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

Author:

Hu Chen12ORCID,Xia Jun1234,She Dunxian123ORCID,Jing Zhaoxia5,Hong Si12,Song Zhihong6ORCID,Wang Gangsheng123

Affiliation:

1. State Key Laboratory of Water Resources and Hydropower Engineering Science Wuhan University Wuhan PR China

2. Hubei Key Laboratory of Water System Science for Sponge City Construction Wuhan University Wuhan PR China

3. Institute for Water‐Carbon Cycles and Carbon Neutrality Wuhan University Wuhan PR China

4. Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences Beijing PR China

5. Changjiang Water Resources Protection Institute Wuhan PR China

6. Changjiang River Scientific Research Institute Wuhan PR China

Abstract

AbstractThe 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.

Publisher

American Geophysical Union (AGU)

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