• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1147-1151
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• 2009

In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1142-1146
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• 2009

A rainfall simulation and forecasting technique that can generate daily rainfall sequences conditional on multi-model ensemble GCMs is developed and applied to data in Korea for the major rainy season. The GCM forecasts are provided by APEC climate center. A Weather State Based Downscaling Model (WSDM) is used to map teleconnections from ocean-atmosphere data or key state variables from numerical integrations of Ocean-Atmosphere General Circulation Models to simulate daily sequences at multiple rain gauges. The method presented is general and is applied to the wet season which is JJA(June-July-August) data in Korea. The sequences of weather states identified by the EM algorithm are shown to correspond to dominant synoptic-scale features of rainfall generating mechanisms. Application of the methodology to seasonal rainfall forecasts using empirical teleconnections and GCM derived climate forecast are discussed.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.314-314
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• 2023

Extreme climate events can have a large impact on human life by hampering social, environmental, and economic development. Global circulation models (GCMs) are the widely used numerical models to understand the anticipated future climate change. However, different GCMs can project different future climates due to structural differences, varying initial boundary conditions and assumptions about the physical phenomena. The multi-model ensemble (MME) approach can improve the uncertainties associated with the different GCM outcomes. In this study, a comprehensive rating metric was used to select the best-performing GCMs out of 11 CMIP5 and 13 CMIP6 GCMs, according to their skills in terms of four temporal and five spatial performance indices, in replicating the 21 extreme climate indices during the baseline (1975-2017) in South Korea. The MME data were derived by averaging the simulations from all selected GCMs and three top-ranked GCMs. The random forest (RF) algorithm was also used to derive the MME data from the three top-ranked GCMs. The RF-derived MME data of the three top-ranked GCMs showed the highest performance in simulating the baseline extreme climate which was subsequently used to project the future extreme climate indices under both the representative concentration pathway (RCP) and the socioeconomic concentration pathway scenarios (SSP). The extreme cold and warming indices had declining and increasing trends, respectively, and most extreme precipitation indices had increasing trends over the period 2031-2100. Compared to all scenarios, RCP8.5 showed drastic changes in future extreme climate indices. The coasts in the east, south and west had stronger warming than the rest of the country, while mountain areas in the north experienced more extreme cold. While extreme cold climatology gradually declined from north to south, extreme warming climatology continuously grew from coastal to inland and northern mountainous regions. The results showed that the socially, environmentally and agriculturally important regions of South Korea were at increased risk of facing the detrimental impacts of extreme climatology.

• Journal of Climate Change Research
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• v.9 no.2
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• pp.133-142
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• 2018

Climate change will affect not only the crop productivity but also the pattern of rice disease epidemics in Korea. Impact assessments for the climate change are conducted using various climate change scenarios from many global climate models (GCM), such as a scenario from a best GCM or scenarios from multiple GCMs, or a combination of both. Here, we evaluated the feasibility of using a climate change scenario from the best GCM for the impact assessment on the potential epidemics of a rice leaf blast disease in Korea, in comparison to a multi?model ensemble (MME) scenario from multiple GCMs. For this, this study involves analyses of disease simulation using an epidemiological model, EPIRICE?LB, which was validated for Korean rice paddy fields. We then assessed likely changes in disease epidemics using the best GCM selected for individual agro?climatic zones and MME scenarios constructed by running 11 GCMs. As a result, the simulated incidence of leaf blast epidemics gradually decreased over the future periods both from the best GCM and MME. The results from this study emphasized that the best GCM selection approach resulted in comparable performance to the MME approach for the climate change impact assessment on rice leaf blast epidemic in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1186-1190
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• 2009

