资源环境科技发展态势分析平台

Raw simulations of global or regional climate models are rarely used in catchment scale hydrological impact assessment or subsequent reservoir storage change assessment studies. Keeping this in mind, this study uses a frequency bias correction alternative for multiple variables to evaluate the impact of climate change on reservoir storage reliability, resilience, and vulnerability across Australia. The bias-corrected time series of daily rainfall and temperature are used as inputs to a hydrological model to derive flows and assess change to reservoir storage attributes. A total of six fifth phase of the Coupled Model Intercomparison Project climate models dynamically downscaled using the Conformal Cubic Atmospheric Model are used. Streamflow data of 222 high-quality catchments in near-natural conditions across Australia are used and change ascertained. The results for the historical climate show that the multivariate frequency bias correction approach outperforms the traditional quantile matching alternative in representing the runoff characteristics related to reservoir storage. For the future climate, the results suggest decrease in the annual mean runoff for most catchments. The proposed approach leads to a smaller decrease in the standard deviation of annual runoff and a reduction in the water supply capability, as indicated by a reduction in reliability and resilience and an increase in vulnerability, to meet the demand in comparison to both raw and quantile matching-based climate simulations across for most catchments. Overall, a reduction in water supply capability to meet a given demand in the future for most regional climate models and catchments is projected.

Plain Language Summary This article follows on from the considerable work our group has done to develop a multivariate bias correction alternative that operates in frequency space. In course of our investigation we had to work on fixing the number of zero rainfall days while dealing with daily rainfall. For historical climate our results are better than the traditionally quantile matching approach in terms of reproduction of statistics of relevance in hydrology across all catchments, which shows the utility of the proposed approach. The multivariate nature of the model makes sure that the bias-corrected time has a cross dependence structure that is physically consistent. The application of the approach to estimate the future changes in the reservoir storage characteristics of the catchments across Australia provides useful information for use by the Australian water resources managers.