

    Article  Google Scholar 

  • Brown ME, Debeurs KM (2008) Evaluation of multi-sensor semi-arid crop season parameters based on NDVI and rainfall. Remote Sens Environ 112(5):2261–2271

    Article  Google Scholar 

  • Brown JF, Reed BC, Hayes MJ, Wilhite DA, Hubbard K (2002) A prototype drought monitoring system integrating climate and satellite data. Pecora 15/Land satellite Information IV/ISPRS commission I/FIEOS 2002 conference proceedings

  • DARE/ICAR (2003) Annual Report of Department of Agriculture Research and Education, Ministry of Agriculture, Government of India, pp 1–271

  • Dutta D, Kundu A, Patel NR (2013) Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index. Geocarto Int 28(3):192–209

    Article  Google Scholar 

  • Edwards DC, McKeeTB (1997) Characteristics of 20th Century Drought in the United States at Multiple Time Scales. Fort Collins, Colorado, Department of Atmospheric Science, Colorado State University 634:1–30

  • Gouveia C, Trigo R (2009) Drought and vegetation stress monitoring in Portugal using satellite data. Nat Hazards Earth Syst 9:185–195

    Article  Google Scholar 

  • Hayes MJ, Decker WL (1998) Using satellite and real-time weather data to predict maize production. Int J Biometeorol 42(1):10–15

    Article  Google Scholar 

  • Jain SK, Keshri R, Goswami A, Sarkar A (2010) Application of meteorological and vegetation indices for evaluation of drought impact: a case study for Rajasthan, India. Nat Hazards 54(3):643–656

    Article  Google Scholar 

  • Ji L, Peters AJ (2003) Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sens Environ 87(1):85–98

    Article  Google Scholar 

  • Kogan FN (1990) Remote sensing of weather impacts on vegetation in non-homogeneous areas. Int J Remote Sens 11:1405–1419

    Article  Google Scholar 

  • Kogan FN (1991) Observations of the 1990 US drought from the NOAA-11 polar-orbiting satellite. Drought Netw News 3(2):7–11

    Google Scholar 

  • Kogan FN (1995) Droughts of the late 1980s in the United States as derived from NOAA polar orbiting satellite data. Bull Am Meteorol Soc 76:655–668

    Article  Google Scholar 

  • Kogan FN (1997) Global drought watch from space. Bull Am Meteor Soc 78:621–636

    Article  Google Scholar 

  • Li B, Su H, Chen F, Wu J, Qi J (2013) The changing characteristics of drought in China from 1982 to 2005. Nat Hazards. doi:10.1007/s11069-013-0649-3

    Google Scholar 

  • Liu S, Wang T, Guo J, Qu J, An P (2009) Vegetation change based on SPOT-VGT data from 1998 to 2007, northern China. Environ Earth Sci 60(7):1459–1466

    Article  Google Scholar 

  • Los SO, Collatz GJ, Sellers PJ et al (2000) A global 9-year biophysical land-surface data set from NOAA AVHRR data. J Hydrometeorol 1:183–199

    Article  Google Scholar 

  • Lucht WI, Colin Prentice WI, Myneni RB, Sitch S, Friedlingstein P, Cramer W, Bousquet P, Buermann W, Smith B (2002) Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science 296:1687–1688

    Article  Google Scholar 

  • McCollum JR, Gruber A, Ba M (2000) Discrepancy between gauges and satellite estimates of rainfall in equatorial Africa. J Appl Meteor 39:666–679

  • McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. Proceedings of the 8th conference on applied climatology. AMS, Boston, pp 179–184

  • Nemani RR, Charles D, Keeling CD, Hashimoto H, Jolly WM, Piper SC, Tucker CJ, Myneni RB, Runnin SW (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300:1560–1563

    Article  Google Scholar 

  • Nicholson SE, Some B, McCollum J, Nelkin E, Klotter D, Berte Y, Diallo BM (2003) Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part II: validation of TRMM rainfall products. J Appl Meteorol 42(10):1337–1354

