Watershed In South Korea Research Articles

A machine learning-based approach to predict groundwater nitrate susceptibility using field measurements and hydrogeological variables in the Nonsan Stream Watershed, South Korea

Identifying and predicting the nitrate inflow and distribution characteristics of groundwater is critical for groundwater contamination control and management in rural mixed-land-use areas. Several groundwater nitrate prediction models have been developed; in particular, a nitrate concentration model that uses dissolved ions in groundwater as an input variable can produce accurate results. However, obtaining sufficient chemical data from a target area remains challenging. We tested whether machine learning models can effectively determine nitrate contamination using field-measured data (pH, electrical conductivity, water temperature, dissolved oxygen, and redox potential) and existing geographic information system (GIS) data (lithology, land cover, and hydrogeological properties) from the Nonsan Stream Watershed in South Korea, an area where nitrate contamination occurs owing to intensive agricultural activities. In total, 183 groundwater samples from different wells, mixed municipal sites, and agricultural activities were used. The results indicated that among the four machine learning models (artificial neural network (ANN), classification and regression tree (CART), random forest (RF), and support vector machine (SVM)), the RF (R2: 0.74; RMSE: 3.5) and SVM (R2: 0.80; RMSE: 2.8) achieved the highest prediction accuracy and smallest error in all groundwater parameter estimates. Land cover, aquifer type, and soil drainage were the primary RF and SVM model input variables, representing agricultural activity-related and hydrogeological infiltration effects. Our research found that in rural areas with limited hydro-chemical data, RF and SVM models could be used to identify areas at high risk of nitrate contamination using spatial variability, GIS-aided visualization, and easily accessible field-measured groundwater quality data.

Update of Empirical Models for Predicting Specific Degradation in South Korea and Future Sediment Management Considering Climate Change

In this study, the existing empirical models for sediment management for ungauged watersheds in South Korea developed in 2019 were updated. The existing models were developed based on the Universal Soil Loss Equation structure with domestic river sediment data obtained from 2005 to 2014 and watershed characteristic values. To update the models, the specific degradation (SD) value was calculated first in the same way as the existing method by adding sediment measurement and daily discharge data obtained from 2015 to 2020. The calculated SD values with recent data decreased compared with the past and differed from the results predicted using the existing models and the watershed characteristics investigated in this study. For this reason, the existing models were updated using the latest watershed characteristics and SD values. The root-mean-square error values of the existing models, which ranged from 131 to 90 tons/km2·year, decreased to 102 to 62 tons/km2·year in the updated models with the same structures. Multicollinearity was tested to evaluate the reliability of the updated models, and a new 95% prediction interval for usability was estimated. In addition, quantitative changes in the future sediment load of domestic rivers were analyzed using the future rainfall data obtained from climatic change scenarios and the watershed area. The results of this study reconfirmed that periodic updates are required for the existing empirical models. It is expected that the empirical models will be used in various ways, such as for the prediction of future sediment loads for river sediment management.

Estimation of Streamflow Depletion Caused by Groundwater Withdrawal in the Bokhacheon Watershed in South Korea Using the Modified SWAT Model

Understanding the effects of groundwater withdrawal on streamflow depletion is important for effectively managing water resources. The Soil and Water Assessment Tool (SWAT) model has a groundwater module to calculate the groundwater budget and groundwater discharge. However, the water pumped from the aquifer is not considered in the SWAT module that estimates groundwater discharge. Therefore, this module was modified to consider the impact of groundwater pumping on the changes in groundwater discharge in the Bokhacheon watershed, South Korea. The model’s water transfer module was improved to allow water from the aquifer to be transferred to destination locations, such as residential, industrial, and agricultural lands. Using the modified SWAT, streamflow responses to groundwater extraction were simulated for 2011–2019. The groundwater withdrawal induced decreases of 14.6 and 24.2% in low and drought flows, respectively, at the watershed’s outlet. The groundwater withdrawals decreased groundwater flow and total water yield by 23.5% and 9.8%, respectively, and increased surface flow, lateral flow, percolation, soil water, and evapotranspiration owing to the increased soil moisture resulting from the partial re-infiltration of the groundwater pumped for agricultural irrigation. The modified SWAT can effectively estimate streamflow depletion resulting from groundwater pumping without extensive hydrogeological input data and computational time.

