Use of meteorological data for identification of agricultural drought in Kumaon region of Uttarakhand

Abstract

Agriculture in hill and mountain ecosystems is predominantly rainfed with common occurrence of moisture stress. It is a natural disaster which evolves in time and its impacts last for a long time. In the present investigation, long-term monthly precipitation data for 40 years (1980–2019) were used for characterizing agricultural drought in Almora and Nainital districts of Uttarakhand in India. Different drought indices based on meteorological data like standard precipitation index (SPI), percentage of departure (Pd) and percent of normal (Pn) were used. Percentage of departure is calculated from deviation of monthly precipitation from the long-term average monthly precipitation. Percent of normal is calculated by dividing the precipitation by normal precipitation for time being considered. SPI values were calculated based on gamma distribution of long-term monthly precipitation data. The Pearson’s correlation coefficient between monthly percentage of departure and different SPI time scales (1, 3 and 6 months) were analyzed. SPI-1 (July and August) for both the stations showed very strong correlation with the corresponding monthly percentage of departure (r > 0.97) than SPI-3 and SPI-6. Therefore, it is suggested that SPI as a stand-alone indicator should not be interpreted to identify drought in a hilly region.

Research highlights

  • Meteorological drought indices have been used to identify agricultural drought.

  • SPI-1 showed very strong correlation with percentage of departure.

  • Meteorological based SPI was well correlated with satellite based drought indices.

  • Study suggest to use multiple drought indices for drought Identification.

This is a preview of subscription content, access via your institution.

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

References

  1. Angelidis P, Maris F, Kotsovinos N and Hrissanthou V 2012 Computation of drought index SPI with alternative distribution functions; Water Resour. Manag. 26(9) 2453–2473.

    Article  Google Scholar 

  2. Basistha A, Arya D S and Goel N K 2009 Analysis of historical changes in precipitation in the Indian Himalayas; Int. J. Climatol. 29 555–572.

    Article  Google Scholar 

  3. Belayneh A, Adamowski J, Khalil B and Ozga-Zielinski B 2014 Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural networks and wavelet support vector regression models; J. Hydrol. 508 418–429.

    Article  Google Scholar 

  4. Bonsal B, Aider R, Gachon P and Lapp S 2013 An assessment of Canadian prairie drought: Past, present, and future; Clim. Dyn. 41 501–516.

    Article  Google Scholar 

  5. Dutta D, Kundu A and Patel N R 2013 Predicting agricultural drought in eastern Rajasthan of India using NDVI and standardized precipitation index (SPI); Geocarto. Int. 28(3) 192–209.

    Article  Google Scholar 

  6. Dogan S, Berktay A and Singh V 2012 Comparison of multi-monthly precipitation-based drought severity indices, with application to semi-arid Konya closed basin, Turkey; J. Hydrol. 470–471 255–268.

    Article  Google Scholar 

  7. Edossa D C, Babel M S and Gupta A D 2010 Drought analysis in the Awash river basin, Ethiopia; Water Resour. Manag. 24(7) 1441–1460.

    Article  Google Scholar 

  8. Edwards D C 1997 Characteristics of 20th-century drought in the United States at multiple time scales; Master of science dissertation submitted to the Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado.

  9. Guttman N B 1998 Comparing the palmer drought index and the standardized precipitation index; J. Am. Water Resour. Assoc. 34(1) 113–121.

    Article  Google Scholar 

  10. Hayes M J, Svoboda M D, Wilhite D A and Vanyarkho O V 1999 Monitoring the 1996 drought using the standardized precipitation index; Bull. Am. Meteorol. Soc. 80(3) 429–438.

    Article  Google Scholar 

  11. Hayes M J, Wilhite D A and Svoboda M D 2000 Monitoring drought using the standardized precipitation index; In: Drought: A Global Assessment (ed.) Wilhite D A, Routledge, London, UK, pp. 168–180.

    Google Scholar 

  12. Jain S K, Keshri R, Goswami A, Sarkar A and Chaudary A 2009 Identification of drought vulnerable areas using NOAA AVHRR data; Int. J. Remote Sens. 30(10) 2653–2668.

    Article  Google Scholar 

  13. Jain V K, Pandey R P, Jain M K and Byun H R 2015 Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin; Wea. Climate Extr. 8 1–11.

    Article  Google Scholar 

  14. Kamble D B, Gautam S, Bisht H, Rawat S and Kundu A 2019 Drought assessment for Kharif rice using Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI); J. Agrometeorol. 21(2) 182–187.

