• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.285-299
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• 2023

Estimating the evapotranspiration is very important factor for effective water resources management, and FAO Penman-Monteith (FAO P-M) model has been applied for reference evapotranspiration estimation by many researchers. However, because various input data are required for the application of FAO P-M model, understanding the effect of each input data on FAO P-M model is necessary. Therefore, in this study, for 56 study stations located in South Korea, the effects of 8 meteorological factors (maximum and minimum temperature, wind speed, relative humidity, solar radiation, vapor pressure deficit, net radiation, ground heat flux), energy and aerodynamic terms of FAO P-M model, and elevation on FAO P-M reference evapotranspiration (RET) estimation were analyzed. The relative sensitivity analysis was performed to determine how 10% increment of each specific independent variable affects a reference evapotranspiration under given set of condition that other independent variables are unchanged. Furthermore, to select the 5 representative stations and perform the monthly relative sensitivity analysis for those stations, 56 study stations were classified into 5 clusters using cluster analysis. The study results showed that net radiation was turned out to be the most sensitive factor in 8 meteorological factors for 56 study stations. The next most sensitive factor was relative humidity, solar radiation, maximum temperature, vapor pressure deficit and wind speed, followed by minimum temperature in order. Ground heat flux was the least sensitive factor. In case of ground surface condition, elevation showed very low positive relative sensitivity. Relativity sensitivities of energy and aerodynamic terms of FAO P-M model were 0.707 for energy term and 0.293 for aerodynamic term respectively, indicating that energy term was more contributable than aerodynamic term for reference evapotranspiration. The monthly relative sensitivities of meteorological factors showed the seasonal effects, and also the relative sensitivity of elevation showed different pattern each other among study stations. Therefore, for the application of FAO P-M model, the seasonal and regional sensitivity differences of each input variable should be considered.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.2
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• pp.72-78
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• 2009

The demand for rainfall data in gridded digital formats has increased in recent years due to the close linkage between hydrological models and decision support systems using the geographic information system. One of the most widely used tools for digital rainfall mapping is the PRISM (parameter-elevation regressions on independent slopes model) which uses point data (rain gauge stations), a digital elevation model (DEM), and other spatial datasets to generate repeatable estimates of monthly and annual precipitation. In the PRISM, rain gauge stations are assigned with weights that account for other climatically important factors besides elevation, and aspects and the topographic exposure are simulated by dividing the terrain into topographic facets. The size of facet or grid cell resolution is determined by the density of rain gauge stations and a $5{\times}5km$ grid cell is considered as the lowest limit under the situation in Korea. The PRISM algorithms using a 270m DEM for South Korea were implemented in a script language environment (Python) and relevant weights for each 270m grid cell were derived from the monthly data from 432 official rain gauge stations. Weighted monthly precipitation data from at least 5 nearby stations for each grid cell were regressed to the elevation and the selected linear regression equations with the 270m DEM were used to generate a digital precipitation map of South Korea at 270m resolution. Among 1.25 million grid cells, precipitation estimates at 166 cells, where the measurements were made by the Korea Water Corporation rain gauge network, were extracted and the monthly estimation errors were evaluated. An average of 10% reduction in the root mean square error (RMSE) was found for any months with more than 100mm monthly precipitation compared to the RMSE associated with the original 5km PRISM estimates. This modified PRISM may be used for rainfall mapping in rainy season (May to September) at much higher spatial resolution than the original PRISM without losing the data accuracy.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.1033-1046
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• 2019

To evaluate the utilization suitability of solar radiation models, estimated solar radiation from 13 solar radiation models were verified by comparing with measured solar radiation at 5 study stations in South Korea. Furthermore, for the evaluation of evaporation estimates according to solar radiation models, 5 different evaporation estimation equations based on Penman's combination approach were applied, and evaporation estimates were compared with pan evaporation. Some solar radiation models require only meteorological data; however, some other models require not only meteorological data but also geographical data such as elevation. The study results showed that solar radiation model based on the ratio of the duration of sunshine to the possible duration of sunshine, maximum temperature, and minimum temperature provided the estimated solar radiation that most closely match measured solar radiation. Accuracy of estimated solar radiation also greatly improved when Angstrőm-Prescott model coefficients are adjusted to the study stations. Therefore, when choosing the solar radiation model for evaporation estimation, both data availability and model capability should be considered simultaneously. When applying measured solar radiation for estimating evaporation, evaporation estimates from Penman, FAO Penman-Monteith, and KNF equations are most close to pan evaporation rates in Jeonju and Jeju, Seoul and Mokpo, and Daejeon respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.29-35
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• 2010

