基于IBUNE方法的水文模型不确定性分析

在贝叶斯理论框架下,根据一种可结合多个水文模型给出模拟或预报结果的IBUNE方法探讨了水文模型的输入、参数以及结构的不确定性问题。将SCEM-UA算法和EM算法嵌入新安江和TOPMODEL水文模型用于参数优化和模型平均,进而将输入与参数的综合不确定性处理后得到的预报量后验分布进行多模型综合,据此对水文模型的不确定性及其对水文模拟结果的影响进行评价。以湖南洣水流域龙家山水文站以上集水区域为例进行了应用研究,结果表明,IBUNE方法能够有效估计水文模型的不确定性,并能给出合理的概率预报区间。

Uncertainty Analysis of Hydrologic Model with IBUNE Method

In order to get more accurate modeling or forecasting results, a Bayesian multi-model comprehensive framework-IBUNE (Integrated Bayesian Uncertainty Estimator) was used in this paper to analyze the uncertainties from observation data, model parameter and structure based on probability and statistics methods. Research based on Bayesian theory, SCEM-UA algorithm and EM algorithm was embedded in XAJ model and TOPMODEL to optimize parameters and average models. Example shows that IBUNE method can efficiently estimate the uncertainty of hydrologic model, and give a reasonable interval of probability forecast.