Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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A study on the analyzing of uncertainty for actual evapotranspiration: flux tower, satellite-based and reanalysis based dataset

실제증발산 자료의 불확실성 파악에 관한 연구: flux tower, 인공위성 및 재분석자료

  • Baik, Jongjin (Center for Built Environment, Sungkyunkwan University) ;
  • Jeong, Jaehwan (Department of Water Resources, Sungkyunkwan University) ;
  • Park, Jongmin (Department of Civil and Environmental Engineering, University of Maryland) ;
  • Choi, Minha (Department of Water Resources, Sungkyunkwan University)
  • 백종진 (성균관대학교 건설환경연구소) ;
  • 정재환 (성균관대학교 수자원전문대학원) ;
  • 박종민 (메릴랜드대학교 건설환경공학과) ;
  • 최민하 (성균관대학교 수자원전문대학원)
  • Received : 2018.09.18
  • Accepted : 2018.10.30
  • Published : 2019.01.31
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Abstract

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.

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) 방법을 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, GLDAS와 GLEAM이 한반도 영역에서 낮은 error variance 와 높은 correlation coefficient를 나타낸 반면, MOD16의 경우, 농지에서 낮은 correlation coefficient과 높은 error variance를 나타내었다.

Keywords

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Fig. 1. Location of two flux tower measurements (SMK and CFK) over korean peninsula

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Fig. 2. Variations of the time series of hydrological variables on each months

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Fig. 3. Statistical results of three ET products with flux tower measurement at two difference sites over korean peninsula: a) SMK and b) CFK

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Fig. 4. Statistical results of three ET products with flux tower measurement at two difference sites over korean peninsula: a) SMK and b) CFK

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Fig. 5. Bar chart of error variance and correlation coefficient estimated from ETC and standard TC for each land classification over korean peninsula

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Fig. 6. Spatial distribution of error variance and correlation coefficient estimated from ETC and standard TC over Korean peninsula during 2003 to 2014.

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