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