• Journal of Korea Water Resources Association
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• v.54 no.9
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• pp.719-730
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• 2021

The objective of this study is to develop the water quality and aquatic ecosystem model for Andong lake using SWAT-WET (Soil and Water Assessment Tool-Water Ecosystem Tool) and to evaluate the applicability of WET. To quantify the pollutants load flowing into Andong lake, a watershed model of SWAT was constructed for Andong Dam basin (1,584 km²). The calibration results for Dam inflow and water quality loads (SS, T-N, T-P) were analyzed that average R² was more than 0.76, 0.69, 0.84, and 0.60 respectively. The calibrated SWAT results of streamflow and nutrients concentration was used into WET input data. WET was calibrated and validated for water temperature, dissolved oxygen, and water quality concentration (T-N, T-P) of Andong lake. The WET calibrated results was analyzed that PBIAS was +19%, -13%, +4%, and +26.5% respectively and showed that it was simulated to a significant level compared with the observation data. The observed dry weight (gDW/m²) of zoobenthos was less than 0.5, but the average value of simulation was analyzed to be 0.8, which is because the WET model considers zoobenthos with a broader concept. Although accurate calibration is difficult due to the lack of observed data, SWAT-WET can analyze the effects of environmental change in the upstream watershed on the lake based on long-term simulation based on watershed model. Therefore, the results of this study can be used as basic data for managing the aquatic environment of Andong lake.

• Journal of the Korean Geographical Society
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• v.51 no.6
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• pp.779-797
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• 2016

A calibrated hydrological model is a useful tool for quantifying the impacts of the climate variations and land use/land cover changes on sediment load, water quality and runoff. In the rainy season each year, the Xe Bang Fai river basin is provisionally flooded because of typhoons, the frequency and intensity of which are sensitive to ongoing climate change. Severe heavy rainfall has continuously occurred in this basin area, often causing severe floods at downstream of the Xe Bang Fai river basin. The main purpose of this study is to investigate the climate change impact on river discharge using a Soil and Water Assessment Tool (SWAT) model based on future climate change scenarios. In this study, the simulation of hydrological river discharge is used by SWAT model, covering a total area of $10,064km^2$ in the central part of country. The hydrological model (baseline) is calibrated and validated for two periods: 2001-2005 and 2006-2010, respectively. The monthly simulation outcomes during the calibration and validation model are good results with $R^2$ > 0.9 and ENS > 0.9. Because of ongoing climate change, three climate models (IPSL CM5A-MR 2030, GISS E2-R-CC 2030 and GFDL CM3 2030) indicate that the rainfall in this area is likely to increase up to 10% during the summer monsoon season in the near future, year 2030. As a result of these precipitation increases, the SWAT model predicts rainy season (Jul-Aug-Sep) river discharge at the Xebangfai@bridge station will be about $800m^3/s$ larger than the present. This calibrated model is expected to contribute for preventing flood disaster risk and sustainable development of Laos

• Journal of the Korean Geotechnical Society
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• v.15 no.2
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• pp.165-180
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• 1999

Static load tests were performed for open-ended piles, closed-ended piles, piles with grouted toe, and base-grouted piles by using calibration chamber. Then vertical bearing capacities determined from load tests were compared with each other. The stability of base-grouted pile during a simulated seaquake was investigated by changing the penetration depth. Also, static load tests and seaquake tests for 2-piles and 4-piles group were performed. The bearing capacity of the pile grouted inside the toe was 11.2~30.8% less than that of open-ended pile because of reduction of base resistance due to disturbance of base soil under pile toe. The bearing capacity of a base-grouted pile was 23.8~33.9% more than that of an open-ended pile and was similar to that of a closed-ended pile. The bearing capacity of base-grouted group pile was increased ; the bearing capacity of base-grouted 2-piles group increased 14.6~31.8% compared to that of open-ended 2-piles group, and that of base-grouted 4-piles group increased 15.3~22.4% compared to that of open-ended 4-piles group. During the simulated seaquake in deep sea, stability of base-grouted pile was found to be dependent on the pile penetration depth. During seaquake motion, single long base-grouted pile longer than 20m was stable and short base-grouted pile shorter than 12m failed. But relatively long base-grouted pile longer than 12m kept mobility state. Bearing capacity of base-grouted group pile with penetration depth less than 7m was degraded a little bit ; so, base-grouted group pile could maintain mobility condition.

