Machine Learning Assessment of the Impact of Global Warming on the Climate Drivers of Water Supply to Australia’s Northern Murray-Darling Basin

Milton Samuel Speer, Joshua Hartigan, Lance M. Leslie

Water · 29 September 2022

In one sentence. Regression-based machine learning ranks 45 climate attributes, linking declining April–May rainfall in the northern Murray-Darling Basin to global warming, ENSO, SAM and IOD.

Abstract

Droughts and long dry spells, interspersed with intense rainfall events, have been characteristic of the northern Murray-Darling Basin (NMDB), a major Australian agricultural region. The NMDB precipitation results from weather systems ranging from thunderstorms to larger scale events. The larger scale events exhibit high seasonal and annual rainfall variability. To detect attributes shaping the NMDB precipitation patterns, and hence net water inflows to the vast Darling River catchment area, numerous (45) possible attributes were assessed for their influence on rainfall trends. Four periods were assessed: annual, April–May (early cool-season), June–September (remaining cool-season), and October–March (warm-season). Linear and non-linear regression machine learning (ML) methods were used to identify the dominant attributes. We show the impact of climate drivers on the increasingly dry April–May months on annual precipitation and warmer temperatures since the early 1990s. The NMDB water supply was further reduced during 1992–2018 by the lack of compensating rainfall trends for the April–May decline. The identified attributes include ENSO, the Southern Annular Mode, the Indian Ocean Dipole, and both local and global sea surface temperatures. A key finding is the prominence of global warming as an attribute, both individually and in combination with other climate drivers.

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