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

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 (NR...

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Bibliographic Details
Published in:Water resources management 2022-03, Vol.36 (4), p.1217-1236
Main Authors: Dixit, Soumyashree, Jayakumar, K. V.
Format: Article
Language:English
Subjects:
Additives
Atmospheric Sciences
Civil Engineering
Climate change
Climatic conditions
Construction
Dipoles
Drought
Drought index
Earth and Environmental Science
Earth Sciences
El Nino
El Nino phenomena
Environment
Extreme drought
Geotechnical Engineering & Applied Earth Sciences
Hydrogeology
Hydrology
Hydrology/Water Resources
Precipitation
Rain
Reconnaissance
Sea surface
Sea surface temperature
Southern Oscillation
Standardized precipitation index
Wind
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Summary: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.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-022-03069-5