• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.106-106
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• 2020

물 이용 측면에서 유출해석은 강수, 증발산, 침투, 유출 등 물 순환의 핵심이다. 또 기상자료는 증발산량을 산정하는데 꼭 필요하다. 그러나 해외 수자원 사업에서 기상자료가 없어 곤란을 겪는 경우가 많다. 여기서, NASA POWER에서 전지구 0.5° 격자로 제공하는 위성기반의 일 기상자료를 이용한 증발산량과, 지상의 기상자료를 이용한 증발산량을, 각각 일 유출 모형에 적용한 결과를 비교하였다. 유역면적 4,134 ㎢인 대청댐 유역에 1984년부터 2001년까지 일별 유입량을 모의하고 관측 유입량과 비교하고, R², RMSE, NSE 등으로 평가하였다. 지상의 기상관측소는 위도 36.21°, 경도 127.34°인 대전 관측소를, 위성자료는 대전 지점과 동일한 위치의 경위도의 기상자료를 적용하였고, 일 증발산량은 Penman-Monteith 방법으로, 일 유출량은 ONE 모형에 의해 모의하였다. 강수량을 대청댐 유역 면적강수량을 적용한 경우, 지상 기상자료를 적용하여 유입량을 모의한 결과는 연평균하여 연 유입량 668.1 mm, 일 최대 82.9 mm, 유출률 56.1%(관측은 연 강수량 1,191.3 mm, 연 유입량 668.3 mm, 일 최대 87.0 mm, 유출률 56.1%)로 나타났고, R² 0.805, RMSE 2.425, NSE 0.802였고, 위성 기상자료를 적용하여 유입량을 모의한 결과는 연평균하여 연 유입량 668.4 mm, 일 최대 83.7 mm, 유출률 57.8%로 나타났고, R² 0.803, RMSE 2.431, NSE 0.801였다. 또한, 강수량을 위성 제공의 강수량을 적용한 경우, 지상 기상자료를 적용하여 유입량을 모의한 결과는 연평균하여 연 유입량 718.0 mm, 일 최대 97.7 mm, 유출률 56.7%(관측은 연 강수량 1,265.3 mm, 유출률 52.8%)로 나타났고, R² 0.582, RMSE 3.524, NSE 0.581였고, 위성 기상자료를 적용하여 유입량을 모의한 결과는 연평균하여 연 유입량 741.5 mm, 일 최대 99.0 mm, 유출률 58.6%로 나타났고, R² 0.578, RMSE 3.541, NSE 0.577였다. 결과적으로 위성 기상자료를 이용하여 증발산량을 산정하여 일 유출해석에 적용한 결과는 지상 기상자료를 이용한 경우와 거의 같은 값을 나타내, NASA POWER가 제공한 기상자료의 활용성은 매우 높게 나타났다. 그러나 위성 제공 강수량의 활용은 R², RMSE, NSE 등의 지표가 낮게 나타나, 신중하게 검토되어야 할 것으로 평가하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.177-185
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• 2008

Accurate monitoring of snow cover is a key component for studying climate and global as well as for daily weather forecasting and snowmelt runoff modelling. The few observed data related to snowmelt was the major cause of difficulty in extracting snowmelt factors such as snow cover area, snow depth and depletion curve. Remote sensing technology is very effective to observe a wide area. Although many researchers have used remote sensing for snow observation, there were a few discussions on the characteristics of spatial and temporal variation. Snow cover maps were derived from NOAA AVHRR images for the winter seasons from 1997 to 2006. Distributed snow depth was mapped by overlapping between snow cover maps and interpolated snowfall maps from 69 meteorological observation stations. Model parameters (Snow Cover Area: SCA, snow depth, Snow cover Depletion Curve: SDC) were built for 7 major watersheds in South Korea. The decrease pattern of SCA for time (day) was expressed as exponentially decay function, and the determination coefficient was ranged from 0.46 to 0.88. The SCA decreased 70% to 100% from the maximum SCA when 10 days passed.

• Journal of the Korean earth science society
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• v.21 no.5
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• pp.623-634
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• 2000

This study was conducted to analyze the patterns associated with the short-term variability of CO$_2$ concentrations over 24-h scale within and across the Korean Peninsula. In the course of our study, we compared the data sets obtained from Moo-Ahn (MAN) station located in the far western coastal area of Korea with those determined from major background observatory stations around the world from the periods of Aug. 1995 to Dec. 1997. The mean CO$_2$ concentration of the MAN area for the whole study periods, when computed using the daily mean values, was found out to be 374.5${\pm}$6.6 ppm (N=884); seasonal mean values were found out to be 378${\pm}$5.2 (spring: N=181), 372${\pm}$10.2 (summer: N =210), 372${\pm}$7.2 (fall: N=243), and 376${\pm}$5.4 ppm (winter: N=206). When the data from MAN was compared with those of major background stations, the effects of both daily and seasonal components appear to vary distinctively across different stations. Those effects are expected to reflect the mixed effects of various factors which include: seasonal pollution patterns, weather conditions, vegetation, and so forth. Based upon this comparative analysis, we suspect that the MAN area is under the strong influence of anthropogenic source processes relative to all the other stations under consideration. If that is not the case, the existence of enhanced CO$_2$ level may be rather ubiquitous phenomena in Korea. More detailed inspection of CO$_2$ behavior from various respects is strongly desired in the future.

