A dynamic drought propagation threshold identification method and system

Abstract

This invention discloses a dynamic drought transmission threshold identification method and system, relating to the field of dynamic drought transmission monitoring technology. The method involves acquiring time-series basic data, segmenting the data using a sliding window of preset window length, and obtaining segmented data for each window. For the segmented data, it calculates the correlation coefficients between various meteorological drought indices within a preset continuous time scale and the soil moisture index at a one-month time scale. Based on the Pearson correlation coefficient, it calculates the correlation coefficients between these indices and the soil moisture index at the one-month time scale, and identifies the preset time scale corresponding to the maximum correlation coefficient as the drought transmission time. Finally, it calculates the drought transmission threshold based on the drought transmission time, the one-month soil moisture index, a joint probability distribution, and a first-order Bayesian network. This invention achieves dynamic identification of drought transmission thresholds at different levels.

Description

Technical Field

[0001] This invention relates to the field of dynamic monitoring technology for drought transmission, and more specifically to a dynamic drought transmission threshold identification method and system. Background Technology

[0002] Existing research on drought transmission mainly focuses on identifying the transmission time, with limited research on transmission thresholds. Furthermore, existing methods for identifying transmission thresholds suffer from the following problems: 1. Most existing methods rely on regression models and use fixed thresholds to determine whether drought has occurred (e.g., SRI < -1 indicates drought). These methods cannot automatically identify the transmission thresholds from meteorological drought to different levels (mild, moderate, severe, and extreme) of agricultural or hydrological drought, nor can they describe the seasonal variations in transmission thresholds, leading to insufficient early warning accuracy. 2. Drought transmission thresholds are influenced by climate change and human activities and are not static over long periods. Existing research methods cannot automatically identify the long-term dynamic changes in transmission thresholds at different levels of drought, making them unsuitable for long-term drought early warning needs.

[0003] Therefore, how to dynamically identify the transmission threshold of different levels of drought is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] In view of this, the present invention provides a dynamic drought propagation threshold identification method and system to solve the problems existing in the above-mentioned background art.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: A dynamic drought propagation threshold identification method, comprising: Acquire basic time series data, and segment the basic time series data using a sliding window of preset window length to obtain segmented data for each window; Calculate the meteorological drought index sequence within a preset continuous time scale and the soil moisture index sequence on a one-month scale for the segmented data. The correlation coefficient between each meteorological drought index sequence and the soil moisture index sequence on a one-month time scale is calculated based on the Pearson correlation coefficient. The preset time scale corresponding to the maximum correlation coefficient is the drought transmission time. The drought transmission threshold is calculated based on the drought transmission time, the soil moisture index on a monthly scale, the joint probability distribution, and a first-order Bayesian network.

[0006] Preferably, the joint probability distribution specifically includes: Constructing SPI using Copula functions The joint probability distribution function with SSMI-1 and the least squares Euclidean distance are calculated. For drought propagation time, SPI- For time scale The meteorological drought index, SSMI-1 is a monthly soil moisture index series; the Copula function corresponding to the least squares Euclidean distance is chosen as SPI- The best-fit function and joint distribution function with SSMI-1 are as follows: ; in, and They represent SPI- The marginal distribution function of SSMI-1, where C is the optimal Copula function. and Let be any real number.

[0007] Preferably, the calculation of the drought transmission threshold specifically includes: Preset the upper limit value y of agricultural drought at different levels and the initial meteorological drought range. Upper limit of the interval Set to -0.5, lower limit Set to -0.6, iterate over the interval at intervals of -0.1 according to the following formula, obtaining a probability value P in each iteration. Stop iterating when P first equals or exceeds the judgment probability threshold; obtain the value at this time. and The mean value is the drought transmission threshold from meteorological drought to agricultural drought. If the lower limit is... If P still does not exceed the decision probability threshold when updated to -10, the iteration stops immediately, which is considered as no propagation behavior has occurred, and the propagation threshold does not exist. ; In the formula, X is SPI- Y is SSMI-1. For SPI- Joint probability distribution with SSMI-1, and They represent SPI- The marginal distribution function of SSMI-1, C is the optimal Copula function.

