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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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. Federica Carfagna & Raffaello Cervigni, 2018. "Mitigating Drought Impacts in Drylands," World Bank Publications - Books, The World Bank Group, number 29597, December.
    5. 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).
    6. 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.
    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.
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    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.

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