• Journal of Wetlands Research
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• v.26 no.3
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• pp.245-253
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• 2024

Blue-green algal bloom, or harmful algal bloom has a negative impact on the aquatic ecosystem and purified water supply system due to oxygen depletion in the water body, odor, and secretion of toxic substances in the freshwater ecosystem. This Blue-green algal bloom is expected to increase in intensity and frequency due to the increase in algae's residence time in the water body after the construction of the Nakdong River weir, as well as the increase in surface temperature due to climate change. In this study, in order to respond to the expected increase in green algae phenomenon, an algal bloom forecast system based on hydro-meteorological factors was presented for preemptive response before issuing a algal bloom warning. Through polyserial correlation analysis, the preceding influence periods of temperature and discharge according to the algal bloom forecast level were derived. Using the decision tree classification, a machine learning technique, Classification models for the algal bloom forecast levels based on temperature and discharge of the preceding period were derived. And a algal bloom forecast system based on hydro-meteorological factors was derived based on the results of the decision tree classification models. The proposed algae forecast system based on hydro-meteorological factors can be used as basic research for preemptive response before blue-green algal blooms.

• Proceedings of the Korea Water Resources Association Conference
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• 1986.07a
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• pp.93-101
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• 1986

This study aims at analysis and application of PMP(Probable Maximum Precipitation) for the determination of design of major hydraulic structures. PMP was estimated by hydro-meteorolgical method and envelope curve method. PMF(Probable Maximum Flood) was then estimated from this PMP by synthetic unit hydrograph method and chow method. From the comparison of three methods for PMP estimation of magnitude of PMP in order of statistical, hydro-meteorlogical, envelope curve method. Amon PMP results estimated by each method it is believed that the hydro-meteorological method gave the best proper value in comparison with historical maximum rainfall because of this method reflected upon all meteorological factor. From the comparison of PMP with probable rainfall and flood, it was shown that estimated value by statistical method and hydro-metelogical method were nearly equivalent to the value of return period 200 to 500 year. It was found that PMF estimated from would be more safe for the design of major hydraulic structures in the consinderation.

• Water for future
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• v.19 no.1
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• pp.75-86
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• 1986

This study aims at the analysis and application of PMP(Probable Maximum Precipitation)for the determination of design flood in the river basin planning and design of major hydraulic structures. PMP was estimated by hydro-meterological method statistical method and envelope curve method. PMF(Probable Maximum Flood)was then estimated from this PMP by synthetic unit hydrograph method and chow method. From the comparison of three methods for PMP estimation of magnitude of PMP in order of statistical, hydro-metrological, envelope curve method. Among PMP results estimated by each method it is believed that the hydro-meteorological method gave the best proper value in comparison with historical maximum rainfall because of this method reflected upon all meterological factor. From the comparison of PMP with probable rainfall and flood, it was shown that estimated value by statistical method and hydro-metrological method were nearly equivalent to the value of return period 100 years and its value of envelope curve method was equivalent to return period 200 to 500 year. It was found that PMF estimated from would be more safe for the design of major hydraulic structures in the consideration.

• Journal of Korea Water Resources Association
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• v.49 no.2
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• pp.107-119
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• 2016

Since future climate scenarios indicate that extreme precipitation events will intensity, probable maximum precipitations (PMPs) without being taken climate change into account are very likely to be underestimated. In this study future PMPs in accordance with the variation of future rainfall are estimated. The hydro-meteorologic method is used to calculate PMPs. The orographic transposition factor is applied in place of the conventional terrain impact factor which has been used in previous PMPs estimation reports. Future DADs are indirectly obtained by using bias-correction and moving-averaged changing factor method based on daily precipitation projection under KMA RCM (HEDGEM3-RA) RCP 8.5 climate change scenario. As a result, future PMPs were found to increase and the spatially-averaged annual PMPs increase rate in 4-hour and $25km^2$ was projected to be 3 mm by 2045. In addition, the increased rate of future PMPs is growing increasingly in the future, but it is thought that the uncertainty of estimating PMPs caused by future precipitation projections is also increased in the distant future.

• Journal of Korea Water Resources Association
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• v.53 no.10
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• pp.903-915
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• 2020

Long-term high-resolution hydro-meteorological data has been recognized as an essential element in establishing the water resources plan. The increasing demand for spatial precipitation in various areas such as climate, hydrology, geography, ecology, and environment is apparent. However, potential limitations of the existing area-weighted and numerical interpolation methods for interpolating precipitation in high altitude areas remains less explored. The proposed PRISM (Precipitation-Elevation Regressions on Independent Slopes Model) model can produce gridded precipitation that can adequately consider topographic characteristics (e.g., slope and altitude), which are not substantially included in the existing interpolation techniques. In this study, the PRISM model was optimized with SCEM-UA (Shuffled Complex Evolution Metropolis-University of Arizona) to produce daily gridded precipitation. As a result, the minimum impact radius was calculated 9.10 km and the maximum 34.99 km. The altitude of coastal weighted was 681.03 m, the minimum and maximum distances from coastal were 9.85 km and 38.05 km. The distance weighting factor was calculated to be about 0.87, confirming that the PRISM result was very sensitive to distance. The results showed that the proposed PRISM model could reproduce the observed statistical properties reasonably well.

