• Proceedings of the Korea Water Resources Association Conference
• /
• 2018.05a
• /
• pp.154-154
• /
• 2018

To interpret the climate projections for the future as well as present, recognition of the consequences of the climate internal variability and quantification its uncertainty play a vital role. The Korean Peninsula belongs to the Far East Asian Monsoon region and its rainfall characteristics are very complex from time and space perspective. Its internal variability is expected to be large, but this variability has not been completely investigated to date especially using models of high temporal resolutions. Due to coarse spatial and temporal resolutions of General Circulation Models (GCM) projections, several studies adopted dynamic and statistical downscaling approaches to infer meterological forcing from climate change projections at local spatial scales and fine temporal resolutions. In this study, stochastic downscaling methodology was adopted to downscale daily GCM resolutions to hourly time scale using an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). After extracting factors of change from the GCM realizations, these were applied to the climatic statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series which can be considered to be representative of future climate conditions. Further, 30 ensemble members of hourly precipitation were generated for each selected station to quantify uncertainty. Spatial map was generated to visualize as separated zones formed through K-means cluster algorithm which region is more inconsistent as compared to the climatological norm or in which region the probability of occurrence of the extremes event is high. The results showed that the stations located near the coastal regions are more uncertain as compared to inland regions. Such information will be ultimately helpful for planning future adaptation and mitigation measures against extreme events.

• Journal of Korean Society of Forest Science
• /
• v.101 no.3
• /
• pp.509-516
• /
• 2012

In this research, it was intended to examine the vulnerability of Pinus rigida to climate changes, a major planting species in Korea. For this purpose, the distribution of Pinus rigida and its changes caused by climate changes were estimated based on the 'A1B' climate change scenario suggested by IPCC. Current distribution of Pinus rigida was analyzed by using the $4^{th}$Forest Type Map and its potential distribution in the recent year (2000), the near future (2050) and the further future (2100) were estimated by analyzing the optimized ranges of three climate indices - warmth index(WI), minimum temperature index of the coldest month (MTCI) and precipitation effectiveness index(PEI). The results showed that the estimated potential distribution of Pinus rigida declines to 56% in the near future(2050) and 15% in the further future (2100). This significant decline was found in most provinces in Korea. However, in Kangwon province where the average elevation is higher than other provinces, the area of potential distribution of Pinus rigida increases in the near future and the further future. Also the result indicated that the potential distribution of Pinus rigida migrates to higher elevation. The potential distributions estimated in this research have relatively high accuracy with consideration of classification accuracy (44.75%) and prediction probability (62.56%).

• Korean Journal of Soil Science and Fertilizer
• /
• v.50 no.5
• /
• pp.391-400
• /
• 2017

Italian ryegrass (IRG) is one of the fastest growing grasses available to farmers. It offers rapid establishment and starts growing early in the following spring and has fast regrowth after defoliation. So, IRG can be utilized as the dominant/single species of grass used in a farming system, or to play a role as a large producing pasture and sacrificial paddock. The objective of this study was to develop the use of unmanned aerial vehicle (UAV) for the evaluation of feed value of IRG. For this study, UAV imagery was taken on the Nonsan regions two times during the IRG growing season. We analyzed the relationships between $NDVI_{UAV}$ and feed value parameters such as fresh matter yield, dry matter yield, acid detergent fiber (ADF), neutral detergent fiber (NDF), total digestible nutrient (TDN) and crude protein at the season of harvest. Correlation analysis between $NDVI_{UAV}$ and feed value parameters of IRG revealed that $NDVI_{UAV}$ correlated well with crude protein (r = 0.745), and fresh matter yield (r = 0.655). According to the relationship, the variation of $NDVI_{UAV}$ was significant to interpret feed value parameters of IRG. Eight different regression models such as Linear, Logarithmic, Inverse, Quadratic, Cubic, Power, S, and Exponential model were used to estimate IRG feed value parameters. The S and exponential model provided more accurate results to predict fresh matter yield and crude protein than other models based on coefficient of determination, p- and F-value. The spatial distribution map of feed values in IRG plot was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when $NDVI_{UAV}$ was applied to regression equation. These lead to the result that the characteristics of variations in feed value of IRG according to $NDVI_{UAV}$ were well reflected in the model.

