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Uncertainty sources in climate change impact studies can be addressed in two distinct phases, climate modelling and impact assessment. Generally, uncertainty from the climate modelling phase is the larger contributor and the impact phase uncertainty is sometimes marginalized. This paper aims to develop integrated uncertainty analysis approaches that deal with uncertainties at the impact level with all sources included. The existing uncertainty analysis method of Reliability Ensemble Averaging (REA) is modified for impact studies by bracketing the hydrological model parameter uncertainty. A novel probabilistic approach referred to as Bootstrapped Ensemble Uncertainty Modelling (BEUM) is also proposed for uncertainty analysis at the impact phase. Modified REA and BEUM are applied to streamflow projections of the Hulu Langat basin with uncertainties stemming from emission scenarios, general circulation models (GCM), downscaling method, and a hydrological model parametrization for a comprehensive integrated uncertainty assessment. The modified REA increased the reliability measure of each streamflow scenario and improved the uncertainty coverage percentage when compared with the original REA. The REA eliminated the projections of the less reliable GCMs, and therefore reduced uncertainty bands compared to the BEUM. The BEUM can create a large ensemble of streamflow projections through nonparametric bootstrapping of the limited impact scenarios and define the streamflow distribution for every quantile. Therefore, BEUM is able to encompass larger uncertainty intervals and explain the behavior of ensemble members through probabilistic distributions that can assist in decision making for climate change mitigation and adaptation planning. [ABSTRACT FROM AUTHOR]

Australian summer heat events have become more frequent and severe in recent times. Temperature-duration-frequency (TDF) curves connect the severity of heat episodes of various durations to their frequencies and thus can be an effective tool for analysing the heat extremes. This study examines Australian heat events using data from 82 meteorological stations. TDF curves have been developed under stationary and non-stationary conditions. Generalised Extreme Value (GEV) distribution is considered to estimate extreme temperatures for return periods of 2, 5, 10, 25, 50 and 100 years. Three major climate drivers for Australia have been considered as potential covariates along with Time to develop the non-stationary TDF curves. According to the Akaike information criterion, the non-stationary framework for TDF modelling provides a better fit to the data than its stationary equivalent. The findings can be beneficial in offering new information to aid climate adaptation and mitigation at the regional level in Australia. [ABSTRACT FROM AUTHOR]