The baseflow separation method based on a digital filter is a simple method for separating the baseflow from streamflow. Appropriate estimation of filter parameters is required to use the digital filter method for analysis. We carried out sensitivity analysis on four digital filter methods: Lyne–Hollick (LH), Chapman, Chapman and Maxwell (CM), and exponentially weighted moving average (EWMA). Furthermore, appropriate filter parameters were suggested for each method in this study. By applying them to 25 stage stations in the Nakdong River in the Republic of Korea, the four methods were evaluated. The results of the evaluation showed that the Chapman and CM methods had problems separating the baseflow during the dry seasons. The EWMA and LH methods were able to achieve reliable baseflow separation of the outcomes by selecting appropriate the filter parameters. Thus, the EWMA and LH methods can be used easily and reasonably among the digital filter methods that have one filter parameter.

Water risk has been continuously rising due to climate change and ownership disputes of water resources. Dam construction to secure water resources may lead to environmental problems and upstream immersion. On the other hand, rainwater harvesting systems can effectively supply water at a low cost, although economic efficiency of these systems is still debatable. This study evaluates financial support programs to promote installation of rainwater harvesting systems, increasing economic feasibility. Based on a cost–benefit analysis, capacity optimization methods are further suggested. A sensitivity analysis is performed to determine the relative importance among uncertain parameters such as inflation and discount rates. In doing so, priority factors to consider in the design of rainwater harvesting systems are ultimately identified. A net present value, although it is sensitive to the inflation rate, is shown to be more appropriate to estimate the economic efficiency of rainwater harvesting system, compared to the typical cost–benefit ratio. Because the high future value overestimates the economic feasibility of rainwater harvesting systems, proper inflation rates should be applied. All in all, a funding program to promote rainwater harvesting systems significantly increases the benefits. Thus, national financial support policies are recommended to ensure economic feasibility of rainwater harvesting systems.

Especially for periods of drought, the higher the accuracy of reservoir inflow forecasting is, the more reliable the water supply from a dam is. This article focuses on the probabilistic forecasting of quarterly inflow to reservoirs, which determines estimates from the probabilistic quarterly inflow according to drought forecast results. The probabilistic quarterly inflow was forecasted by a copula-based Bayesian network employing a Gaussian copula function. Drought forecasting was performed by calculation of the standardized inflow index value. The calendar year is divided into four quarters, and the total inflow volume of water to a reservoir for three months is referred to as the quarterly inflow. Quarterly inflow forecasting curves, conforming to drought stages, produce estimates of probabilistic quarterly inflow according to the drought forecast results. The forecasted estimates of quarterly inflow were calculated by using the inflow records of Soyanggang and Andong dams in the Republic of Korea. After the probability distribution of the quarterly inflow was determined, a lognormal distribution was found to be the best fit to the quarterly inflow volumes in the case of the Andong dam, except for those of the third quarter. Under the threshold probability of drought occurrences ranging from 50% to 55%, the forecasted quarterly inflows reasonably matched the corresponding drought records. Provided the drought forecasting is accurate, combining drought forecasting with quarterly inflow forecasting can produce reasonable estimates of drought inflow based on the probabilistic forecasting of quarterly inflow to a reservoir.

The discrete hedging rule for reservoir operation includes time-varying trigger volumes used for the onset and termination of water rationing, which complicates its optimization problems. A dynamically dimensioned search can be easily applied to complex optimization problems, but the performance is relatively limited in constrained optimization problems such as deriving reservoir operation rules. A dynamically dimensioned search allowing for a flexible search range is proposed in this study to efficiently solve constrained optimization problems. The modified algorithm can recursively update the search ranges of decision variables with limited overlaps. The above two algorithms are applied to derive hedging rule curves for three reservoirs. Objective function values are closely converged to optimum solutions, with fewer evaluations using the modified algorithm than those using the traditional algorithm. The modified algorithm restrains an overlapped search range of decision variables and can reduce redundant computational efforts caused by unreasonable candidate solutions that violate inequality conditions.

Rapid urbanization and industrialization have caused water quality issues in urban rivers. Appropriate measures based on water quality monitoring systems and prediction methods are needed for water quality management. While South Korea has operated a water quality monitoring system that measures various environmental factors and has accumulated water quality data, a water quality prediction system is not in place. This study suggests a water quality prediction method based on a long short-term model using water quality and meteorological monitoring data. Additionally, we present a derived input set of the prediction model that can improve the prediction model performance. The prediction model’s performance was evaluated by the coefficient of determination under various conditions, such as the hyperparameters, temporal resolution of input data, and application of upstream and downstream data. As a result, using the temporal resolution of the input data as hourly data improved predictions by an average of 25.6% over three days of the prediction period compared to daily data. Meanwhile, it was analyzed that the hyperparameters and using upstream and downstream data have a minor effect on the model performance. The results of this study underscore the crucial role of the number, duration, and temporal resolution of available monitoring data in water quality management.

In this study, the catchment hydrology cycle analysis tool (CAT) model was used to conduct a comprehensive hydrological analysis of the water balance of agricultural reservoirs. Data from 2010 to 2017, including precipitation, water level data in the reservoir, groundwater usage, and wastewater discharge, were collected and compiled for the upper reaches of the Hantan River Dam. The current conditions and content curves of the 11 reservoirs within the watershed were investigated and recorded. The results were analyzed by simulating three scenarios: treating the entire watershed as 1 unit, dividing the watershed into 5 sub-watersheds according to the standard watershed criteria, and further subdividing it into 27 watersheds, taking into account the presence of agricultural reservoirs. In cases where watershed information is lacking, it is deemed that subdividing the watershed can enhance efficiency. The highest model efficiency was observed in the 27 sub-basins, particularly when accounting for agricultural reservoirs. This study proposed an efficient method for hydrological analysis of watersheds including ungauged areas.