Daily scale drought and flood sharp turn predictable index construction method and system

Abstract

The invention provides a daily scale drought and flood sharp turn predictable index construction method and system, and relates to the technical field of drought and flood sharp turn prediction.The method comprises the steps that according to weather forecast data, an early-stage accumulated rainfall and evapotranspiration difference index is calculated and converted into a standardized index SAPEI; determining the optimal window size of the waterlogging period window and the drought period window; calculating a drought period average index and a waterlogging period average index; and judging whether a drought and flood sudden turning event occurs, and calculating the drought and flood sudden turning intensity and the drought and flood sudden turning speed. According to the method, by constructing a multi-dimensional index system, different change characteristics of drought and flood sudden change events in the aspects of conversion intensity and speed are reflected, and the characteristic that drought-to-flood is asymmetric in the time scale can be fully considered; the SAPEI is used as basic data, so that the ground moisture state instead of the rainfall condition can be better reflected; the sizes of T and U are calculated according to the drought and flood period window corresponding to the maximum value of T instead of fixing the size of the window, so that the method is suitable for the nationwide range.

Description

Technical Field

[0001] The present invention relates to the technical field of prediction of rapid alternation between drought and flood, and particularly to a method and system for constructing a predictability index of rapid alternation between drought and flood at daily scale. Background Art

[0002] The occurrence background of rapid alternation between drought and flood events is usually accompanied by long-term drought in the early stage, which leads to soil hardening and changes in runoff generation and concentration conditions. Under the same precipitation, the superposition of previous drought and subsequent heavy precipitation makes the occurrence of flood events more rapid and violent, and the affected area is also wider. In addition, this characteristic of rapid alternation between drought and flood brings great challenges to disaster warning. Traditional warning criteria are not applicable in the context of rapid alternation between drought and flood. For example, the original threshold for flood warning is 50 mm of precipitation, but after long-term drought, a warning may be required with only 40 mm of precipitation. This problem of prediction and warning of rapid alternation between drought and flood further exacerbates the complexity of its disaster management.

[0003] Existing research on rapid alternation between drought and flood indices is mostly limited to the analysis at summer or monthly scale, such as the long and short cycle indices LDFAI and SDFAI. In fact, rapid alternation between drought and flood occurs throughout the year, and the neutralization of drought and flood occurring at dekad and monthly scales is often ignored, resulting in insufficient accuracy in determining the occurrence and intensity of events. The current indicators are only calculated based on precipitation, and cannot reflect the actual dry and wet conditions of the surface because conditions such as evapotranspiration are not considered. In addition, past research has focused more on the review and analysis of historical data, while the research on predicting future rapid alternation between drought and flood events is relatively lacking. Summary of the Invention

[0004] The present invention provides a method and system for constructing a predictability index of rapid alternation between drought and flood at daily scale to solve the defects in the prior art.

[0005] On the one hand, the present invention provides a method for constructing a predictability index of rapid alternation between drought and flood at daily scale, including: Collect meteorological forecast data from a meteorological database, and calculate the potential evapotranspiration value using the Penman-Monteith formula according to the meteorological forecast data; Calculate the difference index APEI between the cumulative precipitation and evapotranspiration in the early stage according to the potential evapotranspiration value and historical precipitation, output the APEI sequence, and perform probability distribution fitting on the APEI sequence to convert it into a standardized index SAPEI; Divide the drought level in combination with SAPEI; a negative value of SAPEI indicates drought, otherwise it indicates wetness, and output the level division table; Set the initial drought-flood rapid transition window, calculate the average index of SAPEI according to the size of the drought-flood rapid transition window; calculate the intensity of drought-flood rapid transition according to the average index of SAPEI; and set up a drought-flood rapid transition intensity matrix according to the intensity of drought-flood rapid transition to determine the optimal window sizes of the flood period window and the drought period window. According to the optimal window sizes and the average index, combined with the convective precipitation data in the drought period window, determine whether it is a drought-flood rapid transition event; if it is a drought-flood rapid transition event, calculate the drought-flood rapid transition rate.

