• 제목/요약/키워드: RCP 8.5 scenario

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Assessment of climate disaster vulnerability of Gangwon-do based on RCP 8.5 climate change scenario (RCP 8.5 기후변화시나리오 기반 강원도 기후 재난취약성 평가)

  • Lee, Hyeon Ji;Jeung, Se Jin;Kim, Byung Sik
    • Proceedings of the Korea Water Resources Association Conference
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    • 한국수자원학회 2022년도 학술발표회
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    • pp.335-335
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    • 2022
  • 남한상세 기후변화 전망보고서(2021)는 2100년대 강원도 강수량이 현재보다 19% 증가하고, 평균기온이 현재보다 6.5℃ 상승할 것으로 공표했다. 강원도는 영동지역과 영서지역으로 분리돼 기후 차이가 분명하다. 기상청 ASOS 데이터(1986~2020)를 이용해 기후 특성을 확인한 결과 영동지역 강수량은 1,463mm, 평균기온은 10.5℃, 상대습도는 66%로 분석됐고, 영서지역 강수량은 1,307mm, 평균기온은 11℃, 상대습도는 68%로 분석됐다. 영동지역 강수량이 영서지역 강수량보다 약 156mm 더 많으며, 이는 영동지역에서 큰 규모의 우심 피해가 발생할 가능성이 존재함을 의미한다. 강원도 평년 우심 피해 현황을 살펴본 결과 영동지역은 5회(피해액: 62억 원), 영서지역은 24회(피해액: 62억원)가 발생했다. 이는 미래로 갈수록 더 심해질 것으로 판단되며, 이런 기상 재난을 객관적으로 판단할 수 있는 기준이 필요하다. 이에 본 연구에서는 기후변화에 따른 강원도 기후 재난취약성을 평가했다. 이를 위해 기후변화 위험성, 기후변화 민감도, 기후변화 적응능력 지표를 활용해 기후변화 취약성 지표를 선정했다. 기후변화 위험성 지표는 홍수(CWD, Rx5day, R30mm), 가뭄(CDD, SU, TX90p), 폭염(SU, TR, TN90p), 한파(ID, TX10p, FD)로 RCP 8.5 기후변화시나리오를 ETCCDI 지수에 적용했다. 기후변화 민감도와 기후변화 적응능력 지표는 국가통계포털, 강원통계정보, WAMIS에서 자료를 수집해 선정했다. 또한 재난취약성 지표를 4단계(Very Low, Low, High, Very High)로 구분했다. 홍수 취약성 평가 결과 2090년대 원주시, 춘천시, 횡성군이 Low에서 Very High로 단계가 격상됐다. 가뭄 취약성 평가 결과 2090년대 양양군, 영월군, 정선군이 Very Low에서 Very High로 단계가 격상됐다. 폭염 취약성 평가 결과 2090년대 삼척시, 태백시, 영월군이 Very Low에서 Very High로 단계가 격상됐다. 한파 취약성 평가 결과 삼척시, 태백시, 영월군이 High에서 Very Low로 단계가 격하됐다. 고로 강원도는 기후 재난취약성 평가 결과에 따른 미래 기후변화를 대비하고, 각 지역 특성에 맞는 복원력 관점 기후 재난 관리가 필요하다고 사료된다.

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Estimation and validation of the genetic coefficient of cv. Superior for the DSSAT-CSM (DSSAT 작물모형을 위한 수미품종의 품종모수의 결정과 기후변화에서의 활용)

  • Bak, Gyeryeong;Lee, Gyejun;Lee, Eunkyeong
    • Korean Journal of Agricultural and Forest Meteorology
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    • 제20권2호
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    • pp.166-174
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    • 2018
  • Potato(Solanum tuberosum L.) is one of the major food crop in the world following rice, wheat, and maize. It is thus important to project yield predict of potato under climate change conditions for assessment of food security. A crop growth modelling is widely used to simulate crop growth condition and total yield of various crops under a given climate condition. The decision support system for agrotechnology transfer (DSSAT) cropping system model, which was developed by U.S. which package integrating several models of 27 different crops, have been used to project crop yield for the impact assessment of climate change on crop production. In this study, we simulated potato yield using RCP 8.5 climate change scenario data, as inputs to the DSSAT model in five regions of Korea. The genetic coefficients of potato cultivar for 'superior', which is one of the most widely cultivated potato variety in Korea were determined. The GenCalc program, which is a submodule of the DSSAT package, was used to determine the genetic coefficients for the superior cultivar. The values of genetic coefficients were validated using results of 39 experiments performed over seven years in five regions. As a case study, the potato yield was projected that total yields of potato across five regions would increase by 26% in 2050s but decrease by 17% in 2090s, compared with 2010s. These results suggested that the needs for cultivation and irrigation technologies would be considerably large for planning and implementation of climate change adaptation for potato production in Korea.

