Regional drought and flood sudden turn disaster monitoring method with balanced sudden turn rate and sudden turn degree

Abstract

The invention discloses a regional drought and flood sudden turn disaster monitoring method with balanced sudden turn rate and sudden turn degree. The method comprises the following steps: acquiring soil humidity and rainfall data, calculating standardization indexes SSI and SPI, performing moving average to obtain SPIM and SSIM, and identifying grid drought and flood attributes; determining the number b of ahead days through the lead-lag regression of the mean values of the two regions, screening drought and flood transfer grid points, and counting regional drought and flood transfer events; taking the date with the maximum drought and flood transfer grid points as a reference date, obtaining a change slope through difference, and calculating a multiple parameter a; determining a drought-to-waterlogging event occurrence day, synthesizing sequences before and after the occurrence day, and calculating a sudden rotation speed rate DFAAV and a sudden rotation degree DFAAM; and performing normalization summation on the two to obtain a drought and flood sudden change index DFAAI, and screening drought and flood sudden change events. According to the method, the accuracy and reliability of drought and flood sudden change event monitoring can be remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of meteorological disaster monitoring, and particularly relates to a regional drought-flood rapid transition disaster monitoring method with balanced rapid transition rate and rapid transition degree. BACKGROUND

[0002] Under the background of global warming, the frequency, intensity and spatial range of drought-flood rapid transition events have significantly increased. As a complex disaster that suddenly changes from extreme drought to extreme flood in a short time, drought-flood rapid transition has a greater impact on water safety, food production and social economy than a single extreme event, especially on crop yield in areas with weak drought and flood drainage capacity. However, existing indexes are calculated mainly based on precipitation data, ignoring the assessment of soil moisture on drought degree, which leads to misjudgment of the event. Therefore, it is of great significance to develop a monitoring method for drought-flood rapid transition.

[0003] Accurate identification of drought and flood processes is the basis for monitoring drought-flood rapid transition events. The essence of drought-flood rapid transition is a hydrological-meteorological rapid transition process involving multi-time scale interaction. One of its complexities lies in the inherent difference in physical properties between drought and flood stages. Drought is a slowly changing soil moisture deficit caused by persistent lack of precipitation, while flood is caused by the sudden outbreak of heavy precipitation. Previous studies have mainly monitored drought-flood rapid transition events based on precipitation data, generally ignoring the background of soil moisture and its synergistic effect with subsequent precipitation, which makes it difficult to fully reveal the systematic mechanism of the transition from hydrological drought to flood. Therefore, incorporating soil moisture data into the drought-flood rapid transition index can enhance the accuracy of event identification.

[0004] Considering the importance of soil moisture in drought monitoring, existing technologies have gradually incorporated it into the identification of drought-flood rapid transition events. For example, soil moisture departure percentage is used to determine drought-flood status, or a coupling model of soil moisture and precipitation is constructed for drought-flood rapid transition prediction. These methods have improved the weight of soil moisture in event identification. However, most current inventions are still limited to single-factor identification based on soil moisture or precipitation, while methods involving double-factor coupling mainly focus on prediction models, and there is still a lack of a multi-factor drought-flood rapid transition identification method suitable for climate characteristic analysis, model evaluation and mechanism diagnosis in scientific research scenarios.

[0005] The current drought-flood abrupt change index still has certain limitations in intensity evaluation. The drought-flood abrupt change degree and the abrupt change rate, as important components of the drought-flood abrupt change index, have been improved by various methods to improve the accuracy of the two. For example, some studies use dynamic optimization methods to quantify the cumulative effect of the mean state difference and the state recovery rate in the sliding window to reflect the actual intensity of the asymmetric drought-flood abrupt change event. Another technology optimizes the traditional drought-flood abrupt change index by assigning different weight coefficients to different events, or by setting a conversion coefficient and a fixed weight mechanism to regulate the influence of the abrupt change rate in the overall index. Although these methods have improved the weight setting of the abrupt change degree and rate to some extent, it is still difficult to achieve a full balance between the two in the evaluation system, and there is still room for optimization in weight allocation. Especially in practical applications, the importance of the abrupt change rate is often underestimated, leading to biased disaster risk assessment. With the increasing extremity of the event, a higher abrupt change rate often means a more dramatic change in the drought-flood state, which can quickly break through the buffering capacity of the hydrological system, exacerbate the disaster pressure on infrastructure, physiological stress on crops, and degradation of ecological system stability, thereby significantly amplifying the disaster of the event.