[0006] According to a method for constructing a daily-scale drought-flood rapid transition predictability index provided by the present invention, the steps of converting the APEI sequence into a standardized index SAPEI include: Perform data preprocessing on the APEI values in the APEI sequence and output a standard APEI sequence. Adopt a three-parameter logarithmic Logistic distribution to determine the probability density function of the APEI sequence, and use the maximum likelihood estimation method to fit the APEI sequence and output the fitted probability density function. According to the fitted probability density function, calculate the cumulative distribution function of the APEI sequence, substitute the daily APEI value into the cumulative distribution function to obtain the corresponding APEI probability value. Inverse-normally transform the APEI probability value into the standardized index SAPEI through the quantile function of the standard normal distribution.

[0007] According to a method for constructing a daily-scale drought-flood rapid transition predictability index provided by the present invention, the steps of performing data preprocessing on the APEI values in the APEI sequence include: Calculate the mean and standard deviation of all APEI values in the APEI sequence. According to the mean and standard deviation, use Z-score to standardize all APEI values and output a standard APEI sequence, which is expressed by the formula: ; where is the standard APEI sequence, μ is the mean of the APEI value, and σ is the standard deviation of the APEI value.

[0008] According to a method for constructing a daily-scale drought-flood rapid transition predictability index provided by the present invention, the steps of calculating the potential evapotranspiration value using the Penman-Monteith formula include: Calculate the relevant evapotranspiration parameters according to the meteorological forecast data. Calculate the standard evapotranspiration value according to the relevant evapotranspiration parameters. Correct the standard evapotranspiration value according to the carbon dioxide CO2 concentration in the meteorological forecast data and output the potential evapotranspiration value.

[0009] According to a method for constructing a daily-scale drought-flood abrupt change predictability index provided by the present invention, the average index of SAPEI includes the average index SAPEI_pre_sum in the drought period and the average index SAPEI_post_sum in the flood period, and the formula is expressed as: ; ; In the formula, SAPEI pre_sum_i is the average index in the drought period at the i-th moment, SAPEI post_sum_i is the average index in the flood period at the i-th moment, Pre_day represents the window size of the drought period, Post_day represents the window size of the flood period, i is the current moment, j is the index of the drought period window, and k is the index of the flood period window.

[0010] According to a method for constructing a daily-scale drought-flood abrupt change predictability index provided by the present invention, the steps for determining the optimal window sizes of the flood period window and the drought period window include: Set the traversal ranges of the drought period window and the flood period window; for the drought period window, Pre_day ∈ {15, 16,..., 50}, and for the flood period window, Post_day ∈ {1, 2,..., 15}; According to the traversal ranges and the average index of SAPEI, calculate the intensity of drought-flood abrupt change, and the formula is expressed as: ; ; ; In the formula, index_pre and index_post are the traversal ranges of the drought period window and the flood period window respectively; According to the intensity of drought-flood abrupt change, generate a T pre value matrix T1 with a size of 36×15; Traverse the maximum value in the time window matrix T1, and determine the position of the maximum value in the T pre value matrix T1, and record its row number and column number; According to the row number and column number, determine the optimal drought period window and the optimal flood period window, which are expressed as: ; ; In the formula, row is the row number corresponding to the maximum value, and col is the column number corresponding to the maximum value.

[0011] According to a method for constructing a daily-scale drought-flood abrupt change predictability index provided by the present invention, the way to determine whether it is a drought-flood abrupt change event is as follows: If there is no convective precipitation at the end of the drought period window, it is determined as a drought-flood abrupt change event when and only when SAPEI_pre_sum ≤ -0.5 and SAPEI_post_sum ≥ 0.5; if there has been convective precipitation at the end of the drought period window, adjust the value of SAPEI and then determine the drought-flood abrupt change event.

[0012] According to a method for constructing a daily-scale drought-flood abrupt change predictability index provided by the present invention, the steps of adjusting the value of SAPEI include: According to meteorological data, statistically analyze the precipitation intensity and duration of convective precipitation; According to the precipitation intensity and duration, calculate the weighted intensity of convective precipitation, expressed as: ; In the formula, I is the weighted intensity, m is the total number of segments of convective precipitation, P a is the precipitation of the a-th segment of convective precipitation, and t a is the effective duration of the a-th segment of precipitation; According to the weighted intensity, correct the value of SAPEI in the following way: ; In the formula, |SAPEI| is the absolute value of the corrected value of SAPEI.