Evaluation and selection of CMIP6 GCMs for long-term hydrological projections based on spatial performance assessment metrics across South Korea

Abstract The selection of an appropriate subset of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project 6 (CMIP6) for long-term hydrological simulation at the basin scale is a necessity. This study selected high-performing GCMs among 32 available CMIP6 GCMs on the basis of reproducing the observed precipitation over the main watershed in South Korea during the historical period. An integrated selection approach based on four spatial performance assessment metrics was proposed to better estimate changes in precipitation using the simulated GCMs precipitation. Results revealed that the spatial performance over different GCMs could provide an effective means of selecting GCMs for hydrological study over the major river watersheds in South Korea. The four top-ranked GCMs are FIO-ESM-2-0, CESM2-WACCM, CESM2, and CMCC-ESM2, and they overestimated precipitation in South Korea during the historical period, with a bias of 10–25%. However, this study confirmed that a formal bias correction for these GCMs is not recommended prior to model selection, and the ranking of GCMs under the bias correction could be problematic. The proposed approach in this study can be applied to numerous GCMs, climate variables, and other regions to select representative GCMs to reduce the uncertainties in terms of the spatial patterns observed.

Diffuse pollutant load predictions in areas that implement the total maximum daily load due to climate change

Total maximum daily load (TMDL), which is implemented for water quality management in a watershed, necessitates the use of a watershed model to predict the load. However, such a model consumes a lot of time and effort until the results are simulated. Therefore, the present study aimed to compare and evaluate load changes following the revision of the land-based diffuse pollutant unit load values, which are used to calculate the diffuse pollutant discharge loads in the total maximum daily load (TMDL) guidelines established for TMDL control by watersheds in South Korea, using the Natural Resources Conservation Service (NRCS) method. When the revised unit load was used, the loads were reduced by an average of 56.8% compared with the prior unit load. The descending order of load change rate was biochemical oxygen demand (BOD) > total phosphorus (TP) > total nitrogen (TN). Although the difference in load discharge trends between the Representative Concentration Pathway (RCP) 2.6 and RCP8.5 scenarios was insignificant, the load discharge under the worst scenario (RCP8.5) may increase. When the future period was split into 30-year increments, the load of precipitation and diffuse pollutants increased in the intermediate future (2041–2070). This study examined how the management ratio of diffuse pollutants in a watershed changes when the legal standard value of institutionally managed diffuse pollutants changes. The findings of this study provide useful information for the efficient management of present and future diffuse pollutants.

미래 기후변화 시나리오에 따른 한반도 유역별 극한 강수 변화 전망

For the assessment of the future flood risk of 26 watersheds in South Korea, we analyze the change in extreme precipitation with a 100-year return period under global warming. We use downscaled high-resolution observation and bias-corrected future climate change scenario data from the National Institute of Meteorological Sciences for this purpose. In the high-CO₂ emission scenario (SSP5-8.5), the average rates of change in 100-year return period precipitation at 26 watersheds is projected to increase about 29%, 46%, and 53% in the early (2021 ~ 2040), mid (2041 ~ 2060), and late (2081 ~ 2100) 21st century, respectively, relative to the present climate. The number of watersheds with a 100-year return period precipitation increase of 50% or more compared to the present climate is predicted to increase significantly in the late 21st century. On the other hand, in the low-CO₂ emission scenario (SSP1-2.6), after the mid-21st century, the increase in 100-year return period precipitation is much less than that of the high-CO₂ emission scenario since the rate of change is projected to increase about 31%, 31%, and 29% in the early, mid, and late periods, respectively. Furthermore, most watersheds show a 100-year return period precipitation increase of 50% or less. These results imply that the possibility of flooding by extreme precipitation can be reduced through achievement of a carbon-neutral policy.

