• Korean Journal of Ecology and Environment
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• v.43 no.3
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• pp.385-399
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• 2010

The objective of this study was to analyze long term temporal trends of water chemistry and spatial heterogeneity for 83 sampling sites of Geum-River watershed using water quality dataset during 2003~2007 (obtained from the Ministry of Environment, Korea). The water quality, based on multi-parameters of temperature, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), suspended solids (SS), total nitrogen (TN), total phosphorus (TP), and electric conductivity (EC), largely varied depending on the landuse patterns, years and seasons. The watershed was classified into three different landuse types: forest stream (Fo), agricultural stream (Ag), and urban stream (Ur). Largest seasonal variabilities in most parameters occurred during the two months of July to August and these were closely associated with large spate of summer monsoon rain. Conductivity, used as a key indicator for an ionic dilution during rainy season, and nutrients of TN and TP had inverse functions of precipitation. BOD, COD decrease during the rainy season. Minimum values in the conductivity, TN, and TP were observed during the summer monsoon, indicating an ionic and nutrient dilution of river water by the rainwater. In contrast, major inputs of suspended solids (SS) occurred during the period of summer monsoon. The landuse patterns analyses, based on the variables of BOD, COD, TN, TP and SS, showed that the values were greater in the agricultural stream (Ag) than in the forest stream (Fo) and urban stream (Ur) and that water quality was worst in the urban stream (Ur). The overall dataset suggest that efficient water quality management, especially in Gap-Stream and Miho-Stream, which showed worst water quality is required along with some of urban stream (Ur), based on the analysis of landuse patterns.

• Korean Journal of Remote Sensing
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• v.33 no.6_1
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• pp.917-930
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• 2017

To understand the temporal and spatial variations of surface chlorophyll-a concentration (Chl-a) distribution in the Indian Ocean ($30^{\circ}E{\sim}120^{\circ}E$, $30^{\circ}S{\sim}30^{\circ}N$) by the Indian Ocean Dipole (IOD), we conducted EOF and K means analyses of monthly satellite-derived Chl-a data in the region during 1998~2016 periods. Chl-a showed low values in the central region of the Indian Ocean and relatively high values in the upwelling region and around the marginal regions of the Indian Ocean. It also had a strong seasonal variation of Chl-a, showing the lowest value in the spring and the highest value in summer due to the change of the monsoon and current system. The EOF analysis showed that Chl-a variation in EOF mode 1 is related to ENSO (El $Ni{\tilde{n}}o$/Southern Oscillation) and that of mode 2 is linked to IOD. Both modes explained spatially opposite trends of Chl-a in the east and west Indian Ocean. From K means analysis, the Chl-a variation in the east and west Indian Ocean, and around India have relatively good relationship with IOD while that in the tropical and middle Indian Ocean closely associated with ENSO. The spatial and temporal distribution of Chl-a also showed distinct spatial and temporal variations depend on the different types of IOD events. IOD classifies two patterns, which occurred during the developing ENSO (First Type IOD) and the year following ENSO event (Second Type IOD). Chl-a variation in the First Type IOD started in summer and peaked in fall around the east and west Indian Ocean. Chl-a variation in the Second Type IOD occurred started in spring, peaked in summer and fall, and disappeared in winter. In the Chl-a variation related to IOD, developing process appearing in the Chl-a difference between the east and west Indian ocean was similar. Chl-a variation in the northern Indian Ocean were opposite trend with changing developing phase of IOD.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.15 no.2
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• pp.51-61
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• 2010

Surface particulate organic carbon (POC) concentration was measured in the Northeastern Gulf of Mexico on 9 cruises from November 1997 to August 2000 to investigate the seasonal and spatial variability related to synchronous remote sensing data (Sea-viewing Wide Field-of-view Sensor (SeaWiFS), sea surface temperature (SST), sea surface height anomaly (SSHA), and sea surface wind (SSW)) and recorded river discharge data. Surface POC concentrations have higher values (>100 $mg/m^3$) on the inner shelf and near the Mississippi Delta, and decrease across the shelf and slope. The inter-annual variations of surface POC concentrations are relatively higher during 1997 and 1998 (El Nino) than during 1999 and 2000 (La Nina) in the study area. This phenomenon is directly related to the output of Mississippi River and other major rivers, which associated with global climate change such as ENSO events. Although highest river runoff into the northern Gulf of Mexico Coast occurs in early spring and lowest flow in late summer and fall, wide-range POC plumes are observed during the summer cruises and lower concentrations and narrow dispersion of POC during the spring and fall cruises. During the summer seasons, the river discharge remarkably decreases compared to the spring, but increasing temperature causes strong stratification of the water column and increasing buoyancy in near-surface waters. Low-density plumes containing higher POC concentrations extend out over the shelf and slope with spatial patterns and controlled by the Loop Current and eddies, which dominate offshore circulation. Although river discharge is normal or abnormal during the spring and fall seasons, increasing wind stress and decreasing temperature cause vertical mixing, with higher surface POC concentrations confined to the inner shelf.

