CN111080059A - Non-uniform multi-source flood encounter risk analysis method - Google Patents
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Abstract
The invention relates to a non-uniform multi-source flood encounter risk analysis method, which is characterized by comprising the following steps: the analysis method comprises the following steps: 1) preparing data; 2) carrying out non-uniformity diagnosis on the flood peak sequence; 3) determining time sequence distribution of flood peaks of main and branch flows; 4) fitting and parameter estimation are carried out on the flood peak sequences of the main and branch flows; 5) constructing combined distribution; 6) flood peak encounter analysis was performed. The method has scientific and reasonable design, can analyze the actual encounter condition of the actual flood sequence, characterizes the flood sequence by the obvious characteristic quantity, grasps the characteristics of the researched flood, and can more flexibly and effectively use a joint analysis method to evaluate the influence of the flood; the flood control response mechanism is perfected, peaks are adjusted and staggered in time, life and property losses of people are reduced, and a flood control standard basis is provided for watershed hydraulic engineering construction.
Description
Technical Field
The invention belongs to the technical field of flood frequency analysis, relates to flood encounter risk analysis, and particularly relates to a non-uniform multi-source flood encounter risk analysis method.
Background
The extra-large flood is usually formed by the combined action of multi-source flood, particularly the watershed large flood, and the mutual influence among the flood of each main branch and branch is particularly prominent. For a large flood peak, peak clipping and flood blocking can be performed in advance to avoid flood disasters, however, for a plurality of rivers in a large watershed, particularly flood peaks generated in a plurality of rain areas, timely peak staggering needs to be performed to prevent the flood peaks from being overlapped to cause the larger flood disasters. Therefore, in flood forecasting, it is necessary to consider a plurality of river flood encounters and accurately judge the possibility of the encounter. However, with the increasing influence of global climate change and human large-scale activities, the river basin rainfall convergence rule and the space-time distribution of natural runoff are changed, so that the consistency of hydrological sequences is damaged, the assumption of random independent uniform distribution is not satisfied, the traditional single fitting distribution form cannot accurately reflect the statistical characteristics of the original sequences, and the reliability of the design result obtained according to the statistical characteristics is questioned.
Aiming at multi-source flood risk analysis with complex conditions, the common method in the current hydrological work is to consider flood from all sources as irrelevant and carry out statistical analysis on actual measurement data of the flood, the method neglects the internal relation among the floods from different sources in the same flow area, the obtained result obviously cannot accurately describe the actual flood characteristics, and only can qualitatively analyze the encountering conditions of the main and branch flows and cannot analyze the designed flood conditions. Therefore, the internal relation of the flood of the main and branch flows is comprehensively considered, the encounter probability of the flood of the main and branch flows is quantitatively analyzed, and the method has important significance for river flood control and reservoir dispatching.
Through a search for a patent publication, no patent publication similar to the present patent application is found.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a non-uniform multi-source flood encounter risk analysis method, which is used for carrying out flood encounter analysis on main and branch flows by selecting flood peak flow as a significant characteristic quantity of flood, more accurately evaluating flood risks and providing a flood frequency basis for flood control work and water conservancy and hydropower engineering construction of various drainage basins.
