• Korean Journal of Environmental Agriculture
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• v.28 no.3
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• pp.233-237
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• 2009

It has widely been observed that the effect of elevating atmospheric $CO_2$ concentrations on rice productivity depends largely on soil N availabilities. However, the responses of ammonia volatilization from flooded paddy soil that is an important pathway of N loss and thus affecting fertilizer N availability to concomitant increases in atmospheric $CO_2$ and temperature has rarely been studied. In this paper, we first report the interactive effect of elevated $CO_2$ and temperature on ammonia volatilization from rice paddy soils applied with urea. Urea labeled with $^{15}N$ was used to quantitatively estimate the contribution of applied urea-N to total ammonia volatilization. This study was conducted using Temperature Gradient Chambers (TGCs) with two $CO_2$ levels [ambient $CO_2$ (AC), 383 ppmv and elevated $CO_2$ (EC), 645 ppmv] as whole-plot treatment (main treatment) and two temperature levels [ambient temperature (AT), $25.7^{\circ}C$ and elevated temperature (ET), $27.8^{\circ}C$] as split-plot treatments (sub-treatment) with triplicates. Elevated temperature increased ammonia volatilization probably due to a shift of chemical equilibrium toward $NH_3$ production via enhanced hydrolysis of urea to $NH_3$ of which rate is dependent on temperature. Meanwhile, elevated $CO_2$ decreased ammonia volatilization and that could be attributed to increased rhizosphere biomass that assimilates $NH_4^+$ otherwise being lost via volatilization. Such opposite effects of elevated temperature and $CO_2$ resulted in the accumulated amount of ammonia volatilization in the order of ACET>ACAT>ECET>ECAT. The pattern of ammonia volatilization from applied urea-$^{15}N$ as affected by treatments was very similar to that of total ammonia volatilization. Our results suggest that elevated $CO_2$ has the potential to decrease ammonia volatilization from paddy soils applied with urea, but the effect could partially be offset when air temperature rises concomitantly.

• Journal of Environmental Impact Assessment
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• v.33 no.2
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• pp.74-83
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• 2024

This study aims to estimate roadkill occurrences and investigate influential factors in Chungcheongnam-do, contributing to the establishment of roadkill prevention measures. By comprehensively considering weather, road, and environmental information, machine learning was utilized to estimate roadkill incidents and analyze the importance of each variable, deriving primary influencing factors. The Gradient Boosting Machine (GBM) exhibited the best performance, achieving an accuracy of 92.0%, a recall of 84.6%, an F1-score of 89.2%, and an AUC of 0.907. The key factors affecting roadkill included average local atmospheric pressure (hPa), average ground temperature (℃), month, average dew point temperature (℃), presence of median barriers, and average wind speed (m/s). These findings are anticipated to contribute to roadkill prevention strategies and enhance traffic safety, playing a crucial role in maintaining a balance between ecosystems and road development.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.107-107
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• 2022

The increase in atmospheric temperature due to climate change prolongs the period of exposure to high-temperature environments during rice cultivation. In particular, high-temperature during early seed development greatly affects on the productivity and quality of rice. The high temperature at this time not only affects the transport and distribution of assimilates from leaves to seeds and the accumulation of starch in the seeds, but also affects the leaves, which are the production organs of assimilates, and increases the consumption of assimilation products due to an increase in respiration. Therefore, in this study, rice was grown in temperature gradient chambers(TGC) to analyze the effects of high temperature on physiological responses, assimilate production, and changes in gene expression in rice leaves. Analysis of chlorophyll and sugar contents and RNA-seq experiments were performed using flag leaves collected under normal and elevated temperature conditions, respectively, during the early seed development stage, and then these results were comprehensively discussed.

• Proceedings of KOSOMES biannual meeting
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• 2007.05a
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• pp.43-50
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• 2007

In order to calculate the strength and to. see the variation af the stratification in the Southern Waters af Korea, the stratification parameter defined as potential energy anomaly (PEA, $V(J/m^3)$) introduced by Simpson and Hunter (1974) was used The data used in this paper were observed in August 1999 and February 2000 by National Fisheries Research and Development Institute (NFRDI). Also to know the effects af the temperature and the salinity an the stratification respectively, averaged temperature and salinity were used in the process af calculation the parameter. V is generally high in the offshore. However, in February, V in the onshore is higher than that of the offshore due to the vertical temperature gradient caused by the expansion of South Korean Coastal Waters (SKCW). In the summer, the increase af the atmospheric heating, the temperature inversion phenomenon act an the stratification as the buoyancy forcing. In most cases, the effects of the temperature on the stratification is stronger than that of the salinity. The temperature effect is predominantly due to the extent af the intrusion of Tsushima Warm Current into the study area. However, at stations where V is high the effect af the salinity is also significant. In the winter, V is very low due to the decrease of the buoyancy forcing, but same stations show the relatively high V due to the expansion of SKCW and Tsushima Warm Current.

