Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Prof. Jie Yin
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Sea level rise (SLR) and subsidence are expected to increase the risk of flooding and reliance on flood defenses for cities built on deltas. Here, we combine reliability analysis with hydrodynamic modeling to quantify the effect of projected relative SLR on dike failures and flood hazards for Shanghai, one of the most exposed delta cities. We find that flood inundation is likely to occur in low‐lying and poorly protected periurban/rural areas of the city even under the present‐day sea level. However, without adaptation measures, the risk increases by a factor of 3–160 across the densely populated floodplain under projected SLR to 2100. Impacts of frequent flood events are predicted to be more affected by SLR than those with longer return periods. Our results imply that including reliability‐based dike failures in flood simulations enables more credible flood risk assessment for global delta cities where conventional methods have assumed either overtopping only or complete failure.
Intensification of short‐duration rainfall extremes contributes to increased urban flood risk. Yet, it remains unclear how upper‐tail rainfall statistics could change with regional warming. Here, we characterize the non‐stationarity of rainfall extremes over durations of 1–24 hr for the rapidly developing coastal megalopolis of the Greater Bay Area, China. Using high‐resolution, multi‐source, merged and gridded data we observe greater increases in rainfall intensities over the north‐central part of the region compared with the southern coastal region. Our results show, for the first time, that urbanization nonlinearly increases rainfall intensities at different durations and return periods. Over short durations (≤3‐hr) and short return periods (2‐yr), urban areas have the greatest scaling rates (≥19.9%/°C). However, over longer durations (≥9‐hr) rural areas have greater scaling rates, with a lower degree of dependency on both durations and return periods.
20 July 2021 in Journal of Geophysical Research: Oceans
This paper describes an integrated climatological‐hydrodynamic method that couples probabilistic hurricane model, storm surge model, inundation model, coastal protection data, and sea level rise projections to estimate tropical cyclone‐induced coastal flood inundation hazard in a coastal megacity‐Shanghai, China. We identify three “worst‐case” scenarios (extracted from over 5,000 synthetic storms) that generate unprecedentedly high flood levels in Shanghai. Nevertheless, we find that the mainland Shanghai is relatively safe from coastal flooding under the current climate, thanks to its high‐standard seawall protection. However, the city is expected to be increasingly at risk due to future sea level rise, with inundation two times and 20 times more likely to occur by mid‐ and late‐21st century, respectively, and inundation depth and area to greatly increase (e.g., 60%–1,360% increase in the inundation area for the “worst cases” by 2,100). The low‐lying and poorly protected area (e.g., Chongming Island) is likely to be moderately affected by flood events with long return periods at the current state but could be largely inundated in future sea‐level‐rise situations.
This article describes a new method of urban pluvial flood modeling by coupling the 1D storm water management model (SWMM) and the 2D flood inundation model (ECNU Flood-Urban). The SWMM modeling results (the overflow of the manholes) are used as the input boundary condition of the ECNU Flood-Urban model to simulate the rainfall–runoff processes in an urban environment. The analysis is applied to the central business district of East Nanjing Road in downtown Shanghai, considering 5-, 10-, 20-, 50-, and 100-year return period rainfall scenarios. The results show that node overflow, water depth, and inundation area increase proportionately with the growing return periods. Water depths are mostly predicted to be shallow and surface flows generally occur in the urban road network due to its low-lying nature. The simulation result of the coupled model proves to be reliable and suggests that urban surface water flooding could be accurately simulated by using this methodology. Adaptation measures (upgrading of the urban drainage system) can then be targeted at specific locations with significant overflow and flooding.