IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i7d10.1007_s11269-024-03789-w.html
   My bibliography  Save this article

Meteorological Drought Prediction Based on Evaluating the Efficacy of Several Prediction Models

Author

Listed:
  • Abdol Rassoul Zarei

    (Fasa University)

  • Mohammad Reza Mahmoudi

    (Fasa University)

  • Alireza Pourbagheri

    (Fasa University)

Abstract

The prediction of drought is critically important for early warning and mitigation of its impacts. Selecting the most appropriate prediction model provides opportunities for reducing the drought’s adverse effects on different sectors. So, in this study, the capability of several prediction models to predict drought (based on the Reconnaissance Drought Index (RDI) in 1 and 3-month time scales) was compared. The models included stationary time series models (ST), cyclostationary time series (CST) models, autoregressive fractionally integrated moving average (ARFIMA), periodic autoregressive fractionally integrated moving average (PARFIMA), first-order Markov chain (FOMC), and second-order Markov chain (SOMC). For choosing the best-fitted model, the correlation coefficient (R2) and absolute values of T-Statistics (AVTS) between predicted (using each model) and observed RDI in 1 and 3-month time scales at 15 stations during the period 2017–2021 in Iran were used. For this purpose, the 1967 to 2016 data series was used. Then the best prediction model (with the highest performance level) was used to predict 1 and 3-month RDI in the investigated stations from 2022 to 2031. For this purpose, 1967 to 2021 data series was used. The results showed that CST models with the highest R2 values (significantly at the 5% level in both time scales in all stations) and the lowest AVTS values (significantly at the 1% level in both time scales in all stations) for the best-fitted models in 1 and 3-month time scales had the best performance in predicting monthly and seasonal RDI. To predict monthly and seasonal RDI in all stations, the PARIMA (24, 0, 0), 12 and PARIMA (20, 0, 0), 4 models were used as the best models, respectively. The predictions indicated that normal (No) and moderately (Mod) dry classes would be more frequent in both time scales. This study demonstrates that CST models can be useful tools for drought prediction and management.

