CN113268710B - Drought extremum information output method and device, electronic equipment and medium - Google Patents

Drought extremum information output method and device, electronic equipment and medium Download PDF

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CN113268710B
CN113268710B CN202110533425.9A CN202110533425A CN113268710B CN 113268710 B CN113268710 B CN 113268710B CN 202110533425 A CN202110533425 A CN 202110533425A CN 113268710 B CN113268710 B CN 113268710B
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鲁帆
周毓彦
严登华
唐颖复
孙高虎
江明
王晓钰
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China Institute of Water Resources and Hydropower Research
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Abstract

The application provides a drought extremum information output method, a drought extremum information output device, electronic equipment and a medium, wherein the drought extremum information output method comprises the following steps: acquiring drought recurrence period, historical drought data, historical average temperature, future drought data and future average temperature of a target river basin; performing fitting calculation on the historical drought data and the future drought data by using extremum distribution and the drought reproduction period to obtain a historical drought extremum and a future drought extremum; and obtaining drought extremum information according to the historical drought extremum, the future drought extremum, the historical average temperature and the future average temperature, and outputting the drought extremum information. According to the application, the historical drought data and the future drought data in the target flow area are subjected to fitting processing by utilizing the extremum distribution, and drought extremum information with high accuracy can be output.

Description

Drought extremum information output method and device, electronic equipment and medium
Technical Field
The application belongs to the technical field of hydrologic analysis, and particularly relates to a drought extremum information output method, a drought extremum information output device, electronic equipment and a drought extremum information output medium.
Background
Global climate change, which is primarily characterized by global warming, has had a serious impact on fragile ecosystems and social systems. Such as local and regional drought disasters occurring in China in successive years, are one of the devil fruits caused by global climate warming.
At present, due to the climate influence of global warming, data such as drought extremum and the like mostly show unsteady state change, and if the influence degree of global warming on the drought extremum is calculated through hydrological data corresponding to historical drought, the accuracy of the outputted drought extremum information is lower.
Disclosure of Invention
The embodiment of the application provides a drought extremum information output method, device, electronic equipment and medium, which are used for solving the problem that the accuracy of the drought extremum information output in a target flow area is low.
In a first aspect, an embodiment of the present application provides a drought extremum information output method, including:
Acquiring drought recurrence period, historical drought data, historical average temperature, future drought data and future average temperature of a target river basin;
Acquiring drought recurrence period, historical drought data, historical average temperature, future drought data and future average temperature of a target river basin;
fitting the historical drought data and the future drought data by utilizing extremum distribution to obtain a drought extremum function;
Obtaining a historical drought extremum and a future drought extremum according to the drought recurrence period and the drought extremum function;
according to the historical drought extremum and the future drought extremum, the historical average temperature and the future average temperature are used for obtaining drought extremum information, wherein the drought extremum information is the ratio of a drought extremum difference to an average temperature difference, the drought extremum difference is the difference between the future drought extremum and the historical drought extremum, and the average temperature difference is the difference between the future average temperature and the historical average temperature;
and outputting the drought extremum information.
In a second aspect, an embodiment of the present application provides a drought extremum information output apparatus, including:
the acquisition module is used for acquiring the drought reappearance period, the historical drought data, the historical average temperature, the future drought data and the future average temperature of the target river basin;
The fitting module is used for fitting the historical drought data and the future drought data by utilizing extremum distribution so as to obtain a drought extremum function;
The first calculation module is used for obtaining a historical drought extremum and a future drought extremum according to the drought reproduction period and the drought extremum function;
The second calculation module is used for obtaining drought extremum information according to the historical drought extremum and the future drought extremum, the historical average temperature and the future average temperature, the drought extremum information is the ratio of a drought extremum difference to an average temperature difference, the drought extremum difference is the difference between the future drought extremum and the historical drought extremum, and the average temperature difference is the difference between the future average temperature and the historical average temperature;
And the output module is used for outputting the drought extremum information.
In a third aspect, an embodiment of the present application provides an electronic device, including:
A processor, a memory, and a program or instruction stored on the memory and executable on the processor, which when executed by the processor, performs the steps in the drought extremum information output method as described in the first aspect above.
In a fourth aspect, an embodiment of the present application provides a readable storage medium, where a program or an instruction is stored, where the program or the instruction, when executed by a processor, implement the steps in the drought extremum information output method according to the first aspect above.
According to the technical scheme provided by the embodiment of the application, the drought extremum information output method is based on the historical hydrologic information (historical drought data and historical average temperature) and the future hydrologic information (future drought data and future average temperature) of the target river basin, and the degree of influence of global warming on the drought index of the target river basin is subjected to fitting analysis in an extremum distribution mode, so that the accuracy of the drought extremum information output by the target river basin is improved.
