• 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 CROP SCIENCE
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• v.42 no.5
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• pp.579-596
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• 1997

A simulation study was carried out to delineate potential effects of the lake-induced climate change on crop productivity around Lake Imha which was formed after a multi-purpose dam construction in Andong, Korea. Twenty seven cropping zones were identified within the 30 km by 25 km study area. Five automated weather stations were installed within the study area and operated for five years after the lake formation. A geostatistical method was used to calculate the monthly climatological normals of daily maximum and minimum temperature, solar radiation and precipitation for each cropping zone before and after the dam construction. Daily weather data sets for 30 years were generated for each cropping zone from the monthly normals data representing "No lake" and "After lake" climatic scenarios, respectively. They were fed into crop models (ORYZA1 for rice, SOYGRO for soybean, CERES-maize for corn) to simulate the yield potential of each cropping zone. Calculated daily maximum temperature was higher after the dam construction for the period of October through March and lower for the remaining months except June and July. Decrease in daily minimum temperature was predicted for the period of April through August. Monthly total radiation was predicted to decrease after the lake formation in all the months except February, June, and September and the largest drop was found in winter. But there was no consistent pattern in precipitation change. According to the model calculation, the number of cropping zones which showed a decreased yield potential was 2 for soybean and 6 for corn out of 27 zones with a 10 to 17% yield drop. Little change in yield potential was found at most cropping zones in the case of paddy rice, but interannual variation was predicted to increase after the lake formation. the lake formation.

• Journal of Korea Water Resources Association
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• v.55 no.11
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• pp.931-939
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• 2022

In Korea, about two-thirds of the precipitation is concentrated in the summer season, so the problem of turbidity in the summer flood season varies from year to year. Concentrated rainfall due to abnormal rainfall and extreme weather is on the rise. The inflow of turbidity caused a sudden increase in turbidity in the water, causing a problem of turbidity in the dam reservoir. In particular, in Korea, where rivers and dam reservoirs are used for most of the annual average water consumption, if turbidity problems are prolonged, social and environmental problems such as agriculture, industry, and aquatic ecosystems in downstream areas will occur. In order to cope with such turbidity prediction, research on turbidity modeling is being actively conducted. Flow rate, water temperature, and SS data are required to model turbid water. To this end, the national measurement network measures turbidity by measuring SS in rivers and dam reservoirs, but there is a limitation in that the data resolution is low due to insufficient facilities. However, there is an unmeasured period depending on each dam and weather conditions. As a sensor for measuring turbidity, there are Optical Backscatter Sensor (OBS) and YSI, and a sensor for measuring SS uses equipment such as Laser In-Situ Scattering and Transmissometry (LISST). However, in the case of such a high-tech sensor, there is a limit due to the stability of the equipment. Therefore, there is an unmeasured period through analysis based on the acquired flow rate, water temperature, SS, and turbidity data, so it is necessary to develop a relational expression to calculate the SS used for the input data. In this study, the AEM3D model used in the Water Resources Corporation SURIAN system was used to improve the accuracy of prediction of turbidity through the turbidity-SS relationship developed based on the measurement data near the dam outlet.