• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.6-7
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• 2021

Vegetation development in rivers is one of the important issues not only in academic fields such as geomorphology, ecology, hydraulics, etc., but also in river management practices. The problem of river vegetation is directly connected to the harmony of conflicting values of flood management and ecosystem conservation. In Korea, since the 2000s, the issue of river vegetation and land formation has been continuously raised under various conditions, such as the regulating rivers downstream of the dams, the small eutrophicated tributary rivers, and the floodplain sites for the four major river projects. In this background, this study proposes a method for classifying the distribution of vegetation in rivers based on remote sensing data, and presents the results of applying this to the Naeseong Stream. The Naeseong Stream is a representative example of the river landscape that has changed due to vegetation development from 2014 to the latest. The remote sensing data used in the study are images of Sentinel 1 and 2 satellites, which is operated by the European Aerospace Administration (ESA), and provided by Google Earth Engine. For the ground truth, manually classified dataset on the surface of the Naeseong Stream in 2016 were used, where the area is divided into eight types including water, sand and herbaceous and woody vegetation. The classification method used a random forest classification technique, one of the machine learning algorithms. 1,000 samples were extracted from 10 pre-selected polygon regions, each half of them were used as training and verification data. The accuracy based on the verification data was found to be 82~85%. The model established through training was also applied to images from 2016 to 2020, and the process of changes in vegetation zones according to the year was presented. The technical limitations and improvement measures of this paper were considered. By providing quantitative information of the vegetation distribution, this technique is expected to be useful in practical management of vegetation such as thinning and rejuvenation of river vegetation as well as technical fields such as flood level calculation and flow-vegetation coupled modeling in rivers.

• Archives of design research
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• v.20 no.1 s.69
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• pp.5-20
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• 2007

The aim of this research is to find out the flows of mega-trends and design trends by analyzing the factors that influence trend and design trends in the late 20th century. Moreover, it is to forecast and recommend design color trends by evaluating color trends in design trends for the near future. Secondary and primary research were used in parallel. In the late 20th century, mega-trends were analyzed from secondary research based on PEST. Design trends were analyzed from case studies in fashion, space, product and visual design. On this basis, design color trends were analyzed. Also, color trends were forecast for the near future. The results are as follows. Firstly, the main trends in the late 20th century were 'female thinking', 'back to the nature' and 'heaven of peace'. Second, main design trends in the 1970s were modernism, post-modernism and high-tech. In the 1980s, with those of the 1970s, ecology was introduced In the late 1980s. In the 1990s, modernism rose again and ecology had an influence. The trends of 'female thinking' and 'back to the nature' controled the design in the early 2000s. Third, design colors in the late 20th century changed from Red to Purple Blue. Tones changed from 'grayish' to 'dull' Finally, it was forecast that Purple Blue, Yellow Red and Green colors with 'grayish', 'dull' and 'deep' tones were going to be used mainly in the near future. Also, achromatic colors with female and warm nuances would be reflected in design parts. This research will be very useful in that it has built a concrete database reflected on design trends forecasting in the near future by organizing academically a methodology to identify trends reflected on design and identifying relation between mega-trends and design trends based on analyzing factors that influence trend.

• Journal of Korea Water Resources Association
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• v.49 no.6
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• pp.551-563
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• 2016

According to the 4th and 5th assessment of the Intergovernmental Panel on Climate Change (IPCC), global climate has been rapidly changing because of the human activities since Industrial Revolution. The perceived changes were appeared strongly in temperature and concentration of carbon dioxide ($CO_2$). Global average temperature has increased about $0.74^{\circ}C$ over last 100 years (IPCC, 2007) and concentration of $CO_2$ is unprecedented in at least the last 800,000 years (IPCC, 2014). These phenomena influence precipitation, evapotranspiration and soil moisture which have an important role in hydrology, and that is the reason why there is a necessity to study climate change. In this study, Asia region was selected to simulate primary energy index from 1951 to 2100. To predict future climate change effect, Common Land Model (CLM) which is used for various fields across the world was employed. The forcing data was Representative Concentration Pathway (RCP) data which is the newest greenhouse gas emission scenario published in IPCC 5th assessment. Validation of net radiation ($R_n$), sensible heat flux (H), latent heat flux (LE) for historical period was performed with 5 flux tower site-data in the region of AsiaFlux and the monthly trends of simulation results were almost equaled to observation data. The simulation results for 2006-2100 showed almost stable net radiation, slightly decreasing sensible heat flux and quite increasing latent heat flux. Especially the uptrend for RCP 8.5 has been about doubled compared to RCP 4.5 and since late 2060s, variations of net radiation and sensible heat flux would be significantly risen becoming an extreme climate condition. In a follow-up study, a simulation for energy index and hydrological index under the detailed condition will be conducted with various scenario established from this study.

