• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.695-698
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• 2008

The SWAT model was used to assess the impacts of potential future climate change on the hydrology of the Upper Geum River Basin(UGRB). Calibration and validation of SWAT were performed on a monthly basis for 1982-1995 and 1996-2005, respectively. The impact of ten 15-year(1988-2002) scenarios were then analyzed relative to a scenario baseline. Among them, scenario 1-6 were set to show the sensitivity response. A doubling of atmospheric CO2 concentration was predicted to result in an maximum monthly flow increase of 11 percent. Non-linear impacts were predicted among precipitation change scenarios of -42, -17, 17, and 42 percent, which resulted in average annual flow changes in UGRB of -55, -24, 26, and 65 percent.

• Journal of Korea Water Resources Association
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• v.35 no.3
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• pp.275-284
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• 2002

In this study, a method of random resampling of residuals from stochastic models such as the Monte-Carlo model, the lag-one autoregressive model(AR(1)) and the periodic lag-one autoregressive model(PAR(1)), has been adopted to generate a large number of long traces of annual and monthly steamflows. Main advantage of this resampling scheme called the Bootstrap method is that it does not rely on the assumption of population distribution. The Bootstrap is a method for estimating the statistical distribution by resampling the data. When the data are a random sample from a distribution, the Bootstrap method can be implemented (among other ways) by sampling the data randomly with replacement. This procedure has been applied to the Yongdam site to check the performance of Bootstrap method for the streamflow generation. and then the statistics between the historical and generated streamflows have been computed and compared. It has been shown that both the conventional and Bootstrap methods for the generation reproduce fairly well the mean, standard deviation, and serial correlation, but the Bootstrap technique reproduces the skewness better than the conventional ones. Thus, it has been noted that the Bootstrap method might be more appropriate for the preservation of skewness.

• Water for future
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• v.20 no.4
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• pp.257-266
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• 1987

The existing stochastic models for the data with hydrologic persistence can be classified into two categories; the short-term and long-term models.For the present study, the Hurst coefficients which are the dominant parameter in the Fast Fractional Gaussian Noise(FFGN)model, one of the long-term models. are estimated with historical annual and monthly streamflows. In order to verify the applicability of these estimators the statistical properties of the generated annual streamflows by FFGN model are compared with those of the historical annual streamflows. Then the generated annual streamflows by FFGN model are disaggregated into the monthly streamflows by disaggregation model at two sites, i.e. Waekman and Jindong, in the Nakdong River Basin. On the other hand, the monthly stream flows at the two sites were also generated by the two-site Matalas model which is one of the short-term models. To evaluate the applicability of the above models and to select the better model the statistical properties of the generated monthly streamflows by two models were compared with those of the historicals, respectively.

• Water for future
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• v.15 no.2
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• pp.33-39
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• 1982

For the determination of a reservoir capacity Rippl's mass-curve method has long been used with the past river flow data assuming the same flow records will be repeated in the future. In this study the synthetic generation methods of thomas-Fiering type and harmonic analysis were used to synthetically generate 50 years of monthly river inflows to three single-purpose reservoris(Chuncheon, Chungpyong, Hwacheon) and three multi-purpose reservoirs(Soyany, Andon, Daichung). The generated sequences of monthly flows were analyzed based on the range concept, and hence the so-determined ranges for single-prupose and multi-purpose rewervoirs were correlated with the number of monthly flow subseries, resulting an empirical equation of the Feller's type. (1) Single-purpose reservoir $$R_n=2.8357 I\sqrt{n}$$ (2) Multi-purpose reservoir $$R_n=2.5145 I\sqrt{n}$$ where, $R_n$:Range(㎥/S-M) n:periodic(12 months, ……120 months) I:Input mean(㎥/S-M) In Korea, the monthly inflow data generation will be fit to the Thomas-Fiering type, and this paper shows that the periodic range is easily calculated without the Rippl's mass-curve method as shown above formula.

• Journal of Korean Society on Water Environment
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• v.24 no.3
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• pp.317-327
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• 2008

This study developed the methodology to calculate the total daily maximum load (TMDL) using the instreamflow requirement because the previous TMDLs were too simple to easily achieve. Instreamflow requirement which was the average low flow ($Q_{275}$) in the previous planning cannot consider the seasonal variation of streamflow. Therefore, this study used the instreamflow requirement which is a maximum value among hydrologic drought flow ($Q_{355}$), and environmental flows for ecology and scenery. The environmental flows for ecology were calculated using Physical HABitat SIMulation system (PHABSIM) which can estimate the necessary flow for fish survival by life cycle. Using the proposed method, all monthly TMDLs of streams in the Anyangcheon were calculated for the application.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1152-1156
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• 2004