[0013] According to a method for constructing a daily-scale drought-flood abrupt change predictability index provided by the present invention, the formulas for calculating the final drought-flood abrupt change intensity and the drought-flood abrupt change rate are expressed as: ; ; ; ; In the formula, SAPEI pre_sum_pre represents the average drought period index size under the optimal drought period window, SAPEI post_sum_pre represents the average flood period index size under the optimal flood period window, T pre is the final drought-flood abrupt change intensity under the optimal drought period window, U pre is the drought-flood abrupt change rate under the optimal drought period window, n is the number of days in the flood period, and SAPEI b and SAPEI0 are the SAPEI values on the b-th day of the flood period and the last day of the drought period, respectively.

[0014] The present invention also provides a system for constructing a daily-scale drought-flood abrupt change predictability index, including: A data acquisition module for acquiring meteorological data from a meteorological database; A standardized index calculation module, which is used to calculate the potential evapotranspiration value according to meteorological data; calculate the index APEI based on the potential evapotranspiration value and historical precipitation, output the APEI sequence, and perform probability distribution fitting on the APEI sequence to convert it into a standardized index SAPEI; A grade division module, which is used to divide the drought grade in combination with SAPEI and output a grade division table; An event determination module, which is used to determine whether it is a drought-flood rapid transition event according to the grade division table in combination with meteorological data and output a determination result; An event severity determination module, which is used to calculate the drought-flood rapid transition intensity and the drought-flood rapid transition rate according to the determination result.

[0015] A method and system for constructing a daily-scale drought-flood rapid transition predictability index provided by the present invention can calculate the future change of SAPEI by inputting meteorological forecast data for the next 1-15 days, so as to realize the prediction of drought-flood rapid transition events for the next 1-15 days. By identifying the indexes of drought-flood rapid transition events at multiple scales, covering days, months, seasons, etc. Thus, it can avoid the neglect of drought-flood neutralization occurring at dekad and monthly scales caused by the low time scale of traditional methods, so it is more accurate. By constructing an index system with two dimensions, it fully reflects the different change characteristics of drought-flood rapid transition events in terms of conversion intensity and rate; drought has a cumulative effect and usually lasts for a long time, so the window size range is large; while heavy precipitation usually occurs rapidly, so the window size range is small; this index can fully consider the asymmetric characteristics of drought-to-flood transition. Using SAPEI as the basic data can better reflect the ground moisture status rather than the precipitation situation compared with the precipitation-related indexes commonly used in other methods. By calculating the sizes of T and U according to the drought-flood period window corresponding to the maximum value of T, the characteristics of drought-flood rapid transition events can be obtained instead of a fixed window size. Therefore, the window sizes of this method are different across the country and it is applicable to the whole country. Brief Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a step diagram of a method for constructing a daily-scale drought-flood rapid transition predictability index provided by an embodiment of the present invention; Figure 2 It is a structural schematic diagram of a system for constructing a daily-scale drought-flood rapid transition predictability index provided by an embodiment of the present invention; Figure 3 These are the characteristics of the DWAAI event in the middle and lower reaches of the Yangtze River region on June 17, 2011, provided in the embodiments of the present invention. Detailed implementation manners

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1

[0020] The following Figures 1-3 describes a method and system for constructing a daily-scale drought-flood abrupt change predictability index of the present invention.

[0021] As Figure 1 shown, a method for constructing a daily-scale drought-flood abrupt change predictability index provided in an embodiment of the present invention includes: Obtaining meteorological forecast data from a meteorological database, and calculating a potential evapotranspiration value according to the meteorological forecast data. The specific steps include: Calculating relevant evapotranspiration parameters according to the meteorological forecast data. The parameters include: daily average temperature, daily maximum / minimum temperature, relative humidity, wind speed, solar radiation, atmospheric pressure, soil heat flux, etc.

