IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v115y2023i1d10.1007_s11069-022-05579-2.html
   My bibliography  Save this article

Determining the most appropriate drought index using the random forest algorithm with an emphasis on agricultural drought

Author

Listed:
  • Abdol Rassoul Zarei

    (Fasa University)

  • Mohammad Reza Mahmoudi

    (Fasa University)

  • Mohammad Mehdi Moghimi

    (Fasa University)

Abstract

Since drought can mainly affect agriculture, especially rain-fed agriculture with high dependence on rainwater, and consequently jeopardize food security and social protection, it is absolutely essential to determine an appropriate drought index to assess the agricultural drought more accurately. In this paper, the random forest algorithm (RF) was employed to compare the capabilities of six more commonly used drought indices in agricultural drought assessment, namely standardized precipitation evapotranspiration index (SPEI), modified SPEI (M-SPEI), reconnaissance drought index (RDI), modified RDI (M-RDI), standardized precipitation index (SPI) and modified SPI (M-SPI) based on the correlation between the drought indices (i.e., independent variables) and yield of rain-fed barley (i.e., response variable). In this study, the data series of 10 stations with appropriate climate variety from 1968 to 2017 were used on 1-, 3-, 6-, and 12-month time scales (27 time periods in total). The results indicated that the linear regression between the simulated and predicted yield of rain-fed barley using the AquaCrop model and the RF algorithm (respectively) had no difference with a perfect reliable line (Y = X) in 0.05 or 0.01 significant levels. The R2 between simulated and predicted barley yield (YB) was also significant at 0.01 levels in all stations, indicating the appropriate capability of the RF algorithm in YB prediction. The results also showed that some indices had better outcomes than others in stations; for example, the SPEI index in Arak, Mashhad, Shahre Kord, and Shiraz stations, the M-SPEI index in Babolsar, Gazvin, and Ramsar, the SPEI and M-SPEI indices in Esfahan and Gorgan stations, and the RDI and SPI indices in Rasht stations had the best outcome. Finally, it was recommended to use the SPEI and M-SPEI indices for agricultural drought assessment.

