• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.101-110
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• 2018

This study experimentally investigated the compressive and flexyral strength development characteristics of polymer concrete using four different type polymeric resins such as unsaturated polyester, vinyl ester, epoxy, and PMMA (polymethyl methacrylate) as binders. The test results show that the average compressive strength of those four different polymer concretes was 88.70 MPa, the average flexural strength was 20.30 MPa. Those test results show that compressive and flexural strengths of polymer concrete were much stronger than compressive and flexural strengths of ordinary Portland cement concrete. In addition, the relative gains of the compressive strength development at the age of 24 hrs compared to the age of 168 hrs were 68.6~88.3 %. Also, the relative gains of the flexural strength development at the age of 24 hrs compared to the age of 168 hrs were 73.8~93.4 %. These test results show that compressive and flexural strengths of each polymer concrete tested in this study were developed at the early age. Moreover, the prediction equations of compressive and flexural strength developments regarding the age were determined. The determined prediction equations could be applied to forecast the compressive and flexural strength developments of polymer concrete investigated in this study because those prediction equations have the high coefficients of correlation. Last, the relations between the compressive strength and the flexural strength of polymer concrete were determined and the flexural/compressive strength ratios were from 1/4 to 1/5. These results show that polymer concretes investigated in this study were appropriate as a flexural member of a concrete structure because the flexural/compressive strength ratios of polymer concrete were much higher than the flexural/compressive strength ratios of Portland cement concrete.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.381-394
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• 2020

Accelerated soil wind erosion still remains to date to cause severe economic and environmental impacts. Revised and updated models to quantitatively evaluate wind induced soil erosion have been made for specific factors in the wind erosion equation (WEQ) framework. Because of increasing quantities of accumulated data, the WEQ, the revised wind erosion equation (RWEQ), the wind erosion prediction system (WEPS), and other soil wind erosion models have been established. These soil wind erosion models provide essential knowledge about where and when wind erosion occurs although naturally, they are less accurate than the field-scale. The WEQ was a good empirical model for comparing the effects of various management practices on potential erosion before the RWEQ and the WEPS showed more realistic estimates of erosion using easily measured local soil and climatic variables as inputs. The significant relationship between the observed and predicted transport capacity and soil loss makes the RWEQ a suitable tool for a large scale prediction of the wind erosion potential. WEPS developed to replace the empirical WEQ can calculate soil loss on a daily basis, provide capability to handle nonuniform areas, and obtain predictions for specific areas of interest. However, the challenge of precisely estimating wind erosion at a specific regional scale still remains to date.

• Journal of Environmental Impact Assessment
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• v.19 no.2
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• pp.117-125
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• 2010

Korea's researchers have recently studied the prediction of forest change, but they have not considered landuse/cover change compared to distribution of forest vegetation. The purpose of our study is to predict forest vegetation based on landuse/cover change on the Korean Peninsula in the 2090's. The methods of this study were Multi-layer perceptrom neural network for Landuse/cover (water, urban, barren, wetland, grass, forest, agriculture) change and Multinomial Logit Model for distribution prediction for forest vegetation (Pinus densiflora, Quercus Spp., Alpine Plants, Evergreen Broad-Leaved Plants). The classification accuracy of landuse/cover change on the Korean Peninsula was 71.3%. Urban areas expanded with large cities as the central, but forest and agriculture area contracted by 6%. The distribution model of forest vegetation has 63.6% prediction accuracy. Pinus densiflora and evergreen broad-leaved plants increased but Quercus Spp. and alpine plants decreased from the model. Finally, the results of forest vegetation based on landuse/cover change increased Pinus densiflora to 38.9% and evergreen broad-leaved plants to 70% when it is compared to the current climate. But Quercus Spp. decreased 10.2% and alpine plants disappeared almost completely for most of the Korean Peninsula. These results were difficult to make a distinction between the increase of Pinus densiflora and the decrease of Quercus Spp. because of they both inhabit a similar environment on the Korean Peninsula.

• Atmosphere
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• v.28 no.4
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• pp.479-490
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• 2018

This study assesses the prediction skill of regional scale model for the mean temperature anomaly over South Korea produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. The initial and boundary conditions of WRF are derived from PNU CGCM. The hindcast period is 11 years from 2007 to 2017. The model's prediction skill of mean temperature anomaly is evaluated in terms of the temporal correlation coefficient (TCC), root mean square error (RMSE) and skill scores which are Heidke skill score (HSS), hit rate (HR), false alarm rate (FAR). The predictions of WRF and PNU CGCM are overall similar to observation (OBS). However, TCC of WRF with OBS is higher than that of PNU CGCM and the variation of mean temperature is more comparable to OBS than that of PNU CGCM. The prediction skill of WRF is higher in March and April but lower in October to December. HSS is as high as above 0.25 and HR (FAR) is as high (low) as above (below) 0.35 in 2-month lead time. According to the spatial distribution of HSS, predictability is not concentrated in a specific region but homogeneously spread throughout the whole region of South Korea.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.367-385
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• 2022