[0008] A dynamic drought propagation threshold identification system, comprising: The data segmentation module acquires time-series basic data, segments the time-series basic data through a sliding window of preset window length, and acquires the segmented data for each window. The index calculation module calculates the meteorological drought index sequence within a preset continuous time scale and the soil moisture index sequence on a one-month scale for the segmented data. The drought propagation time acquisition module calculates the correlation coefficient between each meteorological drought index sequence and the soil moisture index sequence on a one-month scale within a preset continuous time scale based on the Pearson correlation coefficient, and obtains the preset time scale corresponding to the maximum correlation coefficient as the drought propagation time. The drought transmission threshold calculation module calculates the drought transmission threshold based on the drought transmission time, the soil moisture index on a monthly scale, the joint probability distribution, and a first-order Bayesian network.

[0009] Preferably, the joint probability distribution in the transmission threshold calculation module specifically includes: Constructing SPI using Copula functions The joint probability distribution function with SSMI-1 and the least squares Euclidean distance are calculated. For drought propagation time, SPI- For time scale The meteorological drought index, SSMI-1 is a monthly soil moisture index series; the Copula function corresponding to the least squares Euclidean distance is chosen as SPI- The best-fit function and joint distribution function with SSMI-1 are as follows: ; in, and They represent SPI- The marginal distribution function of SSMI-1, where C is the optimal Copula function. and Let be any real number.

[0010] Preferably, the calculation of the drought transmission threshold in the transmission threshold calculation module specifically includes: Preset the upper limit value y of agricultural drought at different levels and the initial meteorological drought range. Upper limit of the interval Set to -0.5, lower limit Set to -0.6, iterate over the interval at intervals of -0.1 according to the following formula, obtaining a probability value P in each iteration. Stop iterating when P first equals or exceeds the judgment probability threshold; obtain the value at this time. and The mean value is the drought transmission threshold from meteorological drought to agricultural drought. If the lower limit is... If P still does not exceed the decision probability threshold when updated to -10, the iteration stops immediately, which is considered as no propagation behavior has occurred, and the propagation threshold does not exist. ; In the formula, X is SPI- Y is SSMI-1. For SPI- Joint probability distribution with SSMI-1, and They represent SPI- The marginal distribution function of SSMI-1, C is the optimal Copula function.

[0011] As can be seen from the above technical solutions, compared with the prior art, the present invention discloses a dynamic drought transmission threshold identification method and system, which automatically identifies the threshold for the transmission of meteorological drought to other types of drought at different levels based on readily available basic data; through the sliding window technique, it can describe the seasonal changes of the transmission threshold and automatically capture the long-term change characteristics of the drought transmission threshold in each month over a long period of time, thereby improving the comprehensiveness of the transmission threshold identification. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0013] Figure 1 A flowchart of the method steps provided by the present invention; Figure 2 The dynamic change process diagram provided by this invention; Figure 3 A technical flowchart provided for this invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] This invention discloses a dynamic drought propagation threshold identification method, such as... Figure 1 As shown, it includes: Acquire basic time series data, segment the basic time series data using a sliding window of preset window length, and obtain the segmented data for each window; Calculate the meteorological drought index series within a preset continuous time scale and the soil moisture index series on a one-month scale for the segmented data. The correlation coefficient between each meteorological drought index sequence and the soil moisture index sequence on a one-month time scale is calculated based on the Pearson correlation coefficient. The preset time scale corresponding to the maximum correlation coefficient is the drought transmission time. The drought transmission threshold was calculated based on drought transmission time, a monthly soil moisture index, a joint probability distribution, and a first-order Bayesian network.

[0016] In one specific embodiment, the joint probability distribution specifically includes: Constructing SPI using Copula functions The joint probability distribution function with SSMI-1 and the least squares Euclidean distance are calculated. For drought propagation time, SPI- For time scale The standardized precipitation index series (meteorological drought index) and SSMI-1 are a monthly soil moisture index series (agricultural drought index); the Copula function corresponding to the least squares Euclidean distance is chosen as SPI- The best-fit function and joint distribution function with SSMI-1 are as follows: ; in, and They represent SPI- The marginal distribution function of SSMI-1, where C is the optimal Copula function. and Let be any real number.

[0017] In one specific embodiment, calculating the drought propagation threshold specifically includes: Preset the upper limit value y of agricultural drought at different levels and the initial meteorological drought range. Upper limit of the interval Set to -0.5, lower limit Set to -0.6, iterate over the interval at intervals of -0.1 according to the following formula. Each iteration yields a probability value P (the probability that meteorological drought will propagate to different levels of agricultural drought within this interval; all P values ​​in the following formula have the same meaning). Iteration stops when P first equals or exceeds the probability threshold. [The remaining text appears to be incomplete and requires further context.] and The mean value is the drought transmission threshold from meteorological drought to agricultural drought. If the lower limit... When P is updated to -10, if it still does not exceed the decision probability threshold, the iteration stops immediately. This indicates that no propagation behavior occurs and the propagation threshold does not exist.