강수량이 예년에 비해 적은 양상은 여름강수량에 대한 부족으로 기인한다. 우리나라의 경우 장마기간의 강수와 태풍으로 인해 발생하는 강수가 전체 강수량에 많은 부분을 차지하고 있기 때문에 여름강수량이 적게 나타나게 되면 가을 가뭄 및 봄 가뭄에 대한 발생 압력도 그 만큼 커지게 되는 것이 일반적이다. 기존 연구들이 단순히 강수량을 가정하거나 시나리오를 기반으로 가뭄을 전망하는데 그치고 있으나 본 연구에서는 2009년 가뭄전망을 위해서 전지구기후모형(GCMs)의 3개월 기상예측 결과를 활용하고자 한다. 즉, APEC 기후예측 센터로부터 제공 받은 3개월 GCM Multi-Model Ensemble 예측 결과를 바탕으로 가뭄상태를 평가하였다. 따라서 본 연구의 목적은 Large-scale의 기후예측 시스템과 기상관측지점의 강수 및 온도를 연결시켜 가뭄을 전망할 수 있는 시스템을 구축하는데 있다. GCM 예측 결과를 바탕으로 2009년도 매월 강수량 및 평균 온도를 추정하여 PDSI 가뭄지수 산정에 이용하였다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.2
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• pp.327-340
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• 2015

General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the spatio-temporal discrepancy between GCM and observed value, therefore, the models deliver output that are generally required calibration for applied studies. Which is generally done by Multi-Model Ensemble (MME) approach. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a MME of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.198-202
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• 2009

기후변화는 강수와 기온을 변화시켜 수자원에 지대한 영향을 미칠 것으로 알려져 있다. 따라서 이에 대한 안정적인 수자원 관리를 위해서는 기후변화 영향을 정량적으로 평가하는 것이 필요하다. 기본적으로 기후변화에 대한 수자원의 영향을 연구할 때 '온실가스 배출시나리오, GCMs을 통한 기후모의, 시공간적 편차보정을 위한 상세화, 유출모형 적용을 통한 유출시나리오 생산'의 과정을 거친다. 그러나 유출시나리오를 얻기까지 과정에는 각각 불확실성을 가지고 있기 때문에 최종결과의 불확실성은 각 과정을 거치면서 매우 커진다고 할 수 있다. 다양한 배출시나리오, GCM 결과, 유출모형에 대해 단순평균 혹은 가중치를 주는 multi-model ensemble 기법은 각 경우에 따른 값의 범위를 제시할 수있다는 점 때문에 불확실성 평가에서 주로 이용되고 있다. 본 연구에서는 우리나라 5대강 유역 109개 중권역에 대해 multi-model ensemble을 적용하여 기후변화에 의한 수자원 영향을 평가하였다. 1971년에서 2100년까지 120년 기간에 대해 3개의 온실가스 배출시나리오, 13개의 GCMs 결과들을 수집하여 총 39개의 기후시나리오를 이용하였고, 이를 8개의 유출모형에 적용하여 총 312개의 유출시나리오를 생산하였다. 생산된 유출시나리오를 기준시간(1971${\sim}$2000)에 대한 미래의 세 기간(2020s, 2050s, 2080s)으로 나누어 변화율을 분석한 결과 여름철 유출량과 겨울철 유출량이 증가될것으로 나타났으나 겨울철 유출량 전망은 여름철에 비해 불확실성이 큰 것으로 나타났다. 공간적으로는 한강유역이 위치한 북쪽유역이 남쪽에 비해 불확실성이 큰 것으로 나타났다. 결과적으로 유출의 시공간적 편차에 의해 우리나라 수자원은 홍수피해 증가가 예상되었으며, 월별유출량의 변화로 인해 용수확보와 관리에 어려움이 증가할 것으로 전망되었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.516-516
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• 2016

