• Korean journal of applied entomology
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• v.39 no.2
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• pp.73-82
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• 2000

Two Psyllidae species of Cacopsylla pyricola (Foerster) and C. pyrisuga (Foerster)damaging pear trees have been reported in Korea. However, their ecological characteristics and damagepatterns have not been evaluated yet. To establish basic control measures of C. pyricola, field phenology,overwintering ecology, seasonal fluctuation and temperature-dependent development of C. pyricola wereexamined. C. pyricola overwintered under the bark scale of pear trees as winter form adults and theymoved to fruiting twigs from mid-February. Honeydew produced by C. pyricola nymphs and adults asthey feed caused serious black sooty mold on leaves and fruits. The seasonal occurrence of C. pyricolawas different every year. In 1993, characterized by cold temperature and heavy precipitation, C. pyricolapopulation was maintained highly during growing season. However, the population was decreased rapidlyfrom early July in 1994, year of hot and dry weather condition. In 1995, year of average temperature, thedensity of C. pyricola population was decreased during hot months of July and August, and rebuilt up inSeptember and October. The development periods of C. pyricola eggs were 13.33 days at 15"C, 9.32 daysat 20$^{\circ}$C, 7.82 days at 25"C, 6.60 days at 30$^{\circ}$C, and 7.75 days at 35$^{\circ}$C. The development periods ofnymphs were 33.75 days at 15OC, 23.77 days at 20$^{\circ}$C, 15.21 days at 25"C, and 17.40 days at 30$^{\circ}$C. Theirdevelopment periods and mortalities were increased in higher temperatures. The parameters of nonlineardevelopment model, Weibull and linear development models of Cacopsylla pyricola were estimated.models of Cacopsylla pyricola were estimated.

• The Journal of Engineering Geology
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• v.26 no.1
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• pp.1-13
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• 2016

Groundwater flow paths and groundwater ages at a radioactive waste repository located in a coastal area of South Korea were evaluated using the hydrochemical and hydrogeological characteristics of groundwater, surface water, rain water, and seawater, as well as by numerical modeling. The average groundwater travel time in the top layer of the model, evaluated by numerical modeling and groundwater age (34 years), approximately corresponds to the groundwater age obtained by chlorofluorocarbon (CFC)-12 analysis (26-34 years). The data suggest that the groundwater in wells in the study area originated up-gradient at distances of 140-230 m. Results of CFC analyses, along with seasonal variations in the δ¹⁸O and δD values of groundwater and the relationships between ²²²Rn concentrations and δ¹⁸O values and between ²²²Rn concentrations and δD values, indicate that groundwater recharge occurs in the summer rainy season and discharge occurs in the winter dry season. Additionally, a linear relationship between dissolved SiO₂ concentrations and groundwater ages indicates that natural mineralization is affected by the dilution of groundwater recharge in the rainy summer season.

• Korean Journal of Ecology and Environment
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• v.42 no.4
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• pp.452-464
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• 2009

The objectives of this study were to evaluate spatial and temporal variability of water quality in 10 reservoirs and identify the key nutrients (N, P) influencing chlorophyll-a (CHL) along with analysis of empirical models and zonal patterns of total phosphorus (TP) and CHL. We analyzed total nitrogen (TN), TP, CHL, water clarity (Secchi depth, SD), and evaluated potential limiting nutrient using ambient N:P ratios and previous criteria of ambient nutrients. Water clarity and CHL varied largely depending on the seasonal monsoon and type of reservoir, but trophic state was diagnosed as eutrophy, base on mean CHL in most reservoirs. The peak of TP did not match the contents of CHL due to rapid flushing during the high run-off period. In the reservoir of DR, regression coefficient in the $P_r$ was 0.510 but was 0.159 in the $M_o$, while the TP-CHL relation in the YR increased during the monsoon compared to the premonsoon. The regression coefficient in the $P_r$ was not statistically significant but the value of $M_o$ was 0.250. TP showed similar longitudinal zonal gradients among the reservoirs of DR, YR and JR. Empirical models of TP-CHL, based on overall data, showed that CHL was determined by phosphorus($R^2=0.244$, p=0.0019). Regression analysis of CHL-SD showed a stronger linear fit ($R^2=0.638$, p<0.001) than the TP-CHL model.

