Skip to main content

Advertisement

SpringerLink
Log in

Predicting long term regional drought pattern in Northeast India using advanced statistical technique and wavelet-machine learning approach

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

Understanding drought and its multifaceted challenges is crucial for safeguarding food security, promoting environmental sustainability, and fostering socio-economic well-being across the globe. As a consequence of climate change and anthropogenic factors, the occurrence and severity of drought has risen globally. In India, droughts are regular phenomenon affecting about 16% area of country each year which leads to a loss of about 0.5–1% of country’s annual GDP. Hence, the study aims to analyse and predict the meteorological drought in northeast India during 1901 to 2015 using standardised precipitation index (SPI) and analytical techniques such as Mann–Kendall test (MK), innovative trend analysis (ITA), and wavelet approach. In addition, the periodicity of the drought was estimated using Morlet wavelet technique, while discrete wavelet transform (DWT) was applied for decomposing the time series SPI-6 & SPI-12. Study shows that the northeast India experienced moderate drought conditions (SPI-6) in short term and two significant severe droughts (SPI-12) in long term between 1901 and 2015. The trend analysis shows a significant increase in SPI-6 & SPI-12 (p-value 0.01). Further, the combination of parameters i.e. approximation and levels result in the best drought prediction model with higher correlation coefficient and lower error. By using PSO-REPTtree, this study pioneers the use of decomposed parameters to detect trends and develop a drought prediction model. The study is the first step towards establishing drought early warning system that will help decision-makers and farmers to mitigate the impact of drought at the regional level.

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

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Adinehvand M, Singh BN (2017) Monitoring drought status using precipitation factor: a case study of Jaisalmer meteorological station in Rajasthan. India Forum Geografic 16(2):119

    Google Scholar 

  • Alam J, Saha P, Mitra R, Das J (2023) Investigation of spatio-temporal variability of meteorological drought in the Luni River Basin, Rajasthan. India Arab J Geosci 16(3):201

    Google Scholar 

  • Allen MR, O.P. Dube, W. Solecki, F. Aragón-Durand, W. Cramer, S. Humphreys, M. Kainuma, J. Kala, N. Mahowald, Y. Mulugetta, R. Perez, M. Wairiu, and K. Zickfeld (2018) Framing and Context. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty.

  • Almazroui M, Şen Z (2020) Trend analyses methodologies in hydro-meteorological records. Earth Syst Environ 4:713–738

    Google Scholar 

  • Anderegg WR, Kane JM, Anderegg LD (2013) Consequences of widespread tree mortality triggered by drought and temperature stress. Nat Clim Chang 3(1):30–36

    Google Scholar 

  • Ault TR (2020) On the essentials of drought in a changing climate. Science 368(6488):256–260

    Google Scholar 

  • Bandyopadhyay N, Bhuiyan C, Saha AK (2016) Heat waves, temperature extremes and their impacts on monsoon rainfall and meteorological drought in Gujarat, India. Nat Hazards 82:367–388

    Google Scholar 

  • Borji M, Malekian A, Salajegheh A, Ghadimi M (2016) Multi-time-scale analysis of hydrological drought forecasting using support vector regression (SVR) and artificial neural networks (ANN). Arab J Geosci 9:1–10

    Google Scholar 

  • Byun HR, Lee SJ, Morid S, Choi KS, Lee SM, Kim DW (2008) Study on the periodicities of droughts in Korea. Asia-Pac J Atmos Sci 44(4):417–441

    Google Scholar 

  • Chen RJ, Bloomfield P, Fu JS (2003) An evaluation of alternative forecasting methods to recreation visitation. J Leis Res 35(4):441–454

    Google Scholar 

  • Chen Y, Guan Y, Shao G, Zhang D (2016) Investigating trends in streamflow and precipitation in Huangfuchuan Basin with wavelet analysis and the Mann-Kendall test. Water 8(3):77

    Google Scholar 

  • Chong KL, Huang YF, Koo CH, Ahmed AN, El-Shafie A (2022) Spatiotemporal variability analysis of standardized precipitation indexed droughts using wavelet transform. J Hydrol 605:127299