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FIG. 1 is a flow chart of a drought extremum information output method provided by an embodiment of the application;
Fig. 2 is a schematic structural diagram of a drought extremum information output device according to an embodiment of the present application;
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and in the claims are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
Referring to fig. 1, fig. 1 is a flowchart of a drought extremum information output method according to an embodiment of the present application, where the method may be performed by a drought extremum information output device, and the drought extremum information output device may be composed of hardware and/or software and may be generally integrated in a device with a drought extremum information output function, where the device may be an electronic device such as a server, a mobile terminal or a server cluster. As shown in fig. 1, the drought extremum information output method includes the following steps:
And 101, acquiring drought reappearance period, historical drought data, historical average temperature, future drought data and future average temperature of a target river basin.
And 102, fitting the historical drought data and the future drought data by using extremum distribution to obtain a drought extremum function.
And step 103, obtaining a historical drought extremum and a future drought extremum according to the drought reproduction period and the drought extremum function.
And 104, obtaining drought extremum information according to the historical drought extremum and the future drought extremum, the historical average temperature and the future average temperature.
The drought extremum information is the ratio of a drought extremum difference to an average temperature difference, the drought extremum difference is the difference between the future drought extremum and the historical drought extremum, and the average temperature difference is the difference between the future average temperature and the historical average temperature.
And 105, outputting the drought extremum information.
Drought disasters are main disasters affecting the development of China economic society, are affected by global warming climates, and occur in China locally and regionally. In recent years, the average annual precipitation amount in China has a weak increasing trend, but the average annual precipitation day has a remarkable decreasing trend. In order to better cope with drought disasters that may occur in the future, it is necessary to analyze the impact of global warming on drought disasters accordingly, so as to estimate the frequency and intensity of drought disasters that may occur in the future in the climate background that is warmed throughout the year.
In the prior art, the traditional hydrologic analysis method generally uses hydrologic information in a history period and takes the steady state change of hydrologic data as a precondition to deduce and analyze the influence degree of the drought disaster intensity by various weather condition changes. The traditional hydrologic analysis method mainly has two defects, namely, the data source is not comprehensive enough, and hydrologic information in the future is missing; and secondly, the precondition is not satisfied (because the drought disaster is influenced by global change, the hydrological data related to the drought disaster show unsteady state change characteristics). Because the conventional hydrologic analysis method at least has the two defects, the accuracy of drought extremum information (namely the degree of influence of the variation of various meteorological conditions on the intensity of drought disasters) output by the conventional hydrologic analysis method is low.
According to the drought extremum information output method provided by the application, the degree of influence of the annual warming on the intensity of drought disasters is analyzed by supplementing hydrologic information (future drought data and future average temperature) in a future period and combining hydrologic information (historical drought data and historical average temperature) in a historical period and fitting the distribution situation of the drought data of a target river basin by means of extremum distribution applicable to unsteady state change data. The drought extremum information output method provided by the application overcomes the two defects, so that the accuracy of the finally output drought extremum information can be improved.
The historical drought data and the future drought data may be obtained by processing the historical runoff flow and the future runoff flow of the river section in the target flow area, respectively.
Wherein, the step of processing the historical runoff flow to obtain the historical drought data may be:
And acquiring a historical flow set of the river section.
And obtaining the historical average flow for many years according to the historical flow set.
And obtaining the historical drought data according to the years of historical average flow and the historical flow set.
The examples are:
Assuming that the history period of the target river basin is 1980-2020, setting the history flow set as Q A and the elements in Q A as
Wherein, the parameter i is used to indicate the year of a certain element in Q A (when i=1, it indicates 1980; when i=2, it indicates 1981, and so on, the other values of the parameter i correspond to the meanings of the other values), and the parameter m A is used to indicate the total number of years of the history period of the target river basin, in this example, the parameter m A should be 41.
The parameter j is used to indicate the unit scale of a certain element in Q A, and assuming that the unit scale in this example is a daily scale (when j=1, 1 month and 1 day is indicated, when j=2, 1 month and 2 days are indicated, and the other values of the parameter j correspond to the meanings and the like), and the parameter n A is used to indicate the total number of unit scales of the history period of the target drainage basin, and in this example, the parameter n A should be 365 or 366.
In practical applications, the unit scale may be not only a daily scale, but also a ten-day scale (when j=1, the last ten-day of 1 month is indicated, and the like, and the parameter n A should be 36) or a month scale (when j=1, the month is indicated, and the like, and the parameter n A should be 12), and the unit scale of other time periods (such as 3 days, 5 days, 7 days, etc.) may be set based on the actual requirement.