[0018] ; In the formula, X is SPI- Y is SSMI-1. For SPI- Joint probability distribution with SSMI-1, and They represent SPI- The marginal distribution function of SSMI-1, C is the optimal Copula function.

[0019] A dynamic drought propagation threshold identification system, comprising: The data segmentation module acquires time-series basic data, segments the time-series basic data through a sliding window of preset window length, and acquires the segmented data for each window. The index calculation module calculates the meteorological drought index sequence within a preset continuous time scale and the soil moisture index sequence on a one-month scale for the segmented data. The drought propagation time acquisition module calculates the correlation coefficient between each meteorological drought index sequence and the soil moisture index sequence on a one-month scale within a preset continuous time scale based on the Pearson correlation coefficient, and obtains the preset time scale corresponding to the maximum correlation coefficient as the drought propagation time. The drought transmission threshold calculation module calculates the drought transmission threshold based on the drought transmission time, the soil moisture index on a monthly scale, the joint probability distribution, and a first-order Bayesian network.

[0020] In one specific embodiment, the joint probability distribution in the transmission threshold calculation module specifically includes: Constructing SPI using Copula functions The joint probability distribution function with SSMI-1 and the least squares Euclidean distance are calculated. For drought propagation time, SPI- For time scale The meteorological drought index, SSMI-1 is a monthly soil moisture index series; the Copula function corresponding to the least squares Euclidean distance is chosen as SPI- The best-fit function and joint distribution function with SSMI-1 are as follows: ; in, and They represent SPI- The marginal distribution function of SSMI-1, where C is the optimal Copula function. and Let be any real number.

[0021] In one specific embodiment, the calculation of the drought transmission threshold in the transmission threshold calculation module specifically includes: Preset the upper limit value y of agricultural drought at different levels and the initial meteorological drought range. Upper limit of the interval Set to -0.5, lower limit Set to -0.6, iterate over the interval at intervals of -0.1 according to the following formula, obtaining a probability value P in each iteration. Stop iterating when P first equals or exceeds the judgment probability threshold; obtain the value at this time. and The mean value is the drought transmission threshold from meteorological drought to agricultural drought. If the lower limit is... If P still does not exceed the decision probability threshold when updated to -10, the iteration stops immediately, which is considered as no propagation behavior has occurred, and the propagation threshold does not exist. ; In the formula, X is SPI- Y is SSMI-1. For SPI- Joint probability distribution with SSMI-1, and They represent SPI- The marginal distribution function of SSMI-1, C is the optimal Copula function.

[0022] In one specific embodiment, the present invention includes the following steps: (1) Window selection and data segmentation The long-term dynamic monitoring of the transmission threshold is calculated using a sliding window, and the window length can be adjusted according to actual needs. First, the acquired long-term basic data (precipitation, soil moisture) is divided using a sliding window with a set window length, and the sliding step size is set to 1 year. Assuming that 60 years of monthly data can be obtained, and the window length is set to n years, the original data can be divided into (60-n+1) different windows. Then, the calculation in part (2) is performed in each window.

[0023] (2) Calculation of Standardized Drought Index The SPI (SPI-1 to SPI-12) and SSMI-1 were calculated for 12 consecutive time scales. Meteorological drought was represented using the standardized meteorological drought index (SPI), and agricultural drought was represented using the standardized soil moisture index (SSMI).

[0024] (3) Determination of transmission time The correlation coefficient between SPI-1 to SPI-12 and SSMI-1 for each month in each window was calculated using the Pearson correlation coefficient. The SPI time scale corresponding to the maximum correlation coefficient value for each month was then identified. . This refers to the monthly drought propagation time, indicating that the agricultural drought in the current month is caused by the previous drought. The drought was transmitted from the meteorological drought of the month. Then, the transmission time of each month in different windows was used to calculate the corresponding transmission threshold according to Part (4).

[0025] (4) Calculation of transmission threshold ①SPI- Joint distribution construction with SSMI-1.

