Development of an integrated method for long-term water quality prediction
using seasonal climate forecast
1Research Department, APEC Climate Center, Busan, 48058, Korea
2National Institute of Environmental Research, Incheon, 22689, Korea
3Seoul National University, Pyeongchang, 25354, Korea
Abstract. The APEC Climate Center (APCC) produces climate prediction information utilizing a multi-climate model ensemble (MME) technique. In this study, four different downscaling methods, in accordance with the degree of utilizing the seasonal climate prediction information, were developed in order to improve predictability and to refine the spatial scale. These methods include: (1) the Simple Bias Correction (SBC) method, which directly uses APCC's dynamic prediction data with a 3 to 6 month lead time; (2) the Moving Window Regression (MWR) method, which indirectly utilizes dynamic prediction data; (3) the Climate Index Regression (CIR) method, which predominantly uses observation-based climate indices; and (4) the Integrated Time Regression (ITR) method, which uses predictors selected from both CIR and MWR. Then, a sampling-based temporal downscaling was conducted using the Mahalanobis distance method in order to create daily weather inputs to the Soil and Water Assessment Tool (SWAT) model. Long-term predictability of water quality within the Wecheon watershed of the Nakdong River Basin was evaluated. According to the Korean Ministry of Environment's Provisions of Water Quality Prediction and Response Measures, modeling-based predictability was evaluated by using 3-month lead prediction data issued in February, May, August, and November as model input of SWAT. Finally, an integrated approach, which takes into account various climate information and downscaling methods for water quality prediction, was presented. This integrated approach can be used to prevent potential problems caused by extreme climate in advance.
Cho, J., Shin, C.-M., Choi, H.-K., Kim, K.-H., and Choi, J.-Y.: Development of an integrated method for long-term water quality prediction
using seasonal climate forecast, Proc. IAHS, 374, 175-185, doi:10.5194/piahs-374-175-2016, 2016.