CN111680912B - Drought and waterlogging sudden turning risk assessment method - Google Patents
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Abstract
The invention discloses a drought and flood sudden turn risk assessment method which is based on a hydrological meteorological variable and calculates the average time sequence of continuous N time periods as P t Extracting a time sequence P delayed by N time periods from the time sequence t‑1 Then according to a time sequence P t And P t‑1 Determining drought and flood thresholds by using a threshold level method, and constructing P based on copula combined distribution function t And P t‑1 A joint probability distribution function between the P and P, and further constructing P t And P t‑1 A condition distribution function model of drought transferring waterlogging and waterlogging transferring drought between the drought transferring waterlogging and the waterlogging transferring drought so as to realize risk probability evaluation of drought transferring waterlogging and waterlogging transferring drought; after the model is established in the mode, the drought and waterlogging rush turning risk assessment method can effectively assess the possibility of drought and waterlogging disasters in a certain area at a certain time, and can also predict the possibility of the drought and waterlogging disasters in the area under future climate conditions according to meteorological data simulated and predicted in the future, so that an accurate and reliable basis is provided for drought and waterlogging disaster resistance and risk management decision making.
Description
Technical Field
The invention relates to the field of risk assessment of natural disasters, in particular to a drought and waterlogging sudden turning risk assessment method.
Background
Disaster risk assessment is the core content of disaster management, acquires disaster risk information, and can provide accurate and reliable basis for drought and flood disaster resistance and risk management decision.
With the development of mathematics, decision-making, remote sensing, computers and other subjects, disaster risk assessment is rapidly developed, and more mathematical tools are introduced to accelerate the pace of natural disaster risk assessment quantification.
Common disaster risk assessment methods include fuzzy comprehensive evaluation methods, chromatographic analysis methods, probability statistics methods, gray systems, artificial neural networks, and the like. The probabilistic statistical method estimates the probability of occurrence of a disaster by a mathematical method of probabilistic statistics based on historical data samples. With the further development of mathematical theories and methods, many scholars at home and abroad continuously introduce and put forward new disaster assessment theories and models aiming at different disaster types, for example, many scholars estimate the possibility of natural disasters such as flood, drought and the like by constructing various joint probability distribution models. However, most of these models are used for traditional disaster research, for example, the drought risk is evaluated by considering characteristics of duration, strength, intensity and the like of drought, and evaluation of disasters that alternate between drought and flood disasters, such as drought and flood rush, is rare.
Disclosure of Invention
The invention aims to provide a drought and flood sudden turn risk assessment method to solve the problem of lack of assessment on alternate occurrence of drought and flood disasters.
In order to solve the technical problem, the invention provides a drought and waterlogging sudden turning risk assessment method, which comprises the following steps:
s1, calculating an average time sequence P of a certain hydrometeorology variable in continuous N times t Extracting a time series P lagging by one N time period t-1 Wherein said P is t Is the first time series of drought-flood and turn-fast events, P t-1 A second time series of drought-flood sharp turn events;
s2, drought and flooding are defined, namely P is defined according to a time sequence t And said P t-1 Determining the threshold values of drought and flooding by using a threshold level method;
s3, constructing the P based on copula combined distribution function t And said P t-1 A joint probability distribution function therebetween;
s4, further constructing the P based on the constructed joint probability distribution function t And said P t-1 And a condition distribution function model for converting drought into waterlogging and converting waterlogging into drought is formed between the drought, the waterlogging and the waterlogging so as to realize risk probability evaluation on the drought, the waterlogging and the waterlogging.
In one embodiment, in S1, the hydrometeorological variables include rainfall, runoff, water level, and soil humidity, and the units of the time period include hours, days, months, and years.
In one embodiment, in said S2, said P is t And said P t-1 Respectively fitting an edge distribution, estimating each distribution parameter by using a maximum likelihood method, selecting the optimal distribution according to chi-square fitting goodness test, and determining drought and flood conditions by using 10% -30% and 70% -90% quantile thresholds of the optimal distribution, namely determining that the drought and flood conditions exist when the hydrological meteorological variable is less than 10% -30% quantile threshold, and determining that the flood conditions exist when the hydrological meteorological variable is greater than 70% -90% quantile threshold.
