Parameter Regionalization With Donor Catchment Clustering Improves Urban Flood Modeling in Ungauged Urban Catchments.

The lack of discharge observations and reliable drainage information is a pervasive problem in urban catchments, resulting in difficulties in parameterizing urban hydrological models. Current parameterization methods for ungauged urban catchments mostly rely on subjective experiences or simplified models, resulting in inadequate accuracy for urban flood prediction. Parameter regionalization has been widely used to tackle model parameterization issues, but has rarely been employed for urban hydrological models. How to conduct effective parameter regionalization for urban hydrological models remains to be investigated. Here we propose a parameter regionalization framework (PRF) that integrates donor catchment clustering and the optimal regression‐based methods in each cluster. The PRF is applied to an urban hydrological model, the Time Variant Gain Model in urban areas (TVGM_Urban), in 37 urban catchments in Shenzhen City, China. We first show satisfactory flood simulation performance of TVGM_Urban for all urban catchments. Subsequently, we employ the PRF for parameter regionalization of TVGM_Urban. PRF classifies 37 urban catchments into three groups, and the partial least‐squares regression is identified as optimal regression‐based method for Groups 1 and 2, while the random forest model is found to be best for Group 3. To evaluate the simulation performance of PRF, we compare it with eight single regionalization methods. The results indicate better simulation performance and lower uncertainty of PRF, and donor catchment clustering can effectively enhance the simulation performance of linear regression‐based methods. Lastly, we identify curve number, land cover area ratios, and slope as critical factors for most TVGM_Urban parameters based on PRF results. Plain Language Summary: Frequent urban flooding and waterlogging pose significant threats to human life and property. Accurate modeling urban floods is crucial for minimizing damages caused by urban flooding. However, the delayed initiation of urban observations has resulted in prevalent data scarcity in urban areas, leading to difficulties and low accuracy in estimating model parameters for ungauged urban catchments. This study proposes a parameter regionalization framework for an urban hydrological model to improve the accuracy of flood simulations in ungauged urban catchments. This framework involves classifying gauged catchments into multiple groups and identifying the optimal regression‐based methods in each group for parameter regionalization. The flood simulation performance and stability of the parameter regionalization framework is evaluated by comparing it with eight widely‐used regionalization methods in 37 urban catchments located in Shenzhen City, China. Our results show that the classification of gauged catchments can significantly improve the simulation performance of linear regression methods. Additionally, the parameter regionalization framework outperforms the other regionalization methods for both urban flood simulation in the study area and urban flood prediction in the ungauged urban catchments. This study provides a novel and efficient parameterization method for accurate flood prediction in ungauged urban catchments to reduce potential losses. Key Points: TVGM_Urban presents satisfying performance in urban flood modelingThe PRF integrating donor catchment clustering and optimal regression methods in each cluster outperforms single regionalization methodsCurve number, land cover area ratio, and slope are critical factors for runoff generation parameters of TVGM_Urban [ABSTRACT FROM AUTHOR]