• Journal of Korea Water Resources Association
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• v.42 no.11
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• pp.911-920
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• 2009

Virtual water is defined as the volume of water required to produce a commodity or service. The degree of food self-sufficiency is currently about 27 % in South Korea, so that Korea is one of the largest net virtual water import countries for agricultural product, thus it is necessary to estimate suitable virtual water for South Korea. The objective of this paper is to quantify the agricultural virtual water use (AWU) and virtual water content (VWC) using the method suggested by Chapagain and Hoekstra during the period 1991-2007. To calculate the virtual water content, 44 different crop production quantity and harvested area data were collected for 17 years and FAO Penman-Monteith equation was adapted for computing crop consumptive use of water. As the results, AWU has been estimated at 15.1 billion $m^3$ in average showing a tendency to decrease. Rice has the largest share in the AWU, consuming about 10.1 billion $m^3$/yr which is about 75 % of gross AWU, and the VWC is 1600.1 $m^3$/ton for paddy rice. The largest VWCs of crops are oilseed and tuber crop, and the smallest are leaf and root vegetables. The primary crop production VWC can be used for calculating the VWC of various secondary products using the contribution ratio, therefore the results of this study are expected to be used as basic data for national agricultural water footprint.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.833-842
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• 2013

The quantile mapping is utilized to reproduce reliable GCM(Global Climate Model) data by correct systematic biases included in the original data set. This scheme, in general, projects the Cumulative Distribution Function (CDF) of the underlying data set into the target CDF assuming that parameters of target distribution function is stationary. Therefore, the application of stationary quantile mapping for nonstationary long-term time series data of future precipitation scenario computed by GCM can show biased projection. In this research the Nonstationary Quantile Mapping (NSQM) scheme was suggested for bias correction of nonstationary long-term time series data. The proposed scheme uses the statistical parameters with nonstationary long-term trends. The Gamma distribution was assumed for the object and target probability distribution. As the climate change scenario, the 20C3M(baseline scenario) and SRES A2 scenario (projection scenario) of CGCM3.1/T63 model from CCCma (Canadian Centre for Climate modeling and analysis) were utilized. The precipitation data were collected from 10 rain gauge stations in the Han-river basin. In order to consider seasonal characteristics, the study was performed separately for the flood (June~October) and nonflood (November~May) seasons. The periods for baseline and projection scenario were set as 1973~2000 and 2011~2100, respectively. This study evaluated the performance of NSQM by experimenting various ways of setting parameters of target distribution. The projection scenarios were shown for 3 different periods of FF scenario (Foreseeable Future Scenario, 2011~2040 yr), MF scenario (Mid-term Future Scenario, 2041~2070 yr), LF scenario (Long-term Future Scenario, 2071~2100 yr). The trend test for the annual precipitation projection using NSQM shows 330.1 mm (25.2%), 564.5 mm (43.1%), and 634.3 mm (48.5%) increase for FF, MF, and LF scenarios, respectively. The application of stationary scheme shows overestimated projection for FF scenario and underestimated projection for LF scenario. This problem could be improved by applying nonstationary quantile mapping.

