| dc.description.abstract | Many catchments in Southern Africa, including the Muzvezve Subcatchment in the central part of Zimbabwe have been negatively affected by the deterioration of water quality. This study aimed at assessing the impacts of land use and land cover changes on the water quality of Muzvezve River and Claw dam in Zimbabwe. Five sampling campaigns across the wet and dry seasons were conducted from Mid-December 2015 to end of February 2016 at six systematically selected sampling points. Eight physico-chemical parameters including turbidity, electrical conductivity (EC) Total Dissolved Solids (TDS), pH, Biological Oxygen Demand (BOD), Chemical Oxygen Demand (COD), phosphorus and nitrogen, were analysed using Standard Methods. The biological parameters measured were faecal coliforms and total coliforms. Heavy metals analysed included; Copper, Zinc, Cadmium and Lead. The river water quality was compared to EMA ambient water quality guidelines. Land cover was characterized using the Normalized Difference Vegetation Index (NDVI) and was correlated with selected ground measured parameters of turbidity and TDS. A rapid water quality assessment for Claw Dam was done using remotely sensed and ground measured data. Analysis was conducted within a GIS environment and statistical software. Results showed that, for the measured parameters along the Muzvezve River, most were below the EMA standard guidelines, indicating that the river water quality is fairly good. The parameters which were below the threshold levels include EC, TDS, coliforms, BOD, COD, turbidity, phosphates and coliforms. However, based on WHO standards, the Muzvezve River water is not safe for drinking, without some form of appropriate effective treatment. Agriculture and settlement had no significant effect on water quality (p >0.05). Mining had significant effect (p< 0.05) as seen by high zinc and lead especially in the dry season. Results of the NDVI correlation with measured water quality parameters of turbidity and TDS showed that NDVI had a strong and significant positive relationship (p <0.05) with turbidity (r=0.998) and TDS (r ranging from 0.961 to 0.980) in the dry season. From these results, it was concluded that land cover could be used to predict turbidity during the dry season and TDS during the wet season. Turbidity showed positive correlation with the Blue, Red and NIR bands of Landsat 8 imagery. Results, however, showed that Secchi depth and Chlorophyll a correlations were weak. It was concluded that more work needs to be done on testing whether remotely sensed data can be used to estimate water quality in environments similar to the study area. | en_US |
