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

Francesco De Paola,Fernando Nardi,Francesco Pugliese,Antonio Annis,Maurizio Giugni

doi:10.3390/hydrology5020028

Francesco De Paola, Fernando Nardi + Show 3 more

Open Access

https://doi.org/10.3390/hydrology5020028

Copy DOI

Abstract

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.

Highlights

Nowadays, the increased flood risk is recognized as one of the most important threats, from both the actual and, more importantly, the climate change scenario, with frequent severe flooding at the global scale causing significant loss of property and life
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
The results showed the dependence of Non-Stationary effects of climate change to be conveniently accounted for engineering design and management

Summary

Introduction

The increased flood risk is recognized as one of the most important threats, from both the actual and, more importantly, the climate change scenario, with frequent severe flooding at the global scale causing significant loss of property and life. Worldwide flooding events caused by extreme rainfall events are causing the degradation of water quality, damages and potential loss of life [1,2]. These events prompted public debates on the apparent causes of the increased frequency of extreme events. Scientists are querying the changing statistics of rainfall intensity, especially considering the climate model predictions of increasing frequency and intensity of heavy rainfall in the high latitudes under enhanced greenhouse conditions.

Methods

Results

Conclusion