Integration of physicochemical assessment of water quality with remote sensing techniques for the Dikgathong Damin Botswana.
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Water quality has become a global concern due to ever increasing population and developmental activities that are polluting water resources. Botswana’s water resources are threatened by various pollution sources such as agricultural runoff, industrial and domestic effluents.This study was carried out to assess the water quality of Dikgathong Dam in Botswana using physicochemical analysis of water quality parameters and remote sensing techniques.The study first assessed landuse patternsbefore construction (2010) and after construction (2015) to establish dominant landuse inorder to select water quality parameters related to the principal landuse in the catchment. Images for 2010 and 2015 were acquired from Landsat and were classified using the supervised classification through the Maximum Likelihood algorithm. Results showed that forest and shrubs were the dominant landuse covering 73.7 % of the total area, followed by settlements (21.1 %) and agricultural fields (2.76 %).Chl_a, COD, EC, TP, TN, TSS, NO3 and NO2 were selected for testing and analysis based on their relationship with forest, settlements and agricultural fields.For assessment of water quality, ten points were sampled in the dam from 15th January to 07th April 2016. Temperature,pH, EC, COD, TDS, TSS, turbidity, chloride, nitrates, sodium, potassium, calcium, magnesium, sulphates, phosphates, total phosphorus, alkalinity, hardness and algae were tested and analysedaccording to standard methods. Only COD, turbidity and TSSexceededthe limits set by Environmental Protection Agency (EPA)surface water standards of 2001, making Dikgathong Dam slightly polluted.One way ANOVA showed significant variations (p<0.05) between water quality values in all sampling points only for NO3, SO4, pH, algae and Na. Five different groups of sites were identified from ten sites using cluster analysis.The principal component analysis identified ten parameters (COD, EC, turbidity, TSS, Ca, Mg, NO3, SO4, total hardness and alkalinity)based on similarities of water quality characteristics. The Water Utilities Corporation, which is responsible for the dam,can therefore monitor water quality at five points focusing mainly on ten parametersfound to be principal. This study also investigated the likelihood of integrating remote sensing and in-situ measurements to assess the water quality status of the dam. Quasi analytical algorithms andMODIS datawere used to quantify Chl_aand TSS concentrations in the dam. Values forChl_awerebetween 1.74and 24.4 mg/m3, whileTSS ranged from 2.34 mg/l to 59.2 mg/l. Based on chlorophyll concentrations thedam can be classified as both oligotrophic and mesotrophic as per the EPA 2001 standard.The QAA and MODIS can therefore be deployed as a mechanism for near real time monitoring of water quality in Botswana reservoirs.Spearman’s correlation was used to test whether satellite retrieved water quality parameters relate to in-situ measurements.Strong positive significant correlation was observed between chl_a and turbidity (r=0.794 and 0.830), TSS (r = 0.819 and 0.770), SO4 COD (r=0.781 and 0.769).), SO4 (r= 0.851 and 0.646) and alkalinity (r= 0.847). Moderate positive and non-significant relationship is observed for temp (r= 0.055), pH (r= 0.587), EC (r= 0.409), TDS (r=0.348), Na (r= 0.406) and Cl (r= 0.394).Strong positive and significant correlation was observed between remote sensing retrieved TSSand in-situ measured TSS (r= 0.733) andturbidity (r= 0.867).This study concludes that there is strong positive correlation between parameters retrieved through remote sensing and in-situ measurements and therefore can be used in monitoring and assessment of the water quality in the lake at any point in time.
Additional Citation InformationMosimanegape, Kagiso. (2016). Integration of physicochemical assessment of water quality with remote sensing techniques for the Dikgathong Damin Botswana. (Unpublished Masters Thesis). University of Zimbabwe, Harare.
geographic information systems
Quasi Analytical Algorithms