In one embodiment, in said S3, said P is constructed t And said P t-1 Of the joint probability distribution function between, said P t And said P t-1 The corresponding two-dimensional joint probability distribution function is: f (x) 1 ,x 2 )＝C(F X1 (x 1 ),F X2 (x 2 ))＝C(u 1 ，u 2 ) (ii) a Said F X1 (x 1 ) And said F X2 (x 2 ) Respectively represent the P t And said P t-1 The edge distribution function of (c), the u 1 And said u 2 Each represents said x 1 And said x 2 C is a copula function.
In one embodiment, in the step S4, a conditional probability distribution model of drought transferring waterlogging and waterlogging transferring drought is respectively constructed; for drought vs. flood conditions, given X 1 ≤x 1 Under the condition of X 2 >x 2 The probability of (d) is expressed as: f (X) 2 ＞x 2 |X 1 ≤x 1 )＝1-C(u 1 ，u 2 )/u 1 (ii) a For flood to drought conditions, given X 1 >x 1 Under the condition of X 2 ≤x 2 The probability of (d) is expressed as: f (X) 2 ≤x 2 |X 1 ＞x 1 )＝(u 1 -C(u 1 ，u 2 ))/(1-u 1 ) (ii) a Said x 1 And x 2 Respectively refer to the P t And said P t-1 And (4) determining drought and flood thresholds.
The invention has the following beneficial effects:
the invention further constructs the P based on the constructed joint probability distribution function t And said P t-1 After the model is established in the above way, the drought-waterlogging and flood-drought risk assessment method can effectively assess the possibility of drought-waterlogging disasters in a certain area in a certain period, and also can predict the possibility of the drought-waterlogging disasters in the area under the future climate situation according to meteorological data simulated and predicted in the future, thereby providing accurate and reliable basis for drought-waterlogging disaster resistance and risk management decision.
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In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart provided by a preferred embodiment of the drought and flood sudden-turn risk assessment method;
FIG. 2 is a spatial distribution diagram of risk assessment of drought-to-flood (1961-1985) in the Dongjiang river basin;
FIG. 3 is a spatial distribution diagram of risk assessment of drought-to-flood (1986-2010) in the Dongjiang river basin;
FIG. 4 is a water-logging drought-transferring risk assessment spatial distribution map of the Dongjiang river basin (1961-1985);
FIG. 5 is a water-logging drought-transferring risk assessment spatial distribution diagram of the Dongjiang river basin (1986-2010).
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides a drought and flood sudden turning risk assessment method, the embodiment of which is shown in figure 1 and comprises the following steps:
s1, calculating an average time sequence P of a certain hydrometeorology variable in continuous N times t Extracting a time series P lagging by one N time period t-1 In which P is t Is the first time series of drought and flood sudden turn events, P t-1 The second time series of drought and flood sudden turn events.
It should be noted that in S1, the hydrometeorology variables include rainfall, runoff, water level and soil humidity, the units of the time period include hours, days, months and years, namely, any index of rainfall, runoff, water level and soil humidity can be used for risk assessment, and the time unit selected for assessment is not unique, so for the sake of example detailed below, the east river basin will be described below, rainfall is used as the hydrometeorology variable, and the unit of the time period will be set as days.
S2, drought and flooding are defined, namely according to the time sequence P t And P t-1 And determining the drought and flooding thresholds by using a threshold level method.
Specifically, in S2, P can be t And P t-1 Respectively fitting an edge distribution, estimating each distribution parameter by using a maximum likelihood method, selecting the optimal distribution according to chi-square fitting goodness test, and determining drought and flood conditions by using 10% -30% and 70% -90% quantile thresholds of the optimal distribution, namely determining that the drought and flood conditions are the case when the hydrological meteorological variable is less than 10% -30% quantile threshold, and determining that the flood conditions are the case when the hydrological meteorological variable is greater than 70% -90% quantile threshold.