• Atmosphere
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• v.15 no.2
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• pp.75-90
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• 2005

This study examines the impacts of land cover changes on the East Asia summer monsoon with the National Center for Atmospheric Research Regional Climate Model (NCAR RegCM2), coupled with Biosphere Atmosphere Transfer Scheme (BATS). To assess the goals, two types of land cover maps were used in the simulation of summer climate. One type was NCAR land cover map (CTL) and the other was current land cover map derived from satellite data (land cover: LCV). Warm and cold surface temperature biases of $1-3^{\circ}C$ occurred over central China and Mongolia in CTL. The model produced excessive precipitation over northern land area but less over southern ocean of the model domain. Changes of biophysical parameters, such as albedo, minimum stomatal resistance and roughness length, due to the land cover changes resulted in the alteration of land-atmosphere interactions. Latent heat flux and wind speed in LCV increased noticeably over central China where deciduous broad leaf trees have been replaced by mixed farm and irrigated crop. As a result, the systematic warm biases over central China were greatly reduced in LCV. Strong cooling of central China decreased pressure gradient between East Asian continent and Pacific Ocean. The decreased pressure gradient suppressed the northward transport of moisture from south China and South China Sea. These changes reduced not only the excessive precipitation over north China and Mongolia but also less precipitation over south China. However, the land cover changes increased the precipitation over the Korean Peninsula and the Japan Islands, especially in July and August.

• Atmosphere
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• v.16 no.2
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• pp.97-110
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• 2006

This study investigates the response of a typhoon model to the change of the sea surface temperature (SST) throughout the model integration. The SST change is parameterized as a formulae of which the magnitude is given as a function of not only the intensity and the size but the moving speed of tropical cyclone. The formulae is constructed by referring to many previous observational and numerical studies on the SST cooling with the passage of tropical cyclones. Since the parameterized cooling formulae is based on the mathematical expression, the resemblance between the prescribed SST cooling and the observed one during the period of the numerical experiment is not complete nor satisfactory. The agreements between the prescribed and the observed SST even over the swath of the typhoon passage differ from case to case. Numerical experiments are undertaken with and without prescribing the SST cooling. The results with the SST cooling do not show clear evidence in improving the track prediction compared to those of the without-experiments. SST cooling in the model shows its swath along the incomplete simulated track so that the magnitude and the distribution of the sea surface cooling does not resemble completely with the observed one. However, we have observed a little improvement in the intensity prediction in terms of the central pressure of the tropical cyclone in some cases. In case where the model without the SST treatment is not able to yield a correct prediction of the filling of the tropical cyclone especially in the decaying stage, the pulling effect given by the SST cooling alleviates the over-deepening of the model so that the central pressure approaches toward the observed value. However, the opposite case when the SST treatment makes the prediction worse may also be possible. In general when the sea surface temperature is reduced, the amount of the sensible and the latent heat from the ocean surface become also reduced, which results in the weakening of the storms comparing to the constant SST case. It turns out to be the case also in our experiments. The weakening is realized in the central pressure, maximum wind, horizontal temperature gradient, etc.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.4
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• pp.268-276
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• 2007

Urban atmosphere may play as a harbinger for the future climate change with respect to temperature and $CO_2$ concentration. The Seoul metropolitan area is unique in rapid urbanization and industrialization during the last several decades, providing a natural $CO_2$ dome with increased temperature. This study was carried out to evaluate the feasibility of using the urban-rural environmental gradient in replacement of the IPCC mid-term scenario (after 30-50 years). For this, we measured atmospheric $CO_2$ concentration and air temperature at three sites with different degree of urbanization (Seoul, Suwon, and Icheon). Results from 11-month measurement can be summarized as follows: (1) The annual mean $CO_2$ concentration across 3 sites was in the order of Seoul (439 ppm) > Suwon (419 ppm) > Icheon (416 ppm), showing a substantial urban-rural environmental gradient. (2) The diurnal fluctuation in $CO_2$ concentration was greater in summer than in winter, showing the effect of photosynthesis on local $CO_2$ concentration. (3) The daily maximum $CO_2$ concentration was observed at 0500 LST in spring and summer, 0800 LST in autumn, and 0900 LST in winter, showing the sunrise-time dependence. (4) The observed hourly maximum $CO_2$ concentration averaged for the whole period was 446 ppm in Seoul at 0700 LST, while the minimum was 407 ppm in Suwon at 1500 LST. (5) Compared with the background atmospheric concentration of $CO_2$ in Anmyeon-do (377.4 ppm annual mean), $CO_2$ concentration of the study sites was higher by 14% in Seoul, by 10% in Suwon, and by 9% in Icheon. The observed $CO_2$ concentration in Seoul reached already 98% of the 2030-2040 projection (450 ppm) and 80% of the 2040-2050 projection (550 ppm) under the IPCC BAU scenario, showing a feasibility of using the $CO_2$ dome of Seoul as a natural experimental setting for the mid-term climate change impact assessment.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.1-19
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• 2008

