Van Rensburg, L. D.Woyessa, Yali E.Mengistu, Achamyeleh Girma2020-02-192020-02-192019-10http://hdl.handle.net/11660/10410The hydrologic processes and their behaviours in arid and semi-arid areas are poorly understood and differ highly from humid/sub-humid areas. Hydrologic models play critical roles in understanding such complex processes. However, the application of hydrologic models is limited due to the unavailability or scarcity of data for model calibration, uncertainty and validation procedures. Therefore, this study was aimed at evaluating the application of the Soil and Water Assessment Tool (SWAT model) in simulating the components of water balance in an arid and semi-arid catchment. Moreover, the spatio-temporal variabilities of the different components of the water balance were quantified and analysed. The intensity of water stress was also evaluated in the catchment. All the components of the catchment water balance in this study were estimated using the SWAT model. The regionalization with physical similarity approach was adopted here for the calibration, uncertainty and validation processes due to the unavailability of streamflow data in the study catchment. Based on the sensitivity analysis, the top sixteen parameters were calibrated, from which the first three (the base flow alpha factor, curve number II and initial depth of water in the shallow aquifer) were found to be the most sensitive parameters, at p < 0.01. The result for model uncertainty also indicated acceptable values of both the R-factor (0.8) and P-factor (0.7), which is the average of the calibration and validation periods. Regarding the model performance evaluation, four statistical indicators were used, namely the Nash-Sutcliffe Coefficient (NS), the coefficient of determination (R2), the percent bias (PBIAS), and the ratio of the root mean squared error to the standard deviation of measured data (RSR). The results showed that all the model performance indicators were in fairly acceptable ranges; taking the average of calibration and validation periods, NS was 0.76; R2 was 0.78; and RSR was 0.49. The PBIAS indicated a slight over-estimation during calibration (by 11.8%) and under-estimation during validation periods (by 8.1%). The model performance was also verified by the comparison of the in situ measured and simulated soil water content outside the SWAT-CUP programmes, and showed an average R2 of 0.71 for the verification of four hydrologic response units (HRUs). The analyses of the model output indicated that all the components of the soil water balance exhibited a higher spatial and temporal variation in the study catchment. Hence, the long-term precipitation showed no trend on an annual time scale; however, it showed a decreasing trend (with 0.01 mm per month) on a monthly time scale. Similarly, the monthly total runoff showed a decrease of 0.002 mm per month. Evapotranspiration and revap water showed a decreasing trend in both monthly and annual time scales. Hence, evapotranspiration decreased by 0.01 and 1.25 mm, whereas revap decreased by 0.07 and 1.1 mm on monthly and annual time scales, respectively. The analyses also indicated that no significant trend was found with regard to soil water content, percolation and recharge components on both time scales. Generally, it was indicated that the variations of weather parameters were responsible for the spatio-temporal variabilities. However, topography, land use/land cover (LULC) and soil type played a role mainly for the spatial variations of water balance in the catchment. The study also showed that the catchment under study (Soutloop Catchment) is one of the driest catchments in South Africa, with an aridity index of 0.07–0.15 (classified as arid catchment). Due to this, the area is water stress almost throughout the year. The intensity of water stress was also evaluated using available hydro-meteorological and environmental indicators, such as the standardized precipitation index (SPI), soil water anomaly (SWA), evaporative stress index (ESI), and normalized difference vegetation index (NDVI). The analyses of water stress generally revealed that the use of a satellite-based NDVI and model output-based SWA and ESI were important alternatives and/or additional indicators, other than the usual and widely applied SPI method. The study was successful in conceptualizing the major components of the hydrometeorological processes with a focus on the natural hydrological processes (excluding the anthropogenic impacts). However, it is understandable that the human-induced components like the LULC change and groundwater abstraction, which are related to the large-scale mining activity, could have a significant impact on the soil, water resources and the environment as a whole. Therefore, further research is recommended to investigate the impacts of human activity on the soil, water resources and environmental influences of the area.enThesis (Ph.D. (Soil, Crop and Climate Sciences))--University of the Free State, 2019Arid catchmentsCalibrationHydrologic modelsRegionalizationSpatial variationSWAT modelTemporal variationTrend analysisTime series analysisWater balanceWater deficitThesisUniversity of the Free State