    Google Scholar 

  • Dai A (2011) Drought under global warming: a review. Wiley Interdiscip Rev 2(1):45–65

    Google Scholar 

  • Darlong V, Hore DK, Barma SD (2020) Gender, food security and rice farming in Tripura. Agriculture and a changing environment in Northeastern India. Routledge, India, pp 184–211

    Google Scholar 

  • Das PK, Dutta D, Sharma JR, Dadhwal VK (2016) Trends and behaviour of meteorological drought (1901–2008) over Indian region using standardized precipitation–evapotranspiration index. Int J Climatol 36(2):909–916

    Google Scholar 

  • Despotovic M, Nedic V, Despotovic D, Cvetanovic S (2015) Review and statistical analysis of different global solar radiation sunshine models. Renew Sustain Energy Rev 52:1869–1880

    Google Scholar 

  • Dikshit A, Pradhan B, Huete A (2021) An improved SPEI drought forecasting approach using the long short-term memory neural network. J Environ Manage 283:111979

    Google Scholar 

  • Dunne A, Kuleshov Y (2022) Drought risk assessment and mapping for the Murray-Darling Basin, Australia. Nat Hazards 115:839–863

    Google Scholar 

  • Elbeltagi A, Kumar M, Kushwaha NL, Pande CB, Ditthakit P, Vishwakarma DK, Subeesh A (2023) Drought indicator analysis and forecasting using data driven models: case study in Jaisalmer, India. Stoch Env Res Risk Assess 37(1):113–131

    Google Scholar 

  • Grossmann A, Morlet J (1984) Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15(4):723–736

    Google Scholar 

  • Guttman NB (1999) Accepting the standardized precipitation index: a calculation algorithm. J Am Water Resour Assoc 35(2):311–322

    Google Scholar 

  • Gyamfi C, Amaning-Adjei K, Anornu GK, Ndambuki JM, Odai SN (2019) Evolutional characteristics of hydro-meteorological drought studied using standardized indices and wavelet analysis. Model Earth Syst Environ 5:455–469

    Google Scholar 

  • Hayes MJ, Svoboda MD, Wiihite DA, Vanyarkho OV (1999) Monitoring the 1996 drought using the standardized precipitation index. Bull Am Meteorol Soc 80(3):429–438

    Google Scholar 

  • Jain VK, Pandey RP, Jain MK, Byun HR (2015) Comparison of drought indices for appraisal of drought characteristics in the Ken River Basin. Weather Clim Extremes 8:1–11

    Google Scholar 

  • Jayanthi SK, Sasikala S (2013) Reptree classifier for identifying link spam in web search engines. IJSC 3(2):498–505

    Google Scholar 

  • Joshi N, Gupta D, Suryavanshi S, Adamowski J, Madramootoo CA (2016) Analysis of trends and dominant periodicities in drought variables in India: a wavelet transform based approach. Atmos Res 182:200–220

    Google Scholar 

  • Katipoğlu OM (2023) Revealing the trend and change point in Hargreaves equation based on potential evapotranspiration values with various statistical approaches. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-023-27417-z

    Article  Google Scholar 

  • Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1/2):81–93

    Google Scholar 

  • Kendall MG (1955) Rank correlation methods. Griffin, London

    Google Scholar 

  • Kim S, Kim H (2016) A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast 32(3):669–679

    Google Scholar 

  • Kisi O, Gorgij AD, Zounemat-Kermani M, Mahdavi-Meymand A, Kim S (2019) Drought forecasting using novel heuristic methods in a semi-arid environment. J Hydrol 578:124053

    Google Scholar 

  • Kumar S, Chong I (2018) Correlation analysis to identify the effective data in machine learning: Prediction of depressive disorder and emotion states. Int J Environ Res Public Health 15(12):2907

    Google Scholar 

  • Kumar N, Middey A (2023) Extreme climate index estimation and projection in association with enviro-meteorological parameters using random forest-ARIMA hybrid model over the Vidarbha region. India Environ Monit Assess 195(3):380

    Google Scholar 

  • Kumar NM, Murthy CS, Sesha Sai MVR, Roy PS (2012) Spatiotemporal analysis of meteorological drought variability in the Indian region using standardized precipitation index. Meteorol Appl 19(2):256–264

