Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models

Wang, F.; Huang, Guohe; Fan, Yurui; Li, Y. P.

doi:10.1007/s11269-020-02608-2

Cited by 37 publications

(17 citation statements)

References 50 publications

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“…In aggregate, 784 subsamples were used to calculate the mean unbiased estimate of total uncertainty fractions (i.e., the averages obtained from all subsamples) attributable to each source (Figures 10B,C). It should be noted that the ANOVA is sensitive to the subsampling approaches (Wang et al, 2020); in this regard, the reader is referred to the Supplementary Material, Part G for further discussion. In all sites, the contribution of emission scenario uncertainty (alone) to total uncertainty is between 10 and 20%.…”

Section: Resultsmentioning

confidence: 99%

Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

et al. 2022

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This study quantifies the uncertainties in the projected changes in potential longshore sediment transport (LST) rates along a non-straight coastline. Four main sources of uncertainty, including the choice of emission scenarios, Global Circulation Model-driven offshore wave datasets (GCM-Ws), LST models, and their non-linear interactions were addressed through two ensemble modelling frameworks. The first ensemble consisted of the offshore wave forcing conditions without any bias correction (i.e., wave parameters extracted from eight datasets of GCM-Ws for baseline period 1979–2005, and future period 2081–2100 under two emission scenarios), a hybrid wave transformation method, and eight LST models (i.e., four bulk formulae, four process-based models). The differentiating factor of the second ensemble was the application of bias correction to the GCM-Ws, using a hindcast dataset as the reference. All ensemble members were weighted according to their performance to reproduce the reference LST patterns for the baseline period. Additionally, the total uncertainty of the LST projections was decomposed into the main sources and their interactions using the ANOVA method. Finally, the robustness of the LST projections was checked. Comparison of the projected changes in LST rates obtained from two ensembles indicated that the bias correction could relatively reduce the ranges of the uncertainty in the LST projections. On the annual scale, the contribution of emission scenarios, GCM-Ws, LST models and non-linear interactions to the total uncertainty was about 10–20, 35–50, 5–15, and 30–35%, respectively. Overall, the weighted means of the ensembles reported a decrease in net annual mean LST rates (less than 10% under RCP 4.5, a 10–20% under RCP 8.5). However, no robust projected changes in LST rates on annual and seasonal scales were found, questioning any ultimate decision being made using the means of the projected changes.

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Section: Resultsmentioning

confidence: 99%

Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

et al. 2022

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“…Uncertainty exists in the climate model, the greenhouse gas emission scenarios, and the population scenarios. As a powerful statistical tool, the main contribution (and their multidimensional interactions) of GCM, RCP, and SSP to the sources of uncertainty on population exposure projections can be quantified through the analysis (F. Wang et al., 2020). It has been commonly used in many fields, especially for the experimental aspect (Barros et al., 2020).…”

Section: Methodology and Data Collectionmentioning

confidence: 99%

Ensemble Drought Exposure Projection for Multifactorial Interactive Effects of Climate Change and Population Dynamics: Application to the Pearl River Basin

et al. 2021

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With a changing climate, drought has become a common natural disaster. In our study, population exposure to drought over the Pearl River Basin (PRB) under climate change is investigated. Drought frequency is evaluated through the Standardized Precipitation Evapotranspiration Index (SPEI). The data needed for SPEI calculation are obtained based on the ensemble of multiple global climate model (GCM)outputs. Population exposure to drought for the future is assessed by combining drought frequency under two emission scenarios (RCP4.5 and RCP8.5) with three downscaled population scenarios (SSP1, SSP2, and SSP3). Moreover, the main contribution (and their interactions) of GCM, RCP, and SSP to the sources of uncertainty on population exposure projections are explored through multi-level factorial analysis. Results indicate that the temperature and precipitation would continually increase for the future, and the increase in drought frequency is more substantial during the 2080s than in the 2050s. Meanwhile, population exposure to drought accounts for 21.60% of the total population in 1976-2005 over the PRB area. During the 2050s, it would decrease to 11.98-12.28% under RCP4.5 and 14.15-14.40%, under RCP8.5, respectively. By the 2080s, population exposure would slightly reduce under RCP4.5 and increase to 28.86-29.44% under RCP8.5. GCM is the primary uncertainty source of drought exposure in the 2050s, with contribution rates of 72.41%, 57.47%, 51.10%, and 78.71% to the four Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

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“…The strength of the MK test is that as a nonparametric procedure it does not assume a specific joint distribution of the precipitation indices, which is minimally affected by departures from normality (Han et al, 2014). The limitations of this trend test are frequently associated with its own null hypothesis (H0), which assumes that the precipitation indices are independently and identically distributed (Wang et al, 2020). More details about MK test are available in (Blain, 2013).…”

Section: Methodsmentioning

confidence: 99%

Extreme Summer Precipitation Events in China and Their Changes during 1982 ~ 2019

Wang¹,

Duan²,

Li³

et al. 2021

J ENVIRON INFORM LET

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This work analyzed the spatial and temporal variations of the extreme precipitation over China during the period 1982 to 2019, based on GPCC data. Meanwhile, the spatially clustering characteristic of China is evaluated based on the trends of summer precipitation through K-means. This is the first to clustering based on the trend of summer precipitation. The results indicated that the spatial distribution of MK trends for the summer precipitation indices over China during 1982 ~ 2019 shows increasing and decreasing trends in different regions. The extreme precipitation has been found mainly in Pearl River Basin, Southeastern River Basin, Huaihe River Basin, and the lower reaches of the Yangtze River. The risk of floods in SERB (the Southeastern River Basin), especially those caused by short-term heavy precipitation, may increase in recent decades. Four clusters are identified through K-means, which can be named as stable (59%), extreme wetter (5%), dryer (19%), and wetter (17%) zones. Combining the spatial distribution of the multi-year average of precipitation indicators and four clusters, the 'wet to wetter and dry to dryer' is found in China summer precipitation.

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Robust Subsampling ANOVA Methods for Sensitivity Analysis of Water Resource and Environmental Models

Cited by 37 publications

References 50 publications

Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

Uncertainties in the Projected Patterns of Wave-Driven Longshore Sediment Transport Along a Non-straight Coastline

Ensemble Drought Exposure Projection for Multifactorial Interactive Effects of Climate Change and Population Dynamics: Application to the Pearl River Basin

Extreme Summer Precipitation Events in China and Their Changes during 1982 ~ 2019

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