This study intends to clarify the characteristics and causes of current changes in wintertime precipitation in Korea and to predict the future directions based on surface observational $(1973/04\sim2006/07)$ and modeled (GFDL 2.1) climate data. Analyses of surface observation data demonstrate that without changes in the total amount of precipitation, snowfall in winter (November-April) has reduced by 4.3cm/decade over the $1973\sim2007$ period. Moreover, the frequency and intensity of snowfall have decreased; the duration of snow season has shortened; and the snow-to-rain day ratio (STDR) has decreased. These patterns indicate that the type of wintertime precipitation has changed from snow to rain in recent decades. The snow-to-rain change in winter is associated with the increases of air temperature (AT) over South Korea. Analyses of synoptic charts reveal that the warming pattern is associated with the formation of a positive pressure anomaly core over northeast Asia by a hemispheric positive winter Arctic Oscillation (AO) mode. Moreover, the differentiated warming of AT versus sea surface temperature (SST) under the high pressure anomaly core reduces the air-sea temperature gradient, and subsequently it increases the atmospheric stability above oceans, which is associated with less formation of snow cloud. Comparisons of modeled data between torrent $(1981\sim2000)$ and future $(2081\sim2100)$ periods suggest that the intensified warming with larger anthropogenic greenhouse gas emission in the $21^{st}$ century will amplify the magnitude of these changes. More reduction of snow impossible days as well as more abbreviation of snow seasons is predicted in the $21^{st}$ century.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.581-584
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• 2019

Food consumption is growing worldwide every year owing to a growing population. Hence, the increasing population needs the production of sufficient and good quality food products. Strawberry is one of the world's most famous fruit. To obtain the highest strawberry output, we worked with three strawberry varieties supplied with three kinds of nutrient water in a greenhouse and with the outcome of the strawberry production, the highest yielding strawberry variety is detected. This Study uses the nutrient water consumed every day by the highest yielding strawberry variety. The atmospheric temperature, humidity and CO2 levels within the greenhouse are identified and used for the prediction, since the water consumption by any plant depends primarily on weather conditions. Machine learning techniques show successful outcomes in a multitude of issues including time series and regression issues. In this study, daily nutrient water consumption of strawberry plants is predicted using machine learning algorithms is proposed. Four Machine learning algorithms are used such as Linear Regression (LR), K nearest neighbour (KNN), Support Vector Machine with Radial Kernel (SVM) and Gradient Boosting Machine (GBM). Gradient Boosting System produces the best results.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.545-551
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• 2018

For domestic application of the temperature gradient model proposed by foreign design standards, a specimen of steel box girder bridge was fabricated with the following dimensions: 2.0 m width, 2.0 m height and 3.0 m length. Temperature was measured using 24 temperature gauges during the summer of 2016. The reliability of the measured data was verified by comparing the measured air temperature with the ambient air temperature of the Korea Meteorological Administration. Of the measured gauges, four temperature gauges that can be compared with the temperature difference of the Euro code were selected and used to analyze the distribution of the measured temperatures at each point. The reference atmospheric temperature for the selection of the maximum temperature difference was determined by considering the standard error. Maximum and minimum temperatures were calculated from the four selected points and the resulting temperature difference was calculated. The model for the temperature difference in the steel box girder bridge was shown by graphing the temperature difference. Compared to the temperature distribution of the Euro code, the presented temperature difference model showed a temperature difference of $0.9^{\circ}C$ at the top and of $0.3^{\circ}$ to $0.4^{\circ}C$ at the intermediate part. These results suggested that the presented model could be considered relatively similar to the Euro code The calculated standard error coefficient was 2.71 to 2.84 times the standard error and represents a range of values. The proposed temperature difference model may be used to generate basic data for calculating the temperature difference in temperature load design.