NDLI: Estimation of the Extreme Flow Distributions by Stochastic Models

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Estimation of the Extreme Flow Distributions by Stochastic Models

Content Provider	Springer Nature Link
Author	Li, Yuanzhang Saxena, K. M. Lal Cong, Shuzheng
Copyright Year	1999
Abstract	The t-year event is a commonly used characteristic to describe the extreme flood peak in hydrological designs. The annual maximum series (AMS) and the partial duration series (PDS) are two basic approaches in flood analyses. In this paper, we first derive the distribution of the maximum extreme or the joint distribution of two or more maximum extremes from historical records based on a stochastic model, and then estimate statistical characteristics, including the t-year event, from the distribution. In addition to the two classical approaches (AMS and PDS), two additional approaches are proposed for estimating the unknown parameters in this paper. The first one uses two or more annual maximums (MAMS) as the sample to estimate the distribution of the maximum extremes. The second one uses multi-variate shock model to estimate the distribution of the maximum extremes for a multi-modal streamflow. The distribution of the extreme streamflow and the associated characteristics in the Bird Creek in Avant, Oklahoma, in the St. Johns River in Deland, Florida, and in the West Walker River in Coleville, California are estimated by using the stochastic model. To investigate further the performance of the estimation, the stochastic models based on AMS, MAMS and PDS related are also applied to the simulated data. The results show that the stochastic model and the related methods are reliable.
Starting Page	423
Ending Page	448
Page Count	26
File Format	PDF
ISSN	13861999
Journal	Extremes
Volume Number	1
Issue Number	4
e-ISSN	1572915X
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	1999-01-01
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hydrogeology Statistics Statistics for Business/Economics/Mathematical Finance/Insurance Quality Control, Reliability, Safety and Risk Civil Engineering Environmental Management
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Economics, Econometrics and Finance Engineering

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