• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.55-67
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• 2021

Hornbeams (Carpinus spp.), which are widely distributed in South Korea, are recognized as one of the most abundant species at climax stage in the temperate forests. Although the distribution and vegetation structure of the C. laxiflora community have been reported, little ecological information of C. tschonoskii is available. Little effort was made to examine the distribution shift of these species under the future climate conditions. This study was conducted to predict potential shifts in the distribution of C. laxiflora and C. tschonoskii in 2050s and 2090s under the two sets of climate change scenarios, RCP4.5 and RCP8.5. The MaxEnt model was used to predict the spatial distribution of two species using the occurrence data derived from the 6th National Forest Inventory data as well as climate and topography data. It was found that the main factors for the distribution of C. laxiflora were elevation, temperature seasonality, and mean annual precipitation. The distribution of C. tschonoskii, was influenced by temperature seasonality, mean annual precipitation, and mean diurnal rang. It was projected that the total habitat area of the C. laxiflora could increase by 1.05% and 1.11% under RCP 4.5 and RCP 8.5 scenarios, respectively. It was also predicted that the distributional area of C. tschonoskii could expand under the future climate conditions. These results highlighted that the climate change would have considerable impact on the spatial distribution of C. laxiflora and C. tschonoskii. These also suggested that ecological information derived from climate change impact assessment study can be used to develop proper forest management practices in response to climate change.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.1-30
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• 2024

The international community adopts the SSP (Shared Socioeconomic Pathways) scenario as a new greenhouse gas emission pathway. As part of efforts to reflect these international trends and support for climate change adaptation measure in the agricultural sector, the National Institute of Agricultural Sciences (NAS) produced high-resolution (1 km) climate change scenarios for the Korean Peninsula based on SSP scenarios, certified as a "National Climate Change Standard Scenario" in 2022. This paper introduces SSP climate change scenario of the NAS and shows the results of the climate change projections. In order to produce future climate change scenarios, global climate data produced from 18 GCM models participating in CMIP6 were collected for the past (1985-2014) and future (2015-2100) periods, and were statistically downscaled for the Korean Peninsula using the digital climate maps with 1km resolution and the SQM method. In the end of the 21^st century (2071-2100), the average annual maximum/minimum temperature of the Korean Peninsula is projected to increase by 2.6~6.1℃/2.5~6.3℃ and annual precipitation by 21.5~38.7% depending on scenarios. The increases in temperature and precipitation under the low-carbon scenario were smaller than those under high-carbon scenario. It is projected that the average wind speed and solar radiation over the analysis region will not change significantly in the end of the 21st century compared to the present. This data is expected to contribute to understanding future uncertainties due to climate change and contributing to rational decision-making for climate change adaptation.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.419-431
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• 2021

In Seoul, it has been confirmed that the duration of rainfall is shortened and the frequency and intensity of heavy rains are increasing with a changing climate. In addition, due to high population density and urbanization in most areas, floods frequently occur in flood-prone areas for the increase in impermeable areas. Furthermore, the Seoul City is pursuing various projects such as structural and non-structural measures to resolve flood-prone areas. A disaster prevention performance target was set in consideration of the climate change impact of future precipitation, and this study conducted to reduce the overall flood damage in Seoul for the long-term. In this study, 29 GCMs with RCP4.5 and RCP8.5 scenarios were used for spatial and temporal disaggregation, and we also considered for 3 research periods, which is short-term (2006-2040, P1), mid-term (2041-2070, P2), and long-term (2071-2100, P3), respectively. For spatial downscaling, daily data of GCM was processed through Quantile Mapping based on the rainfall of the Seoul station managed by the Korea Meteorological Administration and for temporal downscaling, daily data were downscaled to hourly data through k-nearest neighbor resampling and nonparametric temporal detailing techniques using genetic algorithms. Through temporal downscaling, 100 detailed scenarios were calculated for each GCM scenario, and the IDF curve was calculated based on a total of 2,900 detailed scenarios, and by averaging this, the change in the future extreme rainfall was calculated. As a result, it was confirmed that the probability of rainfall for a duration of 100 years and a duration of 1 hour increased by 8 to 16% in the RCP4.5 scenario, and increased by 7 to 26% in the RCP8.5 scenario. Based on the results of this study, the amount of rainfall designed to prepare for future climate change in Seoul was estimated and if can be used to establish purpose-wise water related disaster prevention policies.