• Magazine of the Korean Society of Agricultural Engineers
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• v.44 no.7
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• pp.64-73
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• 2002

This study was performed to evaluate effects of physical characteristics of both watershed and reservoir on nutrient-chlorophyll relationship in Korean reservoirs. Simple linear models were developed with published data in Korea including 415 reservoirs and 11 multi-purpose dams, and physico-chemical parameters of reservoirs and characteristics relationship of models were analyzed. Theoretical residence time in Korean reservoirs was strongly correlated with the ratio of TA/ST (drainage area + surface area / storage volume) in the logarithmic function. As a result of monthly nutrients-chlorophyll-a regression analysis, significant Chl-a-TP relationship appeared during May～July. The high Chl-a yields per total phosphorus appeared during this time (R$\^$2/=0.51, p<0.001, N= 1088). Chlorophyll-a demonstrated much stronger relationship with TP. than TN. Seasonal algal-nutrient coupling were closely related with N:P ratio in the reservoir water, and it was, in turn, dependent on the monsoon climatic condition (precipitation). Based on the results of regression analysis and high N:P ratio, a major limiting factor of algal growth appeared to be phosphorus during this time. Unlikely TA/ST ratio, DA/SA ratio (drainage area f surface area) was likely to influence directly on the nutrient-Chl-a relationship, indicating that if storage volume and inflowing water volume were the same, algal biomass could be developed more in reservoirs with large surface area. Thus, DA/SA ratio seemed to be an important factor to affect the development of algal biomass in Korean reservoirs. With low determination coefficient of TP-Chl-a relationship, our findings indicated not only nutrient (phosphorus) but also other physical factors, such as DA/SA ratio, may affect algal biomass development in Korean reservoirs, where actual residence time appears to be more closely related to reservoir surface area rather than storage volume.

• Journal of the Korean Society of Urban Environment
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• v.18 no.4
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• pp.495-502
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• 2018

In this study, the AERMOD air diffusing model was used to estimate the range of influence of Hazardous chemicals (hydrogen fluoride) in case of small accidents in Jeju area. The impact scope were in the order of Seogwipo Fire Station, Dongbu Fire Station, Jeju Fire Station, and Seobu Fire Station. Seasonal orders were summer, spring, autumn and winter. The correlation between the meteorological factors shows a strong positive correlation with the wind speed of 0.998 and has a negative correlation with the temperature of -0.463. Through the linear regression analysis, we could estimate the equation of Impact scope = 13.922WS (Wind Speed) - 5.195 and the reliability ($R^2$) was as high as 0.995.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.112-112
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• 2018

