Considering temporal distribution of design rainfall for enhancing urban flood resilience in response to climate change

A representative example of how extreme rainfall events caused by climate change can be directly recognized is the increase of property damage due to urban flood. This surge in urban flood damage is not merely corresponding to an augmentation in probable rainfall depth but rather a result of urbanization with densely populated and concentrated flooding. Despite this circumstance, we have mostly revolved around increasing the return period, primarily focusing on the augmentation of probable rainfall depth to mitigate the flood damage by climate change. In terms of the temporal distribution of design rainfall, conventional methods such as the Huff method and alternating blocking method are still in use; however, they cannot accurately capture the real rainfall distribution. In this context, we investigate the viability of enhancing design standards by adjusting the temporal distribution of design rainfall without artificially inflating a return period for design. To achieve that, it is necessary to understand the impact of temporal rainfall distribution on the design flood.

We generate 449 dimensionless time-rainfall distributions for short-term(less than 6 hours) and 5,789 for long-term(6 hours or more) durations considering rainfall data from both meteorological observations by the Korea Meteorological Administration and d4PDF(Data for Policy Decision Making for Future) scenarios. Based on these distributions, total 25,860 hyetographs are synthesized for five rainfall durations. We repeatedly estimated the design flood using a rainfall-runoff model, revealing that the peak discharges varied up to 10 times depending on the time-rainfall distribution. Considering that increasing the return period from 50 years to 100 years generally results in only a 10% rise in probable rainfall depths, adjusting the temporal distribution of design rainfall provides a more adaptable approach to increasing design floods. These outcomes have the potential to broaden the perspective for applications of rainfall scenarios in data-based model or establishing the design flood standards.

 

Acknowledgement: This research was supported by a grant(2022-MOIS61-002(RS-2022-ND 634021)) of ‘Development Risk Prediction Technology of Storm and Flood for Climate Change based on Artificial Intelligence’ funded by Ministry of Interior and Safety(MOIS, Korea).