• Korean Journal of Soil Science and Fertilizer
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• v.44 no.5
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• pp.727-733
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• 2011

A new Korean standard for the determination of Cr(VI) in soils has been officially published as ES 07408.1 in 2009. This analytical method is based on the hot alkaline digestion and colorimetric detection prescribed by U.S. EPA method 3060A and 7196A. The hot alkaline digestion accomplished using 0.28 M $Na_2CO_3$ and 0.5 M NaOH solution (pH 13.4) at $90{\sim}95^{\circ}C$ determines total Cr(VI) in soils extracting all forms of Cr(VI), including water-soluble, adsorbed, precipitated, and mineral-bound chromates. This aggressive alkaline digestion, however, proved to be problematic for certain soils which contain large amounts of soluble humic substances or active manganese oxides. Cr(III) could be oxidized to Cr(VI) by manganese oxides during the strong alkaline extraction, resulting in overestimation (positive error) of Cr(VI). In contrast, Cr(VI) reduction by dissolved humic matter or Fe(II) could occur during the neutralization and acidic colorimetric detection procedure, resulting in underestimation (negative error) of Cr(VI). Futhermore, dissolved humic matter hampered the colorimetric detection of Cr(VI) using UV/Vis spectrophotometer due to the strong coloration of the filtrate, resulting in overestimation (positive error) of Cr(VI). Without understanding the mechanisms of Cr(VI) and Cr(III) transformation during the analysis it could be difficult to operate the experiment in laboratory and to evaluate the Cr(VI) results. For this reason, in this paper we described the theoretical principles and limitations of Cr(VI) analysis and provided useful guidelines for laboratory work and Cr(VI) data analysis.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1719-1729
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• 2021

Leaf area index (LAI) provides valuable information necessary for sustainable and effective management of forests. Although global high resolution LAI data are provided by European Space Agency using Sentinel-2 satellite images, they have not considered forest characteristics in model development and have not been evaluated for various forest ecosystems in South Korea. In this study, we proposed a LAI estimation model combining machine learning and the PROSAIL radiative transfer model using Sentinel-2 satellite data over a local forest area in South Korea. LAI-2200C was used to measure in situ LAI data. The proposed LAI estimation model was compared to the existing Sentinel-2 LAI product. The results showed that the proposed model outperformed the existing Sentinel-2 LAI product, yielding a difference of bias ~ 0.97 and a difference of root-mean-square-error ~ 0.81 on average, respectively, which improved the underestimation of the existing product. The proposed LAI estimation model provided promising results, implying its use for effective LAI estimation over forests in South Korea.

• Journal of Korea Water Resources Association
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• v.54 no.5
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• pp.321-334
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• 2021

A fixed reflectivity-rainfall relationship approach, such as the Marshall-Palmer relationship, for an entire year and different seasons, can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of long-term radar reflectivity for South Korea to obtain a nationwide calibrated Z-R relationship and the associated uncertainties within a Bayesian inference framework. A calibrated spatially structured pattern in the parameters exists, particularly for the wet season and parameter for the dry season. A pronounced region of high values during the wet and dry seasons may be partially associated with storm movements in that season. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields. In contrast, the radar rainfall fields obtained from the existing Marshall-Palmer relationship show a systematic underestimation. In the event of high impact weather, it is expected that the value of national radar resources can be improved by establishing an active watershed-level hydrological analysis system.

• Journal of Korea Water Resources Association
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• v.52 no.7
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• pp.451-461
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• 2019

The dam reservoir inflow prediction is utilized to ensure for water supply and prevent future droughts. In this study, we predicted the dam reservoir inflow and analyzed how seasonal weather forecasting affected the accuracy of the inflow for even multi-purpose dams. The hindcast and forecast of GloSea5 from KMA were used as input for rainfall-runoff models. TANK, ABCD, K-DRUM and PRMS models which have individual characteristics were applied to simulate inflow prediction. The dam reservoir inflow prediction was assessed for the periods of 1996~2009 and 2015~2016 for the hindcast and forecast respectively. The results of assessment showed that the inflow prediction was underestimated by comparing with the observed inflow. If rainfall-runoff models were calibrated appropriately, the characteristics of the models were not vital for accuracy of the inflow prediction. However the accuracy of seasonal weather forecasting, especially precipitation data is highly connected to the accuracy of the dam inflow prediction. It is recommended to consider underestimation of the inflow prediction when it is used for operations. Futhermore, for accuracy enhancement of the predicted dam inflow, it is more effective to focus on improving a seasonal weather forecasting rather than a rainfall-runoff model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.165-174
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• 2019

As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles.

