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http://dx.doi.org/10.3741/JKWRA.2017.50.12.837

Development of Land Surface Model for Soyang river basin  

Lee, Jaehyeon (Department of Civil Engineering, Hongik University)
Cho, Huidae (Department of Civil and Construction Engineering, Southern Polytechnic College of Engineering and Engineering Technology, Kennesaw State University)
Choi, Minha (Department of Water Resources, Graduate School of Water Resources, Sungkyunkwan University)
Kim, Dongkyun (Department of Civil Engineering, Hongik University)
Publication Information
Journal of Korea Water Resources Association / v.50, no.12, 2017 , pp. 837-847 More about this Journal
Abstract
Land Surface Model (LSM) was developed for the Soyang river basin located in Korean Peninsula to clarify the spatio-temporal variability of hydrological weather parameters. Variable Infiltration Capacity (VIC) model was used as a LSM. The spatial resolution of the model was 10 km and the time resolution was 1 day. Based on the daily flow data from 2007 to 2010, the 7 parameters of the model were calibrated using the Isolated Particle Swarm Optimization algorithm and the model was verified using the daily flow data from 2011 to 2014. The model showed a Nash-Sutcliffe Coefficient of 0.90 and a correlation coefficient of 0.95 for both calibration and validation periods. The hydrometeorological variables estimated for the Soyang river basin reflected well the seasonal characteristics of summer rainfall concentration, the change of short and shortwave radiation due to temperature change, the change of surface temperature, the evaporation and vegetation increase in the cover layer, and the corresponding change in total evapotranspiration. The model soil moisture data was compared with in-situ soil moisture data. The slope of the trend line relating the two data was 1.087 and correlation coefficient was 0.723 for the Spring, Summer and Fall season. The result of this study suggests that the LSM can be used as a powerful tool in developing precise and efficient water resources plans by providing accurate understanding on the spatio-temporal variation of hydrometeorological variables.
Keywords
Land Surface Model; VIC model; Soil moisture; Particle Swarm Optimization;
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