Pe Weinmann

Monte Carlo simulation of flood frequency curves from rainfall

Abstract The currently adopted rainfall-based design flood estimation techniques, for example the Design Event Approach, do not account for the probabilistic nature of the key variables except for the rainfall depth. This arbitrary treatment of key inputs and parameters can lead to inconsistencies and significant bias in flood estimates for a given average recurrence interval. This paper presents a Monte Carlo simulation technique that makes explicit allowance for the probability-distributed nature of the key flood producing variables and the dependencies between them to determine derived flood frequency curves. The proposed approach employs joint probability principles to develop a design flood estimation technique that can incorporate commonly applied rainfall–runoff models and design data. The application of the proposed technique to three catchments in Victoria has shown that the new method provides a relatively precise reproduction of the observed frequency curves. The new technique is relatively easy to apply for catchments with good rainfall data and a limited streamflow record. The technique thus shows a strong potential to become a practical design tool; further work is needed to allow its routine application in a wider range of design situations.

Design flood estimation in ungauged catchments: a comparison between the probabilistic rational method and quantile regression technique for NSW

Abstract Design flood estimation for ungauged catchments is often required in hydrologie design. The most commonly adopted regional flood frequency analysis methods used for this purpose include the index flood method, regression based techniques and various forms of the rational method. This paper first examines the similarities and differences between the probabilistic rational method (PRM) (the currently recommended method for Victoria and eastern NSW in Australian Rainfall and Runoff) and the generalised least squares (GLS) based quantile regression technique (QRT). It then uses data from 107 catchments in NSW to compare the performance of these two methods. To make a valid comparison, the same predictor variables and data set have been used for both methods. Based on one-at-a-time and split sample validation tests and a range of evaluation statistics, it has been found that the GLS-based QRT performs better than the PRM. The particular advantage of the QRT is that it does not require a contour map of the runoff coefficient as with the PRM. The QRT also offers potential for improvement through inclusion of additional predictor variables. The QRT can also be integrated with the region of influence approach, where a region can be formed around an ungauged catchment by selecting an “appropriate number” of neighbouring gauged catchments. Overall, the QRT offers much greater flexibility and potential in terms of error analysis and further development as compared to the PRM.

Monte Carlo simulation of flood frequency curves from rainfall : the way ahead

Abstract This paper summarises the results of a 3-year research project by the CRC for Catchment Hydrology (CRCCH) on the joint probability approach (Monte Carlo simulation technique) to design flood estimation and the subsequent research activities to further the CRCCH method towards industrial applications. It identifies significant shortcomings in the current design event approach to rainfall-based design flood estimation, and argues that substantial improvements in the accuracy and reliability of flood estimates can be obtained from a more rigorous treatment of probability aspects in the generation of design floods. Applications of the proposed Monte Carlo simulation approach to test catchments in Victoria and Queensland have produced promising results, and demonstrated the feasibility and in-principle advantages of the approach. More recently, the Monte Carlo simulation approach has been integrated with the industry-based flood estimation model URBS thus significantly broadening its range of application. The paper discusses how far the recent research on the joint probability approach has advanced towards resolving the main research issues, and outlines desirable future development work to allow the new method to be routinely applied as a design tool.

Estimation of Major Floods: Applicability of a Simple Probabilistic Model

Abstract Estimation of major flood flows is needed in the design and operation of large water infrastructure. This paper presents a simple probabilistic model (PM) that can be used to derive “easy to apply” prediction equations for estimation of major flood flows. The proposed method assumes that the maximum observed flood data over a large number of sites in a region can be pooled together by accounting for the across-site variations in the mean and standard deviation values. The method is developed and tested in this paper using data from 227 catchments across Victoria and NSW. The application to ungauged catchments involves the development of prediction equations using generalised least squares regression for the mean and coefficient of variation of the annual maximum flood series as a function of catchment characteristics. An independent test shows that the PM provides quite reasonable design flood estimation in the study area for average recurrence intervals in the range of 20 to 200 years, with median relative error values (compared to at-site flood frequency estimates) in the range of 10% to 35%. The method is under further development, eg. consideration of the inter-station correlation in pooling the standardised data from the nearby stations and comparison of results with the rainfall-based methods. The method has the potential to derive regional flood prediction equations for major floods by pooling the flood data from all the Australian states.