the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Historical trends of seasonal droughts in Australia
Abstract. Australia frequently experiences severe and widespread droughts, causing impacts on food security, the economy, and human health. Despite this, recent research to comprehensively understand the past trends in Australian droughts is lacking. We analyse the past changes in seasonal-scale meteorological, agricultural and hydrological droughts – defined using the 15th percentile threshold of precipitation, soil moisture, and runoff, respectively. We complement these traditional metrics with an impact-based drought indicator built from government drought reports using machine learning. Calculating trends in time and area under drought, for the various drought types, we find that while there have been widespread decreases in Australian droughts since the early 20th century, extensive regions have experienced an increase in recent decades. However, these recent changes largely remain within the range of observed variability, suggesting they are not unprecedented in the context of the historical drought events. The drivers behind these drought trends are multi-faceted and we show that the trends can be driven by both mean and variability changes in the underlying hydrological variable. Additionally, using explainable machine learning techniques, we unpick the key hydrometeorological variables contributing to agricultural and hydrological drought trends. The influence of these variables varies considerably between regions and seasons, with precipitation often shown to be important but rarely the main driver behind observed drought trends. This suggests the need to consider multiple drivers when assessing drought trends.
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Status: open (until 02 Apr 2025)
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RC1: 'Comment on egusphere-2024-4024', Anonymous Referee #1, 23 Feb 2025
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This manuscript analyzes historical drought trends in Australia using multiple drought indicators and explainable machine learning to assess hydrometeorological drivers and variability influences. While well-structured with a robust methodology and significant findings, improvements in clarity, statistical transparency, and rigor are needed. The attached document evaluates the manuscript against HESS criteria with recommendations to enhance its impact and alignment.
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