• Journal of Korea Water Resources Association
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• v.37 no.1
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• pp.21-29
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• 2004

The growth of water resources engineering associated with stable supply, management, development is essential to overcome the coming water deficit of our country. Large scale remote sensing and the analysis of sub-pixel variability of soil moisture fields are necessary in order to understand water cycle and to develop appropriate hydrologic model. The target resolution of coming Global monitoring of soil moisture field is about 10km which is not appropriate for the regional scale hydrologic model. Therefore, we need a downscaling scheme to generate hydrologic variables which are suitable for the regional hydrologic model. The results of the analysis of sub-pixel soil moisture variability show that the relationship between ancillary data and soil moisture fields shows there is very weak linear relationship. A downscaling scheme was developed using physically-based classification scheme and Neural Networks which are able to link the nonlinear relationship between ancillary data and soil moisture fields. The model is demonstrated by downscaling soil moisture fields from 4km to 0.2km resolution using remotely-sensed data from the Washita'92 experiment.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.863-876
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• 2016

Climate change projections for precipitation are in general provided at daily time step. However, sub-daily precipitation data is necessarily required for hydrologic design and management. Thus, a reliable downscaling model is needed to analyze impact of climate change on water resources. While daily downscaling models have been widely developed and applied in hydrologic and climate community, hourly downscaling models have not been properly developed. In this regard, this study aims at developing a hourly downscaling model that can better reproduce sub-daily extreme rainfalls using conditional copula model. The proposed model was applied to generate extreme rainfalls under the RCP 8.5 scenario for weather stations in South Korea, and design rainfalls were then finally provided. We expected that the future design rainfalls can be used for baseline data to evaluate impact of climate change on water resources.

• Proceedings of the Korea Water Resources Association Conference
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• 2012.05a
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• pp.124-124
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• 2012

기후변화가 수자원에 미치는 영향을 예측하는 데에 널리 사용되는 GCMs (General Circulation Models)는 모의 결과의 시 공간적 해상도가 낮기 때문에 상세화 (Downscaling) 기법을 거쳐 수문 모형에 적용된다. 상세화 기법은 크게 역학적 상세화 (Dynamical downscaling)와 통계적 상세화 (Statistical downscaling)로 구분되며, 종류가 매우 다양하고 각각의 모의 능력에 차이가 있으므로 적절한 기법을 선택할 필요가 있다. 본 연구의 목적은 통계적 상세화 기법 중 인공신경망과 LARS-WG 모형을 활용하여 CGCM3.1 T63의 모의 결과를 상세화하고, 두 모형의 모의 결과를 비교하는 데에 있다. 인공신경망은 비선형함수에 의한 전이함수 모형인 반면 LARS-WG는 추계학적 기상 발생기 모형으로, 각 모형을 이용해 CGCM3.1 T63의 강수량 및 평균기온 모의 결과를 서울 지역에 대해 공간적으로 상세화하였다. 모형의 검 보정은 1971년부터 2000년까지 30년 동안의 서울 관측소 일 기상 자료와 CGCM3.1 T63 (20C3M 시나리오) 모의 결과를 이용하여 수행하였다. 각 기법의 비교 및 평가는 2001년부터 2011년까지 11년 동안의 일 기상 자료와 CGCM3.1 T63 (IPCC SRES A1B 시나리오) 모의 결과를 이용하였다. 분석 결과, 인공신경망 모형은 입력 자료의 형태에 따라 모의 결과가 크게 달라지는 특성을 보였으며, LARS-WG 모형은 강수량을 실제보다 과소 추정하는 경향을 보였다. 본 연구에서는 강수량과 평균기온만을 대상으로 하였으나, 추후에 다른 기상인자를 고려함으로써 모형의 적용성을 보다 종합적으로 판단할 수 있을 것이다.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.1173-1178
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• 2002

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.20 no.1
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• pp.34-41
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• 2008

Future impact of anthropogenic climate-induced change on ecological regime has been an issue and information on water temperature is required for estimating coastal aquatic environment. One way to induce water temperature is to relate water temperature to air temperature and GCM is able to provide future air temperature data to do this. However, GCM data of low spatial resolution doesn't incorporate local or sitespecific air temperature in need of application, and downscaling processes are essential. In this study, a linear regression is used to relate nationally averaged air temperature to local area for the time period of 2000-2005. The RMSE for calibration (2000-2005) is 1.584, while the RMSE for validation is 1.675 for the year 2006 and 1.448 for the year 2007. The NSC for calibration (2000-2005) is 0.962, while the NSC for validation is 0.955 for the year 2006 and 0.963 for the year 2007. The results show that the linear regression is a good tool to relate local air temperature to nationally averaged air temperature with $1.0{\sim}2.0^{\circ}C$ of RMSE. The study will contribute to estimate future impact of climate-induced change on aquatic environment in Korean coastal zone.