    Google Scholar 

  15. Karavitis C A, Alexandris S, Tsesmelis D E and Athanasopoulos G 2011 Application of the Standardized Precipitation Index (SPI) in Greece; Water (Switzerland) 3(3) 787–805.

    Google Scholar 

  16. Keyantash J and Dracup J A 2002 The quantification of drought: An evaluation of drought indices; Bull. Am. Meteorol. Soc. 83(8) 1167–1180.

    Article  Google Scholar 

  17. Kumar M N, Murthy C S, Sesha Sai M V R and Roy P S 2009 On the use of Standardized Precipitation Index (SPI) for drought intensity assessment; Meteorol. Appl. 16 381–389.

    Article  Google Scholar 

  18. Kumar M N, Murthy C S, Sesha Sai M V R and Roy P S 2012 Spatiotemporal analysis of meteorological drought variability in the Indian region using standardized precipitation index; Meteorol. Appl. 19 256–264.

    Article  Google Scholar 

  19. Kumar V, Shanu and Jahangeer 2017 Statistical distribution of precipitation in Uttarakhand, India; Appl. Water Sci. 7 4765–4776.

    Article  Google Scholar 

  20. McKee T B, Doesken N J and Kleist J 1993 The relationship of drought frequency and duration to time scales; In: Proceedings of the IX Conference on Applied Climatology, Am. Meteorol. Soc. 17(22) 179–183.

  21. Mishra A K and Singh V P 2009 Analysis of drought severity-area-frequency curves using a general circulation model and scenario uncertainty; J. Geophys. Res. 114 (D6).

  22. Mishra A K and Singh V P 2010 A review of drought concepts; J. Hydrol. 391(1–2) 202–216.

    Article  Google Scholar 

  23. Meshram S, Kant S and Sahu K C 2014 Identification of meteorological drought year for varnasi district, UP; Recent Res. Sci. Technol. 6(1) 245–247.

    Google Scholar 

  24. Morid S, Smakhtin V and Moghaddasi M 2006 Comparision of seven meteorological indices for drought monitoring in Iran; Int. J. Climatol. 26(7) 971–985.

    Article  Google Scholar 

  25. Pai D S, Sridhar L, Guhathakurta P and Hatwar H R 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 

  26. Patel N R, Parida B R, Venus V, Saha S K and Dadhwal V K 2012 Analysis of agricultural drought using vegetation temperature condition index (VTCI) from Terra/MODIS satellite data; Environ. Monit. Assess. 184(12) 7153–7163.

    Article  Google Scholar 

  27. Pashiardis S and Michaelides S 2008 Implementation of the standardized precipitation index (SPI) and the reconnaissance drought index (RDI) for regional drought assessment: A case study for Cyprus; European Water 23 57–65.

    Google Scholar 

  28. Pandey R P, Dash B B, Mishra S K and Singh R 2008 Study of indices for drought characterization in KBK districts in Orissa (India); Hydrol. Process. 22(12) 1895–1907.

    Article  Google Scholar 

  29. Pandey R P, Pandey A, Galkate R V, Byun H R and Mal B C 2010 Integrating hydro-meteorological and physiographic factors for assessment of vulnerability to drought; Water Resour. Manag. 24(15) 4199–4217.

    Article  Google Scholar 

  30. Patel N R, Chopra P and Dadhwal V K 2007 Analyzing spatial patterns of meteorological drought using standard precipitation index; Meteorol. Appl. 14(4) 329–336.

    Article  Google Scholar 

  31. Poonia S and Rao A S 2012 Analysis of meteorological drought at arid Rajasthan using Standardized Precipitation Index; In: 92nd Am. Meteorol. Soc. Annual. Meet. (January 22–26, 2012).

  32. Quiring S M and Ganesh S 2010 Evaluating the utility of the vegetation condition index (VCI) for monitoring meteorological drought in Texas; Agr. Forest Meteorol. 150(3) 330–339.

    Article  Google Scholar 

  33. Rathore M S 2004 State-level analysis of drought policies and impacts in Rajasthan, India; International Water Management Institute, Paper 93.

  34. Raziei T, Saghafian B, Paulo A A, Pereira L S and Bordi I 2009 Spatial patterns and temporal variability of drought in Western Iran; Water Resour. Manag. 23(3) 439–455.

