• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.1
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• pp.73-80
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• 2016

The aim of this study is to analyze the contribution of regional water resources through the gap between water used for rice production and water used for consumption. The blue water use for rice production and for consumption was quantified and the regional blue water flows were estimated using the virtual water concept from 1995 to 2010. About $1134.4Mm^3/yr$ of blue water flowed among the provinces and metropolises of Korea, and about 28.5 % of total blue water flows came from Jeonnam province. In addition, blue water usage for rice was classified into three categories: water for production, internal consumption, and overproduction in each region. In Jeonnam, $633.8Mm^3/yr$ of blue water totally used for rice production, and 50.9 % and 15.5 % were used for external and internal rice consumption, respectively. The other 33.6 % was used for over production of rice for food security. This study assumed the blue water flows depended on the gap between virtual water use for rice production and consumption. However, the analysis of regional blue water usage and flows might show the importance of other region's water resources, and make policy decision-makers aware of the integrated water management among the regions.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.211-211
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• 2020

The concept of virtual water has been largely applied in the study of regional, national, and global water flows with particular emphasis on water scarcity. Despite water traditionally being managed locally, certain global forces influence the local water resource scarcity/availability and hence virtual water exchanges worldwide. It is therefore of necessity that the significant forces be examined to understand the relationship between available water in a region and the variability and trends in environmental, social, and economic factors that are of utmost importance in the formulation of water resources management policies. This study therefore reviewed recent literature from 2003 - 2019 to determine the significant indicators of virtual water trade at different spatiotemporal levels. The study examined and compared the major approaches to virtual water trade flows accounting, and also identified and discussed policy implications and future research options concerning the analysis of virtual water trade. Available information has shown that virtual water trade is significantly influenced by economic (GDP, Demand-Supply of goods and services), geographical (Distance), institutional (population) and environmental (water availability, arable land, precipitation) factors. Reports further show that the selection of a given approach for virtual water trade flows accounting will depend on the scope of the study, the available datasets, and other research preferences. Accordingly, this study suggests that the adoption of multidisciplinary approaches to virtual water trade, taking into consideration the spatial and temporal variations in water resources availability and the complexity of environmental and socioeconomic factors will be pivotal for establishing the basis for the conservation of water resources worldwide.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.85-97
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• 2017

In recent years, the damage scales of water disasters such as typhoons, tsunamis, and heavy snow have been increasing globally as a result of global warming and climate changes. In particular, the economic loss caused by typhoons has been increasing for overpopulated areas that have undergone economic development and urbanization since the 1960s. In this study, we investigated and analyzed satellite images captured before and after typhoons on the Korean peninsula, including Typhoon Chaba (2016), Typhoon Rusa ('02), and Typhoon Maemi ('03). There was a limitation in utilizing existing satellites. Domestic satellites have mostly been developed and operated for the observation of the weather, ocean, and topography, as well as for use in communication. There are therefore insufficient temporal and spatial observations for water management and disaster response. In this work, we expanded the scope to overseas satellites and collected data from GMS, TRMM, COMS, and GPM. In the future, it will be necessary to develop and launch water resources satellites that can provide sufficient temporal and spatial data analysis units to obtain rapid and accurate water hazard information for the Korean peninsula.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1273-1281
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• 1993

Estimation of daily and seasonal evaportranspiration is essential for water resource planning irrigation feasibility study, and real-time irrigation water management . This paper is to evaluate the applicability of neural networks to the estimation of evapotranspiration . A neural network was developed to forecast daily evapotranspiration of the rice crop. It is a three-layer network with input, hidden , and output layers. Back-propagation algorithm with delta learning rule was used to train the neural network. Training neural network wasconducted usign daily actural evapotranspiration of rice crop and daily climatic data such as mean temperature, sunshine hours, solar radiation, relative humidity , and pan evaporation . During the training, neural network parameters were calibrated. The trained network was applied to a set of field data not used in the training . The created response of the neural network was in good agreement with desired values. Evaluating the neural networ performance indicates that neural network may be applied to the estimation of evapotranspiration of the rice crop.