• Korean Journal of Agricultural and Forest Meteorology
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• v.5 no.2
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• pp.61-69
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• 2003

Surface energy and $CO_2$ fluxes have been measured over a farmland in Haenam, Korea since July 2002. Eddy covariance technique, which is the only direct flux measurement method, was employed to quantitatively understand the interaction between the farmland ecosystem and the atmospheric boundary layer. Maintenance of eddy covariance system was the main concern during the early stage of measurement to minimize gaps and uncertainties in the dataset. Half-hourly averaged $CO_2$ concentration showed distinct diurnal and seasonal variations, which were closely related to changes in net ecosystem exchange (NEE) of $CO_2$. Daytime maximum $CO_2$ uptake was about -1.0 mg $CO_2$ m$^{-2}$ s$^{-1}$ in August whereas nighttime $CO_2$ release was up to 0.3 mg $CO_2$ m$^{-2}$ s$^{-1}$ during the summer. Both daytime $CO_2$ uptake and nighttime release decreased gradually with season. During the winter season, NEE was from near zero to 0.05 mg $CO_2$ m$^{-2}$ s$^{-1}$ . FK site was a moderate sink of atmospheric $CO_2$ until September with daily NEE of 22 g $CO_2$ m$^{-2}$ d$^{-1}$ . In October, it became a weak source of $CO_2$ with an emission rate of 2 g $CO_2$ m$^{-2}$ d$^{-1}$ . Long-term flux measurements will continue at FK site to further investigate inter-annual variability in NEE. to better understand these exchange mechanism and in-depth analysis, process-level field experiments and intensive short-term intercomparisons are also expected to be followed.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.1
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• pp.1-19
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• 2018

In order to compare significant wave height (SWH) data from multi-satellites (GFO, Jason-1, Envisat, Jason-2, Cryosat-2, SARAL) and SWH measurements from Ieodo Ocean Research Station (IORS), we constructed a 12 year matchup database between satellite and IORS measurements from December 2004 to May 2016. The satellite SWH showed a root mean square error (RMSE) of about 0.34 m and a positive bias of 0.17 m with respect to the IORS wave height. The satellite data and IORS wave height data did not show any specific seasonal variations or interannual variability, which confirmed the consistency of satellite data. The effect of the wind field on the difference of the SWH data between satellite and IORS was investigated. As a result, a similar result was observed in which a positive biases of about 0.17 m occurred on all satellites. In order to understand the effects of topography and the influence of the construction structures of IORS on the SWH differences, we investigated the directional dependency of differences of wave height, however, no statistically significant characteristics of the differences were revealed. As a result of analyzing the characteristics of the error as a function of the distance between the satellite and the IORS, the biases are almost constant about 0.14 m regardless of the distance. By contrast, the amplitude of the SWH differences, the maximum value minus the minimum value at a given distance range, was found to increase linearly as the distance was increased. On the other hand, as a result of the accuracy evaluation of the satellite SWH from the Donghae marine meteorological buoy of Korea Meteorological Administration, the satellite SWH presented a relatively small RMSE of about 0.27 m and no specific characteristics of bias such as the validation results at IORS. In this paper, we propose a conversion formula to correct the significant wave data of IORS with the satellite SWH data. In addition, this study emphasizes that the reliability of data should be prioritized to be extensively utilized and presents specific methods and strategies in order to upgrade the IORS as an international world-wide marine observation site.