    Article  Google Scholar 

  • Oladipio EO (1985) A comparative performance analysis of three meteorological drought indices. Int J Climatol 5:655–664

    Article  Google Scholar 

  • Pai DS, Sridhar L, Guhathakurta P, Hatwar HR (2011) District-wide drought climatology of the southwest monsoon season over India based on standardized precipitation index (SPI). Nat Hazards 59(3):1797–1813

    Article  Google Scholar 

  • Patel NR, Chopra P, Dadhwal VK (2007) Analyzing spatial patterns of meteorological drought using standardized precipitation index. Meteorol Appl 14:329–336

    Article  Google Scholar 

  • Rahimzadeh Bajgiran P, Darvishsefat A, Khalili A, Makhdoum MF (2008) Using AVHRR-based vegetation indices for drought monitoring in the Northwest of Iran. J Arid Environ 72(6):1086–1096

    Article  Google Scholar 

  • Reed BC (1993) Using remote sensing and Geographic Information Systems for analyzing landscape/drought interaction. Int J Remote Sens 14:3489–3503

    Article  Google Scholar 

  • Rundquist BC, Harrington JA Jr (2000) The effects of climatic factors on vegetation dynamics of tall grass and short grass cover. Geocarto Int 15(3):31–36

    Article  Google Scholar 

  • Schneider U, Becker A, Meyer-Christoffer A, Ziese M, Rudolf B (2011) Global precipitation analysis products of the GPCC. ftp-anon.dwd.de/pub/data/gpcc/PDF/GPCC_intro_products_2008.pdf Accessed 2 Dec 2011

  • Singh RP, Roy S, Kogan F (2003) Vegetation and temperature condition indices from NOAA AVHRR data for drought monitoring over India. Int J Remote Sens 24(22):4393–4402

    Article  Google Scholar 

  • Thenkabail PS, Gamage N, Smakhin V (2004) The use of remote sensing data for drought assessment and monitoring in south west Asia, vol 85. IWMI Research report, IWMI, Colombo, p 25

    Google Scholar 

  • Verbesselt J, Somers B, Lhermitte S, Jonckheere I, van Aardt J, Coppin P (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sens Environ 108(4):357–368

    Article  Google Scholar 

  • Wan Z, Wang P, Li X (2004) Using MODIS land surface tempera- true and normalized difference vegetation index for monitoring drought in the southern Great Plains, USA. Int J Remote Sens 25:61–72

    Article  Google Scholar 

  • Wilhite DA (1992) Drought encyclopedia of Earth system science, vol 2. Academic Press, San Diego, pp 81–92

    Google Scholar 

  • WMO (1998) El Niño Updates, 1–5. [http://www.wmo.ch]

  • Xiao X, Boles S, Liu J, Zhuang D (2002) Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sens Environ 82:335–348

    Article  Google Scholar 

  • Zhou L, Tucker CJ, Kaufmann RK, Slayback D, Shabanov N, Myneni RB (2001) Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J Geophys Res 106(D17):20069–20083

    Article  Google Scholar 

Download references

Acknowledgments

The authors acknowledge the grant received from ICAR (GEF-NAIP) under the project “Strategies to enhance adaptive capacity to climate change in vulnerable regions” for carrying out the present work.

Author information

Authors and Affiliations

  1. Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi, India

    R. N. Sahoo

  2. Department of Remote Sensing and GIS, Vidyasagar University, West Bengal, India

    Dipanwita Dutta

  3. Water Technology Centre, Indian Agricultural Research Institute, New Delhi, India

    M. Khanna

  4. Centre for Environment Science and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi, India

    N. Kumar & S. K. Bandyopadhyay

Authors
  1. R. N. Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dipanwita Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Khanna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. N. Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. K. Bandyopadhyay
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. N. Sahoo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahoo, R.N., Dutta, D., Khanna, M. et al. Drought assessment in the Dhar and Mewat Districts of India using meteorological, hydrological and remote-sensing derived indices. Nat Hazards 77, 733–751 (2015). https://doi.org/10.1007/s11069-015-1623-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-015-1623-z

Keywords

Search

Navigation