Skip to main content

Advertisement

SpringerLink
Log in

Observed and projected changes in extreme drought and flood-prone regions over India under CMIP5 RCP8.5 using a new vulnerability index

  • Published:
Climate Dynamics Aims and scope Submit manuscript

Abstract

Past versions of the vulnerability indices have shown the ability to detect susceptible regions by assessing climatic and socioeconomic parameters at local scales. These parameters significantly vary over geographic regions, therefore such an index may not be suitable to identify and predict susceptibility over a large domain. The present endeavour aims to develop a new vulnerablity index that identifies and predicts the spatiotemporal imprint of extreme drought and flood cases at various scales in India by analyzing monthly observed and Coupled Model Intercomparison Phase 5 (CMIP5) rainfall data at a spatial scale of 1° × 1° from 1901 to 2100. It is proposed by consolidating the outcomes of the Standard Precipitation Index (SPI) at different time scales, such as 3 and 12 months, along with the weights of individual grids. The weights of individual grids are calculated through the occurrence of extreme drought and flood years in the recent past to include a climate change factor in the proposed index. Based on the spatial distribution of high index values, the vulnerable regions concerning extreme droughts are expected to be in the Northeast, Northeast-central, East-coast, West, Northwest, North-central, and some grids in South India. Similarly, vulnerable regions concerning extreme flood cases are likely to be in the Northeast, West-coast, East-coast, and some grids in the Peninsular region.

Furthermore, a conceptual model is presented to quantify the severity of extreme cases. The analyses reveal that on the CMIP5 model data, 2024, 2026–2027, 2035, 2036–2037, 2043–2044, 2059–2060, and 2094 are likely to be the most prominent extreme drought years in all India monsoon rainfall, and their impacts will persist for a longer time than others. Similarly, the most prominent extreme flood cases are likely to occur in the year 2076, 2079–2080, 2085, 2090, 2092, and 2099.

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

Similar content being viewed by others

References

  • Azad S, Rajeevan M (2016) Possible shift in the ENSO-Indian monsoon rainfall relationship under future global warming. Sci Rep 6:20145

    Google Scholar 

  • Becker A, Finger P, Meyer-Christoffer A, Rudolf B, Schamm K, Schneider U, Ziese M (2013) A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst Sci Data 5(1):71

    Google Scholar 

  • Bhalme HN, Mooley DA (1980) Large-scale droughts/wets and monsoon circulation. Mon Weather Rev 108(8):1197–1211

    Google Scholar 

  • Bhalme HN, Mooley DA, Jadhav SK (1983) Fluctuations in the drought/wet area over India and relationships with Southern Oscillation. Mon Weather Rev 111(1):86–94

    Google Scholar 

  • Bonaccorso B, Bordi I, Cancelliere A, Rossi G, Sutera A (2003) Spatial variability of drought: and analysis of the SPI in Sicily. Water Resour Manag 17:273–296

    Google Scholar 

  • Brooks N, Adger W, Kelly P (2005) The determinants of vulnerability and adaptive capacity at the national level and the implications for adaptation. Global Environ Change 15(2):151–163

    Google Scholar 

  • Cook BI, Ault TR, Smerdon JE (2015) Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci Adv 1(1):e1400082. https://doi.org/10.1126/sciadv.1400082

    Article  Google Scholar 

  • Dabanli I (2018) Drought hazard, vulnerability, and risk assessment in Turkey. Arab J Geosci 11(18):538

    Google Scholar 

  • Dhar ON, Nandargi S (2003) Hydrometeorological aspects of wets in India. Nat Hazards 28(1):1–33

    Google Scholar 

  • Dickey DA, Fuller WA (1981) Fuller likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49:1057–1072

    Google Scholar 

  • Diffenbaugh NS, Giorgi F (2012) Climate change hotspots in the CMIP5 global climate model ensemble. Clim Change 114(3–4):813–822

