###### Abstract  
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. correction models are especially beneficial for special hydrodynamic modeling where model predictions can be affected by rapidly changing environmental conditions such as tides, waves, and storms ([PERSON], 2017; [PERSON] et al., 2018). However, it is particularly challenging for real-time models that require a high temporal or spatial resolution in large river networks, as the data that need to be processed and assimilated can be substantial. Real-time correction models may not always be feasible or practical in some applications due to limitations in data availability, accessibility, or timeliness. The direct measurement of roughness coefficients requires knowledge of the physical properties of the overland environment, and can be rather challenging to handle spatially varying coefficients, rendering practical measurements unfeasible ([PERSON] et al., 2018). Given this limitation, and considering the importance of roughness calibration in engineering applications, theoretical research on roughness identification and optimization based on historical observational water-flow data has attracted the attention of many researchers ([PERSON] et al., 2018; [PERSON] et al., 2018). The calibrated roughness is then used as input into hydrodynamic models. Existing methods mainly focus on optimization algorithms to minimize the difference between hydraulic simulation and observed values. A common method that calibrates roughness is trial-and-error due to scarce hydraulic information in practice. For example, [PERSON] et al. (2018) used field observations to estimate Manning's roughness values, and applied the Hydrologic Engineering Center's River Analysis System (HEC-RAS) model for calibration. The calibration process involved a trial-and-error approach, and the findings showed that the estimation and calibration methods were efficient. However, empirical or trial-and-error methods used to calibrate roughness suffer from several drawbacks. The selection of parameters is time-consuming ([PERSON] et al., 2018) and depends on researchers' knowledge or experience, which lacks clarity ([PERSON] et al., 2018). Further, the workload associated with determining parameters for a large number of river sections is high, requiring highly skilled technical personnel. Additionally, the lack of systematic theoretical guidance undermines the accuracy of the results and the reliability of rate determination, hindering assessing their accuracy. Consequently, further research is required to address this significant issue that still poses a challenge for engineering designers.