Suggested Citation

  • Abdol Rassoul Zarei & Mohammad Reza Mahmoudi & Mohammad Mehdi Moghimi, 2023. "Determining the most appropriate drought index using the random forest algorithm with an emphasis on agricultural drought," 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 923-946, January.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:1:d:10.1007_s11069-022-05579-2
    DOI: 10.1007/s11069-022-05579-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05579-2
    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/s11069-022-05579-2?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. Guillermo N. Murray-Tortarolo & Víctor J. Jaramillo, 2019. "The impact of extreme weather events on livestock populations: the case of the 2011 drought in Mexico," Climatic Change, Springer, vol. 153(1), pages 79-89, March.
    2. Wei Pei & Qiang Fu & Dong Liu & Tianxiao Li & Kun Cheng & Song Cui, 2019. "A Novel Method for Agricultural Drought Risk Assessment," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(6), pages 2033-2047, April.
    3. Araya, A. & Habtu, Solomon & Hadgu, Kiros Meles & Kebede, Afewerk & Dejene, Taddese, 2010. "Test of AquaCrop model in simulating biomass and yield of water deficient and irrigated barley (Hordeum vulgare)," Agricultural Water Management, Elsevier, vol. 97(11), pages 1838-1846, November.
    4. Tavakoli, Ali Reza & Mahdavi Moghadam, Mehran & Sepaskhah, Ali Reza, 2015. "Evaluation of the AquaCrop model for barley production under deficit irrigation and rainfed condition in Iran," Agricultural Water Management, Elsevier, vol. 161(C), pages 136-146.
    5. G. Tsakiris & D. Pangalou & H. Vangelis, 2007. "Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 821-833, May.
    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. López-Urrea, R. & Domínguez, A. & Pardo, J.J. & Montoya, F. & García-Vila, M. & Martínez-Romero, A., 2020. "Parameterization and comparison of the AquaCrop and MOPECO models for a high-yielding barley cultivar under different irrigation levels," Agricultural Water Management, Elsevier, vol. 230(C).
    2. Razzaghi, Fatemeh & Zhou, Zhenjiang & Andersen, Mathias N. & Plauborg, Finn, 2017. "Simulation of potato yield in temperate condition by the AquaCrop model," Agricultural Water Management, Elsevier, vol. 191(C), pages 113-123.
    3. Soyeon Lim & Seungyub Lee & Donghwi Jung, 2021. "Identifying the Drought Impact Factors and Developing Drought Scenarios Using the DSD Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4809-4823, November.
    4. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Tong, Ling & Ding, Risheng & Du, Taisheng & Li, Sien & Zhang, Xiaotao, 2017. "Performance of AquaCrop and SIMDualKc models in evapotranspiration partitioning on full and deficit irrigated maize for seed production under plastic film-mulch in an arid region of China," Agricultural Systems, Elsevier, vol. 151(C), pages 20-32.
    5. Mohammad Ghabaei Sough & Hamid Zare Abyaneh & Abolfazl Mosaedi, 2018. "Assessing a Multivariate Approach Based on Scalogram Analysis for Agricultural Drought Monitoring," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3423-3440, August.
    6. Muhammad Waseem & Muhammad Ajmal & Joo Heon Lee & Tae-Woong Kim, 2016. "Multivariate Drought Assessment Considering the Antecedent Drought Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4221-4231, September.
    7. Zhai, Yijie & Zhang, Tianzuo & Ma, Xiaotian & Shen, Xiaoxu & Ji, Changxing & Bai, Yueyang & Hong, Jinglan, 2021. "Life cycle water footprint analysis of crop production in China," Agricultural Water Management, Elsevier, vol. 256(C).
    8. Tran, Thi Xuyen, 2021. "Typhoon and Agricultural Production Portfolio -Empirical Evidence for a Developing Economy," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242411, Verein für Socialpolitik / German Economic Association.
    9. Lampros Vasiliades & Athanasios Loukas & Nikos Liberis, 2011. "A Water Balance Derived Drought Index for Pinios River Basin, Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(4), pages 1087-1101, March.
    10. 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.
    11. Ran, Hui & Kang, Shaozhong & Li, Fusheng & Du, Taisheng & Tong, Ling & Li, Sien & Ding, Risheng & Zhang, Xiaotao, 2018. "Parameterization of the AquaCrop model for full and deficit irrigated maize for seed production in arid Northwest China," Agricultural Water Management, Elsevier, vol. 203(C), pages 438-450.
    12. Dimitrios Myronidis & Konstantinos Ioannou & Dimitrios Fotakis & Gerald Dörflinger, 2018. "Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(5), pages 1759-1776, March.
    13. Zhang, Chao & Xie, Ziang & Wang, Qiaojuan & Tang, Min & Feng, Shaoyuan & Cai, Huanjie, 2022. "AquaCrop modeling to explore optimal irrigation of winter wheat for improving grain yield and water productivity," Agricultural Water Management, Elsevier, vol. 266(C).
    14. Brunella Bonaccorso & David Peres & Antonio Castano & Antonino Cancelliere, 2015. "SPI-Based Probabilistic Analysis of Drought Areal Extent in Sicily," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 459-470, January.
    15. Alex Zizinga & Jackson Gilbert Majaliwa Mwanjalolo & Britta Tietjen & Bobe Bedadi & Ramon Amaro de Sales & Dennis Beesigamukama, 2022. "Simulating Maize Productivity under Selected Climate Smart Agriculture Practices Using AquaCrop Model in a Sub-humid Environment," Sustainability, MDPI, vol. 14(4), pages 1-17, February.
    16. Pere Quintana-Seguí & Anaïs Barella-Ortiz & Sabela Regueiro-Sanfiz & Gonzalo Miguez-Macho, 2020. "The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(7), pages 2135-2156, May.
    17. Efrosyni Kanellou & Nicos Spyropoulos & Nicolas Dalezios, 2012. "Geoinformatic Intelligence Methodologies for Drought Spatiotemporal Variability in Greece," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(5), pages 1089-1106, March.
    18. Jagadish Padhiary & Kanhu Charan Patra & Sonam Sandeep Dash, 2022. "A Novel Approach to Identify the Characteristics of Drought under Future Climate Change Scenario," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(13), pages 5163-5189, October.
    19. Peng Qi & Y. Jun Xu & Guodong Wang, 2020. "Quantifying the Individual Contributions of Climate Change, Dam Construction, and Land Use/Land Cover Change to Hydrological Drought in a Marshy River," Sustainability, MDPI, vol. 12(9), pages 1-16, May.
    20. Lo, Yueh-Hsin & Blanco, Juan A. & Canals, Rosa M. & González de Andrés, Ester & San Emeterio, Leticia & Imbert, J. Bosco & Castillo, Federico J., 2015. "Land use change effects on carbon and nitrogen stocks in the Pyrenees during the last 150 years: A modeling approach," Ecological Modelling, Elsevier, vol. 312(C), pages 322-334.

    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:nathaz:v:115:y:2023:i:1:d:10.1007_s11069-022-05579-2. 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.