[0022] Calculating a standard evapotranspiration value according to the relevant evapotranspiration parameters, and the formula is expressed as: ; In the formula, PET is the evapotranspiration value, Δ is the slope of the saturated water vapor pressure-temperature curve, R n is the net radiation at the canopy surface, G is the soil flux, γ is the thermometer constant, T is the average temperature, U2 is the wind speed at 2m, e s is the saturated water vapor pressure, e a is the actual water vapor pressure. The Penman-Monteith equation is a commonly used method for calculating the reference crop evapotranspiration (ETo). This formula combines the energy balance and aerodynamic principles, and takes into account various factors affecting the evaporation process, such as solar radiation, temperature, wind speed, and relative humidity. The calculation methods of the saturated water vapor pressure (es) and the actual water vapor pressure (ea) are as follows: ; ; In the formula, RH is the relative humidity. The calculation methods of the slope Δ of the saturated water vapor pressure and temperature curve and the thermometer constant γ are as follows: ; ; In the formula, P is the atmospheric pressure.

[0023] Net radiation R n is calculated as follows: ; In the formula, σ is the Stefan-Boltzmann constant, σ = 0.00000004903 MJ / K 4 . T α is the daily average temperature + 273.15. R s is the solar radiation. The soil heat flux is G = 0.5·R n , and this value is an approximation.

[0024] According to the carbon dioxide CO2 concentration in the meteorological forecast data, the standard evapotranspiration value is corrected to output the potential evapotranspiration value, and the formula is expressed as: ; In the formula, PET[CO2] is the potential evapotranspiration value, and CO2 is the concentration of CO2 in the atmosphere.

[0025] In order to reflect the dry and wet conditions of the day, according to the potential evapotranspiration value and historical precipitation, the antecedent cumulative precipitation and evapotranspiration difference index APEI is calculated, and the APEI sequence is output. Then, the probability distribution of the APEI sequence is fitted and converted into the standardized index SAPEI. SAPEI is the Standardized Antecedent Precipitation Evapotranspiration Index, which is an index used to quantify drought conditions. By integrating the cumulative difference between historical precipitation and potential evapotranspiration (PET) and introducing a time weight coefficient to reflect the impact of the previous water balance on the current drought. Generally speaking, this index has the following advantages: First, it considers the impact of water evapotranspiration caused by factors such as temperature on the development of drought, and the response effect to the intensification of drought caused by high temperature is more in line with the actual situation. Second, it has the characteristics of multiple time scales, and can evaluate the evolution of drought characteristics at long time scales such as daily, monthly, and dekadal, and can also be used for drought analysis of different types such as agriculture and hydrology. The calculation method of APEI is as follows: ; In the formula, PET[CO2] is the potential evapotranspiration value, P rainwhere \(P\) is the cumulative precipitation, \(N\) is the number of days pushed forward, with a value of 100. \(\delta\) is the attenuation coefficient, with a value of 0.98. \(n\) is the ordinal number of the day pushed forward (the current day is 0).

[0026] The steps of performing probability distribution fitting on the APEI sequence and converting it into the standardized index SAPEI include: Performing data preprocessing on the APEI values in the APEI sequence. The steps of outputting the standard APEI sequence include: Calculating the mean and standard deviation of all APEI values in the APEI sequence.

[0027] According to the mean and the standard deviation, using Z - score to standardize all APEI values, and outputting the standard APEI sequence, which is expressed by the formula: ; In the formula, is the standard APEI sequence, \(\mu\) is the mean of APEI values, and \(\sigma_1\) is the standard deviation of APEI values.

[0028] Using the three - parameter log - logistic distribution to determine the probability density function of the APEI sequence, which is expressed by the formula: ; In the formula, \(x\) is the APEI sequence, \(\mu\) is the location parameter, \(\sigma_2\) is the scale parameter, and \(\gamma_1\) is the shape parameter.

[0029] Then, using the maximum likelihood estimation method to fit the APEI sequence, and outputting the fitted probability density function, which is expressed as: ; In the formula, \(L\) is the likelihood function, \(y\) is the length of the APEI sequence, and \(s\) is the index of the APEI sequence, usually indexed by date.

[0030] According to the fitted probability density function, calculating the cumulative distribution function of the APEI sequence, substituting the daily APEI value into the cumulative distribution function, and obtaining the corresponding APEI probability value, which is expressed as: ; In the formula, \(F(x s )\) is the cumulative distribution function, and \(x s is the standardized APEI value.