[0026] Five commonly used two-dimensional Copula functions in hydrometeorology (Frank, Clayton, Gumbel, Gaussian, and Student's t) were initially selected as candidates, and SPI- was constructed using each Copula function. The joint probability distribution function of SSMI-1 is calculated, and the least squares Euclidean distance (SED) is computed. The Copula function corresponding to the minimum SED is chosen as SPI- The best-fit function with SSMI-1. The joint distribution function is as follows: in, and They represent SPI- The marginal distribution function of SSMI-1, where C is the optimal Copula function. and Let be any real number.

[0027] ② Transmission threshold calculation The propagation threshold is solved using a joint probability distribution and a first-order Bayesian network. First, an upper limit value y for agricultural drought at different levels and an initial meteorological drought range are preset. Upper limit of the interval Set to -0.5, lower limit Set to -0.6, iterate over the interval at intervals of -0.1 according to the following formula, obtaining a probability value P in each iteration. Stop iterating when P first equals or exceeds the judgment probability threshold; obtain the value at this time. and The mean value is the drought transmission threshold from meteorological drought to agricultural drought. If the lower limit is... If P still does not exceed the decision probability threshold when updated to -10, the iteration stops immediately, which is considered as no propagation behavior has occurred, and the propagation threshold does not exist. ; In the formula, y represents the upper limit value for different levels, with values ​​of -0.5, -1, -1.5, and -2, representing mild, moderate, severe, and extreme, respectively; X represents the SPI- Y is SSMI-1. For SPI- Joint probability distribution with SSMI-1, and They represent SPI- The marginal distribution function of SSMI-1, C is the optimal Copula function.

[0028] It should be noted that the initial upper limit of the meteorological drought interval, -0.5, cannot be changed, because SPI < -0.5 is generally considered the critical value for the occurrence of meteorological drought. The iteration interval of -0.1 and the lower limit of the interval can be changed according to the user's needs, but an interval range that is too large will lead to inaccurate threshold recognition, while an interval that is too small will consume a lot of memory. The probability discrimination criterion of 0.8 can also be changed as needed, and multiple probability discrimination criteria can be set according to actual conditions, thereby obtaining dynamic transmission thresholds under multiple scenarios.

[0029] The core innovations of this invention are as follows: Grading thresholds and Copula optimization: Joint distributions of SPI and SSMI / SRI were constructed for four scenarios: mild, moderate, severe, and extreme drought, respectively, to improve the transmission thresholds for different levels. Based on the minimum Euclidean squared distance (SED) criterion, the optimal Copula function was selected from five Copula functions (Gaussian, Gumbel, Frank, Clayton, and Student's t) for construction, avoiding the error caused by using a single Copula function.

[0030] Iterative optimization of Bayesian networks: Calculating conditional probabilities using a first-order Bayesian network. Dynamically adjust meteorological drought range When P≥0.8, the interval mean is taken as the transmission threshold, and the probability threshold P can be customized (e.g., 0.6~0.9).

[0031] Dynamic sliding window mechanism: Data is segmented using a sliding window with a length of 20 to 30 years, which can balance data stability and sensitivity. Then, it is updated year by year with a step size of 1 year, which can dynamically capture the dynamic changes of the transmission threshold under the influence of climate change and human activities.

[0032] Multi-source data utilization: The computational framework can accept soil moisture and runoff data separately as needed to calculate the transmission thresholds for meteorological-agricultural and meteorological-hydrological drought, without being limited to identifying only one type of transmission threshold. Furthermore, it can obtain a richer set of transmission thresholds for different types of drought by calculating other types of drought indices.

[0033] Monthly precipitation and soil moisture data are used to calculate the transmission threshold of meteorological-agricultural drought, while monthly precipitation and runoff (or runoff depth) data are used to calculate the transmission threshold of meteorological-hydrological drought.

[0034] Assuming that monthly precipitation and soil moisture data for a certain region from 1965 to 2024 (a total of 60 years) are obtained, the method proposed in this invention can be used to obtain the dynamic change process of the transmission threshold from meteorological drought to different levels of agricultural drought in each month during different periods. Figure 2 This paper demonstrates the dynamic changes in the meteorological-agricultural drought transmission thresholds at different levels calculated using this method. Users can select specific months or all months for plotting, using May as an example. The dynamic changes in the drought transmission thresholds at different levels are clearly observed, and accurate transmission thresholds for each period are provided. This solves the problems of existing methods failing to automatically identify different levels of drought transmission thresholds and failing to dynamically identify changes in transmission thresholds.

[0035] The implementation steps are as follows Figure 3 As shown, they are: data input Sliding window splitting Multiscale standardized drought index calculation Copula Joint Distribution Construction Bayesian Network Iteration Dynamic threshold output. In actual use, the final result can be obtained simply by inputting data.