Development and testing of updated curve number models for efficient runoff estimation in steep-slope watersheds

The globally adopted and computationally efficient curve number (CN) model fills an active hydrological professional niche and has a well-documented history. However, it is structurally inconsistent and fails to reliably estimate runoff from rainfall. This is mainly due to the much-debated fixed initial abstraction (λ) and associated sudden jumps in runoff based on CN obtained from the documented tables. In this study, three new variants (M4, M5, M6) of the CN model are proposed that consider the hydrological imbalance between pre-storm soil moisture and initial abstraction after a rainfall event. A total of 1837 rainfall-runoff events were analyzed from 41 steep-slope watersheds in South Korea to test the robustness of the proposed CN models. The results were compared to the recently updated original CN model (M1) and two other recent variants (M2, M3) of this model. These models were evaluated using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), percent bias (PB), Kling-Gupta efficiency (KGE), a proposed overall weighted score, 1:1 line graph, and the FITEVAL tool. Using data from 41 watersheds, the lowest mean (median) RMSE of M5, M6, M4 [15.54(16.56), 15.84(11.45), 18.00(16.56)], PB for M4, M6, M5 [−1.28(−1.03), 1.29(0.03), −7.06(−7.27)]; the highest mean (median) NSE for M5, M6, M4 [0.86(0.87), 0.85(0.87), 0.81(0.83)], KGE for M6, M5, M4 [0.81(0.84), 0.79(0.81), 0.79(0.81)] and other graphical assessments show a better agreement between the observed and the runoff estimated by the proposed models. The corresponding mean (median) of RMSE, PB, NSE, and KGE statistics for M2 [19.26(17.69), 9.50(10.34), 0.78(0.80), 0.77(0.79)], and M3 [19.99(18.47), 05.56(7.17), 0.76(0.79), 0.77(0.79)] models show comparatively inferior results. Based on the same statistics, the M1 [24.60(22.48), 33.62(32.77), 0.63(0.68), 0.60(0.64)] model yields unrealistic results. It is inferred that both λ and CN should be kept flexible for a systematic and region-specific revision of the CN model to improve runoff estimation. In addition, a structurally consistent model with a stable soil moisture accounting (SMA) procedure is vital to get more reliable runoff estimates without compromising the simplicity and applicability to ungauged watersheds.

Framework for Healthiness Assessment of Water Cycle to Decide the Priority of Enhancement and Restoration Plans

The water cycle in watersheds is vulnerable due to climate change; hence, the need for sustainable watershed management is increasing. This paper suggests a framework for a healthiness assessment of the water cycle to provide a guideline for systematic watershed management considering the previous and current states. The suggested framework aims to prioritize restoration and enhancement plans based on the graded healthiness of the water cycle elements by the watersheds. The framework is composed of two assessment procedures: a problem-focused assessment to identify problems such as flood, drought, and river depletion in the watershed and the highest priority assessment to select the watershed for enhancement and restoration plans. The healthiness assessment method for each metric is suggested based on the Korean Framework Act on Water Management. The framework was applied to four different watersheds in South Korea. The framework is proven to be an effective method to identify practical emerging problems for the water cycle in each watershed. The framework can contribute to providing technical information to detect the water problem of the watershed by objectively diagnosing the watersheds with various potential water problems via the healthiness assessment.

Generating More Hydroelecticity While Ensuring the Safety: Resilience Assessment Study for Bukhangang Watershed in South Korea

The recent integrated water management policy and carbon-neutral policy can be seen as a turning point that changed the major frameworks of water resource policy and energy policy in the world. Values of hydropower reservoirs, directly related to both policies, should be re-evaluated in terms of resilience. In the past, hydropower reservoirs in Korea have contributed both to flood control and to generating electricity when operating dams within the limited water level during flood seasons. Under such limited operations, the power loss would be inevitable. Therefore, in this study, the concept of resilience was introduced for application to the operation of the hydropower reservoir to minimize such power loss. Also, the framework was able to be used for evaluating power generation performance when setting the target function to the maximization of electricity sale profit. HEC-5 was used for deriving the optimal operation rule, and the scenario was established by referring to the procedure of the general multiple-reservoir operation plan in Korea. As a result of application to the proposed framework, the operation rule that produces the maximum amount of electricity sales was presented, and it was confirmed that flood control and water usage performance could additionally be evaluated. When comparing the past data with optimal operation results for the period 2006~2013, it was found that the resilient operation increased by about 19.83% in terms of electricity generation. In the near future, if various scenarios are added and economic analysis is accompanied, it will be able to judge the best economic effects and the least opportunity costs.

