• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Korean Journal of Ecology and Environment
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• v.55 no.1
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• pp.10-18
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• 2022

This study analyzed the relationship between the community structure of benthic macroinvertebrates and habitat changes in open estuaries among the sites included in the national estuary monitoring program. The estuary survey was conducted under the "Guidelines for Investigation and Evaluation of Biometric Networks" and classified by sea area, 80 places in the East Sea, 102 places in the South Sea, and 19 places in the West Sea were investigated. In a total of 201 open estuaries, benthic macroinvertebrates were identified with 4 phyla, 9 classes, 41 orders, 139 families, 269 species and 196 species in the East Sea, 182 species in the South Sea, and 90 species in the West Sea. The highest population densities were Insecta in the East Sea, the Malacostraca in the South Sea, and the Annelida in the West Sea. Through SIMPER analysis, species contributing to the similarity of benthic macroinvertebrates communities in each sea area were identified. Some species greatly influenced the similarity of clusters. The benthic community in the East Sea was affected by the salinity, so the contribution rate of freshwater species was high. On the other hand, the benthic communities of the South and West Seas showed species compositions are influenced by the substrate composition. As results, the benthic macroinvertebrate community in Korean estuaries was impacted by salinity and substrate simultaneously, and the close relationship with geographical distance was not observed. The result of this study is expected to be used to respond to environmental changes by identifying and predicting changes in the diversity and distribution of benthic macroinvertebrates in Korea estuaries.

• Journal of Korea Water Resources Association
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• v.56 no.5
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• pp.311-323
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• 2023

In order to reduce disaster damage by localized heavy rains, floods, and urban inundation, it is important to know in advance whether natural disasters occur. Currently, heavy rain watch and heavy rain warning by the criteria of the Korea Meteorological Administration are being issued in Korea. However, since this one criterion is applied to the whole country, we can not clearly recognize heavy rain damage for a specific region in advance. Therefore, in this paper, we tried to reset the current criteria for a special weather report which considers the regional characteristics and to predict the damage caused by rainfall after 1 hour. The study area was selected as Gyeonggi-province, where has more frequent heavy rain damage than other regions. Then, the rainfall inducing disaster or hazard-triggering rainfall was set by utilizing hourly rainfall and heavy rain damage data, considering the local characteristics. The heavy rain damage prediction model was developed by a decision tree model and a random forest model, which are machine learning technique and by rainfall inducing disaster and rainfall data. In addition, long short-term memory and deep neural network models were used for predicting rainfall after 1 hour. The predicted rainfall by a developed prediction model was applied to the trained classification model and we predicted whether the rain damage after 1 hour will be occurred or not and we called this as 1ST-Model. The 1ST-Model can be used for preventing and preparing heavy rain disaster and it is judged to be of great contribution in reducing damage caused by heavy rain.