[0031] By using the quantile function of the standard normal distribution to perform inverse normal transformation on the APEI probability value to obtain the standardized index SAPEI, which is expressed as: ; where \(\varPhi −1Is the inverse function of the standard normal CDF. For a given value x s , CDF-F(x s ) represents the probability that the random variable is less than or equal to x s , and its value range is 0, 1. The inverse function means that for a given probability p ∈ [0, 1], it returns the x s value that satisfies F(x s ).

[0032] Combine the above SAPEI to divide the drought level. A negative value of the SAPEI indicates drought, otherwise it indicates wetness, and output the level division table, as shown in Table 1.

[0033] Table 1: ; In Table 1, it reflects the division of drought or wetness degrees corresponding to different SAPEI value ranges.

[0034] This method only considers the situation of drought turning into waterlogging.

[0035] Set the initial drought-flood rapid transition window, calculate the average index of SAPEI according to the size of the drought-flood rapid transition window; calculate the drought-flood rapid transition intensity according to the average index of SAPEI; and set the drought-flood rapid transition intensity matrix according to the drought-flood rapid transition intensity to determine the optimal window sizes of the waterlogging period window and the drought period window.

[0036] The above steps for determining the optimal window sizes of the waterlogging period window and the drought period window include: Set the traversal ranges of the drought period window and the waterlogging period window. Pre_day traverses from 15 to 50, and Post_day traverses from 1 to 15. That is, the drought period window: Pre_day Î {15, 16,... 50}; the waterlogging period window: Post_day Î {1, 2,... 15}.

[0037] According to the traversal ranges and the average index of SAPEI, calculate the drought-flood rapid transition intensity, and the formula is expressed as: ; ; ; In the formula, index_pre and index_post are the traversal ranges of the drought period window and the waterlogging period window respectively.

[0038] According to the drought-flood rapid transition intensity, generate a T pre value matrix T1 with a size of 36×15.

[0039] Traverse the maximum value in the time window matrix T1 and determine the position of the maximum value in T preRecord the row number and column number of its position in the value matrix T1.

[0040] Determine the optimal early drought window and the optimal flood window according to the row number and column number, expressed as: ; ; In the formula, row is the row number corresponding to the maximum value, and col is the column number corresponding to the maximum value.

[0041] Calculate the average index of the SAPEI according to the optimal window size, including the average early drought index SAPEI_pre_sum and the average flood index SAPEI_post_sum. The formula for the average index of the SAPEI is expressed as: ; ; In the formula, SAPEI pre_sum_i is the average early drought index at time i, SAPEI post_sum_i is the average flood index at time i, Pre_day represents the early drought window size, Post_day represents the flood window size, i is the current time, j is the early drought window index, and k is the flood window index.

[0042] According to the average index and the grade division table, combined with the convective precipitation data in the early drought window, determine whether it is a rapid drought-flood transition event. The determination method is: if there is no convective precipitation at the end of the early drought window, and only when SAPEI_pre_sum ≤ -0.5 and SAPEI_post_sum ≥ 0.5, it is determined as the rapid drought-flood transition event. If there has been convective precipitation at the end of the early drought window, adjust the value of SAPEI and then determine the rapid drought-flood transition event. The steps to adjust the value of SAPEI include: According to the meteorological data, count the precipitation intensity and duration of the convective precipitation.

[0043] Calculate the weighted intensity of the convective precipitation according to the precipitation intensity and the duration, expressed as: ; In the formula, I is the weighted intensity, m is the total number of segments of the convective precipitation, P a is the precipitation of the a-th segment of the convective precipitation, t a is the effective duration of the a-th segment of the precipitation.

[0044] According to the weighted intensity, correct the value of SAPEI as follows: ; Where, |SAPEI| is the absolute value of the SAPEI correction value.

[0045] For example, when convective precipitation has occurred within the early drought window and the weighted intensity of the convective precipitation is calculated to be 1.5, the conditions for determining a rapid drought-flood transition event are changed to: SAPEI_pre_sum ≤ -0.4 and SAPEI_post_sum ≥ 0.4.

