EAI International Conference for Research, Innovation and Development for Africa

Research Article

A remote sensing and GIS based application for monitoring water levels at Kariba dam.

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  • @INPROCEEDINGS{10.4108/eai.20-6-2017.2270774,
        author={Adonia Shumba and Sydney Togarepi and Webster Gumindoga and Tarirai Masarira and Edward Chikuni},
        title={A remote sensing and GIS based application for monitoring water levels at Kariba dam.},
        proceedings={EAI International Conference for Research, Innovation and Development for Africa},
        publisher={EAI},
        proceedings_a={ACRID},
        year={2018},
        month={4},
        keywords={water level monitoring kariba dam remote sensing and gis tool},
        doi={10.4108/eai.20-6-2017.2270774}
    }
    
  • Adonia Shumba
    Sydney Togarepi
    Webster Gumindoga
    Tarirai Masarira
    Edward Chikuni
    Year: 2018
    A remote sensing and GIS based application for monitoring water levels at Kariba dam.
    ACRID
    EAI
    DOI: 10.4108/eai.20-6-2017.2270774
Adonia Shumba1, Sydney Togarepi1,*, Webster Gumindoga1, Tarirai Masarira1, Edward Chikuni1
  • 1: University of Zimbabwe
*Contact email: stogarepi@gmail.com

Abstract

The Global warming effect and the Elnino have had a negative impact on water resources in Zimbabwe due to erratic rainfall patterns and escalating temperatures. The overall effect in the energy production sector is the reduction in power generation on hydropower stations as a result of low water levels. Water level monitoring at hydro power generation reservoirs is thus of utmost importance. Currently employed in situ based water level monitoring techniques are less efficient, time consuming and do not provide the synoptic coverage of the lake and the surrounding basin at large. We present the relationship between factors (natural and anthropogenic) affecting water levels and the measured water levels. The factors were derived from remote sensed data. To ascertain the most significant factors contributing to water level and electricity generation fluctuations, correlation and regression were used. The analysis revealed that rainfall, evapo-transpiration, forest and shrubs area and grassland area were the most significant contributing factors to the variation in water levels and electricity production. The regression models generated were used to design an application using visual basic.net. The application automated the processing of CHIRPS rainfall data and MODIS classification in ILWIS. The automated extraction of rainfall data and land use/cover classification water level monitoring was achieved.