    Google Scholar 

  • Eakin H, Conley J (2002) Climate variability and the vulnerability of ranching in southeastern Arizona: a pilot study. Clim Res 21(3):271–282

    Google Scholar 

  • Edwards DC, McKee TB (1997) Characteristics of 20th century drought in the United States at multiple timescales. Colorado State University Fort Collins. Climatology Report No. 97-2

  • Fowler AM, Hennessy KJ (1995) Potential impacts of global warming on the frequency and magnitude of heavy precipitation. Nat Hazards 11(3):283–303

    Google Scholar 

  • Geil KL, Serra YL, Zeng X (2013) Assessment of CMIP5 model simulations of the North American monsoon system. J Clim 26(22):8787–8801

    Google Scholar 

  • Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations–the CRU TS3 10 dataset. Int J Climatol 34(3):623–642

    Google Scholar 

  • Hayes MJ, Svoboda MD, Wilhite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteor Soc 80:429–438

    Google Scholar 

  • Houghton E (1996) Climate change 1995: The science of climate change: contribution of working group I to the second assessment report of the Intergovernmental Panel on Climate Change, vol 2. Cambridge University Press

  • IPCC (2001) Climate change 2001. The scientific basis. In: Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds) Contributions of working Group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, New York

    Google Scholar 

  • Jena P, Azad S (2019) Weakening of triennial oscillation of the Indian summer monsoon rainfall (at 1° × 1° gridded scale) under future global warming. Earth Space Sci 6(7):1262–1272

    Google Scholar 

  • Jena P, Azad S (2021) A novel method to detect drought and flood years in Indian rainfall associated with weak and strong monsoon. Theor Appl Climatol. https://doi.org/10.1007/s00704-021-03652-7 

    Article  Google Scholar 

  • Jena P, Azad S, Rajeevan MN (2015) Statistical selection of the optimum models in the CMIP5 dataset for climate change projections of Indian monsoon rainfall. Climate 3(4):858–875

    Google Scholar 

  • Jena P, Azad S, Rajeevan MN (2016) CMIP5 projected changes in the annual cycle of Indian monsoon rainfall. Climate 4:1–14

    Google Scholar 

  • Jena P, Kasiviswanathan KS, Azad S (2020) Spatiotemporal characteristics of extreme droughts and their association with sea surface temperature over the Cauvery River basin, India. Nat Hazards 104(3):2239–2259

    Google Scholar 

  • Jones PD et al (2012) Hemispheric and large-scale land-surface air temperature variations: an extensive revision and an update to 2010. J Geophys Res 117:D05127

    Google Scholar 

  • Kar SK, Thomas T, Singh RM, Patel L (2018) Integrated assessment of drought vulnerability using indicators for Dhasan basin in Bundelkhand region, Madhya Pradesh, India. Curr Sci 115(2):338–346

    Google Scholar 

  • Kim H, Park J, Yoo J, Kim TW (2015) Assessment of drought hazard, vulnerability, and risk: A case study for administrative districts in South Korea. J Hydrol-Environ Res 9(1):28–35

    Google Scholar 

  • Koirala S, Hirabayashi Y, Mahendran R, Kanae S (2014) Global assessment of agreement among streamflow projections using CMIP5 model outputs. Environ Res Lett 9(6):064017

  • Lavell A, Oppenheimer M, Diop C, Hess J, Lempert R, Li J, Muir-Wood R, Myeong S, Moser S, Takeuchi K, Cardona OD, Hallegatte S, Lemos Maria, Little C, Lotsch A, Weber E (2012) Climate change: new dimensions in disaster risk, exposure, vulnerability, and resilience. In: Field CB, Barros V, Stocker TF, Dahe Q (eds) Managing the risks of extreme events and disasters to advance climate change adaptation: special report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 25–64

  • Mallya G, Mishra V, Niyogi D, Tripathi S, Govindaraju RS (2016) Trends and variability of droughts over the Indian monsoon region. Weather Clim Extremes 12:43–68