Setting the average flow rate of the history of years asThe elements in (a) areThe calculation formula of (2) is as follows:
setting the historical drought data as elements in DT A,DTA Element(s)The method can be used for indicating the duration time of the river section in the historical drought state, and the judgment process of the historical drought state is as follows: when an element isAnd elementIs kept consistent with the parameter j of (1) and the elementIs smaller than the elementWhen the value of (2) is the same as the value of the element, the river section is determined to be the elementThe corresponding historical time node is in a drought state; otherwise, determining that the river section is at the elementThe corresponding historical time node is not in drought state.
Wherein the parameter t A is used for indicating the elementThe sequence number in DT A, the parameter k A is the total number of elements in DT A.
In practical application, the elements areAnd can also be used for indicating the accumulated underflow of the river section in a historical drought state.
Further described is that the river section is assumed to correspond to elements from 1 day to 10 days in the period from 1 day in 1980 to 1 day in 1980(In cubic meters per second) of {30, 30 }, respectively 30, 70, 30, 70}; corresponding elements of 1 day to 10 days(In cubic meters per second) of 50, then when the elements areWhen the duration time of the river section in the historical drought state is indicated, the element corresponding to the river section in 1 day to 10 days3 (Days) and 4 (days), respectively; while when the element isWhen the accumulated underwater quantity used for indicating that the river section is in the historical drought state, the element corresponding to the river section in 1 day to 10 days60 (Cubic meters per second) and 80 (cubic meters per second), respectively.
The step of processing the future runoff flow to obtain the future drought data may be:
And acquiring a future flow set of the river section.
And obtaining the average future flow for many years according to the future flow set.
And obtaining the future drought data according to the average flow in the future for many years and the set of the future flows.
The examples are:
Assuming that the future period of the target river basin is 2021-2100 years, setting the future flow set as Q B, and enabling elements in Q B to be
Wherein, the parameter e is used to indicate the year of a certain element in Q B (when e=1, 2021 is indicated, when e=2, 2022 is indicated, and the other values of the parameter e correspond to the meanings and the like), the parameter m B is used to indicate the total number of years in the future of the target river basin, and the parameter m B should be 80 in this example.
The parameter f is used to indicate the unit scale of a certain element in Q B, and assuming that the unit scale in this example is a daily scale (when f=1, 1 month and 1 day is indicated, when f=2, 1 month and 2 days are indicated, and the other values of the parameter f correspond to the meanings and the like), and the parameter n B is used to indicate the total number of unit scales of the future period of the target drainage basin, and the parameter n B should be 365 or 366 in this example.
Setting the average flow rate in the future for a plurality of years asThe elements in (a) areThe calculation formula of (2) is as follows:
Setting the future drought data as elements in DT B,DTB Element(s)Can be used to indicate the duration of the river section in a future drought state, the decision process for the future drought state being: when an element isAnd elementIs kept consistent with the parameter j of (1) and the elementIs smaller than the elementWhen the value of (2) is the same as the value of the element, the river section is determined to be the elementThe corresponding future time node is in drought state; otherwise, determining that the river section is at the elementThe corresponding future time node is not in drought.
Wherein the parameter t B is used for indicating the elementThe sequence number in DT B, the parameter k B is the total number of elements in DT B.
In practical application, the elements areAnd can also be used to indicate the cumulative underflow of the river section in a future drought state.
Notably, the step of obtaining the future traffic set may be:
Dividing the target river basin into a plurality of grid objects, and extracting attribute information of each grid object. The attribute information at least comprises historical hydrologic information, soil information, vegetation information and elevation information of a block to which the grid object belongs.
Substituting the plurality of attribute information into a distributed hydrological model, and calibrating parameters of the distributed hydrological model to obtain the parameters of the distributed hydrological model;
and obtaining the future flow set according to the parameters of the distributed hydrologic model, the distributed hydrologic model and future climate factor data.
The future climate factor data may be simulation data (based on a shared socioeconomic scenario) of a sixth coupling mode comparison plan (Coupled Model Intercomparison Project, CMIP) after the downscaling process, where the simulation data includes at least estimated precipitation information, temperature information, radiation information, and wind speed information.
The historical average temperature is preferably the average of the average temperatures of the target river basin in a plurality of years in the historical period, but in practical application, the historical average temperature may also be the average of the average temperatures of the target river basin in a plurality of months in the historical period/the average temperature of the target river basin in the week/the average temperature of the day; as for the definition of the future average temperature is similar to the definition of the historical average temperature, and the time scales (annual scale/monthly scale/Zhou Chedu/daily scale) of the two are consistent all the time, the specific calculation modes of the historical average temperature and the future average temperature are not limited in the embodiment of the application.
