• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.1-10
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• 2023

This study aimed to confirm the importance ratio of climate and management variables on production of orchardgrass in Korea (1982-2014). For the climate, the mean temperature in January (MTJ, ℃), lowest temperature in January (LTJ, ℃), growing days 0 to 5 (GD 1, day), growing days 5 to 25 (GD 2, day), Summer depression days (SSD, day), rainfall days (RD, day), accumulated rainfall (AR, mm), and sunshine duration (SD, hr) were considered. For the management, the establishment period (EP, 0-6 years) and number of cutting (NC, 2nd-5th) were measured. The importance ratio on production of orchardgrass was estimated using the neural network model with the perceptron method. It was performed by SPSS 26.0 (IBM Corp., Chicago). As a result, EP was the most important variable (100%), followed by RD (82.0%), AR (79.1%), NC (69.2%), LTJ (66.2%), GD 2 (63.3%), GD 1 (61.6%), SD (58.1%), SSD (50.8%) and MTJ (41.8%). It implies that EP, RD, AR, and NC were more important than others. Since the annual rainfall in Korea is exceed the required amount for the growth and development of orchardgrass, the damage caused by heavy rainfall exceeding the appropriate level could be reduced through drainage management. It means that, when cultivating orchardgrass, factors that can be controlled were relatively important. Although it is difficult to interpret the specific effect of climates on production due to neural networking modeling, in the future, this study is expected to be useful in production prediction and damage estimation by climate change by selecting major factors.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.423-429
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• 2008

The object of the present study is to estimate the potential effects of climate change and land use on soil erosion in the mid-east Korea. Simulated precipitation by CCCma climate model during 2030-2050 is used to model predicted soil erosion, and results are compared to observation. Simulation results allow relative comparison of the impact of climate change on soil erosion between current and predicted future condition. Expected land use changes driven by socio-economic change and plant growth driven by the increase of temperature and are taken into accounts in a comprehensive way. Mean precipitation increases by 17.7% (24.5%) for A2 (B2) during 2030-2050 compared to the observation period (1966-1998). In general predicted soil erosion for the B2 scenario is larger than that for the A2 scenario. Predicted soil erosion increases by 48%~90% under climate change except the scenario 1 and 2. Predicted soil erosion under the influence of temperature-induced fast plant growth, higher evapotranspiration rate, and fertilization effect (scenario 5 and 6) is approximately 25% less than that in the scenario 3 and 4. On the basis of the results it is said that precipitation and the corresponding soil erosion is likely to increase in the future and care needs to be taken in the study area.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.3
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• pp.19-28
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• 2023

The purpose of this study is to estimate carbon dioxide emissions from soil microbial respiration by forest type of Sobaeksan National Park. As a result of estimating the annual soil microbiological respiration volume by forest type in Sobaeksan National Park, broad-leaved forests, coniferous forest, artificial forests were similar to around 19.5 CO₂-ton/ha/yr. In the case of coniferous forests in sub-alpine and grassland near Birobong Peak, 12.2 CO₂-ton/ha/yr and 8.1 CO₂-ton/ha/yr, respectively, were lower than general forest areas. And as a result of analyzing the changes in soil microbiological respiration rate according to forest type in Sobaeksan National Park, the soil microbiological respiration rate in coniferous forests, broad-leaved forests, artificial forests, and sub-alpine areas was the highest in the July survey in summer and the lowest in November in late autumn. The change in soil microbial respiratory volume according to the measurement time in Sobaeksan National Park was the highest between 12:00 and 16:00, when the soil temperature was generally the highest among the days. It is known that the soil temperature is relatively low and the amount of soil microbial respiration decreases during winter, and the change in respiratory volume over the measurement time during the day was the smallest in November, when the amount of soil microbial respiration was relatively smaller than the May-September survey. However, this study has limitations in revealing the causal relationship of various environmental factors that affect the soil microbial respiration. Therefore, it is suggested that long-term research and investigation of various factors affecting soil respiration are needed to understand the carbon cycle of forest ecosystems.

• Ecology and Resilient Infrastructure
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• v.10 no.4
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• pp.161-170
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• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• Resources Recycling
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• v.33 no.2
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• pp.62-72
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• 2024

Although the Paris Agreement in 2015 aimed to limit global temperature increases to below 2℃ and eventually to 1.5℃ to address the climate crisis, global temperature continues to rise. Developed countries have proposed a circular economy as a major strategy to tackle this issue. Detailed implementation methods include reusing, remanufacturing, recycling, and energy recovery. Remanufacturing has a greater potential to achieve high added value and carbon neutrality than other resource circulation methods. However, currently, no standardized method for quantitatively evaluating the greenhouse gas (GHG) reduction effects of remanufacturing exists. This study compares and analyzes recent research trends since 2020 on the calculation of GHG emission reduction effects from remanufacturing. It also examines international standards for environmental impact assessment, including GHGs and environmental performance labeling systems. This study derives the key factors for standardizing the calculation of the GHG emission reduction effects of remanufactured products.

• Journal of Korea Water Resources Association
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• v.57 no.9
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• pp.615-626
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• 2024

To accurately and efficiently monitor soil moisture (SM) across South Korea, this study developed a SM estimation model that integrates the cloud computing platform Google Earth Engine (GEE) and Automated Machine Learning (AutoML). Various spatial information was utilized based on Terra MODIS (Moderate Resolution Imaging Spectroradiometer) and the global precipitation observation satellite GPM (Global Precipitation Measurement) to test optimal input data combinations. The results indicated that GPM-based accumulated dry-days, 5-day antecedent average precipitation, NDVI (Normalized Difference Vegetation Index), the sum of LST (Land Surface Temperature) acquired during nighttime and daytime, soil properties (sand and clay content, bulk density), terrain data (elevation and slope), and seasonal classification had high feature importance. After setting the objective function (Determination of coefficient, R² ; Root Mean Square Error, RMSE; Mean Absolute Percent Error, MAPE) using AutoML for the combination of the aforementioned data, a comparative evaluation of machine learning techniques was conducted. The results revealed that tree-based models exhibited high performance, with Random Forest demonstrating the best performance (R² : 0.72, RMSE: 2.70 vol%, MAPE: 0.14).

