A fuzzy‐based simulation method for modelling hydrological processes under uncertainty

In this study, a fuzzy‐based simulation method (FBSM) is developed for modelling hydrological processes associated with vague information through coupling fuzzy vertex analysis technique with distributed hydrological model. The FBSM can handle uncertainties existed as fuzzy sets in the hydrological modelling systems, and solutions under an associated number of α‐cut levels can be generated by solving 2ⁿ deterministic models. The lower reach of the Tarim River Basin in China is selected as a study case for demonstrating applicability of the proposed method. The developed model is calibrated and validated against observed groundwater elevation for four wells during the period 2000–2001, and generally performed acceptable for model Nash–Sutcliffe coefficient (R2) and correlation coefficient (R). The R2 is approximately over 0·65 and the correlation coefficient is higher than 0·90. Based on the technique of fuzzy simulation, the uncertainties of two parameters (K_H and LC) are reflected under different α‐cut levels. The results indicate that, under a lower degree of plausibility, the interval between the lower and upper bounds of the groundwater elevation is wider; conversely, a higher degree of plausibility would lead to a narrow interval. The main effect of K_H is more significant than the effect of LC at most well sites. The proposed method is useful for studying hydrological processes within a system containing multiple factors with uncertainties and providing support for identifying proper water resources management strategies. Copyright © 2010 John Wiley & Sons, Ltd.     

基于局部采样MCMC方法的时移探地雷达反演

马尔科夫链蒙特卡洛方法(MCMC)是一种启发式的全局寻优算法,可以用来解决概率反演的问题.基于MCMC方法的反演不依赖于准确的初始模型,可以引入任意复杂的先验信息,通过对先验概率密度函数的采样来获得大量的后验概率分布样本,在寻找最优解的过程中可以跳出局部最优得到全局最优解.MCMC方法由于计算量巨大,应用难度较高,在地...     

A regional Bayesian POT model for flood frequency analysis

Flood frequency analysis is usually based on the fitting of an extreme value distribution to the local streamflow series. However, when the local data series is short, frequency analysis results become unreliable. Regional frequency analysis is a convenient way to reduce the estimation uncertainty. In this work, we propose a regional Bayesian model for short record length sites. This model is less restrictive than the index flood model while preserving the formalism of “homogeneous regions”. The performance of the proposed model is assessed on a set of gauging stations in France. The accuracy of quantile estimates as a function of the degree of homogeneity of the pooling group is also analysed. The results indicate that the regional Bayesian model outperforms the index flood model and local estimators. Furthermore, it seems that working with relatively large and homogeneous regions may lead to more accurate results than working with smaller and highly homogeneous regions.