• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.155-163
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• 2022

The optimization of long-range ensemble climate prediction for rice phenology model with advanced bias correction method is conducted. The daily long-range forecast(6-month) of mean/ minimum/maximum temperature and observation of January to October during 1991-2021 is collected for rice phenology prediction. In this study, the concept of "buffer period" is newly introduced to reduce the problem after bias correction by quantile mapping with constructing the transfer function by month, which evokes the discontinuity at the borders of each month. The four experiments with different lengths of buffer periods(5, 10, 15, 20 days) are implemented, and the best combinations of buffer periods are selected per month and variable. As a result, it is found that root mean square error(RMSE) of temperatures decreases in the range of 4.51 to 15.37%. Furthermore, this improvement of climatic variables quality is linked to the performance of the rice phenology model, thereby reducing RMSE in every rice phenology step at more than 75~100% of Automated Synoptic Observing System stations. Our results indicate the possibility and added values of interdisciplinary study between atmospheric and agriculture sciences.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.81-92
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• 2019

The objective of this study is to evaluate hydrologic impacts of climate change according to downscaling methods using the Soil and Water Assessment Tool (SWAT) model at watershed scale. We used the APCC Integrated Modeling Solution (AIMS) for assessing various General Circulation Models (GCMs) and downscaling methods. AIMS provides three downscaling methods: 1) BCSA (Bias-Correction & Stochastic Analogue), 2) Simple Quantile Mapping (SQM), 3) SDQDM (Spatial Disaggregation and Quantile Delta Mapping). To assess future hydrologic responses of climate change, we adopted three GCMs: CESM1-BGC for flood, MIROC-ESM for drought, and HadGEM2-AO for Korea Meteorological Administration (KMA) national standard scenario. Combined nine climate change scenarios were assessed by Expert Team on Climate Change Detection and Indices (ETCCDI). SWAT model was established at the Mankyung watershed and the applicability assessment was completed by performing calibration and validation from 2008 to 2017. Historical reproducibility results from BCSA, SQM, SDQDM of three GCMs show different patterns on annual precipitation, maximum temperature, and four selected ETCCDI. BCSA and SQM showed high historical reproducibility compared with the observed data, however SDQDM was underestimated, possibly due to the uncertainty of future climate data. Future hydrologic responses presented greater variability in SQM and relatively less variability in BCSA and SDQDM. This study implies that reasonable selection of GCMs and downscaling methods considering research objective is important and necessary to minimize uncertainty of climate change scenarios.

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

• Ecology and Resilient Infrastructure
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• v.8 no.3
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• pp.154-163
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• 2021

This study was carried out to identify the factors affecting the distribution of suitable habitats for Geranium carolinianum, which was naturalized in South Korea, and to predict the changes of distribution in the future. We collected occurrence data of G. carolinianum at 68 sites in South Korea, and applied the MaxEnt model under climate change scenarios (SSP2-4.5, and SSP5-8.5). Precipitation seasonality (bio15), mean temperature of warmest quarter (bio10), and mean temperature of driest quarter (bio09) had high contribution for potential distribution of G. carolinianum. According to climate change scenarios, high suitable habitats of G. carolinianum occupied 6.43% of the land of South Korea in historical period (1981~2010), and 92.60% under SSP2-4.5, and 98.36% undr SSP5-8.5 in far future (2071~2100).

• Journal of Environmental Impact Assessment
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• v.23 no.2
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• pp.101-111
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• 2014

The research was carried out in order to find climate factors which determine the distribution of Neolitsea sericea, and the potential habitats (PHs) under the current climate and three climate change scenario by using species distribution models (SDMs). Four climate factors; the minimum temperature of the coldest month (TMC), the warmth index (WI), summer precipitation (PRS), and winter precipition (PRW) : were used as independent variables for the model. Three general circulation models under A1B emission scenarios were used as future climate scenarios for the 2050s (2040~2069) and 2080s (2070~2099). Highly accurate SDMs were obtained for N. sericea. The model of distribution for N. sericea constructed by SDMs showed that minimum temperature of the coldest month (TMC) is a major climate factor in determining the distribution of N. sericea. The area above the $-4.4^{\circ}C$ of TMC revealed high occurrence probability of the N. sericea. Future PHs for N. sericea were projected to increase respectively by 4 times, 6.4 times of current PHs under 2050s and 2080s. It is expected that the potential of N. sericea habitats is expanded gradually. N. sericea is applicable as indicator species for monitoring in the Korean Peninsula. N. sericea is necessary to be monitored of potential habitats.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.83-96
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• 2018

