• Journal of Korea Water Resources Association
• /
• v.53 no.6
• /
• pp.409-416
• /
• 2020

An extreme climate monitoring is essential to the reduction of socioeconomic damages from extreme events. The objective of this study was to produce the near-realtime weekly root-zone Soil Moisture Index (SMI) on the basis of soil moisture using the Noah 3.3 Land Surface Model (LSM) for potentially monitoring extreme drought events. The Yangtze basin was selected to evaluate the Noah LSM performance for the East Asia region (15-60°N, 70-150°E) and the evapotranspiration (ET) and sensible heat flux (SH) were compared with ET and SH from FluxNet and with ET from FluxCom, Global Land Evaporation Amsterdam Model (GLEAM), ERA-5, and Generalized Complementary Relationship (GCR). For the ET, the coefficients of determination (R²) were higher than 0.96, while the R² value for the SH was 0.71 with slightly lower than those. A time series of the weekly root-zone SMI revealed that the regions with Extreme drought had been expanded from the northern part of East China to the entire East China between July to October 2019. The trend analysis of the number of extreme drought events showed that extreme drought events in spring had reduced in South Korea over the past 20 years, while those in fall had a tendency to increase. It is concluded that this study can be useful to reduce the socioeconomic damages resulted from climate extremes by comprehensively characterizing extreme drought events.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.18 no.2
• /
• pp.65-73
• /
• 2016

We estimated the averaged maximum incidences of bacterial canker at suitable sites for kiwifruit cultivation in 2020s and 2050s using D-PSA-K model with RCP4.5 and RCP8.5 climate change scenarios. Though there was a little difference between the estimation using RCP4.5 and that using RCP8.5, the estimated maximum disease incidences were more than 75% at all the suitable sites in Korea except for some southern coastal areas and Jeju island under the assumption that there are a plenty of infections to cause the symptoms. We also analyzed the intermediate and final outputs of D-PSA-K model to find out the trends on the change in disease incidence affected by climate change. Whereas increase of damage to kiwifruit canes in a non-frozen environment caused by bacterial canker was estimated at almost all the suitable sites in both the climate change scenarios, rate of necrosis increase caused by the bacterial canker pathogen in a frozen environment during the last overwintering season was predicted to be reduced at almost all the suitable sites in both the climate change scenarios. Directions of change in estimated maximum incidence varied with sites and scenarios. Whereas the maximum disease incidence at 3.14% of suitable sites for kiwifruit cultivation in 2020s under RCP4.5 scenario was estimated to increase by 10% or more in 2050s, the maximum disease incidence at 25.41% of the suitable sites under RCP8.5 scenario was estimated so.

• Journal of the Korean association of regional geographers
• /
• v.19 no.4
• /
• pp.600-614
• /
• 2013

This study aims to examine spatially-detailed changes and projection of subtropical climate zones based on the modified K$\ddot{o}$ppen-Trewartha's climate classification and extreme temperature indices using $1km{\times}1km$ high resolution RCP 4.5 and RCP 8.5 climate change scenarios based on PRIDE model over the Republic of Korea. Subtropical climate zones currently located along the southern coastal region. Future subtropical climate zones would be pushed northwards expanding to the western and the eastern coastal regions as well as some metropolitan areas. For both scenarios, the frequency of cold-related extreme temperatures projects to be reduced while the frequency of hot-related ones projects to be increased. Especially, hot days with $33^{\circ}C$ or higher temperature projects to occur more than 30 days over the most of regions except for some mountain areas with high altitudes during the period of 2070~2100. This study might provide essential information to make climate change adaptation processes be enhanced.

• Communications for Statistical Applications and Methods
• /
• v.27 no.1
• /
• pp.109-128
• /
• 2020

A climate-impact projection usually consists of several stages, and the uncertainty of the projection is known to be quite large. It is necessary to assess how much each stage contributed to the uncertainty. We call an uncertainty quantification method in which relative contribution of each stage can be evaluated as uncertainty decomposition. We propose a new Bayesian model for uncertainty decomposition in climate change impact assessments. The proposed Bayesian model can incorporate uncertainty of natural variability and utilize data in control period. We provide a simple and efficient Gibbs sampling algorithm using the auxiliary variable technique. We compare the proposed method with other existing uncertainty decomposition methods by analyzing streamflow data for Yongdam Dam basin located at Geum River in South Korea.

• Environmental Engineering Research
• /
• v.22 no.1
• /
• pp.87-94
• /
• 2017

This study was conducted to determine the effect of the climate change to the level of groundwater recharging. This research was conducted on the watershed of Bangga by using the Soil Water Balance of MockWyn-UB model. Input data compose of evapotranspiration, monthly rainfall, watershed area, canopy interception, heavy rain factor and the influence of climate change factors (rainfall and temperature). The conclusion of this study indicates that there is a decreasing trend in annual groundwater recharge observed from 1995 to 2011. The amount of groundwater recharge varied linearly with monthly rainfall and between 3% to 25% of the rainfall. This result implies that rain contributed more than groundwater recharge to runoff and evaporation and the groundwater recharge and Bangga River discharge depends largely on the rainfall. In order to increase the groundwater recharge in the study area, reforestation programmes should be intensified.

