• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.3
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• pp.190-198
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• 2023

This study was conducted to calculate the damage of Italian ryegrass (IRG) by abnormal climate using machine learning and present the damage through the map. The IRG data collected 1,384. The climate data was collected from the Korea Meteorological Administration Meteorological data open portal.The machine learning model called xDeepFM was used to detect IRG damage. The damage was calculated using climate data from the Automated Synoptic Observing System (95 sites) by machine learning. The calculation of damage was the difference between the Dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of IRG data (1986~2020). The level of abnormal climate was set as a multiple of the standard deviation applying the World Meteorological Organization (WMO) standard. The DMY_normal was ranged from 5,678 to 15,188 kg/ha. The damage of IRG differed according to region and level of abnormal climate with abnormal temperature, precipitation, and wind speed from -1,380 to 1,176, -3 to 2,465, and -830 to 962 kg/ha, respectively. The maximum damage was 1,176 kg/ha when the abnormal temperature was -2 level (+1.04℃), 2,465 kg/ha when the abnormal precipitation was all level and 962 kg/ha when the abnormal wind speed was -2 level (+1.60 ㎧). The damage calculated through the WMO method was presented as an map using QGIS. There was some blank area because there was no climate data. In order to calculate the damage of blank area, it would be possible to use the automatic weather system (AWS), which provides data from more sites than the automated synoptic observing system (ASOS).

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.120-129
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• 2017

Agrometeorological advisories for farms and orchards are issued when daily weather exceeds a predefined range of the local reference climate, which is a long-term average of daily weather for the location. The reference climate at local scales is prepared by various simplification methods, resulting in uncertainty in the agrometeorological advisories. We restored daily weather data for the 1981-2010 period and analyzed the differences in prediction results of weather risk by comparing with the temporal and spatial simplified normal climate values. For this purpose, we selected the agricultural drought index (ADI) among various disaster related indices because ADI requires many kinds of weather data to calculate it. Ten rural counties within the Seomjin River Basin were selected for this study. The normal value of 'temporal simplification' was calculated by using the daily average value for 30 years (1981-2010). The normal value of 'spatial simplification' is the zonal average of the temporally simplified normal values falling within a standard watershed. For residual moisture index, temporal simplification normal values were overestimated, whereas spatial simplification normal values were underestimated in comparison with non-simplified normal values. The ADI's calculated from January to July 2017 showed a significant deviation in terms of the extent of drought depending on the normal values used. Through this study, we confirmed that the result of weather risk calculation using normal climatic values from 'simplified' methods can affect reliability of the agrometeorological advisories.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.110-119
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• 2017

The agroclimatic indices are produced by statistical analysis based on primary climate data (e.g., temperature, precipitation, and solar irradiance) or driving agronomic models. This study was carried out to evaluate how selection of daily temperature for a climate normal (1983-2012) affected the precision of the agroclimatic indices. As a first step, averaged daily 0600 and 1500 LST temperature for a climate normal were produced by geospatial schemes based on topo-climatology ($365days{\times}1$ set, EST normal year). For comparison, 30 years daily temperature data were generated by applying the same process ($365days{\times}30sets$), and calculated mean of daily temperature (OBS normal year). The flowering date of apple 'Fuji' cultivar, the last frost date, and the risk of late frost were estimated based on EST normal year data and compared with the results from OBS normal year. The results on flowering date showed 2.9 days of error on average. The last frost date was of 11.4 days of error on average, which was relatively large. Additionally, the risk of the late frost was determined by the difference between the flowering and the last frost date. When it was determined based on the temperature of EST normal year, Akyang was classified as a risk area because the results showed that the last frost date would be the same or later than the flowering date in the 12.5% of area. However, the temperature of OBS normal year indicated that the area did not have the risk of a late frost. The results of this study implied that it would be necessary to reduce the error by replacing the EST method with the OBS method in the future.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.3
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• pp.29-39
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• 2017

In this study, the GPM (Global Precipitation Mission) IMERG (Integrated Multi-satellitE retrievals for GPM) rainfall data was verified and evaluated using ground AWS (Automated Weather Station) and radar in order to investigate the availability of GPM IMERG rainfall data. The SPI (Standardized Precipitation Index) was calculated based on the GPM IMERG data and also compared with the results obtained from the ground observation data for the Hoengseong Dam and Yongdam Dam areas. For the radar data, 1.5 km CAPPI rainfall data with a resolution of 10 km and 30 minutes was generated by applying the Z-R relationship ($Z=200R^{1.6}$) and used for accuracy verification. In order to calculate the SPI, PERSIANN_CDR and TRMM 3B42 were used for the period prior to the GPM IMERG data availability range. As a result of latency verification, it was confirmed that the performance is relatively higher than that of the early run mode in the late run mode. The GPM IMERG rainfall data has a high accuracy for 20 mm/h or more rainfall as a result of the comparison with the ground rainfall data. The analysis of the time scale of the SPI based on GPM IMERG and changes in normal annual precipitation adequately showed the effect of short term rainfall cases on local drought relief. In addition, the correlation coefficient and the determination coefficient were 0.83, 0.914, 0.689 and 0.835, respectively, between the SPI based GPM IMERG and the ground observation data. Therefore, it can be used as a predictive factor through the time series prediction model. We confirmed the hydrological utilization and the possibility of real time drought monitoring using SPI based on GPM IMERG rainfall, even though results presented in this study were limited to some rainfall cases.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.2
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• pp.37-42
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• 2017

