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Model and parameter uncertainty in IDF relationships under climate change

Rupa, Chandra and Saha, Ujjwal and Mujumdar, PP (2015) Model and parameter uncertainty in IDF relationships under climate change. In: ADVANCES IN WATER RESOURCES, 79 . pp. 127-139.

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Official URL: http://dx.doi.org/10.1016/j.advwatres.2015.02.011


Quantifying distributional behavior of extreme events is crucial in hydrologic designs. Intensity Duration Frequency (IDF) relationships are used extensively in engineering especially in urban hydrology, to obtain return level of extreme rainfall event for a specified return period and duration. Major sources of uncertainty in the IDF relationships are due to insufficient quantity and quality of data leading to parameter uncertainty due to the distribution fitted to the data and uncertainty as a result of using multiple GCMs. It is important to study these uncertainties and propagate them to future for accurate assessment of return levels for future. The objective of this study is to quantify the uncertainties arising from parameters of the distribution fitted to data and the multiple GCM models using Bayesian approach. Posterior distribution of parameters is obtained from Bayes rule and the parameters are transformed to obtain return levels for a specified return period. Markov Chain Monte Carlo (MCMC) method using Metropolis Hastings algorithm is used to obtain the posterior distribution of parameters. Twenty six CMIP5 GCMs along with four RCP scenarios are considered for studying the effects of climate change and to obtain projected IDF relationships for the case study of Bangalore city in India. GCM uncertainty due to the use of multiple GCMs is treated using Reliability Ensemble Averaging (REA) technique along with the parameter uncertainty. Scale invariance theory is employed for obtaining short duration return levels from daily data. It is observed that the uncertainty in short duration rainfall return levels is high when compared to the longer durations. Further it is observed that parameter uncertainty is large compared to the model uncertainty. (C) 2015 Elsevier Ltd. All rights reserved.

Item Type: Journal Article
Additional Information: Copy right for this article belongs to the ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND
Keywords: Bayesian method; Intensity Duration Frequency relationships; Climate change; Short duration rainfall; Uncertainties
Department/Centre: Division of Mechanical Sciences > Divecha Centre for Climate Change
Division of Mechanical Sciences > Civil Engineering
Date Deposited: 13 May 2015 06:40
Last Modified: 13 May 2015 06:40
URI: http://eprints.iisc.ac.in/id/eprint/51509

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