Soil organic carbon (SOC), being a help to forest formation and control of carbon dioxide in the air, is found to be an important factor by which global warming is influenced. Excavating the samples by whole area is very inefficient method to discovering the distribution of SOC. So, the development of suitable model for expecting the relative amount of the SOC makes better use of expecting the SOC. In the present study, a model based on a decision tree algorithm is introduced to estimate the amount of SOC along with accessing influencing factors such as altitude, aspect, slope and type of trees. The model was applied to a real site and validated by 10-fold cross validation using two softwares, See 5 and Weka. From the results given by See 5, it can be concluded that the amount of SOC in surface layers is highly related to the type of trees, while it is, in middle depth layers, dominated by both type of trees and altitude. The estimation accuracy was rated as 70.8% in surface layers and 64.7% in middle depth layers. A similar result was, in surface layers, given by Weka, but aspect was, in middle depth layers, found to be a meaningful factor along with types of trees and altitude. The estimation accuracy was rated as 68.87% and 60.65% in surface and middle depth layers. The introduced model is, from the tests, conceived to be useful to estimation of SOC amount and its application to SOC map production for wide areas.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.3
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• pp.37-48
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• 2010

The purpose of this paper is to evaluate how the rainfall field effect on a runoff simulation using grid radar rainfall data and ground gauge rainfall. The Gwangdeoksan radar and ground-gauge rainfall data were used to estimate a spatial rainfall field, and a hydrologic model was used to evaluate whether the rainfall fields created by each method reproduced a realistically valid spatial and temporal distribution. Pilot basin in this paper was the Naerin stream located in Inje-gun, Gangwondo, 250m grid scale digital elevation data, land cover maps, and soil maps were used to estimate geological parameters for the hydrologic model. For the rainfall input data, quantitative precipitation estimation(QPE), adjusted radar rainfall, and gauge rainfall was used, and then compared with the observed runoff by inputting it into a $Vflo^{TM}$ model. As a result of the simulation, the quantitative precipitation estimation and the ground rainfall were underestimated when compared to the observed runoff, while the adjusted radar rainfall showed a similar runoff simulation with the actual observed runoff. From these results, we suggested that when weather radars and ground rainfall data are combined, they have a greater hydrological usability as input data for a hydrological model than when just radar rainfall or ground rainfall is used separately.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.83-96
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• 2017

This study presented a surface water quality modeling framework considering the spatial resolution of pollutant load estimation to better represent stream water quality characteristics in the Saemangeum watershed which has been focused on keeping its water resources sustainable after the Saemangeum embankment construction. The watershed delineated into 804 sub-watersheds in total based on the administrative districts, which were units for pollutant load estimation and counted as 739 in the watershed, Digital Elevation Model (DEM), and agricultural structures such as drainage canal. The established model consists of 7 Mangyung (MG) sub-models, 7 Dongjin (DJ) sub-models, and 3 Reclaimed sub-models, and the sub-models were simulated in a sequence of upstream to downstream based on its connectivity. The hydrologic calibration and validation of the model were conducted from 14 flow stations for the period of 2009 and 2013 using an automatic calibration scheme. The model performance to the hydrologic stations for calibration and validation showed that the Nash-Sutcliffe coefficient (NSE) ranged from 0.66 to 0.97, PBIAS were -31.0~16.5 %, and $R^2$ were from 0.75 to 0.98, respectively in a monthly time step and therefore, the model showed its hydrological applicability to the watershed. The water quality calibration and validation were conducted based on the 29 stations with the water quality constituents of DO, BOD, TN, and TP during the same period with the flow. The water quality model were manually calibrated, and generally showed an applicability by resulting reasonable variability and seasonality, although some exceptional simulation results were identified in some upstream stations under low-flow conditions. The spatial subdivision in the model framework were compared with previous studies to assess the consideration of administrative boundaries for watershed delineation, and this study outperformed in flow, but showed a similar level of model performance in water quality. The framework presented here can be applicable in a regional scale watershed as well as in a need of fine-resolution simulation.

• Archives of design research
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• v.15 no.1
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• pp.191-202
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• 2002

As family types have been diverse, patterns of living and living space became diverse as much as users are. Therefore, it is needed to provide various remodeled design of living space corresponding to changes of users'living patterns, and to provide these remodeling process to users directly on the web. In this paper, use scenario for the Internet-based Virtual Apartment Remodeling Simulator is researched as an export system to remodel space in accordance with users diverse lifestyle paradigm and the website is developed. The study consists of four parts. First, the general concept of remodeling, including the range and types of remodeling, are defined, and the misleading terms in this field are reviewed and organized by secondary research Second, fixed factors and variable factors are differentiated in the complex building for residence and business that was decided as a basic building type in this study. Third, there needed a database for consulting, final material, pre-estimation real estimation for simulation of remodeling. This database was introduced along with floor plan and elevation. Finally, the remodeling simulator is presented by the case study developed on the web. The system structure and use scenario are also presented. In order to present and inspect design alternatives, prototype was produced. The Final simulator was enhanced by defeating problems regarding interface efficiency and missing information of existing online site.

