• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.4
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• pp.240-249
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• 2005

The normal Indian Ocean is characterized by warmer waters over the eastern region and cooler waters over the western region. Changes in sea surface temperature (SST) over the western and eastern Indian Ocean give birth to a phenomenon now referred to as the Indian Ocean Dipole Mode (IODM). The positive phase of this mode is characterized by positive SST anomalies over the western Indian Ocean and negative anomalies over the southeastern Indian Ocean, while the negative phase is characterized by a reversed SST anomaly pattern. On the other hand, the normal Pacific Ocean has warm (cool) waters over the western (eastern) parts. Positive (negative) SST anomalies over the central/eastern (western) Pacific Ocean characterize the E1 Nino phenomenon. The reverse situation leads to the La Nina phenomenon. The coupled ocean-atmosphere phenomenon over the Pacific is referred to as the E1 Nino Southern Oscillation (ENSO) phenomenon. In this study the impact of IODM and ENSO on the East Asian monsoon variability has been studied using observational data and using the Community Atmospheric Model (CAM) of the National Center for Atmospheric Research (NCAR). Five sets of model experiments were performed with anomalous SST patterns associated with IODM/ENSO superimposed on the climatological SSTs. The empirical and dynamic approaches reveal that it takes about 3-4 seasons fur the peak IODM mode to influence the summer monsoon activity over East Asia. On the other hand, the impact of ENSO on the East Asian monsoon could occur simultaneously. Further, the negative (positive) phase of IODM and E1 Nino (La Nina) over the Pacific enhances (suppresses) monsoon activity over the Korea-Japan Sector. Alternatively, IODM appears to have no significant impact on monsoon variability over China. However, El Nino (La Nina) suppresses (enhances) monsoon activity over China. While the IODM appears to influence the North Pacific subtropical high, ENSO appears to influence the Aleutian low over the northwest Pacific. Thus, the moisture supply towards East Asia from the Pacific is determined by the strengthening/weakening of the subtropical high and the Aleutian low.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Korean Journal of Environmental Biology
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• v.40 no.2
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• pp.224-238
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• 2022

North Korea has actively participated in the international community related to environmental agreements. It has proposed various environmental policies internally since the Kim Jong-un regime. In particular, it emphasizes activities related to climate change response, the Sustainable Development Goals, and the conservation of ecosystems including forests and wetlands. In this study, a new security cooperation plan was proposed with an understanding of the climate crisis and environmental regime as a starting point. To this end, trends and recent activities for climate-environment cooperation in the international community and on the Korean Peninsula were analyzed. In addition, North Korea's conditions for cooperation on the Korean Peninsula, technology demand, and the projected future environment of the Korean Peninsula were dealt with. Ultimately, through advice of experts, we were able to discover cooperation agendas by sector and propose short-term and long-term environmental cooperation strategies for the Korean Peninsula based on them. In this study, conditions and directions for cooperation in fields of climate technology, biological resources, air/weather, water environment, biodiversity, renewable energy, bioenergy, and so on were considered comprehensively. Among 21 cooperation agendas discovered in this study, energy showed the largest number of areas. Renewable energy, forest resources, and environmental and meteorological information stood out as agendas that could be cooperated in the short term. As representative initiatives, joint promotion of 'renewable energy' that could contribute to North Korea's energy demand and carbon neutrality and 'forest cooperation' that could be recognized as a source of disaster reduction and greenhouse gas sinks were suggested.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.1-30
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• 2024

The international community adopts the SSP (Shared Socioeconomic Pathways) scenario as a new greenhouse gas emission pathway. As part of efforts to reflect these international trends and support for climate change adaptation measure in the agricultural sector, the National Institute of Agricultural Sciences (NAS) produced high-resolution (1 km) climate change scenarios for the Korean Peninsula based on SSP scenarios, certified as a "National Climate Change Standard Scenario" in 2022. This paper introduces SSP climate change scenario of the NAS and shows the results of the climate change projections. In order to produce future climate change scenarios, global climate data produced from 18 GCM models participating in CMIP6 were collected for the past (1985-2014) and future (2015-2100) periods, and were statistically downscaled for the Korean Peninsula using the digital climate maps with 1km resolution and the SQM method. In the end of the 21^st century (2071-2100), the average annual maximum/minimum temperature of the Korean Peninsula is projected to increase by 2.6~6.1℃/2.5~6.3℃ and annual precipitation by 21.5~38.7% depending on scenarios. The increases in temperature and precipitation under the low-carbon scenario were smaller than those under high-carbon scenario. It is projected that the average wind speed and solar radiation over the analysis region will not change significantly in the end of the 21st century compared to the present. This data is expected to contribute to understanding future uncertainties due to climate change and contributing to rational decision-making for climate change adaptation.

