Variability analysis of monthly precipitation vector time series in Australia by a new spatiotemporal entropy statistic

Abstract

Changing climate in Australia has significant impacts on the country's economy, environment and social well-being. Addressing such impacts, particularly that of precipitation change, entails immediate action due to the more frequent occurrence of extreme dry or wet events in Australia in recent decades. In this paper we investigate the intra-annual Australian precipitation variability and how it changes over space and time. We quantify this variability using information entropy -- a statistical tool for measuring the uncertainty of a random variable over its sample space, and propose a compositional data model to compute optimal spatiotemporal estimators of this entropy using 1/1979-to-3/2022 monthly satellite precipitation estimates from the National Oceanic and Atmospheric Administration (NOAA). The results enable us to identify those locations/times where/when extreme intra-annual precipitation variation or unevenness occurred. We find this variability has been changing over time in large regions of southeastern Queensland and on the coast of South Australia, which would be difficult to find without using the proposed approach. We uncover the development of extreme entropy in the months leading up to, and in the location of, four extreme precipitation events in Australia where inter-annual precipitation amounts and/or trends proved insignificant. In marked contrast to annual precipitation, we found entropy has a weak association with the El Niño Southern Oscillation (ENSO) cycle.