• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1053-1060
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• 2021

Current water quality monitoring systems in Korea carried based on in-situ grab sample analysis. It is difficult to improve the current water quality monitoring system, i.e. shorter sampling period or increasing sampling points, because the current systems are both cost- and labor-intensive. One possible way to improve the current water quality monitoring system is to adopt a modeling approach. In this study, a modeling technique was introduced to support the current water quality monitoring system, and an artificial neural network model, the computational tool which mimics the biological processes of human brain, was applied to predict water quality of the river. The approach tried to predict concentrations of Total coliform at the outlet of the river and this showed, somewhat, poor estimations since concentrations of Total coliform were rapidly fluctuated. The approach, however, could forecast whether concentrations of Total coliform would exceed the water quality standard or not. As results, modeling approaches is expected to assist the current water quality monitoring system if the approach is applied to judge whether water quality factors could exceed the water quality standards or not and this would help proper water resource managements.

• Journal of Korea Water Resources Association
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• v.55 no.5
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• pp.321-331
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• 2022

In this study, we tried to present a method for calculating the amount of regression using a watershed modeling method that can simulate the hydrological mechanism of water balance analysis and agricultural water based on watershed unit. Using the soil water assessment tool (SWAT), a watershed water balance analysis was conducted considering the simulation of paddy fields for the Manbongcheon Standard Basin (97.34 km²), which is a representative agricultural area of the Yeongsan river basin. Before evaluating return flow, the SWAT was calibrated and validated using the daily streamflow observation data at Naju streamflow gauge station (NJ). The coefficient of determination (R²), Nash-Sutcliffe Efficiency (NSE), Root-Mean-Square Error (RMSE) of NJ were 0.73, 0.70, 0.64 mm/day. Based on the calibration results for three years (2015-2017), the quick return flow and the return rate compared to the water supply amount for the irrigation period (April 1 to September 30) were calculated, and the average return flow rate was 53.4%. The proposed method of this study may be used as foundation data to optimal agricultural water supply plan for rational watershed management.

• Journal of Korea Water Resources Association
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• v.51 no.2
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• pp.141-150
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• 2018

The purpose of this study is to evaluate the future hydrologic behavior affected by the potential climate and land use changes in upstream of Anseong-cheon watershed ($366.5km^2$) using SWAT. The HadGEM3-RA RCP 4.5 and 8.5 scenarios were used for 2030s (2020-2039) and 2050s (2040-2059) periods as the future climate change scenario. It was shown that maximum changes of precipitation ranged from -5.7% in 2030s to +18.5% in 2050s for RCP 4.5 scenarios and the temperature increased up to $1.8^{\circ}C$ and $2.6^{\circ}C$ in 2030s RCP 4.5 and 2050s 8.5 scenarios respectively based on baseline (1976-2005) period. The future land uses were predicted using the CLUE-s model by establishing logistic regression equation. The 2050 urban area were predicted to increase of 58.6% (29.0 to $46.0km^2$). The SWAT was calibrated and verified using 14 years (2002-2015) of daily streamflow with 0.86 and 0.76 Nash-Sutcliffe model efficiency (NSE) for stream flow (Q) and low flow 1/Q respectively focusing on 2 drought years (2014-2015) calibration. For future climate change only, the stream discharge showed maximum decrease of 24.2% in 2030s RCP 4.5 and turned to maximum increase of 10.9% in 2050s RCP 4.5 scenario compared with the baseline period stream discharge of 601.0 mm by the precipitation variation and gradual temperature increase. While considering both future climate and land use change, the stream discharge showed maximum decrease of 14.9% in 2030s RCP 4.5 and maximum increase of 19.5% in 2050s RCP 4.5 scenario by the urban growth and the related land use changes. The results supported that the future land use factor might be considered especially for having high potential urban growth within a watershed in the future climate change assessment.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1285-1300
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• 2022