    Google Scholar 

  • Manikandan M, Tamilmani D (2013) Development of drought vulnerability maps in the Parambikulam-Aliyar Basin, Tamil Nadu, India. Sci Res Essays 8(20):778–790

    Google Scholar 

  • Manjunatha BR, Balakrishna K, Krishnakumar KN, Manjunatha HV, Avinash K, Mulemane AC, Krishna KM (2015) Increasing trend of rainfall over Agumbe, Western Ghats, India in the scenario of global warming. Open Oceanogr J 8(1):39–44

    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. AMS, Boston, pp 179–184

  • Meehl GA, Arblaster JM, Tebaldi C (2005) Understanding future patterns of precipitation intensity in climate model simulations. Geophys Res Lett 32:L18719

    Google Scholar 

  • Metzger MJ, Leemans R, Schröter D (2005) A multidisciplinary multiscale framework for assessing vulnerabilities to global change. Int J Appl Earth Obs 7:253–267

    Google Scholar 

  • Mishra A, Liu SC (2014) Changes in precipitation pattern and risk of drought over India in the context of global warming. J Geophys Res Atmos 119(13):7833–7841

    Google Scholar 

  • Mishra A, Singh V (2010) A review of drought concepts. J Hydrol 391:202–216

    Google Scholar 

  • Murthy CS, Laxman B, Sai MS (2015) Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity. Int J Disaster Risk Reduct 12:163–171

    Google Scholar 

  • Murugan M, Krishnappa Shetty P, Anandhi A, Ravi R, Alappan S, Vasudevan M, Gopalan S (2009) Rainfall changes over tropical montane cloud forests of southern Western Ghats India. Curr Sci 97(12):1755–1760

    Google Scholar 

  • Nagarajan R (2003) Drought assessment, monitoring, management & resources conservation. Capital Publishing Co., Bangalore, pp 1–32

    Google Scholar 

  • Narasimhan B, Srinivasan R (2005) Development and evaluation of Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) for agricultural drought monitoring. Agric for Meteor 133:69–88

    Google Scholar 

  • Narula P, Sarkar K, Azad S (2018) A functional evaluation of the spatiotemporal patterns of temperature change in India. Int J Climatol 38(1):264–271

    Google Scholar 

  • Oikonomou PD, Tsesmelis DE, Waskom RM, Grigg NS, Karavitis CA (2019) Enhancing the standardized drought vulnerability index by integrating spatiotemporal information from satellite and in situ data. J Hydrol 569:265–277

    Google Scholar 

  • Ojha R, Nagesh Kumar D, Sharma A, Mehrotra R (2013) Assessing severe drought and wet events over India in a future climate using a nested bias-correction approach. J Hydrol Eng 18(7):760–772

    Google Scholar 

  • Orlowsky B, Seneviratne SI (2013) Elusive drought: uncertainty in observed trends and short-and long-term CMIP5 projections. Hydrol Earth Syst Sci 17:1765–1781

    Google Scholar 

  • Parthasarathy B, Sontakke NA, Munot AA, KoPaithawale DR (1987) Droughts/wets in summer monsoons season over different meteorological sub-divisions of India for the period 1871–1980. J Climatol 7:57–70

    Google Scholar 

  • Parthasarathy B, Munot AA, Kothawale DR (1994) All India monthly and seasonal rainfall series 1871–1993. Theor Appl Climatol 49:217–224

    Google Scholar 

  • Peters E, Van Lanen HAJ, Torfs PJJF, Bier G (2005) Drought in groundwater—drought distribution and performance indicators. J Hydrol 306(1–4):302–317

    Google Scholar 

  • Preethi B, Ramya R, Patwardhan SK, Mujumdar M, Kripalani RH (2019) Variability of Indian summer monsoon droughts in CMIP5 climate models. Clim Dyn 53(3–4):1937–1962

