• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.221-232
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• 2016

Models to estimate solar radiation have been used because solar radiation is measured at a smaller number of weather stations than other variables including temperature and rainfall. For example, solar radiation has been estimated using the Angstrom-Prescott (AP) model that depends on two coefficients obtained empirically at a specific site ($AP_{Choi}$) or for a climate zone ($AP_{Frere}$). The objective of this study was to identify the coefficients of the AP model for reliable estimation of solar radiation under a wide range of spatial and temporal conditions. A global optimization was performed for a range of AP coefficients to identify the values of $AP_{max}$ that resulted in the greatest degree of agreement at each of 20 sites for a given month during 30 years. The degree of agreement was assessed using the value of Concordance Correlation Coefficient (CCC). When $AP_{Frere}$ was used to estimate solar radiation, the values of CCC were relatively high for conditions under which crop growth simulation would be performed, e.g., at rural sites during summer. The statistics for $AP_{Frere}$ were greater than those for $AP_{Choi}$ although $AP_{Frere}$ had the smaller statistics than $AP_{max}$ did. The variation of CCC values was small over a wide range of AP coefficients when those statistics were summarized by site. $AP_{Frere}$ was included in each range of AP coefficients that resulted in reasonable accuracy of solar radiation estimates by site, year, and month. These results suggested that $AP_{Frere}$ would be useful to provide estimates of solar radiation as an input to crop models in Korea. Further studies would be merited to examine feasibility of using $AP_{Frere}$ to obtain gridded estimates of solar radiation at a high spatial resolution under a complex terrain in Korea.

• Korean Journal of Remote Sensing
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• v.28 no.2
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• pp.215-226
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• 2012

NASA MODIS GPP provides a useful tool to monitor global terrestrial vegetation productivity. Two major problems of NASA GPP in regional applications are coarse spatial resolution ($1.25^{\circ}{\times}1^{\circ}$) of DAO meteorological data and cloud contamination of MODIS FPAR product. In this study, we improved the NASA GPP by using enhanced input data of high spatial resolution (3 km${\times}$3 km) WRF meteorological data and cloud-corrected FPAR over the North Korea. The improved GPP was utilized to investigate characteristics of GPP interannual variation and spatial patterns from 2000 to 2008. The GPP varied from 645 to 863 $gC\;m^{-2}\;y^{-1}$ in 2000 and 2008, respectively. Mixed forest showed the highest GPP (1,076 $gC\;m^{-2}\;y^{-1}$). Compared to NASA GPP (790 $gC\;m^{-2}\;y^{-1}$);FPAR enhancement increased GPP (861) but utilization of WRF data decreased GPP (710). Enhancements of both FPAR and meteorological input resulted in GPP increase (809) and the improvement was the greatest for mixed forest regions (+10.2%). The improved GPP showed better spatial heterogeneity reflecting local topography due to high resolution WRF data. It is remarkable that the improved and NASA GPPs showed distinctly different interannual variations with each other. Our study indicates improvement of NASA GPP by enhancing input variables is necessary to monitor region-scale terrestrial vegetation productivity.

• Korean Journal of Ecology and Environment
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• v.35 no.3 s.99
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• pp.160-171
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• 2002

The impact of summer monsoon on water quality of the lower Nakdong River was evaluated during the summer (June-August) in 1997. Several limnological variables were measured in the interval of $1{\sim}3$ day using an automatic monitoring system (Hydrolab $Recorder^{TM}$) to detect water quality changes caused by rainfall on onehour basis. During the monsoon period (from late June to mid July), 5 times of major rainfall events of >50 mm were recorded in the river basin. Dynamic changes of water quality were observed during the monsoon, and the first rainfall event (June$25{\sim}27$) had a significant influence on the water quality at the lower part of the river. All Parameters were largely changed due to the first rain event, and the changed level was maintained until the end of monsoon period. Nutrient concentrations and turbidity increased and values of the other parameters were declined as a result of water dilution. This rainfall event, Changma, is a meteorological phenomenon caused by the East-Asian monsoon climate. The magnitude and frequency of the rainfall during the early monsoon play an important role in change of water quality and ecosystem characteristics of large river systems.

• Korean Journal of Breeding Science
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• v.42 no.1
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• pp.40-49
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• 2010

This study was conducted to examine genetic variation on leaf characteristics of Zelkova serrata populations. Leaf samples were obtained from eighteen populations and fourteen leaf characteristics such as leaf blade length, leaf width, leaf area and etc. were measured. In all leaf characteristics, there were significant differences among populations and among individuals within population. Most variance of leaf characteristics was contributed to among individuals within population except for length from leaf base to maximum width (x3) and the ratio of x3 to leaf blade length (x10). The relatively high variations of leaf characteristics were found at Gurye, Jungeup and Gyungju populations while Youngwol, Cheongsong, Youngchun populations showed the lower variation. There was high correlation among leaf characteristics related to leaf size, but not among the variables of ratio between leaf characteristics. Length from leaf base to maximum width(x3) and the ratio of x3 to x10 showed significant positive correlation with latitude and altitude of populations, which reflect the differences of mean annual temperature among populations. Mean annual rainfall of populations showed negative correlation with leaf blade length, leaf width, length from apex to first serration and leaf area. Four principal components (PC) were deduced from principal component analysis, which explain the 88.5% of total variance of leaf characteristics. Leaf area, length from leaf base to maximum width, serration number and petiole length showed the highest contribution to PC1, PC2, PC3, PC4, respectively. According to cluster analysis, the populations of Z. serrata were divided into two groups, which reflect the difference of mean annual temperature between groups. Within group, however, specific tendency of clustering was not observed among populattions.