[0006] In addition, given that drought-flood abrupt change events are increasingly characterized by short duration and high frequency, the scientific selection of spatial and temporal scales is increasingly critical to their identification and determination. For short-period drought-flood abrupt change events, in terms of spatial range, the existing index is calculated using the regional average method, which has limitations in terms of range and subjectivity, leading to misjudgment due to different locations of drought and flood.

[0007] Therefore, it is of great scientific significance to develop a new short-period drought-flood abrupt change index based on soil moisture and precipitation, balanced consideration of abrupt change rate and degree, and suitable for regional scale identification, to improve the identification ability of the event, deepen the mechanism research, and improve the prediction accuracy of the disaster. SUMMARY

[0008] In view of the limitations in the prior art, such as the single factor used to calculate the drought-flood abrupt change index leading to biased identification of the drought process, the inconsistent weight of the abrupt change rate and degree, and the large range or strong subjectivity in the event region division, the present application proposes a regional drought-flood abrupt change disaster monitoring method with balanced abrupt change rate and degree, based on soil moisture and precipitation, which can effectively represent the drought and flood states, and by balancing the consideration of the abrupt change rate and degree, a short-period drought-flood abrupt change index suitable for regional scale identification is constructed, which significantly improves the accuracy and reliability of drought-flood abrupt change event monitoring.

[0009] To achieve the above technical purposes, the technical scheme adopted by the present application is as follows:

[0010] A method for monitoring regional drought-flood transition disasters with a balance between the rate and severity of rapid transition, the method comprising the following steps:

[0011] S1: Obtain daily soil moisture data and precipitation data of the study area during the study period. Perform weighted processing according to the depth weight of each soil layer in the soil moisture data, and calculate the standardized soil moisture index SSI and standardized precipitation index SPI respectively.

[0012] S2, the standardized soil moisture index (SSI) and standardized precipitation index (SPI) of the study area were processed by moving average to obtain the SSIM and SPIM sequences of the study area, and the drought and flood attributes of each grid point in the corresponding moving window were identified.

[0013] S3. Select the regional average values ​​of the SSIM and SPIM sequences in the study area and perform lead-lag regression to obtain the number of days with the largest correlation coefficient, b. Combine the number of days with the lead to analyze the drought and flood attribute change patterns of each grid point in the study area and screen out the drought and flood transition grid points in the study area.

[0014] S4. During the study period, the identified drought-flood transition points are statistically analyzed to screen out regional drought-flood transition events and their corresponding consecutive dates within the study area.

[0015] S5. Taking the date with the most drought-flood transition points within each consecutive date range as the base date, the mean SPIM and mean SSIM values ​​for each drought-flood transition point within M days before and after the base date are taken, forming a mean sequence for each. The slopes of the two mean sequences, Diff(SPIM) and Diff(SSIM), are obtained by forward differencing, and the ratio of their maximum values ​​is selected as the multiplier parameter. :

[0016] ;

[0017] S6. Taking the first date before the base date where Diff(SPIM) is greater than zero as the occurrence date of the drought-flood transition event, the SPIM and SSIM values ​​of each drought-flood transition event are synthesized N days before and after the occurrence date to obtain the SPIM sequence and SSIM sequence for each drought-flood transition event, combined with the multiplier parameter. The rapid turning rate was calculated. :

[0018] ;

[0019] In the formula, j = 1, 2, ..., b, where b is the number of days leading the SSIM and SPIM correlation coefficients. and These represent the SPIM values ​​for day i and day i+1, respectively. and Let i and j represent the SSIM values ​​for day i+j and day i+j+1, respectively. The index i is the index of the day the drought-flood transition event occurred, and the index j is the index of the number of days preceding the event. , , and All are timestamps; Within the allowed range of values ​​for variable j, calculate the expression. The maximum value;

[0020] For the SSIM and SPIM sequences of each event, the difference between their respective maximum and minimum values ​​is taken as the degree of abrupt change. The calculation formula is as follows:

[0021] ;

[0022] S7. After calculating the rate and degree of abrupt change for all events, normalize them separately and sum them to obtain the short-cycle drought-flood abrupt change index. According to the short-cycle drought-flood transition index Select rapid drought-flood transition events from drought-flood transition events.