기후변화는 농업생산량 감소와 식량 안보 문제와 같이 농업에 심각한 영향을 미칠 수 있다. 또한 기존의 농업수리 및 관개배수 시설 운영에 영향을 줄 수 있다. 따라서 지속가능한 농업 수자원 관리를 위해서는 기후변화의 영향을 고려한 장기적인 계획 수립이 필요하다. 따라서 본 연구에서는 논벼 지역의 설계용수량의 확률론적 분석을 통한 논벼 필요수량 및 설계용수량에 대한 기후변화영향 평가를 실시하였다. 이를 위해서 본 연구에서는 23개 GCM의 36개 산출물을 활용하여 Multi-model ensemble 구축하였다. 먼저 GCM별 증발산량과 유효우량을 산정한 결과 중부지역에서는 IPSL-CM5A 모델의 기후변화자료를 활용할 경우 증발산량과 유효우량이 타 GCM 모델들과 비하여 크게 산정되었다. 남부지역에서는 CanESM2 모델을 적용할 경우 가장 많은 증발산량과 유효우량이 모의되는 것으로 나타났다. 이처럼 GCM별로 다양한 결과가 모의되기 때문에 농업시설 설계에 적용되는 설계용수량의 경우 안전성을 위하여 Multi-GCM models을 활용할 필요가 있다. Multi-model ensemble의 RCP 4.5와 RCP 8.5 시나리오를 적용한 결과, 모든 경우에서 1995s(1981-2014)에 비해 설계용수량은 점차적으로 증가하는 것으로 나타났다. 평균 증가율은 RCP 4.5에서 중부지역이 9.4%, 남부지역이 6.0% 증가하는 것으로 나타난 반면, RCP 8.5에서는 중부지역이 11.1%, 남부지역이 8.2% 증가하는 것으로 나타났다. 또한 여러 GCM 산출물간의 불확실성은 RCP 4.5보다는 RCP 8.5 시나리오가, 중부 지역보다는 남부 지역이, 논벼 증발산량 보다는 유효우량이 더 큰 것으로 분석되었다. 본 연구는 향후 미래 가뭄 위험성을 최소화하기 위한 농업 수자원관리 전략수립에 활용될 수 있을 것이다. 또한 본 연구결과는 기후변화 영향 평가에 있어서 적합한 GCM 자료를 선택하는데 있어, 불확실성을 가늠할 수 있는 유용한 척도로 이용될 수 있을 것으로 기대된다.

• Journal of Korea Water Resources Association
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• v.44 no.8
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• pp.653-666
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• 2011

The purposes of this study are to suggest flood vulnerability assessment method on climate change with evaluation of this method over the 5 river basins and to present the uncertainty range of assessment using multi-model ensemble scenarios. In this study, the data related to past historical flood events were collected and flood vulnerability index was calculated. The vulnerability assessment were also performed under current climate system. For future climate change scenario, the 39 climate scenarios are obtained from 3 different emission scenarios and 13 GCMs provided by IPCC DDC and 312 hydrology scenarios from 3 hydrological models and 2~3 potential evapotranspiration computation methods for the climate scenarios. Finally, the spatial and temporal changes of flood vulnerability and the range of uncertainty were performed for future S1 (2010~2039), S2 (2040~2069), S3 (2070~2099) period compared to reference S0 (1971~2000) period. The results of this study shows that vulnerable region's were Han and Sumjin, Youngsan river basins under current climate system. Considering the climate scenarios, variability in Nakdong, Gum and Han river basins are large, but Sumjin river basin had little variability due to low basic-stream ability to adaptation.

• Journal of The Korean Society of Agricultural Engineers
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• v.57 no.5
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• pp.1-12
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• 2015

The study aimed to project inflows and demmands for the agricultural reservoir watersheds in South Korea considering a variety of regional characteristics and the uncertainty of future climate information. The study bias-corrected and spatially downscaled retrospective daily Global Climate Model (GCM) outputs under Representative Concentration Pathways (RCP) 4.5 and 8.5 emission scenarios using non-parametric quantile mapping method to force Soil and Water Assessment Tool (SWAT) model. Using the historical simulation, the skills of un-calibrated SWAT model (without calibration process) was evaluated for 5 reservoir watersheds (selected as well-monitored representatives). The study then, evaluated the performance of 9 GCMs in reproducing historical upstream inflow and irrigation demand at the five representative reservoirs. Finally future inflows and demands for 58 watersheds were projected using 9 GCMs projections under the two RCP scenarios. We demonstrated that (1) un-calibrated SWAT model is likely applicable to agricultural watershed, (2) the uncertainty of future climate information from different GCMs is significant, (3) multi-model ensemble (MME) shows comparatively resonable skills in reproducing water balances over the study area. The results of projection under the RCP 4.5 and RCP 8.5 scenario generally showed the increase of inflow by 9.4% and 10.8% and demand by 1.4% and 1.7%, respectively. More importantly, the results for different seasons and reservoirs varied considerably in the impacts of climate change.