• Korean Journal of Ecology and Environment
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• v.51 no.4
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• pp.331-344
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• 2018

Lotic organisms in streams are affected by natural and anthropogenic disturbances such as increase of heavy rainfall event caused by climate change and flow regime change caused by weir constructions. Based on domestic and foreign literature, 157 Korean benthic macroinvertebrate taxa were selected species as potential lotic candidates. Three shoreline sites (total 54 samples) were surveyed consecutively before ('08~'09), during ('10~'12) and after ('13~'16) the construction of the weirs (Gangcheon, Yeoju and Ipo weir) in the Namhan-River for tracing changes of lotic communities. As a result, water flow of the Ipo-wier and water quality variables such as T-N, T-P, BOD5, etc. of the weir section revealed no significant changes. Physical habitat conditions such as the flow velocity and streambed substrate evidently changed. Particulary, flow velocity measured at sampling points along with each microhabitat drastically decreased and particle size of streambed substrate steadily decreased after weir constructions. Lotic organisms also decreased after construction, especially Hydropsychidae (insecta: Trichoptera) acutely decreased from $3,526ind.\;m^{-2}$ to $2ind.\;m^{-2}$ As a result of CCA, lotic species such as Hydropsyche valvata, Hydropsyche kozhantschikovi, Cheumatopsyche brevilineata, Cheumatopsyche KUa, Macrostemum radiatum, etc. correlated with the flow velocity, streambed substrate. Therefore, the decrease of the flow velocity and substrate size after weir construction seemed to be closely related to the decrease of the individual abundances of the lotic organisms independently of water quality. In order to evaluate the influence of the ecosystem on the flow regime change more accurately, it is necessary to study the indicator species based on the resistance or preference of the flow.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.

• Journal of Korea Water Resources Association
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• v.56 no.6
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• pp.403-417
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• 2023

In this research, a methodology was developed for constructing an appropriate rainfall image database for estimating rainfall intensity based on CCTV video. The database was constructed in the Large-Scale Climate Environment Chamber of the Korea Conformity Laboratories, which can control variables with high irregularity and variability in real environments. 1,728 scenarios were designed under five different experimental conditions. 36 scenarios and a total of 97,200 frames were selected. Rain streaks were extracted using the k-nearest neighbor algorithm by calculating the difference between each image and the background. To prevent overfitting, data with pixel values greater than set threshold, compared to the average pixel value for each image, were selected. The area with maximum pixel variability was determined by shifting with every 10 pixels and set as a representative area (180×180) for the original image. After re-transforming to 120×120 size as an input data for convolutional neural networks model, image augmentation was progressed under unified shooting conditions. 92% of the data showed within the 10% absolute range of PBIAS. It is clear that the final results in this study have the potential to enhance the accuracy and efficacy of existing real-world CCTV systems with transfer learning.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.1
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• pp.46-58
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• 2024

Pollinators are organisms that carry out the pollination process of plants and include Hymenoptera, Lepidoptera, Diptera, and Coleoptera. Among them, bees not only pollinate plants but also improve urban green spaces damaged by land use changes, providing a habitat and food for birds and insects. Today, however, the number of pollinating plants is decreasing due to issues such as early flowering due to climate change, fragmentation of green spaces due to urbanization, and pesticide use, which in turn leads to a decline in bee populations. The decline of bee populations directly translates into problems, such as reduced biodiversity in cities and decreased food production. Urban beekeeping has been proposed as a strategy to address the decline of bee populations. However, there is a problem asurban beekeeping strategies are proposed without considering the complex structure of the socio-ecological system consisting of bees foraging and pollination activities and are therefore unsustainable. Therefore, this study aims to analyze the socio-ecological system of honeybees, which are pollinators, structurally using system thinking and propose a green space planning strategy to revitalize urban beekeeping. For this study, previous studies that centered on the social and ecological system of bees in cities were collected and reviewed to establish the system area and derive the main variables for creating a causal loop diagram. Second, the ecological structure of bees' foraging and pollination activities and the structure of bees' ecological system in the city were analyzed, as was the social-ecological system structure of urban beekeeping by creating an individual causal loop diagram. Finally, the socio-ecological system structure of honey bees was analyzed from a holistic perspective through the creation of an integrated causal loop diagram. Citizen participation programs, local government investment, and the creation of urban parks and green spaces in idle spaces were suggestedas green space planning strategies to revitalize urban beekeeping. The results of this study differ from previous studies in that the ecological structure of bees and the social structure of urban beekeeping were analyzed from a holistic perspective using systems thinking to propose strategies, policy recommendations, and implications for introducing sustainable urban beekeeping.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.69 no.1
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• pp.49-60
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• 2024

A region can be divided into cultivation zones based on homogeneity in weather variables that have the greatest influence on crop growth and yield. This study classified the cultivation zone of soybean using weather indices as a prior study to classify the agroclimatic zone of soybean. Meteorological factors affecting soybeans were determined through correlation analysis over a 10 year period (from 2013 to 2022) using data from the Miryang and Suwon regions collected from the soybean yield trial database of the Rural Development Administration, Korea and the meteorological database of the Korea Meteorological Administration. The correlation between growth characteristics and the minimum temperature, daily temperature range, and precipitation were high during the vegetative growth stages. Moreover, the correlation between yield components and the maximum temperature, daily temperature range, and precipitation were high during the reproductive growth stages. As a result of k-means clustering, soybean cultivation zones were divided into three zones. Zone 1 was the central inland region and southern Gyeonggi-do; Zone 2 was the southern part of the west coast, the southern part of the east coast, and the South Sea; and Zone 3 included parts of eastern Gyeonggi-do, Gangwon-do, and areas with high altitudes. Zone 1, which has a wide latitude range, was further subdivided into three cultivation zones. The results of this study may provide useful information for estimating agrometeorological characteristics and predicting the success of soybean cultivation in South Korea.