통계적 상세화 기법을 통한 기후변화기반 지속시간별 연최대 대표 강우시나리오 생산기법 소개

Introduction to the production procedure of representative annual maximum precipitation scenario for different durations based on climate change with statistical downscaling approaches

Abstract

기후변화는 홍수의 가장 큰 원인이 되는 극치강우의 빈도와 크기에 매우 큰 영향을 미치고 있다. 특히, 우리나라에서 발생하는 대규모 재해는 강우에 의한 홍수피해가 대부분을 차지하고 있다. 이러한 홍수피해는 기후변화에 의한 극한강우의 발생 빈도가 높아짐에 따라 새로운 재해양상으로 전개되고 있다. 하지만, 미래 기후변화 시나리오 자료는 해상도의 한계로 인하여 중소규모 하천 및 도시유역에 요구되는 수준의 자료 수집이 불가능한 상태이다. 이러한 문제점을 개선하기 위하여 본 연구에서는 전지구모형에서 생산된 기후변화 시나리오에 대해서 여러 단계의 통계적 상세화 기법을 통하여 우리나라 전역에 대하여 미래 시나리오에 대한 빈도해석이 가능하도록 각 지점의 특성에 따라 시간적으로 상세화하기 위해 개발된 방법 및 과정을 소개하였다. 이를 통해, 시간상세화 자료를 토대로 미래 강우에 대한 빈도해석과 기후변화에 따른 방재성능 목표강우량을 산정하는데 활용할 수 있도록 하였다.

Climate change has been influenced on extreme precipitation events, which are major driving causes of flooding. Especially, most of extreme water-related disasters in Korea occur from floods induced by extreme precipitation events. However, future climate change scenarios simulated with Global Circulation Models (GCMs) or Reigonal Climate Models (RCMs) are limited to the application on medium and small size rivers and urban watersheds due to coarse spatial and temporal resolutions. Therefore, the current study introduces the state-of-the-art approaches and procedures of statistical downscaling techniques to resolve this limitation It is expected that the temporally downscaled data allows frequency analysis for the future precipitation and estimating the design precipitation for disaster prevention.

Fig. 1. Procedure of producing the representative climate scenarios of annual maximum precipitation for different durations with statistical downscaling approaches. Note that‘base’ indicates the period of 1979~2005 and ‘future’ does the period of 2006~2100

Fig. 2. Model development of nonparametric temporal downscaling from lee and jeong (2014) (left panel) to lee and park (2017) (right panel)

Fig. 3. Employed weather stations in the current study

Fig. 4. Performance measurement as likelihood, RMSE, and AIC for each non-mixture and mixture distributions

Fig. 5. Return periods versus annual maximum precipitation for different durations at goheung station

Fig. 6. The results of the mean of annual maximum precipitation (x-axis) for different durations at mokpo from lee and jeong (2014) (left panel) to lee and park (2017) (right panel)

Fig. 7. Time series of annual maximum precipitation for different durations at Mokpo station. note that the base period is between 1979~2005 and future period for the rcp8.5 scenario is 2006~2100. solid line indicates the observed data and the gray lines are 100 downscaled series and their median with red dotted line

Fig. 8. Trace selection method of RCP8.5 employing (a) mean and std.(see Eq.(3)), (b) density estimate(see Eq.(4)), (c ) empirical CDF(see Eq.(5))

Fig. 9. Mean of selected representative annual maximum precipitation for different durations for the base period and future periods of RCP8.5. Here, P1: 2006~2040, P2: 2041~2070, P3: 2071~2100