Development of Field Pollutant Load Estimation Module and Linkage of QUAL2E with Watershed-Scale L-THIA ACN Model

The Long Term Hydrologic Impact Assessment (L-THIA) model was previously improved by incorporating direct runoff lag time and baseflow. However, the improved model, called the L-THIA asymptotic curve number (ACN) model cannot simulate pollutant loads from a watershed or instream water quality. In this study, a module for calculating pollutant loads from fields and through stream networks was developed, and the L-THIA ACN model was combined with the QUAL2E model (The enhanced stream water quality model) to predict instream water quality at a watershed scale. The new model (L-THIA ACN-WQ) was applied to two watersheds within the Korean total maximum daily loads management system. To evaluate the model, simulated results of total nitrogen (TN) and total phosphorus (TP) were compared with observed water quality data collected at eight-day intervals. Between simulated and observed data for TN pollutant loads in Dalcheon A watershed, the R2 and Nash–Sutcliffe efficiency (NSE) were 0.81 and 0.79, respectively, and those for TP were 0.79 and 0.78, respectively. In the Pyungchang A watershed, the R2 and NSE were 0.66 and 0.64, respectively, for TN and both statistics were 0.66 for TP, indicating that model performed satisfactorily for both watersheds. Thus, the L-THIA ACN-WQ model can accurately simulate streamflow, instream pollutant loads, and water quality

Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation

In this study, 52 asymptotic Curve Number (CN) regression equations were developed for combinations of representative land covers and hydrologic soil groups. In addition, to overcome the limitations of the original Long-term Hydrologic Impact Assessment (L-THIA) model when it is applied to larger watersheds, a watershed-scale L-THIA Asymptotic CN (ACN) regression equation model (watershed-scale L-THIA ACN model) was developed by integrating the asymptotic CN regressions and various modules for direct runoff/baseflow/channel routing. The watershed-scale L-THIA ACN model was applied to four watersheds in South Korea to evaluate the accuracy of its streamflow prediction. The coefficient of determination (R2) and Nash–Sutcliffe Efficiency (NSE) values for observed versus simulated streamflows over intervals of eight days were greater than 0.6 for all four of the watersheds. The watershed-scale L-THIA ACN model, including the asymptotic CN regression equation method, can simulate long-term streamflow sufficiently well with the ten parameters that have been added for the characterization of streamflow

Methodology for Determining the Key Factors for Non-Point Source Management

Due to the growing significance of water quality degradation by non-point source (NPS) pollution, regions in which NPS management is required should be designated as the management areas. Relevant management measures should be established to control water quality items related to degradation. It is advantageous that the area where the water environment is negatively affected by NPS is provided with legal grounds for NPS management, namely the designation of an NPS management area. This is because if it is designated as an NPS management area, the government can support the budget necessary for the installation of non-point pollution reduction facilities. In order to effectively utilize the limited budget, it is necessary to select and concentrate the area that should be managed first in the NPS management area. For the efficiency of the NPS pollution management within a management region, priority locations or key management sub-regions should be determined to implement differential management plans. Also, in selecting priority management regions, evaluation factors that can reflect the effects of NPS, such as the water quality target excess ratio in the mid-level region (or the total maximum daily load (TMDL) management) which includes the target region (low-level region), the NPS load in land, and non-permeable area ratio, should be quantified and the management order should be defined. Since NPS has local characteristics, the management items should be determined based on turbidity, suspended solid (SS), or total phosphorus (TP) that affect the local water quality. When the water environment is polluted due to non-point pollutants, various materials such as turbidity, SS, TP, Escherichia coli, and heavy metals can be set as management items according to local characteristics. However, the most important items to be managed are turbidity, SS, and TP, because if the solid (SS) is present in the water, which is highly turbid and does not sink easily, people can feel unpleasant and feel that the water is not clean, even if they do not analyze the water quality. In addition, in the case of TP, nutrients accumulated in the land are introduced into the river by rainfall, causing eutrophication. People feel uncomfortable because it changes the water color. Other pollutants can only be found to be contaminated after water quality analysis is performed. The water quality target of the management items should be set realistically, based on the situation of the watershed by considering the watershed model, management flow, NPS pollutant reduction plan, the river flow in the management area, and load. All these reflect the characteristics of the region. To evaluate whether the water quality target is achieved after NPS management, a method similar to the one to set the water quality target should be used to review the performance of the management plan. This study introduces specific examples of key factors in establishing an NPS management plan, including consideration factors and methods for the designation of NPS management regions, consideration factors and the selection method for key management areas within a management region, the selection method of management items, the selection method of the water quality target, and an evaluation method of the water quality target

