SpringerLink
Log in

Assessing the suitability of McKee et al. (1993) drought severity classification across India

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

Abstract

Drought analysis has become very important in water resources management and has brought attention to many researchers in recent years owing to its severity which leads to economic and life-threatening impacts in the affected region. Many studies have been carried out across different domains of drought at regional and global scales using various drought indices. Drought indices such as SPI, RDI, and SPEI have been widely used for meteorological drought assessment across the globe but their differences in terms of classification of drought severity are yet to be assessed. Currently, the classification table given by McKee et al. (Proceedings of the 8th conference on applied climatology, 1993) is being used for finding the severity of meteorological drought conditions irrespective of the index being used. However, all these indices differ in data requirements, and their formulations. This study focuses on assessing the suitability of McKee et al. (1993) drought severity classification for the two most popular meteorological drought indices, namely, SPI and SPEI across 36 meteorological subdivisions within India having varied climatic conditions. For this purpose, the SPI and SPEI are calculated for varying assessment periods of 3, 6, 9, 12 and 24 months for all these subdivisions employing the precipitation and temperature data obtained from the Indian Meteorological Department, Pune. In the next step, the meteorological subdivisions with the maximum and minimum number of extreme drought and extreme wet events across different assessment periods have been identified for these indices. The coefficient of determination (R2 Value) between SPI and SPEI has been estimated for all the meteorological subdivisions and is found to be maximum at 0.98 for Assam & Meghalaya and minimum at 0.40 for Saurashtra & Rann of Kutch and Gujarat. Further Matthews correlation coefficient (MCC) has been estimated for all the meteorological subdivisions and is found to be maximum at 0.73 for West Bengal & Sikkim and minimum at 0.32 for West Rajasthan. The analysis based on R2 and MCC shows that the meteorological subdivisions in hot desert and arid climate, and steppe hot and arid climate, have low agreement among SPI and SPEI. The disparity between SPI and SPEI drought severity is quite evident for Rajasthan, Gujarat, Madhya Maharashtra, Vidarbha, Marathwada, Rayalseema, and West Madhya Pradesh subdivisions, where effect of high temperature is an influential factor in determining the climate of these regions. The overall conclusion drawn from this study is that the use of McKee et al. (1993) drought severity classification table for drought indices other than SPI is not always advisable and opens a discussion for the requirement of separate drought classification tables for different indices for getting better assessment of drought in a specific climatic region.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Germany)

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

Data availability

The data used in this study has been obtained freely online from Indian Meteorological Department (https://www.imdpune.gov.in/) and is duly acknowledged.

References

  • Abatan AA, Collins M, Babel MS et al (2023) Sub-seasonal to seasonal drivers of dry extreme rainfall events over Northeast Thailand. Front Clim 4:1031226

    Article  Google Scholar 

  • Adib A, Marashi SS (2019) Meteorological drought monitoring and preparation of long-term and short-term drought zoning maps using regional frequency analysis and L-moment in the Khuzestan province of Iran. Theoret Appl Climatol 137:77–87

    Article  Google Scholar 

  • Adib A, Tavancheh F (2019) Relationship between hydrologic and metrological droughts using the streamflow drought indices and standardized precipitation indices in the Dez Watershed of Iran. Int J Civil Eng 17:1171–1181

    Article  Google Scholar 

  • Apurv T, Xu YP, Wang Z et al (2019) Multidecadal changes in meteorological drought severity and their drivers in mainland China. J Geophys Res Atmos 124(23):12937–12952

    Article  Google Scholar 

  • Artusi R, Verderio P, Marubini E (2002) Bravais-Pearson and Spearman correlation coefficients: meaning, test of hypothesis and confidence interval. Int J Biol Markers 17(2):148–151

    Article  CAS  Google Scholar 

  • Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:1–13

    Article  Google Scholar 

  • Costa V, Silva A, Palmier LR et al (2021) Assessing the propagation from meteorological to hydrological drought in the São Francisco river catchment with standardized indexes: exploratory analysis, influential factors, and forecasting strategies. J Water Resour Plan Manag 147(11):05021020

    Article  Google Scholar 

  • Dayal KS, Deo RC, Apan AA (2018) Investigating drought duration-severity-intensity characteristics using the standardized precipitation-evapotranspiration index: case studies in drought-prone Southeast Queensland. J Hydrol Eng 23(1):05017029