• Journal of Korean Society of Water and Wastewater
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• v.23 no.6
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• pp.743-752
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• 2009

One of the major problems associated with operation of domestic sewer lines involves hydraulic problems such as insufficient conveyance capacity, exceeding maximum velocity, and deficiency of minimum velocity. It has also been pointed out that influent concentration lower than design concentration of pollutants, which is mainly caused by unidentified inflow and infiltration, degrades the operational efficiency of many sewage treatment plants (STPs). A computer-added analysis method supporting a coupled simulation of sewage quality and quantity is essentially required to evaluate the status of existing STPs and to improve their efficiency by a proper sewer rehabilitation work. In this study, dynamic water quality simulations were conducted using MOUSE TRAP to investigate the principal parameters that governs the changes of BOD, ${NH_4}^+$, and ${PO_4}^{3-}$3- concentrations within the sewer networks based on data acquired through on-site and laboratory measurements. The BOD, ${NH_4}^+$ and ${PO_4}^{3-}$3- concentrations estimated by MOUSE TRAP was lower than theoretical pollution loads because of sedimentation and decomposition in the sewer. The results revealed that sedimentation is a most important factor than other biological reactions in decreasing pollutant load in the sewers of C-city. The sensitivity analysis of parameters pertaining to water quality changes indicated that the effect of the BOD decay rate, the initial DO concentration, the half-saturation coefficient of dissolved BOD, and the initial sediment depth is marginal. However, the influence of settling rate and temperature is relatively high because sedimentation and precipitation, rather than biological degradation, are dominant processes that affect water quality in the study sewer systems.

• Journal of Ecology and Environment
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• v.32 no.2
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• pp.67-73
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• 2009

Soil $CO_2$ efflux can vary markedly in magnitude over both time and space, and understanding this variation is crucial for the correct measurement of $CO_2$ efflux in ecological studies. Although considerable research has quantified temporal variability in this flux, comparatively little effort has focused on its spatial variability. To account for spatial heterogeneity, we must be able to determine the number of sampling points required to adequately estimate soil $CO_2$ efflux in a target ecosystem. In this paper, we report the results of a study of the number of sampling points required for estimating soil $CO_2$ efflux using a closed-dynamic chamber in young and old Japanese cedar plantations in central Japan. The spatial heterogeneity in soil $CO_2$ efflux was significantly higher in the mature plantation than in the young stand. In the young plantation, 95% of samples of 9 randomly-chosen flux measurements from a population of 16 measurements made using 72-$cm^2$ chambers produced flux estimates within 20% of the full-population mean. In the mature plantation, 20 sampling points are required to achieve means within $\pm$ 20% of the full-population mean (15 measurements) for 95% of the sample dates. Variation in soil temperature and moisture could not explain the observed spatial variation in soil $CO_2$ efflux, even though both parameters are a good predictor of temporal variation in $CO_2$ efflux. Our results and those of previous studies suggest that, on average, approximately 46 sampling points are required to estimate the mean and variance of soil $CO_2$ flux in temperate and boreal forests to a precision of $\pm$ 10% at the 95% confidence level, and 12 points are required to achieve a precision of $\pm$ 20%.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.7
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• pp.921-927
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• 2013

The performance estimation of a landing gear with uncertain parameters is presented. In actual use, many parameters can have certain degrees of variations that affect the energy absorbing performance. For example, the shock strut gas pressure, oil volume, tire pressure, and temperature can deviate from their nominal values. The objective function in this study is the ground reaction during touchdown, which is a function of the abovementioned parameters and time. To consider the uncertain properties, convex modeling and interval analysis are used to calculatethe objective function. The numerical results show that the ground reaction characteristics are quite different from those of the deterministic method. The peak load, which affects the efficiency and structural integrity, is increases considerably when the uncertainties are considered. Therefore, it is important to consider the uncertainties, and the proposed methodology can serve as an efficient method to estimate the effect of such uncertainties.

• Journal of Environmental Impact Assessment
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• v.20 no.6
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• pp.891-905
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• 2011

Climate vulnerability index is usually defined as a function of the climate exposure, sensitivity, and adaptive capacity, which requires adequate selection of proxy variables of each variable. We selected and used 9 proxy variables related to climate exposure in the literature, and diagnosed the adequacy of them for application in Korean peninsula. The selected proxy variables are: four variables from temperature, three from precipitation, one from wind speed, and one from relative humidity. We collected climate data over both previous year (1981~2010) and future climate scenario (A1B scenario of IPCC SERES) for 2020, 2050, and 2100. We introduced the spatial and temporal diagnostic statistical parameters, and evaluated both spatial and time variabilities in the relative scale. Of 9 proxy variables, effective humidity indicated the most sensitive to climate change temporally with the biggest spatial variability, implying a good proxy variable in diagnostics of climate change vulnerability in Korea. The second most sensitive variable is the frequency of strong wind speed with a decreasing trend, suggesting that it should be used carefully or may not be of broad utility as a proxy variable in Korea. The A1B scenario of future climate in 2020, 2050 and 2100 matches well with the extension of linear trend of observed variables during 1981~2010, indicating that, except for strong wind speed, the selected proxy variables can be effectively used in calculating the vulnerability index for both past and future climate over Korea. Other local variabilities for the past and future climate in association with climate exposure variables are also discussed here.