• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.985-997
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• 2020

SWAT (Soil and Water Assessment Tool)-MODFLOW (Modular Groundwater Flow) is a coupled model that linking semi-distributed watershed hydrology with fully-distributed groundwater behavior. In this study, the groundwater simulation results of SWAT and SWAT-MODFLOW were compared for Bokhacheon watershed in Namhan river basin. The models were calibrated and validated with 9 years (2009~2017) daily streamflow (Q) data of Heungcheon (HC) water level gauge station and the daily groundwater level observation data of Yulheon (YH). For SWAT, the groundwater parameters of GW_DELAY, GWQMN, and ALPHA_BF affecting baseflow and recession phase were treated. The SWAT results showed the coefficient of determination (R²) of 0.7 and Nash-Sutcliffe model efficiencies (NES_Q, NSE_inQ) for Q and 1/Q with 0.73 and -0.1 respectively. For SWAT-MODFLOW, the spatio-temporal aquifer hydraulic conductivity (K, m/day), specific storage (S_s, 1/m), and specific yield (S_y) were applied. The SWAT-MODFLOW showed R², NSE_Q, and NSE_inQ of 0.69, 0.74, and 0.51 respectively. The SWAT-MODFLOW considerably enhanced the low flow simulation with the help of aquifer physical information. The total streamflow of SWAT and SWAT-MODFLOW were 718.6 mm and 854.9 mm occupying baseflow of 342.9 mm and 423.5 mm respectively.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1081-1095
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• 2020

This study is to evaluate stream flow and water quality changes of Yeongsan river basin (3,371.4 km2) by inter-basin water transfer (IBWT) from Juam dam of Seomjin river basin using SWAT (Soil and Water Assessment Tool). The SWAT was established using inlet function for IBWT between donor and receiving basins. The SWAT was calibrated and validated with 14 years (2005 ~ 2018) data of 1 stream (MR) and 2 multi-functional weir (SCW, JSW) water level gauging stations, and 3 water quality stations (GJ2, NJ, and HP) including data of IBWT and effluent from wastewater treatment plants of Yeongsan river basin. For streamflow and weir inflows (MR, SCW, and JSW), the coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), root mean square error (RMSE), and percent bias (PBIAS) were 0.69 ~ 0.81, 0.61 ~ 0.70, 1.34 ~ 2.60 mm/day, and -8.3% ~ +7.6% respectively. In case of water quality, the R2 of SS, T-N, and T-P were 0.69 ~ 0.81, 0.61 ~ 0.70, and 0.54 ~ 0.63 respectively. The Yeongsan river basin average streamflow was 12.0 m3/sec and the average SS, T-N, and T-P were 110.5 mg/L, 4.4 mg/L, 0.18 mg/L respectively. Under the 130% scenario of IBWT amount, the streamflow, SS increased to 12.94 m3/sec (+7.8%), 111.26 mg/L (+0.7%) and the T-N, T-P decreased to 4.17 mg/L (-5.2%), 0.165 mg/L (-8.3%) respectively. Under the 70% scenario of IBWT amount, the streamflow, SS decreased to 11.07 m3/sec (-7.8%), 109.74 mg/L (-0.7%) and the T-N, T-P increased to 4.68 mg/L (+6.4%), 0.199 mg/L (+10.6%) respectively.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.3
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• pp.97-111
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• 2022

This study was conducted as restoration work to improve the discharge in forested wetlands where there is a concern of damage and observed changes in the discharge and groundwater level. The monthly changes showed that during the wet season, the amount of discharge decreased after restoration and GWL increased. It showed that during the dry season, the GWL and discharge increased. The increased discharge after restoration seems to be the difference in the number of days with no rainfall duration. The change in discharge for each unit of rainfall showed a tendency to increase the baseflow and decrease the direct discharge after restoration. The recharge ratio of GWL showed a decreasing tendency as rainfall was higher. After restoration, it showed a higher tendency under rainfall with less than 20mm. It has been confirmed that the restoration implemented by the study caused such an effect as the increased baseflow and increased GWL. It would be an effective restoration method to maintain water resources in forested wetlands. In the initial rainfall, it demonstrated a certain level of effect, but it is necessary to develop a restoration technology that can decrease the amount of water discharged after the end of rainfall or during the period of no rainfall to protect and maintain the forested wetlands. Streamflow should be identified by each type of terrain of wetlands and a proper restoration countermeasure should be devised for the site where the discharge frequently occurs.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• Korean Journal of Agricultural Science
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• v.38 no.3
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• pp.513-524
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• 2011

To evaluate the applicability of inflow runoff model to reservoir operation in Korea, DAWAST model and TPHM model which are conceptual lumped daily runoff model and were developed in Korea, were selected and applied to simulate inflows to Daecheong multipurpose dam with watershed area of 4,134 $km^2$, and water storages in Geryong reservoir with watershed area of 15.1 $km^2$ and total water storage of 3.4 M $m^3$. Evaluating inflows on an yearly, monthly, ten-day, and daily basis, inflows by DAWAST model showed balanced scatters around equal value line. But inflow by TPHM model showed high in high flows. Annual mean water balance by DAWAST model was rainfall of 1,159.9 mm, evapotranspiration of 622.1 mm, and inflow of 644.6 mm, from which rainfall was 104.8 mm less than sum of evapotranspiration and inflow, and showed unbalanced result. Water balance by TPHM model showed satisfactory result. Reservoir water storages were shown to simulate on a considerable level from applying DAWAST and TPHM models to simulate inflows to Geryong reservoir. But it was concluded to be needed to improve DAWAST and TPHM model together from imbalance of water balance and low estimation in high flow.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.287-287
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• 2023

