본 연구에서는 인공위성 및 재분석 자료인 Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MOD16의 실제증발산량 산출물을 활용하여 한국수자원조사기술원(Korea Institute of Hydrological Survey, KIHS)에서 관리하고 있는 청미천(cheongmicheon farmland site, CFK)과 설마천(seolmacheon site, SMK) flux tower에서 검증하였고, Triple collocation (TC) 방 법을 활용하여 자료간의 불확실성 및 상관성분석을 수행하였다. 플럭스타워와의 검증 결과에서는 전반적으로 GLEAM>GLDAS>MOD16순으로 좋은 결과를 나타내었으며, 세가지 산출물의 조합(S1: flux tower vs. GLDAS vs. MOD16, S2: flux tower vs. GLDAS vs. GLEAM, S3: flux tower vs. GLEAM vs. MOD16)을 통한 TC 결과에서는 청미천(설마천)에서 GLEAM>GLDAS>MOD16>flux tower (GLDAS>GLEAM>MOD16>flux tower)순으로 좋은 결과를 나타내었다. TC 분석 결과에서 Flux tower의 error variance와 correlation coefficient가 상대적으로 좋은 결과를 나타내지 못하였으므로, 한반도 지역에서 인공위성과 재분석 자료(GLDAS vs. GLEAM vs. MOD16)만을 활용하여 TC를 적용하였다. 그 결과, GLDAS 와 GLEAM이 한반도 영역에서 낮은 error variance 와 높은 correlation coefficient를 나타낸 반면, MOD16의 경우, 농지에서 낮은 correlation coefficient과 높은 error variance를 나타내었다.

In this study, the actual evapotranspiration products of Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM) and MOD16, which are satellite- and reanalysis-based dataset, were validated at the flux tower sites (i.e., CFK and SMK) managed by Korea Institute of Hydrological Survey, and the uncertainty and correlation analysis were conducted using Triple Collocation (TC) method. The result of validation with the flux tower showed better agreement in the order of GLEAM> GLDAS>MOD16. At the result of three combinations (S1: flux tower vs. GLDAS vs. MOD16, S2: flux tower vs. GLDAS vs. GLEAM, S3: flux tower vs. GLEAM vs. MOD16), the order of best to worst is GLEAM, GLDAS, MOD16, and flux tower for CFK (GLDAS> GLEAM>MOD16>flux tower for SMK). Since the error variance and correlation coefficients of the flux tower show relatively worse performance in TC analysis than the other products, By applying TC method to three products (GLDAS vs. GLEAM vs. MOD16), the uncertainty of each dataset were evaluated at the Korean Peninsula, As a results, the GLDAS and GLEAM performed reasonable performance (low error variance and high correlation coefficient), whereas results of MOD16 showed high error variance and low correlation coefficient at the cropland.

Abstract
 요 지
 1. 서 론
 2. 연구지역 및 데이터
  2.1 연구지역
  2.2 인공위성 및 재분석 증발산 자료
 3. 방법론
  3.1 Triple Collocation (TC) and Extended TC (ETC)
 4. 결 과
  4.1 계절에 따른 수문기상인자의 변동성 파악
  4.2 인공위성 및 재분석 증발산 자료와 flux tower와의 비교
  4.3 Triple Collocation 방법을 이용한 증발산 자료 분석
 5. 결 론
 감사의 글
 References

• Baik Jongjin(Center for Built Environment, Sungkyunkwan University, 성균관대학교 건설환경연구소 박사후연구원) | 백종진
• Jeong Jaehwan(Department of Water Resources, Sungkyunkwan University, 성균관대학교 수자원전문대학원 박사과정) | 정재환
• Park Jongmin(Department of Civil and Environmental Engineering, University of Maryland, 메릴랜드대학교 건설환경공학과 박사과정) | 박종민
• Choi Minha(Department of Water Resources, Sungkyunkwan University, 성균관대학교 수자원전문대학원 교수) | 최민하 Corresponding Author