[0023] Furthermore, in step S1, the calculation process of the standardized precipitation index (SPI) includes the following steps:

[0024] S11, calculate the 5-day sliding cumulative precipitation from the daily precipitation data for the study period. ;

[0025] S12, the parameter estimation is performed using the maximum likelihood estimation method, and the calculation formula is as follows:

[0026] ;

[0027] ;

[0028] in, Let n be the mean of the cumulative precipitation series, and n be the sample size. For shape parameters, A is the scale parameter, and A is the first intermediate variable used to calculate the shape parameter;

[0029] S13, Calculate the sliding cumulative precipitation probability density function The calculation formula is as follows:

[0030] ;

[0031] in, It is the Gamma function;

[0032] S14, for a given sliding cumulative precipitation The cumulative probability of its Gamma distribution is:

[0033] ;

[0034] In the formula, t represents the integration variable. Let x be the cumulative distribution function of the sliding cumulative precipitation; total cumulative probability. for:

[0035] ;

[0036] The proportion of samples with zero precipitation in the total sample was... , This represents the number of samples where precipitation is zero.

[0037] Total cumulative probability The Z-value, converted to a standard normal distribution, is calculated using the following formula:

[0038] make , when hour:

[0039] ;

[0040] ;

[0041] when hour:

[0042] ;

[0043] ;

[0044] The constant term takes the following values: , , , , , p represents the total cumulative probability. The second intermediate variable when converting to the Z-value of a standard normal distribution.

[0045] Step S2 further includes:

[0046] The standardized soil moisture index (SSI) and standardized precipitation index (SPI) of the study area were subjected to N1-day and N2-day moving averages, respectively, to obtain the SSIM and SPIM sequences of the study area.

[0047] Grid points with SSIM values ​​less than or equal to a preset humidity threshold for N1 consecutive days are identified as drought, and grid points with SPIM values ​​greater than or equal to a preset rainfall threshold for N2 consecutive days are identified as floods.

[0048] Step S3 further includes:

[0049] The regional average values ​​of the SSIM and SPIM sequences in the study area were selected for lead-lag regression to obtain the number of days b with the highest correlation coefficient.

[0050] If a grid point's attribute changes from drought to flood within M days, and the SSIM value on day b+1 after the change to flood attribute is greater than a preset humidity threshold, then the grid point is identified as a drought-to-flood transition grid point.

[0051] Further, in step S4, within the study period and study area, the total number of grid points that will change from drought to flood in the next few days is calculated for each day to obtain a grid point total sequence. The 95th percentile of this grid point total sequence is used as the regional event threshold to filter out all dates that exceed the regional event threshold. Among them, dates that meet the condition of exceeding the regional event threshold for two consecutive days or more are merged into one event to obtain the regional drought-flood transition events and corresponding consecutive dates within the study area.

[0052] Furthermore, in step S7, based on the short-cycle drought-flood transition index... The drought-flood transition events in the study area are ranked, and the drought-flood transition events that are ranked before the preset ranking threshold are identified as drought-flood abrupt transition events.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] First, the regional drought-flood transition disaster monitoring method of the present invention, which balances the rate and degree of rapid transition, introduces a dual-factor synergistic mechanism of soil moisture and precipitation, and first identifies the drought-to-flood process of a single point at the grid scale. Then, through regional threshold determination and quantitative screening of rapid transition rate and degree, a complete technical route for identifying drought-flood transition events is constructed, thereby significantly improving the accuracy and reliability of drought-flood transition event monitoring.

[0055] Secondly, the regional drought-flood transition disaster monitoring method of the present invention, which balances the rate and severity of rapid transition, uses a short-cycle drought-flood transition index composed of both the rate and severity of rapid transition, with their weights balanced and unified. This allows for a more accurate and effective assessment of the risk level of drought-flood transition events. Unlike traditional methods that use regional averages to calculate the index, the regional threshold and the final screening threshold for drought-flood transition events in this invention can be flexibly adjusted according to the actual climate and geographical characteristics of different regions. Therefore, it has stronger regional adaptability and ensures the comprehensiveness and objectivity of event screening.

