Method and system for identifying drainage basin low water event and electronic equipment

Abstract

The invention discloses a drainage basin low water event identification method and system and electronic equipment, and the method comprises the following steps: considering meteorological and hydrological elements, and constructing a comprehensive drought index calculation model; calculating a dynamic identification threshold value of the comprehensive drought index CDI by adopting a time-varying nonlinear parameter regression method in combination with historical drought data; the comprehensive drought index calculation model is adopted to calculate the comprehensive drought index of the current time period, and the CDI dynamic identification threshold value of the current time period is adopted to carry out comparison to identify the dry water event and the dry water characteristics.

Description

Technical Field

[0001] The invention belongs to the technical field of drought early warning, and relates to a method, system and electronic equipment for identifying low water events in a river basin. Background Art

[0002] Identifying low water events helps to improve early warning of water shortage and drought risks, guide water resource management and utilization, and protect agricultural production and the ecological environment. In recent years, extreme climate conditions such as river basin drought have occurred frequently, and water supply has become increasingly tight. For example, in the Yangtze River Basin, the annual summer rainfall is the lowest in the same period, and the upper, middle and lower reaches of the Yangtze River are all dry. The severe drought in the basin has greatly affected the urban water supply. River basin drought is closely related to climate conditions and human activities. The technical difficulties in identifying low water events are: ① It is difficult to comprehensively evaluate river basin drought events by considering only indicators of a single factor; ② How to build a method to systematically identify river basin drought events to improve the accuracy of river basin drought identification. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method, system and electronic equipment for identifying low water events in a river basin, taking into account various drought data indicators, and comprehensively and accurately evaluating low water events.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for identifying low water events in a river basin, comprising the following steps: Step 1: Considering meteorological and hydrological elements, a comprehensive drought index calculation model is constructed; Step 2: Combined with historical low water data, the dynamic identification threshold of the comprehensive drought index CDI is calculated using the time-varying nonlinear parameter regression method; Step 3, using the comprehensive drought index calculation model of step 1 to calculate the comprehensive drought index of the current period, and compare it with the CDI dynamic identification threshold of the current period calculated in step 2 to identify the dry events and dry characteristics.

[0005] The method of constructing a comprehensive drought index assessment model in step 1 includes: Step 1-1, considering the meteorological and hydrological elements of the basin, select the Z index, the standardized precipitation evapotranspiration index SPEI and the standardized runoff index SRI as drought indices and calculate them; Step 1-2, calculate the probability of each drought index successfully identifying a dry event based on historical data, and determine the weight of each drought index; Steps 1-3, based on the weight coefficients of each index, a linear weighted approach is used to construct a comprehensive drought index assessment model that takes into account meteorological and hydrological factors.

[0006] In step 1-1, the Z index is calculated as: Assuming that the precipitation in a certain period follows the Pearson-Ⅲ distribution, its probability density function is: ; In the formula, α, β, are shape, scale and location parameters respectively, x represents the precipitation series, is the factorial function; The probability density function Pearson-Ⅲ distribution is converted to the standard normal distribution with Z as the variable. The conversion formula is: ; In the formula, is the skewness coefficient, is the standard variable, and the calculation formula is: ; ; ; Where: represents the precipitation data sample; represents the average precipitation amount, represents the standardized precipitation, represents the standard deviation of precipitation, and n represents the total number of samples.

[0007] In step 1-1, the calculation method of the standardized precipitation evapotranspiration index SPEI is: ; Where: It represents the difference between precipitation and potential evapotranspiration; Indicates precipitation; Represents potential evapotranspiration; Calculated using log-logistic probability distribution The cumulative distribution function of the data series is calculated as: ; The parameters in the above formula are , , are shape, scale and position parameters respectively, which are obtained by fitting using the linear moment method and calculated as follows: ; ; ; In the formula, is the factorial function, , , For data series The probability weighted moment of is calculated as: ; ; Where N is the length of the data involved in the calculation. is a data sequence arranged in ascending order, represents the empirical distribution function of the data, represents the s-order probability weighted moment; The standard normalized cumulative probability density is: ; When the cumulative probability P≤0.5: ; ; In the formula , , , , are all constants; When P>0.5, the P value is 1-P: ; In the formula , , , , are all constants.

