Basin-scale monthly rainfall forecasts with a data-driven model using lagged global climate indices and future predicted rainfall of an adjacent basin

Future long-term rainfall forecasts are valuable for operating water supply facilities and managing unusual droughts. This study proposes a novel approach to forecast basin-scale monthly rainfall from lagged global climate indices, antecedent historical rainfall data of a targeted basin, and forecasted rainfall data from a nearby basin using a data-driven model. The approach is applied to the Han River basin and the Geum River basin, South Korea, for May and June, prone to drought occurrence. An artificial neural network (ANN), a widely used data-driven model, was employed to build forecasting models for the study basins. Two types of ANN models were constructed: one uses predictors of the lagged climate indices and antecedent rainfall of a targeted basin that have been typically used in previous studies, and the other further uses the forecasted rainfall of an adjacent basin that was first attempted in this study by considering the strong concurrent relationship of monthly rainfall data between nearby basins. The optimal network architectures were determined through the Monte Carlo cross-validation (MCCV) process in which repeated data subsampling for training datasets was carried out to reduce the output variance and obtain ensemble forecasts. The results show that the proposed ANN model in this study with input variables of the forecasted rainfall of the nearby basin and the lagged global climate indices and the past rainfall of the target basin provides better predictive performance than the model without using the adjacent basin's forecast rainfall. The categorical forecasting skill based on the proposed approach is good: the hit rates and Heidke skill scores ranged from 50.9 to 66.0% and 0.29 to 0.49, respectively. The results confirm that using rainfall forecasts of a nearby basin as an input variable can enhance the ANN model's ability to predict future monthly rainfall.