Revised guidance on selection of approaches to flood estimation
- Publication Type:
- Conference Proceeding
- The Art and Science of Water - 36th Hydrology and Water Resources Symposium, HWRS 2015, 2015, pp. 173 - 183
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© 2015, Engineers Australia. All rights reserved. Hydrologists are required to estimate flood magnitudes for the design of culverts and bridges for roads and railways, the design of urban drainage systems, the design of flood mitigation levees and other flood mitigation structures, design of dam spillways, and many other situations. The flood characteristic of most importance depends on the nature of the problem under consideration, but it is often necessary to estimate peak flow, peak level, flood volume, and flood rise. Design objectives are most commonly specified using risk-based criteria, and thus the focus of the revised ARR guidance (for Chapter 3 of Book 1) is on the use of methods that provide estimates of flood characteristics for a specified probability of exceedance. A key difference between the proposed guidance and earlier versions of ARR is the focus on "probability-neutrality". This concept is particularly relevant to rainfall-based techniques where it is necessary to ensure that the transformation of design rainfalls into design floods is undertaken in a fashion that minimises bias in the resulting exceedance probabilities. Accordingly, the proposed guidance introduces the use of more computationally intensive procedures (such as ensemble event, Monte Carlo event, and continuous simulation approaches) in an attempt to minimise bias in the resulting flood quantiles. This paper summarises the proposed ARR recommendations covering the selection and application of methods available to the flood practitioner. The methods discussed are divided into two broad classes of procedures based on: (i) the direct analysis of observed flood and related data and (ii) the use of simulation models to transform rainfall into flood maxima. All methods involve the use of some kind of statistical model (or transfer function) to extrapolate information in space or time, and the paper includes discussion of their strengths and limitations and how they vary in their suitability to different types of data and design contexts.
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