Development of High-frequency Data-based Inflow Water Temperature Prediction Model and Prediction of Changesin Stratification Strength of Daecheong Reservoir Due to Climate Change (고빈도 자료기반 유입 수온 예측모델 개발 및 기후변화에 따른 대청호 성층강도 변화 예측)

  • Han, Jongsu;Kim, Sungjin;Kim, Dongmin;Lee, Sawoo;Hwang, Sangchul;Kim, Jiwon;Chung, Sewoong
    • Journal of Environmental Impact Assessment
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    • 제30권5호
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    • pp.271-296
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    • 2021
  • Since the thermal stratification in a reservoir inhibits the vertical mixing of the upper and lower layers and causes the formation of a hypoxia layer and the enhancement of nutrients release from the sediment, changes in the stratification structure of the reservoir according to future climate change are very important in terms of water quality and aquatic ecology management. This study was aimed to develop a data-driven inflow water temperature prediction model for Daecheong Reservoir (DR), and to predict future inflow water temperature and the stratification structure of DR considering future climate scenarios of Representative Concentration Pathways (RCP). The random forest (RF)regression model (NSE 0.97, RMSE 1.86℃, MAPE 9.45%) developed to predict the inflow temperature of DR adequately reproduced the statistics and variability of the observed water temperature. Future meteorological data for each RCP scenario predicted by the regional climate model (HadGEM3-RA) was input into RF model to predict the inflow water temperature, and a three-dimensional hydrodynamic model (AEM3D) was used to predict the change in the future (2018~2037, 2038~2057, 2058~2077, 2078~2097) stratification structure of DR due to climate change. As a result, the rates of increase in air temperature and inflow water temperature was 0.14~0.48℃/10year and 0.21~0.41℃/10year,respectively. As a result of seasonal analysis, in all scenarios except spring and winter in the RCP 2.6, the increase in inflow water temperature was statistically significant, and the increase rate was higher as the carbon reduction effort was weaker. The increase rate of the surface water temperature of the reservoir was in the range of 0.04~0.38℃/10year, and the stratification period was gradually increased in all scenarios. In particular, when the RCP 8.5 scenario is applied, the number of stratification days is expected to increase by about 24 days. These results were consistent with the results of previous studies that climate change strengthens the stratification intensity of lakes and reservoirs and prolonged the stratification period, and suggested that prolonged water temperature stratification could cause changes in the aquatic ecosystem, such as spatial expansion of the low-oxygen layer, an increase in sediment nutrient release, and changed in the dominant species of algae in the water body.

Projections of Future Summer Weather in Seoul and Their Impacts on Urban Agriculture (미래 서울의 여름날씨 전망과 도시농업에의 영향)

  • Kim, Jin-Hee;Yun, Jin I.
    • Korean Journal of Agricultural and Forest Meteorology
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    • 제17권2호
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    • pp.182-189
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    • 2015
  • Climate departure from the past variability was projected to start in 2042 for Seoul. In order to understand the implication of climate departure in Seoul for urban agriculture, we evaluated the daily temperature for the June-September period from 2041 to 2070, which were projected by the RCP8.5 climate scenario. These data were analyzed with respect to climate extremes and their effects on growth of hot pepper (Capsicum annuum), one of the major crops in urban farming. The mean daily maximum and minimum temperatures in 2041-2070 approached to the $90^{th}$ percentile in the past 30 years (1951-1980). However, the frequency of extreme events such as heat waves and tropical nights appeared to exceed the past variability. While the departure of mean temperature might begin in or after 2040, the climate departure in the sense of extreme weather events seems already in progress. When the climate scenario data were applied to the growth and development of hot pepper, the departures of both planting date and harvest date are expected to follow those of temperature. However, the maximum duration for hot pepper cultivation, which is the number of days between the first planting and the last harvest, seems to have already deviated from the past variability.