Turbidity, the measure of the cloudiness of water, is used as an important index for water quality management. The turbidity can vary greatly in small river systems, which affects water quality in national rivers. Therefore, the generation of high-resolution spatial information on turbidity is very important. In this study, a turbidity retrieval model using the Korea Multi-Purpose Satellite-3 and -3A (KOMPSAT-3/3A) images was developed for high-resolution turbidity mapping of Han River system based on eXtreme Gradient Boosting (XGBoost) algorithm. To this end, the top of atmosphere (TOA) spectral reflectance was calculated from a total of 24 KOMPSAT-3/3A images and 150 Landsat-8 images. The Landsat-8 TOA spectral reflectance was cross-calibrated to the KOMPSAT-3/3A bands. The turbidity measured by the National Water Quality Monitoring Network was used as a reference dataset, and as input variables, the TOA spectral reflectance at the locations of in situ turbidity measurement, the spectral indices (the normalized difference vegetation index, normalized difference water index, and normalized difference turbidity index), and the Moderate Resolution Imaging Spectroradiometer (MODIS)-derived atmospheric products(the atmospheric optical thickness, water vapor, and ozone) were used. Furthermore, by analyzing the KOMPSAT-3/3A TOA spectral reflectance of different turbidities, a new spectral index, new normalized difference turbidity index (nNDTI), was proposed, and it was added as an input variable to the turbidity retrieval model. The XGBoost model showed excellent performance for the retrieval of turbidity with a root mean square error (RMSE) of 2.70 NTU and a normalized RMSE (NRMSE) of 14.70% compared to in situ turbidity, in which the nNDTI proposed in this study was used as the most important variable. The developed turbidity retrieval model was applied to the KOMPSAT-3/3A images to map high-resolution river turbidity, and it was possible to analyze the spatiotemporal variations of turbidity. Through this study, we could confirm that the KOMPSAT-3/3A images are very useful for retrieving high-resolution and accurate spatial information on the river turbidity.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.2
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• pp.82-96
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• 2019

This study evaluated the status of watershed health in Geum River Basin by SWAT (Soil and Water Assessment Tool) hydrology and water quality. The watershed healthiness from watershed hydrology and stream water quality was calculated using multivariate normal distribution from 0(poor) to 1(good). Before evaluation of watershed healthiness, the SWAT calibration for 11 years(2005~2015) of streamflow(Q) at 5 locations with 0.50~0.77 average Nash-Sutcliffe model efficiency and suspended solid (SS), total nitrogen(T-N), and total phosphorus(T-P) at 3 locations with 0.67~0.94, 0.59~0.79, and 0.61~0.79 determination coefficient($R^2$) respectively. For 24 years (1985~2008) the spatiotemporal change of watershed healthiness was analyzed with calibarted SWAT and 5 land use data of 1985, 1990, 1995, 2000, and 2008. The 2008 SWAT results showed that the surface runoff increased by 40.6%, soil moisture and baseflow decreased by 6.8% and 3.0% respectively compared to 1985 reference year. The stream water quality of SS, T-N, and T-P increased by 29.2%, 9.3%, and 16.7% respectively by land development and agricultural activity. Based on the 1985 year land use condition. the 2008 watershed healthiness of hydrology and stream water quality decreased from 1 to 0.94 and 0.69 respectively. The results of this study be able to detect changes in watershed environment due to human activity compared to past natural conditions.

• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.875-889
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• 2021