• Journal of Soil and Groundwater Environment
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• v.13 no.4
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• pp.22-29
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• 2008

To understand environmental characteristics and contamination assessment of the Namsan Park soil in Seoul, we divided the Namsan map into 33 sectors and sampled mixed soil in depth 0${\sim}$15 cm, in 5${\sim}$10 points at the sites. We analyzed soil samples collected at 21 sectors twice on May and September. The results were as follows. The hue color ranges of the Namsan soil were 2.5YR${\sim}$10YR, the value ranges were 1${\sim}$4, the water rates were 3.1${\sim}$22.3 and the Ignition losses were 3.4${\sim}$10.4%. The average concentration of Cu and Pb were determined 3.374 and 15.000 mg/kg, Cd and As showed very low level. The mean concentrations of Zn and Ni were showed 103.290 and 11.649 mg/kg and this amount is not different from the nationalwide mean in 2005. The mean pH showed 5.41. The Zn, Ni and Cd in the soil of the circular road of Namsan showed 1.33, 1.48, 1.46 times higher than the other sector of the Namsan soil. The corelation coefficient between water rate and ignition loss were 0.720 and the correlation coefficient between Cu and Pb, Cu and Zn showed 0.827, 0.694 respectively. There was weak corelationship between pH and Zn. The Uniformity coefficient (Uc) of all the survey sites was determined below 5 in the range of 1.5${\sim}$4.4.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.3
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• pp.19-34
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• 2022

This study was conducted to provide basic data that can be used when establishing Net Zero policies and implementation plans for non-urban settlements by quantitatively analyzing the Net Zero contribution to green infrastructure in rural areas corresponding to non-urban settlements. The main purpose is to first, systematize green infrastructure in rural areas, secondly derive basic units for each element of green infrastructure, and thirdly quantify and present the impact on Net Zero in Korea using these. In this study, CVR(Content Validity Ration) analysis was performed to verify the adequacy of green infrastructure elements in rural areas derived through research and analysis of previous studies, is as follows. First, Hubs of Green infrastructure in rural area include village forests, wetlands, farm land, and smart farms with a CVR value of .500 or higher. And Links of Green infrastructure in rural area include streams, village green areas, and LID (rainwater recycling). Second, the basic unit for each green infrastructure element was presented by classifying it into minimum, maximum, and median values using the results of previous studies so that it could be used for spatial planning and design for Net Zero. Third, when Green infrastructure in rural areas is applied to non-urban settlements in Korea, it is analyzed that it has the effect of indirectly reducing CO₂ by at least 70.76 million tons and up to 141.16 million tons. This is 3.4 to 6.7 times the amount of CO₂ emission from the agricultural sector in 2019, and it can be seen that the contribution to Net Zero is very high. It is expected to greatly contribute to the transformation of the ecosystem. This study quantitatively presented the carbon-neutral contribution to settlements located in non-urban areas, and by deriving the carbon reduction unit for each element of green infrastructure in rural areas, it can be used in spatial planning and design for carbon-neutral at the village level. It has significance as a basic research. In particular, the basic unit of carbon reduction for each green infrastructure factors will be usable for Net Zero policy at the village level, presenting a quantitative target when establishing a plan, and checking whether or not it has been achieved. In addition, based on this, it will be possible to expand and apply Net Zero at regional and city units such as cities, counties, and districts.

• Economic and Environmental Geology
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• v.55 no.5
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• pp.551-561
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• 2022

Geophysical exploration methods are very useful for generating high-resolution images of underground structures, and such methods can be applied to investigation of buried cultural properties and for determining their exact locations. In this study, image feature extraction and image segmentation methods were applied to automatically distinguish the structures of buried relics from the high-resolution ground-penetrating radar (GPR) images obtained at the center of Silla Kingdom, Gyeongju, South Korea. The major purpose for image feature extraction analyses is identifying the circular features from building remains and the linear features from ancient roads and fences. Feature extraction is implemented by applying the Canny edge detection and Hough transform algorithms. We applied the Hough transforms to the edge image resulted from the Canny algorithm in order to determine the locations the target features. However, the Hough transform requires different parameter settings for each survey sector. As for image segmentation, we applied the connected element labeling algorithm and object-based image analysis using Orfeo Toolbox (OTB) in QGIS. The connected components labeled image shows the signals associated with the target buried relics are effectively connected and labeled. However, we often find multiple labels are assigned to a single structure on the given GPR data. Object-based image analysis was conducted by using a Large-Scale Mean-Shift (LSMS) image segmentation. In this analysis, a vector layer containing pixel values for each segmented polygon was estimated first and then used to build a train-validation dataset by assigning the polygons to one class associated with the buried relics and another class for the background field. With the Random Forest Classifier, we find that the polygons on the LSMS image segmentation layer can be successfully classified into the polygons of the buried relics and those of the background. Thus, we propose that these automatic classification methods applied to the GPR images of buried cultural heritage in this study can be useful to obtain consistent analyses results for planning excavation processes.