The technical problem to be solved by the invention is realized by the following technical scheme:
a non-uniform multi-source flood encounter risk analysis method is characterized by comprising the following steps: the analysis method comprises the following steps:
1) preparing data: processing a flood sequence of the main and branch flows to be evaluated, and eliminating historical extra-large years so as to avoid interference on non-consistency analysis of the sequence;
2) and (3) carrying out non-uniformity diagnosis on the flood peak sequence: carrying out non-consistency diagnosis on the flood peak sequence based on a hydrological variation comprehensive diagnosis system, wherein the hydrological variation comprehensive diagnosis system comprises preliminary diagnosis, detailed diagnosis and comprehensive diagnosis, and comprehensive human activity and climate change factor analysis, and respectively determining the variation points of the sequences of the trunk and branch lines;
3) determining time sequence distribution of flood peaks of main streams: based on a specific EM algorithm, carrying out punished maximum likelihood estimation and von Mises distribution parameter estimation, and specifically operating as follows:
a) adopting improved likelihood ratio test (MLRT) to test the direction data, determining the distribution form and constructing statistic;
b) estimating von Mises distribution parameters by adopting an EM algorithm: taking the initial and final time of flood season as the upper and lower boundaries, converting the flood peak occurrence time sequence into a directional sequence on the circumference of [0, 2 pi ], and selecting von Mises distribution to fit the flood peak occurrence time;
the von Mises distribution can describe finite variables with periodicity or seasonality, and the conversion formula is as follows:
wherein: l is the flood season length;
Dithe flood peak occurrence time;
the von Mises distribution probability density function is:
wherein: mu is a position parameter, mu is more than or equal to 0 and less than or equal to 2 pi;
k is a scale parameter, and k is more than or equal to 0;
I0(k) is a 0-order deformation Bessel function;
the von Mises distribution is a unimodal distribution on the circumference, while the flood peak time sequence is usually in a multi-modal form, and a single distribution function cannot accurately describe the sequence distribution form, so that a plurality of von Mises distributions need to be selected for mixed distribution fitting, and the form is as follows:
wherein: n is the sequence length;
αiα is satisfied for the weight coefficient1+α2+…+αn＝1；
The improved likelihood ratio test (MLRT) formula is as follows:
wherein: n is the number of data, and when n is larger, approximately obeys χ2Distributing;
r and theta are respectively the radius and the angle corresponding to the direction data;
4) fitting the flood peak sequence of the main and branch flows, and performing parameter estimation: and fitting the flood peak sequence of the main stream and the branch stream by selecting P-III type distribution form mixed distribution, wherein the P-III type distribution density function form is as follows:
wherein epsilon, β and b0Shape, dimension and position parameters respectively;
the density function form of the P-III type mixed distribution is as follows:
wherein:
is a weight coefficient;
εi、βiand b0iFor each element density function parameter;
5) constructing combined distribution: constructing combined distribution for the time sequence distribution of the flood peak of the main tributaries obtained in the step 3) and the flow sequence of the flood peak of the main tributaries obtained in the step 4) based on GumbelCopula functions,
a) on the basis of comprehensively considering the inherent and mutual correlation of each river flood peak sequence, constructing the four-variable joint distribution of the flood peak sequences by using an asymmetric Copula function and based on a semi-nested structure;
b) and (3) performing parameter estimation by adopting a pseudo maximum likelihood estimation method: and calculating the empirical joint frequency by adopting a Gringorten formula, selecting a nonparametric K-S test method, carrying out fitting test on the joint distribution function, and constructing statistic.
The Gumbel Copula function has a two-variable joint distribution expression as follows:
C(u1,u2)＝exp{-[(-ln u1)θ+(-ln u2)θ]1/θ}
wherein: u. of1And u2Is an edge distribution function;
theta is a connection parameter, and theta is more than 1;
the expression of the semi-nested structure is as follows:
wherein: u. of1、u2、u3、u4Is an edge distribution function;
θ1、θ2、θ3is a connection parameter and satisfies 1 < theta3＜θ1，1＜θ3＜θ2；
The Gringorten formula calculates an empirical joint frequency expression as follows:
wherein: m is satisfied in the joint observation samplej＜xi，yj＜yi(ii) a J is more than or equal to 1 and less than or equal to i);
n is the total number of samples;
the nonparametric K-S test is in the form:
wherein: f0(x, y) is a theoretical joint distribution probability;
6) flood peak encounter analysis was performed: and analyzing the occurrence time encounter of the multi-source flood, and performing flood peak encounter analysis by taking the occurrence time encounter as an intermediate quantity, wherein the risk of the flood peak occurrence time encounter can be defined as the probability of the flood occurring at the same time in the ith day:
Pi＝P(ti-1＜T1＜ti,ti-1＜T2＜ti)
wherein: t is1And T2Respectively the flood occurrence time of the two rivers;
the two rivers of the ith day are simultaneously subjected to flood peak with specified magnitude (q)1，q2) The probability of (c) is:
wherein: q1And Q2Respectively the peak flows of the two rivers.
The invention has the advantages and beneficial effects that:
1. the invention relates to a non-uniform multi-source flood encounter risk analysis method, which is used for analyzing the actual encounter condition of an actually-measured flood sequence, characterizing the flood sequence by a significant characteristic quantity, grasping the characteristics of the researched flood, and more flexibly and effectively using a joint analysis method to evaluate the flood influence. The flood control response mechanism is perfected, the peak regulation and peak staggering are carried out in time, and the life and property loss of people is reduced. And provide flood control standard basis for basin hydraulic engineering construction.