This study evaluates the temperature and precipitation results in East Asia simulated from the Hadley Centre Global Environmental Model version 3 regional climate model (HadGEM3-RA) developed by the UK Met Office. The HadGEM3-RA is conducted in the Coordinated Regional climate Downscaling Experiment-East Asia (CORDEX-EA) Phase II domain for 15 year (2000-2014). The spatial distribution of rainbands produced from the HadGEM3-RA by the summer monsoon is in good agreement with the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APRODITE) data over the East Asia. But, precipitation amount is overestimated in Southeast Asia and underestimated over the Korean Peninsula. In particular, the simulated summer rainfall and APRODITE data show the least correlation coefficient and the maximum value of root mean square error in South Korea. Prediction of temperature in Southeast Asia shows underestimation with a maximum error during winter season, while it appears the largest underestimation in South Korea during spring season. In order to evaluate local predictability, the time series of temperature and precipitation compared to the ASOS data of the Seoul Meteorological Station is similar to the spatial average verification results in which the summer precipitation and winter temperature underestimate. Especially, the underestimation of the rainfall increases when the amounts of precipitation increase in summer. The winter temperature tends to underestimate at low temperature, while it overestimates at high temperature. The results of the extreme climate index comparison show that heat wave is overestimated and heavy rainfall is underestimated. The HadGEM3-RA simulated with a horizontal resolution of 25 km shows limitations in the prediction of mesoscale convective system and topographic precipitation. This study indicates that improvement of initial data, horizontal resolution, and physical process are necessary to improve predictability of regional climate model.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.88-96
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• 2013

Much attention has been needed in water resource management at the watershed due to drought and flooding issues caused by climate change in recent years. Increase in air temperature and changes in precipitation patterns due to climate change are affecting hydrologic cycles, such as evaporation and soil moisture. Thus, these phenomena result in increased runoff at the watershed. The Soil and Water Assessment Tool (SWAT) model has been used to evaluate rainfall-runoff at the watershed reflecting effects on hydrology of various weather data such as rainfall, temperature, humidity, solar radiation, wind speed. For bias-correction of RCP data, at least 30 year data are needed. However, for most gaging stations, only precipitation data have been recorded and very little stations have recorded other weather data. In addition, the RCP scenario does not provide all weather data for the SWAT model. In this study, two scenarios were made to evaluate whether it would be possible to estimate streamflow using measured precipitation and long-term average values of other weather data required for running the SWAT. With measured long-term weather data (scenario 1) and with long-term average values of weather data except precipitation (scenario 2), the estimate streamflow values were almost the same with NSE value of 0.99. Increase/decrease by ${\pm}2%$, ${\pm}4%$ in temperature and humidity data did not affect streamflow. Thus, the RCP precipitation data for Hongcheon watershed were bias-corrected with measured long-term precipitation data to evaluate effects of climate change on streamflow. The results revealed that estimated streamflow for 2055s was the greatest among data for 2025s, 2055s, and 2085s. However, estimated streamflow for 2085s decreased by 9%. In addition, streamflow for Spring would be expected to increase compared with current data and streamflow for Summer will be decreased with RCP data. The results obtained in this study indicate that the streamflow could be estimated with long-term precipitation data only and effects of climate change could be evaluated using precipitation data as shown in this study.

• Journal of electromagnetic engineering and science
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• v.8 no.2
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• pp.84-89
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• 2008

In a wireless channel above microwave frequency, rain attenuation is very important. In order to predict rain attenuation, 1-min. rain rate distribution is required. This paper discusses appropriate conversion methods to estimate 1-minute rain rate from that of other integration time. Based on the measurement data filed in ITU-R WP3J including ETRI data for 6 consecutive years, distributions of rain rate with 1-, 5-, 10-, 20-, 30-minute integration time were analyzed, both on the global and regional basis, and the parametric relationship between the statistical characteristics of 1-minute and other measurement data were investigated to deduce the conversion methods. It is shown that the global model works good with good accuracy for 5-, 10-, 20-min integration time, and the global model is also applicable globally with good accuracy for 5-, 10-, 20-min integration time. The global conversion model was adopted last year as an ITU-R document for new recommendation. The regional conversion model would also be very useful for locations of similar climatic zone.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.685-689
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• 2002

This study focused the relationship between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration (ET) temporal changes. Especially, the interannual change of the NDVI and ET from 1982 to 2000 at regional to continental scales was highlighted mainly over Asia. Monthly global NDVI data were acquired from Pathfinder AVHRR Land (PAL) data (1$\times$1 degree resolution). The monthly ET was estimated from assimilated atmospheric data provided from National Centers for Environmental Prediction (NCEP) (2.5$\times$2.5 degree resolution), and gridded global precipitation data of CPC Merged Analysis of Precipitation (CMAP) (2.5$\times$2.5 degree resolution). Significant positive correlations were found between the NDVI and ET interannual changes in May and June over western Siberia. Moreover, it was revealed that the most of area in Asia has positive correlation coefficient in May and June. These results delineate that the vegetation activity significantly contributes to the ET interannual change over extensive areas.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2016.05a
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• pp.285-286
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• 2016

We aim to propose the prediction modeling method of ship's position with extracting ship's trajectory model through pattern recognition based on the data that are being collected in VTS centers at real time. Support Vector Machine algorithm was used for data modeling. The optimal parameters are calculated with k-fold cross validation and grid search. We expect that the proposed modeling method could support VTS operators' decision making in case of complex encountering traffic situations.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.20-27
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• 2011

A system for fine-resolution long-range weather forecast is introduced in this study. The system is basically consisted of a global-scale coupled general circulation model (CGCM) and Weather Research and Forecast (WRF) regional model. The system makes use of a data assimilation method in order to reduce the initial shock or drift that occurs at the beginning of coupling due to imbalance between model dynamics and observed initial condition. The long-range predictions are produced in the system based on a non-linear ensemble method. At the same time, the model bias are eliminated by estimating the difference between hindcast model climate and observation. In this research, the predictability of the forecast system is studied, and it is illustrated that the system can be effectively used for the high resolution long-term weather prediction. Also, using the system, fine-resolution climatological data has been produced with high degree of accuracy. It is proved that the production of agrometeorological variables that are not intensively observed are also possible.