Hydro-environmental runoff projection under GCM scenario downscaled by Artificial Neural Network in the Namgang Dam watershed, Korea

Abstract

Until recently, hydrological impacts and prediction caused by climate change have been popular issues. However, a basin-scale hydro-environmental study has begun to attract attention recently due to the difficulties in watershed model calibration and uncertainty propagation in the data relaying procedure from the GCM (General Circulation Model) process to watershed modeling process, which leads to unrealistic projections. In addition, reliable downscaling scheme is essential in utilizing the GCM model output for hydrological applications. The main objective of this study is to suggest a reliable ANN (Artificial Neural Network)-based GCM scenario and its application for hydro-environmental projection using a watershed model. In this report, the Namgang Dam watershed in the Nakdong river basin was selected as the case study. To examine the vulnerability of the Namgang dam watershed caused by climate change, the change in streamflow and pollutant material runoff due to climate change were predicted using the watershed model, SWAT (Soil Water Assessment Tool), based on the IPCC’s A1B GCM scenario, which is downscaled using the ANN and Nonstationary Quantile Mapping. The results of this study will be used for suggesting an effective counterplan from an engineering point of view, and developing an integrated water sources management system.