[0046] If it is a rapid drought-flood transition event, calculate the rapid drought-flood transition intensity and the rapid drought-flood transition rate, and the formulas are expressed as: ; ; ; ; Where, SAPEI pre_sum_pre represents the magnitude of the average early drought index under the optimal early drought window, SAPEI post_sum_pre represents the magnitude of the average flood period index under the optimal flood period window, T pre is the rapid drought-flood transition intensity under the optimal early drought window, U pre is the rapid drought-flood transition rate under the optimal early drought window, n is the number of days in the flood period, n ∈ {1, post_day_pre}, pre_day_pre is the optimal early drought window, post_day_pre is the optimal flood period window, SAPEI b and SAPEI0 are the SAPEI values on the b-th day of the flood period and the last day of the early drought period respectively. T is equivalent to the intensity difference between the early drought period and the flood period; U is equivalent to the sum of the slopes in the flood period. Since the T values and U values corresponding to different window sizes are calculated by traversing in the front, the window sizes of the early drought period and the flood period corresponding to the maximum T value are taken as the optimal window sizes at this moment. The larger T is, the greater the intensity of the rapid drought-flood transition; the larger U is, the greater the rate of the rapid drought-flood transition. For a rapid drought-flood transition event, T and U respectively characterize its situations in the two dimensions of intensity and rate. By constructing an index system of two dimensions, the different change characteristics of the rapid drought-flood transition event in terms of conversion intensity and rate are fully reflected; drought has a cumulative effect and usually lasts for a long time, so the window size range is large; while heavy precipitation usually occurs rapidly, so the window size range is small; this index can fully consider the asymmetric characteristics of the drought-to-flood transition.

[0047] As shown in Table 2.

[0048] Table 2 (is the rapid drought-flood transition risk matrix table): ; Such as Figure 3As shown in the figure, it presents the characteristics of the DWAAI event in the middle and lower reaches of the Yangtze River on June 17, 2011. At this time, the T value is the largest, and the corresponding optimal drought period window size is 50 days, while the flood period window size is 1 day. News and literature reports indicate that in the late spring and early summer of 2011, especially in the area of (27°N - 32°N, 110°E - 120°E) in the middle and lower reaches of the Yangtze River, there occurred the most significant drought-flood abrupt transition event since 1951, which rapidly changed from a previous severe drought to a regional flood disaster.

[0049] Through the calculation of this method, it can be seen that there is indeed a strong drought-flood abrupt transition in the Yangtze River Basin, especially in the middle and lower reaches of the Yangtze River.

[0050] Whether it is for the calculation of SAPEI or the calculation of T and U during the drought-flood abrupt transition, the meteorological elements used can be obtained from meteorological monitoring and meteorological numerical forecasts. Therefore, when this index is used for forecasting and early warning, the input elements for calculation should obtain the corresponding meteorological element forecast results from meteorological numerical forecasts and perform corresponding calculations, so as to achieve the forecast of different aspects such as the intensity and rate of the drought-flood abrupt transition event in the next 15 days.

[0051] In summary, a method for constructing a daily-scale drought-flood abrupt transition predictability index provided by the present invention can calculate the future change of SAPEI by inputting meteorological forecast data for the next 1 - 15 days, thereby realizing the prediction of the drought-flood abrupt transition event in the next 1 - 15 days. By identifying the indexes of drought-flood abrupt transition events at multiple scales, covering daily, monthly, seasonal, etc. It can thus avoid the neglect of drought-flood neutralization occurring at dekad and monthly scales due to the lower time scale of traditional methods, and is therefore more accurate. By constructing an index system with two dimensions, it fully reflects the different change characteristics of drought-flood abrupt transition events in terms of conversion intensity and rate; drought has a cumulative effect and usually lasts for a long time, so the window size range is relatively large; while heavy precipitation usually occurs rapidly, so the window size range is relatively small; this index can fully consider the asymmetric characteristics of the drought-to-flood transition. Using SAPEI as the basic data, compared with the precipitation-related indexes commonly used in other methods, it can better reflect the ground moisture state rather than the precipitation situation. By adopting the sliding window method, the characteristics of the drought-flood abrupt transition event are obtained instead of a fixed window size. Therefore, the window sizes of this method are different across the country, and thus this method is applicable to the whole country.