• Journal of the Korean Society of Hazard Mitigation
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• v.7 no.5
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• pp.159-166
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• 2007

Large scale flood disasters bring human losses and properties, which lead to the decrease of our productive value and change social environment. Human loss and economic damage are considered to be the same system but they are viewed as separated systems. The total amount of human loss can be represented as the total amount of economic damage estimated in the frame of social system while it will be possible to make mutual changing by clearing the relations between social and economic systems. In this regard, an attempt to estimate economic loss considering per capita Gross Domestic Production (GDP) caused by flood-related mortality was carried out to the typhoon Rusa of 2002 in Kangwon-do. The proposed method tried to capture quantitative factors which are affecting the loss of per capita GDP. The approach has great importance not only to set up governmental policy but also methodological progress in the research due to impact of disaster-related mortality on GDP loss.

• Korean Journal of Environmental Agriculture
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• v.24 no.3
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• pp.303-313
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• 2005

Agricultural environmental indicators (AEIs) are useful tool for evaluating environmental performance induced by agri-environmental policy measures. General and specific criteria have been set to assess the linkages between policy measures and environmental states. In addition, a number of specific AEIs such as nutrient balance indicators and farm management indicators have been posit to review environmental performance associated with agri-environmental policy measures. The proposed environmental subjects encompass soil quality, qualities of underground and surface water, water resource preservation, species and genetic diversity, diversity for wildlife habitats, and agricultural landscapes. The developed AEIs may contribute to establishment or adjustment of environmental targets and ex-ante or ex-post evaluation for environmental performance associated with policy measures. In addition, the AEIs may be useful to consider introduction of new agri-environmental measures and enhance policy efficiency by assessing environmental performance, considering specific locality, and harmonizing support measures.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1107-1118
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• 2021

Climate change brought on by global warming increased the frequency of flood and drought on the Korean Peninsula, along with the casualties and physical damage resulting therefrom. Preparation and response to these water disasters requires national-level planning for water resource management. In addition, watershed-level management of water resources requires flow duration curves (FDC) derived from continuous data based on long-term observations. Traditionally, in water resource studies, physical rainfall-runoff models are widely used to generate duration curves. However, a number of recent studies explored the use of data-based deep learning techniques for runoff prediction. Physical models produce hydraulically and hydrologically reliable results. However, these models require a high level of understanding and may also take longer to operate. On the other hand, data-based deep-learning techniques offer the benefit if less input data requirement and shorter operation time. However, the relationship between input and output data is processed in a black box, making it impossible to consider hydraulic and hydrological characteristics. This study chose one from each category. For the physical model, this study calculated long-term data without missing data using parameter calibration of the Soil Water Assessment Tool (SWAT), a physical model tested for its applicability in Korea and other countries. The data was used as training data for the Long Short-Term Memory (LSTM) data-based deep learning technique. An anlysis of the time-series data fond that, during the calibration period (2017-18), the Nash-Sutcliffe Efficiency (NSE) and the determinanation coefficient for fit comparison were high at 0.04 and 0.03, respectively, indicating that the SWAT results are superior to the LSTM results. In addition, the annual time-series data from the models were sorted in the descending order, and the resulting flow duration curves were compared with the duration curves based on the observed flow, and the NSE for the SWAT and the LSTM models were 0.95 and 0.91, respectively, and the determination coefficients were 0.96 and 0.92, respectively. The findings indicate that both models yield good performance. Even though the LSTM requires improved simulation accuracy in the low flow sections, the LSTM appears to be widely applicable to calculating flow duration curves for large basins that require longer time for model development and operation due to vast data input, and non-measured basins with insufficient input data.

• Korean Journal of Remote Sensing
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• v.39 no.4
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• pp.425-440
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• 2023