For example, if the average rainfall in a certain 5 days is less than the threshold of 20% quantile, it is considered as drought, and if it is more than the threshold of 80% quantile, it is considered as flood.
S3, constructing P based on copula combined distribution function t And P t-1 A joint probability distribution function therebetween.
Specifically, P can be constructed in S3 t And P t-1 Of a joint probability distribution function between, P t And P t-1 Correspond toThe two-dimensional joint probability distribution function of (a) is: f (x) 1 ,x 2 )＝C(F X1 (x 1 ),F X2 (x 2 ))＝C(u 1 ，u 2 )。
F X1 (x 1 ) And F X2 (x 2 ) Each represents P t And P t-1 Of the edge distribution function u 1 And u 2 Each represents x 1 And x 2 C is a copula function.
S4, further constructing P based on the constructed joint probability distribution function t And P t-1 And a condition distribution function model of drought transferring waterlogging and waterlogging transferring drought between the drought transferring waterlogging and the waterlogging transferring drought so as to realize risk probability evaluation of drought transferring waterlogging and waterlogging transferring drought.
Specifically, in S4, a conditional probability distribution model for drought-to-waterlogging and waterlogging-to-drought is constructed.
For drought vs. flood conditions, given X 1 ≤x 1 Under the condition of X 2 >x 2 The probability of (d) is expressed as:
F(X 2 ＞x 2 |X 1 ≤x 1 )＝1-C(u 1 ，u 2 )/u 1 。
for flood vs. drought conditions, given X 1 >x 1 Under the condition of X 2 ≤x 2 The probability of (d) is expressed as:
F(X 2 ≤x 2 |X 1 ＞x 1 )＝(u 1 -C(u 1 ，u 2 ))/(1-u 1 )。
x 1 and x 2 Are respectively referred to as P t And P t-1 And (4) determining drought and flood thresholds.
After the model is built in the mode, the risk of drought and waterlogging emergency is intuitively known through the model, so that timely handling can be carried out, and the loss caused by drought and waterlogging emergency is reduced.
In order to test the effect of the drought and waterlogging sudden turning risk assessment method, a plurality of meteorological sites in the east river valley are selected to carry out drought and waterlogging sudden turning risk assessment: the Dongjiang river is located in the south China, the river basin area is 27040 square kilometers, the river basin is also an important sub-river basin of the Zhujiang river basin, the Dongjiang river basin is in the climate dominated by monsoon, the annual average temperature is about 20-22 ℃, the annual average precipitation is 1500-2400 mm, and the daily rainfall data time span of each station is 1961-2010.
The risk assessment of drought and waterlogging sudden turning comprises the following steps:
s1, calculating average rainfall for 5 continuous days according to the daily rainfall time sequence of each station of the Dongjiang river basin to obtain an average rainfall time sequence P for 5 continuous days t Extracting average rainfall time sequence P of continuous 5 days lagging by 1 day 5 t-1 。
S2, drought and flood are defined, namely according to the historical sequence P t And P t-1 And determining the threshold values of drought and flooding by using a threshold value method.
S3, constructing P based on copula combined distribution function t And P t-1 A joint probability distribution function between.
S4, further constructing P based on the constructed joint probability distribution function t And P t-1 And a distribution function model of drought-to-waterlogging and waterlogging-to-drought conditions is adopted between the drought-to-waterlogging and the waterlogging-to-drought conditions to realize risk probability evaluation of drought-to-waterlogging and waterlogging-to-drought.
To better illustrate the effectiveness of the present invention, in this example, the risk of drought and flood rushing in the east river basin was compared in two periods (1961-1985 and 1986-2010). Wherein the drought-waterlogging threshold level used in the second period is the same as the first period.
