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The APEC Climate Center (APCC) produces climate\udprediction information utilizing a multi-climate model ensemble (MME)\udtechnique. In this study, four different downscaling methods, in accordance\udwith the degree of utilizing the seasonal climate prediction information,\udwere developed in order to improve predictability and to refine the spatial\udscale. These methods include: (1) the Simple Bias Correction (SBC) method,\udwhich directly uses APCC's dynamic prediction data with a 3 to 6 month lead\udtime; (2) the Moving Window Regression (MWR) method, which indirectly\udutilizes dynamic prediction data; (3) the Climate Index Regression (CIR)\udmethod, which predominantly uses observation-based climate indices; and (4)\udthe Integrated Time Regression (ITR) method, which uses predictors selected\udfrom both CIR and MWR. Then, a sampling-based temporal downscaling was\udconducted using the Mahalanobis distance method in order to create daily\udweather inputs to the Soil and Water Assessment Tool (SWAT) model. Long-term\udpredictability of water quality within the Wecheon watershed of the Nakdong\udRiver Basin was evaluated. According to the Korean Ministry of Environment's\udProvisions of Water Quality Prediction and Response Measures, modeling-based\udpredictability was evaluated by using 3-month lead prediction data issued in\udFebruary, May, August, and November as model input of SWAT. Finally, an\udintegrated approach, which takes into account various climate information\udand downscaling methods for water quality prediction, was presented. This\udintegrated approach can be used to prevent potential problems caused by\udextreme climate in advance

Development of an integrated method for long-term water quality prediction using seasonal climate forecast