This study compares and analyzes the Soil and Water Assessment Tool (SWAT) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) as coniferous, deciduous and mixed forest with Yongdam Dam upstream (904.4 km²). The hydrologic evaluation period was set to 10 years from 2010 to 2019, and the applicability of the 8-day MOD15A2 Leaf Area Index (LAI) data, 3 TDR (Time Domain Reflectometry) (GB, JC, CC), and 1 Flux Tower (DU) evaporation volume (YDD) data was simulated. As a result, the R² of coniferous forest, deciduous forest and mixed forest are 0.95, 0.89, 0.90, soil moisture and evaportranspiration stations R² were analyzed at 0.50 to 0.55 and 0.51, respectively, with R² at 0.74, RMSE 2.75 mm/day, NSE 0.70 and PBIAS 14.3% for Yongdam inflow. Based on the calibrated and validated watersheds, the annual average evaportranspiration was calculated as coniferous 469.7 mm, deciduous 501. mm and 511.5 mm mixed forest, total runoff were estimated at coniferous 909.8 mm, deciduous 860.6 mm and 864.2 mm mixed forest. In the case of annual average evaportranspiration, it was evaluated that deciduous were high, but in the case of streamflow, it was evaluated that coniferous were high. Unlike other hydrologic with similar patterns throughout the year, the average annual evapotranspiration was about 7% higher than coniferous due to the higher evapotranspiration of deciduous with high leaf area index in summer and fall. In addition, deciduous were 9% and 6% higher for surface runoff and lateral flow, but the groundwater of coniferous was 77% higher. Therefore, it was confirmed that the total runoff was in order of coniferous, mixed forest, and deciduous.

• Korean Journal of Remote Sensing
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• v.30 no.1
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• pp.61-73
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• 2014

This study reviewed an application of water quality estimation using an Airborne Hyperspectral Imagery (A-HSI) and tested a part of Han River water quality (especially suspended solid) estimation with available in-situ data. The estimation of water quality was processed two methods. One is using observation data as downwelling radiance to water surface and as scattering and reflectance into water body. Other is linear regression analysis with water quality in-situ measurement and upwelling data as at-sensor radiance (or reflectance). Both methods drive meaningful results of RS estimation. However it has more effects on the auxiliary dataset as water quality in-situ measurement and water body scattering measurement. The test processed a part of Han River located Paldang-dam downstream. We applied linear regression analysis with AISA eagle hyperspectral sensor data and water quality measurement in-situ data. The result of linear regression for a meaningful band combination shows $-24.847+0.013L_{560}$ as 560 nm in radiance (L) with 0.985 R-square. To comparison with Multispectral Imagery (MSI) case, we make simulated Landsat TM by spectral resampling. The regression using MSI shows -55.932 + 33.881 (TM1/TM3) as radiance with 0.968 R-square. Suspended Solid (SS) concentration was about 3.75 mg/l at in-situ data and estimated SS concentration by A-HIS was about 3.65 mg/l, and about 5.85mg/l with MSI with same location. It shows overestimation trends case of estimating using MSI. In order to upgrade value for practical use and to estimate more precisely, it needs that minimizing sun glint effect into whole image, constructing elaborate flight plan considering solar altitude angle, and making good pre-processing and calibration system. We found some limitations and restrictions such as precise atmospheric correction, sample count of water quality measurement, retrieve spectral bands into A-HSI, adequate linear regression model selection, and quantitative calibration/validation method through the literature review and test adopted general methods.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1181-1191
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• 2013

This study have implemented finding the optimal water temperature parameter set for Hapcheon dam reservoir using CE-QUAL-W2 model. In particular the sensitivity analysis was carried out for four water temperature parameters of wind sheltering coefficient (WSC), radiation heat coefficient (BETA), light extinction coefficient (EXH2O), heat exchange coefficient at the channel bed (CBHE). Firstly, WSC, BETA, EXH2O shows relatively high sensitivity in common during April to September, and CBHE does during August to November. Secondly, as a result of identifying depth range of parameter influence, BETA and EXH2O show 0~9 m and 8~14 m which is thermocline layer close to water surface, CBHE is deep layer 12 m away from bottom. Finally, applying annual or monthly optimal parameter sets indicates that the bias between two sets does not show much differences for WSC and CBHE parameters, but BETA and EXH2O parameters show $0.20^{\circ}C$ and $0.51^{\circ}C$ of monthly average biases for two parameter sets. In particular the bias reveals to be $0.4^{\circ}C$ and $1.09^{\circ}C$ during May and August that confirms the necessity of use of monthly parameters during that season. It is claimed that the current operational custom use of annual parameters in calibration of reservoir water quality model requires the improvement of using monthly parameters.