Diagnosis of flood events in Brisbane (Australia) using a flood index based on daily effective precipitation

Deo, R. C. and Byun, H. R. and Adamowski, J. F. and Kim, D. W. (2014) Diagnosis of flood events in Brisbane (Australia) using a flood index based on daily effective precipitation. In: International Conference on Analysis and Management of Changing Risks for Natural Hazards, 18‐19 Nov 2014, Padua, Italy.

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Like drought, flood events are extremely detrimental to the community [1, 2]. In the State of Queensland (Australia) the 2010–2011 Summer period saw a very significant flooding that appeared to be exacerbated by La Nina, with damages of magnitudes similar to previous flood in 1974 and mid-1950s [3]. Therefore, a plethora of flood events in this region raises serious questions about how best to address the vulnerability and costs [4]. Several studies documented particularly vulnerable geographic setting of the capital city, Brisbane. To name a few, the worst event was in January 1974 and next in 2010, which flooded most dwellings around Brisbane River catchment, severely in Toowoomba and the Lockyer Creek catchment (where 23 people had drowned). Insurers received some 56,200 claims with payouts totaling $2.55 billion, due to estimated inundations of 18,000 properties. Crucial to any flood mitigation and adaptation is the prediction of events with a good real-time monitoring system. The system should detect precisely the onset dates and corresponding water-intensive properties. A flood event is dependent on how abundant the water resources due to heavy rain are and how the water is dissipated over time. Hence a scientific method for detecting floods should be based on remaining effective precipitation on daily basis, due to heavy rain over a period of time. In this paper we applied an obje400+-ctive flood diagnostic method following an earlier pioneer study [6]. The Flood Index (FI) used in this research was initially developed by [6] based on the concept of daily Effective Precipitation (EP) proposed by [7] using the Available Water Resources Index (AWRI), and later used by [8] for analysis of water abundant seasons. The daily FI was applied to the flood region of Brisbane, the state capital of Queensland, Australia (27°30' S, 153°1' E). In order to compute the FI, the pre-processed daily rainfall data was acquired from Australian Bureau of Meteorology (http://www.bom.gov.au/climate/data-services/). Data for the period 1915–2012 were analysed. Since the FI was a standardized value comparing daily water resources for any Julian date to the yearly maximum values over climatological period, it detected flood starting date (for FI > 0) and represented adequately anomalously high precipitation that potentially triggered flood situation. The severity, intensity and durations were analysed by running sum approach of [9] over identified flood periods between onset and termination dates. Our results demonstrated good skill of the daily Flood Index for objective diagnosis and monitoring of flood events based on water intensive properties. The method allowed for the detection of the event, and quantified its properties for comparison of various events. The method was novel for quantifying floods and appears quite promising for forecasting flood events using time-series approaches.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 11 Mar 2015 21:13
Last Modified: 15 Feb 2017 06:03
Uncontrolled Keywords: flood risk, flood Index, flood onset date, daily flood monitoring
Fields of Research : 04 Earth Sciences > 0401 Atmospheric Sciences > 040107 Meteorology
04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040603 Hydrogeology
04 Earth Sciences > 0406 Physical Geography and Environmental Geoscience > 040604 Natural Hazards
01 Mathematical Sciences > 0102 Applied Mathematics > 010299 Applied Mathematics not elsewhere classified
Socio-Economic Objective: D Environment > 96 Environment > 9603 Climate and Climate Change > 960302 Climate Change Mitigation Strategies
E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences
D Environment > 96 Environment > 9610 Natural Hazards > 961099 Natural Hazards not elsewhere classified
URI: http://eprints.usq.edu.au/id/eprint/26754

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