Bayesian uncertainty decomposition for hydrological projections

온일상,김성현,서승범,김영오,김용대 한국통계학회 2020 Journal of the Korean Statistical Society Vol.49 No.3

There is a considerable uncertainty in a hydrological projection, which arisen from themultiple stages composing the hydrological projection. Uncertainty decomposition analysis evaluates contribution of each stage to the total uncertainty in the hydrological projection. Some uncertainty decomposition methods have been proposed, but they still have some limitations: (1) they do not consider nonstationarity in data and (2) they only use summary statistics of the projected data instead of the full time-series and lack a principled way to choose the summary statistic. We propose a novel Bayesian uncertainty decomposition method which can alleviate such problems. In addition, the proposed method provides probabilistic statements about the uncertainties. We apply the proposed method to the streamflow projection data for Yongdam Dam basin located at Geum River in South Korea.

수자원 전망에서의 단계별 불확실성 평가 기술

온일상,김성현,김용대,On, Il-Sang,Kim, Seong-Hyeon,Kim, Yong-Dae 한국수자원학회 2018 물과 미래(한국수자원학회지) Vol.51 No.11

교호작용 효과를 고려한 수자원 전망의 불확실성 분해

온일상,김용대,김영오 한국수자원학회 2018 한국수자원학회논문집 Vol.51 No.S-1

Water resources projection typically consists of several stages including emission scenarios, global circulation models (GCMs), downscaling techniques, and hydrological models, and each stage is a source of total uncertainty in water resources projection. Several studies proposed methods to quantify the relative contribution of each stage to total uncertainty, and we call such analysis uncertainty decomposition. Uncertainty decomposition enables us to investigate the stages yielding large uncertainties and to establish the uncertainty reduction plan that reflects them. Interactions between stages is one of the important issues to be considered in uncertainty decomposition. This study suggests a new uncertainty decomposition method considering interaction effect. The proposed method has an advantage of decomposing the total uncertainty to the uncertainty from each stage considering both the main and interactions effects. We apply the proposed method to streamflow projection for Chungju Dam basin. The results show that the uncertainties from the main effects are larger than the uncertainties from interaction effects in both summer and winter. Using the proposed uncertainty decomposition method, we show that the GCM stage is the largest source of the total uncertainty in summer and the downscaling technique stage is the one in winter among the following four stages: emission scenarios, GCMs, downscaling techniques, and hydrological models. 기후변화로 인한 수자원 전망은 배출 시나리오, 전지구적 순환모형, 상세화 기법, 수문 모형 등 여러 전망 단계를 거쳐 이루어지며, 각 단계는 수자원 전망의 총 불확실성의 원천이 된다. 몇몇 연구를 통해 개별 전망 단계의 총 불확실성에 대한 상대적인 기여를 계량화하는 방법이 제안되었으며며, 이러한 분석을 불확실성 분해라고 한다. 불확실성 분해 분석은 큰 불확실성을 발생시키는 단계를 진단하고, 이를 반영한 불확실성 저감 계획을 수립할 수 있게 한다. 전망 단계 간의 교호작용은 불확실성 분해 시 고려되어야 하는 중요한 문제 중 하나이다. 본 연구는 교호작용 효과로 인한 불확실성을 계량화하고 이를 불확실성 분해에 반영하는 새로운 방법을 제안한다. 제안한 방법은 전망 단계별 불확실성을 주효과와 교호작용 효과를 모두 고려하여 계량화함과 동시에 총 불확실성에서 개별 전망 단계가 차지하는 상대적인 비중을 제시할 수 있다는 장점이 있다. 제안한 방법을 충주댐 유량 전망의 불확실성 분석에 적용하였다. 충주댐 유역의 불확실성 분석 결과 여름과 겨울 두 계절 모두에서 교호작용 효과의 불확실성은 주효과의 불확실성에 비해 그 크기가 작은 것으로 나타났다. 교호작용 효과를 고려하여 불확실성을 분해한 결과 배출 시나리오, 전지구적 순환모형, 상세화 기법, 수문 모형의 네 단계 중 여름철은 전지구적 순환모형의 불확실성이, 겨울철은 상세화 기법의 불확실성이 가장 큰 것으로 분석되었다.

수자원 전망 불확실성 분해 R Package 'UncDecomp'

김성현,온일상,김용대,Kim, Seong-Hyeon,On, Il-Sang,Kim, Yong-Dae 한국수자원학회 2019 물과 미래(한국수자원학회지) Vol.52 No.11

매립부문 온실가스 목표관리제 지침의 개선 : 성상분석시 최소시료수 결정을 중심으로

이승훈,온일상,김호 한국폐기물자원순환학회 2014 한국폐기물자원순환학회지 Vol.31 No.1

Quality of GHG emission from solid waste disposal depends on level of activity data. Activity data of solid wastedisposal is mass of waste disposal and waste composition. Waste composition is one of the main factors influencingemissions from solid waste disposal. According to GHG target management in Korea, uncertainty of activity datadetermined by level of tier. We suggest minimum sample number for analysis of waste composition. In result, we suggestto revise the guideline for GHG target management that minimum sample number for analysis of waste compositionmust be over 73 times during 3 years for total uncertainty of waste composition must be less than or equal to 7.5%(Tier 1 level).

