• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.82-82
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• 2022

최근 지구온난화로 인한 전 세계적 기후변화가 일어나고 있으며, 이러한 지구온난화 방지 대책으로 탄소의 중요성과 탄소중립을 선언하는 국가가 증가하고 있다. 탄소의 중요성이 증가함에 따라 유역 내의 탄소 중립이 중요 이슈로 떠오르고 있다. 유역 내 탄소 저장원으로는 숲, 하천, 토양 등이 존재하나 하천의 경우 탄소의 저장이 곧 수질 오염과 연결이 되기에 바람직한 방안이 될 수 없다. 그러나 토양의 경우 방대한 양의 탄소를 저장하기에 적합한 기능을 하기에 다른 저장원들에 비해 중요한 저장원으로 대두되고 있다. 토양탄소의 경우 일반적으로 유기물과 무기물의 형태로 토양에 저장된다. 이중 토양유기탄소는 장기간 토양 속에서 대기와의 탄소 조절 역할을 하기에 중요 요인으로 대두되고 있다. 하지만 기후변화로 인한 국지성 호우 및 무분별한 개발 등이 증가함에 따라 유역 내 토양 생태계의 변화가 일어나고 있으며, 이에 따른 유역 내 토양유기탄소 또한 손실이 일어나고 있다. 따라서 본 연구에서는 토양의 특성과 모델을 활용하여 유역단위 토양유기탄소량의 변화량을 산정하여 비교 및 분석을 하고자 한다. 이를 위해서 토양유기탄소의 모의가 가능한 APEX 모델을 활용하였으며, 선정된 연구 대상 지역의 토양 특성 자료를 활용하여 입력자료 전처리를 진행 후 모의를 진행하였다. 이후 선행연구 및 보고서를 통한 실측자료를 기반으로 모델 매개변수 보정을 진행하였으며, 보정된 결과를 통해 유역에 대한 토양유기탄소를 산정을 진행하였고 기간별 변화의 차이를 분석하였다. 해당 연구를 통해 유역 내 잠재되어있는 토양유기탄소량 정량화 등의 연구에 활용될 수 있을 것으로 기대한다.

• Journal of Soil and Groundwater Environment
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• v.12 no.1
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• pp.53-63
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• 2007

Soil risk assessment models were used to determine the goals of soil remediation and to establish the soil quality standards in developed countries. Recently, Korean Ministry of Environment prepared the guideline for soil risk assessment. Soil risk assessment model applicable to Korean situation will be needed in the near future. In this study, three models for soil risk assessment were extensively compared to suggest the fundamental components that required for the soil risk assessment in Korea. The models considered in this study were CalTOX in the United States, CLEA (Contaminated Land Exposure Assessment) in the United Kingdom, and CSOIL in the Netherlands. The major exposure routes and the intake estimation equations suitable for Korean situation were suggested. The exposure routes suggested were intake of the crops, underground water, indoor outdoor soil ingestion, dust inhalation and a volatile matter inhalation. The equations for intake estimation used in CalTOX and CSOIL seem to be applicable for the calculation of the human intake in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.862-865
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• 2012

유사량은 하천의 단면을 단위시간 동안 통과하는 토사의 양을 의미하며, 하천 구조물의 설계 및 유지관리를 위한 기본자료로 활용된다. 유사량은 하천 유역의 지형적인 특성과 기상요소에 영향을 받으며, 이를 규명하기 위한 많은 연구들이 수행되어 왔다. GIS기반의 유사량 예측모델로서 국내에서는 개정범용토양유실공식과 유사운송비(Sediment Delivery Ratio)를 이용하여 유역단위 유사량을 예측하는 연구가 이루어져왔다. Gavrilovi$\acute{c}$ 모델은 유역의 총 연유사량을 예측하고 토양침식의 정도를 정량화할 수 있는 경험적 모형으로 지질 및 토양, 지형조건, 기후인자(연평균 강우량, 연평균 온도), 토지이용의 6가지 입력변수로 구성되어 있다. 본 연구는 Gavrilovi$\acute{c}$ 모델의 국내 적용성을 검토하기 위한 것으로서, 왕숙천 유역을 대상으로 Gavrilovi$\acute{c}$ 모델을 적용하여 유사량을 산정해본 결과, 실측값을 약 20% 내외로 비교적 근사하게 추정할 수 있음을 알 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.115-122
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• 2018

