Chunggil Jung’s research while affiliated with Konkuk University and other places

South Korea’s River Act mandates the maintenance of instream flow to support river ecosystems. This regulation has evolved from early river management practices to more advanced, systematic approaches, including the Instream Flow Incremental Methodology (IFIM). Despite these advancements, river management in South Korea, particularly in the Seomjin River Basin, continues to face numerous challenges. In this study, a three-dimensional numerical model was developed to simulate the hydrodynamic and salinity conditions of the Seomjin River Estuary. This study proposes optimal instream flows to support critical habitats for the Corbicula bivalve, which has seen a significant decline due to salinity intrusion by environmental changes. Using the Environmental Fluid Dynamics Code (EFDC), the model simulates salinity and river discharge with calibration and validation by incorporating historical data. Subsequently, this study evaluates how river discharge affects salinity in four major Corbicula habitats (Dugok, Shinbi, Mokdo, and Hwamok). Finally, we determine the minimum flow (instream flow) needed to sustain Corbicula habitats. In short, this study found that the minimum flow rates (instream flow) required to meet target salinities varied significantly across these sites and under different tidal conditions. These findings highlight the necessity of adapting river flow management practices to preserve the ecological health for Corbicula in the Seomjin River Estuary. Furthermore, this study suggests integrating an additional water supply to be used with local water management plans by suggesting short-term and long-term alternatives in order to sustain adapting river minimum flow (instream flow).

Climate change not only affects the water resource system but also has a great impact on the aquatic ecosystem, which is complexly linked to various organic and inorganic matter. It is difficult to simulate the current aquatic ecosystem and predict the future system due to the immensity and complexity of aquatic ecosystems; however, a spatial analysis of future aquatic ecological health is necessary if we are to adapt and take action against future climate change. In this study, we evaluated the aquatic ecological health of the Han River basin under the future climate change RCP4.5 and RCP8.5 scenarios using three indices: fish assessment index (FAI), trophic diatom index (TDI), and benthic macroinvertebrate index (BMI). For this, we developed the SWAT-XGBoost linkage algorithm, and the algorithm accuracy for the FAI, TDI, and BMI was 89.3~95.2%. In the case of the FAI and BMI assessment of aquatic ecological health, the upstream Han River was classified as a hot spot. In the case of the TDI, the downstream area of the Han River was classified as a cold spot. However, as the current TDI downstream was classified as grades D and E, continuous management is needed.

This study modelled possible ways of reducing salinity in the Seomjin River estuary to improve habitats for corbicula, which are important components of the ecosystem (ecologically and commercially) in the Seomjin River. Additionally, we analyzed the salinity reduction effects of structural measures to sustain optimal salinity. To do this, salinity measurement facilities were installed at crucial habitat locations in the Seomjin River estuary. After ensuring the reliability of the model, numerical simulations were conducted. Salinity changes were analyzed for four major locations (Dugok, Shinbi, Mokdo, Hwamok) and simulated under various conditions, resulting in concentrations of 4.7 psu in Dugok, 16.0 psu in Shinbi, 19.2 psu in Mokdo, and 28.2 psu in Hwamok. Additionally, this study analyzed reduction effects by applying three reduction scenarios (riverbed restoration, submerged weir, and groyne) by simulating the numerical model. The reductions in salinity for the submerged weir (SWS-1 to 3) and groyne (GS-1 to 3) scenarios were minimal, below 1 psu, indicating a very limited reduction effect. In conclusion, the salinity concentration in the Seomjin River estuary is dominated by the neap tide, and the salinity intrusion distance is influenced more by the spring tide. The Songjeong discharge strongly impacts the spring tide, while the neap tide is less dominant due to salinity stratification. Among the salinity reduction scenarios, riverbed restoration has the most critical effect, but artificial reduction measures may pose challenges in terms of cost and practicality.

Anthropogenic development can strongly influence natural river processes, leading to environmental changes that negatively affect important habitats and biodiversity and consequently reduce economically important natural resources. This study investigated the effects of salinity intrusion on the habitat of the clam Corbicula japonica in the Seomjin River estuarine zone. We employed the Environmental Fluid Dynamics Code (EFDC) model, which incorporates topographic data and hydrological changes, to simulate salinity. Two salinity measurement facilities were installed in Seomjin River estuarine and operated to optimize the EFDC model. The results show that reduced flow rates due to intake have a negligible impact on the increased salinity. Maintaining optimal salinity (15–20 psu) during neap tides at the Seomjin River Bridge requires constant high flow rates, which poses significant challenges. Saltwater stratification is identified as the primary cause of pronounced salinity stratification, particularly during neap tides. Addressing this issue through river discharge and intake facility operation is challenging. Structural measures, including riverbed restoration and underwater barriers, are recommended to improve resistance to seawater intrusion. Future research should aim to develop scenarios to reduce salinity, quantify the reduction efficiency, and propose region-specific measures.