[0008] In step 1-1, the standardized runoff index SRI is calculated as follows: Assumed runoff x The probability density function of satisfy Distribution, which is calculated as: ; In the formula, , They are shape parameter and scale parameter respectively; is a factorial function, runoff x The cumulative distribution function of F ( x )for: ; right The SRI can be obtained by normalizing the distribution probability: ; ; In the formula, , , , , They are all constants, represented by h; is the cumulative distribution function ,when hour, , then S=1; hour, , then S=-1.

[0009] In step 1-2, the method for determining the weight of each drought index is: Assuming that there are m low flow events in the basin, the number of low flow events identified by SPEI index, Z index and SRI index is a, b and c. Then the probability of SPEI index identifying low flow events is: , the probability of the Z index identifying a low-flow event is , the probability of the SRI index identifying a low water event is ; The calculation formula of the weight coefficient is as follows: ; ; ; In the formula, They are the weight coefficients of SPEI index, Z index and SRI index respectively.

[0010] In step 2, the method for calculating the dynamic identification threshold of the comprehensive drought index CDI includes: First, assuming that the comprehensive drought index Normal distribution , the formula is as follows: ; Where n is the sample length of the comprehensive drought index, is the normal distribution parameter group, that is, the mean and variance ; Then, the vegetation index VI and the teleconnection index TI are used as covariates of climate change and human activities respectively, and polynomials are used to describe the relationship between distribution parameters and covariates, thereby obtaining the time-varying probability distribution parameters under the changing environment, namely: ; ; In the formula, represents the time-varying mean, represents the time-varying variance, and are the polynomial coefficients, , t represents time; Secondly, the maximum likelihood method is used to solve the parameters and ; Based on the fitted normal distribution, calculate the k% quantile threshold, that is: ; In the formula, is the inverse cumulative distribution function of the normal distribution.

[0011] In step 3, the method for identifying the low water period includes: When the current comprehensive drought index calculated in step 1 The dynamics less than step 2 are thresholded When , it is judged that a low water event occurred in the basin at time t; For the identified dry events, the dry characteristics of the dry duration, dry severity and dry intensity are calculated.

[0012] The system of the method for identifying low water events in a river basin as described above includes a comprehensive drought index calculation model construction module, which is used to construct a comprehensive drought index calculation model based on meteorological and hydrological elements; a dynamic identification threshold calculation module, which is used to calculate the dynamic identification threshold of the comprehensive drought index CDI based on historical low water data using a time-varying nonlinear parameter regression method; and a low water event identification module, which is used to calculate the comprehensive drought index of the current period using the comprehensive drought index calculation model, and compare it with the CDI dynamic identification threshold of the current period to identify low water events and low water characteristics.

[0013] The electronic device of the method for identifying low-flow events in a watershed as described above comprises a memory and a processor, wherein the memory stores a computer program; and the processor executes the steps of the method for identifying low-flow events in a watershed.

[0014] The main beneficial effects of the present invention are: The dynamic identification threshold of the drought index was calculated using the time-varying nonlinear method of low flow non-stationary estimation. The low flow events in the basin were identified based on the dynamic threshold, which effectively adapted to the temporal variation characteristics of the low flow phenomenon under a changing environment and improved the accuracy of low flow event identification.

[0015] It comprehensively considers the complex interactive effects of multiple meteorological and hydrological elements, reflects the multidimensional characteristics of drought, and incorporates climate change and human activities into the calculation of effective impacts to adapt to the climate types and geographical environments of different regions, and more comprehensively characterizes the dry water situation in the basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0017] Figure 1 It is a flow chart of the present invention.

[0018] Figure 2This is a schematic diagram of identifying low water events according to the present invention.

[0019] Figure 3 This is the result diagram of low water event identification in the basin of the present invention.

