Original Research

Modelling non-stationary annual maximum flood heights in the lower Limpopo River basin of Mozambique

Daniel Maposa, James J. Cochran, Maseka Lesaoana
Jàmbá: Journal of Disaster Risk Studies | Vol 8, No 1 | a185 | DOI: https://doi.org/10.4102/jamba.v8i1.185 | © 2016 Daniel Maposa, James J. Cochran, Maseka Lesaoana | This work is licensed under CC Attribution 4.0
Submitted: 12 April 2015 | Published: 12 May 2016

About the author(s)

Daniel Maposa, Department of Statistics and Operations Research, University of Limpopo, South Africa
James J. Cochran, Department of Information Systems, Statistics and Management Science, University of Alabama, United States
Maseka Lesaoana, Department of Statistics and Operations Research, University of Limpopo, South Africa


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Abstract

In this article we fit a time-dependent generalised extreme value (GEV) distribution to annual maximum flood heights at three sites: Chokwe, Sicacate and Combomune in the lower Limpopo River basin of Mozambique. A GEV distribution is fitted to six annual maximum time series models at each site, namely: annual daily maximum (AM1), annual 2-day maximum (AM2), annual 5-day maximum (AM5), annual 7-day maximum (AM7), annual 10-day maximum (AM10) and annual 30-day maximum (AM30). Non-stationary time-dependent GEV models with a linear trend in location and scale parameters are considered in this study. The results show lack of sufficient evidence to indicate a linear trend in the location parameter at all three sites. On the other hand, the findings in this study reveal strong evidence of the existence of a linear trend in the scale parameter at Combomune and Sicacate, whilst the scale parameter had no significant linear trend at Chokwe. Further investigation in this study also reveals that the location parameter at Sicacate can be modelled by a nonlinear quadratic trend; however, the complexity of the overall model is not worthwhile in fit over a time-homogeneous model. This study shows the importance of extending the time-homogeneous GEV model to incorporate climate change factors such as trend in the lower Limpopo River basin, particularly in this era of global warming and a changing climate.

Keywords: nonstationary extremes; annual maxima; lower Limpopo River; generalised extreme value


Keywords

nonstationary extremes; annual maxima; lower Limpopo River; generalised extreme value.

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