Multi-source evaluation of a non-stationary drought index informed by climatic teleconnections across the Murray-Darling Basin, Australia

Nonstationarity in meteorological series presents challenges for drought monitoring, particularly under dynamic climatic influences. This study aims to refine meteorological drought assessment in the Murray-Darling Basin (MDB), Australia, by developing a nonstationary drought index (NeSPI) within the Generalized Additive Models for Location, Scale, and Shape (GAMLSS) framework. The NeSPI incorporates large-scale climate indices, including the Southern Oscillation Index (SOI), Indian Ocean Dipole (IOD), Nino3.4, and Pacific Decadal Oscillation (PDO), to better capture temporal variability and climatic teleconnections. The study identified B-splines as the optimal smoothing function, with the 3-month NeSPI capturing local heterogeneity and the 6-month NeSPI reflecting regional coherence through non-linear, low-precipitation models influenced by broader climatic drivers. The study evaluates the NeSPI against the traditional SPI using historical precipitation data across multiple timescales. Results indicate that nonstationary models demonstrated superior accuracy in capturing short-term drought dynamics, highlighting temporal variability overlooked by stationary indices. Moreover, NeSPI showed superior alignment with the Vegetation Condition Index (VCI) and Root-Zone Soil Moisture (SM-RZ), particularly during the Millennium Drought (2007-2009), capturing drought onset, peak intensity, and recovery more effectively than SPI. Wavelet analysis revealed significant coherence between NeSPI and SOI at interannual scales (2-8 years), as well as between NeSPI and Nino3.4 during 1995-2005 and 2010-2015, with additional influences from the IOD (1995-2005) and PDO at decadal scales (8-16 years). These findings highlight NeSPI's robustness in capturing nonstationary drought dynamics, offering a valuable tool for improved drought monitoring and management in the MDB and other climatically sensitive regions under changing environmental conditions.