• 대한토목학회논문집
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• 제37권6호
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• pp.965-972
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• 2017

수자원 분야에서 기후변화 취약성 평가 연구는 미래를 반영하는 기후변화 시나리오를 다양한 방법으로 적용하고 있다. 하지만 대부분의 미래 취약성 평가 연구에서 미래 사회 및 경제 변화는 반영되지 않고 있다. 이에 본 연구에서는 통합적인 미래 시대상을 반영하기 위하여 Intergovernmental Panel on Climate Change (IPCC)에서 개발한 Reprensentative Concentration Pathway (RCP) 기후 변화 시나리오와 함께 공동 사회 경제 경로 시나리오(Shared Socioeconomic reference Pathway, SSP)를 적용하고자 하였다. 취약성 평가는 현재 상황 뿐 아니라 미래 시나리오를 반영하기에 적절한 지표를 선정하고 다기준 의사결정기법인 TOPSIS (Technique for Order Performance by Similarity to Ideal Solution)를 활용하여 각 지표를 통합하는 방법으로 진행하였다. 지표 자료는 국가 통계 및 보고서, 기후변화 시나리오가 반영된 SWAT (Soil and Water Assessment Tool) 모형의 모의 결과, 사회 경제 시나리오를 활용하였으며, 최종적으로 주요 수계인 한강 유역의 단기 미래(2020)와 중기 미래(2050)에 대한 중권역별 물이용 취약성 순위를 도출하였다. 전반적으로 기후변화만 적용한 결과와 사회 경제 변화를 함께 적용한 결과는 유사한 공간분포를 보였으나, SSP 시나리오에 따라 일부 유역에서 차이를 보였다. 미래 시나리오 적용 시 유역의 순위 변동성이 유사하게 나타났으나 일부에서는 SSP 시나리오 적용 유무에 따른 차이를 확인할 수 있었다. 본 연구에서 기후변화 취약성 분석 시 사회 경제 시나리오 활용가능성을 확인하였고, 이에 사회 경제 변화를 고려하는 것이 보다 효과적인 기후변화 대응에 도움이 될 것으로 판단된다.

• 환경영향평가
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• 제15권4호
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• pp.249-258
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• 2006

Impacts of global warming have been identified in many areas including natural ecosystem. A good number of studies based on climate models forecasting future climate have been conducted in many countries worldwide. Due to its global coverage, GCM, which is a most frequently used climate model, has limits to apply to Korea with such a narrower and complicated terrain. Therefore, it is necessary to perform a study impact assessment of climate changes with a climate model fully reflecting characteristics of Korean climate. In this respect, this study was designed to compare and analyze the GCM and RCM in order to determine a suitable climate model for Korea. In this study, spatial scope was Korea for 10 years from 1981 to 1990. As a research method, current climate was estimated on the basis of the data obtained from observation at the GHCN. Future climate was forecast using 4 GCMs furnished by the IPCC among SRES A2 Scenario as well as the RCM received from the NIES of Japan. Pearson correlation analysis was conducted for the purpose of comparing data obtained from observation with GCM and RCM. As a result of this study, average annual temperature of Korea between 1981 and 1990 was found to be around $12.03^{\circ}C$, with average daily rainfall being 2.72mm. Under the GCM, average annual temperature was between 10.22 and $16.86^{\circ}C$, with average daily rainfall between 2.13 and 3.35mm. Average annual temperature in the RCM was identified $12.56^{\circ}C$, with average daily rainfall of 5.01mm. In the comparison of the data obtained from observation with GCM and RCM, RCMs of both temperature and rainfall were found to well reflect characteristics of Korea's climate. This study is important mainly in that as a preliminary study to examine impact of climate changes such as global warming it chose appropriate climate model for our country. These results of the study showed that future climate produced under similar conditions with actual ones may be applied for various areas in many ways.

• The Plant Pathology Journal
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• 제35권5호
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• pp.459-472
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• 2019

The increasing variation in climatic conditions under climate change directly influences plant-microbe interactions. To account for as many variables as possible that may play critical roles in such interactions, the use of an integrated modeling approach is necessary. Here, we report for the first time a local impact assessment and adaptation study of future epidemics of kiwifruit bacterial blossom blight (KBB) in Jeonnam province, Korea, using an integrated modeling approach. This study included a series of models that integrated both the phenological responses of kiwifruit and the epidemiological responses of KBB to climatic factors with a 1 km resolution, under the RCP8.5 climate change scenario. Our results indicate that the area suitable for kiwifruit cultivation in Jeonnam province will increase and that the flowering date of kiwifruit will occur increasingly earlier, mainly due to the warming climate. Future epidemics of KBB during the predicted flowering periods were estimated using the Pss-KBB Risk Model over the predicted suitable cultivation regions, and we found location-specific, periodic outbreaks of KBB in the province through 2100. Here, we further suggest a potential, scientifically-informed, long-term adaptation strategy using a cultivar of kiwifruit with a different maturity period to relieve the pressures of future KBB risk. Our results clearly show one of the possible options for a local impact assessment and adaptation study using multiple models in an integrated way.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2011년도 학술발표회
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• pp.18-18
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• 2011

