Bias compensation in flood frequency analysis
Bias compensation in flood frequency analysis
复制标题
洪水频率分析中的偏差补偿
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
C. Valeo
中科院分区：
文献类型：
--
作者：
Jianxun He;A. Anderson;C. Valeo
摘要洪水频率分析是水资源管理的重要内容。长流量记录提高了估计分位数的精度;然而，在某些情况下，一个位置的样本量不足以实现统计参数的可靠估计，因此，区域FFA通常用于降低预测的不确定性。本文用Monte Carlo模拟方法研究了几种常用的参数估计（包括L-矩、概率加权矩和极大似然估计）在一般极值（GEV）分布中的偏差。两种偏置补偿方法：在此基础上，提出了基于形状参数的补偿和基于GEV三参数的补偿方法，并将模型应用于阿尔伯塔南部的径流记录。补偿效率因估计量和补偿方法而异。结果总体上表明，由于估计量和短样本量的偏差补偿将显着提高分位数估计的准确性。此外，现场FFA是能够提供可靠的估计的基础上，短的数据，适当考虑估计的偏差。编辑D. Koutsoyiannis;副编辑盛悦
Abstract Flood frequency analysis (FFA) is essential for water resources management. Long flow records improve the precision of estimated quantiles; however, in some cases, sample size in one location is not sufficient to achieve a reliable estimate of the statistical parameters and thus, regional FFA is commonly used to decrease the uncertainty in the prediction. In this paper, the bias of several commonly used parameter estimators, including L-moment, probability weighted moment and maximum likelihood estimation, applied to the general extreme value (GEV) distribution is evaluated using a Monte Carlo simulation. Two bias compensation approaches: compensation based on the shape parameter, and compensation using three GEV parameters, are proposed based on the analysis and the models are then applied to streamflow records in southern Alberta. Compensation efficiency varies among estimators and between compensation approaches. The results overall suggest that compensation of the bias due to the estimator and short sample size would significantly improve the accuracy of the quantile estimation. In addition, at-site FFA is able to provide reliable estimation based on short data, when accounting for the bias in the estimator appropriately. Editor D. Koutsoyiannis; Associate editor Sheng Yue