Enhancing the Accuracy of Water-Level Forecasting with a New Parameter-Inversion Model for Estimating Bed Roughness in Hydrodynamic Models

The new parameter-inversion model for estimating bed roughness in hydrodynamic Models is potentially usefully for the accurate flood water forecasting in reality.The accurate and efficient estimation of bed roughness using limited historical observational data is well-established. This paper presents a new parameter-inversion model for estimating bed roughness in hydrodynamic models that constrains the roughness distribution between river sections. The impact of various factors on the accuracy of inversed roughness was analyzed through a numerical experiment with the number of measurement stations, observed data amount, initial bed roughness, observational noise, and the weight of the regularization term. The results indicate that increasing the number of measurement stations and the amount of observed data significantly improves the robustness of the model, with an optimal parameter setting of 3 stations and 30 observed data. The initial roughness had little impact on the model, and the model showed good noise resistance capacity, with the error significantly reduced by controlling the smoothness level of inversed roughness using a small weight of the regularization term (i.e., 100). An experiment conducted on a real river using the calibrated model parameters shows a forecasted water level RMSE of 0.041 m, 31% less than that from the Federal Emergency Management Agency. The proposed model provides a new approach to estimating bed roughness parameters in hydrodynamic models and can help in improving the accuracy of water-level forecasting.

Automated summary

This study proposes a new parameter-inversion model for estimating bed roughness in hydrodynamic models. A numerical experiment was conducted to analyze the impact of various factors on the accuracy of inversed roughness. The results show that increasing the number of measurement stations and observed data significantly improves the model's robustness, with an optimal parameter setting of 3 stations and 30 observed data. The proposed model provides a new approach to estimating bed roughness parameters and can improve the accuracy of water-level forecasting.