• KIEAE Journal
• /
• v.14 no.4
• /
• pp.127-131
• /
• 2014

The ground source heat pump (GSHP) system is a kind of the temperature differential energy system using relatively stable underground temperature as heat source of space heating and cooling. This system can achieve higher performance of system than it of conventional air source heat pump systems. However, its superiority of the system performance is different according to installation location or local climate, because the system performance depends on the underground condition which is decided by annual average air temperature. In this study, in order to estimate the feasibility of the ground source heat pump system according to the local climate, numerical simulation was conducted using the ground heat transfer model and the surface heat balance model. The case study was conducted in the condition of Seoul, Daejeon, and Busan, In the result, the heat exchange rate of Busan was 34.33 W/m as the largest in heating season and it of Seoul was 40.61 W/m as the largest in cooling.

• Journal of Environmental Impact Assessment
• /
• v.22 no.1
• /
• pp.109-116
• /
• 2013

By IPCC climate change scenario, the socioeconomic actions such as the land use change are closely associated with the climate change as an up zoning action of urban development to increase green gas emission to atmosphere. Prediction of the land use change with rational quality can provide better data for understanding of the climate change in future. This study aims to predict land use change of Cheongju in future and SLEUTH model is used to anticipate with the status quo condition, in which the pattern of land use change in future follows the chronical tendency of land use change during last 25 years. From 40 years prediction since 2000 year, the area urbanized compared with 2000 year increases up to 87.8% in 2040 year. The ratios of the area urbanized from agricultural area and natural area in 2040 are decreased to 53.1% and 15.3%, respectively.

• Ocean Science Journal
• /
• v.40 no.4
• /
• pp.201-208
• /
• 2005

An accurate particle tracking method for a finite difference method model is developed using a constant acceleration method. Being assumed constant temporal and spatial gradients, the new method permits temporal-spatial variability of particle velocity. Test results in a solid rotating flow show that the new method has second-order accuracy. The performance of the new method is compared with that of other methods; the first-order Euler forward method, and the second-order Euler predictor-corrector method. The new method is the most efficient method among the three. It is more accurate and efficient than the other two.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.25 no.2
• /
• pp.80-98
• /
• 2023

This study explores the effectiveness of various data preprocessing algorithms for improving subseasonal to seasonal (S2S) climate predictions from six climate forecast models and their Multi-Model Ensemble (MME) using a deep learning-based postprocessing model. A pipeline of data transformation algorithms was constructed to convert raw S2S prediction data into the training data processed with several statistical distribution. A dimensionality reduction algorithm for selecting features through rankings of correlation coefficients between the observed and the input data. The training model in the study was designed with TimeDistributed wrapper applied to all convolutional layers of U-Net: The TimeDistributed wrapper allows a U-Net convolutional layer to be directly applied to 5-dimensional time series data while maintaining the time axis of data, but every input should be at least 3D in U-Net. We found that Robust and Standard transformation algorithms are most suitable for improving S2S predictions. The dimensionality reduction based on feature selections did not significantly improve predictions of daily precipitation for six climate models and even worsened predictions of daily maximum and minimum temperatures. While deep learning-based postprocessing was also improved MME S2S precipitation predictions, it did not have a significant effect on temperature predictions, particularly for the lead time of weeks 1 and 2. Further research is needed to develop an optimal deep learning model for improving S2S temperature predictions by testing various models and parameters.

• Journal of Climate Change Research
• /
• v.4 no.2
• /
• pp.177-188
• /
• 2013

In this research, forest cover distribution change, forest volume and carbon stock in Yongin-city, Gyeonggi procince were estimated focused on the forest of Yongin-City using forest type map and HyTAG model in relation to climate change. Present forest volume of Yongin-city was estimated using the data from $5^{th}$ Forest Type Map and Korean National Forest Inventory (NFI). And for the future 100 years potential forest distribution by 10-year interval were estimated using HyTAG model. Forest volume was also calculated using algebraic differences form of the growth model. According to the $5^{th}$ Forest Type Map, present needleleaf forest occupied 37.8% and broadleaf forest 62.2% of forest area. And the forest cover distribution after 30 years would be changed to 0.13% of needleleaf forest and 99.97% of broadleaf forest. Finally, 60 years later, whole forest of Yongin-city would be covered by broad-leaf forest. Also the current forest carbon stocks was measured 1,773,862 tC(56.79 tC/ha) and future carbon stocks after 50 years was predicted to 4,432,351 tC(141.90 tC/ha) by HyTAG model. The carbon stocks after 100 years later was 6,884,063 tC (220.40 tC/ha). According to the HyTAG model prediction, Pinus koraiensis, Larix kaempferi, Pinus rigida, and Pinus densiflora are not suitable to the future climate of 10-year, 30-year, 30-year, and 50-year later respectively. All Quercus spp. was predicted to be suitable to the future climate.