Impact of Future Chinese Emissions on Ozone Air Quality and Human Health in Northeast Asia (동북아 지역에서 중국의 미래 배출량 변화가 오존농도와 보건에 미치는 영향)

  • Kim, Hyeon-Kook;Lyu, Youngsook;Woo, Jung-Hun;Hong, Sung-Chul;Kim, Deok-Rae;Seo, Jeonghyeon;Shin, Myunghwan;Kim, Sang-Kyun
    • Journal of Climate Change Research
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    • 제7권4호
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    • pp.451-463
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    • 2016
  • We explore the impact of Chinese future air pollutant emissions on ozone air quality in Northeast Asia (NEA) and health in South-Korea using an assessment framework including ICAMS (The Integrated Climate and Air Quality Modeling System) and BenMAP (The Environmental Benefits Mapping and Analysis Program). The emissions data sets from the climate change scenarios, the Representative Concentration Pathways (RCPs) (emission scenarios, EMSO), are used to simulate ozone air quality in NEA in the current (1996~2005, 2000s), the near future (2016~2025, 2020s) and the distant future (2046~2055, 2050s). Furthermore, the simulated ozone changes in the 2050s are used to analyze ozone-related premature mortality and economic cost in South-Korea. While different EMSOs are applied to the China region, fixed EMSO are used for other country regions to isolate the impacts of the Chinese emissions. Predicted ozone changes in NEA are distinctively affected by large changes in NOx emission over most of China region. Comparing the 2020s with the 2000s situation, the largest increase in mean ozone concentrations in NEA is simulated under RCP 8.5 and similarly small increases are under other RCPs. In the 2050s in NEA, the largest increase in mean ozone concentrations is simulated under RCP 6.0 and leads to the occurrence of the highest premature mortalities and economic costs in South-Korea. Whereas, the largest decrease is simulated under RCP 4.5 leads to the highest avoided premature mortality numbers and economic costs. Our results suggest that continuous reduction of NOx emissions across the China region under an assertive climate change mitigation scenario like RCP 4.5 leads to improved future ozone air quality and health benefits in the NEA countries including South-Korea.

Assessing the Effects of Climate Change on the Geographic Distribution of Pinus densiflora in Korea using Ecological Niche Model (소나무의 지리적 분포 및 생태적 지위 모형을 이용한 기후변화 영향 예측)

  • Chun, Jung Hwa;Lee, Chang-Bae
    • Korean Journal of Agricultural and Forest Meteorology
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    • 제15권4호
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    • pp.219-233
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    • 2013
  • We employed the ecological niche modeling framework using GARP (Genetic Algorithm for Ruleset Production) to model the current and future geographic distribution of Pinus densiflora based on environmental predictor variable datasets such as climate data including the RCP 8.5 emission climate change scenario, geographic and topographic characteristics, soil and geological properties, and MODIS enhanced vegetation index (EVI) at 4 $km^2$ resolution. National Forest Inventory (NFI) derived occurrence and abundance records from about 4,000 survey sites across the whole country were used for response variables. The current and future potential geographic distribution of Pinus densiflora, one of the tree species dominating the present Korean forest was modeled and mapped. Future models under RCP 8.5 scenarios for Pinus densiflora suggest large areas predicted under current climate conditions may be contracted by 2090 showing range shifts northward and to higher altitudes. Area Under Curve (AUC) values of the modeled result was 0.67. Overall, the results of this study were successful in showing the current distribution of major tree species and projecting their future changes. However, there are still many possible limitations and uncertainties arising from the select of the presence-absence data and the environmental predictor variables for model input. Nevertheless, ecological niche modeling can be a useful tool for exploring and mapping the potential response of the tree species to climate change. The final models in this study may be used to identify potential distribution of the tree species based on the future climate scenarios, which can help forest managers to decide where to allocate effort in the management of forest ecosystem under climate change in Korea.

Calculation of Damage to Whole Crop Corn Yield by Abnormal Climate Using Machine Learning (기계학습모델을 이용한 이상기상에 따른 사일리지용 옥수수 생산량에 미치는 피해 산정)

  • Ji Yung Kim;Jae Seong Choi;Hyun Wook Jo;Moonju Kim;Byong Wan Kim;Kyung Il Sung
    • Journal of The Korean Society of Grassland and Forage Science
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    • 제43권1호
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    • pp.11-21
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    • 2023
  • This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)normal and DMYabnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMYnormal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.