Anthropogenic development can strongly influence natural river processes, leading to environmental changes that negatively affect important habitats and biodiversity, and consequently reduce economically important natural resources. This study investigated the effects of salinity intrusion on the habitat of the clam Corbicula japonica in the Seomjin River estuarine zone. We employed the Environmental Fluid Dynamics Code (EFDC) model, which incorporates topographic data and hydrological changes, to simulate salinity concentrations. Two salinity measurement facilities were installed in Seomjin River estuarine and operated to optimize the EFDC model. The results show that reduced flow rates due to intake have a negligible impact on the increased salinity. Maintaining optimal salinity (15–20 psu) during neap tides at the Seomjin River Bridge requires constant high flow rates, which poses significant challenges. Saltwater stratification is identified as the primary cause of pronounced salinity stratification, particularly during neap tides. Addressing this issue through river discharge and intake facility operation is challenging. Structural measures, including riverbed restoration and underwater barriers, are recommended to improve resistance to seawater intrusion. Future research should aim to develop scenarios to reduce salinity concentrations, quantify the reduction efficiency, and propose region-specific measures.

this study estimates soil moisture content (SMC) using Sentinel-1A/B C-band synthetic aperture radar (SAR) images and an artificial neural network (ANN) over a 40 × 50-km 2 area located in the Geum River basin in South Korea. The hydrological components characterized by the antecedent precipitation index (API) and dry days were used as input data as well as SAR (cross-polarization (VH) and copolarization (VV) backscattering coefficients and local incidence angle), topo-graphic (elevation and slope), and soil (percentage of clay and sand)-related data in the ANN simulations. A simple logarithmic transformation was useful in establishing the linear relationship between the observed SMC and the API. In the dry period without rainfall, API did not decrease below 0, thus the Dry days were applied to express the decreasing SMC. The optimal ANN architecture was constructed in terms of the number of hidden layers, hidden neurons, and activation function. The comparison of the estimated SMC with the observed SMC showed that the Pearson's correlation coefficient (R) and the root mean square error (RMSE) were 0.85 and 4.59%, respectively.

This study is to evaluate future agricultural water supply capacity in Geum river basin (9,865 km²) using SWAT and MODSIM-DSS. The MODSIM-DSS was established by dividing the basin into 14 subbasins, and the irrigation facilities of agricultural reservoirs, pumping stations, diversions, culverts and groundwater wells were grouped within each subbasin, and networked between subbasins including municipal and industrial water supplies. The SWAT was calibrated and validated using 11 years (2005-2015) daily streamflow data of two dams (DCD and YDD) and 4 years (August 2012 to December 2015) data of three weirs (SJW, GJW, and BJW) considering water withdrawals and return flows from agricultural, municipal, and industrial water uses. The Nash−Sutcliffe efficiency (NSE) of two dam and three weirs inflows were 0.55∼0.70 and 0.57∼0.77 respectively. Through MODSIM-DSS run for 34 years from 1982 to 2015, the agricultural water shortage had occurred during the drought years of 1982, 1988, 1994, 2001 and 2015. The agricultural water shortage could be calculated as 197.8 × 10⁶ m³, 181.9 × 10⁶ m³, 211.5 × 10⁶ m³, 189.2 × 10⁶ m³ and 182.0 × 10⁶ m³ respectively. The big shortages of agricultural water were shown in water resources unit map number of 3004 (Yeongdongcheon) and 3012 (Geumgang Gongju) areas exceeding 25.1 × 10⁶ m³ and 47.4 × 10⁶ m³. From the estimation of future agricultural water requirement using RCP 8.5 INM-CM4 scenario, the 3004 and 3012 areas showed significant water shortages of 26.1 × 10⁶ m³ (104.1%) and 50.9 × 10⁶ m³ (107.4%) in 2080s (2070∼2099) compared to the present shortages. The water shortages decreased to 23.6 × 10⁶ m³ (94.0%) and 43.3 × 10⁶ m³ (91.4%) below of the present shortages by developing irrigation facilities.