Monitoring water surface has become one of the most prominent areas of research in addressing environmental challenges.Accurate and automated detection of watersurface in remote sensing imagesis crucial for disaster prevention, urban planning, and water resource management, particularly for a country where water plays a vital role in human life. However, achieving precise detection poses challenges. Previous studies have explored different approaches,such as analyzing water indexes, like normalized difference water index (NDWI) derived from satellite imagery's visible or infrared bands and using k-means clustering analysis to identify land cover patterns and segment regions based on similar attributes. Nonetheless, challenges persist, notably distinguishing between waterspectralsignatures and cloud shadow or terrain shadow. In thisstudy, our objective is to enhance the precision of water surface detection by constructing a comprehensive water database (DB) using existing digital and land cover maps. This database serves as an initial assumption for automated water index analysis. We utilized 1:5,000 and 1:25,000 digital maps of Korea to extract water surface, specifically rivers, lakes, and reservoirs. Additionally, the 1:50,000 and 1:5,000 land cover maps of Korea aided in the extraction process. Our research demonstrates the effectiveness of utilizing a water DB product as our first approach for efficient water surface extraction from satellite images, complemented by our second and third approachesinvolving NDWI analysis and k-means analysis. The image segmentation and binary mask methods were employed for image analysis during the water extraction process. To evaluate the accuracy of our approach, we conducted two assessments using reference and ground truth data that we made during this research. Visual interpretation involved comparing our results with the global surface water (GSW) mask 60 m resolution, revealing significant improvements in quality and resolution. Additionally, accuracy assessment measures, including an overall accuracy of 90% and kappa values exceeding 0.8, further support the efficacy of our methodology. In conclusion, thisstudy'sresults demonstrate enhanced extraction quality and resolution. Through comprehensive assessment, our approach proves effective in achieving high accuracy in delineating watersurfaces from satellite images.

• Korean Journal of Environment and Ecology
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• v.33 no.4
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• pp.453-461
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• 2019

This study was conducted to monitor the soil characteristics and growth of Pinus densiflora and to determine the effect of soil characteristics on growth rate five years after an ecological restoration project in Baekdudaegan ridge including Ihwaryeong, Yuksimnyeong, and Beoljae sites. The ecological restoration project was executed with the forest of P. densiflora in 2012-2013. In April 2018, we collected soil samples from each site and measured the height and the diameter at breach height (DBH) of P. densiflora. Although there was no significant change of soil pH compared to the early stage of restoration (one year after the project), it was high in Ihwaryeong, and Beoljae with values of 7.7 and 6.4, respectively. Also, the organic matter decreased by 70-80%, and the available phosphorus (P) was unchanged in three restoration sites. The decreased organic matter can be attributed to restriction of inflow and thus decomposition of litter in the early stage after the restoration. The tree height growth rate ($m\;yr^{-1}$) of P. densiflora in Yuksimnyeong was the highest at 1.02, followed by Beolja at 0.75 and Ihwaryeong at 0.17. The height growth rate showed negative relationships with soil pH and cations, including Na and Ca concentrations and a positive relationship with available phosphate. The low growth rate in the Ihwaryeong site, in particular, might result from the poor nutrient availability due to high soil pH and the decrease in water absorption into the root due to high Na and Ca concentrations. The substantial reduction of organic matter after five years indicates that the need for soil improvement using chemical fertilizer and biochar.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.233-246
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• 2023

In terms of understanding the water cycle and efficient water resource management, the importance of soil moisture has been highlighted. However, in Korea, the lack of qualified in-situ soil moisture data results in very limited utility. Even if satellite-based data are applied, the absence of ground reference data makes objective evaluation and correction difficult. The cosmic-ray neutron probe (CRNP) can play a key role in producing data for satellite data calibration. The installation of CRNP is non-invasive, minimizing damage to the soil and vegetation environment, and has the advantage of having a spatial representative for the intermediate scale. These characteristics are advantageous to establish an observation network in Korea which has lots of mountainous areas with dense vegetation. Therefore, this study was conducted to evaluate the applicability of the CRNP soil moisture observatory in Korea as part of the establishment of a Korean cOsmic-ray Soil Moisture Observing System (KOSMOS). The CRNP observation station was installed with the Gunup-ri observation station, considering the ease of securing power and installation sites and the efficient use of other hydro-meteorological factors. In order to evaluate the CRNP soil moisture data, 12 additional in-situ soil moisture sensors were installed, and spatial representativeness was evaluated through a temporal stability analysis. The neutrons generated by CRNP were found to be about 1,087 counts per hour on average, which was lower than that of the Solmacheon observation station, indicating that the Hongcheon observation station has a more humid environment. Soil moisture was estimated through neutron correction and early-stage calibration of the observed neutron data. The CRNP soil moisture data showed a high correlation with r=0.82 and high accuracy with root mean square error=0.02 m³/m³ in validation with in-situ data, even in a short calibration period. It is expected that higher quality soil moisture data production with greater accuracy will be possible after recalibration with the accumulation of annual data reflecting seasonal patterns. These results, together with previous studies that verified the excellence of CRNP soil moisture data, suggest that high-quality soil moisture data can be produced when constructing KOSMOS.