Sunwoo, W.Y.; Lee, G., and Jun, K.S., 2020. Rainfall-runoff modeling by hydro-meteorological factors in the coastal urban region. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1303-1309. Coconut Creek (Florida), ISSN 0749-0208.
This study aims to improve the rainfall-runoff model by applying and estimating water content on the surface and sub-surface with hydrological factors such as soil moisture and evapotranspiration. Coastal groundwater and water quality depend on runoff changes. Therefore, interest in more accurate rainfall-runoff analysis has recently increased. However, there are few researches which applying input data reflecting the physical reality into rainfall-runoff model. In this study, first, reanalysis and satellite based soil moisture products (i.e., soil moisture from GLDAS and ASCAT) are validated with in situ soil moisture during the test period. Second, the discharge is estimated using the actual evapotranspiration from Global Land Evaporation and Amsterdam Model (GREAM) and reanalysis soil moisture products as input data for the conceptual model, the Probability Distributed Model (PDM). Study area is Hongseong, a coastal urban region in South Korea. The Root Mean Square Error (RMSE) of estimated discharge is reduced by 25% in case of using reanalysis soil moisture and evapotranspiration products and little overall difference is found before and after a major rainfall event. This shows that the excess rainfall on the soil impacts on surface and sub-surface runoff and reanalysis soil moisture products contributes to improve the accuracy of discharge prediction over the limited data region.