[0020] In the figure, A host 1; B host 2; first relay 11; second relay 21. DETAILED DESCRIPTION

[0021] like Figure 1 In the invention, a method, system and electronic device for identifying low water events in a river basin; Embodiment 1, The identification method of low water events in a river basin includes the following steps: Step 1: Considering meteorological and hydrological elements, a comprehensive drought index calculation model is constructed; In this step, the meteorological and hydrological data of the basin, such as rainfall, temperature, and runoff, are first considered, and the standardized precipitation evapotranspiration index (SPEI), Z index, and standardized runoff index (SRI) are selected as drought identification indexes. The calculation formula is as follows: Standardized precipitation evapotranspiration index (SPEI), ; Where: It represents the difference between precipitation and potential evapotranspiration; Indicates precipitation; Represents potential evapotranspiration; Calculated using log-logistic probability distribution The cumulative distribution function of the data series is calculated as: ; The parameters in the above formula are , , are shape, scale and location parameters respectively, x express The data sequence is obtained by fitting using the linear moment method, and the calculation method is: ; ; ; In the formula, is the factorial function, , , For data series The probability weighted moment of is calculated as: ; ; Where N is the length of the data involved in the calculation. is a data sequence arranged in ascending order, represents the empirical distribution function of the data, represents the s-order probability weighted moment, which takes the values ​​of 0, 1, and 2 in this method; The standard normalized cumulative probability density is: ; When the cumulative probability P≤0.5: ; ; In the formula, , , , , are all constants; in this embodiment, , , , , , ; When P>0.5, the P value is 1-P: ; , , , , The value is the same as above; Z index Assuming that the precipitation in a certain period follows the Pearson-Ⅲ distribution, its probability density function is: ; In the formula, α, β, are shape, scale and location parameters respectively, is the factorial function, x represents the precipitation series; The precipitation is normalized and the conversion formula is: ; In the formula, is the skewness coefficient, is the standard variable, and the calculation formula is: ; ; ; Where: represents the precipitation data sample; represents the average precipitation amount, represents the standardized precipitation, n represents the total number of samples, represents the standard deviation of precipitation.

[0022]  Standardized Runoff Index (SRI) Assumed runoff x The probability density function of satisfy Distribution, which is calculated as: ; In the formula, , are shape parameter and scale parameter respectively; runoff x The cumulative distribution function of F ( x )for: ; right The SRI can be obtained by normalizing the distribution probability: ; ; In the formula, =2.515517, =0.802853, =0.010328, =1.432788, =0.189269, =0.001308; is the cumulative distribution function ,when hour, , S=1; hour, , S=-1.

[0023] The instructions calculate the above indexes respectively after obtaining the rainfall, temperature and runoff in the basin study period.

[0024] Secondly, the weight of each drought index is determined according to the probability of successfully identifying a dry event; Based on GB / T 20481-2017 Meteorological Drought Level, the drought thresholds of SPEI index, Z index and SRI index were determined, and the run theory method was used to identify the low flow events in the basin. Assuming that m low flow events occurred in the basin, the number of low flow events identified by SPEI index, Z index and SRI index was a, b and c, then the probability of SPEI index identifying low flow events was: , the probability of the Z index identifying a low-flow event is , the probability of the SRI index identifying a low water event is ; The calculation formula of the weight coefficient is as follows: ; ; ; In the formula, is the weight coefficient of SPEI index, Z index and SRI index; Finally, based on the weight coefficients of each index, a linear weighted method is adopted to construct a comprehensive drought index (CDI) that comprehensively considers meteorological and hydrological elements. The calculation formula is as follows: ; In the formula, , and They are the normalized SPEI index, Z index and SRI index respectively; Step 2: Combined with historical low water data, the dynamic identification threshold of the comprehensive drought index CDI is calculated using the time-varying nonlinear parameter regression method; First, assuming that the comprehensive drought index Normal distribution , the formula is as follows: ; Where n is the sample length of the comprehensive drought index, is the normal distribution parameter group, that is, the mean and variance .

[0025] Then, the vegetation index (VI) and teleconnection index (TI) were used as covariates of climate change and human activities, respectively, and polynomials were used to describe the relationship between distribution parameters and covariates, thereby obtaining the time-varying probability distribution parameters under the changing environment, namely: ; ; In the formula, represents the time-varying mean, represents the time-varying variance, and are the polynomial coefficients, , t represents time; Secondly, the maximum likelihood method is used to solve the parameters and ; Based on the fitted normal distribution, calculate k % Quantile threshold, in this embodiment, k is 95, of course, in other embodiments, the value is determined according to actual needs, that is: ; In the formula, is the inverse cumulative distribution function of the normal distribution.

