In the field of stochastic hydrology, hydrologic series is formed with the non-
periodic component, the periodic component and the stochastic component. Here the 
period characteristics used include approximate periods in the non-periodic 
component, simple periods and complex periods in the periodic component. Spectral 
analysis is an essential and effective tool for extracting such stochastic 
characteristics of time series. In 1948, Shannon developed a mathematical theory of 
entropy and applied it in communications. Nearly a decade later, Jaynes formulated 
the Principle of Maximum Entropy (POME), which makes good winning in solution the 
ill-posed problem. Maximum Entropy Spectra Analysis (MESA), introduced by BURG in 
1975 and based on POME，has certain advantages over the classical and other new 
methods. The statistical characteristics, which are used in stochastic model 
identification, can be estimated using MESA, thus permitting integration of 
spectral analysis and computations related to stochastic model development. It can 
also be used in analyzing short time series, since it resolves low-frequency 
characteristics of the data. It is clear that studies with the use of entropy, POME 
and MESA in hydrology, water resources and water environment have been relatively 
few. Nevertheless, they are promising and justify further research. The former 
studies provided motivation for our following work. Here MESA is used to detect the 
period characteristics of the annual runoff series, monthly runoff series, and 
annual maximum flood peak series of some stations in the Yellow River in China. The 
subsection technique is used to compare whether the obtained periods have 
consistency and stability. Such conclusions are drawn. (a) The 1st to 4th periods 
of monthly runoff series takes the value of 12 month, 6 month, 4 month and 3 month 
respectively; (b) The 1st to 2nd periods of annual runoff series takes the value of 
3 year and 4 year respectively. (c) There is no significant period in the annual 
maximum flood peak series.
