Applying a coupled hydrometeorological simulation system to flash flood forecasting over the Korean Peninsula View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2017-09-27

AUTHORS

Young Ryu, Yoon-Jin Lim, Hee-Sook Ji, Hyun-Hee Park, Eun-Chul Chang, Baek-Jo Kim

ABSTRACT

In flash flood forecasting, it is necessary to consider not only traditional meteorological variables such as precipitation, evapotranspiration, and soil moisture, but also hydrological components such as streamflow. To address this challenge, the application of high resolution coupled atmospheric-hydrological models is emerging as a promising alternative. This study demonstrates the feasibility of linking a coupled atmospheric-hydrological model (WRF/WRFHydro) with 150-m horizontal grid spacing for flash flood forecasting in Korea. The study area is the Namgang Dam basin in Southern Korea, a mountainous area located downstream of Jiri Mountain (1915 m in height). Under flash flood conditions, the simulated precipitation over the entire basin is comparable to the domain-averaged precipitation, but discharge data from WRF-Hydro shows some differences in the total available water and the temporal distribution of streamflow (given by the timing of the streamflow peak following precipitation), compared to observations. On the basis of sensitivity tests, the parameters controlling the infiltration of excess precipitation and channel roughness depending on stream order are refined and their influence on temporal distribution of streamflow is addressed with intent to apply WRF-Hydro to flash flood forecasting in the Namgang Dam basin. The simulation results from the WRF-Hydro model with optimized parameters demonstrate the potential utility of a coupled atmospheric-hydrological model for forecasting heavy rain-induced flash flooding over the Korean Peninsula. More... »

PAGES

421-430

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13143-017-0045-0

DOI

http://dx.doi.org/10.1007/s13143-017-0045-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091922988


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