• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.176-176
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• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.1
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• pp.29-41
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• 2017

Design rainfalls are one of the most important hydrologic data for river management, hydraulic structure design and risk analysis. The design rainfalls are first estimated by a point frequency analysis and the IDF (intensity-duration-frequency) curve is then constructed by a nonlinear regression to either interpolate or extrapolate the design rainfalls for other durations which are not used in the frequency analysis. It has been widely recognised that the more reliable approaches are required to better account for uncertainties associated with the model parameters under circumstances where limited hydrologic data are available for the watershed of interest. For these reasons, this study developed a hierarchical Bayesian based GLM (generalized linear model) for a regional frequency analysis in conjunction with a scaling function of the parameters in probability distribution. The proposed model provided a reliable estimation of a set of parameters for each individual station, as well as offered a regional estimate of the parameters, which allow us to have a regional IDF curve. Overall, we expected the proposed model can be used for different aspects of water resources planning at various stages and in addition for the ungaged basin.

• Journal of Korea Water Resources Association
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• v.46 no.6
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• pp.585-596
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• 2013

The objective of this study is to evaluate the hybrid downscaling method combined Step-Wise Scaling (SWS) method with Regional Climate Model (RCM) simulation data for climate change impact study on hydrology area. The SWS method is divided by 3 categories (extreme event, dry event and the others). The extreme events, wet-dry days and the others are corrected by using regression method, quantile mapping method, mean & variance scaling method. The application and evaluation of SWS method with 3 existing and popular statistical techniques (linear scaling method, quantile mapping method and weather generator method) were performed at the 61 weather stations. At the results, the accuracy of corrected simulation data by using SWS are higher than existing 3 statistical techniques. It is expected that the usability of SWS method will grow up on climate change study when the use of RCM simulation data are increasing.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.301-301
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• 2016

국내의 경우 수공구조물을 설계하기 위해서는 빈도해석을 통해 설계수문량을 산정한다. 일반적으로 실무에서는 지점빈도해석을 수행하게 되는데 설계빈도보다 대부분 짧은 기간의 자료를 이용하여 산정한다. 지역빈도해석은 이러한 자료기간이 가지는 문제점을 극복하기 위하여 확률수문량의 정확도와 신뢰도를 향상시키는 기법이다. 스케일 모델은 지속기간별로 관측된 강우자료를 이용하여 재현기간에 대한 지속기간의 함수로 표현이 가능하며, 이를 통해 강우의 IDF곡선을 제시할 수 있는 수학적 모델이다. 대상지역의 강우관측소에서 관측된 강우자료가 일단위이면, 기준지속기간이 24시간이 되며, 기준지속기간에 대한 확률강우량으로부터 임의의 지속기간에 대한 확률강우량을 스케일 모델을 이용하여 추정할 수 있다. 따라서 짧은 자료를 보유한 지역이거나 미계측 지역에 대한 확률강우량을 추정을 위해 지역빈도해석과 지역 스케일 모델을 이용하여 확률강우량을 추정하여 지점빈도해석과 비교하고자 한다. 본 연구를 위해 한강유역의 강우 관측소를 이용하였으며, 군집분석 중 k-means방법을 적용하여 수문학적 동질성을 확보한 후 지역을 구분하였다. 구분된 지역은 지점 및 지역빈도해석을 수행한 후 상대평균제곱근오차(relative root mean square error, RRMSE)를 비교하여 정확도를 판단하였고, 정확도가 높은 빈도해석에 지역 스케일 모델을 적용하여 미계측 지점에 대한 임의의 시간에 대한 확률강우량을 추정하고자 한다.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.343-346
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• 2002

Toward more accurate determination of the water cycle in association with climate variability and change as well as baseline data on the impacts of this variability on water resources, the Coordinated Enhanced Observing Period (CEOP) was launched on July 1,2001. The preliminary data period, EOP-1, was implemented from July to September in 2001. The first annual enhanced observing period, EOP-3, is going to start on October 1,2002. CEOP is seeking to achieve a database of common measurements from both in situ and satellite remote sensing, model output, and four-dimensional data analyses (4DDA; including global and regional reanalyses) for a specified period. In this context a number of carefully selected reference stations are linked closely with the existing network of observing sites involved in the GEWEX Continental Scale Experiments, which are distributed across the world. The initial step of CEOP is to develop a pilot global hydro-climatological dataset with global consistency under the climate variability that can be used to help validate satellite hydrology products and evaluate, develop and eventually predict water and energy cycle processes in global and regional models. Based on the dataset, we will address the studies on the inter-comparison and inter-connectivity of the monsoon systems and regional water and energy budget, and a path to down-scaling from the global climate to local water resources, as the second step.

