# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
[16] further explored parameter sensitivity in the Storm Water Management Model, demonstrating that hydrological and water quality parameters exhibit significant sensitivity in urban runoff modeling. While local sensitivity analysis is computationally efficient and ideal for preliminary parameter screening, its primary limitation is its inability to capture interactions among parameters, making it unsuitable for comprehensive flood model optimization. However, it remains valuable for identifying the most influential single parameter in models where dominant factors drive system behavior. Unlike local sensitivity analysis, global sensitivity analysis provides a more comprehensive assessment by quantifying both individual and interactive parameter effects. This makes it well suited for evaluating complex urban flood models where multiple vari ables contribute to system behavior. The most commonly used global sensitivity analysis methods include the Sobol method, Mutual Information method, Generalized Likelihood Uncertainty Estimation method, and Enhanced Fourier Amplitude Sensitivity Test method. [PERSON] et al. [17] applied the Sobol method to the Storm Water Management Model simulations, successfully identifying key parameters that drive urban hydrological processes. [PERSON] et al. [18] refined the Sobol variance decomposition method, achieving more consistent sensitivity rankings and improving parameter reliability. [PERSON] et al. [19] utilized the Mutual Information method to analyze sensitivity in the Storm Water Management Model, revealing that Manning's coefficient of permeable areas and minimum infiltration rate were the most significant factors influencing flood flow and runoff coefficients. More advanced global sensitivity analysis applications have incorporated hybrid methodologies. [PERSON] et al. [20] combined the Generalized Likelihood Uncertainty Estimation method and a modified Morris method to examine urban flood model sensitivity in Jinan, highlighting channel roughness and attenuation coefficient as the most sensitive parameters. [PERSON] et al. [9] investigated the effects of calibration event numbers on model performance, integrating BPROME and the Generalized Likelihood Uncertainty Estimation method to determine that 3-5 calibration events optimized accuracy while minimizing data collection costs. Furthermore, studies by [PERSON] et al. [21] and [PERSON] et al. [22] explored variance-based sensitivity analysis in synthetic urban drainage models and FLOWS-Tank models, respectively, emphasizing the importance of water level fluctuations in urban flood risk assessments. [PERSON] et al. [23] extended global sensitivity analysis applications to dynamic flood risk analysis, highlighting the role of parameter interactions under future uncertainty scenarios. While global sensitivity analysis offers a more detailed analysis compared to local sensitivity analysis, it is computationally demanding, particularly for high-dimensional parameter spaces. The Sobol method, despite its accuracy, requires an extensive number of model simulations, making it computationally expensive for real-time flood risk assessments. Recent research has shown that no single sensitivity analysis method is universally optimal for urban flood modeling. The Morris method provides a computationally efficient screening tool, identifying non-influential parameters while maintaining low computational costs. The Sobol method, in contrast, quantifies variance contributions and detects higher-order interactions, making it ideal for comprehensive assessments. Studies have demonstrated the advantages of combining both approaches. [PERSON] et al. [23] showed that Morris-Sobol integration enhances flood model efficiency, allowing for rapid initial screening, followed by detailed sensitivity quantification. [PERSON] et al. [24] further validated this approach in urban flood event analysis, highlighting its superior performance in evaluating complex hydrological interactions. [PERSON] et al. [16] and [PERSON] et al.