• 한국농공학회논문집
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• 제56권2호
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• pp.11-23
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• 2014

The impact and adaption on agricultural water resources considering climate change is significant for reservoirs. The change in rainfall patterns and hydrologic factors due to climate change increases the uncertainty of agricultural water supply and demand. The quantitative evaluation method of uncertainty based on agricultural water resource management under future climate conditions is a major concern. Therefore, it is necessary to improve the vulnerability management technique for agricultural water supply based on a probabilistic and stochastic risk evaluation theory. The objective of this study was to analyse the uncertainty of water resources under future climate change using probability distribution function of water supply in agricultural reservoir and demand in irrigation district. The uncertainty of future water resources in agricultural reservoirs was estimated using the time-specific analysis of histograms and probability distributions parameter, for example the location and the scale parameter. According to the uncertainty analysis, the future agricultural water supply and demand in reservoir tends to increase the uncertainty by the low consistency of the results. Thus, it is recommended to prepare a resonable decision making on water supply strategies in terms of using climate change scenarios that reflect different future development conditions.

• 한국기후변화학회지
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• 제7권4호
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• pp.407-419
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• 2016

The low flow is the necessary and important index to establish national water planning, however there are lots of uncertainty in the low flow estimation. Therefore, the objectives of this study are to assess the climate change uncertainty and the effects of hydrological models on low flow estimation. The 5 RCMs (HadGEM3-RA, RegCM4, MM5, WRF, and RSM), 5 statistical post-processing methods and 2 hydrological models were applied for evaluation. The study area were selected as Chungju dam and Soyang river dam basin, and the 30 days minimum flow is used for the low flow evaluation. The results of the uncertainty analysis showed that the hydrological model was the largest source of uncertainty about 41.5% in the low flow projection. The uncertainty of hydrological model is higher than the other steps (RCM, statistical post-processing). Also, VIC model is more sensitive for climate change compared to SWAT model. Therefore, the hydrological model should be thoroughly reviewed for the climate change impact assessment on low flow.

• 한국수자원학회논문집
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• 제51권spc1호
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• pp.1079-1089
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• 2018

기존 기후변화 영향평가에서 발생하는 불확실성에 대한 연구들은 전체과정에서 총 불확실성과 그 전파에 대한 것보다 각 단계별 불확실성에 초점을 맞추어 연구가 진행되었다. 따라서 본 연구에서는 first-order Taylor series expansion에 기반하여 전망의 분산을 이용하는 Uncertainty Delta Method (UDM)를 제안하였으며, 이 방법은 각 단계별 불확실성 정량화와 증감정도, 단계별 불확실성 비율, 총 불확실성의 전파 과정 제시가 가능하다. 본 연구에서는 기후변화 영향평가 과정의 단계별 불확실성 정량화와 전파과정 분석을 위해 미래 2030년부터 2059년까지를 대상으로 2개 배출 시나리오, 3개 GCM, 2개 상세화기법, 2개 수문모형을 사용하였다. 결과를 분석하면, UDM을 이용한 총 불확실성은 5.45(배출시나리오: 4.45, 상세화기법: 0.45, 상세화기법: 0.27, 수문모형: 0.28)이며, 배출 시나리오의 불확실성(4.45)이 가장 크게 나타났다. 불확실성은 각 단계를 거칠수록 증가하는 것으로 나타났다. 이러한 결과는 어떠한 배출시나리오를 선정하느냐에 따라 미래 수자원전망이 매우 달라질 수 있음을 의미한다. 다음으로 Hawkins and Sutton (2009)가 제안한 Fractional Uncertainty Method (FUM)을 이용한 기후변화 영향평가 불확실성 분석에서 가장 불확실성이 큰 요인은 배출 시나리오(FUM 불확실성: 0.52)이며, 이 결과는 UDM 결과와 동일하게 나타났다. 따라서 이 연구에서 제안한 UDM은 기후변화 영향평가에서의 불확실성 이해와 적합한 분석 및 미래 기후변화 대비 보다 나은 수자원 전망이 가능하도록 기여할 것으로 판단된다.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.109-128
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• 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.