Validity evaluation of a machine-learning model for chlorophyll a retrieval using Sentinel-2 from inland and coastal waters

The MultiSpectral Instrument (MSI) on-board Sentinel-2 provides satellite images at spatiotemporal resolutions suitable for chlorophyll a (Chla) retrieval from inland and coastal waters. Machine-learning (ML) algorithms including light gradient boosting machine (LGBM) were employed for Chl a retrieval from MSI. The study area encompasses 78 lakes and estuaries located across four major river watersheds in South Korea. Matchup data between MSI overpass and near-concurrent in situ Chl a measurements from December 2018 to April 2021 were included. The remote sensing reflectance (Rrs) values of six single spectral bands and four two-band ratios were used as the input features. Despite the difficulty in Chla estimation in optically complex waters, ML algorithms showed overall reasonable accuracy. Among the ML algorithms, LGBM exhibited the best performance (R2 = 0.75, bias = -0.15, slope = 0.73, RMSE = 15.15 mg·m-3, MAE = 9.49 mg·m-3) over a wide range of trophic states. Post-hoc interpretations of the best performing LGBM using Shapley additive explanations indicated that Rrs(704)/Rrs(665) was the most important feature, while Rrs(739)/Rrs(704) and Rrs(492)/Rrs(560) played auxiliary roles in Chl a retrieval through interaction with Rrs(704)/Rrs(665). Among-lake spatial variations of Chla were explained by percent forest and agricultural area within the buffer zone at multiple scales (buffer widths of 50 m and 500 m). The associations between the modeled Chla and buffer land cover types, that is, increase in Chla concentration with increase in percent forest and decrease in percent agricultural area, were consistent with the established ecological knowledge. Overall, the model interpretations and spatial variations in Chla within and among lakes confirmed the validity of LGBM for retrieving MSI-derived Chla from lakes and estuaries. Our study can serve as the reference for evaluating the validity of complex ML models for inland water remote sensing.

Analyses of Community Structure of Phytoplankton in Reservoirs Located in the Geum River Watershed in South Korea

Analyses of Community Structure of Phytoplankton in Reservoirs Located in the Geum River Watershed in South Korea

A Study to Suggest Monthly Baseflow Estimation Approach for the Long-Term Hydrologic Impact Analysis Models: A Case Study in South Korea

Changes in both land use and rainfall patterns can lead to changes in the hydrologic behavior of the watershed. The long-term hydrologic impact analysis (L-THIA) model has been used to predict such changes and analyze the changes in mitigation scenarios. The model is simple as only a small amount of input data are required, but it can predict only the direct runoff and cannot determine the streamflow. This study, therefore, aimed to propose a method for predicting the monthly baseflow while maintaining the simplicity of the model. The monthly baseflows for 20 watersheds in South Korea were estimated under different land use conditions. Calibration of the monthly baseflow prediction method produced values for R2 and the Nash–Sutcliffe efficiency (NSE) within the ranges of 0.600–0.817 and 0.504–0.677, respectively; during validation, these values were in the ranges of 0.618–0.786 and 0.567–0.727, respectively. This indicates that the proposed method can reliably predict the monthly baseflow while maintaining the simplicity of the L-THIA model. The proposed model is expected to be applicable to all the various forms of the model.

Estimating Design Floods at Ungauged Watersheds in South Korea Using Machine Learning Models

With recent increases of heavy rainfall during the summer season, South Korea is hit by substantial flood damage every year. To reduce such flood damage and cope with flood disasters, it is necessary to reliably estimate design floods. Despite the ongoing efforts to develop practical design practice, it has been difficult to develop a standardized guideline due to the lack of hydrologic data, especially flood data. In fact, flood frequency analysis (FFA) is impractical for ungauged watersheds, and design rainfall–runoff analysis (DRRA) overestimates design floods. This study estimated the appropriate design floods at ungauged watersheds by combining the DRRA and watershed characteristics using machine learning methods, including decision tree, random forest, support vector machine, deep neural network, the Elman recurrent neural network, and the Jordan recurrent neural network. The proposed models were validated using K-fold cross-validation to reduce overfitting and were evaluated based on various error measures. Even though the DRRA overestimated the design floods by 160%, on average, for our study areas the proposed model using random forest reduced the errors and estimated design floods at 99% of the FFA, on average.

