Abstract:
For analyzing the intricate watershed processes which are related to hydrology and its direct relationship to topography, weather, land use, and geology, advance mathematical models have been constructed. The hydrology of the Shahpur Dam, which is built on the Nadna Kas stream and is 46 km from Islamabad/Rawalpindi and 14 km from Fateh Jang town, is modelled in this study using the Soil and Water Assessment Tool (SWAT). Its goal is to help reservoir management plan and maintain Shahpur Dam by simulating stream flow, establishing a water balance, and estimating volume input. The Arc SWAT interface in ArcGIS software was used to define the research region and its sub-components, merge data layers, and modify the model database, while model calibration was carried out using Sequential Uncertainty Fitting version 2. The evaluation reveals satisfactory performance for both the calibration and validation periods, as well as adequate agreement between measured and simulated discharge levels, based on required statistical factors. The water balance components were predicted properly, and the Shahpur Dam inflow was successfully replicated with an R2 of 0.74. These findings show that, when correctly calibrated, the SWAT model may be used to assist water management strategies in semi-arid environments. The impact of climate change on reservoir inflow must be assessed. Climate change scenarios and Global Circulation Models include uncertainties that affect projected climate (GCMs). After the basin's hydrological modelling system (SWAT) was successfully developed, streamflow was simulated for three future periods: early-century (2021-2045), mid-century (2046-2075), and late-century (2076-2099) to investigate changes under two climatic scenarios: RCP 4.5 and RCP 8.5. Based on the findings, both the RCP 4.5 and RCP 8.5 scenarios predict an overall rise in mean annual flow in the basin. These findings show that it is critical to consider the impact of climate change on water resources when developing planning and management recommendations.