• 한국기후변화학회지
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• 제9권1호
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• pp.103-115
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• 2018

The uncertainty of climate scenarios, as initial information, is one of the significant factors among uncertainties of climate change impacts and vulnerability assessments. In this sense, the quantification of the uncertainty of climate scenarios is essential to understanding these assessments of impacts and vulnerability for adaptation to climate change. Here we quantified the precision of surface temperature of ensemble scenarios (high resolution (1km) RCP4.5 and 8.5) provided by Korea Meteorological Administration, with spatiotemporal variation of the standard deviation of them. From 2021 to 2050, the annual increase rate of RCP8.5 was higher than that of RCP4.5 while the annual variation of RCP8.5 was lower than that of RCP4.5. The standard deviations of ensemble scenarios are higher in summer and winter, particularly in July and January, when the extreme weather events could occur. In general, the uncertainty of ensemble scenarios in summer were lower than those in winter. In spatial distribution, the standard deviation of ensemble scenarios in Seoul Metropolitan Area is relatively higher than other provinces, while that of Yeongnam area is lower than other provinces. In winter, the standard deviations of ensemble scenarios of RCP4.5 and 8.5 in January are higher than those of December. Especially, the standard deviation of ensemble scenarios is higher in the central regions including Gyeonggi, and Gangwon, where the mean surface temperature is lower than southern regions along with Chungbuk. Such differences in precisions of climate ensemble scenarios imply that those uncertainty information should be taken into account for the implementation of national climate change policy.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2005년도 학술발표회 논문집
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• pp.138-142
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• 2005

The change of precipitation and temperature due to the global. warming eventually caused the variation of water availability in terms of potential evapotranspiration, soil moisture, and runoff. In this reason national long-term water resource planning should be considered the effect of climate change. Study of AOGCM-based scenario to proposed the plausible future states of the climate system has become increasingly important for hydrological impact assessment. Future climate changes over East Asia are projected from the coupled atmosphere-ocean general circulation model (AOGCM) simulations based on Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios using multi-model ensembles (MMEs) method (Min et al. 2004). MME method is used to reduce the uncertainty of individual models. However, the uncertainty increases are larger over the small area than the large area. It is demonstrated that the temperature increases is larger over continental area than oceanic area in the 21st century.

• 한국농공학회논문집
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• 제57권2호
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• pp.1-13
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• 2015

The Coupled Model Intercomparison Project Phase 5 (CMIP5), coordinated by the World Climate Research Programme in support of the Intergovernmental Panel on Climate Change (IPCC) AR5, is the most recent, provides projections of future climate change using various global climate models under four major greenhouse gas emission scenarios. There is a wide selection of climate models available to provide projections of future climate change. These provide for a wide range of possible outcomes when trying to inform managers about possible climate changes. Hence, future agrometeorological indicators estimation will be much impacted by which global climate model and climate change scenarios are used. Decision makers are increasingly expected to use climate information, but the uncertainties associated with global climate models pose substantial hurdles for agricultural resources planning. Although it is the most reasonable that quantifying of the future uncertainty using climate change scenarios, preliminary analysis using reasonable factors for selecting a subset for decision making are needed. In order to narrow the projections to a handful of models that could be used in a climate change impact study, we could provide effective information for selecting climate model and scenarios for climate change impact assessment using maximum/minimum temperature, precipitation, reference evapotranspiration, and moisture index of nine Representative Concentration Pathways (RCP) scenarios.

