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Goyang, South Korea

Kim S.,Pusan National University | Sun H.,Pusan National University | Jung S.,Hydrological Survey Center
Journal of Hydrology | Year: 2011

Variation in soil moisture content throughout soil profiles during several sequential rainfalls represents the internal hydrological response on a hillslope scale. A multiplex TDR system has been operating on a mountainous hillslope to obtain the time series of soil moisture along two transects in the study area. The soil moisture modeling conducted in this study highlights our understanding of the inter-relationships between soil moistures at identical spatial locations, but at different depths. A sequential procedure was used for the time series modeling to delineate an appropriate model for application to all monitoring points. The feedback relationship of soil wetness between two different depths was expressed with the proposed vector autoregressive model. Based on the successful modeling of 31 coupled soil water histories, the vertical distributions of the stochastic model throughout the study area were obtained. The distribution of the delineated models implied a spatial distribution of the hydrological processes, such as vertical infiltration for the upper soil layers and some of the lower soil layers (38 out of 62 models), lateral redistribution and subsurface flow over bedrock mostly for the lower soil layers (24 out of 62 models) on the steep hillslope. With the use of the resultant models, applications were proposed to improve the data acquisition system, i.e. gap filling for missing data and limited prediction for an ungauged location. © 2011 Elsevier B.V. Source


Park J.,Sungkyunkwan University | Byun K.,University of Notre Dame | Choi M.,Sungkyunkwan University | Jang E.,Hanyang University | And 3 more authors.
Stochastic Environmental Research and Risk Assessment | Year: 2015

Over the past few decades, energy and water fluxes have been directly measured by a global flux network, which was established by regional and continental network sites based on an eddy covariance (EC) method. Although, the EC method possesses many advantages, its typical data coverage could not exceed 65 % due to various environmental factors including micrometeorological conditions and systematic malfunctions. In this study, four different methodologies were used to fill the gap in latent heat flux (LE) data. These methods were Food and Agriculture Organization Penman–Monteith (FAO_PM) equation, mean diurnal variation (MDV), Kalman filter, and dynamic linear regression (DLR). We used these methods to evaluate two flux towers at different land cover types located at Seolmacheon (SMC) and Cheongmicheon (CMC) in Korea. The LE estimated by four different approaches was a fairly close match to the observed LE, with the root mean square error ranging from 4.81 to 61.88 W m−2 at SMC and from 0.89 to 60.27 W m−2 at CMC. At both sites, the LE estimated by DLR showed the best result with the value of the coefficient of correlation (R), equal to 0.99. Cost-effectiveness analysis for evaluating four different gap-filling methods also confirmed that DLR showed the best cost effectiveness ratio (C/R). The Kalman filter showed the second highest C/R rank except in the winter season at SMC followed by MDV and FAO_PM. Energy closures with estimated LE led to further improved compare to the energy closure of the observed LE. The results showed that the estimated LE at CMC was a better fit with the observed LE than the estimated LE at SMC due to the more complicated topography and land cover at the SMC site. This caused more complex interactions between the surface and the atmosphere. The estimated LE with all approaches used in this study showed improvement in energy closure at both sites. The results of this study suggest that each method can be used as a gap-filling model for LE. However, it is important to consider the strengths and weaknesses of each method, the purpose of research, characteristics of the study site, study period and data availability. © 2015 Springer-Verlag Berlin Heidelberg Source

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