Single-site rainfall time sequence simulation method based on Markov chain and rainfall event

A rainfall time series and simulation method technology, applied in special data processing applications, complex mathematical operations, instruments, etc., can solve problems such as limiting the application of random rainfall event simulation methods, and achieve the effect of retaining randomness and improving simulation accuracy.

Pending Publication Date: 2021-11-23
北京师范大学珠海校区
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Problems solved by technology

At present, there are also a few methods for directly stochastically simulating the characteristics of rainfall events, but these methods output rainfall events one by one, and cannot be combined with common hydrological models that require rainfall time series input to carry out flood and drought risk assessment, which greatly limits application of these stochastic rainfall event modeling methods

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  • Single-site rainfall time sequence simulation method based on Markov chain and rainfall event
  • Single-site rainfall time sequence simulation method based on Markov chain and rainfall event
  • Single-site rainfall time sequence simulation method based on Markov chain and rainfall event

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Embodiment Construction

[0044] The technical solution of the present invention will be further elaborated below through examples and in conjunction with the accompanying drawings.

[0045] Such as figure 1 As shown, the present invention relates to a single-site rainfall time series simulation method based on Markov chain and rainfall events, specifically comprising the following steps:

[0046] Step 1: Use the Markov chain to generate time series of wet and dry weather states.

[0047] First, the observed daily rainfall data of a station for 30 years from 1971 to 2000 were collected. For example, wet days are defined as daily rainfall ≥ 0.1 mm, and dry days are defined as daily rainfall 0001 ,P 1001 ,P 0101 ,P 0011 ,P 0111 ,P 1011 ,P 1101 ,P 1111 , according to formula (2) the conversion probability of dry days is P 0000 ,P 1000 ,P 0100 ,P 0010 ,P 0110 ,P 1010 ,P 1100 ,P 1110 , so that this transition probability is used to randomly generate a new set of time series of wet and dry stat...

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Abstract

The invention discloses a single-site rainfall time sequence simulation method based on a Markov chain and a rainfall event. The method comprises the following steps: combining a Markov chain with rainfall event simulation for the first time; and expanding the rainfall event simulation into random simulation of a rainfall time sequence. The characteristics of rainfall in time sequence and event can be kept at the same time, and the precision of rainfall simulation is improved; and the limitation that the output of a rainfall event simulation method can only be applied to a hydrological model based on event sequence input is overcome. According to the method, rainfall data of any number of times and any length can be generated, and the method can be conveniently combined with a common hydrological model to carry out hydrological simulation and carry out accurate flood and drought risk assessment.

Description

technical field [0001] The invention relates to the field of random rainfall simulation, in particular to a single-site rainfall time series simulation method based on Markov chains and rainfall events. Background technique [0002] Rainfall is an important part of the hydrological cycle, directly affects the formation of runoff, and is often used as an important input to hydrological models for flood and drought risk assessment and the design of hydraulic structures. However, in some regions, rainfall observation series are short or even absent, which largely affects the accuracy and reliability of hydrological assessment. [0003] In order to solve the problem of shortage of rainfall data, random rainfall simulation came into being. It can not only be used to generate long-sequence rainfall data and provide multiple possible rainfall sequences, but also can generate rainfall sequences for areas without data by interpolating the parameters of adjacent stations. . In addit...

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Application Information

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IPC IPC(8): G06F30/20G06F17/18G06F113/08
CPCG06F30/20G06F17/18G06F2113/08Y02A10/40
Inventor 高超唐雄朋章四龙刘磊王晓艳
Owner 北京师范大学珠海校区
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