无资料或资料稀缺地区的径流概率模拟, 是目前水文研究难点问题之一。 基于此, 利用Kernal核密度估计法估算出流量的月径流概率密度函数, 采用基于自适应采样算法(Adaptive Metropolis algorithm, AM)的马尔可夫链蒙特卡罗(Markov Chain Monte Carlo,MCMC)模拟方法求解, 最后给出月径流量的模拟预测。 实例表明基于Kernel核密度估计的AM-MCMC算法模型计算结果精度较高, 有良好的应用价值, 可在资料较少地区推广使用。
Abstract
The simulation of runoff probability in an area in lack of runoff data is a difficulty in hydrological research. In this article, we try to establish the probability density function of monthly runoff flow by adopting kernal density estimation method, and give the solution by Markov Chain Monte Carlo (MCMC) simulation method based on Adaptive Metropolis (AM) algorithm. Case study shows that the AM-MCMC algorithm model based on kernel density estimation is of high accuracy and good application value. It can be used in areas in lack of data.
关键词
径流模拟 /
概率分布 /
核密度估计 /
AM-MCMC算法 /
罗岙水库
Key words
runoff simulation /
probability distribution /
kernel density estimation /
AM-MCMC algorithm /
Luo’ao Reservoir
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基金
南京水利科学研究院院基金项目(Y516011);水利部公益性项目(201201020)
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