• Journal of Korea Water Resources Association
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• v.56 no.8
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• pp.509-520
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• 2023

Various drought indices are widely used for assessing drought conditions which are affected by many factors such as precipitation, soil moisture, and runoff. The values of drought indices varies depending on hydro-meteorological data and calculation formulas, and the judgment of the drought condition may also vary. This study selected four calculation components such as precipitation data length, accumulation period, probability distribution function, and parameter estimation method as the sources of uncertainty in the calculation of standardized precipitation index (SPI), and evaluated their contributions to the uncertainty using root mean square error (RMSE) and linear mixed model (LMM). The RMSE estimated the overall errors in the SPI calculation, and the LMM was used to quantify the uncertainty contribution of each factor. The results showed that as the accumulation period increased and the data period extended, the RMSEs decreased. The comparison of relative uncertainty using LMM indicated that the sample size had the greatest impact on the SPI calculation. In addition, as sample size increased, the relative uncertainty related to the sample size used for SPI calculation decreased and the relative uncertainty associated with accumulation period and parameter estimation increased. In conclusion, to reduce the uncertainty in the SPI calculation, it is essential to collect long-term data first, followed by the appropriate selection of probability distribution models and parameter estimation methods that represent well the data characteristics.

• Journal of Korea Water Resources Association
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• v.54 no.10
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• pp.835-848
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• 2021

In Jeju Island which has peculiarity for its geological features and hydrology system, hydrological factor analysis for the effective water management is necessary. Because in-situ hydro-meteorological data is affected by surrounding environment, the in-situ dataset could not be the spatially representative for the study area. For this reason, remote sensing data may be used to overcome the limit of the in-situ data. In this study, applicability assessment of MOD16 evapotranspiration data, Globas Land Data Assimilation System (GLDAS) based evapotranspiration/soil moisture data, and Advanced SCATterometer (ASCAT) soil moisture product which were evaluated their applicability on other study areas was conducted. In the case of evapotranspiration, comparison with total precipitation and flux-tower based evapotranspiration were conducted. And for soil moisture, 6 in-situ data and ASCAT soil moisture product were compared on each site. As a result, 57% of annual precipitation was calculated as evapotranspiration, and the correlation coefficient between MOD16 evapotranspiration and GLDAS evapotranspiration was 0.759, which was a robust value. The correlation coefficient was 0.434, indicating a relatively low fit. In the case of soil moisture, in the case of the GLDAS data, the RMSE value was less than 0.05 at all sites compared to the in-situ data, and a statistically significant result was obtained as a result of the significance test of the correlation coefficient. However, for satellite data, RMSE over than 0.05 were found at Wolgak and there was no correlation at Sehwa and Handong points. It is judged that the above results are due to insufficient quality control and spatial representation of the evapotranspiration and soil moisture sensors installed in Jeju Island. It is estimated as the error that appears when adjacent to the coast. Through this study, the necessity of improving the existing ground observation data of hydrometeorological factors is emphasized.

• Korean Journal of Ecology and Environment
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• v.50 no.1
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• pp.1-15
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• 2017

Daecheong Reservoir was made by the construction of a large dam (>15 m in height) on the middle to downstream of the Geum River and the discharge systems have the watergate-spillway (WS), a hydropower penstock (HPP), and two intake towers. The purpose of this study was to investigate the limnological anomalies of turbid water reduction, green algae phenomenon, and oligotrophic state in the lower part of reservoir dam site, and compared with hydro-meteorological factors. Field surveys were conducted in two stations of near dam and the outlet of HPP with one week intervals from January to December 2000. Rainfall was closely related to the fluctuations of inflow, outflow and water level. The rainfall pattern was depended on the storm of monsoon and typhoon, and the increase of discharge and turbidity responded more strongly to the intensity than the frequency. Water temperature and DO fluctuations within the reservoir water layer were influenced by meteorological and hydrological events, and these were mainly caused by water level fluctuation based on temperature stratification, density current and discharge types. The discharges of WS and HPP induced to the flow of water bodies and the outflows of turbid water and nutrients such as nitrogen and phosphorus, respectively. Especially, when hypoxic or low-oxygen condition was present in the bottom water, the discharge through HPP has contributed significantly to the outflow of phosphorus released from the sediment into the downstream of dam. In addition, HPP effluent which be continuously operated throughout the year, was the main factor that could change to a low trophic level in the downreservoir (lacustrine zone). And water-bloom (green-tide) occurring in the lower part of reservoir was the result that the water body of upreservoir being transported and diffused toward the downreseroir, when discharging through the WS. Finally, the hydropower effluent was included the importance and dynamics that could have a temporal and spatial impacts on the physical, chemical and biological factors of the reservoir ecosystem.