Drawings
FIG. 1 is an architectural diagram of the present invention
FIG. 2 is an architecture diagram of the hydrological variant comprehensive diagnosis system.
Detailed Description
The present invention is further illustrated by the following specific examples, which are intended to be illustrative, not limiting and are not intended to limit the scope of the invention.
A non-uniform multi-source flood encounter risk analysis method is characterized by comprising the following steps: the analysis method comprises the following steps:
4) preparing data: processing a flood sequence of the main and branch flows to be evaluated, and eliminating historical extra-large years so as to avoid interference on non-consistency analysis of the sequence;
5) and (3) carrying out non-uniformity diagnosis on the flood peak sequence: carrying out non-consistency diagnosis on the flood peak sequence based on a hydrological variation comprehensive diagnosis system, wherein the hydrological variation comprehensive diagnosis system comprises preliminary diagnosis, detailed diagnosis and comprehensive diagnosis, and comprehensive human activity and climate change factor analysis, and respectively determining the variation points of the sequences of the trunk and branch lines;
6) determining time sequence distribution of flood peaks of main streams: based on a specific EM algorithm, carrying out punished maximum likelihood estimation and von Mises distribution parameter estimation, and specifically operating as follows:
c) adopting improved likelihood ratio test (MLRT) to test the direction data, determining the distribution form and constructing statistic;
d) estimating von Mises distribution parameters by adopting an EM algorithm: taking the initial and final time of flood season as the upper and lower boundaries, converting the flood peak occurrence time sequence into a directional sequence on the circumference of [0, 2 pi ], and selecting von Mises distribution to fit the flood peak occurrence time;
the von Mises distribution can describe finite variables with periodicity or seasonality, and the conversion formula is as follows:
wherein: l is the flood season length;
Dithe flood peak occurrence time;
the von Mises distribution probability density function is:
wherein: mu is a position parameter, mu is more than or equal to 0 and less than or equal to 2 pi;
k is a scale parameter, and k is more than or equal to 0;
I0(k) is a 0-order deformation Bessel function;
the von Mises distribution is a unimodal distribution on the circumference, while the flood peak time sequence is usually in a multi-modal form, and a single distribution function cannot accurately describe the sequence distribution form, so that a plurality of von Mises distributions need to be selected for mixed distribution fitting, and the form is as follows:
wherein: n is the sequence length;
αiα is satisfied for the weight coefficient1+α2+…+αn＝1；
The improved likelihood ratio test (MLRT) formula is as follows:
wherein: n is the number of data, and when n is larger, approximately obeys χ2Distributing;
r and theta are respectively the radius and the angle corresponding to the direction data;
4) fitting the flood peak sequence of the main and branch flows, and performing parameter estimation: and fitting the flood peak sequence of the main stream and the branch stream by selecting P-III type distribution form mixed distribution, wherein the P-III type distribution density function form is as follows:
wherein epsilon, β and b0Shape, dimension and position parameters respectively;
the density function form of the P-III type mixed distribution is as follows:
wherein:
is a weight coefficient;
εi、βiand b0iFor each element density function parameter;
5) constructing combined distribution: constructing combined distribution for the time sequence distribution of the flood peak of the main tributaries obtained in the step 3) and the flow sequence of the flood peak of the main tributaries obtained in the step 4) based on Gumbel Copula function,
a) on the basis of comprehensively considering the inherent and mutual correlation of each river flood peak sequence, constructing the four-variable joint distribution of the flood peak sequences by using an asymmetric Copula function and based on a semi-nested structure;
b) and (3) performing parameter estimation by adopting a pseudo maximum likelihood estimation method: and calculating the empirical joint frequency by adopting a Gringorten formula, selecting a nonparametric K-S test method, carrying out fitting test on the joint distribution function, and constructing statistic.