최근 이상기후로 인한 국부적인 혹은 광역적인 가뭄이 빈번하게 발생하고 있는 추세이며 발생횟수 뿐 아니라 가뭄 심도 및 지속기간이 과거보다 크게 증가하여 그에 따른 피해가 커질 것으로 예측되고 있다. 특히, 2014~2015년도의 유례없는 가뭄으로 인해 저수지 용수공급이 제한되면서 많은 농가들이 피해를 입었다. 본 연구의 목적은 전국 농업용 저수지를 대상으로 기상청 3개월 예보자료를 활용 할 수 있는 농업용 저수지 저수율 다중선형 회귀 모형을 개발하여 저수율 전망정보를 생산하는 것이다. 본 연구에서는 전국에 적용 가능한 저수율 다중선형 회귀 모형개발을 위해 5개의 기상요소(강수량, 최고기온, 최저기온, 평균기온, 평균풍속)와 관측 저수지 저수율을 활용했다. 기상자료는 2002년부터 2017년까지의 기상청 63개 지상관측소로부터 기상관측자료를 수집하였다. 본 연구에서는 저수율 전망 단계를 세 단계로 나누었다. 첫 번째 단계로 농어촌공사에서 전국 511개 용수구역을 대상으로 군집분석 및 의사결정나무 분석을 통해 제시한 65개 대표저수지를 대상으로 기상자료 및 관측 저수율 자료를 이용하여 다중선형 회귀분석을 실시하였다. 수집한 기상요소와 저수율을 독립변수로 하여 월별 회귀식을 산정한 결과 결정계수($R^2$)는 0.51~0.95로 나타났다. 두 번째 단계로 대표저수지의 회귀분석 결과를 전국의 저수지로 확대하기 위해 나이브 베이즈 분류법을 적용하여 전국 3098개의 저수지를 65의 군집으로 분류하고 각각의 군집에 해당되는 월별 회귀식을 산정하였다. 마지막으로 전국 저수지로 산정된 회귀식과 농업 가뭄 예측을 위해 기상청의 GS5(Global Seasonal Forecasting System 5) 3개월 예보자료를 수집하여 회귀식에 적용해 2017년 전국 저수지의 3개월 저수율 전망정보를 생산하였다. 본 연구의 전국 저수지 군집결과 기반의 저수율 전망기술은 2017년도 관측 저수율과 비교한 결과 유의한 상관성을 나타냈으며 이 결과는 추후 농업용 저수지의 물 공급 및 농업가뭄 전망 자료로서 이용이 가능할 것으로 판단된다.

• Journal of Forest and Environmental Science
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• v.27 no.3
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• pp.135-141
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• 2011

The present study was carried out from September 2007 to February 2008 in Umfakarin natural forest reserve, South Kordofan, Sudan. The objective was to analyze the effect of different management strategies on yield of gum talha from Acacia seyal. A total of 493 single target trees were selected, based on their diameters, and assigned to tapping treatments in three different stand densities (making a total of nine treatments per stand density). The treatments are as follows: tapping date with three levels (first of October, 15 October and first of November) and two levels of local tapping tools (sonki, and makmak). Untapped trees were used as control. The first picking of gum was started fifteen days after tapping while the subsequent pickings were done in intervals of fifteen days. Yield per tree throughout the season was obtained by summing up the gum yield from all pickings. Yield throughout the season (from first to the last picking) were analyzed. General linear model (GLM) was used to test the effect of different tapping treatments on the yield of gum talha. Post hoc test after analysis of variance (ANOVA) based on Scheffe test was performed to examine the differences in gum yield as a result of different management strategies. The results showed that tapping has a significant influence on gum yield. Analysis of pick-to-pick yield indicated that only three treatments in dense stand density showed a decreasing pattern while the rest of treatments either have constant or unclear patterns. The results of the present study were based on a single season data and that may underscore the real effect of Acacia seyal stands' management strategies on gum talha yield. Conducting gum yield experiments in permanent trial plots are highly recommended in order to analyze gum yield of seasonal time series.

• Journal of Preventive Medicine and Public Health
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• v.32 no.2
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• pp.191-199
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• 1999

Objectives: To examine the relationship between air pollution exposure and mortality in Seoul for the years of 1991-1995, Methods: Daily counts of death were analyzed by general additive Poisson model, with adjustment for effects of secular trend, seasonal factor, day of the week, heat wave, temperature, and humidity. Pollution variables were ozone, nitrogen dioxide, total suspended particles(TSP), and sulfur dioxide. Results: Daily death counts were associated with ozone(1 day before), nitrogen dioxide(1 day before), TSP(2 days before), sulfur dioxide(2 days before). The association with ozone was most statisfically significant and independent of other air pollutants. Increase of 100 ppb in ozone was associated with 0%(95% Cl= 2%-10%) increase in the daily number of death, This effect was greater in persons aged 65 and older. The relative risks of death from respiratory disease and cardiovascular disease were greater than for all-cause mortality in each pollutant. After ozone level exceeds 25 ppb, the dose-response relationship between mortality and ozone was almost linear. However, the effect of TSP, sulfur dioxide, and nitrogen dioxide on mortality might be confounded with each other. Conclusion: Daily variations in air pollution within the range currently occurring in Seoul might have an adverse effect on daily mortality.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.