This study develops a late spring-early summer rainfall forecasting model using an artificial neural network (ANN) for the Geum River Basin in South Korea. After identifying the lagged correlation between climate indices and the rainfall amount in May and June, 11 significant input variables were selected for the preliminary ANN structure. From quantification of the relative importance of the input variables, the lagged climate indices of East Atlantic Pattern (EA), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), East Pacific/North Pacific Oscillation (EP/NP), and Tropical Northern Atlantic Index (TNA) were identified as significant predictors and were used to construct a much simpler ANN model. The final best ANN model, with five input variables, showed acceptable performance with relative root mean square errors of 25.84%, 32.72%, and 34.75% for training, validation, and testing data sets, respectively. The hit score, which is the number of hit years divided by the total number of years, was more than 60%, which indicates that the ANN model successfully predicts rainfall in the study area. The developed ANN model, incorporated with lagged global climate indices, could allow for more timely and flexible management of water resources and better preparation against potential droughts in the study region.

In this study, artificial neural network (ANN) models were constructed to predict the rainfall during May and June for the Han River basin, South Korea. This was achieved using the lagged global climate indices and historical rainfall data. Monte-Carlo cross-validation and aggregation (MCCVA) was applied to create an ensemble of forecasts. The input-output patterns were randomly divided into training, validation, and test datasets. This was done 100 times to achieve diverse data splitting. In each data splitting, ANN training was repeated 100 times using randomly assigned initial weight vectors of the network to construct 10,000 prediction ensembles and estimate their prediction uncertainty interval. The optimal ANN model that was used to forecast the monthly rainfall in May had 11 input variables of the lagged climate indices such as the Arctic Oscillation (AO), East Atlantic/Western Russia Pattern (EAWR), Polar/Eurasia Pattern (POL), Quasi-Biennial Oscillation (QBO), Sahel Precipitation Index (SPI), and Western Pacific Index (WP). The ensemble of the rainfall forecasts exhibited the values of the averaged root mean squared error (RMSE) of 27.4, 33.6, and 39.5 mm, and the averaged correlation coefficient (CC) of 0.809, 0.725, and 0.641 for the training, validation, and test sets, respectively. The estimated uncertainty band has covered 58.5% of observed rainfall data with an average band width of 50.0 mm, exhibiting acceptable results. The ANN forecasting model for June has 9 input variables, which differed from May, of the Atlantic Meridional Mode (AMM), East Pacific/North Pacific Oscillation (EPNP), North Atlantic Oscillation (NAO), Scandinavia Pattern (SCAND), Equatorial Eastern Pacific SLP (SLP_EEP), and POL. The averaged RMSE values are 39.5, 46.1, and 62.1 mm, and the averaged CC values are 0.853, 0.771, and 0.683 for the training, validation, and test sets, respectively. The estimated uncertainty band for June rainfall forecasts generally has a coverage of 67.9% with an average band width of 83.0 mm. It can be concluded that the neural network with MCCVA enables us to provide acceptable medium-term rainfall forecasts and define the prediction uncertainty interval.

In this study, long-term precipitation forecasting models capable of reflecting constantly changing climate characteristics and providing forecasts for up to 12 months in advance were developed using lagged correlations with global and local climate indices. These models were applied to predict monthly precipitation in the Han River basin, South Korea. Based on the lead month of forecast, 10 climate indices with high correlations were selected and combined to construct four-variable multiple regression models for monthly precipitation forecasting. The forecast results for the analytical period (2010–2019) showed that predictability was low for some summer seasons but satisfactory for other seasons and long periods. In the goodness-of-fit test results, the Nash–Sutcliffe efficiency (0.48–0.57) and the ratio of the root mean square error to the standard deviation of the observation (0.66–0.72) were evaluated to be satisfactory while the percent bias (9.4–15.5%) was evaluated to be between very good and good. Due to the nature of the statistical models, however, the predictability is highly likely to be reduced if climate phenomena that are different from the statistical characteristics of the past appear in the forecast targets or predictors. The forecast results were also presented as tercile probability information (below normal, normal, above normal) through a comparison with the observation data of the past 30 years. The results are expected to be utilized as useful forecast information in practice if the predictability for some periods is improved.

No abstract

No abstract