• Atmosphere
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• v.21 no.4
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• pp.349-359
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• 2011

The climatology in stratospheric ozone over the Korean Peninsula, presented in previous studies (e.g., Cho et al., 2003; Kim et al., 2005), is updated by using daily and monthly data from satellite and ground-based data through December 2009. In addition, long-term satellite data [Total Ozone Mapping Spectrometer (TOMS), Ozone Monitoring Instrument (OMI), 1979~2009] have been also analyzed in order to deduce the spatial distributions and temporal variations of the global total ozone. The global average of total ozone (1979~2009) is 298 DU which shows a minimum of about 244 DU in equatorial latitudes and increases poleward in both hemispheres to a maximum of about 391 DU in Okhotsk region. The recent period, from 2006 to 2009, shows reduction in total ozone by 6% relative to the values for the pre-1980s (1979~1982). The long-term trends were estimated by using a multiple linear regression model (e.g., WMO, 1999; Cho et al., 2003) including explanatory variables for the seasonal variation, Quasi-Biennial Oscillation (QBO) and solar cycle over three different time intervals: a whole interval from 1979 to 2009, the former interval from 1979 to 1992, and the later interval from 1993 to 2009 with a turnaround point of deep minimum in 1993 is related to the effect of Mt. Pinatubo eruption. The global trend shows -0.93% $decade^{-1}$ for the whole interval, whereas the former and the later interval trends amount to -2.59% $decade^{-1}$ and +0.95% $decade^{-1}$, respectively. Therefore, the long-term total ozone variations indicate that there are positive trends showing a recovery sign of the ozone layer in both North/South hemispheres since around 1993. Annual mean total ozone (1985~2009) is distributed from 298 DU for Jeju ($33.52^{\circ}N$) to 352 DU for Unggi ($42.32^{\circ}N$) in almost zonally symmetric pattern over the Korean Peninsula, with the latitudinal gradient of 6 DU $degree^{-1}$. It is apparent that seasonal variability of total ozone increases from Jeju toward Unggi. The annual mean total ozone for Seoul shows 323 DU, with the maximum of 359 DU in March and the minimum of 291 DU in October. It is found that the day to day variability in total ozone exhibits annual mean of 5.7% in increase and -5.2% in decrease. The variability as large as 38.4% in increase and 30.3% in decrease has been observed, respectively. The long-term trend analysis (e.g., WMO, 1999) of monthly total ozone data (1985~2009) merged by satellite and ground-based measurements over the Korean Peninsula shows increase of 1.27% $decade^{-1}$ to 0.80% $decade^{-1}$ from Jeju to Unggi, respectively, showing systematic decrease of the trend magnitude with latitude. This study also presents a new analysis of ozone density and trends in the vertical distribution of ozone for Seoul with data up to the end of 2009. The mean vertical distributions of ozone show that the maximum value of the ozone density is 16.5 DU $km^{-1}$ in the middle stratospheric layer between 24 km and 28 km. About 90.0% and 71.5% of total ozone are found in the troposphere and in the stratosphere between 15 and 33 km, respectively. The trend analysis reconfirms the previous results of significant positive ozone trend, of up to 5% $decade^{-1}$, in the troposphere and the lower stratosphere (0~24 km), with negative trend, of up to -5% $decade^{-1}$, in the stratosphere (24~38 km). In addition, the Umkehr data show a positive trend of about 3% $decade^{-1}$ in the upper stratosphere (38~48 km).

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1653-1667
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• 2020

In this study, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. We investigated the characteristics of fine particulate matter (PM2.5) distributions in a building-congested district. To analyze the effects of road emission on the PM2.5 concentrations, we calculated road emissions based on the monthly, daily, and hourly emission factors and the total amount of PM2.5 emissions established from the Clean Air Policy Support System (CAPSS) of the Ministry of Environment. We validated the simulated PM2.5 concentrations against those measured at the PKNU-AQ Sensor stations. In the cases of no road emission, the LDAPS-CFD model underestimated the PM2.5 concentrations measured at the PKNU-AQ Sensor stations. The LDAPS-CFD model improved the PM2.5 concentration predictions by considering road emission. At 07 and 19 LST on 22 June 2020, the southerly wind was dominant at the target area. The PM2.5 distribution at 07 LST were similar to that at 19 LST. The simulated PM2.5 concentrations were significantly affected by the road emissions at the roadside but not significantly at the building roof. In the road-emission case, the PM2.5 concentration was high at the north (wind speeds were weak) and west roads (a long street canyon). The PM2.5 concentration was low in the east road where the building density was relatively low.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.353-365
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• 2023