Scaled ridge estimator and its application to multimodel ensemble approaches for climate prediction

김용대,김영오,김재석,김우성,온일상 한국통계학회 2016 Journal of the Korean Statistical Society Vol.45 No.2

We propose a new regularized estimator called the scaled ridge estimator. The scaled ridge estimator is a modified version of the ridge estimator devised to reduce the bias of the ridge estimator by multiplying a positive constant to the ridge estimator. We show theoretically as well as numerically that the scaled ridge estimator performs better than the ridge estimator when the covariates are highly correlated and the true regression coefficients are similar. A motivational example is an ensemble approach for climate prediction based on the global circulation model. By analyzing data sets of monthly precipitation of 10 cites in South Korea, we illustrate that the scaled ridge estimator is a useful and efficient alternative to other competitors for ensemble climate prediction.

수자원 전망의 시나리오별 불확실성 분해

이지수(Jisu Lee),온일상(Ilsang Ohn),김용대(Yongdai Kim) 한국데이터정보과학회 2018 한국데이터정보과학회지 Vol.29 No.6

기후변화로 인한 수자원 전망은 배출 시나리오, 전지구적 순환모형, 상세화 기법, 수문 모형 등 여러 전망 단계를 거쳐 이루어지며, 각 단계는 수자원 전망의 총 불확실성의 원천이 된다. 근래에 들어 수자원 전망의 총 불확실성을 개별 전망 단계로, 이를 다시 전망 단계에 속하는 여러 시나리오의 불확실성으로 분해하는 방법의 필요성이 제기되고 있다. 그러나 그 필요성에 비하여 관련 연구는 전무한 실정이다. 본 연구에서는 수자원 전망의 총 불확실성을 시나리오의 불확실성으로 분해나는 방법을 제안한다. 제안하는 방법은 개별 시나리오의 불확실성이 총 불확실성에 기여하는 상대적 비중을 제시함으로써 시나리오 간의 불확실성 비교를 가능하게 한다. 제안한 방법을 이용하여 실제 수자원 전망 자료를 분석한 결과를 제시한다. Water resource projections considering climate change are based on combinations of scenarios in multiple stages including the emission scenarios, global circulation models, downscaling techniques, and hydrological models. Each scenario is a source of total uncertainty of the water resource projections. Decomposing total uncertainty into components from individual scenarios has recently emerged as an important issue due to their usefulness. But there is no comprehensive research. In this study, we propose a method to decompose the total uncertainty into the individual uncertainty of scenarios. The proposed method makes it possible to compare the uncertainties of the scenarios by providing the relative contribution of each scenario to the total uncertainty. The results of analyzing the real data by the proposed method are presented.

온실가스 인벤토리 불확도 산정 방법

오석현(Seok-hyun Oh),온일상(Il-sang Ohn),김호(Ho Kim) 서울대학교 보건환경연구소 2014 보건학논집 Vol.51 No.1

기후변화에 따른 소양호의 수온 장기 모의 및 불확실성 정량화

윤여정 ( Yeojeong Yun ),박형석 ( Hyungseok Park ),정세웅 ( Sewoong Chung ),김용대 ( Yongda Kim ),온일상 ( Ilsang Ohn ),이서로 ( Seoro Lee ) 한국물환경학회(구 한국수질보전학회) 2020 한국물환경학회지 Vol.36 No.1

Future climate change may affect the hydro-thermal and biogeochemical characteristics of dam reservoirs, the most important water resources in Korea. Thus, scientific projection of the impact of climate change on the reservoir environment, factoring uncertainties, is crucial for sustainable water use. The purpose of this study was to predict the future water temperature and stratification structure of the Soyanggang Reservoir in response to a total of 42 scenarios, combining two climate scenarios, seven GCM models, one surface runoff model, and three wind scenarios of hydrodynamic model, and to quantify the uncertainty of each modeling step and scenario. Although there are differences depending on the scenarios, the annual reservoir water temperature tended to rise steadily. In the RCP 4.5 and 8.5 scenarios, the upper water temperature is expected to rise by 0.029 ℃ (±0.012)/year and 0.048 ℃ (±0.014)/year, respectively. These rise rates are correspond to 88.1 % and 85.7 % of the air temperature rise rate. Meanwhile, the lower water temperature is expected to rise by 0.016 ℃ (±0.009)/year and 0.027 ℃ (±0.010)/year, respectively, which is approximately 48.6 % and 46.3 % of the air temperature rise rate. Additionally, as the water temperatures rises, the stratification strength of the reservoir is expected to be stronger, and the number of days when the temperature difference between the upper and lower layers exceeds 5 ℃ increases in the future. As a result of uncertainty quantification, the uncertainty of the GCM models showed the highest contribution with 55.8 %, followed by 30.8 % RCP scenario, and 12.8 % W2 model.