Temporal and spectral governing dynamics of Australian hydrological streamflow time series

TL;DR

This study applies advanced multivariate time series analysis to Australian hydrological streamflow data, revealing collective dynamics and spectral similarities, and introduces a framework for estimating governing hydrological processes across the continent.

Contribution

It introduces a novel Whittle Likelihood-based optimization framework and an algorithmic procedure to identify and analyze the governing hydrological streamflow dynamics across Australia.

Findings

Identified notable similarity in temporal and spectral behaviors among stations

Developed a framework for estimating governing hydrological processes

Analyzed the evolution of dynamics over time using PCA and spectral analysis

Abstract

We use new and established methodologies in multivariate time series analysis to study the dynamics of 414 Australian hydrological stations' streamflow. First, we analyze our collection of time series in the temporal domain, and compare the similarity in hydrological stations' candidate trajectories. Then, we introduce a Whittle Likelihood-based optimization framework to study the collective similarity in periodic phenomena among our collection of stations. Having identified noteworthy similarity in the temporal and spectral domains, we introduce an algorithmic procedure to estimate a governing hydrological streamflow process across Australia. To determine the stability of such behaviours over time, we then study the evolution of the governing dynamics and underlying time series with time-varying applications of principal components analysis (PCA) and spectral analysis.