[0056] Third, the regional drought-flood transition disaster monitoring method of the present invention, which balances the rate and degree of rapid transition, identifies drought-flood transition events that exhibit significant regional characteristics, providing new insights into the mechanism of drought-flood transition. At the same time, this framework can also be extended to the identification of drought-flood transition events, thereby providing reliable technical support for regional climate change monitoring, disaster formation mechanism analysis, and comprehensive risk assessment. Attached Figure Description

[0057] Figure 1 This is a flowchart of the regional drought-flood transition disaster monitoring method that balances the rate and degree of rapid change according to the present invention.

[0058] Figure 2 A schematic diagram of the leading and lagging regression coefficients of SSIM and SPIM in the Yangtze River Basin;

[0059] Figure 3 A schematic diagram showing the frequency distribution of drought-to-flood transitions in the Yangtze River Basin from 1961 to 2020;

[0060] Figure 4 Composite curves of SPIM and SSIM slopes for 167 drought-to-flood events;

[0061] Figure 5 A schematic diagram showing the temporal distribution of drought-to-flood events in the Yangtze River Basin;

[0062] Figure 6 This is a schematic diagram showing the temporal distribution of rapid shifts between drought and flood in the Yangtze River Basin. Detailed Implementation

[0063] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0064] This invention discloses a method for monitoring regional drought-flood transition disasters that balances the rate and degree of rapid change. The method includes the following steps:

[0065] S1: Obtain daily soil moisture data and precipitation data of the study area during the study period. Perform weighted processing according to the depth weight of each soil layer in the soil moisture data, and calculate the standardized soil moisture index SSI and standardized precipitation index SPI respectively.

[0066] S2, the standardized soil moisture index (SSI) and standardized precipitation index (SPI) of the study area were processed by moving average to obtain the SSIM and SPIM sequences of the study area, and the drought and flood attributes of each grid point in the corresponding moving window were identified.

[0067] S3. Select the regional average values ​​of the SSIM and SPIM sequences in the study area and perform lead-lag regression to obtain the number of days with the largest correlation coefficient, b. Combine the number of days with the lead to analyze the drought and flood attribute change patterns of each grid point in the study area and screen out the drought and flood transition grid points in the study area.

[0068] S4. During the study period, the identified drought-flood transition points are statistically analyzed to screen out regional drought-flood transition events and their corresponding consecutive dates within the study area.

[0069] S5. Taking the date with the most drought-flood transition points within each consecutive date range as the base date, the mean SPIM and mean SSIM values ​​for each drought-flood transition point within M days before and after the base date are taken, forming a mean sequence for each. The slopes of the two mean sequences, Diff(SPIM) and Diff(SSIM), are obtained by forward differencing, and the ratio of their maximum values ​​is selected as the multiplier parameter. :

[0070] ;

[0071] S6. Taking the first date before the base date where Diff(SPIM) is greater than zero as the occurrence date of the drought-flood transition event, the SPIM and SSIM values ​​of each drought-flood transition event are synthesized N days before and after the occurrence date to obtain the SPIM sequence and SSIM sequence for each drought-flood transition event, combined with the multiplier parameter. The rapid turning rate was calculated. :

[0072] ;

[0073] In the formula, j = 1, 2, ..., b, where b is the number of days leading the SSIM and SPIM correlation coefficients. and These represent the SPIM values ​​for day i and day i+1, respectively. and Let i and j represent the SSIM values ​​for day i+j and day i+j+1, respectively. The index i is the index of the day the drought-flood transition event occurred, and the index j is the index of the number of days preceding the event. , , and All are timestamps; Within the allowed range of values ​​for variable j, calculate the expression. The maximum value;

[0074] For the SSIM and SPIM sequences of each event, the difference between their respective maximum and minimum values ​​is taken as the degree of abrupt change. The calculation formula is as follows:

[0075] ;

[0076] S7. After calculating the rate and degree of abrupt change for all events, normalize them separately and sum them to obtain the short-cycle drought-flood abrupt change index. According to the short-cycle drought-flood transition index Select rapid drought-flood transition events from drought-flood transition events.

