## 4 Conclusions  
Water resources managers consider accurate lead time forecasting of streamflow as one of the most fundamental components for preparing water hazards such as droughs. For the streamflow prediction with the statistical method, the most basic method is a naive forecast. Additionally, an improved forecast skill has been shown by more advanced models ranging from simple regression method to the data-driven modeling approach such as ANN, SVM, etc. [31; 64]. Many hydrologists focus on predicting hydrologic conditions using teleconnection between streamflow and climate indices [17; 65].  
This study was motivated by the recognition of worsening water shortage in Southern Chungnam Province in South Korea due to emerging development and increasing demand on water resources. We developed statistical reservoir inflow forecast models with a lead time of 3 months based on MLR, ANN and SVM methods using lagged teleconnection between streamflow and regularly updated climate indices. For the model building, a 19-year-long hydrologic data set for the Boryeong Dam basin and various climatic indices were used. In addition to individual model building, we expanded the scope to investigate their integrated predictive capability as an ensemble prediction method in the context of BMA for improving predictability. The important results obtained in this study are summarized as follows.  
First, we found that monthly teleconnection variables can be useful predictors for forecasting the monthly reservoir inflow for a small basin. Although the forecast performance of three models in Boryeong Dam basin were not perfect, the results have shown the possibility that climatic indices based models could be applied to a small basin for sustainable water resources management. Though ANN and SVM were better than MLR, the MLR method still has the advantage of being more directly based on the relationship between climatic indices and reservoir inflow over the forecasts obtained from SVM and ANN model. Ensemble prediction results showed that BMA is more accurate and useful than SMA and NF. In addition, the prediction interval provided by BMA can be very helpful for early decision-making in response to drought situations.  
Wind, temperature and geomorphologic characters are key factors affecting precipitation and reservoir inflow. Despite remarkable recent progress in atmospheric science, our understanding of climatic forcing and how they influence climate variability and predictability at different lead times and over different time scales remains far from being complete [66]. Further understanding of climatic forcing is needed to develop more accurate statistical and numerical streamflow forecasting models.  
This study is considered a promising first step for stable dam operation using statistical model to forecast reservoir inflow at a reasonable lead time. Despite being the promising approach to forecast reservoir inflow from a small basin, the proposed models need to be tested for other basins for more general applicability. In the future study, climatic indices can be further combined with local variables such as precipitation and flows for current and previous months so that the direct cause of the inflow is supplemented by the remotely influential climate variables for better prediction performance.  
All the authors contributed to the conception and the development of this paper. [PERSON] and [PERSON] contributed to the calculations and the analyses. [PERSON] and [PERSON] helped with the figure production and the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea University Grant.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.