Assessing the impact on Future Discharge on Gandak River Basin Using SWAT Model and Machine Learning Techniques

The assessment of future discharge impacts on the Gandak River Basin is crucial for understanding potential climate change effects and planning effective water resource management. This study employs the Soil and Water Assessment Tool (SWAT) model integrated with machine learning techniques to evaluate and predict the future discharge patterns in the basin. The Gandak River Basin, a significant tributary of the Ganges, plays a vital role in regional agriculture, hydropower, and ecosystem services, making it imperative to understand the potential changes in its hydrological dynamics. The SWAT model, a comprehensive, semi-distributed hydrological model, simulates the effects of land management practices, climate variability, and water management strategies on water, sediment, and agricultural chemical yields in large complex watersheds. SWAT’s capability to incorporate various climatic inputs, land use, soil properties, and topography enables it to simulate hydrological processes with high accuracy. However, the complexity and non-linearity of hydrological processes often necessitate the incorporation of advanced data-driven techniques to enhance prediction accuracy and robustness. In this study, machine learning algorithms, including Random Forest, Support Vector Machines, and Neural Networks, are integrated with SWAT to improve the model’s predictive performance. These algorithms are trained on historical discharge data, climate variables, and SWAT-simulated outputs to capture the non-linear relationships and complex interactions within the hydrological system. The hybrid model leverages the strengths of both physically-based and data-driven approaches, providing a more comprehensive understanding of the future discharge scenarios under various climate change projections. The research involves hbias-correcting climate projections from General Circulation Models (GCMs) to derive high-resolution climate inputs for the SWAT model. Scenarios based on Shared Socio-Economic Pathways (SSPs) are employed to simulate future climatic conditions. The SWAT model is calibrated and validated using observed discharge data from the Gandak River Basin, ensuring the reliability of the simulations. Subsequently, the machine learning models are trained on the SWAT outputs and historical data, creating an ensemble approach to predict future discharge. Results indicate significant variability in future discharge patterns under different climate scenarios. The integrated SWAT and machine learning model captures the seasonal and inter-annual variability in discharge more accurately than the standalone SWAT model. The findings suggest potential increases in peak discharge events during the monsoon season, with implications for flood risk management. Conversely, reduced discharge during the dry season could impact water availability for agriculture and domestic use, necessitating adaptive water management strategies. The study highlights the importance of combining physically-based hydrological models with machine learning techniques to enhance the prediction of hydrological responses to climate change. The integrated approach provides valuable insights for policymakers and stakeholders in the Gandak River Basin, aiding in the development of sustainable water resource management plans to mitigate the adverse impacts of future climate variability. This research underscores the need for continuous monitoring, adaptive management, and the incorporation of advanced modeling techniques to address the complexities of climate change impacts on river basins.