수자원분야에서 강우-유출의 해석은 수자원 이용의 측면에서 가장 중요한 문제 중 하나이다. 특히 기존에 수위 측정 자료가 존재하는 지역에 대한 유출의 분석은 측정 자료를 통한 정밀한 강우-유출의 분석이 가능하나 유량 기록이 전혀 없는 산악지역이나 미개발지역의 f하천에서 댐이나 제방과 같은 수공구조물의 설계 및 수자원 개발을 위해선 강우-유출 관계에 의한 유출량 산정은 상당히 복잡한 과정일 것이다. 미계측 유역에 유출모형을 적용하기 위해서는 모형변수의 초기치 설정과 과거 유출자료를 통하여 최적화한 매개변수를 결정해야 하기 때문에 미계측 유역에 유출모형을 적용하기란 그리 쉽지 많은 않은 실정이다. 따라서 본 연구 에서는 월 유출량 산정을 위한 모형 중 기존의 Xu가 제안한 WASMOD의 매개변수를 관측된 유출량과의 검정에 의해 산정하는 것이 아니라 유출에 영향을 주는 인자 중 유역의 지형학적 인자인 토지이용과의 상관관계를 분석하여 미계측 유역의 적용을 위한 방법을 모색하였다.

• Magazine of the Korean Society of Agricultural Engineers
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• v.34 no.1
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• pp.33-40
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• 1992

Up to now, monthly water balance analysis has been dominantly used for the water resources planning. But, it is more reasonable to explain the variation of spatial and temporal distribution of water by the daily water balance model with daily streamflow data. Since we are recently facing the problems of regional unbalance of water quantity, and of multiuse of irrigation water, and of deterioration of water quality, it is urgently needed to develop the daily water balance model to solve those problems and establish the rational plannings of agricultural water resources. In the circumstances, Daily water Balance(DAWABA) model for irrigation reservoirs was developed and the operation rule of irrigation resorvoir during drought season was established.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.195-209
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• 2003

Spatial-Stochastic Neural Networks Model(SSNNM) is used to estimate long-term streamflow in the parallel reservoir groups. SSNNM employs two kinds of backpropagation algorithms, based on LMBP and BFGS-QNBP separately. SSNNM has three layers, input, hidden, and output layer, in the structure and network configuration consists of 8-8-2 nodes one by one. Nodes in input layer are composed of streamflow, precipitation, pan evaporation, and temperature with the monthly average values collected from Andong and Imha reservoir. But some temporal differences apparently exist in their time series. For the SSNNM training procedure, the training sets in input layer are generated by the PARMA(1,1) stochastic model and they covers insufficient time series. Generated data series are used to train SSNNM and the model parameters, optimal connection weights and biases, are estimated during training procedure. They are applied to evaluate model validation using observed data sets. In this study, the new approaches give outstanding results by the comparison of statistical analysis and hydrographs in the model validation. SSNNM will help to manage and control water distribution and give basic data to develop long-term coupled operation system in parallel reservoir groups of the Upper Nakdong River.

• Journal of Korea Water Resources Association
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• v.41 no.11
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• pp.1095-1106
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• 2008

Climate change and global warming are prevalent all over the world in this century and many researchers including hydrologists have studied on the climate change. This study also studied the impact of climate change on streamflows of a basin in Korea. The SWAT model was used to assess the impacts of potential future climate change on the streamflows of the Daecheong Dam Basin. Calibration and validation of SWAT were performed on a monthly basis for the year of 1982-1995 and 1996-2005, respectively. The impact of seven 15-year(1988-2002) scenarios were then analyzed for comparing it to the baseline scenario. Among them, scenario 1 was set to show the result of doubling $CO_2$, scenario 2-6 were set to show the results of temperature and precipitation change, and scenario 7 was set to show the result of the combination of climatologic components. A doubling of atmospheric $CO_2$ concentration is predicted to result in an maximum monthly flow increase of 11 percent. Non-linear impacts were predicted among precipitation change scenarios of -42, -17, 17, and 42 percent, which resulted in average annual flow changes in Daecheong Dam Basin of -55, -24, 25, and 64 percent. The changes in streamflow indicate that the Daecheong Dam Basin is very sensitive to potential future climate changes and that these changes could stimulate the increased period or severity of flood or drought events.

• Journal of Korea Water Resources Association
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• v.52 no.1
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• pp.71-82
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• 2019

The purpose of this study is to analyze the Mekong River streamflow alteration due to climate change. The future climate change scenarios were produced by bias corrections of the data from East Asia RCP 4.5 and 8.5 scenarios, given by HadGEM3-RA. Then, SWAT model was used for discharge simulation of the Kratie, the main point of the Mekong River (watershed area: $646,000km^2$, 88% of the annual average flow rate of the Mekong River). As a result of the climate change analysis, the annual precipitation of the Kratie upper-watershed increase in both scenarios compared to the baseline yearly average precipitation. The monthly precipitation increase is relatively large from June to November. In particular, precipitation fluctuated greatly in the RCP 8.5 rather than RCP 4.5. Monthly average maximum and minimum temperature are predicted to be increased in both scenarios. As well as precipitation, the temperature increase in RCP 8.5 scenarios was found to be more significant than RCP 4.5. In addition, as a result of the duration curve comparison, the streamflow variation will become larger in low and high flow rate and the drought will be further intensified in the future.