Search this journal:     Advanced search
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 (2016), 9 pages. doi: 10.4102/jamba.v8i1.185

Submitted: 12 April 2015
Published:  12 May 2016

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


Full Text:  |  HTML  |  EPUB  |  XML  |  PDF (2MB)

Author affiliations

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

Keywords

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

Metrics

Total abstract views: 1122
Total article views: 2389

Cited-By

No related citations found

Comments on this article

Before posting your comment, please read our policy.
Post a Comment (Login required)


ISSN: 1996-1421 (print) | ISSN: 2072-845X (online)

Connect on: Facebook, Twitter, Google+, LinkedIn and YouTube

Subscribe to our newsletter

All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license, unless otherwise stated.

Website design & content: ©2017 AOSIS (Pty) Ltd. All Rights Reserved. No Unauthorised Duplication Allowed.

AOSIS Publishing | Empowering Africa through access to knowledge
Postnet Suite #110, Private Bag X19, Durbanville, South Africa, 7551
Tel: 086 1000 381
Tel: +27 21 975 2602
Fax: 086 5004 974

publishing(AT)aosis.co.za replace (AT) with @

Please read the privacy statement.