• Fisheries and Aquatic Sciences
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• 제21권4호
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• pp.9.1-9.7
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• 2018

To establish a comprehensive management strategy, as part of the optimization of cultural practice for an oyster rack culture system, we used a numerical model to estimate the primary production in the waters on the eastern coast of Wando island, South Korea. The estimated primary production ranged from 17.12 to $1052.55mgC\;m^{-2}day^{-1}$ ($204.22{\pm}224.75mgC\;m^{-2}day^{-1}$ in average). Except for the times of peak phytoplankton blooms, the estimated primary production (PP) was consistently under $200mgC\;m^{-2}day^{-1}$, which is more similar to the value of PP measured off the western coast of South Korea than the southern coast. No clear relationship was observed between nitrogen content and rainfall with the exception of heavy rainfall events, indicating that precipitation might not be the main source of nutrients in these waters. No clear influence was observed from Doam tidal discharge, located 24 km north from these waters due to main tide comes in this area from the channel between Gunwe-myeon in Wando island and Pukpyeong-myeon in Haenam-gun. Because of the shallow water depth and strong tidal current, resuspension of sediments, which causes an input of nitrogen into the system, could be easily caused by even mild wind and the infrequent passing of ships. Microscopic examination of the phytoplankton composition showed additional contribution of benthic species such as Paralia sulcata into the waters, which increase the productivity of oyster farms in the waters. The availability of nitrate and phosphate for primary production was temporarily limited throughout most of the spring and autumn blooming season.

• 한국수자원학회논문집
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• 제53권6호
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• pp.417-425
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• 2020

홍수와 같은 수문 재해를 예측하고 예방하기 위해서는 강우량을 정확하게 추정해야한다. 본 연구에서는 전파강수계의 다중 고도 관측 데이터를 사용한 면적 평균 강우량 추정 방법을 개발하였다. 소형 전파강수시스템은 K 대역 이중 편파 기술을 사용하여 매우 짧은 거리를 관측하는 소형 레이더이다. 면적 평균 강우 추정 방법은 관측 거리와 시간이 매우 짧기 때문에 관측 범위에서 강우 변동이 작다는 가정을 기반으로 한다. 제안된 방법은 지상우량계 및 파시벨우적계와 같은 지상 장비와 비교하여 평가되었다. 비교 평가된 강우 사상들에 대해 전파강수계로부터 추정된 평균강우와 지상장비로부터 관측된 강우량이 잘 일치함을 보여준다.

• 대한토목학회논문집
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• 제38권5호
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• pp.655-669
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• 2018

본 연구에서는 비정상성 Bayesian 빈도해석모형을 토대로 외부 기상인자에 의한 시변성을 고려할 수 있는 계절강수량 예측모형을 구축한 후 산정된 결과를 입력 자료로 하여 직접적으로 일단위 이하의 극치강수량을 상세화시킬 수 있는 베타 모델(four parameter beta, 4PB)을 연계하여 한강 및 금강유역의 미래 계절 강수량 전망 및 일단위 이하의 확률강수량을 도출하였다. 모형의 적합성 검증을 위하여 2014~2017년의 모의된 사후 확률분포 값과 관측치를 비교하였다. 그 결과 계절강수량 모의에서 한강은 관측 값의 최대 약 86.3%, 금강은 약 98.9% 일치하는 것을 확인할 수 있었다. 지속시간별 극치강우량은 약 65.9~99.7%의 정확성을 나타냈다. 이에 본 연구에서 산정한 결과는 기상변동성을 다양한 시간규모에서 고려하기 위한 정보로 활용할 수 있을 것으로 판단된다.

• 한국수자원학회논문집
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• 제50권8호
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• pp.537-549
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• 2017

본 연구에서는 기상청에서 제공하는 국지예보모델(LDAPS)과 일본 기상청의 중규모모델(Meso-Scale Model, MSM)을 이용하여 태풍 및 정체전선 등 3개의 강우사상과 남강댐 유역 내 산청 유역에 대해 강우 및 홍수 예측 정확도를 평가하고 비교 검토하였다. 강우예측 정확도 평가 결과, LDAPS와 MSM 모두 태풍 사상과 같은 광역적인 예측에 대해서는 예측 정확도가 높은 것으로 나타났으나, 정체전선과 같이 국지적으로 발생하는 강우사상의 경우 예측 오차가 많이 발생하는 것으로 나타났다. 홍수예측 정확도 평가 결과, 선행시간이 증가함에 따라 점점 예측 정확도가 향상되는 것을 확인할 수 있었으며, LDAPS와 MSM 모두 기상 및 수자원간의 연계를 통하여 강우 및 홍수 예측 분야에서의 활용 가능성을 확인할 수 있었다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2016년도 학술발표회
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• pp.203-203
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• 2016

The linkage between climate change and water security, i.e., the response of water resource to the future climate change, have been of great concern to both scientific community and policy makers. In this study, the impact of future climate on water resources in Yellow River Basin in North of China has been investigated using the Coupled Land surface and Hydrology Model System (CLHMS) and IPCC AR5 projected future climate change in the basin. Firstly, the performances of 14 IPCC AR5 models in reproducing the observed precipitation and temperature in China, especially in North of China, have been evaluated, and it's suggested most climate models do show systematic bias compared with the observation, however, CNRM-CM5、HadCM5 and IPSL-CM5 model are generally the best models among those 14 models. Taking the daily projection results from the CNRM-CM5, along with the bias-correction technique, the response of water resources in Yellow river basin to the future climate change in different emission scenarios have been investigated. All the simulation results indicate a reduction in water resources. The current situation of water shortage since 1980s will keep continue, the water resources reduction varies between 28 and 23% for RCP 2.6 and 4.5 scenarios. RCP 8.5 scenario simulation shows a decrease of water resources in the early and mid 21th century, but after 2080, with the increase of rainfall, the extreme flood events tends to increase.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2018년도 학술발표회
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• pp.154-154
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• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• 대기
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• 제29권2호
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• pp.197-211
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• 2019

