• 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.

• Journal of Environmental Policy
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• v.8 no.1
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• pp.73-95
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• 2009

The fact that soil loss causing to increase muddy water and devastate an ecosystem has been appearing upon a hot social and environmental issues which should be solved. Soil losses are occurring in most agricultural areas with rainfall-induced runoff. It makes hydraulic structure unstable, causing environmental and economical problems because muddy water destroys ecosystem and causes intake water deterioration. One of three severe muddy water source areas in Soyanggang-dam watershed is Jawoon-ri region, located in Hongcheon county. In this area, many cash-crops are planted at illegally cultivated agricultural fields, which were virgin forest areas. The purpose of this study is to estimate soil loss with current land uses(including illegal cash-crop cultivation) and soil loss reduction with land use conversion from illegal cultivation back to forest. In this study, the Sediment Assessment Tool for Effective Erosion Control(SATEEC) ArcView GIS was utilized to assess soil erosion. If the illegally cultivated agricultural areas are converted back to forest, it would be expected to 17.42% reduction in soil loss. At the Jawoon-ri region, illegally cultivated agricultural areas located at over 30% and 15% slopes take 47.48 ha(30.83%) and 103.64 ha(67.29%) of illegally cultivated agricultural fields respectively. If all illegally cultivated agricultural fields are converted back to forest, it would be expected that 17.41% of soil erosion and sediment reduction, 10.86% reduction with forest conversion from 30% sloping illegally agricultural fields, and 16.15% reduction with forest conversion from 15% sloping illegally agricultural fields. Therefore, illegally cultivated agricultural fields located at these sloping areas need to be first converted back to forest to maximize reductions in soil loss reduction and muddy water outflow from the Jawoon-ri regions.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.