In addition, the application of the drought extremum information output in step 105 includes at least one of:
And displaying, sending to other devices and printing through the electronic device.
For example, drought early warning information is generated and played according to the drought extreme value information output in the step 105, so that residents in the target flow area store drought resistant materials in advance according to the drought early warning information; or sending the drought extremum information output in the step 105 to a meteorological department in the target flow area, so that staff of the meteorological department execute drought-preventive tasks according to the drought extremum information.
It should be emphasized that, in practical applications, the execution of step 102 may be earlier than the execution of step 103, step 102 may also be later than the execution of step 103, and even step 102 and step 103 may be executed synchronously, and the execution sequence of step 102 and step 103 is not limited in the embodiment of the present application.
Optionally, the future period of the target river basin comprises a plurality of sub-periods, the drought extremum information comprises a plurality of sub-extremum information, and the plurality of sub-extremum information and the plurality of sub-periods are in one-to-one correspondence;
The step of obtaining the future drought data and the future average temperature comprises: acquiring a plurality of sub-drought data and a plurality of sub-average temperatures of the target river basin, wherein the plurality of sub-drought data and the plurality of sub-average temperatures are in one-to-one correspondence with the plurality of sub-periods;
Fitting the historical drought data and the future drought data using an extremum distribution to obtain a drought extremum function, comprising: fitting the historical drought data and the plurality of sub-drought data by utilizing the extremum distribution to obtain a drought extremum function;
According to the drought recurrence period and the drought extremum function, the step of obtaining a historical drought extremum and a future drought extremum comprises the following steps: obtaining a historical drought extremum and a plurality of sub-drought extremums according to the drought recurrence period and the drought extremum function, wherein the sub-drought extremums correspond to the sub-periods one by one;
According to the historical drought extremum and the future drought extremum, the steps of obtaining drought extremum information comprise: according to the historical drought extremum, the plurality of sub-drought extremums, the historical average temperature and the plurality of sub-average temperatures are respectively obtained to obtain a plurality of sub-extremum information, the sub-extremum information is the ratio of sub-extremum difference to sub-average temperature difference, the sub-extremum difference is the difference between the sub-drought extremum and the historical drought extremum, the sub-average temperature difference is the difference between the sub-average temperature and the historical average temperature, and the plurality of sub-extremum information corresponds to the plurality of sub-periods one by one.
The weather effect of global warming is that not only the drought extremum in the future period and the drought extremum in the history period are significantly different, but also the drought extremum in the future period in different temperature rise stages are significantly different in the future period. Based on the method, the future period of the target river basin is divided into a plurality of sub periods through the temperature threshold, and sub drought data, sub average temperature, sub drought extremum and other information of each sub period are correspondingly acquired, so that sub extremum information of different sub periods and historical periods is finally obtained, and the accuracy of the outputted drought extremum information (formed by the plurality of sub extremum information) is further improved.
The temperature threshold is preferably 1.5 degrees celsius and 2 degrees celsius, and the future period can be divided into a first sub-period (a period from the beginning of the future period to a period in which the global average temperature rises by 1.5 degrees celsius from the pre-industrialization level), a second sub-period (a period in which the global average temperature rises by 1.5 degrees celsius from the pre-industrialization level to a period in which the global average temperature rises by 2 degrees celsius from the pre-industrialization level) and a third sub-period (a period from a period in which the global average temperature rises by 2 degrees celsius from the pre-industrialization level to the end of the future period) by the above temperature threshold.
In practical application, the number and specific values of the temperature thresholds may be adaptively adjusted based on practical needs, and the number and specific values of the temperature thresholds are not limited in the embodiment of the present application.
For the step of obtaining the sub-drought data, refer to the step of processing the future runoff flow to obtain the future drought data in the foregoing example, and for avoiding repetition of the description, the description will be omitted.
Optionally, the extremum distribution comprises a generalized pareto distribution, and a function of the generalized pareto distribution is expressed as follows:
Wherein x t is a sequence formed by the historical drought data and the future drought data, and t is used for indicating sequence numbers corresponding to elements in the sequence; the μ (t), the σ (t) and the ζ (t) are respectively a position parameter, a scale parameter and a shape parameter of the function, and the function of the generalized pareto distribution satisfies the conditions μ (t) ∈r, σ (t) > 0, ζ (t) ∈r and 1+ζ (t) (x- μ (t))/σ (t) > 0.
It should be noted that the position parameter, the scale parameter and the shape parameter are all time-varying parameters, i.e. the values thereof are changed correspondingly according to the change of the parameter t.
