• Korean Journal of Soil Science and Fertilizer
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• v.38 no.2
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• pp.59-65
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• 2005

This study was performed to estimate the soil loss on a national scale and grade regions with the potential risk of soil erosion. Universal soil loss equation (USLE) for rainfall and runoff erosivity factors (R), cover management factors (C) and support practice factors (P) and revised USLE for soil erodibility factors (K) and topographic factors (LS) were used. To estimate the soil loss, the whole nation was divided into 21,337 groups according to city county, soil phase and land use type. The R factors were high in the southern coast of Gyeongnam and Jeonnam and part of the western coast of Gyeonggi and low in the inland and eastern coast of Gyeongbuk. The K factors were higher in the regions located on the lower streams of rivers and the plain lands of the western coast of Chungnam and Jeonbuk. The average slope of upland areas in Pyeongchang-gun was the steepest of 30.1%. The foot-slope areas from the Taebaek Mountains to the Sobaek Mountains had steep uplands. Total soil loss of Korea was estimated as $50{\times}10^6Mg$ in 2004. The potential risk of soil erosion in upland was the severest in Gyeongnam and the amount of soil erosion was the greatest in Jeonnam. The regions in which annual soil loss was estimated over $50Mg\;ha^{-1}$ were graded as "the very severe" and their acreage was $168{\times}10^3ha$ in 2004. The soil erosion maps of city/county of Korea were made based on digital soil maps with 1:25,000 scale.

• Economic and Environmental Geology
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• v.56 no.2
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• pp.139-153
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• 2023

This study aimed to understand the spatiotemporal variations in nitrogen content in the Gyeongan stream along the main stream and at the discharge points of the sub-basins, and to identify the origin of the nitrogen. Field surveys and laboratory analyses, including chemical compositions and isotope ratios of nitrate and boron, were performed from November 2021 to November 2022. Based on the flow duration curve (FDC) derived for the Gyeongan stream, the dry season (mid-December 2021 to mid-June 2022) and wet season (mid-June to early November 2022) were established. In the dry season, most samples had the highest total nitrogen(T-N) concentrations, specifically in January and February, and the concentrations continued to decrease until May and June. However, after the flood season from July to September, the uppermost subbasin points (Group 1: MS-0, OS-0, GS-0) where T-N concentrations continually decreased were separated from the main stream and lower sub-basin points (Group 2: MS-1~8, OS-1, GS-1) where concentrations increased. Along the main stream, the T-N concentration showed an increasing trend from the upper to the lower reaches. However, it was affected by those of the Osan-cheon and Gonjiamcheon, the tributaries that flow into the main stream, resulting in respective increases or decreases in T-N concentration in the main stream. The nitrate and boron isotope ratios indicated that the nitrogen in all samples originated from manure. Mechanisms for nitrogen inflow from manure-related sources to the stream were suggested, including (1) manure from livestock wastes and rainfall runoff, (2) inflow through the discharge of wastewater treatment plants, and (3) inflow through the groundwater discharge (baseflow) of accumulated nitrogen during agricultural activities. Ultimately, water quality management of the Gyeongan stream basin requires pollution source management at the sub-basin level, including its tributaries, from a regional context. To manage the pollution load effectively, it is necessary to separate the hydrological components of the stream discharge and establish a monitoring system to track the flow and water quality of each component.

• Journal of Korea Water Resources Association
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• v.57 no.7
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• pp.437-449
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• 2024

Rainfall-runoff prediction studies using deep learning while considering catchment attributes have been gaining attention. In this study, we selected two models: the Transformer model, which is suitable for large-scale data training through the self-attention mechanism, and the LSTM-based multi-state-vector sequence-to-sequence (LSTM-MSV-S2S) model with an encoder-decoder structure. These models were constructed to incorporate catchment attributes and predict the inflow of 10 multi-purpose dam watersheds in South Korea. The experimental design consisted of three training methods: Single-basin Training (ST), Pretraining (PT), and Pretraining-Finetuning (PT-FT). The input data for the models included 10 selected watershed attributes along with meteorological data. The inflow prediction performance was compared based on the training methods. The results showed that the Transformer model outperformed the LSTM-MSV-S2S model when using the PT and PT-FT methods, with the PT-FT method yielding the highest performance. The LSTM-MSV-S2S model showed better performance than the Transformer when using the ST method; however, it showed lower performance when using the PT and PT-FT methods. Additionally, the embedding layer activation vectors and raw catchment attributes were used to cluster watersheds and analyze whether the models learned the similarities between them. The Transformer model demonstrated improved performance among watersheds with similar activation vectors, proving that utilizing information from other pre-trained watersheds enhances the prediction performance. This study compared the suitable models and training methods for each multi-purpose dam and highlighted the necessity of constructing deep learning models using PT and PT-FT methods for domestic watersheds. Furthermore, the results confirmed that the Transformer model outperforms the LSTM-MSV-S2S model when applying PT and PT-FT methods.