1. Introduction
Like many regions in developed countries, demands on water resources of the Southern Chungnam province, fourth largest in South Korea, are continuously growing, but there is only one water source—Boryeong multi-purpose Dam. The available reservoir storage of Boryeong Dam is 116.9 million tons and designed to supply 23,800 tons per day for agricultural, residential and industrial use serving 420,000 of people in Western Chungnam Province [
1]. Boryeong Dam is also supplying water to four major steam power plants, which generate about 25% of South Korea’s electricity.
Currently, the water use of Southern Chungnam Province is about 21,000 tons per day, which is approaching 90% of design demand. Water demand from steam power plants is expected to increase because of population growth. Furthermore, because of the region’s adjacency to Seoul, the capital of South Korea, construction of an industrial complex has been planned in the near future, which will also increase water demand. These increasing water demands have raised several concerns and triggered discussions on additional dam building and water allocation issues. However, those structural measures need at least 5–10 years to solve the water shortage problem in Chungnam Province, which means, if the operational failure of the dam occurs in the meantime during drought, water availability may be threatened because of the storage shortage.
In 2015, Western Chungnam Province had to reduce water supply by 20% for 5 months because of serious drought. Thus, reliable long lead-time reservoir inflow forecasting is essential because water resources manager must decide when to reduce environmental flows to secure more water during water shortage or to increase water release to prevent dam overflows. To reduce the negative impacts of climatic variability on dam operation, dam inflow forecasting model with at least 3 months lead time is needed to prepare for potential threat such as floods and droughts. For the water supply purpose, it is important to know in advance to save some water if the shortage in water is on the horizon of the near future. Boryeong Dam is very vulnerable to drought as its effective storage to annual water supply is 1:1 and is related with many different types of stakeholders including government, municipalities, agriculture, environmental organizations, enterprises, etc. [
1]. To address the complex need for water resources before any decision on the adjustment of dam releases to be reached, a month or two is not a sufficient amount of time to do so. Thus, at least 3-months lead time, thus a season-ahead forecast, is needed, and it is quite common in forecasting practices [
2,
3,
4]. In summary, the time required for local stakeholders to take operational actions and also the medium time horizon for forecasting needs were factored into the selection of lead time to be 3 months.
There are two basic approaches for long-range hydroclimatic predictions: dynamic models and empirical statistical models. The dynamic approach is based on process models for climate and hydrology. The statistical approach is based on data driven models where the predictor–predictand relationship is the essence of the technology. Although it is anticipated that dynamic models may become superior to empirical models in the future, empirical forecasting models are still able to compete because they are less expensive to develop and use [
5].
The hydroclimatic models used to forecast seasonal precipitation or streamflow are still far from perfect, and there are a lot of uncertainties to be considered. Especially, the models need to be in better compliance with climatic observations such as ocean-atmospheric indices, which are connected to climatic variability globally. These oceanic-atmospheric forcing predictors, called hydroclimatic teleconnections are widely used to forecast long range seasonal streamflow or precipitation. Since weather and climate are linked with atmospheric circulation over the continents, previous studies have suggested the relationships between the extreme phases of El Niño 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]. Moss et al. [
11] used the southern oscillation index (SOI) as a predictor of the probability of low flows in New Zealand. Chiew et al. [
12] pointed out the effect of ENSO on Australian rainfall, streamflow and drought. More recently, Chen 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. Chiew et al. [
14] used the ENSO–streamflow relationship to forecast streamflow. Xu et al. [
15] showed the possibility for seasonal streamflow forecasting using climate teleconnections at the Three Gorges Dam. Chandimala and Zubair [
16] tried to forecast streamflow and rainfall, based on ENSO, for water resource management. Karla 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). Ouachani et al. [
18] also showed the impact of teleconnection pattern on precipitation and streamflow. More recently, Hidalgo-Munoz 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.
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 droughts. 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.