• Journal of the Korean earth science society
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• v.44 no.6
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• pp.578-593
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• 2023

Intense convective activity and heavy precipitation inundated Seoul and its metropolitan area on July 15, 2017. This study investigated the synoptic-scale meteorological drivers of cold cloud genesis of this event. The WRF-Chem (Weather Research and Forecasting model coupled with Chemistry) model was employed to explore the intricate interplay between meteorological factors and the indirect effects of PM_2.5 aerosols originating from eastern China. The PM_2.5 aerosols' indirect effect was quantified by contrasting outcomes between the comprehensive Aerosol Radiation Interaction experiment (encompassing aerosol radiation feedback, cloud chemistry processes, and wet scavenging in the WRF-Chem model) and ACR (Aerosol Cloud Radiation interaction) experiment. The ACR experiment specifically excluded aerosol radiation feedback while incorporating only cloud chemistry processes and wet scavenging. Results indicated that in the early hours of July 15, 2017, a convergence of warm, moisture-laden airflow originating from southeast China and the East China Sea unfolded over the Yellow Sea. This convergence was driven by the juxtaposition of a low-pressure system over the Chinese mainland and Northwest Pacific high. Notably, at approximately 12 km altitude, the resultant convective clouds were characterized by the presence of ice crystals, a hallmark of continental-origin cold clouds. The WRF-Chem model simulations elucidated the role of PM_2.5 aerosols from eastern China, attributing 5.7, 10.4, and 10.8% to cloud water, ice crystal column, and liquid water column formation, respectively, within the developing cold clouds. Thus, this study presented a meteorological mechanism elucidating the formation of deep convective clouds over the Yellow Sea and the indirect effects of PM_2.5 aerosols originating from eastern China.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Korean Journal of Ecology and Environment
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• v.40 no.1
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• pp.14-30
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• 2007

The purpose of this study was to evaluate trophic conditions of various Korean reservoirs using Trophic State Index (TSI) and predict the reservoir conditions by empirical models. The water quality dataset (2000, 2001) used here were obtained from the Ministry of Environment, Korea. The water quality, based on multi-parameters of dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), total nitrogen (TN), suspended solid (SS), Secchi depth (SD), chlorophyll-${\alpha}$ (CHL), and conductivity largely varied depending on the sampling watersheds and seasons. In general, trophic conditions declined along the longitudinal axis of headwater-to-the dam and the largest seasonal variations occurred during the summer monsoon of July-August. Major inputs of TP occurred during the monsoon (r=0.656, p=0.002) and this pattern was similar to solid dynamics of SS (r=0.678, p<0.001). Trophic parameters including CHL, TP, SD, and TN were employed to evaluate how the water systems varies with season. Trophic State Index (TSI, Carlson, 1977), based on TSI (CHL), TSI (TP), and TSI (SD), ranged from mesotrophic to eutrophic. However, the trophic state, based on TSI (TN), indicated eutrophic-hypereutrophic conditions in the entire reservoirs, regardless of the seasons, indicating a N-rich system. Overall, nutrient data showed that phosphorus was a primary factor regulating the trophic state. The relationships between CHL (eutrophication index) vs. trophic parameters (TN, TP, and SD) were analysed to develop empirical models which can predict the trophic status. Regression analyses of log-transformed seasonal CHL against TP showed that the value of $R^2$ was 0.31 (p=0.017) in the premonsoon but was 0.69 (p<0.001) during the postmonsoon, indicating a greater algal response to the phosphorus during the postmonsoon. In contrast, SD had reverse relation with TP, CHL during all season. TN had weak relations with CHL during all seasons. Overall, data suggest that TP seems to be a good predictor for algal biomass, estimated by CHL, as shown in the empirical models.

