• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.2
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• pp.111-116
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• 2020

This study was conducted to determine the maintenance energy requirements of growing goats in order to establish an appropriate energy benefit system, to reduce feed costs and improve livestock productivity of goat farmers, and to present basic data for detailed specifications afterwards. This experiment was conducted as a group specification test for a total of 3 months, with 32 goats of three months age and conducted by four treatments with different energy levels (T1: NRC+0%, T2: NRC+10%, T3: NRC+20%, and T4: NRC+30%). The average daily gain was the highest in the treated with NRC + 10% of the energy level of the experimental diet, and the feed conversion ratio was in the range of 6.3 g to 7.3 g in the group feeding experiment. Although there was no significant difference in digestibility between treatments, the digestibility of dry matter, crude protein, and crude fat was higher in T2 treated with NRC + 10% than the other treatments. Through the regression equation of the values of MEI and ADG obtained through the experiment (Y=0.5439X+111.51, R²=0.712), the maintenance energy requirement of the goat in the growing period was estimated to be 111.51 kcal/kgBW^0.75.

• Korean Journal of Environmental Agriculture
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• v.27 no.1
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• pp.35-43
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• 2008

The study was carried out to estimate runoff loads of heavy metals in the valley watershed at the middle of South Korea, during farming season. There were no other pollution sources except agricultural activity. From 27 April 2006 to 31 October 2007, water samples were collected using two methods. The first method was regular sampling wherein water samples were taken every two weeks; and the other method was through regular sampling when water were collected during each rainfall event. Results showed that heavy metals were found in the water from the regular samples, and were highest during May and June. It was presumed that this might have been contributed by farming activities. Heavy metal concentration of the irregular samples was lower than regular samples. The correlation coefficient between each heavy metal of the regular samples were as follows: Fe-Al>Cr-Al>Fe-Cr>Mn-Fe. The correlation coefficient of the irregular samples were the following: Fe-Al>Fe-Cu is positive; and Pb-Cu>Ni-Al is negative. Measured pollutant loads of heavy metals in the valley watershed were : 2.047 kg $day^{-1}$ of Al, 0.008 kg $day^{-1}$ of Cd, 0.034 kg $day^{-1}$ of Cr, 0.311 kg $day^{-1}$ of Cu, 0.601 kg $day^{-1}$ of Fe, and 0.282 kg $day^{-1}$ of Zn in 2006; while in 2007, the following were observed: 2.535 kg $day^{-1}$ of Al, 0.026 kg $day^{-1}$ of Cd, 0.055 kg $day^{-1}$ of Cu, 0.727 kg $day^{-1}$ of Fe, and 0.317 kg $day^{-1}$ of Zn. In the analysis of data gathered, the loading rates of effluents from the valley watershed during the rainy season were : 79.8% of Al, 69.1% of Cu, 82.5% of Fe, and 69.1% of Zn in 2006; while 69.9% of Al, 67.5% of Cu, 70.4% of Fe, and 67.5% of Zn in 2007.

• Current Research on Agriculture and Life Sciences
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• v.4
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• pp.77-86
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• 1986

