IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v35y2021i13d10.1007_s11269-021-02952-x.html
   My bibliography  Save this article

Reduction of Errors in Hydrological Drought Monitoring – A Novel Statistical Framework for Spatio-Temporal Assessment of Drought

Author

Listed:
  • Zulfiqar Ali

    (Tsinghua University)

  • Asad Ellahi

    (Quaid-I-Azam University
    Wah Medical College, National University of Medical Sciences)

  • Ijaz Hussain

    (Quaid-I-Azam University)

  • Amna Nazeer

    (COMSATS University Islamabad)

  • Sadia Qamar

    (University of Sargodha)

  • Guangheng Ni

    (Tsinghua University)

  • Muhammad Faisal

    (University of Bradford
    Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust)

Abstract

Continuous and accurate drought monitoring has an important role in early warning drought mitigation policies. This study aims to provide an accurate standardized drought monitoring indicator by enhancing the representative characteristics of precipitation data using advanced statistical methods. We proposed a two-phase statistical procedure index – the Regional Multi-Component Gaussian Hydrological Drought Assessment (RMcGHDA) – for accurate drought monitoring under a multi-auxiliary variable-based sampling estimator and K-Component Gaussian Mixture Distribution (CGMD) model. The first phase of our proposed method increases the regional representativeness of the data under Spatio-temporal settings and the second phase describes the use of the Twelve-Component Gaussian Mixture Distribution (CGMD) model in the standardization stage of SDIs. We applied the proposed framework to 52 meteorological stations in Pakistan and compared the RMcGHDA performance with existing methods using Pearson correlation (r) and spatial patterns of various drought categories. We found significant differences between RMcGHDA and existing methods (i.e., Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)) for drought assessment. By the rationale of the data improvement under-sampling estimator and the use of multi-component Gaussian function, these differences indicate that RMcGHDA provides a practical and accurate way for drought assessment.

Suggested Citation

  • Zulfiqar Ali & Asad Ellahi & Ijaz Hussain & Amna Nazeer & Sadia Qamar & Guangheng Ni & Muhammad Faisal, 2021. "Reduction of Errors in Hydrological Drought Monitoring – A Novel Statistical Framework for Spatio-Temporal Assessment of Drought," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(13), pages 4363-4380, October.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:13:d:10.1007_s11269-021-02952-x
    DOI: 10.1007/s11269-021-02952-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-021-02952-x
    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/s11269-021-02952-x?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. Benaglia, Tatiana & Chauveau, Didier & Hunter, David R. & Young, Derek S., 2009. "mixtools: An R Package for Analyzing Mixture Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i06).
    2. 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.
    3. Jingli Lu, 2013. "The Chain Ratio Estimator and Regression Estimator with Linear Combination of Two Auxiliary Variables," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-4, November.
    4. Javad Bazrafshan & Somayeh Hejabi & Jaber Rahimi, 2014. "Drought Monitoring Using the Multivariate Standardized Precipitation Index (MSPI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(4), pages 1045-1060, March.
    5. Kang, Hyunwoo & Sridhar, Venkataramana & Mills, Bradford F. & Hession, W. Cully & Ogejo, Jactone A., 2019. "Economy-wide climate change impacts on green water droughts based on the hydrologic simulations," Agricultural Systems, Elsevier, vol. 171(C), pages 76-88.
    6. Federica Carfagna & Raffaello Cervigni, 2018. "Mitigating Drought Impacts in Drylands," World Bank Publications - Books, The World Bank Group, number 29597.
    7. Zuliqar Ali & Ijaz Hussain & Muhammad Faisal & Hafiza Mamona Nazir & Mitwali Abd-el Moemen & Tajammal Hussain & Sadaf Shamsuddin, 2017. "A Novel Multi-Scalar Drought Index for Monitoring Drought: the Standardized Precipitation Temperature Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4957-4969, December.
    8. Narendra K. Jatwa & Rajesh Tailor & Housila P. Singh, 2013. "A Ratio-Cum-Product Estimator of Finite Population Mean in Systematic Sampling," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 14(3), pages 391-398, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Muhammad Ahmad & Zulfiqar Ali & Maryam Ilyas & Muhammad Mohsin & Rizwan Niaz, 2023. "A Common Factor Analysis Based Data Mining Procedure for Effective Assessment of 21st Century Drought under Multiple Global Climate Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(12), pages 4787-4806, September.