• Journal of the Korean Geographical Society
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• v.50 no.3
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• pp.255-276
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• 2015

This paper presents a multi-agent system model of land-use and cover changes, which is developed and applied to the Gariwang-san and its vicinity, located in Pyeongchang and Jeongseon-gun, Gangwon province, Korea. The Land Use Dynamics Simulator (LUDAS) framework of this study is well suited for representing the spatial heterogeneity and dynamic interactions between human and natural environment, and capturing the impacts of forest-opening policy interventions to future socio-economic and natural environment changes. The model consists of four components: (1) a system of human population, (2) a system of landscape environment, (3) decision-making procedures integrating human(or household), environmental and policy information into forest land-use decisions, and (4) a set of policy scenarios that are related to the forest-opening. The results of model simulation by different combination of various forest management scenarios are assessed by the levels of household income, ecosystem service value and income inequality in the study region. As a result, the optimal scenario of forest-opening policies in the study region is to open the forest to local residential community for the purpose of recreation, considering the distinctive topographical feature. The model developed in this research is expected to contribute to a decision support system for sustainable forest management and various land-use policies in Korea.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.427-435
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• 2023

Changes in flowering time due to weather fluctuations impact plant growth and ecosystem dynamics. Accurate prediction of flowering timing is crucial for effective forest ecosystem management. This study uses a process-based model to predict flowering timing in 2023 for five major tree species in Korean forests. Models are developed based on nine years (2009-2017) of flowering data for Abeliophyllum distichum, Robinia pseudoacacia, Rhododendron schlippenbachii, Rhododendron yedoense f. poukhanense, and Sorbus commixta, distributed across 28 regions in the country, including mountains. Weather data from the Automatic Mountain Meteorology Observation System (AMOS) and the Korea Meteorological Administration (KMA) are utilized as inputs for the models. The Single Triangle Degree Days (STDD) and Growing Degree Days (GDD) models, known for their superior performance, are employed to predict flowering dates. Daily temperature readings at a 1 km spatial resolution are obtained by merging AMOS and KMA data. To improve prediction accuracy nationwide, random forest machine learning is used to generate region-specific correction coefficients. Applying these coefficients results in minimal prediction errors, particularly for Abeliophyllum distichum, Robinia pseudoacacia, and Rhododendron schlippenbachii, with root mean square errors (RMSEs) of 1.2, 0.6, and 1.2 days, respectively. Model performance is evaluated using ten random sampling tests per species, selecting the model with the highest R². The models with applied correction coefficients achieve R² values ranging from 0.07 to 0.7, except for Sorbus commixta, and exhibit a final explanatory power of 0.75-0.9. This study provides valuable insights into seasonal changes in plant phenology, aiding in identifying honey harvesting seasons affected by abnormal weather conditions, such as those of Robinia pseudoacacia. Detailed information on flowering timing for various plant species and regions enhances understanding of the climate-plant phenology relationship.

• 한국해양학회지
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• v.21 no.1
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• pp.13-24
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• 1986

The daily net primary production by phytoplankton in the southeastern sea of Korea in October 1985 ranged from 0.7 to 2.7 gCm$\^$-2/ d$\^$-1/ and averaged to be 1.3 gCm$\^$-2/ d$\^$-1/. Surface total chlorophyll ranged from 0.97 to 3.59mg chlm$\^$-3/. Primary production by nano-phytoplankton(〈20$\mu\textrm{m}$) ranged from 43 to 97% in the surface layer. Optimum light intensity(Iopt)was around 300 to 700${\mu}$Es$\^$-1/m$\^$-1/. Surface primary production from 9:00 to 15:00 h was evidently inhibited by strong light intensity beyond the Iopt. Phytoplankton near the base of euphotic zone(30-40m) showed extremely low Iopt suggesting adaptation to a low light environment. Since Iopt represents the history of light experience of phytoplankton at a given depth, the extent of variation in I of phytoplankton at different depth seems to be related to the in tensity of turbulence mixing in the surface mixed layer. From the present study, ammonium excretion by macrozooplankton (〉350$\mu\textrm{m}$) contributes from 3 to 19% of daily total nitrogen requirement by phytoplandton in this area. Calculation of upward flux of nitrate to the surface mixed layer from the lower layer, based on the simple diffusion model, approximates 3% of nitrogen requirement by phytoplankton. However, large portion of nitrogen requirement by phytoplankton remains unexplained in this area. In upwelling area near the coast, adjective flux might be the major source for the nitrogen requirement by phytoplankton. This study suggests that the major nitrogen source for the phytoplankton growth might come from the pelagic regeneration by nano-and micro-sized heterotrophic plandkon. Enhancement of primary production during the passage of the warm Tsushima Current is discussed in relation with nutrient dynamics and hydrlgraphic processes in this area.

