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Open AccessArticle

GEV Parameter Estimation and Stationary vs. Non-Stationary Analysis of Extreme Rainfall in African Test Cities

Department of Civil Architectural and Environmental Engineering (DICEA), University of Naples Federico II, 80125 Naples, Italy
Water Resources Research and Documentation Centre (WARREDOC), University for Foreigners of Perugia, 06123 Perugia, Italy
Author to whom correspondence should be addressed.
Hydrology 2018, 5(2), 28;
Received: 13 March 2018 / Revised: 9 May 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)
Nowadays, increased flood risk is recognized as one of the most significant threats in most parts of the world, with recurring severe flooding events causing significant property and human life losses. This has entailed public debates on both the apparent increased frequency of extreme events and the perceived increases in rainfall intensities within climate changing scenarios. In this work, a stationary vs. Non-Stationary Analysis of annual extreme rainfall was performed with reference to the case studies of the African cities of Dar Es Salaam (TZ) and Addis Ababa (ET). For Dar Es Salaam (TZ) a dataset of 53 years (1958–2010) of maximum daily rainfall records (24 h) was analysed, whereas a 47-year time series (1964–2010) was taken into account for Addis Ababa (ET). Both gauge stations rainfall data were suitably fitted by Extreme Value Distribution (EVD) models. Inference models using the Maximum Likelihood Estimation (MLE) and the Bayesian approach were applied on EVD considering their impact on the shape parameter and the confidence interval width. A comparison between a Non-Stationary regression and a Stationary model was also performed. On this matter, the two time series did not show any Non-Stationary effect. The results achieved under the CLUVA (Climatic Change and Urban Vulnerability in Africa) EU project by the Euro-Mediterranean Centre for Climate Change (CMCC) (with 1 km downscaling) for the IPCC RCP8.5 climatological scenario were also applied to forecast the analysis until 2050 (93 years for Dar Es Salaam TZ and 86 years for Addis Ababa ET). Over the long term, the process seemed to be Non-Stationary for both series. Moreover, with reference to a 100-year return period, the IDF (Intensity-Duration-Frequency) curves of the two case-studies were estimated by applying the Maximum Likelihood Estimation (MLE) approach, as a function of confidence intervals of 2.5% and 97.5% quantiles. The results showed the dependence of Non-Stationary effects of climate change to be conveniently accounted for engineering design and management. View Full-Text
Keywords: climate change; IDF curves; Bayesian analysis; Non-Stationary process climate change; IDF curves; Bayesian analysis; Non-Stationary process
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De Paola, F.; Giugni, M.; Pugliese, F.; Annis, A.; Nardi, F. GEV Parameter Estimation and Stationary vs. Non-Stationary Analysis of Extreme Rainfall in African Test Cities. Hydrology 2018, 5, 28.

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