• Korean journal of applied entomology
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• v.55 no.2
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• pp.139-148
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• 2016

A seasonal outlook for crop insect pests is most valuable when it provides accurate information for timely management decisions. In this study, we investigated probable tele-connections between climatic phenomena and pest infestations in Korea using a statistical method. A rice insect pest, brown planthopper (BPH), was selected because of its migration characteristics, which fits well with the concept of our statistical modelling - utilizing a long-term, multi-regional influence of selected climatic phenomena to predict a dominant biological event at certain time and place. Variables of the seasonal climate forecast from 10 climate models were used as a predictor, and annual infestation area for BPH as a predictand in the statistical analyses. The Moving Window Regression model showed high correlation between the national infestation trends of BPH in South Korea and selected tempo-spatial climatic variables along with its sequential migration path. Overall, the statistical models developed in this study showed a promising predictability for BPH infestation in Korea, although the dynamical relationships between the infestation and selected climatic phenomena need to be further elucidated.

• Journal of Environmental Impact Assessment
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• v.4 no.3
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• pp.73-86
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• 1995

This paper demonstrates Environmental Impact Assessment (EIA) has to be applied for development projects with regard to the ecological, economical and social aspects before any decisions made in the project. Korea has confronted various environmental problems during the last fifteen years, even though EIA has been enacted since 1981. The role of impact assessment in planning and policy processes should be emphasized to investigate the magnitude and intensity of the adverse influences of economic development. In the Seoul Metropolitan Region, it is necessary to apply EIA all urban projects to reduce the adverse effects of urbanization. Special attention should be given to the climatological effects throughout the urbanization process in Korea to keep the urban area energy-efficient. This study intends not only to establish basic data for national-and regional-based land-use policy in the environmental aspects, but also to provide the basic data for the possible climate model (scenarios) that may provide spatial and temporal variability by analyzing the actual climatic record. There is a noticeable impact of urbanization on the atmospheric environment in the Seoul Metropolitan Region. In this sense, the climatic aspect must be taken into consideration in the process of EIA to mitigate the well-known climatic alterations of urbanization. Moreover, the techniques of assessment should be improved by developing geo-reference data sets to build models of the global climate in response to the man-made environmental change.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.18 no.2
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• pp.79-88
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• 2015

Preparations need to be made for Korean pine(Pinus koraiensis) in anticipation of climate change because Korean pine is an endemic species of South Korea and the source of timber and pine nut. Therefore, climate change adaptation policy has been established to conduct an impact assessment on the distribution of Korean pine. Our objective was to predict the distribution of Korean pine while taking into account uncertainty and afforestation conditions. We used the 5th forest types map, a forest site map and BIOCLIM variables. The climate scenarios are RCP 4.5 and RCP 8.5 for uncertainty and the climate models are 5 regional climate models (HadGEM3RA, RegCM4, SNURCM, GRIMs, WRF). The base period for this study is 1971 to 2000. The target periods are the mid-21st century (2021-2050) and the end of the 21st century (2071-2100). This study used the MaxEnt model, and 50% of the presences were randomly set as training data. The remaining 50% were used as test data, and 10 cross-validated replicates were run. The selected variables were the annual mean temperature (Bio1), the precipitation of the wettest month (Bio13) and the precipitation of the driest month (Bio14). The test data's ROC curve of Korean pine was 0.689. The distribution of Korean pine in the mid-21st century decreased from 11.9% to 37.8% on RCP 4.5 and RCP 8.5. The area of Korean pine at an artificial plantation occupied from 32.1% to 45.4% on both RCPs. The areas at the end of the 21st century declined by 53.9% on RCP 4.5 and by 86.0% on RCP 8.5. The area of Korean pine at an artificial plantation occupied 23.8% on RCP 4.5 and 7.2% on RCP 8.5. Private forests showed more of a decrease than national forests for all subsequent periods. Our results may contribute to the establishment of climate change adaptation policies for considering various adaptation options.

• Journal of Korean Society of Forest Science
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• v.99 no.6
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• pp.791-801
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• 2010

Forest soil carbon model is a useful tool for understanding complex soil carbon cycle in forests and estimating dynamics of soil carbon to climate change. However, studies on development and application of the model are insufficient in Korea. The need for development of Korean model is now growing, because there are notable problems and limitations for adapting overseas models in Korea to meet the requirements of the international organizations such as IPCC, which demands highly reliable data for national reports. Therefore, we have studied 7 overseas forest soil carbon models (CBM-CFS3, CENTURY, Forest-DNDC, ROMUL, RothC, Sim-CYCLE, YASSO), analyzed and compared their structure, decomposition mechanism, initializing process and, input and output data. Then we evaluated applicability of these models in Korea with three criteria; availability of input data, performance of model, and possibility of regional modification. Finally, a systematic process for applying a new model was suggested based on these analyses.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• Atmosphere
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• v.34 no.1
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• pp.55-67
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• 2024

This paper aims to provide a detailed introduction to the concept of the Ratio of Predictable Component (RPC) and the Signal-to-Noise Paradox. Then, we derive insights from them by exploring the paradoxical features by conducting a seasonal and regional analysis through ensemble expansion in KMA's climate prediction system (GloSea). We also provide an explanation of the ensemble generation method, with a specific focus on stochastic physics. Through this study, we can provide the predictability limits of our forecasting system, and find way to enhance it. On a global scale, RPC reaches a value of 1 when the ensemble is expanded to a maximum of 56 members, underlining the significance of ensemble expansion in the climate prediction system. The feature indicating RPC paradoxically exceeding 1 becomes particularly evident in the winter North Atlantic and the summer North Pacific. In the Siberian Continent, predictability is notably low, persisting even as the ensemble size increases. This region, characterized by a low RPC, is considered challenging for making reliable predictions, highlighting the need for further improvement in the model and initialization processes related to land processes. In contrast, the tropical ocean demonstrates robust predictability while maintaining an RPC of 1. Through this study, we have brought to attention the limitations of potential predictability within the climate prediction system, emphasizing the necessity of leveraging predictable signals with high RPC values. We also underscore the importance of continuous efforts aimed at improving models and initializations to overcome these limitations.

