With increasing demands on limited water resources and unavailability of suitable
dam sites, it is essential that available storage works be carefully planned and
efficiently operated to meet the present and future water needs.This research
report presents an attempt to: i) use Artificial Neural Networks (ANN) for the
simulation of the Crocodile water resource system located in the Mpumalanga
province of South Africa and ii) use the model to assess to what extent Kwena
dam, the only major dam in the system could meet the required 0.9m3/s cross
border flow to Mozambique. The modelling was confined to the low flow periods
when the Kwena dam releases are significant.
The form of ANN model developed in this study is the standard error
backpropagation run on a daily time scale. It is comprised of 32 inputs being four
irrigation abstractions at Montrose, Tenbosch, Riverside and Karino; current and
average daily rainfall totals for the previous 4 days at the respective rainfall
stations; average daily temperature at Karino and Nelspruit; daily releases from
Kwena dam; daily streamflow from the tributaries of Kaap, Elands and Sand
rivers and the previous day’s flow at Tenbosch. The single output was the current
day’s flow at Tenbosch. To investigate the extent to which the 0.9m3/s flow
requirement into Mozambique could be met, data from a representative dry year
and four release scenarios were used. The scenarios assumed that Kwena dam was
100%, 75%, 50% and 25% full at the beginning of the year. It was found as
expected that increasing Kwena releases improved the cross border flows but the
improvement in providing the 0.9m3/s cross border flow was minimal. For the
scenario when the dam is initially full, the requirement was met with an
improvement of 11% over the observed flows.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/5956 |
| Date | 21 January 2009 |
| Creators | Sebusang, Nako Maiswe |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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