Rainfall Frequency Analysis of Sudan by Using Bayesian Markov chain Monte Carlo (MCMC) methods

DOI

10.2991/icista.2013.21How to use a DOI?

Keywords

frequency analysis; Markov chain Monte Carlo simulation (MCMC); mean annual rainfall; threshold; historical data

Abstract

This paper deals with at-site rainfall frequency estimation in the case when also information on hydrological events from the past with extraordinary magnitude is available. For the joint frequency analysis of systematic observations and historical data, respectively, the Bayesian framework is chosen, which, through adequately defined likelihood functions, allows for incorporation of different sources of hydrological information, e.g., mean annual rainfall, historical events as well as measurement errors. The distribution of the parameters of the fitted distribution function and the confidence intervals of the rain quantiles are derived by means of the Markov chain Monte Carlo simulation (MCMC) technique. The paper presents a sensitivity analysis related to the choice of the most influential parameters of the statistical model, which are the length of the historical period h and the perception threshold X0. These are involved in the statistical model under the assumption that except for the events termed as ‘historical’ ones, none of the (unknown) rains from the historical period h should have exceeded the threshold X0. Both higher values of h and lower values of X0 lead to narrower confidence intervals of the estimated rain quantiles; however, it is emphasized that one should be prudent of selecting those parameters, in order to avoid making inferences with wrong assumptions on the unknown hydrological events having occurred in the past. The Bayesian MCMC methodology is presented on the example of the mean annual rains observed at Sudan in the period 1901–2002.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Badreldin G. H. Hassan
AU  - Isameldin A. Atiem
AU  - Ping Feng
PY  - 2013/06
DA  - 2013/06
TI  - Rainfall Frequency Analysis of Sudan by Using Bayesian Markov chain Monte Carlo (MCMC) methods
BT  - Proceedings of the 2013 International Conference on Information Science and Technology Applications (ICISTA-2013)
PB  - Atlantis Press
SP  - 103
EP  - 110
SN  - 1951-6851
UR  - https://doi.org/10.2991/icista.2013.21
DO  - 10.2991/icista.2013.21
ID  - Hassan2013/06
ER  -