Suggested Citation

  • Abdol Rassoul Zarei & Mohammad Reza Mahmoudi & Alireza Pourbagheri, 2024. "Meteorological Drought Prediction Based on Evaluating the Efficacy of Several Prediction Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(7), pages 2601-2625, May.
    • Handle: RePEc:spr:waterr:v:38:y:2024:i:7:d:10.1007_s11269-024-03789-w
    DOI: 10.1007/s11269-024-03789-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03789-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-024-03789-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Dimitris Tigkas & Harris Vangelis & George Tsakiris, 2020. "Implementing Crop Evapotranspiration in RDI for Farm-Level Drought Evaluation and Adaptation under Climate Change Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(14), pages 4329-4343, November.
    2. Elnaz Ghabelnezam & Raoof Mostafazadeh & Zeinab Hazbavi & Guangwei Huang, 2023. "Hydrological Drought Severity in Different Return Periods in Rivers of Ardabil Province, Iran," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    3. A. R. Nematollahi & A. R. Soltani & M. R. Mahmoudi, 2017. "Periodically correlated modeling by means of the periodograms asymptotic distributions," Statistical Papers, Springer, vol. 58(4), pages 1267-1278, December.
    4. Anwar Hussain & Khan Zaib Jadoon & Khalil Ur Rahman & Songhao Shang & Muhammad Shahid & Nuaman Ejaz & Himayatullah Khan, 2023. "Analyzing the impact of drought on agriculture: evidence from Pakistan using standardized precipitation evapotranspiration index," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 115(1), pages 389-408, January.
    5. Nathan Sunday & Rehema Kahunde & Blessing Atwine & Adesoji Adelaja & Justin George, 2023. "How specific resilience pillars mitigate the impact of drought on food security: Evidence from Uganda," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 15(1), pages 111-131, February.
    6. Jamshid Piri & Mohammad Abdolahipour & Behrooz Keshtegar, 2023. "Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 683-712, January.
    7. Mohammad Reza Mahmoudi & Mohsen Maleki, 2017. "A new method to detect periodically correlated structure," Computational Statistics, Springer, vol. 32(4), pages 1569-1581, December.
    8. Djerbouai Salim & Souag-Gamane Doudja & Ferhati Ahmed & Djoukbala Omar & Dougha Mostafa & Benselama Oussama & Hasbaia Mahmoud, 2023. "Comparative Study of Different Discrete Wavelet Based Neural Network Models for long term Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1401-1420, February.
    9. Neda Khanmohammadi & Hossein Rezaie & Javad Behmanesh, 2022. "Investigation of Drought Trend on the Basis of the Best Obtained Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1355-1375, March.
    10. Wentao Yang & Min Deng & Jianbo Tang & Rui Jin, 2020. "On the use of Markov chain models for drought class transition analysis while considering spatial effects," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(3), pages 2945-2959, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Roohi, Reza, 2019. "A new method to compare the spectral densities of two independent periodically correlated time series," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 160(C), pages 103-110.
    2. Abdol Rassoul Zarei & Mohammad Reza Mahmoudi, 2020. "Ability Assessment of the Stationary and Cyclostationary Time Series Models to Predict Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 5009-5029, December.
    3. Mohammad Reza Mahmoudi & Abdol Rassoul Zarei, 2022. "Using Periodic Copula to Assess the Relationship Between Two Meteorological Cyclostationary Time Series Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(11), pages 4363-4388, September.
    4. Mahmoudi, Mohammad Reza & Baleanu, Dumitru & Mansor, Zulkefli & Tuan, Bui Anh & Pho, Kim-Hung, 2020. "Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    5. Chen, Yanan & Wang, Ying & Wu, Chaoyang & Rosa Ferraz Jardim, Alexandre Maniçoba da & Fang, Meihong & Yao, Li & Liu, Guihua & Xu, Qiuyi & Chen, Lintao & Tang, Xuguang, 2025. "Drought-induced stress on rainfed and irrigated agriculture: Insights from multi-source satellite-derived ecological indicators," Agricultural Water Management, Elsevier, vol. 307(C).
    6. Ruperto Ortiz-Gómez & Roberto S. Flowers-Cano & Guillermo Medina-García, 2022. "Sensitivity of the RDI and SPEI Drought Indices to Different Models for Estimating Evapotranspiration Potential in Semiarid Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2471-2492, May.
    7. Laura Şmuleac & Ciprian Rujescu & Adrian Șmuleac & Florin Imbrea & Isidora Radulov & Dan Manea & Anișoara Ienciu & Tabita Adamov & Raul Pașcalău, 2020. "Impact of Climate Change in the Banat Plain, Western Romania, on the Accessibility of Water for Crop Production in Agriculture," Agriculture, MDPI, vol. 10(10), pages 1-24, September.
    8. Maleki, Mohsen & Mahmoudi, Mohammad Reza & Heydari, Mohammad Hossein & Pho, Kim-Hung, 2020. "Modeling and forecasting the spread and death rate of coronavirus (COVID-19) in the world using time series models," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    9. Farman Ali & Bing-Zhao Li & Zulfiqar Ali, 2021. "Strengthening Drought Monitoring Module by Ensembling Auxiliary Information Based Varying Estimators," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3235-3252, August.
    10. Siddhant Panigrahi & Vikas Kumar Vidyarthi, 2024. "Assessing the suitability of McKee et al. (1993) drought severity classification across India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(14), pages 13543-13572, November.
    11. Rahman, Khalil Ur & Ejaz, Nuaman & Shang, Songhao & Balkhair, Khaled S. & Alghamdi, Khalid Mohammad & Zaman, Kifayat & Khan, Mahmood Alam & Hussain, Anwar, 2024. "A robust integrated agricultural drought index under climate and land use variations at the local scale in Pakistan," Agricultural Water Management, Elsevier, vol. 295(C).
    12. Abdol Rassoul Zarei & Marzieh Mokarram & Mohammad Reza Mahmoudi, 2023. "Comparison of the capability of the Meteorological and Remote Sensing Drought Indices," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 769-796, January.
    13. Mohammad Tavosi & Mehdi Vafakhah & Hengameh Shekohideh & Seyed Hamidreza Sadeghi & Vahid Moosavi & Ziyan Zheng & Qing Yang, 2024. "Rainfall Extreme Indicators Trend and Meteorological Drought Changes Under Climate Change Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(11), pages 4393-4413, September.
    14. Kouao Laurent Kouadio & Jianxin Liu & Serge Kouamelan Kouamelan & Rong Liu, 2023. "Ensemble Learning Paradigms for Flow Rate Prediction Boosting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4413-4431, September.
    15. T. Manouchehri & A. R. Nematollahi, 2019. "Periodic autoregressive models with closed skew-normal innovations," Computational Statistics, Springer, vol. 34(3), pages 1183-1213, September.
    16. Hui Yue & Xiangyu Yu & Ying Liu & Xu Wang, 2023. "The Construction and Migration of a Multi-source Integrated Drought Index Based on Different Machine Learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 5989-6004, December.
    17. Roghayeh Ghasempour & Mohammad Taghi Aalami & Kiyoumars Roushangar, 2022. "Drought Vulnerability Assessment Based on a Multi-criteria Integrated Approach and Application of Satellite-based Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(10), pages 3839-3858, August.
    18. Quoc Bao Pham & Tao-Chang Yang & Chen-Min Kuo & Hung-Wei Tseng & Pao-Shan Yu, 2021. "Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 847-868, February.
    19. Jia-Qi, Liu & Yun-Wen, Feng & Cheng, Lu & Wei-Huang, Pan, 2024. "Decomposed-coordinated framework with intelligent extremum network for operational reliability analysis of complex system," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    20. Weisong Li & Jiahui Wu & Liyan Yang & Wanxu Chen & Xinghua Cui & Mingyu Lin, 2024. "Spatial Variations in Relationships between Urbanization and Carbon Emissions in Chinese Urban Agglomerations," Land, MDPI, vol. 13(8), pages 1-20, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:waterr:v:38:y:2024:i:7:d:10.1007_s11269-024-03789-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.