scholarly journals Uncertainty Analysis of Climate Change Impacts on Streamflow Extremes in Zayandeh-Rud River by Bayesian Model Averaging

2019 ◽  
Vol 23 (4) ◽  
Keyword(s):  
Climate Change ◽  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Climate Change Impacts ◽  
Streamflow Extremes

scholarly journals Quantifying multi-source uncertainties in multi-model predictions using the Bayesian model averaging scheme

2017 ◽  
Vol 49 (3) ◽  
pp. 954-970 ◽  
Author(s):  
Shanhu Jiang ◽  
Liliang Ren ◽  
Chong-Yu Xu ◽  
Shuya Liu ◽  
Fei Yuan ◽  
...  
Keyword(s):  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Prediction Interval ◽  
Model Averaging ◽  
Streamflow Prediction ◽  
Input Uncertainty ◽  
Shuffled Complex Evolution ◽  
Model Predictions ◽  
Combination Approach

Abstract This study focuses on a quantitative multi-source uncertainty analysis of multi-model predictions. Three widely used hydrological models, i.e., Xinanjiang (XAJ), hybrid rainfall–runoff (HYB), and HYMOD (HYM), were calibrated by two parameter optimization algorithms, namely, shuffled complex evolution (SCE-UA) method and shuffled complex evolution metropolis (SCEM-UA) method on the Mishui basin, south China. The input uncertainty was quantified by utilizing a normally distributed error multiplier. The ensemble simulation sets calculated from the three models were combined using the Bayesian model averaging (BMA) method. Results indicate the following. (1) Both SCE-UA and SCEM-UA resulted in good and comparable streamflow simulations. Specifically, the SCEM-UA implied parameter uncertainty and provided the posterior distribution of the parameters. (2) In terms of the precipitation input uncertainty, precision of streamflow simulations did not improve remarkably. (3) The BMA combination not only improved the precision of streamflow prediction, but also quantified the uncertainty bounds of the simulation. (4) The prediction interval calculated using the SCEM-UA-based BMA combination approach appears superior to that calculated using the SCE-UA-based BMA combination for both high flows and low flows. Results suggest that the comprehensive uncertainty analysis by using the SCEM-UA algorithm and BMA method is superior for streamflow predictions and flood forecasting.

Improving Streamflow Prediction Using Uncertainty Analysis and Bayesian Model Averaging

2018 ◽  
Vol 23 (5) ◽  
pp. 05018004 ◽  
Author(s):  
Antonio A. Meira Neto ◽  
Paulo Tarso S. Oliveira ◽  
Dulce B. B. Rodrigues ◽  
Edson Wendland
Keyword(s):  
Uncertainty Analysis ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Streamflow Prediction

scholarly journals Comparison of Three Statistical Downscaling Methods and Ensemble Downscaling Method Based on Bayesian Model Averaging in Upper Hanjiang River Basin, China

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Jiaming Liu ◽  
Di Yuan ◽  
Liping Zhang ◽  
Xia Zou ◽  
Xingyuan Song
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Statistical Downscaling ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Support Vector ◽  
Simulation Accuracy ◽  
Downscaling Method ◽  
Hanjiang River

Many downscaling techniques have been developed in the past few years for projection of station-scale hydrological variables from large-scale atmospheric variables to assess the hydrological impacts of climate change. To improve the simulation accuracy of downscaling methods, the Bayesian Model Averaging (BMA) method combined with three statistical downscaling methods, which are support vector machine (SVM), BCC/RCG-Weather Generators (BCC/RCG-WG), and Statistics Downscaling Model (SDSM), is proposed in this study, based on the statistical relationship between the larger scale climate predictors and observed precipitation in upper Hanjiang River Basin (HRB). The statistical analysis of three performance criteria (the Nash-Sutcliffe coefficient of efficiency, the coefficient of correlation, and the relative error) shows that the performance of ensemble downscaling method based on BMA for rainfall is better than that of each single statistical downscaling method. Moreover, the performance for the runoff modelled by the SWAT rainfall-runoff model using the downscaled daily rainfall by four methods is also compared, and the ensemble downscaling method has better simulation accuracy. The ensemble downscaling technology based on BMA can provide scientific basis for the study of runoff response to climate change.

Probabilistic climate change predictions applying Bayesian model averaging

2007 ◽  
Vol 365 (1857) ◽  
pp. 2103-2116 ◽  
Author(s):  
Seung-Ki Min ◽  
Daniel Simonis ◽  
Andreas Hense
Keyword(s):  
Climate Change ◽  
Bayesian Model ◽  
Bayes Factor ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Surface Air Temperature ◽  
First Century ◽  
Intergovernmental Panel ◽  
Twenty First Century ◽  
Global Mean

This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation–maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070–2099) SATs while there is only a little effect of Bayesian weighting on the 5–95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.

Sign in / Sign up

Export Citation Format

Share Document