    Google Scholar 

  • Rajeevan M, Bhate J, Kale JD, Behera BLS, Krishnan R, Yamagata T (2006) High resolution daily gridded rainfall data for the Indian region: analysis of break and active monsoon spells. Curr Sci 91:296–306

    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. Geophy Res Lett 35(23)

  • Rao BB, Chowdary PS, Sandeep VM, Rao VUM, Venkateswarlu B (2014) Rising minimum temperature trends over India in recent decades: Implications for agricultural production. Glob Planet Change 117:1–8

    Google Scholar 

  • Reeves Eyre JEJ, Zeng X (2017) Evaluation of Greenland near surface air temperature datasets. Cryosphere 11:1591–1605

    Google Scholar 

  • Robertson AW, Bell M, Cousin R, Curtis A, & Li S (2013) Online tools for assessing the climatology and predictability of rainfall and temperature in the Indo-Gangetic Plains based on observed datasets and seasonal forecast models. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). Working paper no. 27. Cali, Colombia. https://cgspace.cgiar.org/handle/10568/28992

  • Roxy MK, Ritika K, Terray P, Murtugudde R, Ashok K, Goswami BN (2015) Drying of Indian subcontinent by rapid Indian Ocean warming and a weakening land-sea thermal gradient. Nat Commun 6:7423

    Google Scholar 

  • Samra JS (2004) Review and analysis of drought monitoring, declaration and management in India. Working Paper 84, Drought Series Paper 2, International Water Management Institute (IWMI), Colombo, Sri Lanka, p 31

  • Seitinthang Lh (2014) Cropping pattern of Northeast India: an appraisal. Am Res Thoughts 1(1):488–498

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423

    Google Scholar 

  • Sheffield J, Wood EF (2008) Projected changes in drought occurrence under future global warming from multi-model, multi-scenario, IPCC AR4 simulations. Clim Dyn 31:79–105

    Google Scholar 

  • Shewale MP, Kumar S (2005) Climatological features of drought incidences in India; National climate centre office of the additional director general of meteorology research, Indian Meteorological Department: Pune, India, pp 1–24

  • Shi H, Li T, Wei J (2017) Evaluation of the gridded CRU TS precipitation dataset with the point rain gauge records over the Three-River Headwaters Region. J Hydrol 548:322–332

    Google Scholar 

  • Smit B, Burton I, Klein RJ, Street R (1999) The science of adaptation: a framework for assessment. Mitig Adapt Strat Glob Chang 4:199–213

    Google Scholar 

  • Svoboda M, Fuchs B (2016) Handbook of drought indicators and indices; National drought mitigation center: Lincoln, NE, USA

  • Swann ALS, Hoffman FM, Koven CD, Randerson JT (2016) Plant responses to increasing CO2 reduce estimates of climate impacts on drought severity. Proc Natl Acad Sci 113:10019–10024

    Google Scholar 

  • Taylor KE, Stouffer R, Meehl GA (2011) An overview of CMIP5 and the Experiment Design. Bull Am Meteorol Soc 93(4):485–498

    Google Scholar 

  • Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteor Soc 93:485–498

    Google Scholar 

  • Taylor IH, Burke E, McColl L, Falloon PD, Harris GR, McNeall D (2013) The impact of climate mitigation on projections of future drought. Hydrol Earth Syst Sci 17:2339–2358

    Google Scholar 

  • Thom HC (1958) A note on the gamma distribution. Mon Weather Rev 86(4):117–122

    Google Scholar 

  • Thorne PW et al (2016) Reassessing changes in diurnal temperature range: intercomparison and evaluation of existing global data set estimates. J Geophys Res Atmos 121:5138–5158

    Google Scholar 

  • Thomas T, Jaiswal RK, Galkate R, Nayak PC, Ghosh NC (2016) Drought indicators-based integrated assessment of drought vulnerability: a case study of Bundelkhand droughts in central India. Nat Hazards 81(3):1627–1652. https://doi.org/10.1007/s11069-016-2149-8