The spatial distribution of soil moisture (SM) was estimated by a multiple quantile regression (MQR) model with Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and filtered SM data from 2013 to 2015 in South Korea. For input data, observed precipitation and SM data were collected from the Korea Meteorological Administration and various institutions monitoring SM. To improve the work of a previous study, prior to the estimation of SM, outlier detection using the isolation forest (IF) algorithm was applied to the observed SM data. The original observed SM data resulted in IF_SM data following outlier detection. This study obtained an average data removal rate of 20.1% at 58 stations. For various reasons, such as instrumentation, environment, and random errors, the original observed SM data contained approximately 20% uncertain data. After outlier detection, this study performed a regression analysis by estimating land surface temperature quantiles. The soil characteristics were considered through reclassification into four soil types (clay, loam, silt, and sand), and the five-day antecedent precipitation was considered in order to estimate the regression coefficient of the MQR model. For all soil types, the coefficient of determination (R2) and root mean square error (RMSE) values ranged from 0.25 to 0.77 and 1.86% to 12.21%, respectively. The MQR results showed a much better performance than that of the multiple linear regression (MLR) results, which yielded R2 and RMSE values of 0.20 to 0.66 and 1.08% to 7.23%, respectively. As a further illustration of improvement, the box plots of the MQR SM were closer to those of the observed SM than those of the MLR SM. This result indicates that the cumulative distribution functions (CDF) of MQR SM matched the CDF of the observed SM. Thus, the MQR algorithm with outlier detection can overcome the limitations of the MLR algorithm by reducing both the bias and variance.

This study was to evaluate the groundwater-level behavior in Geum River Basin (9645.5 km2) of South Korea with HadGEM3-RA RCP 4.5 and 8.5 climate change scenarios and future groundwater use data using the soil and water assessment tool (SWAT). Before evaluating future groundwater behavior, the SWAT model was calibrated and validated using the daily inflows and storage of two dams (DCD and YDD) in the basin for 11 years (2005–2015), the daily groundwater-level observation data at five locations (JSJS, OCCS, BEMR, CASS, and BYBY), and the daily inflow and storage of three weir locations (SJW, GJW, and BJW) for three years and five months (August 2012 to December 2015). The Nash–Sutcliffe efficiency (NSE) and the coefficient of determination (R2) of two dam inflows was 0.55–0.70 and 0.67–0.75. For the inflows of the three weirs, NSE was 0.57–0.77 and R2 was 0.62–0.81. The average R2 value for the groundwater levels of the five locations ranged from 0.53 to 0.61. After verifying the SWAT for hydrologic components, we evaluated the behavior of future groundwater levels by future climate change scenarios and estimated future ground water use by Korean water vision 2020 based on ground water use monitoring data. The future groundwater-level decreased by −13.0, −5.0, and −9.0 cm at three upstream locations (JSJS, OCCS, and BEMR) among the five groundwater-level observation locations and increased by +3.0 and +1.0 cm at two downstream locations (CASS and BYBY). The future groundwater level was directly affected by the groundwater recharge, which was dependent on the seasonal and spatial precipitations in the basin.

The Drying Stream Assessment Tool and Water Flow Tracking (DrySAT-WFT) were modified to simulate the hydrological components of water loss databases (DBs) affecting stream drying phenomena. In this study, the phenomenon is defined based on a method using the 10-day minimum flow (reference Q355). Prior to identifying the method using reference Q355, the DrySAT-WFT model was calibrated and verified for its performance with the total runoff (TQ), evapotranspiration (ET), and soil moisture (SM) at 12 streamflow locations, 3 ET locations, and 58 SM locations. The average R2 for TQ in 2005 to 2015 were 0.66 to 0.84, which demonstrates good performance. Moreover, Nash Sutcliffe model efficiency (NSE) values were 0.52 to 0.72, which are also good. After verifying the DrySAT-WFT model for hydrologic components, in order to apply the method, this study defined the drying progress which was analyzed by the stream drying index (SDI) as decision criteria. In this study, the criteria for the estimation of SDI were calculated as reference Q355 coming from the 10-day minimum flow considering only weather changes from 1976 to 2015. Then, SDI grades were determined by counting the number of days below a reference Q355 from TQ considering all water loss databases (DBs) such as weather changes, groundwater uses, forest heights, soil depths, land use, and road network. On the other hand, SDI represents how many days below the reference Q355 increased when all water loss DBs were applied, in comparison to when only weather changes were applied. The DrySAT-WFT model simulated the hydrological components of the water balance based on each water loss DB, including the application of all DBs. As a result, the change ratios for TQ were measured: −4.8% for groundwater use (GWU), −1.3% for forest height (FH), −0.3% for road network (RN), −0.1% for land use (LU) and −0.1% for soil depth (SD). Overall, TQ values decreased by -8.4%. The change ratios for ET were measured: −2.0% for GWU, +10.5% for FH, +5.6% for RN, −1.8% for LU and +0.3% for SD. Overall, the ET values increased by +14.7%. In addition, based on all water loss DBs, the SDI was evaluated for all watersheds, which intensified recently (2006–2015). Under weather DB conditions, the average SDI was measured as 2.0 for all watersheds. Stream drying processes remained limited, requiring only monitoring. Given baseline conditions, stream drying intensified to grades of 3.1 (1976–1985), 3.2 (1986–1995), 3.3 (1996–2005) and 3.5 (2006–2015) by all water loss DBs.