Surface water-groundwater interaction (SWGI) is an important hydrological process that influences both the quantity and quality of water resources. However, regional scale SWGI model calibration and uncertainty analysis have been a challenge because integrated models inherently carry a vast number of parameters, modeling assumptions, and inputs, potentially leaving little time and budget to explore questions related to model performance and forecasting. In this study, we have proposed the application of iterative ensemble smoother (IES) for uncertainty analysis and calibration of the widely used integrated surface-subsurface model, SWAT-MODFLOW. SWAT-MODFLOW integrates Soil and Water Assessment Tool (SWAT) and a three-dimensional finite difference model (MODFLOW). The model was calibrated using a parameter estimation tool (PEST). The major advantage of the employed IES is that the number of model runs required for the calibration of an ensemble is independent of the number of adjustable parameters. The pilot point approach was followed to calibrate the aquifer parameters, namely hydraulic conductivity, specific storage, and specific yield. The parameter estimation process for the SWAT model focused primarily on surface-related parameters. The uncertainties both in the streamflow and groundwater level were assessed. The work presented provides valuable insights for future endeavors in coupled surface-subsurface modeling, data collection, model development, and informed decision-making.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.503-514
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• 2018

In recent, the hydrological regime of the Mekong river is changing drastically due to climate change and haphazard watershed development including dam construction. Information of hydrologic feature like streamflow of the Mekong river are required for water disaster prevention and sustainable water resources development in the river sharing countries. In this study, runoff simulations at the Kratie station of the lower Mekong river are performed using SWAT (Soil and Water Assessment Tool), a physics-based hydrologic model, and LSTM (Long Short-Term Memory), a data-driven deep learning algorithm. The SWAT model was set up based on globally-available database (topography: HydroSHED, landuse: GLCF-MODIS, soil: FAO-Soil map, rainfall: APHRODITE, etc) and then simulated daily discharge from 2003 to 2007. The LSTM was built using deep learning open-source library TensorFlow and the deep-layer neural networks of the LSTM were trained based merely on daily water level data of 10 upper stations of the Kratie during two periods: 2000~2002 and 2008~2014. Then, LSTM simulated daily discharge for 2003~2007 as in SWAT model. The simulation results show that Nash-Sutcliffe Efficiency (NSE) of each model were calculated at 0.9(SWAT) and 0.99(LSTM), respectively. In order to simply simulate hydrological time series of ungauged large watersheds, data-driven model like the LSTM method is more applicable than the physics-based hydrological model having complexity due to various database pressure because it is able to memorize the preceding time series sequences and reflect them to prediction.

• Journal of Korea Water Resources Association
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• v.48 no.4
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• pp.309-319
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• 2015

River characteristics in South Korea has been affected by seasonal climatic variability due to climate change and by remarkable land cover change due to rapid economic growth. In this regard, the roles of river management is getting more important to eco-system and human community in watersheds of South Korea. Understanding river characteristics including direct runoff and baseflow, the first step of river management, can give a significant contribution to sustainable river environment. Therefore, the objective of this study is to quantify the contributions of the direct runoff and baseflow to river streamflow. For this, we used the BFLOW and WHAT programs to conduct baseflow separation for 71 streamflow gauge stations in Han River system, South Korea. The results showed that baseflow index for 71 stations ranges from 0.42 to 0.78. Also, gauge stations which have baseflow index more than 0.5 occupied 76% of a total stations. However, baseflow index can be overestimated due to human impacts such as discharge from dams, reservoirs, and lakes. This study will be used as fundamental information to understand river characteristics in river management at the national level.

• Journal of Korea Water Resources Association
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• v.32 no.2
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• pp.111-120
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• 1999

The relationship between EI Nino-Southern Oscillation(ENSO) and drought in Korea is investigated using the cross correlation analysis. In this paper, Palmer Drought Severity Index(PDSI) is used as an index of drought and nine ENSO indicators are used. To obtain PDSI for Korea, the PDSI equation is derived using monthly precipitation and temperature in Korea. In addition, ENSO composite percentile analyses for PDSI, precipitation and streamflow in Korea are performed to verify the results of the cross correlation. Results of the cross correlation show that the link between drought in Korea and ENSO is statistically significant with 6% of the variance in PDSI for Korea explained by ENSO. The PDSI is negatively correlated with the equatorial Pacific Sea Surface Temperature and the Sea Level Pressure(SLP) at Darwin leading by about 16 months. However, the relationship of the PDSI with the Southern Oscillation Index and the SLP at Tahiti is positive correlation. The ENSO composite percentile analyses show that drought, precipitation and streamflow in Korea are associated with ENSO during 6 months from December of the ENSO ending year

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.215-224
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• 2008

The effect of streamflow considering future land use change and vegetation index information by climate change scenario was assessed using SLURP (Semi-distributed Land-Use Runoff Process) model. The model was calibrated and verified using 4 years (1999-2002) daily observed streamflow data for the upstream watershed ($260.4km^2$) of Gyeongan water level gauging station. By applying CA-Markov technique, the future land uses (2030, 2060, 2090) were predicted after test the comparison of 2004 Landsat land use and 2004 CA-Markov land use by 1996 and 2000 land use data. The future land use showed a tendency that the forest and paddy decreased while urban, grassland and bareground increased. The future vegetation indices (2030, 2060, 2090) were estimated by the equation of linear regression between monthly NDVI of NOAA AVHRR images and monthly mean temperature of 5 years (1998-2002). Using CCCma CGCM2 simulation result based on SRES A2 and B2 scenario (2030s, 2060s, 2090s) of IPCC and data were downscaled by Stochastic Spatio-Temporal Random Cascade Model (SST-RCM) technique, the model showed that the future runoff ratio was predicted from 13% to 34% while the runoff ratio of 1999-2002 was 59%. On the other hand, the impact on runoff ratio by land use change showed about 0.1% to 1% increase.