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Comparison of stochastic streamflow generators and the use thereof within the water resources yield model and MIKE Hydro Basin

by Lourens Francois Maass

Institution: Stellenbosch University
Year: 2017
Keywords: Stream measurements; UCTD; Hydrology; Stochastic systems
Posted: 02/01/2018
Record ID: 2218020
Full text PDF: http://hdl.handle.net/10019.1/102670


Abstract

ENGLISH ABSTRACT: The development and improvement of hydrological simulation software models is a continuousprocess. These software models, together with hydrological stochastic data generators enhancesthe ability to analyse a hydrological system and determine its reliability. Many different stochasticgenerators exist, but the two that are of interest in this research are monthly streamflowgenerators, namely STOMSA and SAMS.STOMSA is a stochastic streamflow generator developed for the Department of Water andSanitation of South Africa, while SAMS is a stochastic streamflow generator developed in theUnited States of America. Both these generators are able to generate satisfying stochasticstreamflow data, with the only concern being a high variance between the average annualstreamflows of the different stochastic sequences generated by SAMS.Over time stochastic data generators became part of the hydrological analysis process. Thisled to the incorporation of stochastic data generators into hydrological simulation models. Asis the case for stochastic streamflow generators many different hydrological simulation modelsare available, but the two that are of interest in this research are the WRYM and MIKE HydroBasin.The WRYM is the hydrological simulation software model used by the Department of Water andSanitation of South Africa. The model is used to do hydrological yield analyses for hydrologicalsystems in South Africa. MIKE Hydro Basin is a hydrological simulation software modeldeveloped by the Danish Hydraulic Institute (DHI).The WRYM is able to do automated historical yield analyses and can calculate the historicalfirm yield of a hydrological system accurately. The WRYM also uses STOMSA as a built-instochastic streamflow generator and uses the data generated by STOMSA to do a reliability ofsupply analysis for a hydrological system. MIKE Hydro Basin, however, is not able to generatestochastic streamflow data or do system yield analyses. It is used to simulate hydrologicalsystems.When the practicality of the two models is considered, it is argued that MIKE Hydro Basin ismore user-friendly than the WRYM. The WRYM is very technical and difficult to use withoutproper training or assistance. It should also be noted that the WRYM, , makes possiblecalculation errors in the reliability of supply analysis and results should be used with caution. AFRIKAANSE OPSOMMING: Die ontwikkeling en verbetering van hidrologiese simulasie sagteware modelle is n deurlopendeproses. Hierdie sagteware modelle tesame met hidrologiese stogastiese data generators dra by totdie vermoe om n hidrologiese stelsel te ontleed en die betroubaarheid vir die stelsel om waterte lewer, te bepaal. Daar bestaan n wye reeks verskillende stogastiese generators, maar dietwee wat van belang is in hierdie navorsing is die maandelikse stroomvloei generators, naamlikSTOMSA en SAMS.STOMSA is n stogastiese stroomvloei generator wat ontwikkel is vir die Departement vanWateren Sanitasie vanAdvisors/Committee Members: Du Plessis, J. A., Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering..

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