Urban flood modelling and climate change: a Melbourne area case study

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Molavi, Shahram, Muttil, Nitin and Tran, Huu Dung (2011) Urban flood modelling and climate change: a Melbourne area case study. In: MODSIM2011, 19th International Congress on Modelling and Simulation. Chan, F, Marinova, D and Anderssen, RS, eds. Modelling and Simulation Society of Australia and New Zealand, Australia, pp. 3608-3614.


This paper will present a proposed research study to assess the urban flooding due to the combined effects of the above mentioned four factors (namely climate change as well as the three urbanization related factors). Furthermore, it will present the result of simulating climate changes and downscaling their impacts on urban catchment, which is one of the first steps in implementation of the proposed study. Climate change effect could be studied using Global Climate Models (GCMs). These GCMs have been used to project global climate change in next 100 years based on different development trends and global greenhouse gas emission scenarios. The outputs of GCMs are at low resolutions and cannot be used for any local area directly. In this study, we are using Statistical Downscaling Methodology (SDSM) which is a hybrid of stochastic weather generator and regression-based downscaling methods to generate high resolution climate data. The results of the research imply variation in both amount and frequency of maximum daily rainfall for current century in compare to the present baseline. The trends of climate regime are toward more dry days and less precipitation. SDSM downscaling is not enough for undertaking the effects of climate change on extreme subdaily (high intensity and low duration) events that are projected to be more frequent due to climate change. A proper disaggregation is essential for generating extreme events.

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/9529
Official URL http://www.mssanz.org.au/modsim2011/I6/molavi.pdf
ISBN 9780987214317
Subjects Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Historical > FOR Classification > 0905 Civil Engineering
Historical > SEO Classification > 9603 Climate and Climate Change
Keywords ResPubID23192, climate change, global climate models(GCM), statistical downscaling, statistical downscaling model(SDSM)
Citations in Scopus 3 - View on Scopus
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