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

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.