IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v38y2024i8d10.1007_s11269-024-03793-0.html
   My bibliography  Save this article

Innovative Drought Classification Matrix and Acceptable Time Period for Temporal Drought Evaluation

Author

Listed:
  • Ahmad Abu Arra

    (Yildiz Technical University)

  • Eyüp Şişman

    (Yildiz Technical University
    Istanbul Medipol University)

Abstract

Evaluating drought is paramount in water resources management and drought mitigation plans. Drought indices are essential tools in this evaluation, which mainly depends on the time period of the original datasets. Investigating the effects of time periods is critical for a comprehensive understanding and evaluation of drought. Also, It holds particular significance for regions facing data availability challenges. The existing literature reveals a gap in drought assessment and comparison analysis using conventional methods based on drought indices only. This research proposes an innovative drought classification matrix to compare drought indices and spatial and temporal scenarios; the proposed matrix depends on any drought classification for comparison procedure. Furthermore, it aims to investigate the differences between several time period scenarios based on the proposed matrix and several statistical metrics (R2, CC, RMSE, HH, and RB) and determine the acceptable/minimum time period. The application of the proposed matrix and selection of an acceptable/minimum time period is presented to three different climates: Durham station in the United Kingdom, Florya station in Türkiye, and Karapinar station in Türkiye. The results show that the time period scenarios are able to catch the reference time period (RTP) scenario reasonably, with strong correlation and negative relative bias. The 10-year time period is sufficient as an acceptable/minimum time period for short timescales, such as meteorological drought. Conversely, for longer timescales, such as hydrological drought, a 20-year time period is the acceptable/minimum time period. The proposed matrix demonstrates a robust and powerful framework for comparison, making it applicable to various drought assessment scenarios globally.

