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Seasonal meteorological drought projections over Iran using the NMME data

Author

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  • Sogol Moradian

    (K. N. Toosi University of Technology)

  • Farhad Yazdandoost

    (K. N. Toosi University of Technology)

Abstract

Accurate and well-planned forecasts provide critical information for preparedness and mitigation strategies as well as the sustainable practice of water resources conservation. In this paper, an experimental seasonal drought forecasting system has been developed based on meteorological hindcasts, generated by the North American Multi-Model Ensemble (NMME) models. The proposed toolbox comprises (1) NMME data as well as observations, (2) post-processing methods, namely GrandNMME and bias correction methods to statistically post-process precipitation predictions, (3) evaluation metrics of a multi-criteria decision-making method (namely the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)) to choose the best post-processed improved data, and (4) the Standardized Precipitation Index (SPI) calculator as the central engine, where distribution maps of seasonal drought forecasts are generated. The toolbox has been utilized for the case of Iran. The country is located in semiarid and arid regions of the world, facing considerable water crisis including droughts. Results indicated that the proposed NMME-based drought forecasting toolbox has a significant skill in forecasting droughts over the study area and provides critical information for early warnings, medium-term response planning and taking preventive measures.

Suggested Citation

  • Sogol Moradian & Farhad Yazdandoost, 2021. "Seasonal meteorological drought projections over Iran using the NMME data," 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. 108(1), pages 1089-1107, August.
    • Handle: RePEc:spr:nathaz:v:108:y:2021:i:1:d:10.1007_s11069-021-04721-w
    DOI: 10.1007/s11069-021-04721-w
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    References listed on IDEAS

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    1. Peter M. Cox & Richard A. Betts & Chris D. Jones & Steven A. Spall & Ian J. Totterdell, 2000. "Erratum: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model," Nature, Nature, vol. 408(6813), pages 750-750, December.
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    Cited by:

    1. Moradian, Sogol & Olbert, Agnieszka I. & Gharbia, Salem & Iglesias, Gregorio, 2023. "Copula-based projections of wind power: Ireland as a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    2. Youxin Wang & Tao Peng & Qingxia Lin & Vijay P. Singh & Xiaohua Dong & Chen Chen & Ji Liu & Wenjuan Chang & Gaoxu Wang, 2022. "A New Non-stationary Hydrological Drought Index Encompassing Climate Indices and Modified Reservoir Index as Covariates," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2433-2454, May.
    3. Sogol Moradian & Liz Coleman & Bartosz Kazmierczak & Agnieszka I. Olbert, 2024. "How to Choose the Most Proper Representative Climate Model Over a Study Region? a Case Study of Precipitation Simulations in Ireland with NEX-GDDP-CMIP6 Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 215-234, January.
    4. Sogol Moradian & Ali Torabi Haghighi & Maryam Asadi & Seyed Ahmad Mirbagheri, 2023. "Future Changes in Precipitation Over Northern Europe Based on a Multi-model Ensemble from CMIP6: Focus on Tana River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(6), pages 2447-2463, May.

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