• Korean Journal of Environment and Ecology
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• v.32 no.3
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• pp.303-312
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• 2018

This study was conducted to investigate the relationship between the decrease of forest biomass by forest thinning and the change of temperature in the natural forest by measuring forest biomass and temperature before and after forest thinning in the Pusan National University forest where afforestation had been carried out. We intended to investigate the relationship between the forest biomass, estimated by calculating the Basal area, Crown area and Crown volume using the same formula to the same quadrat before and after forest thinning, and the forest temperature. Temperature measurement was carried out on April 20, 2016 through 28 before forest thinning, July 26, 2016 through November 4 around the time of forest thinning, and April 15, 2017 through May 8 after forest thinning. A temperature data logger was installed to point north at the height of 2.0 m above the ground in the center of the quadrat to record data every 10 minutes during the measurement periods. We used the AWS (Automatic Weather Station) data of the Dongnae-gu area located in the nearby city because it was difficult to set the control group since the whole forest was the subject to the forest thinning. The analysis of the relationship between forest biomass change and temperature showed that the change in temperature inside the forest was the greatest in the midday (12:00 - 15: 00) and was highly correlated with the Crown volume in the forest biomass. The temperature increase was much larger (average $1.91^{\circ}C$) 1 year after forest thinning than immediately after forest thinning (average $0.74^{\circ}C$). The comparison of the decrease rate of Crown volume and the increase in temperature showed that the Pitch pine community, which showed the highest decrease of Crown volume by 15.4%, recorded the highest temperature rise of $1.06^{\circ}C$ immediately after forest thinning and $2.49^{\circ}C$ 1 year after forest thinning. The Pitch pine-Korean red pine community, which showed the lowest Crown volume reduction rates with 5.0%, recorded no significant difference immediately after forest thinning but a temperature rise of $0.92^{\circ}C$ 1 year after forest thinning. The results confirmed that the decrease of forest biomass caused by forest thinning led to a rapid increase of the internal temperature. The fact that the temperature increase was more severe after 1 year than immediately after forest thinning confirmed that the microclimate changes due to the removed biomass cannot be recovered in a short time.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.35-49
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• 2014

In order to understand the impacts of surface characteristics and the distance from the urban heat island center to suburban areas on the mean night time air temperature, we analyzed GIS and AWS observational data. Spatial distributions of mean night time air temperature during the summer and winter periods of 2004-2011(six years) were utilized. Results show that the temperature gradients were different by distance and direction. We found high correlation between mean night-time air temperature and artificial land cover area within 1km and 200m radii during both summer(R=0.84) and winter(R=0.78) seasons. Regression models either from 1km and 200m land surface data explained the distribution of night-time temperature equally well if the input data had sufficient resolution with detailed attribute including building area and height.

• Journal of Korea Water Resources Association
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• v.40 no.3
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• pp.191-204
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• 2007

To evaluate the climate change impacts on water resources, this study generates and analyzes the climate change scenarios for 139 sub-basins in Korea using high resolution ($27km\;{\times}\; 27km$) SHES A2 scenario and LARS-WG. The $27km\;{\times}\; 27km$ high resolution NCAR/PSU MM5 scenario is downscaled from 350km horizontal resolution ECHO-G data. The A2 scenario relatively well reproduced Korean spatial precipitation characteristics, but it underestimated the precipitation over the Han River and the Gum River basins. The LARS-WG was selected and evaluated to overcome the limitation of climate model and to create a highly reliable climate scenario. The results show that the monthly mean minimum and maximum temperature and monthly mean precipitation are within ${\pm}20%$ from the observed mean, and ${\pm}50%$ from the standard deviation that represents the generated data are highly reliable. Moreover, the comparison results between observed data and generated data from LARS-WG show that the latter can reflect the regional climate characteristic very well that can not be simulated from the former.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.6
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• pp.713-723
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• 2019

Due to the climate change and various rainfall pattern, it is difficult to estimate a rainfall criterion which cause inundation for urban drainage districts. It is necessary to examine the result of inundation analysis by considering the detailed topography of the watershed, drainage system, and various rainfall scenarios. In this study, various rainfall scenarios were considered with the probabilistic rainfall and Huff's time distribution method in order to identify the rainfall characteristics affecting the inundation of the Hyoja drainage basin. Flood analysis was performed with SWMM and two-dimensional inundation analysis model and the parameters of SWMM were optimized with flood trace map and GA (Genetic Algorithm). By linking SWMM and two-dimensional flood analysis model, the fitness ratio between the existing flood trace and simulated inundation map turned out to be 73.6 %. The occurrence of inundation according to each rainfall scenario was identified, and the rainfall criterion could be estimated through the logistic regression method. By reflecting the results of one/two dimensional flood analysis, and AWS/ASOS data during 2010~2018, the rainfall criteria for inundation occurrence were estimated as 72.04 mm, 146.83 mm, 203.06 mm in 1, 2 and 3 hr of rainfall duration repectively. The rainfall criterion could be re-estimated through input of continuously observed rainfall data. The methodology presented in this study is expected to provide a quantitative rainfall criterion for urban drainage area, and the basic data for flood warning and evacuation plan.