Assessment of Groundwater Recharge in Agro-Urban Watersheds Using Integrated SWAT-MODFLOW Model

Numerical models are employed widely to evaluate the hydrological components of a watershed but, traditionally, watershed models simplify either surface or subsurface flow module. In this setup, as a bridge between groundwater and surface water regimes, aquifer recharge is the most affected segment of the water balance. Since the watershed processes are increasingly changed, the need for a comprehensive model with detailed conceptualizing capacity of both groundwater and surface water flow systems is growing. This work focuses on the spatiotemporal groundwater recharge assessment in gauged and ungauged agro-urban watersheds in South Korea using the updated SWAT-MODFLOW model, which integrates the Soil and Water Assessment Tool (SWAT2012) and Newton–Raphson formulation for Modular Finite Difference Groundwater Flow (MODFLOW-NWT) in a single executable code. Before coupling, the setup, calibration, and verification of each model were performed separately. After integration, irrigation pumps and drain cells mapping to SWAT auto-irrigation and subbasins were initiated. Automatic calibration techniques were used for SWAT and MODFLOW-NWT models, but a manual calibration was used for the integrated model. A physical similarity approach was applied to transfer parameters to the ungauged watershed. Statistical model performance indicators revealed that the low streamflow estimation was improved in SWAT-MODFLOW. The spatiotemporal aquifer recharge distribution from both the stream seepage and precipitation showed a substantial change, and most of the aquifer recharge occurs in July–September. The areal annual average recharge reaches about 18% of the precipitation. Low-lying areas receive higher recharge consistently throughout a year. Overall, SWAT-MODFLOW exhibited reasonable versatility in evaluating watershed processes and produced valuable results with reasonable accuracy. The results can be an important input for policymakers in the development of sustainable groundwater protection and abstraction strategies for the region.

Integrating hydraulic and physiologic factors to develop an ecological habitat suitability model

This study aimed to develop an ecological habitat suitability model (EHSM) for Zacco platypus by integrating hydraulic (HHS) and physiologic (PHS) habitat suitability. Study sites (117 stations) were selected throughout the five major watersheds in South Korea, where hydraulic, water temperature, and fish survey data (2008–2015) were available. The HHS was determined using preference curves for water depth and velocity, while the PHS was determined by growth and stress curves. The geometric mean of HHS and PHS was the most appropriate to calculate the ecological habitat suitability (EHS) when compared with fish abundance. The relation of EHS was lower with PHS (r = 0.750) than with HHS (r = 0.956), likely due to the thermal tolerance of Z. platypus. EHSM represented the effect of water temperature on fish growth and stress better than a conventional model, suggesting a promising tool to estimate the habitat suitability of freshwater fish.

The role of probabilistic precipitation forecasts in hydrologic predictability

Accurate streamflow forecasts enable the appropriate management of water resources. Although there is a general consensus that climate information can enhance hydrological predictability, this might not be the case if the accuracy of the given climate information is unreliable. Hence, this study has developed a modeling framework to estimate the role of climate information in forecasting accurate streamflow. Ensemble streamflow prediction (ESP) technology was adopted as a dynamic hydrologic forecast method to 35 watersheds in South Korea. The probabilistic precipitation forecast (PPF), issued by the Korea Meteorological Administration, was used as climate information for updating the probabilities of climate scenarios. First, we found that the current PPF is not accurate enough for significantly enhancing the streamflow forecasting accuracy. Subsequently, multiple sets of PPF were synthetically generated to evaluate the role of climate information. Given the perfect categorical climate forecasts, we found that there is much potential for the enhancement of streamflow forecast skill especially in the seasons that exhibit greater streamflow variability. However, there is less potential for increasing the streamflow forecasting skill under below-normal conditions. The proposed modeling framework is capable of quantifying the magnitude of potential improvement in hydrological predictability under the assumption that better climate information will be available in the future. We expect that this modeling framework can be effectively applied to other regions across a wide range of climate regimes.

