• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.6
• /
• pp.115-126
• /
• 2022

The study's goal is to determine the amount of climate change's impact on ethnic minority (EM) households' livelihoods, as well as their adaptability to climate change and long-term viability. The research was conducted in Vietnam's Northwestern Sub-region, where ethnic minorities account for more than half of the overall population. The study uses a combination of qualitative and quantitative methods based on a survey of 480 households in 04 provinces severely affected by climate change in the Northwest sub-region of Vietnam. The results show that: climate change (extreme weather events) occurs with increasing frequency, mainly affecting the life expectancy, health, and capital of households; Vulnerable groups (women, ethnic minorities) have a poor adaptive capacity and mainly suffer the consequences of shocks, are afraid to change their livelihoods; Microfinance plays an important role in enhancing the sustainability of livelihoods through increasing capital and financial assets and reducing the vulnerability of ethnic minority households. Finally, research has some solutions for microfinance - special credit specifically for ethnic minority households in the Northwest Sub-region: support for microfinance advice, home credit with transition orientations to adapt to climate change response and relieves its impact on the social lives.

• Journal of Climate Change Research
• /
• v.7 no.3
• /
• pp.357-371
• /
• 2016

Buildings in the city acts as a cause of distorted wind direction, wind speed, causing the stagnation of the air flow. In the recent trend of climate change can not but consider the temperature rise of the urbanization. This study was aimed to analyze the thermal comfort of planetary boundary layer in different artificial constructions areas which has a direct impact on urban climate, and estimating the warming phenomena. Envi-met model was used to consider the urban structure associated with urban growth in order to precisely determine the impact of the building on the city weather condition. The analyzed values of thermal comfort index were temperature, wind speed, horizontal and vertical turbulent diffusivity. In particular, analysis of the PPD(Predicted Percentage of Dissatisfied) represents the human thermal comfort. In this study, by adjusting the arrangement and proportion of the top floor building in the urban it was found that the inflow of the fresh air and cooling can be derived low PPD. Vertical heat flux amount of the city caused by climate change was a factor to form a high potential temperature in the city and the accumulation of cold air does not appear near the surface. Based on this, to make the city effectively respond to climate change may require a long-term restructuring of urban spatial structure and density management.

• Atmosphere
• /
• v.16 no.3
• /
• pp.215-223
• /
• 2006

Among observational, local-environmental, and large-scale factors causing significant changes in climate records, the site relocations and the replacement of the instruments are well-known nonclimatic factors for the analysis of climatic trends, climatic variability, and for the detection of anthropogenic climate change such as heat-island effect and global warming. Using dataset that were contaminated by these nonclimatic factors can affect seriously the assessment of climatic trends and variability, and the detection of the climatic change signal. In this paper, the inhomogeneities, which have been caused by relocation of the observation site, in the climate data of Korea Meteorological Administration (KMA) were examined using two-phase regression model. The observations of pan evaporation and wind speed are more sensitive to site relocations than those of other meteorological elements, such as daily mean, maximum and minimum temperatures, with regardless to region.

• Journal of Korean Society for Geospatial Information Science
• /
• v.25 no.1
• /
• pp.3-8
• /
• 2017

In this study future wind resource maps have been produced under climate change scenario using ensemble regional climate model weather research and forecasting(WRF) for the period from 2045 to 2054(mid 21st century). Then various spatiotemporal analysis has been conducted in terms of monthly and diurnal. As a result, monthly variation(monsoon circulation) was larger than diurnal variation(land-sea circulation) throughout the South Korea. Strong wind area with high wind power energy was varied on months and regions. During whole years, strong wind with high wind resource was pronounced at cold(warm) months in particular Gangwon mountainous and coastal areas(southwestern coastal area) driven by strong northwesterly(southwesterly). Projected strong and weak wind were presented in January and September, respectively. Diurnal variation were large over inland and mountainous area while coastal area were small. This new monthly and diurnal variation would be useful to high resource area analysis and long-term operation of wind power according to wind variability in future.

• Journal of Climate Change Research
• /
• v.5 no.4
• /
• pp.323-329
• /
• 2014

Since the terrain of Korea is complex, micro- as well as meso-climate variability is extreme by locations in Korea. In particular, air temperature of agricultural fields is influenced by topographic features of the surroundings making accurate interpolation of regional meteorological data from point-measured data. This study was carried out to compare spatial interpolation methods to estimate air temperature in agricultural fields surrounded by rugged terrains in South Korea. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging (with the temperature lapse rate) and Cokriging were tested to estimate monthly air temperature of unobserved stations. Monthly measured data sets (minimum and maximum air temperature) from 588 automatic weather system(AWS) locations in South Korea were used to generate the gridded air temperature surface. As the result, temperature lapse rate improved accuracy of all of interpolation methods, especially, spline showed the lowest RMSE of spatial interpolation methods in both maximum and minimum air temperature estimation.