Ζ (t) is a key parameter in a function of the generalized Pareto distribution, and when ζ (t) is a positive number, the generalized Pareto distribution corresponds to a Pareto distribution; when ζ (t) is 0, the generalized pareto distribution corresponds to an exponential distribution; when the ζ (t) is a negative number, the generalized pareto distribution corresponds to a Beta distribution.
The value of σ (t) is preferably set as:
Wherein, beta 1、β2、β3、β4 is a parameter, and the preferable setting of sigma (t) can ensure that sigma (t) is always an integer, which can fully embody the trend of each element in x t along with the change of serial numbers; as for t 1, the sequence x t is used to indicate the sequence number of each element belonging to the history period, and as for t 2, the sequence x t is used to indicate the sequence number of each element belonging to the future period.
Note that, when the future period is divided into the first sub-period, the second sub-period, and the third sub-period as in the foregoing example, the value of σ (t) is preferably set to:
The parameter t 21 is used to indicate the sequence number of each element belonging to the first sub-period in the sequence x t, the parameter t 22 is used to indicate the sequence number of each element belonging to the second sub-period in the sequence x t, and the parameter t 23 is used to indicate the sequence number of each element belonging to the third sub-period in the sequence x t.
Optionally, the step of fitting the historical drought data and the future drought data using the extremum distribution includes:
Substituting the historical drought data and future drought data into a function of the generalized pareto distribution, and performing maximum likelihood estimation on the scale parameter sigma (t) and the shape parameter xi (t) to obtain likelihood scale parameters of the function of the generalized pareto distribution respectively And likelihood shape parameters
According to the likelihood scale parametersThe likelihood shape parametersAnd fitting the historical drought data and future drought data as a function of the generalized pareto distribution.
Specifically, the ζ (t) is set as the non-deformation shape parameter ζ.
When ζ is not equal to 0, a first log-likelihood function is obtained according to the generalized pareto distribution function.
The first log likelihood function is represented as follows:
Wherein the parameter K is used to indicate the total number of elements in the sequence x t.
When ζ=0, a second log-likelihood function is obtained from the function of the generalized pareto distribution.
The second log likelihood function is expressed as follows:
Calculating the maximum value of the first log-likelihood function and the second log-likelihood function according to a numerical algorithm to obtain the likelihood scale parameter And the likelihood shape parameters
Wherein, the likelihood scale parameter set based on the preference valueThe method comprises the following steps:
It should be noted that when the future period is divided into the first sub-period, the second sub-period and the third sub-period according to the previous example, the likelihood scale parameters set based on the optimal selection value The method comprises the following steps:
Optionally, the function of drought extremum is expressed as follows:
Wherein the DDT is an drought threshold value for replacing the position parameter mu (t), the K is the total number of elements in a sequence formed by the historical drought data and the future drought data, the N is the total number of elements larger than the drought threshold value in the sequence, the t is the serial number corresponding to each element in the sequence, the w t is the drought recurrence frequency corresponding to the drought recurrence period, and the drought recurrence frequency meets the condition And T is the drought reappearance period.
Illustratively, assuming that the sequence x t formed from the historical drought data and the future drought data together includes {3,3,3,14,15,3,3}, and the drought threshold is 10, then the K is 7 and the N is 2 at this time.
In addition, in practical application, the drought recurrence period may be set correspondingly (for example, 30 years, 50 years, 70 years, 100 years, etc.) according to practical needs, and the embodiment of the present application is not limited to a specific drought recurrence period.
Assuming that the historical average temperature is s 1, the future average temperature is s 2, a historical drought extremum is r 1 according to the drought extremum function, a future drought extremum is r 2 according to the drought extremum function, and the drought extremum information is Z, the calculation formula of the drought extremum information Z is:
it should be noted that, when the historical drought extremum is obtained according to the drought extremum function, the parameter t in the formula is replaced by the parameter t 1 (the specific definition refers to the description in the previous example), and the parameter in the formula Will be represented by the formulaCalculating to obtain; when the future drought extremum is obtained according to the drought extremum function, the parameter t in the formula is replaced by the parameter t 2 (the specific definition is described in the previous example), and the parameter in the formulaWill be represented by the formulaAnd (5) calculating to obtain the product.
The calculation method of the sub-extremum information between each sub-period and the history period after the future period is divided into a plurality of sub-periods is similar to the calculation method of the drought extremum information, and will not be described again.
Referring to fig. 2, fig. 2 is a schematic structural diagram of a drought extremum information output device according to some embodiments of the present application, where the drought extremum information output device includes:
the acquisition module 201 is configured to acquire drought recurring time, historical drought data, historical average temperature, future drought data, and future average temperature of the target river basin.
The fitting module 202 is configured to fit the historical drought data and the future drought data by using an extremum distribution to obtain a drought extremum function.