Suggested Citation

  • Ahmad Abu Arra & Eyüp Şişman, 2024. "Innovative Drought Classification Matrix and Acceptable Time Period for Temporal Drought Evaluation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(8), pages 2811-2833, June.
  • Handle: RePEc:spr:waterr:v:38:y:2024:i:8:d:10.1007_s11269-024-03793-0
    DOI: 10.1007/s11269-024-03793-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-024-03793-0
    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/s11269-024-03793-0?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Babak Vaheddoost & Babak Mohammadi & Mir Jafar Sadegh Safari, 2023. "The Association between Meteorological Drought and the State of the Groundwater Level in Bursa, Turkey," Sustainability, MDPI, vol. 15(21), pages 1-17, November.
    2. Zekâi Şen, 2021. "Reservoirs for Water Supply Under Climate Change Impact—A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3827-3843, September.
    3. Zekai Şen & Eyüp Şişman & Ismail Dabanli, 2020. "Wet and dry spell feature charts for practical uses," 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. 104(3), pages 1975-1986, December.
    4. Hans Visser & Arthur Petersen & Willem Ligtvoet, 2014. "On the relation between weather-related disaster impacts, vulnerability and climate change," Climatic Change, Springer, vol. 125(3), pages 461-477, August.
    5. Zekâi Şen, 2020. "Water Structures and Climate Change Impact: a Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(13), pages 4197-4216, October.
    6. George Tsakiris & Nikos Kordalis & Dimitris Tigkas & Vasileios Tsakiris & Harris Vangelis, 2016. "Analysing Drought Severity and Areal Extent by 2D Archimedean Copulas," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(15), pages 5723-5735, December.
    7. Chong Du & Jiashuo Chen & Tangzhe Nie & Changlei Dai, 2022. "Spatial–temporal changes in meteorological and agricultural droughts in Northeast China: change patterns, response relationships and causes," 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. 110(1), pages 155-173, January.
    8. Milad Nouri, 2023. "Drought Assessment Using Gridded Data Sources in Data-Poor Areas with Different Aridity Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(11), pages 4327-4343, September.
    9. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).
    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. Laura Devitt & Jeffrey Neal & Gemma Coxon & James Savage & Thorsten Wagener, 2023. "Flood hazard potential reveals global floodplain settlement patterns," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Carlynn Fagnant & Avantika Gori & Antonia Sebastian & Philip B. Bedient & Katherine B. Ensor, 2020. "Characterizing spatiotemporal trends in extreme precipitation in Southeast Texas," 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. 104(2), pages 1597-1621, November.
    3. Matteo Coronese & Francesco Lamperti & Francesca Chiaromonte & Andrea Roventini, 2018. "Natural Disaster Risk and the Distributional Dynamics of Damages," LEM Papers Series 2018/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    4. Wang, Xinzhi & Lin, Qingxia & Wu, Zhiyong & Zhang, Yuliang & Li, Changwen & Liu, Ji & Zhang, Shinan & Li, Songyu, 2025. "Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China," Agricultural Water Management, Elsevier, vol. 307(C).
    5. Álvarez, Xana & Gómez-Rúa, María & Vidal-Puga, Juan, 2019. "Risk prevention of land flood: A cooperative game theory approach," MPRA Paper 91515, University Library of Munich, Germany.
    6. Emmylou Reeve & Andrew B. Watkins & Yuriy Kuleshov, 2024. "The Impact of Climate Variability on Cattle Heat Stress in Vanuatu," Agriculture, MDPI, vol. 14(11), pages 1-18, October.
    7. Fawen Li & Huifeng Liu & Xu Chen & Dong Yu, 2019. "Trivariate Copula Based Evaluation Model of Water Accessibility," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3211-3225, July.
    8. Gebhard Geiger, 2024. "Catastrophic risk: indication, quantitative assessment and management of rare extreme events using a non-expected utility framework," Annals of Operations Research, Springer, vol. 343(1), pages 223-261, December.
    9. Jiawei Zhou & Xiaohong Chen & Chuang Xu & Pan Wu, 2022. "Assessing Socioeconomic Drought Based on a Standardized Supply and Demand Water Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1937-1953, April.
    10. Laino, Emilio & Iglesias, Gregorio, 2023. "Extreme climate change hazards and impacts on European coastal cities: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    11. Bilel Zerouali & Mohamed Chettih & Zaki Abda & Mohamed Mesbah & Celso Augusto Guimarães Santos & Reginaldo Moura Brasil Neto & Richarde Marques Silva, 2021. "Spatiotemporal meteorological drought assessment in a humid Mediterranean region: case study of the Oued Sebaou basin (northern central Algeria)," 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 689-709, August.
    12. Elaheh Motevali Bashi Naeini & Ali Mohammad Akhoond-Ali & Fereydoun Radmanesh & Jahangir Abedi Koupai & Shahrokh Soltaninia, 2021. "Comparison of the Calculated Drought Return Periods Using Tri-variate and Bivariate Copula Functions Under Climate Change Condition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4855-4875, November.
    13. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
    14. Wu, Binrong & Yu, Sihao & Peng, Lu & Wang, Lin, 2024. "Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition," Energy, Elsevier, vol. 294(C).
    15. Martyna Świętochowska & Izabela Bartkowska, 2022. "Optimization of Energy Consumption in the Pumping Station Supplying Two Zones of the Water Supply System," Energies, MDPI, vol. 15(1), pages 1-15, January.
    16. Xingang Wang & Bingxiang Wang & Miaoxin Chang & Lei Li, 2020. "Reliability and sensitivity analysis for bearings considering the correlation of multiple failure modes by mixed Copula function," Journal of Risk and Reliability, , vol. 234(1), pages 15-26, February.
    17. Alexa Tanner & Joseph Árvai, 2018. "Perceptions of Risk and Vulnerability Following Exposure to a Major Natural Disaster: The Calgary Flood of 2013," Risk Analysis, John Wiley & Sons, vol. 38(3), pages 548-561, March.
    18. Ali Nasiri Khiavi & Mehdi Vafakhah & Seyed Hamidreza Sadeghi, 2022. "Comparative prioritization of sub-watersheds based on Flood Generation potential using physical, hydrological and co-managerial approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1897-1917, April.
    19. Yongqiang Fang & Shiqiang Du & Paolo Scussolini & Jiahong Wen & Chunyang He & Qingxu Huang & Jun Gao, 2018. "Rapid Population Growth in Chinese Floodplains from 1990 to 2015," IJERPH, MDPI, vol. 15(8), pages 1-11, July.
    20. Nouri, Milad & Veysi, Shadman, 2024. "CMIP6 multi-model ensemble projection of reference evapotranspiration using machine learning algorithms," Agricultural Water Management, Elsevier, vol. 306(C).

    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:8:d:10.1007_s11269-024-03793-0. 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.