• Korean Journal of Remote Sensing
• /
• v.37 no.3
• /
• pp.503-514
• /
• 2021

Rice production with adequate level of area is important for decision making of rice supply and demand policy. It is essential to grasp rice cultivation areas in advance for estimating rice production of the year. This study was carried out to classify paddy rice cultivation in Gimje-si using sentinel-1 SAR (synthetic aperture radar) and UAV imagery in early July. Time-series Sentinel-1A and 1B images acquired from early May to early July were processed to convert into sigma naught (dB) images using SNAP (SeNtinel application platform, Version 8.0) toolbox provided by European Space Agency. Farm map and parcel map, which are spatial data of vector polygon, were used to stratify paddy field population for classifying rice paddy cultivation. To distinguish paddy rice from other crops grown in the paddy fields, we used the decision tree method using threshold levels and random forest model. Random forest model, trained by mainly rice cultivation area and rice and soybean cultivation area in UAV image area, showed the best performance as overall accuracy 89.9%, Kappa coefficient 0.774. Through this, we were able to confirm the possibility of early estimation of rice cultivation area in Gimje-si using UAV image.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2019.08a
• /
• pp.193-194
• /
• 2019

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.166-166
• /
• 2023

Compound hazards (CHs) are two or more extreme climate events combined which occur simultaneously in the same region at the same time. Compared to individual hazards, the combination of hazards that cause CHs can result in greater economic losses and deaths. While several extreme climate events have been recorded across Asia for the past decades, many studies have only focused on a single hazard. In this study, we assess the spatiotemporal pattern of dry compound hazards which includes drought, heatwave, fire and wind across Asia for the last 42 years (1980-2021) using the historical data from ERA5 Reanalysis dataset. We utilize a daily spatial data of each climate event to assess the occurrence of such compound hazards on a daily basis. Heatwave, fire and wind hazard occurrences are analyzed using daily percentile-based thresholds while a pre-defined threshold for SPI is applied for drought occurrence. Then, the occurrence of each type of compound hazard is taken from overlapping the map of daily occurrences of a single hazard. Lastly, a multivariate assessment are conducted to quantify the occurrence frequency, hotspots and trends of each type of compound hazard across Asia. By conducting a multivariate analysis of the occurrence of these compound hazards, we identify the relationships and interactions in dry compound hazards including droughts, heatwaves, fires, and winds, ultimately leading to better-informed decisions and strategies in the natural risk management.

• Journal of the Korean Society of Environmental Restoration Technology
• /
• v.9 no.5
• /
• pp.32-40
• /
• 2006

This study's objects are to suggest effective forest community-level management measures by identifying the vulnerable forest vegetation communities types to climate change through a comparative analysis with present forest communities identified and delineated in the Actual Vegetation Map. The methods of this study are to classify the climatic life zones based on the correlative climate-vegetation relationship for each forest vegetation community, the Holdridge Bio-Climate Model was employed. This study confirms relationship between forest vegetation and environmental factors using Pearson's correlation coefficient analysis. Then, the future distribution of forest vegetation are predicted derived factors and present distribution of vegetation by utilizing the multinomial logit model. The vulnerability of forest to climate change was evaluated by identifying the forest community shifts slower than the average velocity of forest moving (VFM) for woody plants, which is assumed to be 0.25 kilometers per year. The major findings in this study are as follows : First, the result of correlative analysis shows that summer precipitation, mean temperature of the coldest month, elevation, soil organic matter contents, and soil acidity (pH) are highly influencing factors to the distribution of forest vegetation. Secondly, the result of the vulnerability assessment employing the assumed velocity of forest moving for woody plants (0.25kmjyear) shows that 54.82% of the forest turned out to be vulnerable to climate change. The sub-alpine vegetations in regions around Mount Jiri and Mount Seorak are predicted to shift the dominance toward Quercus mongolica and Pinus densiflora communities. In the identified vulnerable areas centering the southern and eastern coastal regions, about 8.27% of the Pinus densiflora communities is likely to shift to sub-tropical forest communities, and 3.38% of the Quercus mongolica communities is likely to shift toward Quercus acutissima communities. In the vulnerable areas scattered throughout the country, about 8.84% of the Quercus mongolica communities is likely to shift toward Pinus densiflora communities due to the effects of climate change. The study findings concluded that challenges associated with predicting the future climate using RCM and the assessment of the future vulnerabilities of forest vegetations to climate change are significant.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.26 no.1
• /
• pp.75-88
• /
• 2024

