Hydrological Simulation using Process Based and Empirical Models for Flood Peak Estimation
Hydrological modelling plays a pivotal role in understanding and predicting flood events, particularly in regions susceptible to inundation. In this study, we explore the parameterization of hydrological models for the Asan and Song river basins within the Doon Valley. By employing three different models—SWAT, VIC, and HEC-HMS—we aim to generate flood peak estimates at predetermined locations and assess the impact of land use and land cover (LULC) changes on hydrological processes. Remote sensing data from Landsat and Google Earth imagery are utilized for land cover mapping, facilitating the observation of LULC change scenarios between 1995, 2005, and 2014.
Specific objectives include hydrological modelling for peak flow hydrograph generation, comparison and validation of simulated runoff using the three hydrological models, and analyzing the influence of meteorological, discharge, and sediment data on model performance. The VIC model demonstrates good performance, with simulated values closely aligning with observed data for the 2014 LULC map. Calibration of the SWAT model for the period 2006-2010 highlights the significance of the curve number parameter in determining total discharge.
Furthermore, our study underscores the importance of land use and vegetative cover in shaping watershed runoff and stream flow discharge patterns over time, particularly during peak flows. Rapid transitions in land cover due to increased human interventions have adversely impacted watershed processes and the hydrological cycle, emphasizing the need for sustainable land management practices..
Introduction
The occurrence of floods poses significant challenges to communities and ecosystems, necessitating effective flood risk management strategies. Hydrological modelling serves as a valuable tool for predicting flood events and understanding the underlying processes governing hydrological dynamics. In this study, we focus on the Asan and Song river basins within the Doon Valley, where floods are a recurrent phenomenon.
Methodology
We employ three hydrological models—SWAT, VIC, and HEC-HMS—to simulate flood peak generation and assess the impact of LULC changes on hydrological processes. Remote sensing data from Landsat and Google Earth imagery are utilized for land cover mapping, enabling the observation of LULC change scenarios over a span of two decades. Various input parameters, including meteorological, discharge, and sediment data, are processed to facilitate model calibration and validation. Results and Discussion
Our findings highlight the importance of accurately parameterizing hydrological models for flood peak estimation. The VIC model demonstrates robust performance, capturing the dynamics of peak flows with high accuracy. Calibration of the SWAT model emphasizes the influence of the curve number parameter on total discharge, underscoring the significance of land cover characteristics in modulating hydrological processes.
Furthermore, our study elucidates the impact of LULC changes on watershed dynamics, with human interventions leading to rapid transitions in land cover and consequent alterations in hydrological patterns. While land cover changes exert a pronounced effect during low flows, their influence diminishes during high-flow events, emphasizing the complex interplay between land use, vegetation cover, and hydrological processes.
Conclusion
In conclusion, our study sheds light on the intricate relationship between land cover dynamics and hydrological processes, particularly in the context of flood peak estimation. By integrating process-based and empirical hydrological models, we gain valuable insights into watershed behaviour and the influence of human activities on hydrological dynamics. Moving forward, sustainable land management practices are essential for mitigating the adverse impacts of land cover changes on watershed processes and ensuring the resilience of ecosystems to flood events.
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