• Journal of Environmental Impact Assessment
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• v.30 no.5
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• pp.271-296
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• 2021

Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2013.11a
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• pp.23-24
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• 2013

Micrometeorological fluxes measured over a tall forest in a complex terrain are difficult to interpret. $CO_2$ storage often makes significant contributions to net ecosystem exchange of $CO_2$ (NEE) in early morning and during nighttime due to calm and stable conditions. We measured the above-canopy $CO_2$ flux along with its concentration profiles at eight levels within and above the canopy to evaluate $CO_2$ storage term. Our question is whether or not the $CO_2$ storage term can be estimated accurately from a single level measurement of $CO_2$ concentration in a complex terrain. Our objectives are (1) to document vertical profiles of $CO_2$ concentration and (2) to compare the diurnal and seasonal variations of $CO_2$ storages estimated from single and multi-level $CO_2$ concentration data. Seasonally averaged Diurnal variations of $CO_2$ concentration ranged from 398 to 455 ppm near the forest floor at 0.1 m whereas they ranged from 364 to 395 ppm at 40 m in the atmosphere. The diurnal variation of vertical profiles of $CO_2$ concentration shows very interesting features with season. At all eight levels, diurnal variation of $CO_2$ concentration showed little change in winter. In spring, the diurnal variations of $CO_2$ concentration at 8 levels showed three distinct groups of layers with height: the first layer: 0.1m (near surface), second layer: 1.0 m and 4.0m (below canopy) and the third layer: 7.4m to 40.7 m (near canopy and above). In summer, these three groups of layers were further separated with larger variations whereas such distinction became smaller in fall. The diurnal variation of $CO_2$ concentration in the first three layers near surface always showed higher concentration with larger variability. Typically, $CO_2$ concentration showed peaks in early morning and in the evening. After the evening peak, $CO_2$ concentration gradually increased except for those near the surface (i.e., 0.1, 1.0 and 4.0 m) where the concentrations actually decreased. We suspect that this could be attributed to the drainage flow of $CO_2$ along the hill slope from the headwater to downstream, which is not taken into account for net ecosystem $CO_2$ exchange. In comparison to the results of other studies, the distinct and different vertical structures of $CO_2$ concentrations observed at our site may be due to complex terrain and weak turbulent mixing under calm conditions at the site. The annual mean of diurnal variation of $CO_2$ storage flux from single level ranged from -0.6 to $0.9{\mu}mol\;m^{-2}s^{-1}$ and from multi-level from -1.2 to $1.0{\mu}\;{\mu}mol\;m^{-2}s^{-1}$. When compared against the results from the multi-level concentrations, the storage flux estimated from a single-level concentration was generally adequate except for specific hours near sunrise and sunset. Further details and their implication will be discussed in the presentation.

• Atmosphere
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• v.31 no.2
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• pp.229-240
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• 2021

The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.129-142
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• 2023

This study analyzed the ratio of carbon monoxide (CO) and carbon dioxide (CO₂), CO and nitrogen dioxide (NO₂) for cities and regionsin Korea and China using column-averaged carbon dioxide dry-air mole fraction (XCO₂) of the Orbiting Carbon Observatory-2/3, CO and NO₂ vertical column density (named XCO, XNO₂ in thisstudy) of TROPOspheric monitoring instrument from April 2018 to April 2022, and presented the relationship between socioeconomic indicators (population, number of vehicles, Gross Regional Domestic Product) and ratio, and differences in characteristics between Korea and China. First, CO₂ and CO were analyzed after calculating ΔXCO₂ and ΔXCO removing the background value and trend line due to the difference in atmospheric residence time of three gaseous substances (CO₂, CO, and NO₂). Comparing the three values by regions, ΔXCO and ΔXCO₂ were relatively higher in China and XNO₂ were higher in Korea and the ratio of both values (ΔXCO/ΔXCO₂, ΔXCO/XNO₂) was higher in China than in Korea. ΔXCO/ΔXCO₂, ΔXCO/XNO₂ and socioeconomic indicators have a positive correlation suggesting that the concentration of air pollutants and greenhouse gases is higher as the city is large and the economic activity is active. Regarding the differences in the ratio characteristics of Korea and China, the relationship between ΔXCO and ΔXCO₂ showed a negative correlation in Korea and a positive correlation in China. When the relationship between ΔXCO and XNO₂ was examined for summer and winter, the change of ΔXCO by season was not significant in Korea, whereasthe change of ΔXCO and XNO₂ by season waslarge in China resulting in the relationship between two countries appeared differently. These results suggest that seasonal variability and national emission characteristics should be considered in the process of analyzing the ratio of greenhouse gases to air pollutants.