• Korean Journal of Environment and Ecology
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• v.32 no.5
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• pp.469-477
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• 2018

Hanabusaya asiatica is an endemic species whose distribution is limited in the mid-eastern part of the Korean peninsula. Due to its narrow range and small population, it is necessary to protect its habitats by identifying it as Key Biodiversity Areas (KBAs) adopted by the International Union for Conservation of Nature (IUCN). In this paper, we estimated potential natural habitats for H. asiatica using maximum entropy model (MaxEnt) and identified candidate sites for KBA based on the model results. MaxEnt is a machine learning algorithm that can predict habitats for species of interest unbiasedly with presence-only data. This property is particularly useful for the study area where data collection via a field survey is unavailable. We trained MaxEnt using 38 locations of H. asiatica and 11 environmental variables that measured climate, topography, and vegetation status of the study area which encompassed all locations of the border region between South and North Korea. Results showed that the potential habitats where the occurrence probabilities of H. asiatica exceeded 0.5 were $778km^2$, and the KBA candidate area identified by taking into account existing protected areas was $1,321km^2$. Of 11 environmental variables, elevation, annual average precipitation, average precipitation in growing seasons, and the average temperature in the coldest month had impacts on habitat selection, indicating that H. asiatica prefers cool regions at a relatively high elevation. These results can be used not only for identifying KBAs but also for the reference to a protection plan for H. asiatica in preparation of Korean reunification and climate change.

• Journal of Korean Society of Forest Science
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• v.84 no.3
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• pp.277-285
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• 1995

This study was conducted to understand the relationship between climatic factors and matsutake(Tricholoma matsutake) mushroom production. Data on local annual matsutake production collected from 29 locations from 1984 to 1993 were analyzed for stepwise and multiple regression with local climatic data, such as monthly maximum, minimum, and average air temperature, soil temperature, relative humidity, amount of rainfall, and number of rainy days. Correlation between monthly climatic factors and annual matsutake production was calculated in each location(Case 1), each year(Case 2), and each month(Case 3). In Case 1, number of rainy days and minimum temperature in Sep. showed positive correlation with matsutake production. In Case 2, maximum, minimum, and average temperature in June showed negative correlation with matsutake production. In Case 3, amount of precipitation in Sep. and Oct. number of rainy days in Sep., and minimum temperature in Sep. and Oct. showed positive correlation with matsutake production. In conclusion, amount of rainfall and number of rainy days in Sep. were the most important climatic factors and correlated positively with matsutake production. Below average air temperature in June was also beneficial for matsutake production.

• Journal of the Geological Society of Korea
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• v.54 no.5
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• pp.529-544
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• 2018

Dongrae hot spring belongs to the residual magma type and has a long history of bathing since the Silla dynasty in Korea. Due to long development of hot spring water, it is expected that the amount of hot spring water in Dongrae hot spring has been changed. In this study, long-trem water level data of Dongrae hot spring were examined for recognizing the change of the hot spring. By the fluctuation analysis of the hot spring water level from January 1992 to July 2018, the maximum and minimum annual drawdowns of no. 27 well were 137.70 and 71.60 meters, respectively, with an average drawdown of 103.39 m. On the other hand, the maximum and minimum annual drawdowns of no. 29 well were 137.80 and 71.70 meters, with an average drawdown of 103.49 m. Besides, drawdown rate became bigger in recent years. As a result of analyzing autocorrelation of the two wells, the correlation coefficient ranged from 0.919 to 0.991, showing seasonal groundwater level fluctuation. The cross correlation analysis between water level and precipitation as well as water level and hot spring discharge resulted in the correlation coefficients of -0.280 ~ 0.256 and 0.428 ~ 0.553, respectively. Therefore, using Dongnae hot-spring water level data from 1992 to 2018, the Mann-Kendall test and Sen's test showed that the continuous decline of water level was mainly caused by the pumping of the hot spring water among various reasons.

