• Journal of Korea Water Resources Association
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• v.45 no.9
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• pp.861-874
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• 2012

To fulfill applicability of Soil and Water Assessment Tool (SWAT) model, it is important that this model passes through a careful calibration and uncertainty analysis. In recent years, many researchers have come up with various uncertainty analysis techniques for SWAT model. To determine the differences and similarities of typical techniques, we applied three uncertainty analysis procedures to Chungju Dam watershed (6,581.1 $km^2$) of South Korea included in SWAT-Calibration Uncertainty Program (SWAT-CUP): Sequential Uncertainty FItting algorithm ver.2 (SUFI2), Generalized Likelihood Uncertainty Estimation (GLUE), Parameter Solution (ParaSol). As a result, there was no significant difference in the objective function values between SUFI2 and GLUE algorithms. However, ParaSol algorithm shows the worst objective functions, and considerable divergence was also showed in 95PPU bands with each other. The p-factor and r-factor appeared from 0.02 to 0.79 and 0.03 to 0.52 differences in streamflow respectively. In general, the ParaSol algorithm showed the lowest p-factor and r-factor, SUFI2 algorithm was the highest in the p-factor and r-factor. Therefore, in the SWAT model calibration and uncertainty analysis of the automatic methods, we suggest the calibration methods considering p-factor and r-factor. The p-factor means the percentage of observations covered by 95PPU (95 Percent Prediction Uncertainty) band, and r-factor is the average thickness of the 95PPU band.

• Journal of Korea Water Resources Association
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• v.39 no.7 s.168
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• pp.605-615
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• 2006

In order to analyze movement of soil moisture, Time Domain Reflectometry(TDR) with multiplex system has been installed at the Bumreunsa hillslope of Sulmachun Watershed to configure spatial-temporal variation pattern considering seasonal characteristic. An intensive surveying was performed to build a refined digital elevation model(DEM) and flow determination algorithms with inverse surveying have been applied to establish an efficient soil moisture monitoring system. Soil moisture data were collected through an intensive and long term monitoring 380 hrs in November of 2003 and 1037 hrs in May and June of 2004. Soil moisture data shows corresponding variation characteristics of soil moisture on the up slope, buffer, main channel zones of the hillslope which were classified from terrain analysis. Measured soil moisture data were discussed in conjunction with flow characteristic through terrain analysis. Regardless season, immediate responses of soil moisture about rainfall looks similar but recession and recharge are primary characteristics of intermediate soil moisture variation for spring to summer and fall to winter season, respectively.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.357-370
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• 2014

This study aims to develop a rainfall field tracking method for depth-area-duration (DAD) analysis and assess whether the proposed tracking methods are able to properly estimate the maximum average areal rainfall (MAAR) within the study area during a rainfall period. We proposed three different rainfall field tracking algorithms (Box-tracking, Point-tracking, Advanced point-tracking) and then applied them to the virtual rainfall field with 1hr duration and also compared DAD curves of each method. In addition, we applied the three tracking methods and a traditional GIS-based tool to the typhoon 'Nari' rainfall event of the Yongdam-Dam watershed and then assess applicability of the proposed methods for DAD analysis. The results showed that Box-tracking was much faster than the other two tracking methods in terms of searching for the MAAR but it was impossible to describe rainfall spatial pattern during its tracking processes. On the other hand, both Point-tracking and Advanced point-tracking provided the MAAR by considering the spatial distribution of rainfall fields. In particular, Advanced point-tracking estimated the MAAR more accurately than Point-tracking in the virtual rainfall field, which has two rainfall centers with similar depths. The proposed automatic rainfall field tracking methods can be used as effective tools to analyze DAD relationship and also calculate areal reduction factor.

• Korean Journal of Ecology and Environment
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• v.34 no.2 s.94
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• pp.126-132
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• 2001

Urbanization and population increase result in the construction of STPs (Sewage Treatment Plants). Discharge from STPs greatly influences on the water quality in the stream which receives discharges. The decision of STP location should be considered with the discharge capacity of STP and self-purification of river in the water quality perspectively. In this study, a change of dissolved oxygen (DO) in a river being affected by STP discharge was simulated by the STELLA model. Minimum DO was 4.98 ppm in 42.6 km downstream of STP. Approximately, it takes 8days to recover the DO by the self-purification and this location is 340 km down-stream from the STP. If the model run for the consideration of the self-purification without phytoplankton algorithms, minimum DO was 4.92 ppm. It took 0.25 day longer to be the minimum DO than that with the phytoplankton functions. Without the phytoplankton algorithm, it took 11days to recover the DO. This proves the importance of phytoplankton in the self-purification processes. Additionally, the effect of adjacent STP discharge should be considered in the construction of new STP.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.3
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• pp.177-188
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• 2012

This study was mainly focused on the development of GIS based decision support system to easily make mitigation scenarios and to conveniently simulate water quality for TMDL. The study area was the 31km section of upper Sapgyo stream in Geum river basin, and QUAL2E model was adopted. GIS DB was built through the collection of the data which includes point/non-point source attributes and various thematic maps. The amounts of discharged loads of BOD, T-N and T-P from unit watershed were estimated respectively. Finally, the system, which can operate water quality simulation through simply modifying their values, was developed. The hypothetical three mitigation scenarios were applied, thereby the most efficient mitigation scenario could be chosen by comparison of the results based on GIS. Therefore, it is expected that the developed system can facilitate the decision makers to select the best alternative through the analysis of the available BMPs. Also, it can be used to develop new scenarios using different methods and algorithms. In the future, more study need to be made to enhance its applicability in the perspective of developing mitigation scenarios through the management of individual pollutant sources and extending study areas.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.471-479
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• 2024

Flood damage is becoming more serious due to the heavy rainfall caused by climate change. Physically based hydrological models have been utilized to predict stream water level variability and provide flood forecasting. Recently, hydrological simulations using machine learning and deep learning algorithms based on nonlinear relationships between hydrological data have been getting attention. In this study, the Long Short-Term Memory (LSTM) algorithm is used to predict the water level of the Seomjin River watershed. In addition, Climate Prediction Center morphing method (CMORPH)-based gridded precipitation data is applied as input data for the algorithm to overcome for the limitations of ground data. The water level prediction results of the LSTM algorithm coupling with the CMORPH data showed that the mean CC was 0.98, RMSE was 0.07 m, and NSE was 0.97. It is expected that deep learning and remote data can be used together to overcome for the shortcomings of ground observation data and to obtain reliable prediction results.