Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Merging technique for evapotranspiration based on in-situ, satellite, and reanalysis data using modifed KGE fusion method

수정된 KGE 방법을 활용한 지점, 인공위성, 재분석 자료 기반 증발산 융합 기술

  • 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 : 2018.10.08
  • Accepted : 2018.12.04
  • Published : 2019.01.31
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Abstract

The modified Kling-Gupta efficiency fusion method to merge actual evapotranspiration was proposed and compared with the simple Taylor skill's score method using Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MODIS Global Evapotranspiration Project (MOD16), and the flux tower on three different land cover types over the Korean peninsula and China. In the results of the weights estimated from two actual evapotranspiration merging techniques (i.e., STS and KGF), the weights of reanalysis data (i.e, GLDAS and GLEAM) in cropland and grassland showed similar performance, while the results of weights are different according to the merging techniques in forest. Both two merging techniques showed better results than original dataset in grassland and forest. However, there were no improvement in cropland compared to the other land cover types. The results of the KGF method slightly improved compared to those of the STS in grassland and forest.

실제증발산 자료를 융합하기 위한 Modified Kling-Gupta efficiency Fusion (KGF)방법을 제시하였고, 인공위성 및 재분석 증발산 자료인 Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM), MODIS Global Evapotranspiration Project (MOD16)를 활용하여 Simple Taylor skill's Score (STS)와 비교하였다. 한반도와 중국의 세가지 land cover type(i.e., cropland, grassland, forest)을 가진 flux tower에서 비교 검증을 실시하였다. 실제증발산의 융합 방법인 STS와 KGF로 계산된 가중치의 결과를 확인하면, cropland와 grassland에서 재분석 자료(GLDAS, GLEAM)가 높은 가중치 영향을 나타내지만, forest에서 융합 방법에 따라 가중치 영향이 다르게 나타났다. 전반적으로 실제증발산 융합 방법 적용 결과의 비교에서는 cropland에서는 융합에 사용된 자료에 비하여 높은 개선이 이뤄지지 않았지만, grassland와 forest 에서는 개선이 이뤄졌다. 두 방법 중 KGF의 결과가 STS의 결과에 비하여 약간 개선되는 결과를 나타내었다.

Keywords

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Fig. 1. Location map of the study sites

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Fig. 2. Time series of satellite- and reanalysis-datasets with flux tower at three different land cover types; a) cropland, b) grassland, and c) forest areas

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Fig. 3. Statistical analysis of seasonal ET of satellite- and reanalysis datasets with flux tower at three different land cover types; a) cropland, b) grassland, and c) forest areas

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Fig. 4. Bar chart of weighting factor results from the STS and KGF methods at three different land cover types; a) cropland, b) grassland, and c) forest areas

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Fig. 5. Radar chart of statistical analysis of satellite- and reanalysis ET product with merged ET dataset at three different land cover types; a) cropland, b) grassland, and c) forest areas

Table 1. Information of flux tower sites used in this study

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Table 2. Information of reanalysis- and satellite based datasets in this study

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Table 3. Statistical methods for error evaluation

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