IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v37y2023i5d10.1007_s11269-023-03461-9.html
   My bibliography  Save this article

A New Regional Drought Index under X-bar Chart Based Weighting Scheme – The Quality Boosted Regional Drought Index (QBRDI)

Author

Listed:
  • Zulfiqar Ali

    (University of the Punjab)

  • Sadia Qamar

    (University of Sargodha)

  • Nasrulla Khan

    (University of the Punjab)

  • Muhammad Faisal

    (Rawalpindi Cantt)

  • Saad Sh. Sammen

    (University of Diyala)

Abstract

Unlike other natural hazards, drought has severe consequences on numerous aspects of life. After the industrial revolution, drought is prevailing in most parts of the world. Likewise, global warming and climate change have increased the recurrent occurrences of extreme values and the short-distance variability in precipitation. Therefore, accurate and effective reporting of drought characteristics at the regional level is one of the most challenging tasks in hydrology. This research aims to improve the accuracy and quality of drought characterization and its continuous monitoring at the regional level. This article develops a new drought indicator by integrating unequal weights under an X-bar chart with the regional aggregation precipitation data. We called the new index– the Quality Boosted Regional Drought Index (QBRDI). In application, the northern region of Pakistan is considered to assess and evaluate QBRDI. In comparison, the study includes a pairwise comparison of QBRDI and Regional Standardized Precipitation Index (RSPI) using the Pearson correlation coefficient. Comparative to RSPI, a significantly low Coefficient of Variation between the correlations of QBRDI with other meteorological stations reveals that QBRDI has more regional characteristics than RSPI. These outcomes endorse the rationality of using QBRDI for regional drought analysis. In addition, the methodology of QBRDI provides a new way to minimize the impact of outliers and extreme values in the regional aggregation of precipitation data.

Suggested Citation

  • Zulfiqar Ali & Sadia Qamar & Nasrulla Khan & Muhammad Faisal & Saad Sh. Sammen, 2023. "A New Regional Drought Index under X-bar Chart Based Weighting Scheme – The Quality Boosted Regional Drought Index (QBRDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(5), pages 1895-1911, March.
  • Handle: RePEc:spr:waterr:v:37:y:2023:i:5:d:10.1007_s11269-023-03461-9
    DOI: 10.1007/s11269-023-03461-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-023-03461-9
    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/s11269-023-03461-9?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. Kim Phuc Tran, 2022. "Introduction to Control Charts and Machine Learning for Anomaly Detection in Manufacturing," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 1-6, Springer.
    2. Sedigheh Mohamadi & Saad Sh. Sammen & Fatemeh Panahi & Mohammad Ehteram & Ozgur Kisi & Amir Mosavi & Ali Najah Ahmed & Ahmed El-Shafie & Nadhir Al-Ansari, 2020. "Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm," 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(1), pages 537-579, October.
    3. Phuong Hanh Tran & Adel Ahmadi Nadi & Thi Hien Nguyen & Kim Duc Tran & Kim Phuc Tran, 2022. "Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective," Springer Series in Reliability Engineering, in: Kim Phuc Tran (ed.), Control Charts and Machine Learning for Anomaly Detection in Manufacturing, pages 7-42, Springer.
    4. Rizwan Niaz & Mohammed M. A. Almazah & Xiang Zhang & Ijaz Hussain & Muhammad Faisal & Alireza Amirteimoori, 2021. "Prediction for Various Drought Classes Using Spatiotemporal Categorical Sequences," Complexity, Hindawi, vol. 2021, pages 1-11, November.
    5. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Jin, Yu, 2022. "A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization," Energy, Elsevier, vol. 239(PE).
    6. 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.
    7. Gustavo Naumann & Carmelo Cammalleri & Lorenzo Mentaschi & Luc Feyen, 2021. "Increased economic drought impacts in Europe with anthropogenic warming," Nature Climate Change, Nature, vol. 11(6), pages 485-491, June.
    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. Ethel García & Rita Peñabaena-Niebles & Maria Jubiz-Diaz & Angie Perez-Tafur, 2022. "Concurrent Control Chart Pattern Recognition: A Systematic Review," Mathematics, MDPI, vol. 10(6), pages 1-31, March.
    2. Wilson Rojas-Preciado & Mauricio Rojas-Campuzano & Purificación Galindo-Villardón & Omar Ruiz-Barzola, 2023. "Control Chart T2Qv for Statistical Control of Multivariate Processes with Qualitative Variables," Mathematics, MDPI, vol. 11(12), pages 1-32, June.
    3. Arthur Charpentier & Molly James & Hani Ali, 2021. "Predicting Drought and Subsidence Risks in France," Papers 2107.07668, arXiv.org.
    4. Nadjib Haied & Atif Foufou & Samira Khadri & Adel Boussaid & Mohamed Azlaoui & Nabil Bougherira, 2023. "Spatial and Temporal Assessment of Drought Hazard, Vulnerability and Risk in Three Different Climatic Zones in Algeria Using Two Commonly Used Meteorological Indices," Sustainability, MDPI, vol. 15(10), pages 1-25, May.
    5. 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.
    6. Wu, Chunying & Wang, Jianzhou & Hao, Yan, 2022. "Deterministic and uncertainty crude oil price forecasting based on outlier detection and modified multi-objective optimization algorithm," Resources Policy, Elsevier, vol. 77(C).
    7. 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.
    8. Xu, Zhenheng & Sun, Hao & Zhang, Tian & Xu, Huanyu & Wu, Dan & Gao, JinHua, 2023. "Evaluating established deep learning methods in constructing integrated remote sensing drought index: A case study in China," Agricultural Water Management, Elsevier, vol. 286(C).
    9. Wang, Hao & Ye, Jingzhen & Huang, Linxuan & Wang, Qiang & Zhang, Haohua, 2023. "A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction," Energy, Elsevier, vol. 262(PA).
    10. Okan Mert Katipoğlu, 2023. "Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    11. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
    12. Jew Das & N. V. Umamahesh, 2018. "Spatio-Temporal Variation of Water Availability in a River Basin under CORDEX Simulated Future Projections," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1399-1419, March.
    13. 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.
    14. Sheunesu Ruwanza & Gladman Thondhlana & Menelisi Falayi, 2022. "Research Progress and Conceptual Insights on Drought Impacts and Responses among Smallholder Farmers in South Africa: A Review," Land, MDPI, vol. 11(2), pages 1-16, January.
    15. Dai, Min & Yang, Han & Yang, Fusheng & Zhang, Zaoxiao & Yu, Yunsong & Liu, Guilian & Feng, Xiao, 2022. "Multi-strategy Ensemble Non-dominated sorting genetic Algorithm-II (MENSGA-II) and application in energy-enviro-economic multi-objective optimization of separation for isopropyl alcohol/diisopropyl et," Energy, Elsevier, vol. 254(PA).
    16. Romy Carmen Brockhoff & Robbert Biesbroek & Bregje Bolt, 2022. "Drought Governance in Transition: a Case Study of the Meuse River Basin in the Netherlands," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2623-2638, June.
    17. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    18. Neeta Nandgude & T. P. Singh & Sachin Nandgude & Mukesh Tiwari, 2023. "Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies," Sustainability, MDPI, vol. 15(15), pages 1-19, July.
    19. 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.
    20. 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.

    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:37:y:2023:i:5:d:10.1007_s11269-023-03461-9. 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.