[0052] As Figure 2 shown, the present invention also provides a system for constructing a daily-scale drought-flood abrupt transition predictability index, including: The data acquisition module is used to collect meteorological data from the meteorological database. By adopting standard database interface protocols such as ODBC / SQLAPI, it supports docking with multi-source meteorological databases to achieve cross-platform data compatibility. The extracted meteorological elements include core meteorological parameters such as daily temperature, precipitation, wind speed, relative humidity, sunshine hours, etc. At the same time, it records the data time resolution (daily scale / hourly scale), spatial positioning information (latitude and longitude / administrative division code), and quality control flag.

[0053] The standardized index calculation module is used to calculate the potential evapotranspiration value according to the meteorological data. Calculate the index APEI based on the potential evapotranspiration value and historical precipitation, output the APEI sequence, and perform probability distribution fitting on the APEI sequence to convert it into the standardized index SAPEI.

[0054] The grade division module is used to divide the drought grade in combination with SAPEI and output the grade division table. Construct a hierarchical evaluation system based on the SAPEI index. Specifically: set the SAPEI critical threshold (such as -1.5 to -1.0 for mild drought), and use the equal interval / logarithmic distribution method for quantitative grading.

[0055] The event determination module is used to determine whether it is a drought-flood rapid transition event according to the grade division table in combination with the meteorological data and output the determination result.

[0056] The event degree determination module is used to calculate the intensity and rate of drought-flood rapid transition according to the determination result.

[0057] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0058] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for constructing a daily-scale drought-flood rapid transition predictability index, characterized in that Including: Collecting meteorological forecast data from a meteorological database, and calculating the potential evapotranspiration value using the Penman-Monteith formula according to the meteorological forecast data; Calculating the difference index of antecedent cumulative precipitation and evapotranspiration APEI based on the potential evapotranspiration value and historical precipitation, outputting an APEI sequence, fitting the probability distribution of the APEI sequence, and converting it into a standardized index SAPEI; Dividing drought levels in combination with the SAPEI; a negative value of the SAPEI indicates drought, otherwise it indicates wetness, and an output level division table is generated; Setting an initial drought-flood rapid transition window, calculating the average index of SAPEI according to the size of the drought-flood rapid transition window; calculating the drought-flood rapid transition intensity according to the average index of SAPEI; and setting a drought-flood rapid transition intensity matrix according to the drought-flood rapid transition intensity to determine the optimal window sizes of the flood period window and the drought period window; Judging whether it is a drought-flood rapid transition event according to the optimal window size and the average index, in combination with the convective precipitation data in the drought period window; If it is a drought-flood rapid transition event, calculating the drought-flood rapid transition rate.

2. A method for constructing a daily-scale drought-flood rapid transition predictability index according to claim 1, characterized in that, The steps of converting the APEI sequence into a standardized index SAPEI include: Performing data preprocessing on the APEI values in the APEI sequence, and outputting a standard APEI sequence; Determining the probability density function of the APEI sequence using a three-parameter logarithmic Logistic distribution, and fitting the APEI sequence using the maximum likelihood estimation method, and outputting a fitted probability density function; Calculating the cumulative distribution function of the APEI sequence according to the fitted probability density function, substituting the daily APEI value into the cumulative distribution function, and obtaining the corresponding APEI probability value; Inverse-normally converting the APEI probability value into the standardized index SAPEI through the quantile function of the standard normal distribution.

3. The construction method of a daily-scale drought-flood rapid transition predictability index according to claim 2, characterized in that, The steps of performing data preprocessing on the APEI values in the APEI sequence include: Calculating the mean and standard deviation of all APEI values in the APEI sequence; Normalizing all APEI values using Z-score according to the mean and the standard deviation, and outputting a standard APEI sequence, which is expressed by the formula: ； In the formula, is the standard APEI sequence, μ is the mean value of the APEI value, and σ is the standard deviation of the APEI value.

4. A method for constructing a daily-scale drought-flood rapid transition predictability index according to claim 1, wherein The steps of calculating the potential evapotranspiration value using the Penman-Monteith formula include: Calculating relevant evapotranspiration parameters according to the meteorological forecast data; Calculating the standard evapotranspiration value according to the relevant evapotranspiration parameters; Correcting the standard evapotranspiration value according to the carbon dioxide CO2 concentration in the meteorological forecast data, and outputting the potential evapotranspiration value.