    Article  Google Scholar 

  • DAC (2016) Manual of drought management, Department of Agriculture and Cooperation, Government of India retrieved on 7 September 2023

  • Ding Y, Xu J, Wang X et al (2021) Propagation of meteorological to hydrological drought for different climate regions in China. J Environ Manage 283:111980

    Article  Google Scholar 

  • Edwards DC, McKee TB (1997) Characteristics of 20th century drought in the United States at multiple time scales, vol 97, p 155. Fort Collins: Colorado State University

  • European Drought Observatory (EDO) (2020) Indicator factsheet for standardized precipitation index (SPI), European Commission retrieved on 1 September 2023

  • Gong R, Chen J, Liang Z et al (2023) Characterization and propagation from meteorological to groundwater drought in different aquifers with multiple timescales. J Hydrol Reg Studies 45:101317

    Article  Google Scholar 

  • Guhathakurta P, Rajeevan M (2008) Trends in the rainfall pattern over India. Int J Climatol A J Royal Meteorol Soc 28(11):1453–1469

    Google Scholar 

  • Gujarati DN, Porter DC (2009) Basic econometrics

  • Guo Y, Huang S, Huang Q et al (2020) Propagation thresholds of meteorological drought for triggering hydrological drought at various levels. Sci Total Environ 712:136502

    Article  CAS  Google Scholar 

  • Hayes MJ (2000) Revisiting the SPI: clarifying the process

  • Hasan MM, Alabdallah NM, Alharbi BM et al (2021) GABA: a key player in drought stress resistance in plants. Int J Mol Sci 22(18):10136

    Article  CAS  Google Scholar 

  • Hibner H, Kelly M (2023) Making a collection count: a holistic approach to library collection management. Elsevier

    Google Scholar 

  • Hui-Mean F, Yusof F, Yusop Z et al (2019) Trivariate copula in drought analysis: a case study in peninsular Malaysia. Theoret Appl Climatol 138:657–671

    Article  Google Scholar 

  • Karlsson IB, Sonnenborg TO, Jensen KH et al (2013). Evaluating the influence of long term historical climate change on catchment hydrology using drought and flood indices. Hydrol Earth Syst Sci Discuss 10(2)

  • Keyantash J (2021) Indices for meteorological and hydrological drought. Hydrolog Aspect Climate Change. https://doi.org/10.1007/978-981-16-0394-5_11

    Article  Google Scholar 

  • Krishna PS, Krishna BY, Nafisa S, Sravani TR, Madhuri JR, Vanditha C (2023) Prediction of droughts using SPEI. In: 2023 IEEE 12th international conference on communication systems and network technologies (CSNT) IEEE, pp 839–845

  • Krishnan R, Sanjay J, Gnanaseelan C, Mujumdar M, Kulkarni A, Chakraborty S (2020) Assessment of climate change over the Indian region: a report of the ministry of earth sciences (MOES), government of India. Springer Nature, p 226

  • Laimighofer J, Laaha G (2022) How standard are standardized drought indices? Uncertainty components for the SPI and SPEI case. J Hydrol 613:128385

    Article  Google Scholar 

  • Lloyd-Hughes B, Saunders MA (2002) A drought climatology for Europe. Int J Climatol A J R Meteorol Soc 22(13):1571–1592

    Article  Google Scholar 

  • Lotfirad M, Esmaeili-Gisavandani H, Adib A (2022) Drought monitoring and prediction using SPI, SPEI, and random forest model in various climates of Iran. J Water Climate Change 13(2):383–406

    Article  Google Scholar 

  • Lweendo MK, Lu B, Wang M et al (2017) Characterization of droughts in humid subtropical region, upper Kafue river basin (Southern Africa). Water 9(4):242

    Article  Google Scholar 

  • Maity R, Suman M, Verma NK (2016) Drought prediction using a wavelet based approach to model the temporal consequences of different types of droughts. J Hydrol 539:417–428

    Article  Google Scholar 

  • McKee TB, Doesken NJ, Kleist J (1993) The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th conference on applied climatology, vol. 17, no. 22, pp 179–183

  • Mishra AK, Desai VR (2005) Spatial and temporal drought analysis in the Kansabati river basin, India. Int J River Basin Manag 3(1):31–41