[0077] See Figure 1 The monitoring method of the present invention specifically includes the following steps (the specific number of days in the following embodiments is only for the purpose of illustrating the principle of the scheme. In practical applications, it can be set according to the geographical environment and climate characteristics of the study area):

[0078] 1) Data Acquisition

[0079] Acquire daily soil moisture and precipitation data for a desired range within a specified time period, and perform weighted processing based on the depth weights of each soil layer in the soil moisture data. Both datasets must have the same resolution.

[0080] 2) Calculate the standardized soil moisture index and the standardized precipitation index.

[0081] Based on the daily soil moisture and precipitation data of the study area obtained in step 1), the standardized soil moisture index (SSI) and standardized precipitation index (SPI) are calculated respectively. Taking the calculation of SPI as an example, the calculation method of SSI is the same as that of SPI.

[0082] 2.1) Calculate the 5-day moving cumulative precipitation for the daily precipitation data during the study period. .

[0083] 2.2) Perform Gamma distribution fitting. First, estimate the parameters using the maximum likelihood estimation method. The calculation formula is as follows:

[0084] (1)

[0085] (2)

[0086] in, Let n be the mean of the cumulative precipitation series, and n be the sample size. For shape parameters, Let A be the scale parameter, and let A be the first intermediate variable used to calculate the shape parameter.

[0087] 2.3) Calculate the sliding cumulative precipitation probability density function The calculation formula is as follows:

[0088] (3)

[0089] in, This is the Gamma function.

[0090] 2.4) Calculate the cumulative probability. For a given sliding cumulative precipitation... The cumulative probability of its Gamma distribution is:

[0091] (4)

[0092] Considering that precipitation may be zero, the total cumulative probability for:

[0093] (5)

[0094] The proportion of samples with zero precipitation in the total sample was... , This represents the number of samples where precipitation is zero.

[0095] 2.5) Standardization Conversion

[0096] Total cumulative probability The Z-value, or SPI value, is converted to a standard normal distribution. The probability integral transformation here uses the standard method recommended by the World Meteorological Organization, as shown in the following formula:

[0097] make ,when hour:

[0098] (6)

[0099] (7)

[0100] when hour:

[0101] (8)

[0102] (9)

[0103] The constant term takes the following values: , , , , , p represents the total cumulative probability. The second intermediate variable when converting to the Z-value of a standard normal distribution.

[0104] 3) Screening of single-point drought-to-flood transition processes

[0105] To effectively suppress high-frequency fluctuations in the daily sequences and highlight abrupt shifts between drought and flood, 5-day and 15-day moving averages were applied to the SPIM and SSIM sequences, respectively, to obtain smoothed SPIM and SSIM sequences. At each grid point, SSIM values ​​less than or equal to -0.5 for 15 consecutive days were identified as drought, and SPIM values ​​greater than or equal to 0.5 for 5 consecutive days were identified as flood.

[0106] The regional averages of SSIM and SPIM for the study area were selected, and their lead-lag regression coefficients were calculated. The number of days leading with the highest correlation coefficient was [value missing]. If the grid points achieve a transition from drought to flood within 5 days, and the flood begins on the [number]th day... If SSIM > -0.5, it is identified as a single-point drought-to-flood process.

[0107] 4) Screening for regional drought-flood transition events

[0108] Within the study period and region, the total number of grid points that would transition from drought to flood within the next 5 days is calculated for each day. The 95th percentile of this grid point count sequence is used as the regional event threshold, and all dates exceeding the threshold are selected. Dates that meet the regional event threshold condition for two or more consecutive days are merged into a single event.

[0109] 5) Determine the turning point date

[0110] Within each consecutive date range, using the date with the most drought-to-flood grid points as the base date, calculate the mean values ​​of SPIM and SSIM for the 20 days before and after the occurrence of the drought-to-flood grid point within the next 5 days for all events. This yields two sequences, each of length 41. The forward difference between the two sequences represents the slope of the changes in SPIM and SSIM. and Select The first date with a value greater than zero before the base date is taken as the date of occurrence of the drought-to-flood event, i.e., day 0.