The objective of this study was to estimate the climate change impact on water quantity and quality to Saemanguem watershed using SWAT (Soil and water assessment tool) model. The SWAT model was calibrated and validated using observed data from 2008 to 2017 for the study watershed. The $R^2$ (Determination coefficient), RMSE (Root mean square error), and NSE (Nash-sutcliffe efficiency coefficient) were used to evaluate the model performance. RCP scenario data were produced from 10 GCM (General circulation model) and all relevant grid data including the major observation points (Gusan, Jeonju, Buan, Jeongeup) were extracted. The systematic error evaluation of the GCM model outputs was performed as well. They showed various variations based on analysis of future climate change effects. In future periods, the MIROC5 model showed the maximum values and the CMCC-CM model presented the minimum values in the climate data. Increasing rainfall amount was from 180mm to 250mm and increasing temperature value ranged from 1.7 to $5.9^{\circ}C$, respectively, compared with the baseline (2006~2017) in 10 GCM model outputs. The future 2030s and 2070s runoff showed increasing rate of 16~29% under future climate data. The future rate of change for T-N (Total nitrogen) and T-P (Total phosphorus) loads presented from -26 to +0.13% and from +5 to 47%, respectively. The hydrologic cycle and water quality from the Saemanguem headwater were very sensitive to projected climate change scenarios so that GCM model should be carefully selected for the purpose of use and the tendency analysis of GCM model are needed if necessary.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.4
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• pp.65-86
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• 2021

This study aims at testing a hypothesis that the resilience of agricultural water systems is characterized by trade-offs and synergies of effects from climate and socioeconomic change. To achieve this, an Agricultural Water System Climate Resilience Assessment (ACRA) framework is established to evaluate comprehensive resilience of an agricultural water system to the combined impacts of the climate and socioeconomic changes with a case study in South Korea. Understanding dynamic behaviors of the agricultural water systems under climate and socioeconomic drivers is not straightforward because the system structure includes complex interactions with multiple feedbacks across components in water and agriculture sectors and climate and socioeconomic factors, which has not been well addressed in the existing decision support models. No consideration of the complex interactions with feedbacks in a decision making process may lead to counterintuitive and untoward evaluation of the coupled impacts of the climate and socioeconomic changes on the system performance. In this regard, the ACRA framework employs a System Dynamics (SD) approach that has been widely used to understand dynamics of the complex systems with the feedback interactions. In the ACRA framework applied to the case study in South Korea, the SD model works along with HOMWRS simulation. The ACRA framework will help to explore resilience-based strategies with infrastructure investment and management options for agricultural water systems.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.738-739
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• 2015

The main objective of this study, "Spatial Downscaling of Precipitation from GCMs for Assessing Climate Change over Han River and Imjin River Watersheds", is to carry out over Han River and Imjin River watersheds. To this end, a statistical regression method with MOS (Model Output Statistics) corrections at every downscaling step was developed and applied for downscaling the spatially-coarse Global Climate Model Projections (GCMPs) from CCSM3 and CSIRO with respect to precipitation into 0.1 degree (about 11 km) spatial grid over study regions. The spatially archived hydro-climate data sets such as Willmott, GsMap and APHRODITE datasets were used for MOS corrections by means of monthly climatology between observations and downscaled values. Precipitation values downscaled in this study were validated against ground observations and then future climate simulation results on precipitation were evaluated for the projections.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.119-136
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• 2012

The goal of this study is to apply the IPCC(Intergovernmental Panel on Climate Change) concept of vulnerability to climate change and verify the use of a combination of vulnerability index and fuzzy logic to flood vulnerability analysis and mapping in Seoul using GIS. In order to achieve this goal, this study identified indicators influencing floods based on literature review. We include indicators of exposure to climate(daily max rainfall, days of 80mm over), sensitivity(slope, geological, average DEM, impermeability layer, topography and drainage), and adaptive capacity(retarding basin and green-infra). Also, this research used fuzzy model for aggregating indicators, and utilized frequency ratio to decide fuzzy membership values. Results show that the number of days of precipitation above 80mm, the distance from river and impervious surface have comparatively strong influence on flood damage. Furthermore, when precipitation is over 269mm, areas with scare flood mitigation capacities, industrial land use, elevation of 16~20m, within 50m distance from rivers are quite vulnerable to floods. Yeongdeungpo-gu, Yongsan-gu, Mapo-gu include comparatively large vulnerable areas. This study improved previous flood vulnerability assessment methodology by adopting fuzzy model. Also, vulnerability map provides meaningful information for decision makers regarding priority areas for implementing flood mitigation policies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.37 no.6
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• pp.965-972
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• 2017

Assessment of vulnerability of water use to climate change include a variety of climate change scenarios. However, in most future vulnerability studies, only the climate change scenarios are used and not the future scenarios of social and economic indicators. Therefore, in this study, we applied the Representative Concentration Pathway (RCP) climate change scenario and Shared Socioeconomic reference Pathway (SSP) developed by IPCC to reflect the future. We selected indicators for estimating the vulnerability of water use, and indices were integrated with a multi-criteria decision making approach - Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The indicator data utilized national statistics and reports, social and economic scenarios, and simulated results from the Soil and Water Assessment Tool (SWAT) model which reflects climate change scenario. Finally, we derived the rankings of water use vulnerability for the short-term future (2020) and mid-term future (2050) within the Han River watershed. Generally, considering climate change alone and considering climate change plus social and economic changes showed a similar spatial distribution. In the future scenarios, the watershed rankings were similar, but showed differences with SSP scenario in some watersheds. Therefore, considering social and economic changes is expected to contribute to more effective responses to climate change.