• Journal of Wetlands Research
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• v.15 no.1
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• pp.115-125
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• 2013

This study established a methodology for the application of downscaling technique in a mountainous area having large spatial variations of rainfall and tried to estimate the change of rainfall characteristics in the future under climate change using the established method. The Namhan river basin, which is in the mountainous area of the Korean peninsula, has been chosen as the study area. Artificial Neural Network - Simple Kriging with varying local means (ANN-SKlm) has been built by combining artificial neural network, which is one of the general downscaling techniques, and SKlm technique, which can reflect the geomorphologic characteristics like elevation of the study area. The evaluation of SKlm technique was done by using the monthly rainfalls at six weather stations which KMA(Korea Meteorological Administration) is managing in the basin. The ANN-SKlm technique was compared with the Thiessen technique and ordinary kriging(OK) technique. According to the evaluation result of each technique the SKlm technique showed the best result.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.327-340
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• 2015

General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the spatio-temporal discrepancy between GCM and observed value, therefore, the models deliver output that are generally required calibration for applied studies. Which is generally done by Multi-Model Ensemble (MME) approach. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a MME of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.29-38
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• 2010

It is necessary to develop an integration model which can account for various data acquired at different measurement scales in environmental thematic mapping with high-resolution ground survey data and relatively low-resolution remote sensing data. This paper presents and applies a multi-scale geostatistical methodology for downscaling of thematic maps generated from lowresolution remote sensing data. This methodology extends a traditional ordinary kriging system to a block kriging system which can account for both ground data and remote sensing data which can be regarded as point and block data, respectively. In addition, stochastic simulation based on block kriging is also applied to describe spatial uncertainty attached to the downscaling. Two downscaling experiments including SRTM DEM and MODIS Leaf Area Index (LAI) products were carried out to illustrate the applicability of the geostatistical methodology. Through the experiments, multi-scale geostatistics based on block kriging successfully generated relatively high-resolution thematic maps with reliable accuracy. Especially, it is expected that multiple realizations generated from simulation would be effectively used as input data for investigating the effects of uncertain input data on GIS model outputs.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.112-115
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• 2009

The research of climate change impact in hydrometeorology often relies on climate change information. In this paper, neural networks models such as support vector machine neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM) are proposed statistical downscaling of the monthly precipitation. The input nodes of neural networks models consist of the atmospheric meteorology and the atmospheric pressure data for 2 grid points including $127.5^{\circ}E/35^{\circ}N$ and $125^{\circ}E/35^{\circ}N$, which produced the best results from the previous study. The output node of neural networks models consist of the monthly precipitation data for Seoul station. For the performances of the neural networks models, they are composed of training and test performances, respectively. From this research, we evaluate the impact of SVM-NNM and MLP-NNM performances for the downscaling of the monthly precipitation data. We should, therefore, construct the credible monthly precipitation data for Seoul station using statistical downscaling method. The proposed methods can be applied to future climate prediction/projection using the various climate change scenarios such as GCMs and RCMs.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.69-79
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• 2013

This paper presents a two-stage geostatistical integration approach that aims at downscaling of coarse scale remote sensing data. First, downscaling of the coarse scale sedoncary data is implemented using area-to-point kriging, and this result will be used as trend components on the next integration stage. Then simple kriging with local varying means that integrates sparse precise observation data with the downscaled data is applied to generate thematic information at a finer scale. The presented approach can not only account for the statistical relationships between precise observation and secondary data acquired at the different scales, but also to calibrate the errors in the secondary data through the integration with precise observation data. An experiment for precipitation mapping with weather station data and TRMM (Tropical Rainfall Measuring Mission) data acquired at a coarse scale is carried out to illustrate the applicability of the presented approach. From the experiment, the geostatistical downscaling approach applied in this paper could generate detailed thematic information at various finer target scales that reproduced the original TRMM precipitation values when upscaled. And the integration of the downscaled secondary information with precise observation data showed better prediction capability than that of a conventional univariate kriging algorithm. Thus, it is expected that the presented approach would be effectively used for downscaling of coarse scale data with various data acquired at different scales.