In this paper, time series of soil moisture were measured for a steep forest hillslope to model and understand distinct hydrological behaviours along two different transects. The transfer function analysis was presented to characterize temporal response patterns of soil moisture for rainfall events. The rainfall is a main driver of soil moisture variation, and its stochastic characteristic was properly treated prior to the transfer function delineation between rainfall and soil moisture measurements. Using field measurements for two transects during the rainy season in 2007 obtained from the Bumrunsa hillslope located in the Sulmachun watershed, a systematic transfer functional modeling was performed to configure the relationships between rainfall and soil moisture responses. The analysis indicated the spatial variation pattern of hillslope hydrological processes, which can be explained by the relative contribution of vertical, lateral and return flows and the impact of transect topography.

The transfer function was introduced to establish the prediction method for the DO concentration at the intaking point of Kongju Water Works System. In the most cases we analyze a single time series without explicitly using information contained in the related time series. In many forecasting situations, other events will systematically influence the series to be forecasted(the dependent variables), and therefore, there is need to go beyond a univariate forecasting model. Thus, we must build a forecasting model that incorporates more than one time series and introduces explicitly the dynamic characteristics of the system. Such a model is called a multiple time series model or transfer function model. The purpose of this study is to develop the stochastic stream water quality model for the intaking station of Kongju city waterworks in Keum river system. The performance of the multiplicative ARIMA model and the transfer function noise model were examined through comparisons between the historical and generated monthly dissolved oxygen series. The result reveal that the transfer function noise model lead to the improved accuracy.