The first calculation module 203 is configured to obtain a historical drought extremum and a future drought extremum according to the drought recurrence period and the drought extremum function.
And a second calculation module 204, configured to obtain drought extremum information according to the historical drought extremum and the future drought extremum, where the drought extremum information is a ratio of a drought extremum difference to an average temperature difference, the drought extremum difference is a difference between the future drought extremum and the historical drought extremum, and the average temperature difference is a difference between the future average temperature and the historical average temperature.
And the output module 205 is used for outputting the drought extremum information.
Optionally, the future period of the target river basin comprises a plurality of sub-periods, the drought extremum information comprises a plurality of sub-extremum information, and the plurality of sub-extremum information and the plurality of sub-periods are in one-to-one correspondence.
The obtaining module 201 is specifically configured to: and acquiring a plurality of sub-drought data and a plurality of sub-average temperatures of the target river basin, wherein the plurality of sub-drought data and the plurality of sub-average temperatures are in one-to-one correspondence with the plurality of sub-periods.
The fitting module 202 is specifically configured to: and fitting the historical drought data and the plurality of sub-drought data by using the extremum distribution to obtain a drought extremum function.
The first computing module 203 is specifically configured to: and obtaining a historical drought extremum and a plurality of sub drought extremums according to the drought reappearance period and the drought extremum function, wherein the plurality of sub drought extremums and the plurality of sub periods are in one-to-one correspondence.
The second computing module 204 is specifically configured to: according to the historical drought extremum, the plurality of sub-drought extremums, the historical average temperature and the plurality of sub-average temperatures are respectively obtained to obtain a plurality of sub-extremum information, the sub-extremum information is the ratio of sub-extremum difference to sub-average temperature difference, the sub-extremum difference is the difference between the sub-drought extremum and the historical drought extremum, the sub-average temperature difference is the difference between the sub-average temperature and the historical average temperature, and the plurality of sub-extremum information corresponds to the plurality of sub-periods one by one.
Optionally, the fitting module 202 includes a first function library for storing function models of the extremum distribution, the first function library including function models of a generalized pareto distribution, the function models of the generalized pareto distribution being represented as follows:
Wherein x t is a sequence formed according to the historical drought data/future drought data, and t is used for indicating sequence numbers corresponding to elements in the sequence; the μ (t), the σ (t) and the ζ (t) are respectively the position parameter, the scale parameter and the shape parameter of the function model, and the function of the generalized pareto distribution satisfies the conditions μ (t) ∈R, σ (t) >0, ζ (t) ∈R and 1+ζ (t) (x- μ (t))/σ (t) >0.
Optionally, the fitting module 202 is specifically configured to:
Substituting the historical drought data and future drought data into a function of the generalized pareto distribution, and performing maximum likelihood estimation on the scale parameter sigma (t) and the shape parameter xi (t) to obtain likelihood scale parameters of the function of the generalized pareto distribution respectively And likelihood shape parameters
According to the likelihood scale parametersThe likelihood shape parametersAnd fitting the historical drought data and future drought data as a function of the generalized pareto distribution.
Optionally, the first computing module 203 includes a second function library for storing the drought extremum functions, where the functions of the historical drought extremum are expressed as follows:
Wherein the DDT is an drought threshold value for replacing the position parameter mu (t), the K is the total number of elements in a sequence formed by the historical drought data and the future drought data, the N is the total number of elements larger than the drought threshold value in the sequence, the t is the serial number corresponding to each element in the sequence, the w t is the drought recurrence frequency corresponding to the drought recurrence period, and the drought recurrence frequency meets the condition And T is the drought reappearance period.
The drought extremum information output device provided by the embodiment of the application can realize each process of the drought extremum information output method in the method embodiment shown in fig. 1, and in order to avoid repetition, the description is omitted here.
It should be noted that, the drought extremum information output device in the embodiment of the present application may be a device, or may be a component, an integrated circuit or a chip in an electronic device.
Referring to fig. 3, fig. 3 is a block diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 3, an electronic device 300 includes: the drought extremum information output system comprises a memory 301, a processor 302 and a program or instruction stored on the memory 301 and capable of running on the processor 302, wherein the program or instruction realizes the steps in the drought extremum information output method when being executed by the processor 302.
The embodiment of the application also provides a readable storage medium, wherein the readable storage medium stores a program or an instruction, and the program or the instruction realizes each process of the drought extremum information output method embodiment when being executed by a processor, and can achieve the same technical effect, so that repetition is avoided and redundant description is omitted.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.
The embodiments of the present application have been described above with reference to the accompanying drawings, but the present application is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present application and the scope of the claims, which are to be protected by the present application.

