Assessing the Role of Overbank Flow in Hydrological Modeling: A Case Study of Myanmar River basin Using WRF-Hydro

Climate change has significant impacts on water resources, making the study of hydrological cycle alterations essential for understanding regional climate dynamics. Hydrological models are crucial tools for quantifying these changes and assessing their implications.The Weather Research and Forecasting Hydrological Model (WRF-Hydro), is widely used to simulate regional hydrological processes. This study evaluates the performance of an enhanced version of WRF-Hydro, incorporating an overbank flow module, in simulating runoff for the Myanmar region from 2010 to 2012. The model was driven by offline forcing datasets, specifically Integrated Multi-satellite Retrievals for GPM (IMERG) and ECMWF Reanalysis 5th Generation (ERA5) precipitation products, and the simulated results were compared with observed runoff data. The findings indicate that simulations driven by IMERG precipitation data outperformed those driven by ERA5 in terms of accuracy, likely due to IMERG’s superior representation of regional precipitation patterns. Model performance was assessed by comparing simulated runoff with measurements from seven hydrological stations, where the modified model showed consistent improvements over the default model. NSE improved from −0.27 to 0.49 (default) to 0.51 to 0.62 (modified), indicating enhanced accuracy and reliability. A more detailed analysis of the water cycle reveals that the incorporation of the overbank flow module initially reduces surface runoff, which is offset by an increase in soil moisture storage, accompanied by a slight rise in underground runoff and evapotranspiration. Toward the end of the season, surface runoff increases, which can be attributed to the higher soil storage at the start of the season. These results highlight the significant impact of the overbank flow module on hydrological processes, particularly in flood-prone areas, and suggest that the modified model enhances hydrological forecasting capabilities.