• Korean Journal of Ecology and Environment
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• v.51 no.4
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• pp.268-286
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• 2018

The eutrophication and algal blooms by harmful cyanobacteria (CyanoHAs) and freshwater redtide (FRT) that severely experiencing in typical regulated weir system of the Nakdong River are one of the most rapidly expanding water quality problems in Korea and worldwide. To compare with the factors of rainfall, hydrology, and dominant algae, this study explored spatiotemporal variability of the major water environmental factors by weekly intervals in eight weir pools of the Nakdong River from January 2013 to July 2017. There was a distinct difference in rainfall distribution between upstream and downstream regions. Outflow discharge using small-scale hydropower generation, overflow and fish-ways accounted for 37.4%, 60.1% and 2.5%, respectively. Excluding the flood season, the outflow was mainly due to the hydropower release through year-round. These have been associated with the drawdown of water level, water exchange rate, and the significant impact on change of dominant algae. The mean concentration (maximum value) of chlorophyll-a was $17.6mg\;m^{-3}$ ($98.2mg\;m^{-3}$) in the SAJ~GAJ and $29.6mg\;m^{-3}$ ($193.6mg\;m^{-3}$) in the DAS~HAA weir pools reaches, respectively. It has increased significantly in the downstream part where the influence of treated wastewater effluents (TWEs) is high. Indeed, very high values (>50 or $>100mg\;m^{-3}$) of chlorophyll-a concentration were observed at low flow rates and water levels. Algal assemblages that caused the blooms of CyanoHAs and FRT were the cyanobacteria Microcystis and the diatom Stephanodiscus populations, respectively. In conclusion, appropriate hydrological management practices in terms of each weir pool may need to be developed.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of Korea Water Resources Association
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• v.57 no.5
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• pp.333-346
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• 2024

High-resolution medium-range streamflow prediction is crucial for sustainable water quality and aquatic ecosystem management. For reliable medium-range streamflow predictions, it is necessary to understand the characteristics of forcings and to effectively utilize weather forecast data with low spatio-temporal resolutions. In this study, we presented a comparative analysis of medium-range streamflow predictions using the distributed hydrological model, WRF-Hydro, and the numerical weather forecast Global Data Assimilation and Prediction System (GDAPS) in the Geumho River basin, Korea. Multiple forcings, ground observations (AWS&ASOS), numerical weather forecast (GDAPS), and Global Land Data Assimilation System (GLDAS), were ingested to investigate the performance of streamflow predictions with highresolution WRF-Hydro configuration. In terms of the mean areal accumulated rainfall, GDAPS was overestimated by 36% to 234%, and GLDAS reanalysis data were overestimated by 80% to 153% compared to AWS&ASOS. The performance of streamflow predictions using AWS&ASOS resulted in KGE and NSE values of 0.6 or higher at the Kangchang station. Meanwhile, GDAPS-based streamflow predictions showed high variability, with KGE values ranging from 0.871 to -0.131 depending on the rainfall events. Although the peak flow error of GDAPS was larger or similar to that of GLDAS, the peak flow timing error of GDAPS was smaller than that of GLDAS. The average timing errors of AWS&ASOS, GDAPS, and GLDAS were 3.7 hours, 8.4 hours, and 70.1 hours, respectively. Medium-range streamflow predictions using GDAPS and high-resolution WRF-Hydro may provide useful information for water resources management especially in terms of occurrence and timing of peak flow albeit high uncertainty in flood magnitude.