[0026] Embodiment 2, like Figure 2 The identification process of low water events is shown in Figure 1. The comprehensive drought index at time t is calculated based on step 1. ; By using vegetation index (VI) and teleconnection index (TI) as covariates, the time-varying identification threshold is obtained based on the maximum likelihood method. ; Figure 2 The shaded area in the middle is the low water event in the basin identified by the comprehensive drought index CDI.

[0027] Step 3: Calculate the comprehensive drought index of the current period using the comprehensive drought index calculation model of step 1, and compare it with the CDI dynamic identification threshold of the current period calculated in step 2 to identify the dry events and dry characteristics; Based on the comprehensive drought index (CDI) constructed in step 1, the comprehensive drought index result at time t is calculated. ,when Less than the recognition threshold at time t When , it is considered that a low water event occurs in the basin at time t.

[0028] For the identified low-flow events, the following three eigenvalues ​​are calculated: 1) Low water duration: the duration from the occurrence to the end of a low water event; 2) Drought severity: the cumulative absolute value of the Comprehensive Drought Index (CDI) during the drought event; it reflects the overall severity of the drought event; 3) Drought intensity: The ratio of drought severity to drought duration is the drought intensity, which reflects the average intensity of the drought period.

[0029] Embodiment 3, like Figure 3 In this example, the identification results of low water events in the basin in 2006 are plotted. The comprehensive drought index CDI in August and September is less than the time-varying identification threshold of the corresponding time. , the dry duration of this drought event is 2 months, the drought severity is the cumulative absolute value of the comprehensive drought index CDI (i.e. 3.7), and the drought intensity is the ratio of the drought severity and the dry duration, i.e. 1.85.

[0030] Embodiment 4, A system for implementing the above-mentioned method for identifying low-flow events in a river basin comprises: A comprehensive drought index calculation model construction module is used to construct a comprehensive drought index calculation model based on meteorological and hydrological elements; Dynamic identification threshold calculation module, used to calculate the dynamic identification threshold of the comprehensive drought index CDI based on historical low water data using time-varying nonlinear parameter regression method; The low water event identification module is used to calculate the current comprehensive drought index using the comprehensive drought index calculation model, and compare it with the current CDI dynamic identification threshold to identify low water events and low water characteristics.

[0031] Embodiment 5, An electronic device for a method for identifying a low-flow event in a watershed comprises a memory and a processor, wherein the memory stores a computer program; and the processor executes the steps of the method for identifying a low-flow event in a watershed.

[0032] The above-mentioned embodiments are only preferred technical solutions of the present invention and should not be regarded as limitations of the present invention. The embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. The protection scope of the present invention shall be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for identifying low water events in a river basin, characterized by: , including the following steps: Step 1: Considering meteorological and hydrological elements, a comprehensive drought index calculation model is constructed; Step 2: Combined with historical low water data, the dynamic identification threshold of the comprehensive drought index CDI is calculated using the time-varying nonlinear parameter regression method; Step 3, using the comprehensive drought index calculation model of step 1 to calculate the comprehensive drought index of the current period, and compare it with the CDI dynamic identification threshold of the current period calculated in step 2 to identify the dry events and dry characteristics.

2. The method for identifying low water events in a river basin according to claim 1, characterized in that: In step 1, the method of constructing a comprehensive drought index assessment model includes: Step 1-1, considering the meteorological and hydrological elements of the basin, select the Z index, the standardized precipitation evapotranspiration index SPEI and the standardized runoff index SRI as drought indices and calculate them; Step 1-2, calculate the probability of each drought index successfully identifying a dry event based on historical data, and determine the weight of each drought index; Steps 1-3, based on the weight coefficients of each index, a linear weighted approach is used to construct a comprehensive drought index assessment model that takes into account meteorological and hydrological factors.

3. The method for identifying low water events in a river basin according to claim 2, characterized in that: In step 1-1, the Z index is calculated as: Assuming that the precipitation in a certain period follows the Pearson-Ⅲ distribution, its probability density function is: ； In the formula, α, β, are shape, scale and location parameters respectively, x represents the precipitation series, is the factorial function; The probability density function Pearson-Ⅲ distribution is converted to the standard normal distribution with Z as the variable. The conversion formula is: ； In the formula, is the skewness coefficient, is the standard variable, and the calculation formula is: ； ； ； Where: represents the precipitation data sample; represents the average precipitation amount, represents the standardized precipitation, represents the standard deviation of precipitation, and n represents the total number of samples.

