Enhancing river discharge inversion for the midstream Jialing River (China) using the Google Earth Engine platform

ABSTRACT This study focused on the Jinxi section of the Jialing River, utilizing data from Landsat 8, Sentinel-2, and MODIS accessed via the Google Earth Engine (GEE) platform. The objective was to estimate runoff by applying three models: the improved Manning's formula (Model 1), the relationship fitting method (Model 2), and the C/M signal method (Model 3). The models were evaluated based on their accuracy in runoff inversion, the influence of hydraulic parameters, and their suitability for medium-sized rivers. The results indicated that all three models performed well in simulating runoff, with Nash–Sutcliffe Efficiency coefficients exceeding 0.90. The root mean square error (RMSE) for the improved Manning's formula, the relationship fitting method, and the C/M signal method were 50.2, 117.1, and 69.5 m³/s, respectively, corresponding to relative RMSE (RRMSE) of 4.71, 16.15, and 5.88%. It was observed that both the improved Manning's formula and the C/M signal method generally underestimated flow, while the relationship fitting method tended to overestimate it. Overall, the improved Manning's formula and the C/M signal method outperformed the relationship fitting method in terms of accuracy and applicability.