In this embodiment, the evaluation method is as follows:
(1) Effect performance of risk assessment of drought-to-waterlogging
Fig. 2 and fig. 3 show a probability model of drought-to-flood conditions according to the present invention, which is used to estimate the spatial distribution of the likelihood of drought-to-flood in the east river valley, wherein the higher the color of the region is, the higher the risk is, and the lower the risk is; fig. 2 and fig. 3 show the spatial distribution patterns of the occurrence probability of drought, turning into flood in the east river basin in two periods of 1961-1985 and 1986-2010, respectively, wherein the northern area in the graph has a darker color and the southern area has a lighter color, i.e. the risk probability of drought, turning into flood in the northern area of the east river basin is significantly higher than that in the southern area; in addition, comparing fig. 2 and fig. 3, it can be seen that the risk of waterlogging caused by drought in the east river basin in the second period (1986-2010) is obviously increased, and especially in the south area, the risk is most obviously increased.
(2) Effect performance of risk assessment of waterlogging to drought
FIGS. 4 and 5 show a probabilistic model of waterlogging-to-drought conditions of the present invention for estimating the spatial distribution of the likelihood of waterlogging-to-drought in the east river basin; fig. 4 and fig. 5 show spatial distribution patterns of the occurrence probability of waterlogging to drought in the east river basin in two periods, namely 1961-1985 and 1986-2010, respectively, wherein the northern area in the graph has a darker color and the southern area has a lighter color, and the risk probability of waterlogging to drought in the northern area of the east river basin is obviously higher than that in the southern area; in addition, comparing fig. 4 and fig. 5, it can be found that the waterlogging and drought transferring risk of the east river basin in the second period (1986-2010) is obviously increased, the waterlogging and drought transferring probability of the northern area is more than 10%, and the risk of the southern area is also obviously increased.
Generally, the drought and flood sudden turning risk assessment method can effectively assess the possibility of drought and flood disasters in a certain area at a certain time, and can also predict the possibility of the risk in the area under the future climate situation according to the meteorological data simulated and predicted in the future, thereby providing accurate and reliable basis for drought and flood disasters resistance and risk management decision.
The Copula function describes the correlation between variables, and is actually a function that connects the joint distribution function and their respective edge distribution functions, and therefore it is also called a connection function. The theory of relevance dates back to 1959, where SKlar relates multivariate distributions to Copula functions by means of theorem. The theory and method of correlation in the later 90 s of the 20 th century are rapidly developed abroad and applied to the aspects of correlation analysis, investment portfolio analysis, risk management and the like in the fields of finance, insurance and the like.
Copula is latin, the idea is "connection", the concept of Copula was first introduced by Sklar in 1959 when answering the question of m.frechet about the relationship between multidimensional distribution functions and low dimensional edges; copula was primarily used for the development of probability metric space theory in the early days, and later, with the progressive improvement of the theory, it was used for non-parametric measurement to determine the dependency between random variables.
Copula is favored by statisticians for two reasons: the first is that Copula is a method to study dependency measure; the second is Copula as a starting point for constructing a two-dimensional distribution family, which can be used for multivariate model distribution and stochastic simulation. The Copula function is used as a tool of a dependent mechanism between variables, almost contains all dependent information of random variables, and is useful for analyzing the correlation between the variables under the condition that whether the conventional linear correlation coefficient can correctly measure the correlation between the variables cannot be determined, and the occurrence of the Copula function enables the dependence description between the variables to be more perfect.
Risk assessment method while the above is described as a preferred embodiment of the present invention, it should be noted that various modifications and embellishments can be made by those skilled in the art without departing from the principle of the present invention, and these modifications and embellishments are also regarded as the scope of the present invention.