4. The method for identifying low water events in a river basin according to claim 2, characterized in that: In step 1-1, the calculation method of the standardized precipitation evapotranspiration index SPEI is: ； Where: It represents the difference between precipitation and potential evapotranspiration; Indicates precipitation; Represents potential evapotranspiration; Calculated using log-logistic probability distribution The cumulative distribution function of the data series is calculated as: ； The parameters in the above formula are , , are shape, scale and position parameters respectively, which are obtained by fitting using the linear moment method and calculated as follows: ； ； ； In the formula, is the factorial function, , , For data series The probability weighted moment of is calculated as: ； ； Where N is the length of the data involved in the calculation. is a data sequence arranged in ascending order, represents the empirical distribution function of the data, represents the s-order probability weighted moment; The standard normalized cumulative probability density is: ； When the cumulative probability P≤0.5: ； ； In the formula , , , , are all constants; When P>0.5, the P value is 1-P: ; In the formula , , , , are all constants.

5. The method for identifying low water events in a river basin according to claim 2, characterized in that: In step 1-1, the calculation method of the standardized runoff index SRI is: Assumed runoff x The probability density function of satisfy Distribution, which is calculated as: ； In the formula, , They are shape parameter and scale parameter respectively; is a factorial function, runoff x The cumulative distribution function of F ( x )for: ； right The SRI can be obtained by normalizing the distribution probability: ； ； In the formula, , , , , All are constants, represented by h; is the cumulative distribution function ,when hour, , then S=1; hour, , then S=-1.

6. The method for identifying low water events in a river basin according to claim 2, characterized in that: In step 1-2, the method for determining the weight of each drought index is: Assuming that there are m low flow events in the basin, the number of low flow events identified by SPEI index, Z index and SRI index is a, b and c. Then the probability of SPEI index identifying low flow events is: , the probability of the Z index identifying a low-flow event is , the probability of the SRI index identifying a low water event is ; The calculation formula of the weight coefficient is as follows: ； ； ; In the formula, They are the weight coefficients of SPEI index, Z index and SRI index respectively.

7. The method for identifying low water events in a river basin according to claim 1, characterized in that: In step 2, the method for calculating the dynamic identification threshold of the comprehensive drought index CDI includes: First, assuming that the comprehensive drought index Normal distribution , the formula is as follows: ； Where n is the sample length of the comprehensive drought index, is the normal distribution parameter group, that is, the mean and variance ; Then, the vegetation index VI and the teleconnection index TI are used as covariates of climate change and human activities respectively, and polynomials are used to describe the relationship between distribution parameters and covariates, thereby obtaining the time-varying probability distribution parameters under the changing environment, namely: ； ； In the formula, represents the time-varying mean, represents the time-varying variance, and are the polynomial coefficients, , t represents time; Secondly, the maximum likelihood method is used to solve the parameters and ; Based on the fitted normal distribution, calculate the k% quantile threshold, that is: ； In the formula, is the inverse cumulative distribution function of the normal distribution.

8. The method for identifying low water events in a river basin according to claim 1, characterized in that: In step 3, the method for identifying the low water period includes: When the current comprehensive drought index calculated in step 1 The dynamics less than step 2 are thresholded When , it is judged that a low water event occurred in the basin at time t; For the identified dry events, the dry characteristics of the dry duration, dry severity and dry intensity are calculated.

9. A system for identifying a low-flow event in a river basin according to any one of claims 1 to 8, characterized in that: Including comprehensive The drought index calculation model construction module is used to construct a comprehensive drought index calculation model based on meteorological and hydrological elements; the dynamic identification threshold calculation module is used to calculate the dynamic identification threshold of the comprehensive drought index CDI based on historical low water data using the time-varying nonlinear parameter regression method; the low water event identification module is used to calculate the comprehensive drought index of the current period using the comprehensive drought index calculation model, and compare it with the dynamic identification threshold of CDI for the current period to identify low water events and low water characteristics.

10. An electronic device according to any one of claims 1 to 8 for identifying a low water event in a river basin, characterized in that: The invention comprises a memory and a processor, wherein the memory stores a computer program; and the processor executes the steps of the method for identifying low water events in a watershed.

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