Hyun-Suk Shin

Optimal volume of non-point sources management detention considering spatio-temporal variability of land surface moisture condition

AbstractThis study presents the method to estimate the optimal volume of a non-point sources (NPS) management detention considering spatio-temporal variability of urban land surface moisture condition. In order to consider statistical characteristics of rainfall events, 3-parameter mixed exponential probability density function (PDF) is applied, and rainfall events are transformed to runoff events through Natural Resources Conservation Service–curve number method. Urban drainage catchment is divided into square-type grid cells, where runoff curve number is estimated by cells. Using the analytical model developed previously with derived probability distribution theory, the PDF of rainfall event depth is transformed to the PDF of stormwater depth. By cells, in consideration of changes in antecedent soil moisture condition and spatial variations in CN of drainage catchment, stormwater capture curve that can represent the drainage area is drawn and from which the optimal volume of a NPS management detention i...

Development of Hydrologic Forecasting Model Connected with Real Time Watershed and River Monitoring System

In recent, abnormal phenomena in nature occurs due to climate change, which result in increase of flood frequency and rainfall intensity by change of meteorologic and hydrological factors. Especially, flood inundation in urban watershed frequently happens on casualties expected and property damage. Scientific and technological adaptation are acutely needed for unexpected damage in nature, and that, it is considerably important to develop proper model of hydrological forecasting over the short periods and to build a real-time watershed monitoring system. One of important things for solving a variety of problems is to connect real-time watershed monitoring system with short term hydrologic forecasting model. In this research, Suyoung stream in Busan was determined as a study area. After that, real-time watershed monitoring system at ten minute interval has been established for flood warning and forecast in the field. The data from this system was applied to short-term hydrologic forecasting model based on the artificial neural network. When flood happens in urban, input data of forecasting model on each rainfall event was used. In conclusion, after establishing the model being possible to forecast water level from 10 to 60 minutes at 10 minute interval, as a result of simulating the model, the model performances are considerably good as RMSE 0.02~0.6 and more than 0.9. It makes possible effectively to manage the watershed in urban.