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A Non-stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model

Author

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  • Soumyashree Dixit

    (National Institute of Technology)

  • K. V. Jayakumar

    (National Institute of Technology)

Abstract

Under variable climatic conditions, the conventional Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are inadequate for predicting extreme drought characteristics. Non-stationary Standardized Precipitation Index (NSPI) and Non-stationary Reconnaissance Drought Index (NRDI) are, therefore, developed by fitting non-stationary distributions. The Generalized Additive Model in Location, Scale and Shape (GAMLSS) framework, with time varying location parameters considering the external covariates, is used to fit the non-stationary distributions. Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) are considered as external covariates for the non-stationary drought assessment. The performances of stationary and non-stationary models are compared. The study also concentrated on the trivariate and the Pairwise Copula Construction (PCC) models to estimate the drought return periods. The comparison of two copula models revealed that the PCC model performed better than the trivariate Student’s t copula model. The recurrence intervals arrived at for the drought events are different for trivariate copula model and PCC model. This study showed that non-stationary drought indices will be helpful in the accurate estimate of the drought characteristics under the changing climatic scenario.

Suggested Citation

  • Soumyashree Dixit & K. V. Jayakumar, 2022. "A Non-stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1217-1236, March.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:4:d:10.1007_s11269-022-03069-5
    DOI: 10.1007/s11269-022-03069-5
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    1. Xing Liu & Zhaoyang Cai & Yan Xu & Huihui Zheng & Kaige Wang & Fengrong Zhang, 2022. "Suitability Evaluation of Cultivated Land Reserved Resources in Arid Areas Based on Regional Water Balance," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(4), pages 1463-1479, March.
    2. Shuhui Guo & Lihua Xiong & Jie Chen & Shenglian Guo & Jun Xia & Ling Zeng & Chong-Yu Xu, 2023. "Nonstationary Regional Flood Frequency Analysis Based on the Bayesian Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 659-681, January.
    3. Ihsan F. Hasan & Rozi Abdullah, 2022. "Agricultural Drought Characteristics Analysis Using Copula," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5915-5930, December.

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    Keywords

    NSPI; NRDI; MEI; IOD; SST; SOI; GAMLSS; PCC;
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