    Google Scholar 

  • Lesk C, Rowhani P, Ramankutty N (2016) Influence of extreme weather disasters on global crop production. Nature 529(7584):84–87

    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 and Clim Change 13(2):383–406

    Google Scholar 

  • Mahanta R, Sarma D, Choudhury A (2013) Heavy rainfall occurrences in northeast India. Int J Climatol 33(6):1456–1469

    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

    Google Scholar 

  • Mallenahalli NK (2020) Comparison of parametric and nonparametric standardized precipitation index for detecting meteorological drought over the Indian region. Theoret Appl Climatol 142(1–2):219–236

    Google Scholar 

  • Mann HB (1945) Non-parametric tests against trend. Econometrica 13:245–259

    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, 17(22): 179–183.

  • Mishra D, Goswami S, Matin S, Sarup J (2022) Analyzing the extent of drought in the Rajasthan state of India using vegetation condition index and standardized precipitation index. Model Earth Syst Enviro 8:601–610

    Google Scholar 

  • Mohapatra G, Rakesh V, Purwar S, Dimri AP (2021) Spatio-temporal rainfall variability over different meteorological subdivisions in India: analysis using different machine learning techniques. Theoret Appl Climatol 145(1):673–686

    Google Scholar 

  • Morid S, Smakhtin V, Bagherzadeh K (2007) Drought forecasting using artificial neural networks and time series of drought indices. Int J Climatol 27(15):2103–2111

    Google Scholar 

  • Naikoo MW, Talukdar S, Das T, Rahman A (2022) Identification of homogenous rainfall regions with trend analysis using fuzzy logic and clustering approach coupled with advanced trend analysis techniques in Mumbai city. Urban Climate 46:101306

    Google Scholar 

  • Omondi OA (2014) Analysis of meteorological drought in North Eastern Province of Kenya. J Earth Sci Clim Change 5(8):219

    Google Scholar 

  • Owolabi ST, Madi K, Kalumba AM (2021) Comparative evaluation of spatio-temporal attributes of precipitation and streamflow in Buffalo and Tyume Catchments, Eastern Cape, South Africa. Environ Dev Sustain 23:4236–4251

    Google Scholar 

  • Palmer WC (1965) Meteorological drought. US Department of Commerce, Weather Bureau. 45:58.

  • Parida BR, Oinam B (2015) Unprecedented drought in North East India compared to Western India. Curr Sci 109(11):2121–2126

    Google Scholar 

  • Pedrycz W, Park BJ, Pizzi NJ (2009) Identifying core sets of discriminatory features using particle swarm optimization. Expert Syst Appl 36(3):4610–4616

    Google Scholar 

  • Pham MP, Nguyen KQ, Vu GD, Nguyen NT, Tong HT, Trinh LH, Le PV (2022) Drought risk index for agricultural land based on a multi-criteria evaluation. Model Earth Syst Environ 8(4):5535–5546

    Google Scholar 

  • Pisoft P, Kalvova J, Brazdil R (2004) Cycles and trends in the Czech temperature series using wavelet transforms. Int J Climatol 24:1661–1670

    Google Scholar 

  • Poornima S, Pushpalatha M, Jana RB, Patti LA (2023) Rainfall forecast and drought analysis for recent and forthcoming years in India. Water 15(3):592

    Google Scholar 

  • Qtaishat T, El-Habbab MS, Bumblauskas DP, Tabieh M (2023) The impact of drought on food security and sustainability in Jordan. Geo J 88(2):1389–1400

    Google Scholar 

  • Rahman MR, Lateh H (2016) Meteorological drought in Bangladesh: assessing, analysing and hazard mapping using SPI, GIS and monthly rainfall data. Environ Earth Sci 75(12):1026

    Google Scholar 

  • Rao CS, Gopinath KA, Prasad JVNS, Singh AK (2016) Climate resilient villages for sustainable food security in tropical India: concept, process, technologies, institutions, and impacts. Adv Agron 140:101–214

