Extreme climate variability and impacts of future climate change on the streamflow in the southeast Queensland, Australia

Extreme climate variability and impacts of future climate change on the streamflow in the southeast Queensland, Australia

South East Queensland has experienced a series of recent catastrophic climatic events. From December 2010 to January 2011 and in February 2022, heavy rains caused flooding impacting over 2.5 million people and causing approximately 33 deaths. These events challenged the assumption of stationary conditions as no longer viable. The persistent use of this baseline assumption could potentially lead to misestimations in forecasting future floods. The severity and frequency of extremes are escalating; thus, it is necessary to evaluate the impacts of land cover changes and urbanisation, along with climate change. A framework of the trend analysis methods to analyse temporal patterns, spatial analysis techniques utilising the Google Earth Engine (GEE), Generalised Extreme Value (GEV) method, and land cover patterns classification including Random Forest (RF) and Support Vector Machine (SVM) can be useful for hydrometeorological variables extreme events analysis. This research highlights the importance of using spatiotemporal techniques and trend analysis by underscoring the changing frequency and severity of extreme events analysis. The aim of this research is to evaluate extreme events under non-stationary conditions, where the location parameter has a linear function with time. For this study, a unique framework consisting of the hydrological model in line with the Process-informed Non-Stationary Extreme Value Analysis (ProNEVA) GEV model and the ensemble of General Circulation Models (GCMs), mapping land cover patterns using classification methods within the GEE platform, were employed to comprehensively analyse the impacts of climate variability and land cover changes on extreme hydrological events. Runoff was projected under two scenarios for eight GCMs and by incorporating the percentage of each land cover into the hydrological model for two horizons, (2020-2065 and 2066-2085). The outcomes of this study suggest that neglecting non-stationary assumptions of flood frequency can lead to underestimating the magnitude of flooding. This, in turn, can lead to greater and increased risks to infrastructure planning and design. The framework of this research paper is adaptable to various geographical regions for the purposes of estimating extreme conditions; thereby offering valuable insights for infrastructure design, planning, risk assessment, and the sustainable management of future water resources in the context of long-term water management plans.

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