Monte Carlo Simulation Of Flood Frequency Curves From Rainfall

Author : Hitesh D. Patel
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (782 download)

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Download or read book Stochastic Runoff Routing Model Parameter for Design Flood Estimation Using Joint Probability Approach written by Hitesh D. Patel and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The Design Event Approach is currently recommended rainfall-based flood estimation method in Australia according to Australian Rainfall and Runoff. However, Design Event Approach does not account for the probabilistic nature of the key flood producing variables except for the rainfall depth. This arbitrary treatment of key inputs and model parameters in Design Event Approach can lead to inconsistencies and significant bias in flood estimates for a given average recurrence interval. A significant improvement in design flood estimates can be achieved through a Joint Probability Approach, which is more holistic in nature that uses probability-distributed input variables/model parameters and their correlations to obtain probability-distributed flood output. More recently, there have been notable researches in Australia on Monte Carlo Simulation Technique for flood estimation based on the principles of Joint Probability that can employ many of the commonly adopted flood estimation models and design data in Australia. The previous studies in Australia on Joint Probability Approach/Monte Carlo Simulation Technique have considered rainfall duration, rainfall intensity, rainfall temporal pattern and initial loss as random variables in the simulation but the probabilistic nature of runoff routing model storage delay parameter k has been disregarded, which in many circumstances is likely to cause under- or over-estimation of design flood peaks. Application of Monte Carlo Simulation Technique with RORB model, the most widely used hydrologic model in Australia, has not been well investigated. At present, the RORB model has a limited capability in terms of implementation of the Monte Carlo Simulation Technique in flood modeling. In the current RORB modeling, storage delay parameter kc is considered to be a fixed input. In this study, the applicability of the Monte Carlo Simulation Technique with RORB model has been investigated using a large number of selected storm and runoff events from Lismore catchment in New South Wales. It has been found that the value of kc exhibits a high degree of variability from event to event, and use of different possible representative values of kc results in quite different design flood peak estimates and hence it should be considered as a stochastic variable in modeling under the framework of the Monte Carlo Simulation Technique. In this study, the previously developed Monte Carlo Simulation Technique in Australia (old Monte Carlo Simulation Technique) has been upgraded by incorporating runoff routing model storage delay parameter (k) as a stochastic variable. The updated Monte Carlo Simulation Technique has been tested on five catchments in New South Wales. The derived flood frequency curves from the new Monte Carlo Simulation Technique have been compared with at-site flood frequency estimates and the old Monte Carlo Simulation Technique. It has been found that the new Monte Carlo Simulation Technique provides more accurate design flood estimates as compared to the old Monte Carlo Simulation Technique.