This study evaluated five models of rainfall temporal distribution (i.e., the Yen and Chow model, Mononobe model, alternating block method, Huff model, and Keifer and Chu model), with the annual maximum rainfall events selected from Seoul, Korea, from 1961 to 2016. Three different evaluation measures were considered: the absolute difference between the rainfall peaks of the model and the observed, the root mean square error, and the pattern correlation coefficient. Also, sensitivity analysis was conducted to determine whether the model, or the randomness of the rainfall temporal distribution, had the dominant effect on the runoff peak flow. As a result, the Keifer and Chu model was found to produce the most similar rainfall peak to the observed, the root mean square error was smaller for the Yen and Chow model and the alternating block method, and the pattern correlation was larger for the alternating block method. Overall, the best model to approximate the annual maximum rainfall events observed in Seoul, Korea, was found to be the alternating block method. Finally, the sensitivity of the runoff peak flow to the model of rainfall temporal distribution was found to be much higher than that to the randomness of the rainfall temporal distribution. In particular, in small basins with a high curve number (CN) value, the sensitivity of the runoff peak flow to the randomness of the rainfall temporal distribution was found to be insignificant.

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Green roof systems could help reduce peak discharge and retain rainwater in urban areas. The objective of this study was to investigate the hydrological behavior of a green roof system by using the SEEP/W model. The rainfall-runoff relationship within the green roof system was simulated and the results were compared with actual data from a test bed for green roof systems to verify the applicability of SEEP/W. Then, the verified SEEP/W model was used to simulate the green roof system by varying four factors (soil type, rainfall intensity, substrate depth, and green roof slope) to explore the hydrological performance through the peak discharge to rainfall intensity (PD/RI) ratio and the rain water retention rate. The results show that the model presents slightly faster and greater peak time and peak discharge values, respectively, as compared to the observational data. This is attributed to the vegetation conditions in the real green roof system. However, it is also shown that the SEEP/W model can be used to design green roof systems and evaluate their hydrological behavior because of its modeling efficiency. Thus, the SEEP/W model can be used to reliably design and manage green roof systems by further considering the vegetation conditions and water flow dynamics. Furthermore, it would be desirable to consider additional factors, such as vegetation and an insulating pebble layer, in the design and management of green roofs in future work.

This study proposes a new method to estimate the bias correction ratio for the rainfall forecast to be used as input for a flash flood warning system. This method requires a backward tracking to locate where the forecasted storm is at the present time, and the bias correction ratio is estimated at the tracked location, not at the warning site. The proposed method was applied to the rainfall forecasts provided by the Korea Meteorological Administration. A total of 300 warning sites considered in the flash flood warning system for mountain regions in Korea (FFWS-MR) were considered as study sites, along with four different storm events in 2016. As a result, it was confirmed that the proposed method provided more reasonable results, even in the case where the number of rain gauges was small. Comparison between the observed rain rate and the corrected rainfall forecasts by applying the conventional method and the proposed method also showed that the proposed method was superior to the conventional method.

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