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

机译：使用贝叶斯模型平均方案量化多模型预测中的多源不确定性

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摘要

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.
机译：这项研究的重点是对多模型预测的定量多源不确定性分析。通过两种参数优化算法，分别校正了新安江（XAJ），降雨-径流混合（HYB）和HYMOD（HYM）三种水文模型，分别是改组复杂演化（SCE-UA）方法和改组复杂演化中国南部密水盆地的大都市（SCEM-UA）方法。输入不确定度通过使用正态分布误差乘数进行量化。使用贝叶斯模型平均（BMA）方法将由这三个模型计算出的整体模拟集进行组合。结果表明如下。 （1）SCE-UA和SCEM-UA均产生了良好且可比的流量模拟。具体而言，SCEM-UA隐含了参数不确定性，并提供了参数的后验分布。 （2）在降水输入不确定性方面，水流模拟的精度没有明显提高。 （3）BMA组合不仅提高了流量预测的精度，而且还量化了模拟的不确定性范围。 （4）对于高流量和低流量，使用基于SCEM-UA的BMA组合方法计算的预测间隔似乎优于使用基于SCE-UA的BMA组合计算的预测间隔。结果表明，使用SCEM-UA算法和BMA方法进行的综合不确定性分析对于流量预测和洪水预报具有优势。

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