The effects of climate change on green infrastructure and environmental media remain uncertain and context-specific despite numerous climate projections globally. In this study, the extreme weather conditions in seven major cities in South Korea were characterized through statistical analysis of 20-year daily meteorological data extracted fro m the Korea Meteorological Administration (KMA). Additionally, the impacts of extreme weather on Nature-based Solutions (NbS) were determined through a comprehensive review. The results of the statistical analysis and comprehensive review revealed the studied cities are potentially vulnerable to varying extreme weather conditions, depending on geographic location, surface imperviousness, and local weather patterns. Temperature extremes were seen as potential threats to the resilience of NbS in Seoul, as both the highest maximum and lowest minimum temperatures were observed in the mentioned city. Moreover, extreme values for precipitation and maximum wind speed were observed in cities from the southern part of South Korea, particularly Busan, Ulsan, and Jeju. It was also found that extremely low temperatures induce the most impact on the resilience of NbS and environmental media. Extremely cold conditions were identified to reduce the pollutant removal efficiency of biochar, sand, gravel, and woodchip, as well as the nutrient uptake capabilities of constructed wetlands (CWs). In response to the negative impacts of extreme weather on the effectiveness of NbS, several adaptation strategies, such as the addition of shading and insulation systems, were also identified in this study. The results of this study are seen as beneficial to improving the resilience of NbS in South Korea and other locations with similar climate characteristics.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• Korean Journal of Environment and Ecology
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• v.32 no.6
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• pp.698-708
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• 2018

The purpose of this study was to investigate differences in the breeding call characteristics of cicada species found in urban and forest areas in the central region of Korea by examining the interspecific effects and environmental factors affecting the breeding calls and breeding call patterns. The selected research sites were Gyungnam Apartment in Bangbae-dong, Seoul for the urban area and Chiak Mountain National Park in Wonju for the forest area. The research method for both sites was to record cicada breeding calls for 24 hours with a recorder installed at the site and analyze the results. Data from the Korea Meteorological Administration were used for environmental factors. The research period was from June 19, 2017 to September 30, 2017. As a result of the study, there were differences in the emergence of species between the two research sites: while Platypleura kaempferi, Hyalessa fuscata, Meimuna opalifera, Graptopsaltria nigrofuscata, and Suisha coreana were observed at both sites, Cryptotympana atrata was observed in the urban area and Leptosemia takanonis in the forest area only. The emergence periods of cicadas at the two sites were also different. The activities of P. kaempferi and L. takanonis were noticeable in the forest area. In the urban area, however, L. takanonis was not observed and the duration of activity of P. kaempferi was short. In the urban area, C. atrata appeared and sang for a long period; H. fuscata, M. opalifera, and G. nigrofuscata appeared earlier than in the forest area. S. coreana appeared earlier in the forest area than in the urban area. According to the daily call cycle analysis, even cospecific cicada showed a wide variation in their daily cycle depending on the region and the interspecific effects between different cicadas, and the environmental differences between the urban and forest areas affected the calls of cicadas. The results of correlation analysis between each cicada breeding calls and environmental factors of each site showed positive correlation with average temperature of most cicadas except P. kaempferi and C. atrata. The same species of each site showed positive correlations with more diverse weather factors such as solar irradiance. Logistic regression analysis showed that cicadas with overlapping calling times had significant effects on each other's breeding calls. C. atrata, which appeared only in the urban area, had a positive effect on the calling frequency of H. fuscata, M. opalifera, and G. nigrofuscata, which called in the same period. Additionally, L. takanonis, which appeared only in the forest area, and P. kaempferi had a positive effect on each other, and M. opalifera had a positive effect on the calling frequency of H. fuscata and G. nigrofuscata in the forest area. For the environmental factors, the calling frequency of cicadas was affected by the average temperatures of the urban and forest areas, and cicadas that appeared in the forest area were also affected by the amount of solar radiation. According to the results of statistical analysis, urban cicadas with similar activity periods are influenced by species, especially with respect to urban dominant species, C. atrata. Forest cicadas were influenced by species, mainly M. opalifera, which is a forest dominant species. The results of the meteorological impact analysis were similar to those of the correlation analysis, and were influenced mainly by the temperature, and the influence of the insolation was more increased in the forests.

• 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.33 no.5_2
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• pp.721-732
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• 2017

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%),respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR(Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.