Generalizing uncertainty decomposition theory in climate change impact assessments

Kim, Yongdai; Ohn, Ilsang; Lee, Jae-Kyoung; Kim, Young-Oh

doi:10.1016/j.hydroa.2019.100024

Cited by 20 publications

(25 citation statements)

References 20 publications

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“…Since we can write U anova k = U var ({Ȳ k,i : i ∈ X k }) with U var being the variance measure, the ANOVA approach can be generalized to any other uncertainty measure U by replacing U var with U. This generalization is equivalent to the "standard method" referred in Kim et al (2019).…”

Section: Review Of Uncertainty Decomposition Methodsmentioning

confidence: 99%

“…But Lee's approach does not decompose uncertainty properly because ∑ K k=1 U lee k U tot . Kim et al (2019) proposed the cumulative uncertainty approach that is the modified version of Lee's approach. The cumulative uncertainty approach captures the interaction effect reasonably and decomposes the total uncertainty properly.…”

Section: Review Of Uncertainty Decomposition Methodsmentioning

confidence: 99%

“…Using the same method, Bosshard et al (2013) decomposed the total uncertainty of the streamflow projection into uncertainties of GCMs, statistical post-processing, hydrological models and their interactions. On the other hand, Kim et al (2019) proposed a cumulative uncertainty method that provides a unified view of how uncertainty is propagated as each source of the projection proceeds. Kim et al (2019) considered a cascade of several stages (i.e., sources) for streamflow projection and quantified cumulative uncertainties as stages progress.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Kim et al (2019) proposed a cumulative uncertainty method that provides a unified view of how uncertainty is propagated as each source of the projection proceeds. Kim et al (2019) considered a cascade of several stages (i.e., sources) for streamflow projection and quantified cumulative uncertainties as stages progress. Kim et al (2019) defined uncertainties of individual stages as the successive differences of the cumulative uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…Kim et al (2019) considered a cascade of several stages (i.e., sources) for streamflow projection and quantified cumulative uncertainties as stages progress. Kim et al (2019) defined uncertainties of individual stages as the successive differences of the cumulative uncertainties.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Ohn

Seo

Kim

et al. 2020

CSAM

Self Cite

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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.

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Section: Review Of Uncertainty Decomposition Methodsmentioning

confidence: 99%

Section: Review Of Uncertainty Decomposition Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Ohn

Seo

Kim

et al. 2020

CSAM

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Future projection of low flows in the Chungju basin, Korea and their uncertainty decomposition

Lee

Qiu

et al. 2021

Intl Journal of Climatology

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Low flow projections in response to global warming and the uncertainty decomposition into different components are performed with the focus on the Chungju basin, South Korea. Four downscaled climate simulations, two bias correction methods, and two hydrological models with two different potential evapotranspiration computation methods are applied to constitute the 32 ensemble members (4 × 2 × 4) of low flows projections. An analysis of the variance is used to decompose the total variance in the low flow projections into the contribution from three different sources. The result shows that hydrological models, particularly their structures, are the key factor that affects the low flow projections. They contribute more than 85% of the total uncertainty, and the projected change of low flow using different hydrological models can be of opposite signs, even under the same climate input data. On the other hand, the uncertainties related to climate simulation and bias correction are much smaller, comprising around 10 and 2%, respectively. The possible mechanisms behind the large uncertainty of projected low flow are investigated by comparing the relevant hydrological variables obtained from different hydrological simulations. The different representations in the hydrological models for the freezing of the soil layer during winter, which will undergo large changes as temperature increases, are likely to affect the projected change in low flow greatly. The difference in potential evapotranspiration methods also affects the low flow projections, but its contribution is relatively weaker than the effects of the hydrological model structure.

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Model-wise uncertainty decomposition in multi-model ensemble hydrological projections

Ohn

Kim

Seo

et al. 2021

Stoch Environ Res Risk Assess

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Generalizing uncertainty decomposition theory in climate change impact assessments

Cited by 20 publications

References 20 publications

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Uncertainty decomposition in climate-change impact assessments: a Bayesian perspective

Future projection of low flows in the Chungju basin, Korea and their uncertainty decomposition

Model-wise uncertainty decomposition in multi-model ensemble hydrological projections

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