    Article  Google Scholar 

  • Tsakiris G, Vangelis H (2004) Towards a drought watch system based on spatial SPI. Water Resour Manage 18(1):1–12

    Google Scholar 

  • Tyalagadi MS, Gadgil A, Krishnakumar G (2015) Monsoonal droughts in India – a recent assessment. Papers on global change, vol 22. De Gruyter Open, pp 19–35

  • UNDP (2004) Global report. Reducing disaster risk: A challenge for development, UNDP, Bureau for crisis prevention and recovery, New York

  • Vasiliades L, Loukas A, Liberis N (2011) A water balance derived drought index for Pinios River Basin, Greece. Water Resour Manag 25(4):1087–1101

    Google Scholar 

  • Veijalainen N, Vehvilainen B (2008) The effect of climate change on design wets of high hazard dams in Finland. Hydrol Res 39:465–477

    Google Scholar 

  • Wilks DS (1995) Statistical methods in the atmospheric sciences: an introduction. Academic Press, San Diego

    Google Scholar 

  • Willmott CJ, Matsuura K (2001) Terrestrial air temperature and precipitation: monthly and annual time series (1950–1999). Center for Climatic Research, Department of Geography, University of Delaware

  • Xie SP, Deser C, Vecchi GA, Ma J, Teng H, Wittenberg AT (2010) Global warming pattern formation: Sea surface temperature and rainfall. J Clim 23(4):966–986

    Google Scholar 

  • Yang JS, Park JH, Kim NK (2012) Development of drought vulnerability index using trend analysis. J Korean Soc Civ Eng 32(3B):185–192

    Google Scholar 

  • Zhang Q, Xu CY, Gemmer M, Chen YD, Liu CL (2008) Changing properties of precipitation concentration in the Pearl River basin, China. Stoch Environ Res Risk Assess 23(3):377–385

    Google Scholar 

  • Zhao T, Dai A (2015) The magnitude and causes of global drought changes in the twenty-first century under a low moderate emissions scenario. J Clim 28:4490–4512

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, Himachal Pradesh, 175005, India

    Pravat Jena & Sarita Azad

  2. Department of Data Science, SCOPE, VIT, Vellore, 632014, India

    Pravat Jena

Authors
  1. Pravat Jena
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarita Azad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarita Azad.

Additional information

Publisher's Note

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

Appendix

Appendix

1.1 Selection of probability distribution function for the fitting of data

It is mentioned in Thom (1958) and Wilks (1995) that the gamma distribution is a good choice for describing precipitation values at different time scales for a variety of reasons. The advantage of the gamma distribution includes firstly, it is bounded on the left at zero and the gamma distribution is positively skewed, meaning that it has an extended tail to the right of the distribution. Many studies have employed the gamma distribution in the analysis of rainfall. It is reported that the maximum likelihood estimators (MLEs) optimally calculate the shape and scale parameters for the gamma distribution. An alternative to the MLE parameters is the method of moment estimation (MME). It has been shown, however, that the method of moments is a poor estimator, owing to inefficiency, for small shape values (Wilks 1995; Thom 1958). Further, the present study has applied various distributions to fit the rainfall data at different time scales. To verify the efficiency of the distribution, Akkai information criteria (AIC) is calculated for all different data sets and the result is mentioned in Tables 4 and 5. It reveals that the gamma distribution performs uniformly over all kinds of datasets. A rank is assigned to each of distribution based on the performance (best fitting) and mentioned in Table 5, it reveals that the gamma distribution score lowest rank, represents the best fit for the rainfall data.

Table 4 Calculated AIC values for various distribution fitted at different time scales of data
Full size table
Table 5 Rank of the distribution’s performance in fitting data at different time scales
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jena, P., Azad, S. Observed and projected changes in extreme drought and flood-prone regions over India under CMIP5 RCP8.5 using a new vulnerability index. Clim Dyn 57, 2595–2613 (2021). https://doi.org/10.1007/s00382-021-05824-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00382-021-05824-7

Search

Navigation