[0111] 6) Construct a new short-cycle drought-flood transition index

[0112] New short-cycle drought-flood rapid transition index It consists of the abrupt change rate and the abrupt change severity. First, the SPIM and SSIM values ​​for each event are synthesized 20 days before and after the drought-to-flood grid point, resulting in the SPIM and SSIM sequences for each event. These sequences are used to calculate the abrupt change rate and severity for each event. Based on the synthesized values ​​of all events calculated in step 5),... and For sequences, the ratio of the maximum values ​​of the two sequences is taken as the multiplier parameter. Rapid turning rate and multiple parameters The calculation formula is as follows:

[0113] (10)

[0114] (11)

[0115] In the formula, j = 1, 2, ..., b; then, the degree of abrupt change is determined. For each SSIM and SPIM sequence of length 41, the difference between their maximum and minimum values ​​is taken as the degree of abrupt change. The calculation formula is as follows:

[0116] (12)

[0117] 7) Screening for sudden shifts between drought and flood events

[0118] After calculating the rate and degree of abrupt change for all events, the two are normalized separately and then summed to obtain the novel short-cycle drought-flood abrupt change index. Events ranking in the top 50% of the drought-flood abrupt change index are considered drought-flood abrupt change events.

[0119] The following example, using the defined DFAAI to screen short-cycle drought-flood transition events in the Yangtze River basin, further illustrates the specific implementation steps and parameter settings of this invention with reference to the accompanying drawings, ensuring that those skilled in the art can fully reproduce it. See the technical flowchart below. Figure 1 .

[0120] 1) The Yangtze River Basin (24°–36°N, 90°–123°E) was selected as the study area. Hourly soil moisture data from 1961 to 2020 were downloaded from the European Centre for Medium-Range Weather Forecasts (ECMWF) 5th Generation Atmospheric Reanalysis dataset. The depth of the first three layers (0-100cm) was selected. , , The spatial resolution is 0.25°×0.25°. Precipitation data is from the China Meteorological Administration's CN05.1 gridded daily dataset, also with a spatial resolution of 0.25°×0.25°. Soil moisture data is processed into daily data and weighted according to the depth of each soil layer. .

[0121] 2) Extract daily soil and precipitation data for the Yangtze River basin. Using the method described in step 2), calculate the SPI from precipitation data and the SSI from soil data. First, calculate the 5-day moving cumulative precipitation for each grid point from 1961 to 2020. The parameters are calculated based on formulas (1) and (2) in step 2 of the technical solution. and Find the values ​​in formulas (3) and (4). The probability density distribution function and the cumulative probability of the Gamma distribution Then, according to formula (5), Find the total cumulative probability Finally, according to formula (6) Formula (10) is standardized to obtain SPI and SSI.

[0122] 3) To suppress high-frequency disturbances, the SPI and SSI sequences were processed with 5-day and 15-day moving averages, respectively, to obtain smoothed SPIM and SSIM sequences. Drought was defined as SSMI less than or equal to -0.5 for 15 consecutive days, and flooding as SPIM greater than or equal to 0.5 for 5 consecutive days. Lead-lag regression was performed on the regional averages of SSIM and SPIM in the Yangtze River Basin, revealing that SPIM changes lead SSIM changes by 7 days. Figure 2 As shown, that is Therefore, if a grid point achieves a drought-to-flood transition within 5 days, and the SSIM value is greater than -0.5 on the 8th day after the start of the flood, it is identified as a single-point drought-to-flood transition process. Figure 2 The horizontal axis represents the number of days leading / lagging, with negative values ​​indicating leading days and positive values ​​indicating lagging days. The vertical axis represents the linear correlation between the standardized soil moisture mean series (SSIM) and the standardized precipitation mean series (SPIM) under different leading / lagging days. The horizontal dashed line is the baseline within the current statistical day range; above the baseline indicates a positive correlation, and below it indicates a negative correlation. Based on the above determinations, the frequency of drought-to-flood transitions at each grid point within the study area from 1961 to 2020 can be statistically analyzed, and its spatial distribution is shown below. Figure 3 As shown.

[0123] 4) Calculate the total number of grid points in the Yangtze River Basin from 1961 to 2020 that indicate a drought-to-flood transition within the next 5 days. The 95th percentile of this grid point sequence is 163 grid points, which is used as the threshold for regional events in the Yangtze River Basin. All dates exceeding the threshold are selected, and dates with two or more consecutive days are combined into one event, resulting in a total of 167 drought-to-flood transition events.

