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Development of adaptive standardized precipitation index and its application in the Tibet Plateau region

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Abstract

Drought is one of the most complex natural hazards. Therefore, precise drought monitoring and forecasting are the biggest tasks for hydrologists and environmentalists. Under grid data structure, this paper provides a new drought index—the adaptive standardized precipitation index (ASPI), for the evolution of drought, inferring its spatio-temporal patterns and detecting trends. The methodology of the proposed index is based mainly on dynamic time warping clustering algorithm and dynamic principal components. Historical simulated precipitation data from the Australian community climate and earth-system simulator model of coupled model intercomparison project 6 of 727 grid points scattered around the Tibet Plateau has been considered. Results show that as the time scale increases, the severe and extreme drought trends have increased significantly. Further, the significant decreasing magnitude in ASPI reveals the persistence of future drought in the Tibet Plateau region. From a data mining point of view, the outcomes associated with this research recommend the endorsement of ASPI for effective and precise drought monitoring under grid data structure.

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Data and code availability

All the data were analyzed using R software. The data and code used to support the findings of this study are available from the corresponding author upon request.

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Funding

The authors are very grateful to the Natural Science Foundation of Jiangsu Province, China (BK20210369).

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Authors and Affiliations

  1. State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Center for Global Change and Water Cycle, Hohai University, Nanjing, 210098, People’s Republic of China

    Zhenya Li

  2. Yangtze Institute for Conservation and Development, Nanjing, 210098, Jiangsu, People’s Republic of China

    Zhenya Li

  3. Department of Computer Science, SZABIST Islamabad Campus, Islamabad, Pakistan

    Saba Riaz

  4. Department of Statistics, National College of Business Administration and Economics, Lahore, Pakistan

    Sadia Qamar

  5. Department of Statistics, University of Sargodha, Sargodha, Pakistan

    Sadia Qamar

  6. College of Statistical and Actuarial Sciences, University of the Punjab, Lahore, Pakistan

    Zulfiqar Ali

  7. Department of Mathematics, COMSATS University Islamabad, Islamabad, Pakistan

    Javeria Nawaz Abbasi & Rabia Fayyaz

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Li, Z., Riaz, S., Qamar, S. et al. Development of adaptive standardized precipitation index and its application in the Tibet Plateau region. Stoch Environ Res Risk Assess 37, 557–575 (2023). https://doi.org/10.1007/s00477-022-02279-y

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