The Gumbel Copula function has a two-variable joint distribution expression as follows:
C(u1,u2)＝exp{-[(-ln u1)θ+(-ln u2)θ]1/θ}
wherein: u. of1And u2Is an edge distribution function;
theta is a connection parameter, and theta is more than 1;
the expression of the semi-nested structure is as follows:
wherein: u. of1、u2、u3、u4Is an edge distribution function;
θ1、θ2、θ3is a connection parameter and satisfies 1 < theta3＜θ1，1＜θ3＜θ2；
The Gringorten formula calculates an empirical joint frequency expression as follows:
wherein: m is satisfied in the joint observation samplej＜xi，yj＜yi(ii) a J is more than or equal to 1 and less than or equal to i);
n is the total number of samples;
the nonparametric K-S test is in the form:
wherein: f0(x, y) is a theoretical joint distribution probability;
6) flood peak encounter analysis was performed: and analyzing the occurrence time encounter of the multi-source flood, and performing flood peak encounter analysis by taking the occurrence time encounter as an intermediate quantity, wherein the risk of the flood peak occurrence time encounter can be defined as the probability of the flood occurring at the same time in the ith day:
Pi＝P(ti-1＜T1＜ti,ti-1＜T2＜ti)
wherein: t is1And T2Respectively the flood occurrence time of the two rivers;
the two rivers of the ith day are simultaneously subjected to flood peak with specified magnitude (q)1，q2) The probability of (c) is:
wherein: q1And Q2Respectively the peak flows of the two rivers.
1. Wherein the analysis of the non-uniformity of the main and branch streams is realized by a hydrological mutation comprehensive diagnosis system as shown in figure 2,
1) and (3) preliminary diagnosis: generally, a 5-point sliding average line is adopted to judge the trend of the sequence, and the Hurst coefficients of the main and branch streams are respectively calculated. Preliminarily diagnosing whether variation exists in the flood peak time series of the main tributaries;
2) and (3) detailed diagnosis: and (3) carrying out variation detection on the flood peak sequences of the main and branch flows by adopting selected eight detection methods (MWP, sliding T detection, sliding F detection, BF detection, sliding rank sum detection, L-H detection, Cramer detection and ordered clustering method). Calculating Hurst coefficients of subsequences before and after mutation respectively to determine whether the Hurst coefficients meet the requirement of consistency;
3) and (3) comprehensive diagnosis: and (4) integrating human activity and climate change factor analysis, and finally determining the hydrological variation condition of the main and branch streams by combining the results of the primary diagnosis and the detailed diagnosis.
2. And calculating the statistic D of the combined distribution of the flood peak time sequence of the main and branch flows and the combined distribution of the flood peak flow sequence of the main and branch flows by a K-S test method, and if the value D is smaller than a critical value, the statistic D passes the significance test. This shows that the above partially nested joint distribution function constructed based on Gumbel Copula can fit the occurrence time and magnitude of the flood peak of the dry tributary well.
3. And (3) calculating the probability of the peak of the main branch to encounter each day according to the calculated probability of the peak of the main branch to encounter each day and calculating the probability of the peak of the same frequency to encounter each day, wherein the common peak recurrence period/a is 10, 20, 50, 100, 500 and 1000, and providing reference for downstream flood control work and engineering construction according to the calculation result.
Although the embodiments of the present invention and the accompanying drawings are disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the disclosure of the embodiments and the accompanying drawings.
Claims (1)
1. A non-uniform multi-source flood encounter risk analysis method is characterized by comprising the following steps: the analysis method comprises the following steps:
1) preparing data: processing a flood sequence of the main and branch flows to be evaluated, and eliminating historical extra-large years so as to avoid interference on non-consistency analysis of the sequence;
2) and (3) carrying out non-uniformity diagnosis on the flood peak sequence: carrying out non-consistency diagnosis on the flood peak sequence based on a hydrological variation comprehensive diagnosis system, wherein the hydrological variation comprehensive diagnosis system comprises preliminary diagnosis, detailed diagnosis and comprehensive diagnosis, and comprehensive human activity and climate change factor analysis, and respectively determining the variation points of the sequences of the trunk and branch lines;
3) determining time sequence distribution of flood peaks of main streams: based on a specific EM algorithm, carrying out punished maximum likelihood estimation and von Mises distribution parameter estimation, and specifically operating as follows:
a) adopting improved likelihood ratio test (MLRT) to test the direction data, determining the distribution form and constructing statistic;