    Article  Google Scholar 

  • Mishra AK, Singh VP, Desai VR (2009) Drought characterization: a probabilistic approach. Stoch Environ Res Risk Assess 23:41–55

    Article  Google Scholar 

  • Moccia B, Mineo C, Ridolfi E et al (2022) SPI-based drought classification in Italy: influence of different probability distribution functions. Water 14(22):3668

    Article  Google Scholar 

  • Mtilatila L, Bronstert A, Bürger G et al (2020) Meteorological and hydrological drought assessment in Lake Malawi and Shire River basins (1970–2013). Hydrol Sci J 65(16):2750–2764

    Article  Google Scholar 

  • Mukherjee S, Mishra AK (2022) Global flash drought analysis: uncertainties from indicators and datasets. Earth’s Future. https://doi.org/10.1029/2022EF002660

    Article  Google Scholar 

  • Myronidis D, Fotakis D, Ioannou K et al (2018) Comparison of ten notable meteorological drought indices on tracking the effect of drought on streamflow. Hydrol Sci J 63(15–16):2005–2019

    Article  Google Scholar 

  • Naumann G, Alfieri L, Wyser K et al (2018) Global changes in drought conditions under different levels of warming. Geophys Res Lett 45(7):3285–3296

    Article  Google Scholar 

  • Niaz R, Almazah MM, Hussain I et al (2022) Assessing the probability of drought severity in a homogeneous region. Complexity 2022:1–8

    Google Scholar 

  • O’Brien MJ, Philipson CD, Reynolds G et al (2019) Positive effects of liana cutting on seedlings are reduced during El Niño-induced drought. J Appl Ecol 56(4):891–901

    Article  Google Scholar 

  • Oza M, Kishtawal CM (2015) Spatio-temporal changes in temperature over India. Curr Sci, 1154–1158

  • Panigrahi S, Vidyarthi VK (2024) Identification of the most suitable meteorological drought index for a region: a case study of Raigarh district in Chhattisgarh. Nat Hazards. https://doi.org/10.1007/s11069-024-06516-1

    Article  Google Scholar 

  • Pashiardis S, 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. Europ Water 23(24):57–65

    Google Scholar 

  • Pathak AA, Dodamani BM (2021) Connection between meteorological and groundwater drought with copula-based bivariate frequency analysis. J Hydrol Eng 26(7):05021015

    Article  Google Scholar 

  • Pei Z, Fang S, Wang L et al (2020) Comparative analysis of drought indicated by the SPI and SPEI at various timescales in inner Mongolia, China. Water 12(7):1925

    Article  Google Scholar 

  • Rajeevan M, Bhate J, Jaswal AK (2008) Analysis of variability and trends of extreme rainfall events over India using 104 years of gridded daily rainfall data. Geophys Res Lett 35(18)

  • Rajeevan MN, Nayak S (eds) (2017) Observed climate variability and change over the Indian region. Springer, Singapore, pp 145–163

    Book  Google Scholar 

  • Shah D, Mishra V (2020) Integrated Drought Index (IDI) for drought monitoring and assessment in India. Water Res Res. https://doi.org/10.1029/2019WR026284

    Article  Google Scholar 

  • Shamshirband S, Hashemi S, Salimi H et al (2020) Predicting standardized streamflow index for hydrological drought using machine learning models. Eng Appl Comput Fluid Mech 14(1):339–350

    Google Scholar 

  • Sharafati A, Nabaei S, Shahid S (2020) Spatial assessment of meteorological drought features over different climate regions in Iran. Int J Climatol 40(3):1864–1884

    Article  Google Scholar 

  • Singh U, Agarwal P, Sharma PK (2022) Meteorological drought analysis with different indices for the Betwa River basin. India Theoret Appl Climatol 148(3–4):1741–1754

    Article  Google Scholar 

  • Smakhtin VU, Hughes DA (2004) Review, automated estimation and analyses of drought indices in South Asia. Working Paper 83. Colombo, Srilanka: International Water Management Institute

  • Stagge JH, Tallaksen LM, Gudmundsson L et al (2015) Candidate distributions for climatological drought indices (SPI and SPEI). Int J Climatol 35(13):4027–4040

    Article  Google Scholar 

  • Stoica P, Babu P (2023). Pearson–Matthews correlation coefficients for binary and multinary classification and hypothesis testing. ar**v preprint ar**v:2305.05974