Insights from socio-hydrological modeling to design sustainable wastewater reuse strategies for agriculture at the watershed scale

Wastewater reuse in agriculture can be a viable option to solve future freshwater shortages but may need an additional treatment process (Stage-II) to become a safe option. As wastewater reuse interacts with many facets of coupled human and water systems, the introduction of Stage-II treatment systems in wastewater reuse in agriculture must be understood in terms of socio-hydrology. This paper builds on a place-based socio-hydrological model of a wastewater-reused watershed in South Korea and uses it to: (1) identify key parameters in human and water systems that have a significant impact on wastewater reuse in agriculture; (2) explore the impacts of changing agricultural environments by altering the key parameters; and (3) develop the possibility space of future changes from current decision-making. Key parameters concern the characteristics of urbanization, domestic water use, and greenhouse cultivation. Urbanization can reduce the demand for Stage-II irrigation within an urbanizing watershed by reducing irrigation areas and increasing water availability. Domestic water use has a large impact on the economics of indirect wastewater reuse. Greenhouse cultivation influences the demand for Stage-II irrigation, mainly by reducing water availability. Moreover, it could further affect the demand if the communities evolved to have a greater concern for the use of groundwater resources. The possibility space shows that wastewater reuse has a strong influence on groundwater and could relieve agricultural water deficits through the diversification of irrigation sources, and could be a more economical irrigation practice than groundwater irrigation under changing agricultural environments.

Sediment Yield for Ungauged Watersheds in South Korea

Specific degradation (SD) is defined as the ratio of the sediment yield divided by the watershed area to compare sediment yield at the basin scale. The SD from 35 watersheds was calculated from field measurements of discharge and sediment concentrations. The watershed characteristics for each watershed were analyzed using GIS tools. All sediment gauging stations are located in alluvial river reaches and the estimated specific degradation typically ranges between 100 and 1,000 tons/km2·yr. Six regression models based on the watershed characteristics are proposed to estimate the mean annual sediment yield. The most useful relationship is function of the drainage area and mean annual precipitation. The proposed models were tested and validated with 15 additional river stations. The root mean square errors (RMSE) of the predictions are approximately 100 tons/km2·yr which is found to be satisfactory. The proposed models should be useful to estimate the sediment yield from ungauged watershed in South Korea.

Feasible Ranges of Runoff Curve Numbers for Korean Watersheds Based on the Interior Point Optimization Algorithm

Rainfall runoff is a complex phenomenon in nature. It differs from place to place due to different topographical features and rainfall patterns. The Natural Resources Conservation Service - Curve Number (NRCS-CN) is a well-adopted model to account for direct runoff volume from storm events. There are several studies on determining the initial abstraction and the CN from observed rainfall-runoff data; however, few studies demonstrate their statistical characteristics. The major aim of this study is to determine the feasible range and the confidence intervals of the CN. We examined 660 rainfall-runoff events collected from six medium sized watersheds in South Korea. The interior point optimization algorithm was adopted to ascertain the optimum value of CN and the initial abstraction coefficient (λ). The obtained results show that the CN value ranged from 45 to 90 and the average λ = 0.12 was best suited for Korean watersheds. The estimated confidence intervals were highly significant and strongly recommended for Korean watersheds.

Evaluation of Reliable Digital Elevation Model Resolution for TOPMODEL in Two Mountainous Watersheds, South Korea

This study analyzed the result of parameter optimization using the digital elevation model (DEM) resolution in the TOPography-based hydrological MODEL (TOPMODEL). Also, this study investigated the sensitivity of the TOPMODEL efficiency by applying the varying resolution of the DEM grid cell size. This work applied TOPMODEL to two mountainous watersheds in South Korea: the Dongkok watershed in the Wicheon river basin and the Ieemokjung watershed in the Pyeongchang river basin. The DEM grid cell sizes were 5, 10, 20, 40, 80, 160, and 300 m. The effect of DEM grid cell size on the runoff was investigated by using the DEM grid cell size resolution to optimize the parameter sets. As the DEM grid cell size increased, the estimated peak discharge was found to increase based on different parameter sets. In addition, this study investigated the DEM grid cell size that was most reliable for use in runoff simulations with various parameter sets in the experimental watersheds. The results demonstrated that the TOPMODEL efficiencies in both the Dongkok and Ieemokjung watersheds rarely changed up to a DEM grid-size resolution of about 40 m, but the TOPMODEL efficiencies changed with the coarse resolution as the parameter sets were changed. This study is important for understanding and quantifying the modeling behaviors of TOPMODEL under the influence of DEM resolution based on different parameter sets.