• Atmosphere
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• v.28 no.4
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• pp.369-382
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• 2018

Planetary boundary layer height (PBLH), produced by the Local Data Assimilation and Prediction System (LDAPS), was verified using RawinSonde (RS) data obtained from observation at Daegwallyeong (DGW) and Sokcho (SCW) during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). The PBLH was calculated using RS data by applying the bulk Richardson number and the parcel method. This calculated PBLH was then compared to the values produced by LDAPS. The PBLH simulations for DGW and SCW were generally underestimation. However, the PBLH was an overestimation from surface to 200 m and 450 m at DGW and SCW, respectively; this result of model's failure to correctly simulate the Surface Boundary Layer (SBL) and the Mixing Layer (ML) as the PBLH. When the accuracy of the PBLH simulation is low, large errors are seen in the mid- and low-level humidity. The highest frequencies of Planetary boundary layer (PBL) types, calculated by the LDAPS at DGW and SCW, were presented as types Ι and II, respectively. Analysis of meteorological factors according to the PBL types indicate that the PBLH of the existing stratocumulus were overestimated when the mid- and low-level humidity errors were large. If the instabilities of the surface and vertical mixing into clouds are considered important factors affecting the estimation of PBLH into model, then mid- and low-level humidity should also be considered important factors influencing PBLH simulation performance.

• Korean Journal of Environmental Biology
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• v.36 no.4
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• pp.507-516
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• 2018

The closed chamber method, which is one of the most commonly used method for measuring greenhouse gases produced in rice paddy fields, has limitations in measuring dynamic $CH_4$ flux with spatio-temporal constrains. In order to deal with the limitation of the closed chamber method, some studies based on open-path of eddy covariance method have been actively conducted recently. The aim of this study was to compare the $CH_4$ fluxes measured by open-path and closed chamber method in the paddy rice fields. The open-path, one of the gas ($CO_2$, $CH_4$ etc.) analysis methods, is technology where a laser beam is emitted from the source passes through the open cell, reflecting multiple times from the two mirrors, and then detecting. The $CH_4$ emission patterns by these two methods during rice cultivation season were similar, but the total $CH_4$ emission measured by open-path method were 31% less than of the amount measured by closed chamber. The reason for the difference in $CH_4$ emission was due to overestimation by closed chamber and underestimation by open-path. The closed chamber method can overestimate $CH_4$ emissions due to environmental changes caused by high temperature and light interruption by acrylic partition in chamber. On the other hand, the open-path method for eddy covariance can underestimate its emission because it assumes density fluctuations and horizontal homogeneous terrain negligible However, comparing $CH_4$ fluxes at the same sampling time (AM 10:30-11:00, 30-min fluxes) showed good agreements ($r^2=0.9064$). The open-path measurement technique is expected to be a good way to compensate for the disadvantage of the closed chamber method because it can monitor dynamic $CH_4$ fluctuation even if data loss is taken into account.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.367-378
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• 2021

Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.

• Journal of Korea Water Resources Association
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• v.54 no.1
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• pp.27-38
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• 2021

Recently, many studies have been conducted to use the radar rainfall in hydrology. However, in the case of weather radar, the beam is blocked due to the limitation of the observation such as mountain effect, which causes underestimation of the radar rainfall. In this study, the radar rainfall was estimated using the Hybrid Sacn Reflectivity (HSR) technique for hydrological use of weather radar and the runoff analysis was performed using the GRM model which is a distributed rainfall-runoff model. As a result of performing the radar rainfall correction and runoff simulation for 5 rainfall events, the accuracy of the dual-polarization radar rainfall using the HSR technique (Q_H_KDP) was the highest with an error within 15% of the ground rainfall. In addition, the result of runoff simulation using Q_H_KDP also showed an accuracy of R2 of 0.9 or more, NRMSE of 1.5 or less and NSE of 0.5 or more. From this study, we examined the application of the dual-polarization radar and this results can be useful for studies related to the hydrological application of dual-polarization radar rainfall in the future.

• Journal of radiological science and technology
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• v.45 no.3
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• pp.241-248
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• 2022

In this study, a recovery coefficient (RC) calculation was conducted that can correct the underestimation of the standardized uptake value (SUV) due to the partial volume effect (PVE) through phantom measurements and formulas. The experiment was conducted using a dynamic phantom capable of implement cranio-caudal movement at a respiratory rate of 15 times per minute along with the measured phantom experiment of the stopped state, and the RC of the moving state is calculated and compared. Ingenuity TF (Philips Healthcare, Netherland) was used as a positron emission tomography/computed tomography (PET/CT) device. PET-CT Phantom (Biodex Medical System, USA) was used as a phantom for measurement. A phantom image in a stationary state was acquired, and a moving phantom image was acquired using the AZ-733V Respiratory Phantom (Anzai Medical Co, Japan) capable of breathing movement in the cranio-caudal direction under the same acquisition parameters. For RC calculation, the sphere maximum radioactivity concentration and the background mean radioactivity concentration of the acquired images were measured, and the initially determined sphere and background radioactivity concentrations were calculated. The calculated RC was 0.08 to 0.72. The size of sphere smaller, it was confirmed that the RC reduced. And the RC in the moving state reduced than in the stationary state. As a result of this study, the change of the RC was confirmed according to the size of spheres and the phantom moving. Using the RC derived by implement movement of breathing with the respiratory phantom, it is possible to considering correction of underestimated SUV by the partial volume effect of PET images and the patient movements.