• Journal of Korea Water Resources Association
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• v.46 no.8
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• pp.821-831
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• 2013

Recently image velocimetries, including particle image velocimetry (PIV) and surface image velocimetry (SIV), are often used to measure flow velocities in laboratories and rivers. The most difficult point in using image velocimetries may be how to determine the sizes of the interrogation areas and the measurement uncertainties. Especially, it is a little hard for unskilled users to use these instruments, since any standardized measuring techniques or measurement uncertainties are not well evaluated. Sometimes the user's skill and understanding on the instruments may make a wide gap between velocity measurement results. The present study aims to evaluate image velocimetry's uncertainties due to the changes in the sizes of interrogation areas and searching areas with the error analyses. For the purpose, we generated 12 series of artificial images with known velocity fields and various numbers and sizes of particles. The analysis results showed that the accuracy of velocity measurements of the image velocimetry was significantly affected by the change of the size of interrogation area. Generally speaking, the error was reduced as the size of interrogation areas became small. For the same sizes of interrogation areas, the larger particle sizes and the larger number of particles resulted smaller errors. Especially, the errors of the image velocimetries were more affected by the number of particles rather than the sizes of them. As the sizes of interrogation areas were increased, the differences between the maximum and the minimum errors seemed to be reduced. For the size of the interrogation area whose average errors were less than 5%, the differences between the maximum and the minimum errors seemed a little large. For the case, in other words, the uncertainty of the velocity measurements of the image velocimetry was large. In the viewpoint of the particle density, the size of the interrogation area was small for large particle density cases. For the cases of large number of particle and small particle density, however, the minimum size of interrogation area became smaller.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.965-972
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• 2017

Assessment of vulnerability of water use to climate change include a variety of climate change scenarios. However, in most future vulnerability studies, only the climate change scenarios are used and not the future scenarios of social and economic indicators. Therefore, in this study, we applied the Representative Concentration Pathway (RCP) climate change scenario and Shared Socioeconomic reference Pathway (SSP) developed by IPCC to reflect the future. We selected indicators for estimating the vulnerability of water use, and indices were integrated with a multi-criteria decision making approach - Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The indicator data utilized national statistics and reports, social and economic scenarios, and simulated results from the Soil and Water Assessment Tool (SWAT) model which reflects climate change scenario. Finally, we derived the rankings of water use vulnerability for the short-term future (2020) and mid-term future (2050) within the Han River watershed. Generally, considering climate change alone and considering climate change plus social and economic changes showed a similar spatial distribution. In the future scenarios, the watershed rankings were similar, but showed differences with SSP scenario in some watersheds. Therefore, considering social and economic changes is expected to contribute to more effective responses to climate change.

• Journal of Environmental Impact Assessment
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• v.26 no.4
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• pp.227-241
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• 2017

A human in urban areas has depended on ecosystem for well-being, so it is important to evaluate urban ecosystem services which contribute significantly to human well-being. In this study we classified ecosystem functions and set indicators used for evaluating ecosystem services of urban park by Delphi method. As a result, it derived 12 items and 14 indicators of ecosystem services to evaluate them such as vegetable garden, canopy cover, biodiversity, and educational programs. Based on the derived evaluation indicators, the feasibility of the indicators was examined by applying to two urban parks, Maetan park and Seoho-Ggotme park, in Suwon City. We also suggested strategies to improve each ecosystem services based on the results of evaluation. It is significant to recognize unknown services in urban parks. The results can be used for improving urban ecosystem services consistently in response to current rapid urbanization. In the future, the city should make a master plan on ecosystem service on urban area, beyond urban park, considering both of quality and quantity.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.3
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• pp.95-106
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• 2003

This study evaluated the architectural density of urban district using airborne laser scanning(ALS) that is a method used in urban planning, water resources and disaster prevention with high interest recently. First, digital elevation model(DEM) and digital surface model(DSM) was constructed from Light detection and ranging(LiDAR). For getting the height of building, ZONALMEAN filter was used in DEM and ZONALMAJORITY filter was used in DSM. This study compared the floor from filtering with the floor from survey and got standard error, which is ${\pm}0.199$ floor. Also, through the overlay and statistical analysis of total-area layer and zone layer, we could present floor area ratio by zone. As a result of comparison with floor area ratio between airborne laser scanning data and survey data, the standard error of floor area ratio shows ${\pm}2.68%$. Therefore, we expect that airborne laser scanning data can be a very efficient source to decision makers who set up landuse plan in near future.