The observational sensitivity of dual-polarization weather radar was quantitatively analyzed by using two different pulse widths. For this purpose, test radar scan strategy which consisted of consecutive radar scan using long (LP: $2{\mu}s$) and short (SP: $1{\mu}s$) pulses at the same elevation angle was employed. The test scan strategy was conducted at three operational S-band dual-polarization radars (KSN, JNI, and GSN) of Korea Meteorological Administration (KMA). First, the minimum detectable reflectivity (MDR) was analyzed as a function of range using large data set of reflectivity ($Z_H$) obtained from JNI and GSN radars. The MDR of LP was as much as 7~22 dB smaller than that of SP. The LP could measure $Z_H$ greater than 0 dBZ within the maximum observational range of 240 km. Secondly, polarimetric observations and the spatial extent of radar echo between two pulses were compared. The cross-polar correlation coefficient (${\rho}_{hv}$) from LP was greater than that from SP at weak reflectivity (0~20 dBZ). The ratio of $Z_H$ (> 0 dBZ) and ${\rho}_{hv}$(> 0.95) bin to total bin calculated from LP were greater than those from SP (maximum 7.1% and 13.2%). Thirdly, the frequency of $Z_H$ (FOR) during three precipitation events was analyzed. The FOR of LP was greater than that of SP, and the difference in FOR between them increased with increasing range. We conclude that the use of LP can enhance the sensitivity of polarimetric observations and is more suitable for detecting weak echoes.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2015년도 학술발표회
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• pp.243-243
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• 2015

Low-flow simulation and forecasting is one of the emerging issues in hydrology due to the increasing demand of water in dry periods. Even though low-flow simulation and forecasting remains a difficult issue for hydrologists better simulation and earlier prediction of low flows are crucial for efficient water management. The UN has never stated that South Korea is in a water shortage. However, a recent study by MOLIT indicates that Korea will probably lack water by 4.3 billion m3 in 2020 due to several factors, including land cover and climate change impacts. The two main situations that generate low-flow events are an extended dry period (summer low-flow) and an extended period of low temperature (winter low-flow). This situation demands the hydrologists to concentrate more on low-flow hydrology. Korea's annual average precipitation is about 127.6 billion m3 where runoff into rivers and losses accounts 57% and 43% respectively and from 57% runoff discharge to the ocean is accounts 31% and total water use is about 26%. So, saving 6% of the runoff will solve the water shortage problem mentioned above. The main objective of this study is to present the hydrological modelling approach for low-flow simulation and forecasting using a model that have a capacity to represent the real hydrological behavior of the catchment and to address the water management of summer as well as winter low-flow. Two lumped hydrological models (GR4J and CAT) will be applied to calibrate and simulate the streamflow. The models will be applied to Seolmacheon catchment using daily streamflow data at Jeonjeokbigyo station, and the Nash-Sutcliffe efficiencies will be calculated to check the model performance. The expected result will be summarized in a different ways so as to provide decision makers with the probabilistic forecasts and the associated risks of low flows. Finally, the results will be presented and the capacity of the models to provide useful information for efficient water management practice will be discussed.

• 대기
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• 제31권2호
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• pp.185-198
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• 2021

In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.314-314
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• 2023

Extreme climate events can have a large impact on human life by hampering social, environmental, and economic development. Global circulation models (GCMs) are the widely used numerical models to understand the anticipated future climate change. However, different GCMs can project different future climates due to structural differences, varying initial boundary conditions and assumptions about the physical phenomena. The multi-model ensemble (MME) approach can improve the uncertainties associated with the different GCM outcomes. In this study, a comprehensive rating metric was used to select the best-performing GCMs out of 11 CMIP5 and 13 CMIP6 GCMs, according to their skills in terms of four temporal and five spatial performance indices, in replicating the 21 extreme climate indices during the baseline (1975-2017) in South Korea. The MME data were derived by averaging the simulations from all selected GCMs and three top-ranked GCMs. The random forest (RF) algorithm was also used to derive the MME data from the three top-ranked GCMs. The RF-derived MME data of the three top-ranked GCMs showed the highest performance in simulating the baseline extreme climate which was subsequently used to project the future extreme climate indices under both the representative concentration pathway (RCP) and the socioeconomic concentration pathway scenarios (SSP). The extreme cold and warming indices had declining and increasing trends, respectively, and most extreme precipitation indices had increasing trends over the period 2031-2100. Compared to all scenarios, RCP8.5 showed drastic changes in future extreme climate indices. The coasts in the east, south and west had stronger warming than the rest of the country, while mountain areas in the north experienced more extreme cold. While extreme cold climatology gradually declined from north to south, extreme warming climatology continuously grew from coastal to inland and northern mountainous regions. The results showed that the socially, environmentally and agriculturally important regions of South Korea were at increased risk of facing the detrimental impacts of extreme climatology.