• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.2
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• pp.15-26
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• 2015

The purpose of this paper is to develop a software tool, PGA-CC (Projection of hydrology via Grid-based Assessment for Climate Change) to evaluate the present hydrologic cycle and the future watershed hydrology by climate change. PGA-CC is composed of grid-based input data pre-processing module, hydrologic cycle calculation module, output analysis module, and output data post-processing module. The grid-based hydrological model was coded by Fortran and compiled using Compaq Fortran 6.6c, and the Graphic User Interface was developed by using Visual C#. Other most elements viz. Table and Graph, and GIS functions were implemented by MapWindow. The applicability of PGA-CC was tested by assessing the future hydrology of South Korea by HadCM3 SRES B1 and A2 climate change scenarios. For the whole country, the tool successfully assessed the future hydrological components including input data and evapotranspiration, soil moisture, surface runoff, lateral flow, base flow etc. From the spatial outputs, we could understand the hydrological changes both seasonally and regionally.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.69-82
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• 2023

Representative meteorological data of the rural water district, which is the spatial unit of the study, was produced using the grid-based national standard RCP scenario rainfall data provided by the Korea Meteorological Administration. The retrospective reproducibility of the climate model scenario data was analyzed, and the change in climate characteristics in the water district unit for the future period was presented. Finally the data characteristics and differences of each meteorological element according to various spatial resolution conversion and post-processing methods were examined. As a main result, overall, the distribution of average precipitation and R95p of the grid data, has reasonable reproducibility compared to the ASOS observation, but the maximum daily rainfall tends to be distributed low nationwide. The number of rainfall days tends to be higher than the station-based observation, and this is because the grid data is generally calculated using the area average concept of representative rainfall data for each grid. In addition, in the case of coastal regions, there is a problem that administrative districts of islands and rural water districts do not match. and In the case of water districts that include mountainous areas, such as Jeju, there was a large difference in the results depending on whether or not high rainfall in the mountainous areas was reflected. The results of this study are expected to be used as foundation for selecting data processing methods when constructing future meteorological data for rural water districts for future agricutural water management plans and climate change vulnerability assessments.

• International Journal of Contents
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• v.17 no.1
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• pp.61-70
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• 2021

As the resolution of climate change scenario data applied with regional models increased, Earth System Grid Federation (ESGF) was established around major climate-related organizations to jointly operated and manage large-scale climate data. ESGF developed standard software to provide model output, observation data management, dissemination, and analysis using Peer to Peer (P2P) computing technology. Roles of each institution were divided into index and data nodes. Therefore, ESGF data node was established at APEC Climate Center in Korea on behalf of Asia to share data on climate change scenarios of CORDEX-East Asia (CORDEX-EA) to study climate changes in Eastern Asia. Climate researchers are expected to play a large role in researching causes of global warming and responding to climate change by providing CORDEX-EA regional model data to the world through ESGF data node.

• Atmosphere
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• v.23 no.2
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• pp.237-243
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• 2013

A set of scripts for GrADS (Grid Analysis and Display System) and R was implemented to produce agricultural climate information using the future climate scenarios based on the Representative Concentration Pathways. The GrADS script was used to calculate agricultural climate indices including growing degree days and cooling degree days. The script generated agricultural climate maps of these indices, which are compatible with common Geographic Information System (GIS) applications. To perform a statistical analysis using the agricultural climate maps, a script for R, which is open source statistical software, was used. Because a large number of spatial climate data were produced, parallel processing packages such as SNOW, doSNOW, and foreach were used to perform a simple statistical analysis in the R script. The parallel script of R had speedup on workstations with multi-CPU cores.

• Journal of Environmental Impact Assessment
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• v.27 no.5
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• pp.447-474
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• 2018

The purpose of this study is to build a new dataset of spatially interpolated climate data of South Korea by performing various geo-statistical interpolation techniques for comparison with the LDAPS grid data of KMA. Among 595 observation data in 2017, 80 % of the total points and remaining 117 points were used for spatial mapping and quantification,respectively. IDW, cokriging, and kriging were performed via the ArcGIS10.3.1 software and Python3.6.4, and each result was then divided into three clusters and four watersheds for statistical verification. As a result, cokriging produced the most suitable grid climate data for instantaneous temperature. For 1-hr accumulated precipitation, IDW was most suitable for expressing local rainfall effects.

• Journal of Climate Change Research
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• v.6 no.4
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• pp.331-344
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• 2015

A long-term gridded historical data at 3 km spatial resolution has been generated for practical regional applications such as hydrologic modelling. However, overly high or low values have been found at some grid points where complex topography or sparse observational network exist. In this study, the Inverse Distance Weighting (IDW) method was applied to properly smooth the overly predicted values of Improved GIS-based Regression Model (IGISRM), called the IDW-IGISRM grid data, at the same resolution for daily precipitation, maximum temperature and minimum temperature from 2001 to 2010 over South Korea. We tested various effective distances in the IDW method to detect an optimal distance that provides the highest performance. IDW-IGISRM was compared with IGISRM to evaluate the effectiveness of IDW-IGISRM with regard to spatial patterns, and quantitative performance metrics over 243 AWS observational points and four selected stations showing the largest biases. Regarding the spatial pattern, IDW-IGISRM reduced irrational overly predicted values, i. e. producing smoother spatial maps that IGISRM for all variables. In addition, all quantitative performance metrics were improved by IDW-IGISRM; correlation coefficient (CC), Index Of Agreement (IOA) increase up to 11.2% and 2.0%, respectively. Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were also reduced up to 5.4% and 15.2% respectively. At the selected four stations, this study demonstrated that the improvement was more considerable. These results indicate that IDW-IGISRM can improve the predictive performance of IGISRM, consequently providing more reliable high-resolution gridded data for assessment, adaptation, and vulnerability studies of climate change impacts.

