Evan Hajani
University of Sydney
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Publication
Featured researches published by Evan Hajani.
Hydrological Sciences Journal-journal Des Sciences Hydrologiques | 2017
Evan Hajani; Ataur Rahman; Elias H Ishak
ABSTRACT The trends in annual maximum rainfall (AMR) intensity data in New South Wales, Australia, were examined. Data from 60 stations were used covering three study periods, 1955–2010, 1965–2010 and 1978–2010. Mann-Kendall (MK) and Spearman’s rho (SR) tests were applied to assess trends at local stations. Pre-whitening (PW), trend-free pre-whitening (TFPW) and the variance correction (VC) tests were used to assess the effects of serial correlation on trend results. For regional trend analysis, the regional MK test was employed. The impacts of climatic variability modes on the observed trends in AMR intensity and seasonal maximum rainfall data were investigated. It was found that positive trends were more frequent than the negative ones. The PW, TFPW and VC tests resulted in a slight reduction in the count of stations exhibiting significant positive trends. The number of stations exhibiting significant trends decreased when the impact of climate variability modes was considered.
Natural Hazards | 2018
Evan Hajani; Ataur Rahman
Design rainfall, often known as intensity–frequency–duration (IFD) data, is an important input in rainfall runoff modelling exercise. IFD data are derived by fitting a probability distribution to observed rainfall data. Although there are many researches on IFD curves in the literature, there is a lack of systematic comparison among the IFD curves obtained by different distributions and methods. This study compares the latest IFD curves in Australia, published in 2013, as a part of the new Australian rainfall and runoff (ARR) with the at-site IFD curves to examine the expected degree of variation between the at-site and regional IFD data. Ten pluviography stations from eastern New South Wales (NSW) are selected for this study. The IFD curves generated by the two most commonly adopted probability distributions, generalised extreme value (GEV) and log Pearson type 3 (LP3) distributions are also compared. Empirical and polynomial regression methods in smoothing the IFD curves are compared. Based on the three goodness-of-fit tests, it has been found that both GEV and LP3 distributions fit the annual maximum rainfall data (at 1% significance level) for the ten selected stations. The developed IFD curves based on the second-degree polynomial present better fitting than the empirical method. It has been found that the ARR87 and ARR13 IFD curves are generally higher than the at-site IFD curves derived here. The median difference between the at-site and regional ARR-recommended IFD curves is in the range of 13–19%. It is expected that the outcomes of this research will provide better guidance in selecting the correct IFD data for a given application in NSW. The methodology developed here can be adapted to other parts of Australia and other countries.
Water | 2014
Evan Hajani; Ataur Rahman
Journal of Arid Environments | 2014
Evan Hajani; Ataur Rahman
congress on modelling and simulation | 2013
Evan Hajani; Ayesha S Rahman; Al-Amin; Ataur Rahman
International Journal of Climatology | 2018
Evan Hajani; Ataur Rahman
World Academy of Science, Engineering and Technology, International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering | 2014
Evan Hajani; Ataur Rahman; Khaled Haddad
Archive | 2017
Ataur Rahman; Evan Hajani; Saeid Eslamian
H2Olistic Integration: Concept, Design, Construction and Operation: Proceedings of the 9th International Conference on Water Sensitive Urban Design (WSUD), Sydney, 19-23 October 2015 | 2015
Cameron Snook; Ataur Rahman; Marlene van der Sterren; Md. Mahmudul Haque; Evan Hajani
36th Hydrology and Water Resources Symposium: The art and science of water | 2015
Ayesha S Rahman; Ataur Rahman; Khaled Haddad; Elias H Ishak; Evan Hajani; Orpita Urmi Laz; Fazlul Karim
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Commonwealth Scientific and Industrial Research Organisation
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