Claims (5)

1. A method for outputting drought extremum information, the method comprising:
Acquiring drought recurrence period, historical drought data, historical average temperature, future drought data and future average temperature of a target river basin;
fitting the historical drought data and the future drought data by utilizing extremum distribution to obtain a drought extremum function;
Obtaining a historical drought extremum and a future drought extremum according to the drought recurrence period and the drought extremum function;
according to the historical drought extremum and the future drought extremum, the historical average temperature and the future average temperature are used for obtaining drought extremum information, wherein the drought extremum information is the ratio of a drought extremum difference to an average temperature difference, the drought extremum difference is the difference between the future drought extremum and the historical drought extremum, and the average temperature difference is the difference between the future average temperature and the historical average temperature;
outputting the drought extremum information;
The future period of the target river basin comprises a plurality of sub-periods, the drought extremum information comprises a plurality of sub-extremum information, and the sub-extremum information corresponds to the sub-periods one by one;
The step of obtaining the future drought data and the future average temperature comprises: acquiring a plurality of sub-drought data and a plurality of sub-average temperatures of the target river basin, wherein the plurality of sub-drought data and the plurality of sub-average temperatures are in one-to-one correspondence with the plurality of sub-periods;
fitting the historical drought data and the future drought data using an extremum distribution to obtain a drought extremum function, comprising: fitting the historical drought data and the plurality of sub-drought data by utilizing the extremum distribution to obtain the drought extremum function;
According to the drought recurrence period and the drought extremum function, the step of obtaining a historical drought extremum and a future drought extremum comprises the following steps: obtaining a historical drought extremum and a plurality of sub-drought extremums according to the drought recurrence period and the drought extremum function, wherein the sub-drought extremums correspond to the sub-periods one by one;
According to the historical drought extremum and the future drought extremum, the steps of obtaining drought extremum information comprise: according to the historical drought extremum, the plurality of sub-drought extremums, the historical average temperature and the plurality of sub-average temperatures respectively obtain a plurality of sub-extremum information, wherein the sub-extremum information is the ratio of sub-extremum difference to sub-average temperature difference, the sub-extremum difference is the difference between the sub-drought extremum and the historical drought extremum, the sub-average temperature difference is the difference between the sub-average temperature and the historical average temperature, and the plurality of sub-extremum information corresponds to the plurality of sub-periods one by one;
Wherein, the step of obtaining the future drought data comprises:
acquiring a future flow set of a river section of the target river basin;
According to the future flow set, obtaining the average flow in the future for many years;
obtaining the future drought data according to the years of future average flow and the future flow set;
The step of obtaining the future flow set of the river section of the target river basin comprises the following steps:
Dividing the target river basin into a plurality of grid objects, and extracting attribute information of each grid object, wherein the attribute information at least comprises historical hydrologic information, soil information, vegetation information and elevation information of a block to which the grid object belongs;
Substituting the plurality of attribute information into a distributed hydrological model, and calibrating parameters of the distributed hydrological model to obtain the parameters of the distributed hydrological model;
obtaining the future flow set according to parameters of the distributed hydrologic model, the distributed hydrologic model and future climate factor data, wherein the future climate factor data comprises simulation data of a sixth coupling mode comparison plan after downscaling, and the simulation data at least comprises pre-estimated precipitation information, temperature information, radiation information and wind speed information;
Fitting the historical drought data and the future drought data using the extremum distribution, comprising:
Substituting the historical drought data and the future drought data into a function of generalized pareto distribution, and carrying out maximum likelihood estimation on a scale parameter sigma (t) and a shape parameter xi (t) to obtain likelihood scale parameters of the function of generalized pareto distribution respectively And likelihood shape parameters
According to the likelihood scale parametersThe likelihood shape parametersFitting the historical drought data and future drought data by using the function of the generalized pareto distribution;
the drought extremum function is expressed as follows:
wherein the DDT is an drought threshold value for replacing a position parameter mu (t), the K is the total number of elements in a sequence formed by the historical drought data and the future drought data, the N is the total number of elements larger than the drought threshold value in the sequence, the t is a serial number corresponding to each element in the sequence, the w t is a drought recurrence frequency corresponding to the drought recurrence period, and the drought recurrence frequency meets the condition And T is the drought reappearance period.
2. The drought extremum information output method of claim 1, wherein the extremum distribution comprises a generalized pareto distribution, a function of which is expressed as follows:
Wherein x t is a sequence formed by the historical drought data and the future drought data, and t is used for indicating sequence numbers corresponding to elements in the sequence; the μ (t), the σ (t) and the ζ (t) are respectively a position parameter, a scale parameter and a shape parameter of the function, and the function of the generalized pareto distribution satisfies the conditions μ (t) ∈r, σ (t) > 0, ζ (t) ∈r and 1+ζ (t) (x- μ (t))/σ (t) > 0.
