| dc.description.abstract | Groundwater is the world's largest freshwater resource, and it is critical for irrigated agriculture, residential use, and commercial use. As a result, groundwater levels must be stabilized in order for long-term agricultural production and, as a result, global food security to be assured.
Quantifying groundwater recharge is thus a requirement for effective and long-term management of groundwater resources. As aquifers become depleted, recharge estimates are becoming increasingly important in determining appropriate groundwater withdrawal levels. This study aimed at estimating the spatial distribution of ground water recharge in Isingiro District. The Soil- Water Budget Model was used to estimate the annual groundwater recharge in Isingiro district.
The model parameters which include Land use Land Cover map, Digital Elevation Model, Soil data, Wind speed and meteorological data for the study area for the period (2017 and 2018) were converted to ASCII files to be used by the model. The Soil-Water Budget Model was calibrated by comparing the simulated groundwater recharge with the calculated one by using the water balance equation model. The results obtained indicate a very strong positive relationship between calculated and simulated groundwater recharge with correlation coefficients of 0.89 and 0.91, while the root-mean-square errors (RMSEs) were 26.90 and 25.99 mm for 2017 and 2018 respectively. In addition, the results show that the spatially distributed annual recharge values for the year 2017 range from 0 to 313.513mm/year which equates to 0% - 25% of the annual precipitation, whereas those of 2018 range from 0 to 286.325 mm/year which equates to 0% -
22.91% of the annual precipitation distributed over the study area of 2650.054 km2 . The
simulation results revealed that the Soil-Water Budget model can effectively simulate the components of the hydrological water budget.
From the results of this study, it is recommended that more research should be carried out in the study area using various models to understand which models provide more satisfactory results | en_US |