    Article  Google Scholar 

  35. Roudier P and Mahe G 2010 Study of water stress and droughts with indicators using daily data on the Bani River (Niger Basin, Mali). Int. J. Climatol. 30(11) 1689–1705.

    Article  Google Scholar 

  36. Szalai S and Szinell C S 2000 Comparison of two drought indices for drought monitoring in Hungary – A case study; In: Drought and drought mitigation in Europe (eds) Vogt J V and Somma F, Adv. Nat. Technol. Hazards Res. 14, https://doi.org/10.1007/978-94-015-9472-1_12.

  37. Santos J F, Portela M M and Pulido-Calvo I 2011 Regional frequency analysis of droughts in Portugal; Water Resour. Manag. 25(14) 3537–3558.

    Article  Google Scholar 

  38. Sahu Y K, Mishra E P and Rawat S 2018 Quantification of crop water stress index (CWSI) for maize crop under different microclimatic conditions of Allahabad; J. Agrometeorol. 20 368–371.

    Google Scholar 

  39. Siddiqui A R 2004 Regional Evaluation of Desertification Hazards in the Aridlands of Western Rajasthan (an unpublished Ph. D. thesis), AMU, Aligarh, Uttar Pradesh, India, 221p.

  40. Singh R B and Mal S 2014 Trend and variability of monsoon and other precipitation seasons in Western Himalaya, India; Atmos. Sci. Lett. 15 218–226.

    Article  Google Scholar 

  41. Srivastava A, Kumari N and Maza M 2020 Hydrological Response to agricultural land use heterogeneity using variable infiltration capacity model; Water Resour. Manag. 34 3779–3794.

    Google Scholar 

  42. Srivastava A, Sahoo B, Raghuwanshi N S and Chatterjee C 2018 Modelling the dynamics of evapotranspiration using variable infiltration capacity model and regionally-calibrated Hargreaves approach; Irrig. Sci. 36 289–300.

    Article  Google Scholar 

  43. Srivastava A, Sahoo B, Raghuwanshi N S and Singh R 2017 Evaluation of variable infiltration capacity model and MODIS terra satellite-derived grid-scale evapotranspiration estimates in a river basin with tropical monsoon-type climatology; J. Irrig. Drainage Eng. 143(8) 04017028.

    Article  Google Scholar 

  44. Subramanya K 2005 Engineering Hydrology; Tata-Mcgraw Hill, New Delhi, India.

    Google Scholar 

  45. Tabrizi A A, Khalili D, Kamgar-Haghighi A A and Zand-Parsa S 2010 Utilization of time-based meteorological droughts to investigate occurrence of streamflow droughts; Water Resour. Manag. 24(15) 4287–4306.

    Article  Google Scholar 

  46. Zin W Z W, Jemain A A and Ibrahim K 2013 Analysis of drought condition and risk in Peninsular Malaysia using Standardised Precipitation Index; Theor. Appl. Climatol. 111(3–4) 559–568.

    Article  Google Scholar 

  47. Zhai J, Su B, Krysanova V, Vetter T, Gao C and Jiang T 2010 Spatial variation and trends in PDSI and SPI indices and their relation to streamflow in 10 large regions of China; J. Climate 23(3) 649–663.

    Article  Google Scholar 

  48. Zhang A and Jia G 2013 Monitoring meteorological drought in semiarid regions using multi-sensor microwave remote sensing data; Remote Sens. Env. 134 12–23.

    Google Scholar 

Download references

Acknowledgements

Authors are thankful to all the field and laboratory staff for their help in this study. We are very much thankful to the ICAR-VPKAS, Almora as well as ICAR, New Delhi for providing financial support during the course of investigation.

Author information

Affiliations

Authors

Contributions

UK did the modelling exercise with the help of SS, and prepared the manuscript with contributions from all the co-authors. UK along with SS performed the research, literature survey, result interpretation, and manuscript revision during peer-review process. UK and SS conceptualized and supervised the entire research and arranged the necessary data and resources. JKB and LK reviewed the first draft and provided inputs for improvement.

Corresponding author

Correspondence to Utkarsh Kumar.

Additional information

Communicated by Parthasarathi Mukhopadhyay

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, U., Singh, S., Bisht, J.K. et al. Use of meteorological data for identification of agricultural drought in Kumaon region of Uttarakhand. J Earth Syst Sci 130, 121 (2021). https://doi.org/10.1007/s12040-021-01622-1

Download citation

Keywords

  • Hilly region
  • precipitation
  • remote sensing
  • standard precipitation index (SPI)