• Korean Journal of Ecology and Environment
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• v.34 no.4 s.96
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• pp.327-336
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• 2001

Physical, chemical, and biological parameters were measured during the period from July 1993 to August 1994 near the Munui intake tower of Taechung Reservoir to evaluate effects of nutrients and suspended solids on algal chlorophyll-a and water clarity. Large amounts of precipitation during summer 1993 produced minimum conductivity ($88\;{\mu}S/cm$), minimum TN : TP (<40), and maximum total phosphorus (TP;$59\;{\mu}g/L$) and resulted in a chlorophyll-a peak ($79\;{\mu}g/L$) and minimum transparency (<1.5 m) among the seasons. At the same time, ratios of volatile suspended solids (VSS): non-volatile suspended solids (NVSS) were maximum (13.0),indicating that the reduced transparency was mainly attributed to biogenic turbidity in relation to phytoplankton growth. In contrast, severe drought in summer 1994 resulted in greater conductivity (>$120\;{\mu}S/cm$), water clarity (%gt;2 m), and lower TP and chlorophyll- a (<$10\;{\mu}g/L$) relative to those of summer 1993. Total phosphorus ($R^2=0.46$, n=59) accounted more variations of chlorophyll- a compared to total nitrogen ($R^2=0,29$, n=59). The mass ratios of TN : TP ranged from 39 to 222 and were strongly correlated with TP (r = -0.80) but not with concentrations of TN (r = 0.05). Ambient nutrient concentrations and TN : TP mass ratios indicated that seasonality of chlorophyll- a was likely determined by concentrations of phosphorus reflected by the distribution of rainfall. It was concluded that reductions of phosphorus during heavy rainfall may provide better water quality for the drinking water in the intake tower.

• Korean Journal of Ecology and Environment
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• v.48 no.4
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• pp.229-237
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• 2015

This study aims to offer basic data to effectively preserve and manage pine forests using more precise pine forests' distribution status. In this regard, this study predicts the geographical distribution change of pine forests growing in South Korea, due to climate change, and evaluates the spatial distribution characteristics of pine forests by age. To this end, this study predicts the potential distribution change of pine forests by applying the MaxEnt model useful for species distribution change to the present and future climate change scenarios, and analyzes the effects of bioclimatic variables on the distribution area and change by age. Concerning the potential distribution regions of pine forests, the pine forests, aged 10 to 30 years in South Korea, relatively decreased more. As the area of the region suitable for pine forest by age was bigger, the decreased regions tend to become bigger, and the expanded regions tend to become smaller. Such phenomena is conjectured to be derived from changing of the interaction of pine forests by age from mutual promotional relations to competitive relations in the similar climate environment, while the regions suitable for pine forests' growth are mostly overlap regions. This study has found that precipitation affects more on the distribution of pine forests, compared to temperature change, and that pine trees' geographical distribution change is more affected by climate's extremities including precipitation of driest season and temperature of the coldest season than average climate characteristics. Especially, the effects of precipitation during the driest season on the distribution change of pine forests are irrelevant of pine forest's age class. Such results are expected to result in a reduction of the pine forest as the regions with the increase of moisture deficiency, where climate environment influencing growth and physiological responses related with drought is shaped, gradually increase according to future temperature rise. The findings in this study can be applied as a useful method for the prediction of geographical change according to climate change by using various biological resources information already accumulated. In addition, those findings are expected to be utilized as basic data for the establishment of climate change adaptation policies related to forest vegetation preservation in the natural ecosystem field.

• Journal of Korea Water Resources Association
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• v.54 no.7
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• pp.535-544
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• 2021

Agricultural water occupies 48% of water demand, and management of agricultural reservoirs is essential for water resources management within agricultural basins. For more efficient use of agricultural water, monitoring the distribution of water resources in agricultural reservoirs and agricultural basins is required. Therefore, in this study, three threshold determination methods (i.e., fixed threshold, Otsu threshold, Kittler-Illingworth (KI) threshold) were compared to detect terrestrial water bodies using Sentinel-1 images for 3 years from 2018 to 2020. The purpose of this study was to evaluate methods for determining threshold values to more accurately estimate the reservoir area. In addition, by analyzing the relationship between the water surface and water storage at the Edong, Gosam, and Giheung reservoirs, water storage based on the SAR image was estimated and validated with observations. The thresholding method for detecting a waterbody was found to be the most accurate in the case of the KI threshold, and the water storage estimated by the KI threshold indicated a very high agreement (r = 0.9235, KGE' = 0.8691). Although the seasonal error characteristics were not observed, the problem of underestimation at high water levels may occur; the relationship between the water surface and the water storage could change rapidly. Therefore, it is necessary to understand the relationship between the water surface area and water storage through ground observation data for a more accurate estimation of water storage. If the use of SAR data through water resources satellites becomes possible in the future, based on the results of this study, it is judged that it will be beneficial for monitoring water storage and managing drought.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.761-774
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• 2022

Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.11-22
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• 2009

Long-term rainfall-runoff modeling is a key element in the Earth's hydrological cycle, and associated with many different aspects such as dam design, drought management, river management flow, reservoir management for water supply, water right permission or coordinate, water quality prediction. In this regard, hydrologists have used the hydrologic models for design criteria, water resources assessment, planning and management as a main tool. Most of rainfall-runoff studies, however, were not carefully performed in terms of considering reservoir effects. In particular, the downstream where is severely affected by reservoir was poorly dealt in modeling rainfall-runoff process. Moreover, the effects can considerably affect overall the rainfallrunoff process. An objective of this study, thus, is to evaluate the impact of reservoir operation on rainfall-runoff process. The proposed approach is applied to Anseong watershed, where is in a mixed rural/urban setting of the area and in Korea, and has been experienced by flood damage due to heavy rainfall. It has been greatly paid attention to the agricultural reservoirs in terms of flood protection in Korea. To further investigate the reservoir effects, a comprehensive assessment for the results are discussed. Results of simulations that included reservoir in the model showed the effect of storage appeared in spring and autumn when rainfall was not concentrated. In periods of heavy rainfall, however, downstream runoff increased in simulations that do not consider reservoir factor. Flow duration curve showed that changes in streamflow depending upon the presence or absence of reservoir factor were particularly noticeable in ninety-five day flow and low flow.