  • Subramanya K (2008) Engineering hydrology. McGraw-Hill

    Google Scholar 

  • Swain S, Mishra SK, Pandey A (2021) A detailed assessment of meteorological drought characteristics using simplified rainfall index over Narmada River Basin, India. Environ Earth Sci 80:1–15

    Article  Google Scholar 

  • Tefera AS, Ayoade JO, Bello NJ (2019) Comparative analyses of SPI and SPEI as drought assessment tools in Tigray Region, Northern Ethiopia. SN Appl Sci 1:1–14

    Article  Google Scholar 

  • Tigkas D, Vangelis H, Tsakiris G (2020) Implementing crop evapotranspiration in RDI for farm-level drought evaluation and adaptation under climate change conditions. Water Resour Manage 34:4329–4343

    Article  Google Scholar 

  • Touma D, Ashfaq M, Nayak MA et al (2015) A multi-model and multi-index evaluation of drought characteristics in the 21st century. J Hydrol 526:196–207

    Article  Google Scholar 

  • Tsakiris G, Pangalou D, Vangelis H (2007) Regional drought assessment based on the Reconnaissance Drought Index (RDI). Water Resour Manage 21:821–833

    Article  Google Scholar 

  • Vicente-Serrano SM, Beguería S, López-Moreno JI (2010) A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J Clim 23(7):1696–1718

    Article  Google Scholar 

  • Vidyarthi VK, Jain A (2020) Knowledge extraction from trained ANN drought classification model. J Hydrol 585:124804

    Article  Google Scholar 

  • Wang W, Ertsen MW, Svoboda MD et al (2016) Propagation of drought: from meteorological drought to agricultural and hydrological drought. Adv Meteorol 2016:1–5

    Google Scholar 

  • World Meteorological Organization (2012) Standardized precipitation index user guide. (M. Svoboda, M. Hayes and D. Wood). WMO-No. 1090. Geneva. ISBN 978-92-63-11091-6. 16p

  • Wu J, Liu Z, Yao H et al (2018) Impacts of reservoir operations on multi-scale correlations between hydrological drought and meteorological drought. J Hydrol 563:726–736

    Article  Google Scholar 

  • Wu C, Xu Y, ** J, Zhou Y, Nie B, Li R Zhang L (2023) Meteorological to agricultural drought propagation time analysis and driving factors recognition considering time-variant characteristics. Water Resour Manag, 1–20

  • Yildirim G, Rahman A, Singh VP (2022) Meteorological and hydrological drought hazard, frequency and propagation analysis: a case study in southeast Australia. J Hydrol Reg Studies 44:101229

    Article  Google Scholar 

  • Zhang LSVP, Singh VP (2006) Bivariate flood frequency analysis using the copula method. J Hydrol Eng 11(2):150–164

    Article  Google Scholar 

  • Zhang Y, Hao Z, Feng S et al (2021) Agricultural drought prediction in China based on drought propagation and large-scale drivers. Agric Water Manag 255:107028

    Article  Google Scholar 

  • Zhang X, Hao Z, Singh VP et al (2022) Drought propagation under global warming: characteristics, approaches, processes, and controlling factors. Sci Total Environ 838:156021

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The data used in this study has been obtained freely online from Indian Meteorological Department (https://www.imdpune.gov.in/) and is duly acknowledged. The climatic conditions of all the 36 subdivisions have been obtained from Maps of India (https://www.mapsofindia.com/maps/india/climaticregions.htm). This study also appropriately acknowledges the computing resources offered by the Department of Civil Engineering at NIT Raipur.

Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology, Raipur, Chhattisgarh, 492010, India

    Siddhant Panigrahi & Vikas Kumar Vidyarthi

Authors
  1. Siddhant Panigrahi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vikas Kumar Vidyarthi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Siddhant Panigrahi wrote the original draft, and Vikas Kumar Vidyarthi did the review—editing, and Data curation. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Siddhant Panigrahi.

Ethics declarations

Competing interest

The authors have no relevant financial or non-financial interest to disclose and declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Panigrahi, S., Vidyarthi, V.K. Assessing the suitability of McKee et al. (1993) drought severity classification across India. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06762-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11069-024-06762-3

Keywords

Search

Navigation