• 한국농공학회논문집
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• 제62권5호
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• pp.47-62
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• 2020

Rice paddy accounts for approximately 52.5% of all farmlands in South Korea, and it is closely related to the water environment. Climate change is expected to affect not only agricultural productivity also the water and the nutrient circulation. Therefore this study was aimed to evaluate changes of nitrogen load from rice paddy considering climate change scenario uncertainty. APEX-Paddy model which reflect rice paddy environment by modifying APEX (Agricultural Policy and Environmental eXtender) model was used. Using the AIMS (APCC Integrated Modeling Solution) offered by the APEC Climate Center, bias correction was conducted for 9 GCMs using non-parametric quantile mapping. Bias corrected climate change scenarios were applied to the APEX-Paddy model. The changes and uncertainty in runoff and nitrogen load were evaluated using multi-model ensemble. Paddy runoff showed a change of 23.1% for RCP4.5 scenario and 45.5% for RCP8.5 scenario compared the 2085s (2071 to 2100) against the base period (1976 to 2005). The nitrogen load was found to be increased as 43.9% for RCP4.5 scenario and 76.0% for RCP8.5 scenario. The uncertainty analysis showed that the annual standard deviation of nitrogen loads increased in the future, and the maximum entropy indicated an increasing tendency. And Duncan's analysis showed significant differences among GCMs as the future progressed. The result of this study seems to be used as a basis for mid- and long-term policies for water resources and water system environment considering climate change.

• 한국기후변화학회지
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• 제6권1호
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• pp.33-40
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• 2015

Forests are to be recognized as an important carbon sink under the UNFCCC that consist of above- and below-biomass, dead organic matter (DOM) such as dead wood and litter, and soil organic matter (SOM). In order to asses for DOM and SOM, however, it is relevant to land-use change matrices over last 20 years for each land-use category. In this study, a land-use change matrix was produced and its uncertainty was assessed using a point sampling technique with permanent sample plots in national forest inventory at Chungbuk province. With point sampling estimated areas at 2012 year for each land-use category were significantly similar to the true areas by given six land-use categories. Relative standard error in terms of uncertainty of land-use change among land-use categories ranged in 4.3~44.4%, excluding the other land. Forest and cropland covered relatively large areas showed lower uncertainty compared to the other land-use categories. This result showed that selected permanent samples in the NFI are able to support for producing land-use change matrix at a national or province level. If the $6^{th}$ NFI data are fully collected, the uncertainty of estimated area should be improved.

• 한국농공학회논문집
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• 제55권4호
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• pp.95-106
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• 2013

It is now generally known that dynamical climate modeling outputs include systematic biases in reproducing the properties of atmospheric variables such as, preciptation and temerature. There is thus, general consensus among the researchers about the need of bias-correction process prior to using climate model results especially for hydrologic applications. Among the number of bias-correction methods, distribution (e.g., cumulative distribution fuction, CDF) mapping based approach has been evaluated as one of the skillful techniques. This study investigates the uncertainty of using various CDF mapping-based methods for bias-correciton in assessing regional climate change Impacts. Two different dynamicailly-downscaled Global Circulation Model results (CCSM and GFDL under ARES4 A2 scenario) using Regional Spectial Model for retrospective peiod (1969-2000) and future period (2039-2069) were collected over the west central Florida. Total 12 possible methods (i.e., 3 for developing distribution by each of 4 for estimating biases in future projections) were examined and the variations among the results using different methods were evaluated in various ways. The results for daily temperature showed that while mean and standard deviation of Tmax and Tmin has relatively small variation among the bias-correction methods, monthly maximum values showed as significant variation (~2'C) as the mean differences between the retrospective simulations and future projections. The accuracy of raw preciptiation predictions was much worse than temerature and bias-corrected results appreared to be more significantly influenced by the methodologies. Furthermore the uncertainty of bias-correction was found to be relevant to the performance of climate model (i.e., CCSM results which showed relatively worse accuracy showed larger variation among the bias-correction methods). Concludingly bias-correction methodology is an important sourse of uncertainty among other processes that may be required for cliamte change impact assessment. This study underscores the need to carefully select a bias-correction method and that the approach for any given analysis should depend on the research question being asked.