• Journal of Environmental Science International
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• v.25 no.7
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• pp.1029-1042
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• 2016

The initial and boundary conditions are important factors in regional chemical transport modeling systems. The method of generating the chemical boundary conditions for regional air quality models tends to be different from the dynamically varying boundary conditions in global chemical transport models. In this study, the impact of real time Copernicus atmosphere monitoring service (CAMS) re-analysis data from the modeling atmospheric composition and climate project interim implementation (MACC) on the regional air quality in the Korean Peninsula was carried out using the community multi-scale air quality modeling system (CMAQ). A comparison between conventional global data and CAMS for numerical assessments was also conducted. Although the horizontal resolution of the CAMS re-analysis data is not higher than the conventionally provided data, the simulated particulate matter (PM) concentrations with boundary conditions for CAMS re-analysis is more reasonable than any other data, and the estimation accuracy over the entire Korean peninsula, including the Seoul and Daegu metropolitan areas, was improved. Although an inland area such as the Daegu metropolitan area often has large uncertainty in PM prediction, the level of improvement in the prediction for the Daegu metropolitan area is higher than in the coastal area of the western part of the Korean peninsula.

• New & Renewable Energy
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• v.20 no.1
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• pp.88-94
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• 2024

This study explored the potential and implementation of renewable energy sources in Sri Lanka, focusing on the theoretical potential of solar and wind energy to develop self-reliant energy models. Using advanced climate data from the European Centre for Medium-Range Weather Forecasts and Global Solar/Wind Atlas provided by the World Bank, we assessed the renewable energy potential across Sri Lanka. This study proposes off-grid and minigrid systems as viable solutions for addressing energy poverty in rural regions. Rural villages were classified based on solar and wind resources, via which we proposed four distinct energy self-reliance models: Renewable-Dominant, Solar-Dominant, Wind-Dominant, and Diesel-Dominant. This study evaluates the economic viability of these models considering Sri Lanka's current energy market and technological environment. The outcomes highlight the necessity for employing diversified energy strategies to enhance the efficiency of the national power supply system and maximize the utilization of renewable resources, contributing to Sri Lanka's sustainable development and energy security.

• Atmosphere
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• v.25 no.4
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• pp.693-706
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• 2015

A shift of first fowering date (FFD) of spring blossoms (cherry, peach and pear) over the northest Asia under global warming is investiaged using dynamically downscaled daily temperature data with 12.5 km resolution. For the study, we obatained gridded daily data with Historical (1981~2010), and Representative Concentration Pathway (RCP) (2021~2100) 4.5 and 8.5 scenarios which were produced by WRFv3.4 in conjunction with HadGEM2-AO. A change on FFDs in 21st century is estimated by applying daily outputs of WRFv3.4 to DTS phonological model. Prior to projection on future climate, the performances of both WRFv3.4 and DTS models are evaluated using spatial distribution of climatology and SCR diagram (Normalized standard deviation-Pattern correlation coefficient-Root mean square difference). According to the result, WRFv3.4 and DTS models well simulated a feature of the terrain following characteristics and a general pattern of observation with a marigin of $1.4^{\circ}C$ and 5~6 days. The analysis reveals a projected advance in FFDs of cherry, peach and pear over the northeast Asia by 2100 of 15.4 days (9.4 days). 16.9 days (10.4 days) and 15.2 days (9.5 days), respectively, compared to the Historical simulation due to a increasing early spring (Februrary to April) temperature of about $4.9^{\circ}C$ ($2.9^{\circ}C$) under the RCP 8.5 (RCP 4.5) scenarios. This indicates that the current flowering of the cherry, peach and pear over analysis area in middle or end of April is expected to start blooming in early or middle of April, at the end of this century. The present study shows the dynamically downscaled daily data with high-resolution is helpeful in offering various useful information to end-users as well as in understanding regional climate change.

• Journal of the Korean association of regional geographers
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• v.20 no.1
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• pp.70-91
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• 2014

There have been a lot of efforts to adapt climate change around the world, and Korea is no exception. The low carbon green cities for overseas have had many different forms through their own special models and strategies. Korea needs a model and strategy of Korean low carbon green city, which is suitable for Korea climate and topography. This study pays attention to the Pungsu, which is Korean traditional thinking system for space, and examines the way for selecting locations and space planning to create the Korean low carbon green city through the contemporary interpretation of the Pungsu. For this purpose, first of all, this study makes efforts for the contemporary interpretation of the past Pungsu theory from the modern city's perspective, through understanding the difference between the Korea's historic villages(cities) and the modern cities. Based on the contemporary interpretation of the Pungsu theory, this study finds ways of application the system on selecting locations and space planning in the Pungsu theory to create the Korean low carbon green city.