b) estimating von Mises distribution parameters by adopting an EM algorithm: taking the initial and final time of flood season as the upper and lower boundaries, converting the flood peak occurrence time sequence into a directional sequence on the circumference of [0, 2 pi ], and selecting von Mises distribution to fit the flood peak occurrence time;
the von Mises distribution can describe finite variables with periodicity or seasonality, and the conversion formula is as follows:
wherein: l is the flood season length;
Dithe flood peak occurrence time;
the von Mises distribution probability density function is:
wherein: mu is a position parameter, mu is more than or equal to 0 and less than or equal to 2 pi;
k is a scale parameter, and k is more than or equal to 0;
I0(k) is a 0-order deformation Bessel function;
the von Mises distribution is a unimodal distribution on the circumference, while the flood peak time sequence is usually in a multi-modal form, and a single distribution function cannot accurately describe the sequence distribution form, so that a plurality of von Mises distributions need to be selected for mixed distribution fitting, and the form is as follows:
wherein: n is the sequence length;
αiα is satisfied for the weight coefficient1+α2+…+αn＝1；
The improved likelihood ratio test (MLRT) formula is as follows:
wherein: n is the number of data, and when n is larger, approximately obeys χ2Distributing;
r and theta are respectively the radius and the angle corresponding to the direction data;
4) fitting the flood peak sequence of the main and branch flows, and performing parameter estimation: and fitting the flood peak sequence of the main stream and the branch stream by selecting P-III type distribution form mixed distribution, wherein the P-III type distribution density function form is as follows:
wherein epsilon, β and b0Shape, dimension and position parameters respectively;
the density function form of the P-III type mixed distribution is as follows:
wherein:
is a weight coefficient;
εi、βiand b0iFor each element density function parameter;
5) constructing combined distribution: constructing combined distribution for the time sequence distribution of the flood peak of the main tributaries obtained in the step 3) and the flow sequence of the flood peak of the main tributaries obtained in the step 4) based on Gumbel Copula function,
a) on the basis of comprehensively considering the inherent and mutual correlation of each river flood peak sequence, constructing the four-variable joint distribution of the flood peak sequences by using an asymmetric Copula function and based on a semi-nested structure;
b) and (3) performing parameter estimation by adopting a pseudo maximum likelihood estimation method: and calculating the empirical joint frequency by adopting a Gringorten formula, selecting a nonparametric K-S test method, carrying out fitting test on the joint distribution function, and constructing statistic.
The Gumbel Copula function has a two-variable joint distribution expression as follows:
C(u1,u2)＝exp{-[(-lnu1)θ+(-lnu2)θ]1/θ}
wherein: u. of1And u2Is an edge distribution function;
theta is a connection parameter, and theta is more than 1;
the expression of the semi-nested structure is as follows:
wherein: u. of1、u2、u3、u4Is an edge distribution function;
θ1、θ2、θ3is a connection parameter and satisfies 1 < theta3＜θ1，1＜θ3＜θ2；
The Gringorten formula calculates an empirical joint frequency expression as follows:
wherein: m is satisfied in the joint observation samplej＜xi，yj＜yi(ii) a J is more than or equal to 1 and less than or equal to i);
n is the total number of samples;
the nonparametric K-S test is in the form:
wherein: f0(x, y) is a theoretical joint distribution probability;
6) flood peak encounter analysis was performed: and analyzing the occurrence time encounter of the multi-source flood, and performing flood peak encounter analysis by taking the occurrence time encounter as an intermediate quantity, wherein the risk of the flood peak occurrence time encounter can be defined as the probability of the flood occurring at the same time in the ith day:
Pi＝P(ti-1＜T1＜ti,ti-1＜T2＜ti)
wherein: t is1And T2Respectively two rivers floodThe time of water generation;
the two rivers of the ith day are simultaneously subjected to flood peak with specified magnitude (q)1，q2) The probability of (c) is:
wherein: q1And Q2Respectively the peak flows of the two rivers.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN112016051A (en) * | 2020-08-14 | 2020-12-01 | 长江水利委员会水文局 | Probability analysis method and system for encounter in multi-source flood process |
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| CN112396297A (en) * | 2020-11-03 | 2021-02-23 | 华中科技大学 | Method and system for analyzing encounter time and magnitude occurrence rule in flood process |
| CN114022304A (en) * | 2021-09-30 | 2022-02-08 | 西北农林科技大学 | Method and device for calculating ecological water demand of river channel under condition of runoff inconsistency |
| CN114022304B (en) * | 2021-09-30 | 2024-06-07 | 西北农林科技大学 | River channel ecological water demand calculation method and device under runoff non-uniformity condition |
| CN115730829A (en) * | 2022-12-05 | 2023-03-03 | 中国水利水电科学研究院 | Rare flood peak flow calculation method |
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