Optimal Domain Scale for Stochastic Urban Flood Damage Assessment Considering Triple Spatial Uncertainties

As a key factor in the flood damage assessment process, the damage ratio is defined as the ratio of the potential damage value to the total risk assets at a certain inundation depth. Spatial variability of flood-bearing elements caused spatial heterogeneity, local correlation and neighborhood non-stationarity to the damage ratio. Thus, it is challenging to scientifically and rationally measure the damage ratio. In this study, a modeling framework of stochastic flood damage assessment functions was established to measure triple uncertainties. First, based on the maximum likelihood estimation method, migration data of the flood damage ratio were constructed as the beta distribution by heterogeneous multi-attribute analogy. Then, the Markov chain Monte Carlo method, combined with a variant sliding window method, is used for sampling to generate spatially distributed samples. In addition, three risk factors were integrated grid by grid to assess flood damage. By taking the Jinshui District of Zhengzhou City in China as an example, economic losses during the 1%, 2%, 5% and 10% annual exceedance probability (AEP) floods were assessed, respectively. The results show that the economic losses showed a significant increasing trend with AEP. The optimal domain scales for industrial, commercial, residential and public service land uses were 19, 14, 25 and 32 m, respectively, and also varied with different AEP floods. The model was effectively verified by simulating and comparing historical data of the "7 center dot 20" flood event in Zhengzhou, China. This study aims to provide feasible ideas for uncertainty research of damage assessment methods.