The Global Warming Hiatus Simulated in HadGEM2-AO Based on RCP8.5 (HadGEM2-AO RCP8.5 모의에서 나타난 지구온난화 멈춤)

  • Wie, Jieun;Moon, Byung-Kwon;Kim, Ki-Young;Lee, Johan
    • Journal of the Korean earth science society
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    • 제35권4호
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    • pp.249-258
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    • 2014
  • Despite the greenhouse gases like carbon dioxide have steadily increased in atmosphere, the overall trend of the global average surface air temperature has stalled during the last decade (2002-present). This phenomenon is often called hiatus or warming pause, which is challenging the prevailing view that anthropogenic forcing causes warming environment. Our study characterized the hiatus by analyzing the HadGEM2-AO (95 yrs) simulation data based on RCP8.5 scenario. The PC2 time series from the EOF of the zonal mean vertical ocean temperature has been defined as the index that represents the warming pause. The relationship between the hiatus, ENSO and the changes in climate system are identified by utilizing the newly defined PC2. Since the La Nina index (defined as the negative of NINO3 index) leads PC2 by about 11 months, it may be possible that the La Nina causes the warming to be interrupted. We also show that the cooling of the climate system closed tied to the heat penetration into the deep ocean, indicating the weakening the warming rate is due to the oceanic heat uptake. Finally, the global warming hiatus is characterized by the anomalous warming in Arctic region as well as the intensification of the trade wind in the equatorial Pacific.

Development of Extreme Event Analysis Tool Base on Spatial Information Using Climate Change Scenarios (기후변화 시나리오를 활용한 공간정보 기반 극단적 기후사상 분석 도구(EEAT) 개발)

  • Han, Kuk-Jin;Lee, Moung-Jin
    • Korean Journal of Remote Sensing
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    • 제36권3호
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    • pp.475-486
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    • 2020
  • Climate change scenarios are the basis of research to cope with climate change, and consist of large-scale spatio-temporal data. From the data point of view, one scenario has a large capacity of about 83 gigabytes or more, and the data format is semi-structured, making it difficult to utilize the data through means such as search, extraction, archiving and analysis. In this study, a tool for analyzing extreme climate events based on spatial information is developed to improve the usability of large-scale, multi-period climate change scenarios. In addition, a pilot analysis is conducted on the time and space in which the heavy rain thresholds that occurred in the past can occur in the future, by applying the developed tool to the RCP8.5 climate change scenario. As a result, the days with a cumulative rainfall of more than 587.6 mm over three days would account for about 76 days in the 2080s, and localized heavy rains would occur. The developed analysis tool was designed to facilitate the entire process from the initial setting through to deriving analysis results on a single platform, and enabled the results of the analysis to be implemented in various formats without using specific commercial software: web document format (HTML), image (PNG), climate change scenario (ESR), statistics (XLS). Therefore, the utilization of this analysis tool is considered to be useful for determining future prospects for climate change or vulnerability assessment, etc., and it is expected to be used to develop an analysis tool for climate change scenarios based on climate change reports to be presented in the future.

Assessing the Climate Change Impacts on Paddy Rice Evapotranspiration Considering Uncertainty (불확실성을 고려한 논벼 증발산량 기후변화 영향 평가)

  • Choi, Soon-Kun;Jeong, Jaehak;Cho, Jaepil;Hur, Seung-Oh;Choi, Dongho;Kim, Min-Kyeong
    • Journal of Climate Change Research
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    • 제9권2호
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    • pp.143-156
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    • 2018
  • Evapotranspiration is a key element in designing and operating agricultural hydraulic structures. The profound effect of climate change to local agro-hydrological systems makes it inevitable to study the potential variability in evapotranspiration rate in order to develop policies on future agricultural water management as well as to evaluate changes in agricultural environment. The APEX-Paddy model was used to simulate local evapotranspiration responses to climate change scenarios. Nine Global Climate Models(GCMs) downscaled using a non-parametric quantile mapping method and a Multi?Model Ensemble method(MME) were used for an uncertainty analysis in the climate scenarios. Results indicate that APEX-Paddy and the downscaled 9 GCMs reproduce evapotranspiration accurately for historical period(1976~2005). For future periods, simulated evapotranspiration rate under the RCP 4.5 scenario showed increasing trends by -1.31%, 2.21% and 4.32% for 2025s(2011~2040), 2055s(2041~2070) and 2085s(2071~2100), respectively, compared with historical(441.6 mm). Similar trends were found under the RCP 8.5 scenario with the rates of increase by 0.00%, 4.67%, and 7.41% for the near?term, mid?term, and long?term periods. Monthly evapotranspiration was predicted to be the highest in August, July was the month having a strong upward trend while. September and October were the months showing downward trends in evapotranspiration are mainly resulted from the shortening of the growth period of paddy rice due to temperature increase and stomatal closer as ambient $CO_2$ concentration increases in the future.