• The Plant Pathology Journal
• /
• v.35 no.1
• /
• pp.1-10
• /
• 2019

Wheat blast occurred in Bangladesh for the first time in Asia in 2016. It is caused by a fungal pathogen, Magnaporthe oryzae Triticum (MoT) pathotype. In this review, we focused on the current status of the wheat blast in regard to host, pathogen, and environment. Despite the many efforts to control the disease, it expanded to neighboring regions including India, the world's second largest wheat producer. However, the disease occurrence has definitely decreased in quantity, because of many farmers chose to grow alternate crops according to the government's directions. Bangladesh government planned to introduce blast resistant cultivars but knowledges about genetics of resistance is limited. The genome analyses of the pathogen population revealed that the isolates caused wheat blast in Bangladesh are genetically close to a South American lineage of Magnaporthe oryzae. Understanding the genomes of virulent strains would be important to find target resistance genes for wheat breeding. Although the drier winter weather in Bangladesh was not favorable for development of wheat blast before, recent global warming and climate change are posing an increasing risk of disease development. Bangladesh outbreak in 2016 was likely to be facilitated by an extraordinary warm and humid weather in the affected districts before the harvest season. Coordinated international collaboration and steady financial supports are needed to mitigate the fearsome wheat blast in South Asia before it becomes a catastrophe.

• Journal of Climate Change Research
• /
• v.9 no.1
• /
• pp.13-29
• /
• 2018

This study presented evaluation procedure for selecting appropriate GCMs and downscaling method by focusing on the climate extreme indices suitable for climate change adaptation. The procedure includes six stages of processes as follows: 1) exclusion of unsuitable GCM through raw GCM analysis before bias correction; 2) calculation of the climate extreme indices and selection of downscaling method by evaluating reproducibility for the past and distortion rate for the future period; 3) selection of downscaling method based on evaluation of reproducibility of spatial correlation among weather stations; and 4) MME calculation using weight factors and evaluation of uncertainty range depending on number of GCMs. The presented procedure was applied to 60 weather stations where there are observed data for the past 30 year period on Korea Peninsula. First, 22 GCMs were selected through the evaluation of the spatio-temporal reproducibility of 29 GCMs. Between Simple Quantile Mapping (SQM) and Spatial Disaggregation Quantile Delta Mapping (SDQDM) methods, SQM was selected based on the reproducibility of 27 climate extreme indices for the past and reproducibility evaluation of spatial correlation in precipitation and temperature. Total precipitation (prcptot) and annual 1-day maximum precipitation (rx1day), which is respectively related to water supply and floods, were selected and MME-based future projections were estimated for near-future (2010-2039), the mid-future (2040-2069), and the far-future (2070-2099) based on the weight factors by GCM. The prcptot and rx1day increased as time goes farther from the near-future to the far-future and RCP 8.5 showed a higher rate of increase in both indices compared to RCP 4.5 scenario. It was also found that use of 20 GCM out of 22 explains 80% of the overall variation in all combinations of RCP scenarios and future periods. The result of this study is an example of an application in Korea Peninsula and APCC Integrated Modeling Solution (AIMS) can be utilized in various areas and fields if users want to apply the proposed procedure directly to a target area.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.52 no.5
• /
• pp.1-9
• /
• 2010

Evapotranspiration (ET) is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. In this study, artificial neural network (ANN) models for reference crop evapotranspiration ($ET_0$) estimation were developed on a monthly basis (May~October). The models were trained and tested for Suwon, Korea. Four climate factors, daily maximum temperature ($T_{max}$), daily minimum temperature ($T_{min}$), rainfall (R), and solar radiation (S) were used as the input parameters of the models. The target values of the models were calculated using Food and Agriculture Organization (FAO) Penman-Monteith equation. Future climate data were generated using LARS-WG (Long Ashton Research Station-Weather Generator), stochastic weather generator, based on HadCM3 (Hadley Centre Coupled Model, ver.3) A1B scenario. The evapotranspirations were 549.7 mm/yr in baseline period (1973-2008), 558.1 mm/yr in 2011-2030, 593.0 mm/yr in 2046-2065, and 641.1 mm/yr in 2080-2099. The results showed that the ANN models achieved good performances in estimating future reference crop evapotranspiration.

• Environmental Engineering Research
• /
• v.18 no.1
• /
• pp.9-20
• /
• 2013

In this paper, the rainfall elasticity of streamflow was estimated to quantify the effects of climate change on 5 river basins. Rainfall elasticity denotes the sensitivity of annual streamflow for the variations of potential annual rainfall. This is a simple, useful method that evaluates how the balance of a water cycle on river basins changes due to long-term climate change and offers information to manage water resources and environment systems. The elasticity method was first used by Schaake in 1990 and is commonly used in the United States and Australia. A semi-distributed hydrological model (SLURP, semi-distributed land use-based runoff processes) was used to simulate the variations of area streamflow, and potential evapotranspiration. A nonparametric method was then used to estimate the rainfall elasticity on five river basins of Korea. In addition, the A2 (SRES IPCC AR4, Special Report on Emission Scenarios IPCC Fourth Assessment Report) climate change scenario and stochastic downscaling technique were used to create a high-resolution weather change scenario in river basins, and the effects of climate change on the rainfall elasticity of each basin were then analyzed.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.57 no.5
• /
• pp.69-80
• /
• 2015

The majority of projections of future climate come from Global Circulation Models (GCMs), which vary in the way they were modeled the climate system, and so it produces different projections about conceptualizing of the weather system. To implement climate change impact assessment, it is necessary to analyze trends of various GCMs and select appropriate GCM. In this study, climate data in 25 GCMs 41 outputs provided by Coupled Model Intercomparison Project Phase 5 (CMIP5) was downscaled at eight stations. From preliminary analysis of variations in projected temperature, precipitation and evapotranspiration, five GCM outputs were identified as candidates for the climate change impact analysis as they cover wide ranges of the variations. Also, GCM outputs are compared with trends of HadGCM3-RA, which are established by the Korean Meteorological Administration. From the results, it can contribute to select appropriate GCMs and to obtain reasonable results for the assessment of climate change.