• Journal of Energy Engineering
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• v.24 no.4
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• pp.200-210
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• 2015

The modeling for fast pyrolysis of biomass in fluidized bed reactor has been developed for accurate prediction of bio-oil and gas products and for yield improvement. The purpose of this study is to analyze and to compare the CFD(Computational Fluid Dynamics) simulation results with the experimental data from the CFD simulation results with the experimental data from the reference(Mellin et al., 2014) for gas products generated during fast pyrolysis of biomass in fluidized bed reactor. CFD(ANSYS FLUENT v.15.0) was used for the simulation. Complex pyrolysis reaction scheme of biomass subcomponents was applied for the simulation of pyrolysis reaction. This pyrolysis reaction scheme was included reaction of cellulose, hemicellulose, lignin in detail, gas products obtained from pyrolysis were mainly $CO_2$, CO, $CH_4$, $H_2$, $C_2H_4$. The deviation between the simulation results from this study and experimental data from the reference was calculated about 3.7%p, 4.6%p, 3.9%p for $CH_4$, $H_2$, $C_2H_4$ respectively, whereas 9.6%p and 6.7%p for $CO_2$ and CO which are relatively high. Through this study, it is possible to predict gas products accurately by using CFD simulation approach. Moreover, this modeling approach should be developed to predict fluidized bed reactor performance and other gas product yields.

• Journal of Distribution Research
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• v.17 no.1
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• pp.1-36
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• 2012

Service failure is one of the major reasons for customer defection. As the business environment gets tougher and more competitive, a single service failure might bring about fatal consequences to a service provider or a firm. Sometimes a failure won't end up with an unsatisfied customer's simple complaining but with a wide-spread animosity against the service provider or the firm, leading to a threat to the firm's survival itself in the society. Therefore, we are in need of comprehensive understandings of complainants' attitudes and behaviors toward service failures and firm's recovery efforts. Even though a failure itself couldn't be fixed completely, marketers should repair the mind and heart of unsatisfied customers, which can be regarded as an successful recovery strategy in the end. As the outcome of recovery efforts exerted by service providers or firms, recovery of the relationship between customer and service provider need to put on the top in the recovery goal list. With these motivations, the study investigates how service failure and recovery makes the changes in dynamics of fundamental elements of customer-firm relationship, such as customer affection, customer trust and loyalty intention by comparing two time points, before the service failure and after the recovery, focusing on the effects of recovery satisfaction and the failure severity. We adopted La & Choi (2012)'s framework for development of the research model that was based on the previous research stream like Yim et al. (2008) and Thomson et al. (2005). The pivotal background theories of the model are mainly from relationship marketing and social relationships of social psychology. For example, Love, Emotional attachment, Intimacy, and Equity theories regarding human relationships were reviewed. As the results, when recovery satisfaction is high, customer affection and customer trust that were established before the service failure are carried over to the future after the recovery. However, when recovery satisfaction is low, customer-firm relationship that had already established in the past are not carried over but broken up. Regardless of the degree of recovery satisfaction, once a failure occurs loyalty intention is not carried over to the future and the impact of customer trust on loyalty intention becomes stronger. Such changes imply that customers become more prudent and more risk-aversive than the time prior to service failure. The impact of severity of failure on customer affection and customer trust matters only when recovery satisfaction is low. When recovery satisfaction is high, customer affection and customer trust become severity-proof. Interestingly, regardless of the degree of recovery satisfaction, failure severity has a significant negative influence on loyalty intention. Loyalty intention is the most fragile target when a service failure occurs no matter how severe the failure criticality is. Consequently, the ultimate goal of service recovery should be the restoration of customer-firm relationship and recovery of customer trust should be the primary objective to accomplish for a successful recovery performance. Especially when failure severity is high, service recovery should be perceived highly satisfied by the complainants because failure severity matters more when recovery satisfaction is low. Marketers can implement recovery strategies to enhance emotional appeals as well as fair treatments since the both impacts of affection and trust on loyalty intention are significant. In the case of high severity of failure, recovery efforts should be exerted to overreach customer expectation, designed to directly repair customer trust and elaborately designed in the focus of customer-firm communications during the interactional recovery process to affect customer trust rebuilding indirectly. Because it is a longer and harder way to rebuild customer-firm relationship for high severity cases, low recovery satisfaction cannot guarantee customer retention. To prevent customer defection due to service failure of high severity, unexpected rewards as a recovery will be likely to be useful since those will lead to customer delight or customer gratitude toward the service firm. Based on the results of analyses, theoretical and managerial implications are presented. Limitations and future research ideas are also discussed.

