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Copula function-based multivariate hydrologic uncertainty processing method

A technology of uncertainty and processing method, applied in electrical digital data processing, special data processing applications, instruments, etc., can solve problems such as deviation of results from optimal values, affecting applicability, and unstable effects, achieving a wide range of applications. Effect

Active Publication Date: 2017-12-01
WUHAN UNIV
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Problems solved by technology

This normal quantile transformation is not robust when extrapolating extreme events, and the reverse transformation may also make the result deviate from the optimal value, which affects the applicability of the method
At present, there is no literature to introduce the Copula function into the research of PTF and MHUP

Method used

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  • Copula function-based multivariate hydrologic uncertainty processing method
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  • Copula function-based multivariate hydrologic uncertainty processing method

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Embodiment Construction

[0036] The specific implementation of the Copula function-based multivariable hydrological uncertainty processing method involved in the present invention will be described in detail below in conjunction with the accompanying drawings.

[0037]

[0038] Such as figure 1 As shown, the Copula function-based multivariate hydrological uncertainty processing method provided in this embodiment includes the following steps:

[0039] Step 1. Collect basic hydrometeorological data and quantitative precipitation forecast data of the basin:

[0040] The basic hydrometeorological data of the basin collected in this embodiment include measured rainfall, evaporation and flow data. The rainfall data refers to the surface average rainfall of the research basin, which is calculated by the arithmetic mean method of several representative rainfall stations on the basin. The evaporation data of the basin can be obtained from the meteorological station, and the flow data refers to the represent...

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Abstract

The invention provides a Copula function-based multivariate hydrologic uncertainty processing method. The method can be used for hydrologic forecasting, and is characterized by comprising the following steps: step 1, collecting hydrometeorological basic data and quantitative precipitation forecast data of a basin; step 2, establishing a hydrologic model to obtain forecast discharge processes of different forecast periods; step 3, determining marginal distribution functions of actually measured flow and forecast flow; step 4, utilizing a Copula function to construct a joint probability distribution function of the actually measured flow and the forecast flow; step 5, solving a Bayesian posterior transition probability density function of the actually measured flow of the different forecast periods according to the marginal distribution functions estimated in the step 3 and the joint probability distribution function constructed in the step 4; and step 6, acquiring a Bayesian posterior joint probability density function of the actually measured discharge processes through a total probability formula according to the Bayesian posterior transition probability density function, obtained in the step 5, of the actually measured flow of the different forecast periods.

Description

technical field [0001] The invention belongs to the field of reservoir flood forecasting, and in particular relates to a multivariable hydrological uncertainty processing method based on a Copula function. technical background [0002] Flood forecasting is one of the important contents of non-engineering measures for flood control, and it directly serves flood control and emergency rescue, rational utilization and protection of water resources, construction of water conservancy projects, and management of operation and dispatch. The existence of uncertainties in the input, parameters and structure of the hydrological model will inevitably lead to uncertainty in the flood forecast results output by the hydrological model. Therefore, quantitatively describing and estimating the uncertainty of hydrological forecast in the form of probability distribution is not only more scientific and reasonable in theory, but also enables decision makers to quantitatively consider risk inform...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 刘章君郭生练何绍坤巴欢欢
Owner WUHAN UNIV
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