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http://dx.doi.org/10.11108/kagis.2021.24.4.065

Calculation of Soil Moisture and Evapotranspiration for KLDAS(Korea Land Data Assimilation System) using Hydrometeorological Data Set  

PARK, Gwang-Ha (Water Resources Management Research Center K-water Research Institute)
LEE, Kyung-Tae (Earth System Science Interdisciplinary Center (ESSIC-UMD), NASA GSFC)
KYE, Chang-Woo (SELab Inc., Research Institute)
YU, Wan-Sik (Water Resources Management Research Center K-water Research Institute)
HWANG, Eui-Ho (Water Resources Management Research Center K-water Research Institute)
KANG, Do-Hyuk (Earth System Science Interdisciplinary Center (ESSIC-UMD), NASA GSFC)
Publication Information
Journal of the Korean Association of Geographic Information Studies / v.24, no.4, 2021 , pp. 65-81 More about this Journal
Abstract
In this study, soil moisture and evapotranspiration were calculated throughout South Korea using the Korea Land Data Assimilation System(KLDAS) of the Korea-Land Surface Information System(K-LIS) built on the basis of the Land Information System (LIS). The hydrometeorological data sets used to drive K-LIS and build KLDAS are MERRA-2(Modern-Era Retrospective analysis for Research and Applications, version 2) GDAS(Global Data Assimilation System) and ASOS(Automated Synoptic Observing System) data. Since ASOS is a point-based observation, it was converted into grid data with a spatial resolution of 0.125° for the application of KLDAS(ASOS-S, ASOS-Spatial). After comparing the hydrometeorological data sets applied to KLDAS against the ground-based observation, the mean of R2 ASOS-S, MERRA-2, and GDAS were analyzed as temperature(0.994, 0.967, 0.975), pressure(0.995, 0.940, 0.942), humidity (0.993, 0.895, 0.915), and rainfall(0.897, 0.682, 0.695), respectively. For the hydrologic output comparisons, the mean of R2 was ASOS-S(0.493), MERRA-2(0.56) and GDAS (0.488) in soil moisture, and the mean of R2 was analyzed as ASOS-S(0.473), MERRA-2(0.43) and GDAS(0.615) in evapotranspiration. MERRA-2 and GDAS are quality-controlled data sets using multiple satellite and ground observation data, whereas ASOS-S is grid data using observation data from 103 points. Therefore, it is concluded that the accuracy is lowered due to the error from the distance difference between the observation data. If the more ASOS observation are secured and applied in the future, the less error due to the gridding will be expected with the increased accuracy.
Keywords
K-LIS; KLDAS; ASOS; ASOS-S; Soil Moisture; Evapotranspiration;
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