    Google Scholar 

  • Roushangar K, Ghasempour R (2022) Multi-temporal analysis for drought classifying based on SPEI gridded data and hybrid maximal overlap discrete wavelet transform. Int J Environ Sci Technol 19(4):3219–3232

    Google Scholar 

  • Roy P, Pal SC, Chakrabortty R, Chowdhuri I, Saha A, Shit M (2022) Climate change and groundwater overdraft impacts on agricultural drought in India: vulnerability assessment, food security measures and policy recommendation. Sci Total Environ 849:157850

    Google Scholar 

  • Roy S, Hazra S, Chanda A (2023) Changing characteristics of meteorological drought and its impact on monsoon-rice production in sub-humid red and laterite zone of West Bengal. India Theor Appl Climatol 151(3):1419–1433

    Google Scholar 

  • Saha S, Kundu B, Paul GC, Pradhan B (2023) Proposing an ensemble machine learning based drought vulnerability index using M5P, dagging, random sub-space and rotation forest models. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-023-02403-6

    Article  Google Scholar 

  • Saharwardi MS, Kumar P, Dubey AK, Kumari A (2022) Understanding spatiotemporal variability of drought in recent decades and its drivers over identified homogeneous regions of India. Q J R Meteorol Soc 148(747):2955–2972

    Google Scholar 

  • Saini D, Singh O, Sharma T, Bhardwaj P (2022) Geoinformatics and analytic hierarchy process based drought vulnerability assessment over a dryland ecosystem of north-western India. Nat Hazards 114(2):1427–1454

    Google Scholar 

  • Sam AS, Padmaja SS, Kächele H, Kumar R, Müller K (2020) Climate change, drought and rural communities: understanding people’s perceptions and adaptations in rural eastern India. Int J Disaster Risk Reduct 44:101436

    Google Scholar 

  • Sarkar H, Soni S, Ahmad I, Verma MK (2020) Assessment of agricultural drought in upper seonath sub-basin of Chhattisgarh (India) using remote sensing and GIS-based indices. J Indian Soc Remote Sens 48(6):921–933

    Google Scholar 

  • Şen Z (2012) Innovative trend analysis methodology. J Hydrol Eng 17(9):1042–1046

    Google Scholar 

  • Sezen C, Partal T (2020) Wavelet combined innovative trend analysis for precipitation data in the Euphrates-Tigris basin. Turkey Hydrol Sci J 65(11):1909–1927

    Google Scholar 

  • Sharma A, Goyal MK (2020) Assessment of drought trend and variability in India using wavelet transform. Hydrol Sci J 65(9):1539–1554

    Google Scholar 

  • Sharma S, Mujumdar P (2017) Increasing frequency and spatial extent of concurrent meteorological droughts and heatwaves in India. Sci Rep 7(1):1–9

    Google Scholar 

  • Sharma A, Sharma D, Panda SK (2022) Assessment of spatiotemporal trend of precipitation indices and meteorological drought characteristics in the Mahi River basin. India J Hydrol 605:127314

    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 

  • Singh TP, Nandimath P, Kumbhar V, Das S, Barne P (2021) Drought risk assessment and prediction using artificial intelligence over the southern Maharashtra state of India. Model Earth Syst Environ 7:2005–2013

    Google Scholar 

  • Sneyers R, Tuomenvirta H, Heino R (1998) Observations inhomogeneities and detection of climate change the case of the Oulu (Finland) air temperature series. Transp Res Rec J Transp Res Board 34(3):159–178

    Google Scholar 

  • Souza DP, Martinho AD, Rocha CC, Christo ES, Goliatt L (2022) Hybrid particle swarm optimization and group method of data handling for short-term prediction of natural daily streamflows. Model Earth Syst Environ 8(4):5743–5759

    Google Scholar 

  • Spearman C (1961) The proof and measurement of association between two things. Am J Psychol 15(1):72–101

    Google Scholar 

  • Swain S, Mishra SK, Pandey AA (2021) A detailed assessment of meteorological drought characteristics using simplified rainfall index over Narmada River Basin. India Environ Earth Sci 80:221