In this study, we were carried out the classification of Korea's climate zone. $K{\ddot{o}}ppen$ climate classification and Warmth Index were used for classification of climate zone and we have predicted how the climate zone will be changed during the 21st century. Especially, $K{\ddot{o}}ppen$ climate classification is one of the most widely used method in the world. The climate data used monthly climate normal data (1981-2010) and future climate data (2051-2060 and 2091-2100) by considering RCP 8.5 scenarios, which was made from geospatial climate models at 1km grid cell estimated. In conclusion, the temperature will rise steadily and the climate zone will be simplified in the future as a result.

• 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 Solar Energy Society
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• v.30 no.6
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• pp.108-117
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• 2010

Although many researches of simulation programs to predict climate under the current climate change have been performed but more detailed studies of weather date which might influence the load of buildings seem insufficient. In this study, in Incheon are analyzed IES (Integrated Environmental Solutions)6.0, Ecotect 2010, EnergyPlus v4.0's IWEC file and ISO-TRY, the Korean standard weather data provided by the Korean Solar Energy Society for direct normal radiation which is used in load calculation programs. The results show that the radiation of the programs is the same as that of direct normal radiation per month but has a mere difference, compared with the radiation per hour and IWEC has also 77.12% when compared with ISO-TRY, meaning that it could affect load values of buildings when applied to them. And in case of ISO-TRY, it could be judged that the application of test reference year applied by the data measured has higher reliability than IWEC file.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.55-64
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• 2008

The evidence of changes in the climate system is obvious in the world. Nevertheless, at the current techniques for flood frequency analysis, the flood distribution can not reflect climate change or long-term climate cycles. Using a linear regression and a Mann-Kendall test, trends in annual maximum precipitation and flood data for several major gauging sites were evaluated. Moreover, this research considered incorporating flood trends by climate change effects in flood frequency analyses. For five rainfall gauging sites (Seoul, Incheon, Ulleungdo, Jeonju, and Gangneung), upward trends were observed in all gauged annual maximum precipitation records but they were not statistically significant. For three streamflow gauging sites (Andong Dam, Soyanggang Dam, and Daecheong Dam), upward trends were also observed in all gauged annual maximum flood records, but only the flood at Andong Dam was statistically significant. A log-normal trend model was introduced to reflect the observed linear trends in annual maximum flood series and applied to estimate flood frequency and risk for Andong Dam and Soyanggang Dam. As results, when the target year was 2005, 50-year floods of the log-normal trend model were 41% and 21% larger then those of a log-normal model for Andong Dam and Soyanggang Dam, respectively. Moreover, the estimated floods of the log-normal trend model increases as the target year increases.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.203-208
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• 2015

This study was conducted to provide the agricultural climatological basic data for the reset of sowing period of the winter crop on the double cropping system with rice. During the past 30 years from 1981 to 2010, mean air temperature has risen by $0.45^{\circ}C$ per 10 years (with statistical significance), while precipitation has decreased by 6.74 mm per 10 years and the numbers of days for precipitation has reduced by 0.23 days per 10 years (with no statistical significance) in the sowing period ($1^{st}$ Oct. to $5^{th}$ Nov.) of winter crop. It was analyzed that double cropping system of rice and winter crops need to be reset in the way of delaying the sowing time of winter crops, because rising trend of temperature was clear while variability of precipitation was great and the trend was not clear in the sowing period of winter crops. We have also analyzed the meteorological features of the sowing period of winter crops in 2014, and found that mean air temperature in 2014 was higher than that in normal years (similar to recent temperature change feature) while precipitation in 2014 was much more frequent than that in normal years (unlike recent precipitation features). Such tendency in 2014 made the sowing of winter crops difficult because mechanical sowing could not be worked in flooded paddy fields. Heavy rain in October 2014 was also analyzed as a rare phenomenon.

• Journal of Korean Society on Water Environment
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• v.36 no.6
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• pp.568-580
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• 2020

Calibrating a conceptual hydrologic model necessitates selection of a calibration period that produces the most reliable prediction. This often must be chosen randomly, however, since there is no objective guidance. Observation plays the most important role in the calibration or uncertainty evaluation of hydrologic models, in which the key factors are the length of the data and the hydro-climate conditions in which they were collected. In this study, we investigated the effect of the calibration period selected on the predictive performance and uncertainty of a model. After classifying the inflows of the Hapcheon Dam from 1991 to 2019 into four hydro-climate conditions (dry, wet, normal, and mixed), a conceptual hydrologic partitioning model was calibrated using data from the same hydro-climate condition. Then, predictive performance and post-parameter statistics were analyzed during the verification period under various hydro-climate conditions. The results of the study were as follows: 1) Hydro-climate conditions during the calibration period have a significant effect on model performance and uncertainty, 2) calibration of a hydrologic model using data in dry hydro-climate conditions is most advantageous in securing model performance for arbitrary hydro-climate conditions, and 3) the dry calibration can lead to more reliable model results.