[0124] 5) Next, within each consecutive date segment, the date with the most drought-to-flood grid points is selected as the baseline date. Within the next 5 days of each baseline date from the 167 events, the SPIM and SSIM sequences for the 20 days before and after the drought-to-flood grid point are synthesized, resulting in SPIM and SSIM sequences of length 41 days each. The forward difference between the two sequences represents the slope of the SPIM and SSIM changes. and ,like Figure 4 As shown, Figure 4 The horizontal axis represents the number of days leading / lagging, and the vertical axis represents the slope of change. The base date is selected. The first date greater than zero is considered the turning point from drought to flood, i.e., day 0. The date distribution of drought-to-flood events in the Yangtze River basin is as follows: Figure 5 As shown, the bar chart represents the number of drought-to-flood events that occurred in the corresponding month, and the curve is the interdecadal trend line of the number of drought-to-flood events in the Yangtze River Basin in each month.

[0125] 6) Next, calculate the abrupt change rate and degree of abrupt change for each event. Select the 5-day period following the turning point of each event, and synthesize the SPIM and SSIM sequences for the 20 days before and after the drought-to-flood grid point, resulting in 167 SPIM and SSIM sequences. Calculate the multiplier parameter according to formula (10) in step 6) of the technical solution. .Will , Substituting the SPIM and SSIM sequences of each event into formulas (11) and (12) yields the abrupt change rate and abrupt change degree for each event. After normalizing the abrupt change rate and abrupt change degree of all events and summing them, a novel short-period drought-flood abrupt change index for each event is obtained. .

[0126] 7) Finally, by selecting the events ranking in the top 50% of the drought-flood abrupt transition index, we can obtain 83 drought-flood abrupt transition events in the Yangtze River Basin from 1961 to 2020. The date distribution of these events is as follows: Figure 6 As shown, the bars represent the number of drought-to-flood abrupt transition events occurring in the corresponding month, and the curve represents the characteristic value of the drought-to-flood abrupt transition index (DFAAI) for the corresponding month, reflecting the intensity level of the drought-to-flood abrupt transition events. The fluctuations of the curve correspond to the intensity changes of drought-to-flood abrupt transition events in each month. Compared to drought-to-flood events, the screened drought-to-flood abrupt transition events are mainly concentrated in May, June, and August, which is consistent with the actual situation in the Yangtze River Basin. Furthermore, the events screened are more comprehensive than those in existing studies, further demonstrating the objectivity and reliability of the method.

[0127] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0128] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for monitoring regional drought-flood transition disasters with a balance between the rate and degree of rapid transition, characterized in that, The method includes the following steps: S1: Obtain daily soil moisture data and precipitation data of the study area during the study period. Perform weighted processing according to the depth weight of each soil layer in the soil moisture data, and calculate the standardized soil moisture index SSI and standardized precipitation index SPI respectively. S2, the standardized soil moisture index (SSI) and standardized precipitation index (SPI) of the study area were processed by moving average to obtain the SSIM and SPIM sequences of the study area, and the drought and flood attributes of each grid point in the corresponding moving window were identified. S3. Select the regional average values ​​of the SSIM and SPIM sequences in the study area and perform lead-lag regression to obtain the number of days with the largest correlation coefficient, b. Combine the number of days with the lead to analyze the drought and flood attribute change patterns of each grid point in the study area and screen out the drought and flood transition grid points in the study area. S4. During the study period, the identified drought-flood transition points are statistically analyzed to screen out regional drought-flood transition events and their corresponding consecutive dates within the study area. S5. Taking the date with the most drought-flood transition points within each consecutive date range as the base date, the mean SPIM and mean SSIM values ​​for each drought-flood transition point within M days before and after the base date are taken, forming a mean sequence for each. The slopes of the two mean sequences, Diff(SPIM) and Diff(SSIM), are obtained by forward differencing, and the ratio of their maximum values ​​is selected as the multiplier parameter. : ； S6. Taking the first date before the base date where Diff(SPIM) is greater than zero as the occurrence date of the drought-flood transition event, the SPIM and SSIM values ​​of each drought-flood transition event are synthesized N days before and after the occurrence date to obtain the SPIM sequence and SSIM sequence for each drought-flood transition event, combined with the multiplier parameter. The rapid turning rate was calculated. : ； In the formula, j = 1, 2, ..., b, where b is the number of days leading the SSIM and SPIM correlation coefficients. and These represent the SPIM values ​​for day i and day i+1, respectively. and Let i and j represent the SSIM values ​​for day i+j and day i+j+1, respectively. The index i is the index of the day the drought-flood transition event occurred, and the index j is the index of the number of days preceding the event. , , and All are timestamps; Within the allowed range of values ​​for variable j, calculate the expression. The maximum value; For the SSIM and SPIM sequences of each event, the difference between their respective maximum and minimum values ​​is taken as the degree of abrupt change. The calculation formula is as follows: ； S7. After calculating the rate and degree of abrupt change for all events, normalize them separately and sum them to obtain the short-cycle drought-flood abrupt change index. According to the short-cycle drought-flood transition index Select rapid drought-flood transition events from drought-flood transition events.