5. A method for constructing a daily-scale drought-flood rapid transition predictability index according to claim 1, characterized in that, The average index of the SAPEI includes the average index of the drought period SAPEI_pre_sum and the average index of the flood period SAPEI_post_sum, which is expressed by the formula: ； ； where, SAPEI pre_sum_i is the average early index at time i, SAPEI post_sum_i is the average waterlogging index at time i, Pre_day represents the early window size, Post_day represents the waterlogging window size, i is the current time, j is the early window index, and k is the waterlogging window index.

6. The construction method of a daily-scale drought-flood rapid transition predictability index according to claim 5, characterized in that, The steps of determining the optimal window sizes of the flood period window and the drought period window include: Setting the traversal ranges of the drought period window and the flood period window; the drought period window Pre_day ∈ {15, 16,..., 50}, and the flood period window Post_day ∈ {1, 2,..., 15}; Calculating the drought-flood rapid transition intensity according to the traversal range and the average index of the SAPEI, which is expressed by the formula: ； ； ； where index_pre and index_post are the traversal ranges of the early drought window and the late waterlogging window respectively; Generate a T value matrix T1 of size 36×15 according to the intensity of the rapid alternation of drought and flood pre ; Traverse the maximum value in the time window matrix T1 and determine the position of the maximum value in the T pre value matrix T1, record its row number and column number; Determine the optimal early drought window and the optimal late waterlogging window according to the row number and the column number, expressed as: ； ； where row is the row number corresponding to the maximum value and col is the column number corresponding to the maximum value.

7. A method for constructing a daily-scale drought-flood rapid transition predictability index according to claim 1, characterized in that, The method for determining whether it is a drought-flood rapid transition event is as follows: If there is no convective precipitation at the end of the early drought window, it is determined as the drought-flood rapid transition event if and only if SAPEI_pre_sum ≤ -0.5 and SAPEI_post_sum ≥ 0.5; If there has been convective precipitation at the end of the early drought window, adjust the value of SAPEI and then determine the drought-flood rapid transition event.

8. A method for constructing a daily-scale drought-flood rapid transition predictability index according to claim 7, characterized in that, The steps for adjusting the value of SAPEI include: According to the meteorological data, count the precipitation intensity and duration of the convective precipitation; Calculate the weighted intensity of the convective precipitation according to the precipitation intensity and the duration, expressed as: ； Wherein, I is the weighted intensity, m is the total number of segments of shower precipitation, P a is the precipitation of the ath segment of shower precipitation, t a is the effective duration of the ath segment of precipitation; According to the weighted intensity, correct the value of SAPEI in the following way: ； where |SAPEI| is the absolute value of the SAPEI correction value.

9. A method for constructing a daily-scale drought-flood rapid transition predictability index according to claim 1, characterized in that The formulas for calculating the final drought-flood rapid transition intensity and the drought-flood rapid transition rate are expressed as: ； ； ； ； In the formula, SAPEI pre_sum_pre represents the magnitude of the average drought index under the optimal early drought window, and SAPEI post_sum_pre represents the magnitude of the average waterlogging index under the optimal late waterlogging window. T pre is the final drought-flood rapid transition intensity under the optimal early drought window, and U pre is the drought-flood rapid transition rate under the optimal early drought window. n is the number of days in the waterlogging period. SAPEI b and SAPEI0 are the SAPEI values on the b-th day of the waterlogging period and the last day of the drought period, respectively.

10. A construction system for a daily-scale drought-flood rapid transition predictability index, which adopts a construction method for a daily-scale drought-flood rapid transition predictability index according to any one of claims 1 to 9, and is characterized in that, including: A data acquisition module for acquiring meteorological data from a meteorological database; A standardized index calculation module for calculating the potential evapotranspiration value according to the meteorological data; calculating the index APEI according to the potential evapotranspiration value and the historical precipitation, outputting an APEI sequence, and performing a probability distribution fitting on the APEI sequence to convert it into a standardized index SAPEI; A grade division module for dividing the drought grade in combination with the SAPEI and outputting a grade division table; An event determination module for determining whether it is a drought-flood rapid transition event according to the grade division table in combination with meteorological data and outputting a determination result; An event degree determination module for calculating the drought-flood rapid transition intensity and the drought-flood rapid transition rate according to the determination result.

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