수문순환 과정 중 일부인 유출량을 산정하기 위해서는 지형학적 변수, 강우량, 토양수분, 증발산량 등의 인자들이 필요하다. 본 연구에서는 미계측 지역의 유출량 산정을 위한 주요 인자인 토양수분과 증발산량을 지표면 모델을 통해 산출하고자 한다. 사용한 시스템은 미국 NASA에서 개발한 LIS(Land Information System) 프레임워크이며 LIS에 적용된 지표면 모델 중 Noah-MP을 초기 매개변수로 사용하였다. 입력 자료는 전지구 범위로 제공되는 자료를 사용하여 남한 지역을 대상으로 토양수분 및 증발산량을 산출하고 지상 관측 자료, 원격탐사 기반의 토양수분과 증발산량을 통해 정확도를 평가하였고 ASOS 관측 자료를 내삽하여 산출된 토양수분 및 증발산량의 정확도도 평가하였다. 남한 지역을 대상으로 정확도를 평가한 후 대표표적 미계측 지역인 북한을 대상으로 토양수분 및 증발산량을 산출하였다. LIS의 Noah-MP 지표면 모델로 토양수분 및 증발산량을 산출한 결과 ASOS를 내삽하여 산출한 결과가 설마천의 경우 정확도는 오히려 낮아졌고 청미천, 서산의 정확도는 높아졌다. 이는 초기 매개변수 설정을 이용한 것과 전지구 범위의 자료를 사용하여 토양수분 및 증발산량을 산출하여 발생된 오차이며 매개변수 최적화 및 고해상도의 입력자료를 사용하면 보다 높은 정확도를 확보할 수 있다. 이를 통해 미계측 지역에서도 충분히 활용 가능한 토양수분 및 증발산량을 산출하여 유출량을 산정할 수 있을 것으로 사료된다.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.7
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• pp.544-551
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• 2011

The objective of this study was to analyze VIRULO model, a probabilistic quantitative model, which had been developed by US Environmental Protection Agency. The model could assess the viral attenuation capacity of soil as hydrogeologic barrier using Monte Carlo simulation. The governing equations used in the model were composed of unsaturated flow equations and viral transport equations. Among the model parameters, those related to water flow for 11 soil types were from UNDODA data, and those related to 5 virus species were from the literatures. The model compared the attenuation factor with threshold of attenuation to determine the probability of failure and presented the exceedances and Monte Carlo runs as output. The analysis indicated that among 11 USDA soil types, the viral attenuation capacity of loamy sand and sand were far lower than those of clay and silt soils. Also, there were differences in the attenuation in soil among 5 viruses with poliovirus showing the highest attenuation. The viral attenuation capacity of soil decreased sharply with increasing soil water content and increased nonlinearly with increasing soil barrier length. This study indicates that VIRULO model could be considered as a useful screening tool for viral risk assessment in subsurface environment.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.4 no.4
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• pp.365-372
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• 2006

A dynamic compartment model is presented to predict the contamination level of agricultural plant by $^{137}C_s$ as a result of a soil deposition. The model considered the processes of a percolation, soil mixing by a plowing before transplanting, plant uptake, leaching to a deep soil, and fixation to a clay mineral. The effects of the soil properties (pH, clay mineral, organic matter content, and exchangeable K), which are spatially varied, on a plant uptake and the leaching rates of $^{137}C_s$ in a root zone soil were modeled by the Absalom model. To test the validity of the model, the $^{137}C_s$ aggregated transfer factors(TFa) for rice plants were compared with those observed from some simulated $^{137}C_s$ soil deposition experiments, which were carried out with respect to rice plants cultivated in seventeen paddy soils of different properties for two consecutive years. Observed $^{137}C_s$ TFa values of the rice plants did not show an evident trend for the pH and clay content of the soil properties, while they increased with an increasing organic matter content or a decreasing exchangeable K concentration. Predicted $^{137}C_s$ TFa values of the rice plants were found to be comparable with those observed.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.6
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• pp.522-528
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• 2009