• Journal of Distribution Science
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• v.21 no.2
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• pp.35-43
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• 2023

Purpose: As Thailand seeks to become a regional startup hub, Thai startups have been acquiring growth and scalability in the last ten years. Hence, this paper examines influential factors in Thailand's growth of logistics tech startups. The conceptual framework incorporates sensing user needs, sensing technological options, conceptualizing, scaling, and stretching, co-producing, and orchestrating, business strategy, strategic flexibility, and startup growth. Research design, data, and methodology: The quantitative method was applied to distribute the questionnaire to 500 managers and above in logistics tech startups in Thailand. The sampling techniques involve judgmental, convenience, and snowball samplings. Before the data collection, The Item Objective Congruence (IOC) Index and pilot test (n=45) were employed for content validity and reliability. The data were mainly analyzed by Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM). Results: The findings revealed that sensing technological options, scaling, and stretching, co-producing, and orchestrating, and business strategy significantly influence the growth of startups in Thailand. Nevertheless, sensing user needs, conceptualizing, and strategic flexibility have no significant relationship with startup growth. Conclusions: For Thailand to accelerate its digital economy driven by tech startups, firms must emphasize influential factors to accelerate growth by providing the right tech solutions for people's lives.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2001.06a
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• pp.151-152
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• 2001

A common problem of the model simulations of the land surface - atmosphere interaction is to choose the appropriate spatial scale and resolution at which the simulations are to be performed. The accuracy of energy and water exchange predictions between the land surface and the atmosphere in regional and global scale atmospheric models is mainly influenced by: model simplifications applied to describe the spatial heterogeneity of land surface properties within individual grid cells; ignoring the variability of sub-grid properties (e.g. relief, vegetation, soils), and; lacks of necessary input meteorological and biophysical data.(omitted)

• Transactions of Materials Processing
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• v.31 no.5
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• pp.296-301
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• 2022

In this study, influence of the process parameters of the roller leveling process on the straightness of the steel wire was analyzed using numerical analysis. To construct the numerical analysis model, cross-sectional and longitudinal element sizes, which affect the prediction accuracy of longitudinal stress caused by bending deformation of the steel wire, were optimized, and mass scaling that satisfies prediction accuracy while reducing computational time was confirmed. By using the constructed numerical analysis model, the influence of various process parameters such as input direction of the steel wire, initial diameter of the steel wire, back tension and intermesh on the straightness was confirmed. The simulation result shows that the 3^rd and 4^th roller of vertical straightener had a significant influence on vertical shape of the steel wire.

• Journal of the Korean Institute of Landscape Architecture
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• v.33 no.3 s.110
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• pp.18-30
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• 2005

Since the 1990s, with the beginning of local autonomy, most Korean cities have tried to establish and reinforce their own identity. The Law on the Planning and the Use of National Land, which took effect in January 2003, requires major and minor cities to manage and develop a city image and a marketing strategy to reflect their current condition. However, many cities continue to experience confusion in implementing urban landscape planning because no efficient and systematic method has been provided for the analysis of a city's image. The absence of systematic analysis methods makes it difficult not only to assess the current condition of a city accurately but also to choose an appropriate policy for the given city. Consequently, many cities suffer excessive trials and errors in implementing the correct policies for their city. The purpose of this study was to analyze the image: of Kongju, which has many historical properties. For this purpose, adjective questionnaires and multi-dimensional scaling (MDS) were made use of in order to assess the city image. The results of this study can be summarized as follows: 1. There are five properties that serve as landmarts lie symbolize Kongju: Muryeong royal tomb, Castle Kong, Mt. Gyeoiryong, Forest Museum, and Kongju National Museum. 2. Based on the citizen survey regarding the city type, Kongju is seen as a historical and an educational city. 3. Based on the image positioning (image spatial plot), Kongju is described as an old and familiar city. There we, however, no landmarks which are in accord with the image of Kongju. It is difficult to establish and reinforce the image of a city with a single element like a landmark However, with steady follow-up research, this study may serve as a systematic and logical model to improve the urban landscape and image.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1121-1130
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• 2012

Projected changes and their impacts on water quality are simulated in response to climate change stressors. CGHR (T63) simulation on the A1B scenario is converted to regional scale data using a statistical down-scaling method and applied to SWAT model to assess water quality impacts in Nakdong River basin. The results demonstrate that rainfall-runoff and pollutant loading in the future (2011~2100) will clearly increase as compared to the last 30-year average. The rate of pollutant loading increase is expected to continue its acceleration until 2040s. Runoff also shows similar patterns to the precipitation, increasing by 60%. Accordingly, the runoff increase results in escalation of pollutant loading by 35~45% for TSS and 5~20% for T-P. This phenomenon is more pronounced in the upper basin during winter and spring season.