The purpose of this study is to estimate an optimum formula of rainfall intensity on basis of the characteristics for short period of rainfall duration in Kyungpook province for the design of urban sewerage and small basin drain system. Results studied are as follows; 1. The optimum method for Taegu and Pohang, Iwai's and Gumbel-Chow's method are recommended respectively. 2. The opotimum type of rainfall intensity for these area, $I=\frac{a}{\sqrt{t}+b}$ (Japanese type), is confirmed with 2.52~4.17 and 1.86~4.54 as a standard deviation for Taegu and Pohang respectively. The optimum formula of rainfall intensity are as follows. Taegu : T : 200 year - $I=\frac{824}{\sqrt{t}+1.5414}$ T : 100 year - $I=\frac{751}{\sqrt{t}+1.4902}$ T : 50 year - $I=\frac{678}{\sqrt{t}+1.4437}$ T : 30 year - $I=\frac{623}{\sqrt{t}+1.4017}$ T : 20 year - $I=\frac{580}{\sqrt{t}+1.3721}$ T : 10 year - $I=\frac{502}{\sqrt{t}+1.3145}$ T : 5 year - $I=\frac{418}{\sqrt{t}+1.2515}$ Pohang : T : 200 year - $I=\frac{468}{\sqrt{t}+1.1468}$ T : 100 year - $I=\frac{429}{\sqrt{t}+1.1605}$ T : 50 year - $I=\frac{391}{\sqrt{t}+1.1852}$ T : 30 year - $I=\frac{362}{\sqrt{t}+1.2033}$ T : 20 year - $I=\frac{339}{\sqrt{t}+1.2229}$ T : 10 year - $I=\frac{299}{\sqrt{t}+1.2578}$ T : 5 year - $I=\frac{257}{\sqrt{t}+1.3026}$ 3. Significant I.D.F. curves derived should be applied to estimate a suitable rainfall intensity and rainfall duration.

• Journal of Korea Water Resources Association
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• v.54 no.12
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• pp.1243-1254
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• 2021

For water resources operation or agricultural water management, it is important to accurately predict evapotranspiration for a long-term future over a seasonal or monthly basis. In this study, reference evapotranspiration forecast (up to 12 months in advance) was performed using statistically predicted monthly temperatures and temperature-based Hamon method for the Han River basin. First, the daily maximum and minimum temperature data for 15 meterological stations in the basin were derived by spatial-temporal downscaling the monthly temperature forecasts. The results of goodness-of-fit test for the downscaled temperature data at each site showed that the percent bias (PBIAS) ranged from 1.3 to 6.9%, the ratio of the root mean square error to the standard deviation of the observations (RSR) ranged from 0.22 to 0.27, the Nash-Sutcliffe efficiency (NSE) ranged from 0.93 to 0.95, and the Pearson correlation coefficient (r) ranged from 0.97 to 0.98 for the monthly average daily maximum temperature. And for the monthly average daily minimum temperature, PBIAS was 7.8 to 44.7%, RSR was 0.21 to 0.25, NSE was 0.94 to 0.96, and r was 0.98 to 0.99. The difference by site was not large, and the downscaled results were similar to the observations. In the results of comparing the forecasted reference evapotranspiration calculated using the downscaled data with the observed values for the entire region, PBIAS was 2.2 to 5.4%, RSR was 0.21 to 0.28, NSE was 0.92 to 0.96, and r was 0.96 to 0.98, indicating a very high fit. Due to the characteristics of the statistical models and uncertainty in the downscaling process, the predicted reference evapotranspiration may slightly deviate from the observed value in some periods when temperatures completely different from the past are observed. However, considering that it is a forecast result for the future period, it will be sufficiently useful as information for the evaluation or operation of water resources in the future.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.61-75
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• 2021