This study focused on establishing prediction models using visible-near infrared spectrum to simultaneously detect multiple components of soils and enhancing the performance quality by suitably transformed input spectra and classification of soil spectral types for prediction model input. The continuum-removed spectra showed significant result for all cases in terms of soil properties and classified or bulk predictions. The prediction model using classified soil spectra at an absorption peak area around 500nm and 950nm efficiently indicating soil color showed slightly better performance. Especially, Ca and CEC were well estimated by the classified prediction model at $R^{2}$ > 0.8. For organic carbon, both classified and bulk prediction model had a good performance with $R^{2}$ > 0.8 and RPD> 2. This prediction model may be applied in global soil mapping, soil classification, and remote sensing data analysis.

• Economic and Environmental Geology
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• v.55 no.4
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• pp.389-398
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• 2022

Many studies have been conducted to accurately predict the correlations between As and heavy metals content in contaminated soil and cultivated crops; however, due to the low correlation between the two, few clear results were obtained to date. This study aimed to create statistical models that predict the As content transferred from soil to polished rice, considering the physicochemical properties of the soil, as well as the total content and the single-extracted content of As in the soil. Predictive models were derived through regression analysis while sequentially classifying soil samples according to pH, soluble As content by single extraction, and organic matter content of the soil. The correlation coefficients between the As content in 80 polished rice and total As content and Mehlich soluble As content in the soil were low, 0.533 and 0.493, respectively. However, the models derived after sequential classification of the soil by pH, a ratio of total As content to Mehlich soluble As content, and organic matter content greatly increased the predictive power; ① 0.963 for 13 soils with a pH higher than 6.5, ② 0.849 for 15 soils with pH lower than 6.5 and a high ratio of As_Tot/As_Mehlich, ③ 0.935 for 30 soils with pH lower than 6.5, a high ratio of As_Tot/As_Mehlich, and organic matter content lower than 8.5%. The suggested prediction model of As transfer from soil to polished rice derived by soil classification may serve as a statistically significant methodology in establishing a rice cultivation standard for arsenic-contaminated soil.

• Journal of Korea Water Resources Association
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• v.53 no.3
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• pp.181-191
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• 2020

In order to produce accurate initial condition of soil moisture for global Numerical Weather Prediction (NWP), spin-up experiment is carried out using Noah Land Surface Model (LSM). The model is run repeatedly through 10 years, under the atmospheric forcing condition of 2008-2017 until climatological land surface state is achieved. Spin-up time for the equilibrium condition of soil moisture exhibited large variability across Koppen-Geiger climate classification zone and soil layer. Top soil layer took the longgest time to equilibrate in polar region. From the second layer to the fourth layer, arid region equilibrated slower (7 years) than other regions. This result means that LSM reached to equilibrium condition within 10 year loop. Also, spin-up time indicated inverse correlation with near surface temperature and precipitation amount. Initialized from the equilibrium state, LSM was spun up to obtain land surface state in 2018. After 6 months from restarted run, LSM simulates soil moisture, skin temperature and evaportranspiration being similar land surface state in 2018. Based on the results, proposed LSM spin-up system could be used to produce proper initial soil moisture condition despite updates of physics or ancillaries for LSM coupled with NWP.

• Korean Journal of Soil Science and Fertilizer
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• v.42 no.spc
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• pp.8-13
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• 2009

Mathematical models have become increasingly popular in both research and management problems involving flow and transport processes in the subsurface. Rosetta is a program to estimate unsaturated hydraulic properties from surrogate soil data such as soil texture data and bulk density. Models of this type are called pedotransfer functions (PTFs) as an alternative measurements since they translate basic soil data into hydraulic properties. These functions may be either measured directly or estimated indirectly through prediction from more easily measured data based using quasi-empirical models.