It would be advantageous to predict acreage and yield of crops in major grain-exporting countries, which would improve decisions on policy making and grain trade in Korea. A climate suitability model can be used to assess crop acreage and yield in a region where the availability of observation data is limited for the use of process-based crop models. The objective of this study was to determine the climate suitability index of wheat by province in Ukraine, which would allow for the spatial assessment of acreage and yield for the given crop. In the present study, the official data of wheat acreage and yield were collected from the State Statistics Service of Ukraine. The EarthStat data, which is a data product derived from satellite data and official crop reports, were also gathered for the comparison with the map of climate suitability index. The Fuzzy Union model was used to create the climate suitability maps under the historical climate conditions for the period from 1970 to 2000. These maps were compared against actual acreage and yield by province. It was found that the EarthStat data for acreage and yield of wheat differed from the corresponding official data in several provinces. On the other hand, the climate suitability index obtained using the Fuzzy Union model explained the variation in acreage and yield at a reasonable degree. For example, the correlation coefficient between the climate suitability index and yield was 0.647. Our results suggested that the climate suitability index could be used to indicate the spatial distribution of acreage and yield within a region of interest.

• Korean Journal of Environmental Agriculture
• /
• v.28 no.4
• /
• pp.365-370
• /
• 2009

The aim of this study was to investigate the vegetation types of Minarimot, in Jeju Islands. The vegetation types were classified by the Z-M school method and cluster analysis. The vegetation in Minarimot was classified into 6 communities and 2 subcommunities: Persicaria thunbergii-Isachne globosa community (vegetation type: A), Scirpus tribangulatus-Eleocharis manillata var. cyclocarpa community (B) (Aneilema keisak subcommunity (B-1) and Caldesia parnassifolia-Potamogeton distinctus subcommunity (B-2)), Eleocharis kuroguwai community (C), Phragmites communis community (D), Scirpus tabernaemontani community(E) and Typha orientalis community (F). These communities were grouped into three main categories according to cluster analysis. The community (A) established at the edge of the wetland which has the driest condition was distinguished as Group I, while the community (B) emerged in the submerged zone was distinguished as Group III. The Group II was designated as the communities (C, D, E, F) between Group I and III, whose communities were occasionally submerged. The result of principal coordinate analysis (PCoA) appeared that the different vegetation established along the wetland were depending on water environment such as water depth and the period submerged.

• Journal of Korean Society for Geospatial Information Science
• /
• v.19 no.2
• /
• pp.19-27
• /
• 2011

The objective of this study was to suggest technical approaches for preparation and down scaling of climate data used for predicting the potential forest distribution. To predict the forest distribution, we employed a Korean-specific forest distribution model, so-called the TAG(Thermal Analogy Group), and defined the PFT(Plant Functional Types) based on the HyTAG(Hydrological and Thermal Analogy Group). The climate data with 20km spatial resolution were interpolated to fit on the input data format with 1km spatial resolution. Two potential forest distribution maps were estimated using climate data constructed by kriging, one of the interpolation and down-scaling approaches, with and without lapse rate considered. Through the verification process by comparing two potential maps with the actual vegetation map, the forest distribution using the lapse rate was proven to be 38% more accurate.