• Journal of Bio-Environment Control
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• v.15 no.4
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• pp.296-305
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• 2006

The heterogeneity of crop transpiration is important to clearly understand the microclimate mechanisms and to efficiently handle the water resource in greenhouses. A computational fluid dynamic program (Fluent CFD version 6.2) was developed to study the internal climate and crop transpiration distributions of greenhouses. Additionally, the global solar radiation model and a crop heat exchange model were programmed together. Those models programmed using $C^{++}$ software were connected to the CFD main module using the user define function (UDF) technology. For the developed CFD validity, a field experiment was conducted at a $17{\times}6 m^2$ plastic-covered mechanically ventilated single-span greenhouse located at Pusan in Korea. The CFD internal distributions of air temperature, relative humidity, and air velocity at 1m height were validated against the experimental results. The CFD computed results were in close agreement with the measured distributions of the air temperature, relative humidity, and air velocity along the greenhouse. The averaged errors of their CFD computed results were 2.2%,2.1%, and 7.7%, respectively.

• Journal of Soil and Groundwater Environment
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• v.12 no.5
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• pp.19-32
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• 2007

In Korea, there have been various methods of estimating groundwater recharge which generally can be subdivided into three types: baseflow separation method by means of groundwater recession curve, water budget analysis based on lumped conceptual model in watershed, and water table fluctuation method (WTF) by using the data from groundwater monitoring wells. However, groundwater recharge rate shows the spatial-temporal variability due to climatic condition, land use and hydrogeological heterogeneity, so these methods have various limits to deal with these characteristics. To overcome these limitations, we present a new method of estimating recharge based on water balance components from the SWAT-MODFLOW which is an integrated surface-ground water model. Groundwater levels in the interest area close to the stream have dynamics similar to stream flow, whereas levels further upslope respond to precipitation with a delay. As these behaviours are related to the physical process of recharge, it is needed to account for the time delay in aquifer recharge once the water exits the soil profile to represent these features. In SWAT, a single linear reservoir storage module with an exponential decay weighting function is used to compute the recharge from soil to aquifer on a given day. However, this module has some limitations expressing recharge variation when the delay time is too long and transient recharge trend does not match to the groundwater table time series, the multi-reservoir storage routing module which represents more realistic time delay through vadose zone is newly suggested in this study. In this module, the parameter related to the delay time should be optimized by checking the correlation between simulated recharge and observed groundwater levels. The final step of this procedure is to compare simulated groundwater table with observed one as well as to compare simulated watershed runoff with observed one. This method is applied to Mihocheon watershed in Korea for the purpose of testing the procedure of proper estimation of spatio-temporal groundwater recharge distribution. As the newly suggested method of estimating recharge has the advantages of effectiveness of watershed model as well as the accuracy of WTF method, the estimated daily recharge rate would be an advanced quantity reflecting the heterogeneity of hydrogeology, climatic condition, land use as well as physical behaviour of water in soil layers and aquifers.