[0036] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0037] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic drought propagation threshold identification method, characterized in that, include: Acquire basic time series data, and segment the basic time series data using a sliding window of preset window length to obtain segmented data for each window; Calculate the meteorological drought index sequence within a preset continuous time scale and the soil moisture index sequence on a one-month scale for the segmented data. The correlation coefficient between each meteorological drought index sequence and the soil moisture index sequence on a one-month time scale is calculated based on the Pearson correlation coefficient. The preset time scale corresponding to the maximum correlation coefficient is the drought transmission time. The drought transmission threshold is calculated based on the drought transmission time, the soil moisture index on a monthly scale, the joint probability distribution, and a first-order Bayesian network. The joint probability distribution specifically includes: Constructing SPI using Copula functions The joint probability distribution function with SSMI-1 and the least squares Euclidean distance are calculated. For drought propagation time, SPI- For time scale The meteorological drought index, SSMI-1 is a monthly soil moisture index series; the Copula function corresponding to the least squares Euclidean distance is chosen as SPI- The best-fit function and joint distribution function with SSMI-1 are as follows: ； in, and They represent SPI- The marginal distribution function of SSMI-1, where C is the optimal Copula function. and Let be any real number; The calculation of the drought transmission threshold specifically includes: Preset the upper limit value y of agricultural drought at different levels and the initial meteorological drought range. Upper limit of the interval Set to -0.5, lower limit Set to -0.6, iterate over the interval at intervals of -0.1 according to the following formula, obtaining a probability value P in each iteration. Stop iterating when P first equals or exceeds the judgment probability threshold; obtain the value at this time. and The mean value is the drought transmission threshold from meteorological drought to agricultural drought. If the lower limit is... If P still does not exceed the decision probability threshold when updated to -10, the iteration stops immediately, which is considered as no propagation behavior has occurred, and the propagation threshold does not exist. ； In the formula, X is SPI- Y is SSMI-1. For SPI- Joint probability distribution with SSMI-1, and They represent SPI- The marginal distribution function of SSMI-1, C is the optimal Copula function.

2. A dynamic drought transmission threshold identification system, employing the dynamic drought transmission threshold identification method described in claim 1, characterized in that, include: The data segmentation module acquires time-series basic data, segments the time-series basic data through a sliding window of preset window length, and acquires the segmented data for each window. The index calculation module calculates the meteorological drought index sequence within a preset continuous time scale and the soil moisture index sequence on a one-month scale for the segmented data. The drought propagation time acquisition module calculates the correlation coefficient between each meteorological drought index sequence and the soil moisture index sequence on a one-month scale within a preset continuous time scale based on the Pearson correlation coefficient, and obtains the preset time scale corresponding to the maximum correlation coefficient as the drought propagation time. The drought transmission threshold calculation module calculates the drought transmission threshold based on the drought transmission time, the soil moisture index on a monthly scale, the joint probability distribution, and a first-order Bayesian network. The joint probability distribution in the transmission threshold calculation module specifically includes: Constructing SPI using Copula functions The joint probability distribution function with SSMI-1 and the least squares Euclidean distance are calculated. For drought propagation time, SPI- For time scale The meteorological drought index, SSMI-1 is a monthly soil moisture index series; the Copula function corresponding to the least squares Euclidean distance is chosen as SPI- The best-fit function and joint distribution function with SSMI-1 are as follows: ； in, and They represent SPI- The marginal distribution function of SSMI-1, where C is the optimal Copula function. and Let be any real number; The calculation of the drought transmission threshold in the transmission threshold calculation module specifically includes: Preset the upper limit value y of agricultural drought at different levels and the initial meteorological drought range. Upper limit of the interval Set to -0.5, lower limit Set to -0.6, iterate over the interval at intervals of -0.1 according to the following formula, obtaining a probability value P in each iteration. Stop iterating when P first equals or exceeds the judgment probability threshold; obtain the value at this time. and The mean value is the drought transmission threshold from meteorological drought to agricultural drought. If the lower limit is... If P still does not exceed the decision probability threshold when updated to -10, the iteration stops immediately, which is considered as no propagation behavior has occurred, and the propagation threshold does not exist. ； In the formula, X is SPI- Y is SSMI-1. For SPI- Joint probability distribution with SSMI-1, and They represent SPI- The marginal distribution function of SSMI-1, C is the optimal Copula function.

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