Comparative analysis of hydrological modeling using ground-observed and global reanalysis precipitation datasets in Secchia River Basin

The accurate prediction and management of hydrological processes are critical for effective water resource management and flood mitigation. In my thesis I will present a comparative analysis of hydrological modeling using two distinct precipitation datasets: ground-observed data and Global reanalysis data, specifically, ERA5-Land reanalysis data, within the Secchia River Basin in Italy. The study employs the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to perform continuous simulations based on the Soil Moisture Accounting (SMA) approach. This method is utilized to simulate streamflow and assess the hydrological response of the basin using the two precipitation datasets. Evaluation metrics such as the Nash-Sutcliffe Efficiency (NSE) and Root Mean Square Error (RMSE) are used to quantify model performance. Ground-observed data generally provide higher accuracy in streamflow simulations, whereas ERA5-Land reanalysis data demonstrate better spatial and temporal coverage, crucial for ungauged or sparsely gauged basins. This comparative analysis underscores the potential of integrating reanalysis datasets into hydrological models to improve water resource management and contribute valuable data for ungauged basins. The findings of this study enhance our understanding of the utility of reanalysis data in hydrological modeling and provide practical insights for their application in water resource management and future hydrological research.