    Google Scholar 

  • Talukdar S, Ali R, Nguyen KA, Naikoo MW, Liou YA, Islam ARMT, Mallick J, Rahman A (2022) Monitoring drought pattern for pre-and post-monsoon seasons in a semi-arid region of western part of India. Environ Monit Assess 194(6):1–19

    Google Scholar 

  • Torrence C, Compo G (1998) A practical guide to wavelet analysis. Bull Am Meteorol Soc 79:61–78

    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 23:1696–1718

    Google Scholar 

  • Wang T, Tu X, Singh VP, Chen X, Lin K, Lai R, Zhou Z (2022) Socioeconomic drought analysis by standardized water supply and demand index under changing environment. J Clean Prod 347:131248

    Google Scholar 

  • Wilhite DA (2000) Drought as a natural hazard: concepts and definitions. In: Wilhite DA (ed) Drought: a global assessment. Routledge, New York, pp 3–18

    Google Scholar 

  • Wilhite DA, Sivakumar MV, Pulwarty R (2014) Managing drought risk in a changing climate: the role of national drought policy. Weather Clima Extremes 3:4–13

    Google Scholar 

  • World Bank (2006) Natural disaster hotspots. Case studies. World Bank, Washington, DC. https://documents1.worldbank.org/curated/en/414331468339091745/pdf/370900Natural0disaster01PUBLIC1.pdf

  • Wu Q (2010) A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization. Expert Syst Appl 37(3):2388–2394

    Google Scholar 

  • Yue S, Wang CY (2004) The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water Resour Manage 18:201–218

    Google Scholar 

  • Zhang Q, Han L, Jia J, Song L, Wang J (2016) Management of drought risk under global warming. Theoret Appl Climatol 125:187–196

    Google Scholar 

Download references

Acknowledgements

The authors would like to express their gratitude to the India Meteorological Department (IMD) for providing the daily temperature data for this study. The authors are grateful to the anonymous reviewers for their valuable and scholarly remarks, which considerably improved the work.

Funding

No funding has been received for this research.

Author information

Authors and Affiliations

  1. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India

    Shahfahad, Swapan Talukdar, Ishita Afreen Ahmed,  Asif, Aruna Paarcha & Atiqur Rahman

  2. Department of Disaster Management, Begum Rokeya University, Rangpur, 5400, Bangladesh

    Bonosri Ghose, Abu Reza Md. Towfiqul Islam & Md. Hasanuzzaman

  3. School of Humanities and Social Sciences, Indian Institute of Technology Indore, Simrol, Indore, 453552, India

    Bushra Praveen

  4. Department of Geography, Liverpool John Moores University, Liverpool, UK

    A. S. Gagnon

  5. UK Centre for Ecology & Hydrology (UK CEH), Wallingford, OX10 8BB, Oxfordshire, UK

    Muhammad Afzal

  6. School of Natural Sciences, The University of Central Lancashire, Preston, UK

    Muhammad Afzal

Authors
  1. Shahfahad
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Swapan Talukdar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bonosri Ghose
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abu Reza Md. Towfiqul Islam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Md. Hasanuzzaman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ishita Afreen Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bushra Praveen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Asif
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Aruna Paarcha
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Atiqur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. A. S. Gagnon
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Muhammad Afzal
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S, ST and BG designed the study and were responsible for data collection, modeling, analysis and wrote the initial draft; ARMTI, MH and IAA, were responsible for data analysis, data curation and editing of the initial draft; AR, A and ASG supervised the project and reviewed the final manuscript; BP, AP and MA helped in modeling and provided the technical support.

Corresponding authors

Correspondence to Swapan Talukdar or Atiqur Rahman.

Ethics declarations

Conflict of interest

No potential conflict of interest has been reported among the authors on any issue.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent for publishing

All authors have read the manuscript and agreed to publish the manuscript.

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

Shahfahad, Talukdar, S., Ghose, B. et al. Predicting long term regional drought pattern in Northeast India using advanced statistical technique and wavelet-machine learning approach. Model. Earth Syst. Environ. 10, 1005–1026 (2024). https://doi.org/10.1007/s40808-023-01818-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-023-01818-y

Keywords

Search

Navigation