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.
Highlights
There is a general notion that the occurrence of extreme events has changed over these recent years and is anticipated to continue to change in terms of intensity, frequency and complexity of the risks
The order of the models is maintained for the annual maximum series (AMS) moving sums, for example, for AM2 time series data model M1 still refers to a time-dependent generalised extreme value (GEV) model with a linear trend in both the location and scale parameters as in AM1
The study considered the use of statistics of extremes in a changing climate for the lower Limpopo River basin (LLRB) of Mozambique
Summary
There is a general notion that the occurrence of extreme events has changed over these recent years and is anticipated to continue to change in terms of intensity, frequency and complexity of the risks. The anticipated climate-induced changes are of major concern as they have the potential to render our estimates biased and/or useless, those estimates based on traditional approaches that do not take climate changes into consideration. These probable climate changes can cause negative societal impacts and disruptions, for instance, destruction of schools, children dropping out of schools leading to early marriages, for girls, and creating a vicious poverty circle in the community (Katz 2010; Mudavanhu 2014). According to Katz (2010), previous studies in extreme value theory have shown that the frequency of all forms of extreme events, whether in the form of a single value or a sequence of annual maxima, is more sensitive to variations in the scale parameter (or, in particular, the standard deviation) than to the location parameter (or mean) of a distribution. Cooley (2009) wrote a commentary on the potential application of statistics of extremes to climate change based on the previous work of Wigley
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
