Thesis (M. Sc. (Statistics)) -- University of Limpopo, 2021 / The effects of ozone depletion on climate change has, in recent years, become a
reality, impacting on changes in rainfall patterns and severity of extreme floods
or extreme droughts. The majority of people across the entire African continent
live in semi-arid and drought-prone areas. Extreme droughts are prevalent
in Somalia and eastern Africa, while life-threatening floods are common
in Mozambique and some parts of other SADC (Southern African Development
Community) countries. Research has cautioned that climate change in South
Africa might lead to increased temperatures and reduced amounts of rainfall,
thereby altering their timing and putting more pressure on the country’s scarce
water resources, with implications for agriculture, employment and food security.
The average annual rainfall for South Africa is about 464mm, falling far
below the average annual global rainfall of 860mm.
The Limpopo Province, which is one of the nine provinces in South Africa, and
of interest to this study, is predominantly agrarian, basically relying on availability
of water, with rainfall being the major source for water supply. It is,
therefore, pertinent that the rainfall pattern in the province be monitored effectively
to ascertain the rainy period for farming activities and other uses.
Modelling and forecasting rainfall have been studied for a long time worldwide.
However, from time to time, researchers are always looking for new models
that can predict rainfall more accurately in the midst of climate change and
capture the underlying dynamics such as seasonality and the trend, attributed
to rainfall.
This study employed Exponetial Smoothing (ETS) State Space and Seasonal
Autoregressive Integrated Moving Average (SARIMA) models and compared
their forecasting ability using root mean square error (RMSE). Both models
were used to capture the sporadic behaviour of rainfall. These two models have
been widely applied to climatic data by many scholars and adjudged to perform
creditably well. In an attempt to find a suitable prediction model for monthly
rainfall patterns in Limpopo Province, data ranging from January 1900 to December
2015, for seven weather stations: Macuville Agriculture, Mara Agriculture,
Marnits, Groendraal, Letaba, Pietersburg Hospital and Nebo, were
analysed. The results showed that the two models were adequate in predicting
rainfall patterns for the different stations in the Limpopo Province. / National Research Foundation (NRF)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:ul/oai:ulspace.ul.ac.za:10386/3755 |
| Date | January 2021 |
| Creators | Molautsi, Selokela Victoria |
| Contributors | Lesaoana, M., Moeletsi, M. E. |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | xiii, 124 leaves |
| Relation |
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