    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. Zulfiqar Ali & Ijaz Hussain & Muhammad Faisal & Dost Muhammad Khan & Rizwan Niaz & Elsayed Elsherbini Elashkar & Alaa Mohamd Shoukry, 2020. "Propagation of the Multi-Scalar Aggregative Standardized Precipitation Temperature Index and its Application," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(2), pages 699-714, January.
    2. Zhenya Li & Zulfiqar Ali & Tong Cui & Sadia Qamar & Muhammad Ismail & Amna Nazeer & Muhammad Faisal, 2022. "A comparative analysis of pre- and post-industrial spatiotemporal drought trends and patterns of Tibet Plateau using Sen slope estimator and steady-state probabilities of Markov Chain," 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. 113(1), pages 547-576, August.
    3. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    4. Minjung Kyung & Ju-Hyun Park & Ji Yeh Choi, 2022. "Bayesian Mixture Model of Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 87(3), pages 946-966, September.
    5. Xue, Jiacheng & Yao, Weixin, 2022. "Machine Learning Embedded Semiparametric Mixtures of Regressions with Covariate-Varying Mixing Proportions," Econometrics and Statistics, Elsevier, vol. 22(C), pages 159-171.
    6. Zahra Fahimirad & Nazanin Shahkarami, 2021. "The Impact of Climate Change on Hydro-Meteorological Droughts Using Copula Functions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 3969-3993, September.
    7. Derek S. Young & Xi Chen & Dilrukshi C. Hewage & Ricardo Nilo-Poyanco, 2019. "Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1053-1082, December.
    8. Zihao Wang & Wenxi Wang & Xiaoming Xie & Yongfa Wang & Zhengzhao Yang & Huiru Peng & Mingming Xin & Yingyin Yao & Zhaorong Hu & Jie Liu & Zhenqi Su & Chaojie Xie & Baoyun Li & Zhongfu Ni & Qixin Sun &, 2022. "Dispersed emergence and protracted domestication of polyploid wheat uncovered by mosaic ancestral haploblock inference," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    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. Dalla Valle, Luciana & De Giuli, Maria Elena & Tarantola, Claudia & Manelli, Claudio, 2016. "Default probability estimation via pair copula constructions," European Journal of Operational Research, Elsevier, vol. 249(1), pages 298-311.
    11. Zhang, Liyuan & Zhang, Huihui & Han, Wenting & Niu, Yaxiao & Chávez, José L. & Ma, Weitong, 2021. "The mean value of gaussian distribution of excess green index: A new crop water stress indicator," Agricultural Water Management, Elsevier, vol. 251(C).
    12. Dongxing Zhang & Dang Luo, 2022. "Assessment of agricultural drought loss using a skewed grey cloud ordered clustering model," 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. 114(3), pages 2787-2810, December.
    13. Ivana Malá, 2013. "Použití konečných směsí logaritmicko-normálních rozdělení pro modelování příjmů českých domácností [The Use of Finite Mixtures of Lognormal Distribution for the Modelling of Household Income Distri," Politická ekonomie, Prague University of Economics and Business, vol. 2013(3), pages 356-372.
    14. Christian Schellhase & Göran Kauermann, 2012. "Density estimation and comparison with a penalized mixture approach," Computational Statistics, Springer, vol. 27(4), pages 757-777, December.
    15. Alireza Shokoohi & Reza Morovati, 2015. "Basinwide Comparison of RDI and SPI Within an IWRM Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 2011-2026, April.
    16. Kotapati Narayana Loukika & Venkata Reddy Keesara & Venkataramana Sridhar, 2021. "Analysis of Land Use and Land Cover Using Machine Learning Algorithms on Google Earth Engine for Munneru River Basin, India," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    17. Ihsan F. Hasan & Rozi Abdullah, 2023. "Multivariate index for monitoring drought (case study, Northeastern of Iraq)," 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. 116(3), pages 3817-3837, April.
    18. Antonello Maruotti & Antonio Punzo, 2021. "Initialization of Hidden Markov and Semi‐Markov Models: A Critical Evaluation of Several Strategies," International Statistical Review, International Statistical Institute, vol. 89(3), pages 447-480, December.
    19. Cheng Peng & Stanislav Uryasev, 2023. "Factor Model of Mixtures," Papers 2301.13843, arXiv.org, revised Mar 2023.
    20. Ang Shan & Fengkai Yang, 2021. "Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm," Mathematics, MDPI, vol. 9(6), pages 1-13, March.

    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:35:y:2021:i:13:d:10.1007_s11269-021-02952-x. 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.