Claims (1)
1. A drought and waterlogging sudden turning risk assessment method is characterized by comprising the following steps:
s1, calculating an average time sequence P of a certain hydrometeorology variable in continuous N times t Extracting a time series P lagging by one N time period t-1 Wherein said P is t The first time series of drought and flood sudden turn events, the P t-1 A second time series of drought and flood hard turn events;
in the S1, the hydrometeorology variables include rainfall, runoff, water level, and soil humidity, and the units of the time period include hours, days, months, and years;
s2, drought and flooding are defined, namely P is defined according to a time sequence t And said P t-1 Determining the threshold values of drought and flooding by using a threshold level method;
in said S2, for said P t And said P t-1 Respectively fitting an edge distribution, estimating each distribution parameter by using a maximum likelihood method, selecting the optimal distribution according to chi-square fitting goodness test, and determining drought and flood conditions by using 10% -30% and 70% -90% quantile thresholds of the optimal distribution, namely determining that the drought and flood conditions exist when the hydrological meteorological variable is less than 10% -30% quantile threshold, and determining that the flood conditions exist when the hydrological meteorological variable is greater than 70% -90% quantile threshold;
s3, constructing the P based on copula combined distribution function t And said P t-1 A joint probability distribution function therebetween;
in the S3, the P is constructed t And said P t-1 Of the joint probability distribution function between, said P t And said P t-1 The corresponding two-dimensional joint probability distribution function is: f (x) 1 ,x 2 )＝C(F X1 (x 1 ),F X2 (x 2 ))＝C(u 1 ，u 2 )；
Said F X1 (x 1 ) And said F X2 (x 2 ) Respectively represent the P t And said P t-1 The edge distribution function of u 1 And said u 2 Each represents the x 1 And said x 2 C is a copula function;
s4, further constructing the P based on the constructed joint probability distribution function t And said P t-1 A condition distribution function model for converting drought into waterlogging and converting waterlogging into drought is formed between the drought, the waterlogging and the waterlogging so as to realize risk probability evaluation on the drought, the waterlogging and the waterlogging;
in the S4, respectively constructing a conditional probability distribution model for drought transferring waterlogging and waterlogging transferring drought;
for drought vs. flood conditions, given X 1 ≤x 1 Under the condition of X 2 >x 2 The probability of (d) is expressed as:
F(X 2 ＞x 2 |X 1 ≤x 1 )＝1-C(u 1 ，u 2 )/u 1 ；
for flood to drought conditions, given X 1 >x 1 Under the condition of，X 2 ≤x 2 The probability of (d) is expressed as:
F(X 2 ≤x 2 |X 1 ＞x 1 )＝(u 1 -C(u 1 ，u 2 ))/(1-u 1 )；
said x 1 And x 2 Respectively refer to the P t And said P t-1 The determined drought and flooding thresholds.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750589A (en) * | 2012-06-30 | 2012-10-24 | 北京师范大学 | Water environment and water ecology safety guarantee management system |
| CN106355332A (en) * | 2016-04-08 | 2017-01-25 | 中国水利水电科学研究院 | Flood disaster risk response method based on three-layer risk evaluation |
| CN107463901A (en) * | 2017-08-07 | 2017-12-12 | 中国科学院遥感与数字地球研究所 | Multiple dimensioned Regional Flooding disasters danger remote sensing evaluation method and system |
| CN107944219A (en) * | 2017-12-13 | 2018-04-20 | 广东电网有限责任公司电力科学研究院 | A kind of method and apparatus for characterizing different periods drought and waterlogging and causing calamity feature |
| CN108288128A (en) * | 2018-02-28 | 2018-07-17 | 中国电力科学研究院有限公司 | A kind of transmission line of electricity methods of risk assessment and system |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102750589A (en) * | 2012-06-30 | 2012-10-24 | 北京师范大学 | Water environment and water ecology safety guarantee management system |
| CN106355332A (en) * | 2016-04-08 | 2017-01-25 | 中国水利水电科学研究院 | Flood disaster risk response method based on three-layer risk evaluation |
| CN107463901A (en) * | 2017-08-07 | 2017-12-12 | 中国科学院遥感与数字地球研究所 | Multiple dimensioned Regional Flooding disasters danger remote sensing evaluation method and system |
| CN107944219A (en) * | 2017-12-13 | 2018-04-20 | 广东电网有限责任公司电力科学研究院 | A kind of method and apparatus for characterizing different periods drought and waterlogging and causing calamity feature |
| CN108288128A (en) * | 2018-02-28 | 2018-07-17 | 中国电力科学研究院有限公司 | A kind of transmission line of electricity methods of risk assessment and system |
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