• 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 the Korean Association of Geographic Information Studies
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• v.23 no.2
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• pp.53-69
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• 2020

The increase of the impermeable area due to industrialization and urban development distorts the hydrological circulation system and cause serious stream drying phenomena. In order to manage this, it is necessary to develop a technology for impact assessment of stream drying phenomena, which enables quantitative evaluation and prediction. In this study, the cause of streamflow reduction was assessed for dam and weir watersheds in the five major river basins of South Korea by using distributed hydrological model DrySAT-WFT (Drying Stream Assessment Tool and Water Flow Tracking) and GIS time series data. For the modeling, the 5 influencing factors of stream drying phenomena (soil erosion, forest growth, road-river disconnection, groundwater use, urban development) were selected and prepared as GIS-based time series spatial data from 1976 to 2015. The DrySAT-WFT was calibrated and validated from 2005 to 2015 at 8 multipurpose dam watershed (Chungju, Soyang, Andong, Imha, Hapcheon, Seomjin river, Juam, and Yongdam) and 4 gauging stations (Osucheon, Mihocheon, Maruek, and Chogang) respectively. The calibration results showed that the coefficient of determination (R²) was 0.76 in average (0.66 to 0.84) and the Nash-Sutcliffe model efficiency was 0.62 in average (0.52 to 0.72). Based on the 2010s (2006~2015) weather condition for the whole period, the streamflow impact was estimated by applying GIS data for each decade (1980s: 1976~1985, 1990s: 1986~1995, 2000s: 1996~2005, 2010s: 2006~2015). The results showed that the 2010s averaged-wet streamflow (Q95) showed decrease of 4.1~6.3%, the 2010s averaged-normal streamflow (Q185) showed decreased of 6.7~9.1% and the 2010s averaged-drought streamflow (Q355) showed decrease of 8.4~10.4% compared to 1980s streamflows respectively on the whole. During 1975~2015, the increase of groundwater use covered 40.5% contribution and the next was forest growth with 29.0% contribution among the 5 influencing factors.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.329-339
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• 2021

Soybeans (Glycine max), one of major upland crops, require precise management of environmental conditions, such as temperature, water, and soil, during cultivation since they are sensitive to environmental changes. Application of spectral technologies that measure the physiological state of crops remotely has great potential for improving quality and productivity of the soybean by estimating yields, physiological stresses, and diseases. In this study, we developed and validated a soybean growth prediction model using multispectral imagery. We conducted a linear regression analysis between vegetation indices and soybean growth data (fresh weight and LAI) obtained at Miryang fields. The linear regression model was validated at Goesan fields. It was found that the model based on green ratio vegetation index (GRVI) had the greatest performance in prediction of fresh weight at the calibration stage (R²=0.74, RMSE=246 g/m², RE=34.2%). In the validation stage, RMSE and RE of the model were 392 g/m² and 32%, respectively. The errors of the model differed by cropping system, For example, RMSE and RE of model in single crop fields were 315 g/m² and 26%, respectively. On the other hand, the model had greater values of RMSE (381 g/m²) and RE (31%) in double crop fields. As a result of developing models for predicting a fresh weight into two years (2018+2020) with similar accumulated temperature (AT) in three years and a single year (2019) that was different from that AT, the prediction performance of a single year model was better than a two years model. Consequently, compared with those models divided by AT and a three years model, RMSE of a single crop fields were improved by about 29.1%. However, those of double crop fields decreased by about 19.6%. When environmental factors are used along with, spectral data, the reliability of soybean growth prediction can be achieved various environmental conditions.