Determination of NPS Pollutant Unit Loads from Different Landuses

This study aimed to estimate pollutant unit loads for different landuses and pollutants that reflected long-term runoff characteristics of nonpoint source (NPS) pollutants and recent environmental changes. During 2008–2014, 2026 rainfall events were monitored. The average values of antecedent dry days, total rainfall, rainfall intensity, rainfall duration, runoff duration, and runoff coefficient for each landuse were 3.8–5.9 d, 35.2–65.0 mm, 2.9–4.1 mm/h, 12.5–20.4 h, 12.4–27.9 h, and 0.24–0.45, respectively. Uplands (UL) exhibited high suspended solids (SS, 606.2 mg/L), total nitrogen (TN, 7.38 mg/L), and total phosphorous (TP, 2.27 mg/L) levels, whereas the runoff coefficient was high in the building sites (BS), with a high impervious surface ratio. The event mean concentration (EMC) for biological oxygen demand (BOD) was the highest in BS (8.0 mg/L), while the EMC was the highest in BS (in the rainfall range 50 mm). The unit loads for BOD (1.49–17.76 kg/km2·d), TN (1.462–10.147 kg/km2·d), TP (0.094–1.435 kg/km2·d), and SS (15.20–327.70 kg/km2·d) were calculated. The findings can be used to manage NPS pollutants and watershed environments and implement relevant associated management systems

Development of Daily Flow Expansion Regression and Web GIS-Based Pollutant Load Evaluation System

This study accounted for the importance of daily expansion flow data in compensating for insufficient flow data in a watershed. In particular, the 8-day interval flow measurement data (intermittent monitoring data) could cause uncertainty in the high- or low-flow conditions that have been used to estimate the flow duration curve (FDC) and the load duration curve (LDC) used in Total Maximum Daily Load (TMDL) evaluation in Korea. Thus, this study developed a method to expand the 8-day interval flow data (missing data) to daily flow data in order to evaluate the Total Maximum Daily Load (TMDL) appropriately in a watershed. We employed the machine learning technique (the gradient descent method provided by the Google TensorFlow package) to develop a regression for expanding the 8-day interval flow data. The method was applied in the Nakdong River basin located in Korea to collect the 8-day interval and daily flow data from a number of gauging stations. The results of the expanded daily flow were evaluated through the RMSE, MAE, IOA, and NSE, and the valid expanded daily flow data were obtained for the 29 TMDL gauging stations (IOA 0.84~0.99, NSE −0.18~0.99). A good performance in the creation of daily flow data (continuous data) from the 8-day interval flow data (intermittent data) was shown using the proposed method. In addition, the Web GIS-based pollutant load assessment system was developed to evaluate the TMDL; it included the daily data expansion method and provided the pollution load characteristics objectively and intuitively. This system will help decision makers, such as environmental regulators, researchers, and the general public, and support their decision making for pollution source management with accessible and efficient tools for understanding and addressing water quality issues

Correction: Ryu, J., et al. Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation. Water 2016, 8, 153

The authors wish to make the following corrections to this paper [1]: The author name “Bernard A. Engle” should be “Bernard A. Engel”[...

Projecting Future Climate Change Scenarios Using Three Bias-Correction Methods

We performed bias correction in future climate change scenarios to provide better accuracy of models through adaptation to future climate change. The proposed combination of the change factor (CF) and quantile mapping (QM) methods combines the individual advantages of both methods for adjusting the bias in global circulation models (GCMs) and regional circulation models (RCMs). We selected a study site in Songwol-dong, Seoul, Republic of Korea, to test and assess our proposed method. Our results show that the combined CF + QM method delivers better performance in terms of correcting the bias in GCMs/RCMs than when both methods are applied individually. In particular, our proposed method considerably improved the bias-corrected precipitation by capturing both the high peaks and amounts of precipitation as compared to that from the CF-only and QM-only methods. Thus, our proposed method can provide high-accuracy bias-corrected precipitation data, which could prove to be highly useful in interdisciplinary studies across the world