• Spatial Information Research
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• v.22 no.1
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• pp.55-63
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• 2014

Near surface air temperature data which are one of the essential factors in hydrology, meteorology and climatology, have drawn a substantial amount of attention from various academic domains and societies. Meteorological observations, however, have high spatio-temporal constraints with the limits in the number and distribution over the earth surface. To overcome such limits, many studies have sought to estimate the near surface air temperature from satellite image data at a regional or continental scale with simple regression methods. Alternatively, we applied various Kriging methods such as ordinary Kriging, universal Kriging, Cokriging, Regression Kriging in search of an optimal estimation method based on near surface air temperature data observed from automatic weather stations (AWS) in South Korea throughout 2010 (365 days) and MODIS land surface temperature (LST) data (MOD11A1, 365 images). Due to high spatial heterogeneity, auxiliary data have been also analyzed such as land cover, DEM (digital elevation model) to consider factors that can affect near surface air temperature. Prior to the main estimation, we calculated root mean square error (RMSE) of temperature differences from the 365-days LST and AWS data by season and landcover. The results show that the coefficient of variation (CV) of RMSE by season is 0.86, but the equivalent value of CV by landcover is 0.00746. Seasonal differences between LST and AWS data were greater than that those by landcover. Seasonal RMSE was the lowest in winter (3.72). The results from a linear regression analysis for examining the relationship among AWS, LST, and auxiliary data show that the coefficient of determination was the highest in winter (0.818) but the lowest in summer (0.078), thereby indicating a significant level of seasonal variation. Based on these results, we utilized a variety of Kriging techniques to estimate the surface temperature. The results of cross-validation in each Kriging model show that the measure of model accuracy was 1.71, 1.71, 1.848, and 1.630 for universal Kriging, ordinary Kriging, cokriging, and regression Kriging, respectively. The estimates from regression Kriging thus proved to be the most accurate among the Kriging methods compared.

• Journal of Korea Water Resources Association
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• v.53 no.7
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• pp.545-555
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• 2020

A feasibility modeling for potential hydroelectric dam site selection was suggested using 1 sec ASTGTM (ASTER Global Digital Elevation Model) and Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) derived land use (MCD12Q1) data. The modeling includes DEM pre-processing of peak, sink, and flat, river network generation, watershed delineation and segmentation, terrain analysis of stream cross section and reservoir storage, and estimation of submerged area for compensation. The modeling algorithms were developed using Python and as an open source GIS. When a user-defined stream point is selected, the model evaluates potential hydroelectric head, reservoir surface area and storage capacity curve, watershed time of concentration from DEM, and compensation area from land use data. The model was tested for 4 locations of already constructed Buhang, BohyunMountain, Sungdeok, and Yeongju dams. The modeling results obtained maximum possible heads of 37.0, 67.0, 73.0, 42.0 m, surface areas of 1.81, 2.4, 2.8, 8.8 ㎢, storages of 35.9, 68.0, 91.3, 168.3×10⁶ ㎥ respectively. BohyunMountain and Sungdeok show validity but in case of Buhang and Yeongju dams have maximum head errors. These errors came from the stream generation error due to ASTGTM. So, wrong dam watershed boundary limit the head. This study showed a possibility to estimate potential hydroelectric dam sites before field investigation especially for overseas project.

• Geophysics and Geophysical Exploration
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• v.17 no.1
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• pp.11-20
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• 2014

To reveal shear-wave velocities ($v_s$) and site characterization of Wonju, Korea, Rayleigh waves were recorded at 78 sites of lower altitude using 12 to 24 4.5-Hz vertical geophones for 20 days during the period of February to September 2013. Dispersion curves of the Rayleigh waves obtained by the extended spatial autocorrelation method were inverted using the damped least-squares method to derive $v_s$ models. From these 1-D models, the average $v_s$ to a depth of 30 m ($v_s30$), $v_s$ of weathered rocks, depths to these basement rocks, and average $v_s$ of the overburden layer were derived to be $16.3{\pm}0.7m$, $576{\pm}8m/s$, $290{\pm}7m/s$, and $418{\pm}13m/s$, respectively, in the 95% confidence range. To determine adequate proxies for $v_s30$, we computed correlation coefficients of $v_s30$ with topographic slope (r = 0.46) and elevation (r = 0.43). An empirical linear relationship is presented as a combination of individually estimated $v_s30$ with weighting factors of 0.45, 0.45, and 0.1 for topographic slope, elevation, and mapped lithology, respectively. Due to a weak correlation between $v_s30$ obtained from inversion of dispersion curves and the proxy-based estimation (r = 0.50), however, the relatively large error range should be considered for applications of this relationship.