• Journal of Korea Water Resources Association
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• v.45 no.8
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• pp.815-826
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• 2012

The underestimating trend using existing ordinary regression (OR) based trend analysis has been a well-known problem. The existing OR method based on least squares approximate the conditional mean of the response variable given certain values of the time t, and the usual assumption of the OR method is normality, that is the distribution of data are not dissimilar form a normal distribution. In this regard, this study proposed a quantile regression that aims at estimating either the conditional median or other quantiles of the response variable. This study assess trend in annual daily maximum rainfall series over 64 weather stations through both in OR and QR approach. The QR method indicates that 47 stations out of 67 weather stations are a strong upward trend at 5% significance level while OR method identifies a significant trend only at 13 stations. This is mainly because the OR method is estimating the condition mean of the response variable. Unlike the OR method, the QR method allows us flexibly to detect the trends since the OR is designed to estimate conditional quantiles of the response variable. The proposed QR method can be effectively applied to estimate hydrologic trend for either non-normal data or skewed data.

• Korean Journal of Environmental Biology
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• v.34 no.4
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• pp.240-245
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• 2016

This study was conducted to predict the changes of potential distribution for invasive alien plant, Amaranthus viridis in Korea. The habitats of A. viridis were roadside, bare ground, farm area, and pasture, where the interference by human was severe. We used maximum entropy modeling (MaxEnt) for analyzing the environmental influences on A. viridis distribution and projecting on two different representative concentration pathways (RCP) scenarios, RCP 4.5 and RCP 8.5. The results of our study indicated annual mean temperature, elevation and precipitation of coldest month had higher contribution for A. viridis potential distribution. Projected potential distribution of A. viridis will be increased by 110% on RCP 4.5, 470% on RCP 8.5.

• Korean Journal of Agricultural and Forest Meteorology
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• v.11 no.4
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• pp.233-246
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• 2009

The partitioning of evapotranspiration (ET) into evaporation (E) and transpiration (T) is critical in understanding the water cycle and the couplings between the cycles of energy, water, and carbon. In forests, the total ET measured above the canopy consists of T from both overstory and understory vegetation, and E from soil and the intercepted precipitation. To quantify their relative contributions, we have measured ET from the floors of deciduous and coniferous forests in Gwangneung using eddy covariance technique from 1 June 2008 to 31 May 2009. Due to smaller eddies that contribute to turbulent transfer near the ground, we performed a spectrum analysis and found that the errors associated with sensor separation were <10%. The annual sum of the understory ET was 59 mm (16% of total ET) in the deciduous forest and 43 mm (~7%) in the coniferous forest. Overall, the understory ET was not negligible except during the summer season when the plant area index was near its maximum. In both forest canopies, the decoupling factor ($\Omega$) was about ~0.15, indicating that the understory ET was controlled mainly by vapor pressure deficit and soil moisture content. The differences in the understory ET between the two forest canopies were due to different environmental conditions within the canopies, particularly the contrasting air humidity and soil water content. The non-negligible understory ET in the Gwangneung forests suggests that the dual source or multi-level models are required for the interpretation and modeling of surface exchange of mass and energy in these forests.

• Korean Journal of Remote Sensing
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• v.13 no.1
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• pp.1-12
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• 1997

The western equatorial Pacific Ocean, where sea surface temperature is the warmest on the globe, is characterized by numerous convective systems and large annual precipitation. In this region, the cloudiness data with tops higher than 8km level obtained from the GMS-IR data are used to investigate the diurnal variation of cloudiness. The amplitude and phase of diurnal and semi-diurnal cycles are mainly investigated to examine details on the temporal and spatial structure of clouds. Cloudiness variation has typical cycles and each cycle is associated with the air-sea interactive phenomena. Spectral analysis on the cloudiness time series data indicates that 30-60 day, 17-20day, 7-8 day, diurnal and semi diurnal cycle are peaked. During Northern Winter and Southern Summer, the large cloudiness exsists over New Guinea, the adjacent seas of North Australia, and the open oceanic regions east of $160^{\circ}$E. Cloudiness diurnal variability over the lands and their adjacent seas is about 2.0 times larger than that over the open sea regions. That may be due to the difference of specific heat between the land and sea. The maximum and minimum cloudiness appeared at 18:00 and 09:00 hours over the land, and at noon and 21:00 hours over the sea, respectively. The amplitude of diurnal component over the land is 4,7 times larger than that of semi-diurnal component, and 1.5 times over the sea.