## 1 Introduction
Since weather and climate are linked with atmospheric circulation over the continents, previous studies have suggested the relationships between the extreme phases of El Nino southern oscillation (ENSO) and the fluctuations of stream flow and precipitation [6; 7]. There is growing evidence of the effects of atmospheric-oceanic features on the hydrology of the western basins [8; 9; 10]. [PERSON] et al. [11] used the southern oscillation index (SOI) as a predictor of the probability of low flows in New Zealand. [PERSON] et al. [12] pointed out the effect of ENSO on Australian rainfall, streamflow and drought. More recently, [PERSON] and lee [13] suggested variations in correlations and possible climate regime shifts. There are also several past studies for streamflow prediction with long lead times using ocean-atmospheric oscillation indices. [PERSON] et al. [14] used the ENSO-streamflow relationship to forecast streamflow. [PERSON] et al. [15] showed the possibility for seasonal streamflow forecasting using climate teleconnections at the Three Gorges Dam. [PERSON] and [PERSON] [16] tried to forecast streamflow and rainfall, based on ENSO, for water resource management. [PERSON] et al. [17] predicted annual streamflow volume with a 1-year lead time by a data-driven model, the support vector machine (SVM), using the North Atlantic oscillation (NAO), ENSO and sea surface temperature (SST). [PERSON] et al. [18] also showed the impact of teleconnection pattern on precipitation and streamflow. More recently, [PERSON] et al. [19] employed multiple linear regression using large-scale atmospheric and oceanic information for seasonal streamflow forecasting and found fair forecasting skill. Although previous studies revealed a significant relationship between large-scale climate teleconnections and stream flow, most of those applications have investigated the correlation between teleconnection indices and streamflow in the large-scale basins on a yearly or seasonal basis. However, there have been few studies evaluating the applicability of climate indices in predicting monthly runoff in a small watershed such as Boryeong Dam watershed (163 km\({}^{2}\)) in South Korea (20,21). At the same time, there were no attempts to use multiple data-driven models as an ensemble for a monthly reservoir inflow forecasting purpose in the context of simple model averaging (SMA) and Bayesian model averaging (BMA), which is an ensemble averaging technique based on multiple model results. BMA considers the uncertainty of each model's forecasts explicitly and uses this uncertainty to calculate a predictive distribution. This method has two desirable advantages. One is that it provides deterministic forecast and the other is that it offers prediction interval of forecast distribution, which is very helpful to make a decision probabilistically. The main objective of this research is identifying the monthly best combination of climate indices and developing a monthly reservoir inflow forecasting model based on them with a three-month lead time. For the efficient dam operation, monthly reservoir inflow prediction with a lead time of at least three months is needed, which means climate predictors measured at least three months in advance are used. Another objective is to evaluate whether the ensemble prediction by BMA with data-driven models for reservoir inflows in a small basin is reliable or useful in forecasting monthly reservoir inflows, which is a very important tool for the stable dam operation.