• Ocean Science Journal
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• v.41 no.4
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• pp.245-254
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• 2006

Long term wave climate of both extreme wave and operational wave height is essential for planning and designing coastal structures. Since the field wave data for the waters around Korean peninsula is not enough to provide reliable wave statistics, the wave climate information has been generated by means of long-term wave hindcasting using available meteorological data. Basic data base of hindcasted wave parameters such as significant wave height, peak period and direction has been established continuously for the period of 25 years starting from 1979 and for major 106 typhoons for the past 53 years since 1951 for each grid point of the North East Asia Regional Seas with grid size of 18 km. Wind field reanalyzed by European Center for Midrange Weather Forecasts (ECMWF) was used for the simulation of waves for the extra-tropical storms, while wind field calculated by typhoon wind model with typhoon parameters carefully analyzed using most of the available data was used for the simulation of typhoon waves. Design wave heights for the return period of 10, 20, 30, 50 and 100 years for 16 directions at each grid point have been estimated by means of extreme wave analysis using the wave simulation data. As in conventional methodsi of design criteria estimation, it is assumed that the climate is stationary and the statistics and extreme analysis using the long-term hindcasting data are used in the statistical prediction for the future. The method of extreme statistical analysis in handling the extreme vents like typhoon Maemi in 2003 was evaluated for more stable results of design wave height estimation for the return periods of 30-50 years for the cost effective construction of coastal structures.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.303-303
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• 2017

Japanese agriculture has faced to several threats: aging and decrease of farmer population, global competition, and the risk of climate change as well as harsh and variable weather. On the other hands, the number of large scale farms is increasing, because farm lands have been being aggregated to fewer numbers of farms. Cost cutting, development of efficient ways to manage complicatedly scattered farm lands, maintaining yield and quality under variable weather conditions, are required to adapt to changing environments. Information and communications technology (ICT) would contribute to solve such problems and to create innovative technologies. Thus we have been developing an early warning and decision support system to reduce weather and climate risks for rice, wheat and soybean production in Japan. The concept and prototype of the system will be shown. The system consists of a weather data system (Agro-Meteorological Grid Square Data System, AMGSDS), decision support contents where information is automatically created by crop models and delivers information to users via internet. AMGSDS combines JMA's Automated Meteorological Data Acquisition System (AMeDAS) data, numerical weather forecast data and normal values, for all of Japan with about 1km Grid Square throughout years. Our climate-smart system provides information on the prediction of crop phenology, created with weather forecast data and crop phenology models, as an important function. The system also makes recommendations for crop management, such as nitrogen-topdressing, suitable harvest time, water control, pesticide spray. We are also developing methods to perform risk analysis on weather-related damage to crop production. For example, we have developed an algorism to determine the best transplanting date in rice under a given environment, using the results of multi-year simulation, in order to answer the question "when is the best transplanting date to minimize yield loss, to avoid low temperature damage and to avoid high temperature damage?".

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.5
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• pp.149-162
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• 2018

It is necessary to select the appropriate global climate model (GCM) to take into account the impacts of climate change on integrated water management. The objective of this study was to develop the selection technique of representative GCMs for uncertainty in climate change scenario. The selection technique which set priorities of GCMs consisted of two steps. First step was evaluating original GCMs by comparing with grid-based observational data for the past period. Second step was evaluating whether the statistical downscaled data reflect characteristics for the historical period. Spatial Disaggregation Quantile Delta Mapping (SDQDM), one of the statistical downscaling methods, was used for the downscaled data. The way of evaluating was using explanatory power, the stepwise ratio of the entire GCMs by Expert Team on Climate Change Detection and Indices (ETCCDI) basis. We used 26 GCMs based on CMIP5 data. The Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios were selected for this study. The period for evaluating reproducibility of historical period was 30 years from 1976 to 2005. Precipitation, maximum temperature, and minimum temperature were used as collected climate variables. As a result, we suggested representative 13 GCMs among 26 GCMs by using the selection technique developed in this research. Furthermore, this result can be utilized as a basic data for integrated water management.

• Journal of Environmental Science International
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• v.15 no.12
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• pp.1141-1154
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• 2006

Orographic effect is one of the important factors to induce Local circulations and to make atmospheric turbulence, so it is necessary to use the exact topographic data for prediction of local circulations. In order to clarify the sensitivity of the spatial resolution of topography data, numerical simulations using several topography data with different spatial resolution are carried out under stable and unstable synoptic conditions. The results are as follows: 1) Influence of topographic data resolution on local circulation tends to be stronger at simulation with fine grid than that with coarse grid. 2) The hight of mountains in numerical model become mote reasonable with high resolution topographic data, so the orographic effect is also emphasized and clarified when the topographic data resolution is higher. 2) The higher the topographic resolution is, the stronger the mountain effect is. When used topographic data resolution become fine, topography in numerical model becomes closer to real topography. 3) The topographic effect tends to be stronger when atmospheric stability is strong stable. 4) Although spatial resolution of topographic data is not fundamental factor for dramatic improvement of weather prediction accuracy, some influence on small scale circulation can be recognized, especially in fluid dynamic simulation.