In this study, an experiment was performed to assess the trip difficulty for urban residents of different age groups walking in various depths of water, and the data were corroborated with the real urban rainstorm waterlogging scenarios in downtown (Daoli district) Ha-Erbin (China). Mathematical models of urban rainstorm waterlogging were constructed using scenario simulation methods, aided by the GIS spatial analysis technology and hydrodynamic analysis of the waterway systems in the study area. Then these models were used to evaluate the impact of waterlogging on the safety of residents walking in the affected area. Results are summarized as: (1) for an urban rainstorm waterlogging scenario reoccurring once every 10 years, three grid regions would have waterlogging above 0.5 m moving at a velocity of 1.5 m/s. Under this scenario, waterlogging would accumulate on traffic roads only in small areas, affecting the safety and mobility of residents walking in the neighborhood; (2) for an urban rainstorm waterlogging scenario reoccurring once every 20 years, 13 grids experienced the same waterlogging situation affecting a larger area of the city; (3) for an urban rainstorm waterlogging scenario reoccurring once every 50 years, 86 grid regions were affected (waterlogging above 0.5 m moving at 1.5 m/s), and those areas would become impassable for residents.

Understanding storm surge inundation risk is essential for developing countermeasures and adaptation strategies for tackling climate change. Consistent assessment of storm surge inundation risk that links probability of hazard occurrence to distribution of economic consequence are scarce due to the lack of historical data and uncertainty of climate change, especially at local scales. This paper proposes a simulation-based method to construct exceedance probability (EP) curves for representing storm surge risk and identifying the economic impact of climate change in the coastal areas of Ise Bay, Japan. The region-specific exceedance probability curves show that risk could be different among different districts. The industry-specific exceedance probability curves show that manufacturing, transport and postal activities, electricity, gas, heat supply and water, and wholesale and retail trade are the most affected sectors in terms of property damage. Services also need to be of concern in terms of business interruption loss. Exceedance probability curves provide complete risk information and our simulation-based approach can contribute to a better understanding of storm surge risk, improve the quantitative assessment of the climate change-driven impacts on coastal areas, and identify vulnerable regions and industrial sectors in detail.

A key issue in assessing the spatial distribution of flood risk is considering risk information derived from multiple flood sources (river flooding, drainage inundation, etc.) that may affect the risk assessment area. This study proposes a method for assessing spatial flood risk that includes flooding and inundation in small-basin areas through multivariate design rainfall. The concept of critical rainfall duration, determined by the time of concentration of flooding, is used to represent the characteristics of flooding from different sources. A copula method is adopted to capture the correlation of rainfall amounts in different critical rainfall durations to reflect the correlation of potential flooding from multiple flood sources. Rainfalls for different return periods are designed based on the copula multivariate analysis. Using the design rainfalls as input, flood risk is assessed following the rainfall–runoff–inundation–loss estimation procedure. A case study of the Otsu River Basin, Osaka Prefecture, Japan, was conducted to demonstrate the feasibility and advantages of this method. Compared to conventional rainfall design, this method considers the response characteristics of multiple flood sources, and solves the problem of flood risk assessment from multiple flood sources. It can be applied to generate a precise flood risk assessment to support integrated flood risk management.