3. An drought extremum information output device, characterized in that the drought extremum information output device comprises:
the acquisition module is used for acquiring the drought reappearance period, the historical drought data, the historical average temperature, the future drought data and the future average temperature of the target river basin;
The fitting module is used for fitting the historical drought data and the future drought data by utilizing extremum distribution so as to obtain a drought extremum function;
The first calculation module is used for obtaining a historical drought extremum and a future drought extremum according to the drought reproduction period and the drought extremum function;
The second calculation module is used for obtaining drought extremum information according to the historical drought extremum and the future drought extremum, the historical average temperature and the future average temperature, the drought extremum information is the ratio of a drought extremum difference to an average temperature difference, the drought extremum difference is the difference between the future drought extremum and the historical drought extremum, and the average temperature difference is the difference between the future average temperature and the historical average temperature;
the output module is used for outputting the drought extremum information;
The future period of the target river basin comprises a plurality of sub-periods, the drought extremum information comprises a plurality of sub-extremum information, and the sub-extremum information corresponds to the sub-periods one by one;
the acquisition module is used for acquiring a plurality of sub-drought data and a plurality of sub-average temperatures of the target river basin, wherein the plurality of sub-drought data and the plurality of sub-average temperatures are in one-to-one correspondence with the plurality of sub-periods;
The fitting module is used for fitting the historical drought data and the plurality of sub-drought data by utilizing the extremum distribution so as to obtain a drought extremum function;
the first calculation module is used for obtaining a historical drought extremum and a plurality of sub-drought extremums according to the drought reappearance period and the drought extremum function, and the plurality of sub-drought extremums and the plurality of sub-periods are in one-to-one correspondence;
the second calculation module is configured to obtain, according to the historical drought extremum, the plurality of sub-drought extremums, the historical average temperature and the plurality of sub-average temperatures, respectively, a plurality of sub-extremum information, where the sub-extremum information is a ratio of a sub-extremum difference to a sub-average temperature difference, the sub-extremum difference is a difference between the sub-drought extremum and the historical drought extremum, the sub-average temperature difference is a difference between the sub-average temperature and the historical average temperature, and the plurality of sub-extremum information corresponds to the plurality of sub-periods one by one;
the acquisition module is specifically configured to:
acquiring a future flow set of a river section of the target river basin;
According to the future flow set, obtaining the average flow in the future for many years;
obtaining the future drought data according to the years of future average flow and the future flow set;
The step of obtaining the future flow set of the river section of the target river basin comprises the following steps:
Dividing the target river basin into a plurality of grid objects, and extracting attribute information of each grid object, wherein the attribute information at least comprises historical hydrologic information, soil information, vegetation information and elevation information of a block to which the grid object belongs;
Substituting the plurality of attribute information into a distributed hydrological model, and calibrating parameters of the distributed hydrological model to obtain the parameters of the distributed hydrological model;
obtaining the future flow set according to parameters of the distributed hydrologic model, the distributed hydrologic model and future climate factor data, wherein the future climate factor data comprises simulation data of a sixth coupling mode comparison plan after downscaling, and the simulation data at least comprises pre-estimated precipitation information, temperature information, radiation information and wind speed information;
the fitting module is specifically configured to:
Substituting the historical drought data and the future drought data into a function of generalized pareto distribution, and carrying out maximum likelihood estimation on a scale parameter sigma (t) and a shape parameter xi (t) to obtain likelihood scale parameters of the function of generalized pareto distribution respectively And likelihood shape parameters
According to the likelihood scale parametersThe likelihood shape parametersFitting the historical drought data and future drought data by using the function of the generalized pareto distribution;
Optionally, the first calculation module includes a second function library for storing the drought extremum function, where the function of the drought extremum function is expressed as follows:
wherein the DDT is an drought threshold value for replacing a position parameter mu (t), the K is the total number of elements in a sequence formed by the historical drought data and the future drought data, the N is the total number of elements larger than the drought threshold value in the sequence, the t is a serial number corresponding to each element in the sequence, the w t is a drought recurrence frequency corresponding to the drought recurrence period, and the drought recurrence frequency meets the condition And T is the drought reappearance period.
4. An electronic device comprising a processor, a memory and a program or instruction stored on the memory and executable on the processor, which program or instruction when executed by the processor implements the steps of the method of any of claims 1-2.
5. A readable storage medium, characterized in that it has stored thereon a program or instructions which, when executed by a processor, implement the steps of the method according to any of claims 1-2.
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