Climate change is impacting and will increasingly impact both the quantity and quality of the world's water resources in a variety of ways. In some areas warming climate results in increased rainfall, surface runoff, and groundwater recharge while in others there may be declines in all of these. Water quality is described by a number of variables. Some are directly impacted by climate change. Temperature is an obvious example. Notably, increased atmospheric concentrations of $CO_2$ triggering climate change increase the $CO_2$ dissolving into water. This has manifold consequences including decreased pH and increased alkalinity, with resultant increases in dissolved concentrations of the minerals in geologic materials contacted by such water. Climate change is also expected to increase the number and intensity of extreme climate events, with related hydrologic changes. A simple framework has been developed in New Zealand for assessing and predicting climate change impacts on water resources. Assessment is largely based on trend analysis of historic data using the non-parametric Mann-Kendall method. Trend analysis requires long-term, regular monitoring data for both climate and hydrologic variables. Data quality is of primary importance and data gaps must be avoided. Quantitative prediction of climate change impacts on the quantity of water resources can be accomplished by computer modelling. This requires the serial coupling of various models. For example, regional downscaling of results from a world-wide general circulation model (GCM) can be used to forecast temperatures and precipitation for various emissions scenarios in specific catchments. Mechanistic or artificial intelligence modelling can then be used with these inputs to simulate climate change impacts over time, such as changes in streamflow, groundwater-surface water interactions, and changes in groundwater levels. The Waimea Plains catchment in New Zealand was selected for a test application of these assessment and prediction methods. This catchment is predicted to undergo relatively minor impacts due to climate change. All available climate and hydrologic databases were obtained and analyzed. These included climate (temperature, precipitation, solar radiation and sunshine hours, evapotranspiration, humidity, and cloud cover) and hydrologic (streamflow and quality and groundwater levels and quality) records. Results varied but there were indications of atmospheric temperature increasing, rainfall decreasing, streamflow decreasing, and groundwater level decreasing trends. Artificial intelligence modelling was applied to predict water usage, rainfall recharge of groundwater, and upstream flow for two regionally downscaled climate change scenarios (A1B and A2). The AI methods used were multi-layer perceptron (MLP) with extended Kalman filtering (EKF), genetic programming (GP), and a dynamic neuro-fuzzy local modelling system (DNFLMS), respectively. These were then used as inputs to a mechanistic groundwater flow-surface water interaction model (MODFLOW). A DNFLMS was also used to simulate downstream flow and groundwater levels for comparison with MODFLOW outputs. MODFLOW and DNFLMS outputs were consistent. They indicated declines in streamflow on the order of 21 to 23% for MODFLOW and DNFLMS (A1B scenario), respectively, and 27% in both cases for the A2 scenario under severe drought conditions by 2058-2059, with little if any change in groundwater levels.

• 환경영향평가
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• 제21권5호
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• pp.757-765
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• 2012

The purpose of this study was to estimate the spatial-temporal NPP(Net Primary Productivity) and SCS(Soil Carbon Storage) of forest ecosystem under climate change in the capital area of South Korea using Mapss-Century1 (MC1), one of Dynamic Global Vegetation Models (DGVMs). The characteristics of the NPP and SCS changes were simulated based on a biogeochemical module in this model. As results of the simulation, the NPP varies from 2.02 to 7.43 tC $ha^{-1}\;yr^{-1}$ and the SCS varies from 34.55 to 84.81 tC $ha^{-1}$ during 1971~2000 respectively. Spatial mean NPP showed a little decreasing tendency in near future (2021~2050) and then increased in far future (2071~2100) under the condition of increasing air temperature and precipitation which were simulated by the A1B climate change scenario of Intergovernmental Panel on Climate Change (IPCC). But it was estimated that the temporal change of spatial mean NPP indicates 4.62% increasing tendency in which elevation is over 150m in this area. However, spatial mean SCS was decreased in the two future periods under same climate condition.

• 한국기후변화학회지
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• 제2권4호
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• pp.269-281
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• 2011

전구 및 지역 기후 모델 결과를 분석하여 동아시아와 한반도 지역에 대해 상세한 지역기후의 변화를 전망한 연구를 소개하였다. 특히 IPCC 4차 평가 보고서의 배출 시나리오를 기반으로 국내 연구그룹이 산출한 한반도 지역기후 변화 전망을 소개하고 그 특성을 파악하였다. 배출 시나리오에 따라 강도의 차이가 다소 있지만 미래 한반도의 온난화 경향은 명확한 것으로 나타났다. 강수량의 경우 배출 시나리오와 시기에 따라 다소 상이한 변화 경향을 보이지만, 대부분의 연구에서 공통적으로 미래 집중호우의 발생 빈도와 강도가 증가하는 것으로 나타났다. 이와 같은 지역기후 변화로 인하여 고지대를 제외한 대부분의 남한지역이 아열대 기후구로 점차 변해갈 것으로 전망되며, 이로 인한 생태계와 계절의 변화가 야기될 것으로 예상된다.

