미래기후변화에 대한 영향분석 및 유역단위의 수자원 영향평가

Abstract

17 GCMs' simulation of late 20th century climate in Korea are examined. A regionally averaged time series formed by averaging the temperature and precipitation values at all the Korean grid points. In order to compare global models with observations, observed spatially averaged temperature and precipitation is calculated using 24 gauging stations for 1971-2000. Most models have a slight cold bias. The models with least bias in annual average temperature are NIES(MIROC3.2 hires), GISS(AOM) and INGV(SXG2005). For precipitation, almost all models have a dry bias, and for some the bias exceeds 50%. Models with lowest bias are NIES(MIROC3.2 hires), CCCma(CGCM3-T47) and MPI-M( ECHAM5-OM). The models' simulated seasonal cycles show that for temperature, CSIRO(Mk3.0) has the best followed by CCCma(CGCM3-T47) and CCCma(CGCM3-T63), and for precipitation, NIES(MIROC3.2 hires) has the best followed by CSIRO(Mk3.0) and CNRM(CM3). In the assessment using Taylor diagram, NIES(MIROC3.2 hires) ranks the best for temperature, and CSIRO(Mk3.0) ranks the best for precipitation. As additional process, for the assessment of climate change impacts for the Byeongseong stream, CSMK3 is selected as future climate information. The projections come from CSMK3 used to simulate the GHG emission scenarios known as A2, A1B, B1. Air temperature and precipitation information from the GCM simulations are converted to regional scale data using anomaly scaling method. Downscaled climate data from GCM are then used as the input data for the SWAT model to generate regional runoff and water quality estimates in the Byeongseong stream. As a result of simple sensitivity analysis, the increase of CO2 concentration leads to increase water yield through reduction of evapotranspiration and increase of soil water. Hydrologic responses to climate change are in phase with precipitation change. According to future scenarios' results, streamflow, SS and TN are more fluctuated than precipitation. this means watershed's response are more sensitive than future climate change. However, it should be noted that there are a lot of uncertainties in such multiple-step analysis used to convert climate information from GCM-based future climate projections into hydrologic information.