Recently, as the occurrence frequency of sudden floods due to climate change increased and the aging of the existing spillway, it is necessary to establish a plan to utilize an auxiliary spillway to minimize the flood damage of downstream rivers. Most studies have been conducted on the review of flow characteristics according to the operation of auxiliary spillway through the hydraulic experiments and numerical modeling. However, the studies on examination of flood damage in the downstream rivers and the stability of the revetment according to the operation of the auxiliary spillway were relatively insufficient in the literature. In this study, the stability of the revetment on the downstream river according to the outflow conditions of the existing and auxiliary spillway was examined by using 3D numerical model, FLOW-3D. The velocity, water surface elevation and shear stress results of FLOW-3D were compared with the permissible velocity and shear stress of design criteria. It was assumed the sluice gate was fully opened. As a result of numerical simulations of various auxiliary spillway operations during flood season, the single operation of the auxiliary spillway showed the reduction effect of maximum velocity and the water surface elevation compared with the single operation of the existing spillway. The stability of the revetment on downstream was satisfied under the condition of outflow less than 45% of the design flood discharge. However, the potential overtopping damage was confirmed in the case of exceeding the 45% of the design flood discharge. Therefore, the simultaneous operation with the existing spillway was important to ensure the stability on design flood discharge condition. As a result of examining the allocation ratio and the total allowable outflow, the reduction effect of maximum velocity was confirmed on the condition, where the amount of outflow on auxiliary spillway was more than that on existing spillway. It is because the flow of downstream rivers was concentrated in the center due to the outflow of existing spillway. The permissible velocity and shear stress were satisfied under the condition of less than 77% of the design flood discharge with simultaneous operation. It was found that the flood damage of downstream rivers can be minimized by setting the amount allocated to the auxiliary spillway to be larger than the amount allocated to the existing spillway for the total outflow with simultaneous operation condition. However, this study only reviewed the flow characteristics around the revetment according to the outflow of spillway under the full opening of the sluice gate condition. Therefore, the various sluice opening conditions and outflow scenarios will be asked to derive more efficient utilization of the auxiliary spillway in th future.

• Journal of Korean Society of Forest Science
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• v.111 no.4
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• pp.613-621
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• 2022

The study aim was to investigate changes in the diameter, number of standing trees, stand volume per ha and site index by the forest survey order, climate zone (northern temperate, central temperate, southern temperate, and warm temperate regions), and altitude in 100 m intervals) by collecting samples of Robinia pseudoacacia from the fifth, sixth, and seventh national forest survey datasets. The rotation cutting age, which is a standard used for wood, was calculated. The changes were statistically analyzed by performing ANOVA and the Duncan multiple test. Diameter growth naturally increased according to the forest survey order and was lowest in the southern temperate region by climate zone and lowest at the 301-400 m altitude. The number of standing trees per ha did not change according to the forest survey order and altitude, and the density was highest in the central temperate region and lowest in the southern temperate region. The stand volume per ha increased according to the forest survey order, and the climate zone was divided into two groups: ① northern temperate region and central temperate region, ② southern temperate region and warm temperate region. The stand volume growth was highest at the 201-300 m point. Thesite index showed results similar to the change pattern of the stand volume per ha. The growth curve, which can be seen by the change in stand volume per ha, was estimated by applying theWeibull formula, and the stand volume per ha was estimated to reach approximately 200 m³/ha at 50-60 years. The rotation of the highest production in volume, which is the standard for using trees as wood rather than honey sources, was calculated to be 34 years.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.377-388
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• 2023

Carbon management is emerging as an important factor for global warming control, and land use change is considered one of the causes. To quantify the changes in carbon stocks due to development, this study attempted to calculate carbon storage by borrowing the formula of the InVEST Carbon Storage and Sequestration Model (InVEST Model). Before analyzing carbon stocks, a carbon pool was compiled based on previous studies in Korea. Then, we estimated the change in carbon stocks according to the development of Osong National Industrial Park (ONIP) and the application of alternatives. The analysis shows that 16,789.5 MgC will be emitted under Alternative 1 and 16,305.3 MgC under Alternative 2. These emissions account for 44.4% and 43.1% of the pre-project carbon stock, respectively, and shows that choosing Alternative 2 is advantageous for reducing carbon emissions. The difference is likely due to the difference in grassland area between Alternatives 1 and 2. Even if Alternative 2 is selected, efforts are needed to increase the carbon storage effect by managing the appropriate level of green cover in the grassland, creating multi-layered vegetation, and installing low-energy facilities. In addition, it is suggested to conserve wetlands that can be lost during the stream improvement process or to create artificial wetlands to increase carbon storage. The assessment of carbon storage using carbon pools by land cover can improve the objectivity of comparison and evaluation analysis results for land use plans in Environmental Impact Assessment and Strategic Environmental Impact Assessment. In addition, the carbon pool generated in this study is expected to be used as a basis for improving the accuracy of such analyses.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.