• 한국농림기상학회지
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• 제25권4호
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• pp.368-375
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• 2023

본 연구에서는 농촌진흥청과 홍콩과학기술대학교의 공동 개발로 생산된 1개월 예측 자료의 오차를 분석하고, 통계적 보정 기법을 활용한 오차 개선 효과를 살펴보고자 하였다. 이를 위해 2013년부터 2021년까지의 과거 예측(hindcast) 자료, 기상관측자료, 다양한 환경정보들을 수집하고 다양한 환경 조건에서의 오차 특성을 분석하였다. 최고기온과 최저기온의 경우, 해발고도와 위도가 높을 수록 예측 오차가 더 크게 나타났다. 평균적으로, 선형회귀모형과 XGBoost로 보정한 예측자료는 보정 전 예측자료보다 각각 0.203, 0.438(최고기온) 및 0.069, 0.390(최저기온) 정도의 RMSE가 감소했으며, 높은 고도와 위도에서의 오차 개선이 더 크게 나타났다. 모든 분석 조건에서 XGBoost가 선형회귀모형보다 우수한 오차 개선 효과를 나타냈다. 본 연구를 통해 예측 자료의 오차가 지형적 조건에 영향을 받는다는 사실을 확인하였고, XGBoost와 같은 기계학습법이 다양한 환경인자들을 고려하여 효과적으로 오차를 개선할 수 있다는 것을 확인하였다.

• 한국농림기상학회지
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• 제23권4호
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• pp.391-404
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• 2021

본 연구에서는 최신의 연구 트렌드인 빅데이터와 인공지능을 농업분야에 접목하여 유전자 알고리즘(GA)과 전지구 기후 재분석 자료를 활용한 마늘 생산량의 장기 예측 모형을 개발하고 그 예측성능을 평가해 보았다. 해당 모형은 마늘의 파종량을 수정할 수 있는 11월에 예측 자료를 생산하므로, 마늘의 생산 시기와 시간공간적으로 떨어진 전지구 기후 재분석 자료로부터 마늘생산량의 예측 인자로 활용할 수 있는 시그널을 찾아 장기적 마늘 생산량 예측에 활용하였다. 그 결과 결정론적 예측과 확률론적 예측 모두 마늘 생산량의 경년변동성을 통계적으로 99% 신뢰수준에서 관측과 유사하게 모의하였으며, 범주형 예측에서도 이분위 예측에서 93.3%, 삼분위 예측에서 73.3%의 적중률을 보이며 우수한 예측 성능을 나타내었다. 또한, 예측인자들 사이의 선형 및 비선형적 관계를 모두 고려하는 GA방법을 사용하였을 때, 선형적 앙상블 방법을 적용하였을 때 보다 높은 예측성능과 안정적인 예측결과를 보이는 것을 알 수 있다. 본 연구에서 개발된 마늘 생산량 예측 모형은 기존의 단기예측 위주의 농산물 생산량 예측의 한계를 극복하고 한 해의 농사가 시작되기 전 잠재 생산량을 전망 정보를 생산하여 농산물의 수요·공급 및 가격안정화를 위한 장기적 계획을 수립하는 것에 도움이 될 것으로 생각된다.

• 한국물환경학회지
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• 제25권4호
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• pp.507-514
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• 2009

In this study, a dynamic modeling scheme is presented to describe the probabilistic structure of soil water and plant water stress index under stochastic precipitation conditions. The proposed model has the form of the Fokker-Planck equation, and its applicability as a model for the probabilistic evolution of the soil water and plant water stress index is investigated under a climate change scenario. The simulation results of soil water confirm that the proposed soil water model can properly reproduce the observations and show that the soil water behaves with consistent cycle based on the precipitation pattern. The simulation results of plant water stress index show two different PDF patterns according to the precipitation. The simple impact assessment of climate change to soil water and plant water stress is discussed with Korean Meteorological Administration regional climate model.

• Entomological Research
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• 제48권6호
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• pp.505-513
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• 2018

The red imported fire ant is considered one of the most notorious invasive species because of its adverse impact on both humans and ecosystems. Public concern regarding red imported fire ants has been increasing, as they have been found seven times in South Korea. Even if red imported fire ants are not yet colonized in South Korea, a proper quarantine plan is necessary to prevent their widespread distribution. As a basis for quarantine planning, we modeled the potential distribution of the red imported fire ant under current climate conditions using six different species distribution models (SDMs) and then selected the random forest (RF) model for modeling the potential distribution under climate change. We acquired occurrence data from the Global Biodiversity Information Facility (GBIF) and bioclimatic data from WorldClim. We modeled at the global scale to project the potential distribution under the current climate and then applied models at the local scale to project the potential distribution of the red imported fire ant under climate change. Modeled results successfully represent the current distribution of red imported fire ants. The potential distribution area for red imported fire ants increased to include major harbors and airports in South Korea under the climate change scenario (RCP 8.5). Thus, we are able to provide a potential distribution of red imported fire ant that is necessary to establish a proper quarantine plan for their management to minimize adverse impacts of climate change.