2. The regional drought-flood transition disaster monitoring method with balanced transition rate and degree as described in claim 1, characterized in that, In step S1, the calculation process of the Standardized Precipitation Index (SPI) includes the following steps: S11, calculate the 5-day sliding cumulative precipitation from the daily precipitation data for the study period. ; S12, the parameter estimation is performed using the maximum likelihood estimation method, and the calculation formula is as follows: ； ； in, Let n be the mean of the cumulative precipitation series, and n be the sample size. For shape parameters, A is the scale parameter, and A is the first intermediate variable used to calculate the shape parameter; S13, Calculate the sliding cumulative precipitation probability density function The calculation formula is as follows: ； in, It is the Gamma function; S14, for a given sliding cumulative precipitation The cumulative probability of its Gamma distribution is: ； In the formula, t represents the integration variable. Let x be the cumulative distribution function of the sliding cumulative precipitation; total cumulative probability. for: ； The proportion of samples with zero precipitation in the total sample was... , This represents the number of samples where precipitation is zero. Total cumulative probability The Z-value, converted to a standard normal distribution, is calculated using the following formula: make , when hour: ； ； when hour: ； ； The constant term takes the following values: , , , , , p represents the total cumulative probability. The second intermediate variable when converting to the Z-value of a standard normal distribution.

3. The regional drought-flood transition disaster monitoring method with balanced transition rate and degree as described in claim 1, characterized in that, Step S2 further includes: The standardized soil moisture index (SSI) and standardized precipitation index (SPI) of the study area were subjected to N1-day and N2-day moving averages, respectively, to obtain the SSIM and SPIM sequences of the study area. Grid points with SSIM values ​​less than or equal to a preset humidity threshold for N1 consecutive days are identified as drought, and grid points with SPIM values ​​greater than or equal to a preset rainfall threshold for N2 consecutive days are identified as floods.

4. The regional drought-flood transition disaster monitoring method with balanced transition rate and degree as described in claim 1, characterized in that, Step S3 further includes: The regional average values ​​of the SSIM and SPIM sequences in the study area were selected for lead-lag regression to obtain the number of days b with the highest correlation coefficient. If a grid point's attribute changes from drought to flood within M days, and the SSIM value on day b+1 after the change to flood attribute is greater than a preset humidity threshold, then the grid point is identified as a drought-to-flood transition grid point.

5. The regional drought-flood transition disaster monitoring method with balanced transition rate and degree as described in claim 1, characterized in that, In step S4, within the study period and study area, the total number of grid points that will change from drought to flood in the next few days is calculated for each day to obtain a grid point total sequence. The 95th percentile of this grid point total sequence is used as the regional event threshold to filter out all dates that exceed the regional event threshold. Among them, dates that meet the condition of exceeding the regional event threshold for two consecutive days or more are merged into one event to obtain the regional drought-flood transition events and corresponding consecutive dates within the study area.

6. The regional drought-flood transition disaster monitoring method with balanced transition rate and degree as described in claim 1, characterized in that, In step S7, based on the short-cycle drought-flood transition index... The drought-flood transition events in the study area are ranked, and the drought-flood transition events that are ranked before the preset ranking threshold are identified as drought-flood abrupt transition events.

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