A method, media, and program product for real-time monitoring and assessment of drought and flood disaster severity based on the multi-scale, multi-index DFI index.

Abstract

The application discloses a kind of based on multi-scale multi-index DFI index drought and flood disaster grade intensity real-time monitoring evaluation method, medium and program product, comprising: collect and pre-process station 30 years above daily precipitation and potential evaporation data, form long time series database.Select the historical 10-day rolling precipitation climate average value of station as the annual precipitation, select the historical minimum annual maximum daily potential evaporation of station as the evaporation threshold value.Construct the DFI index considering 10-day precipitation relative anomaly index, 30-day relative humidity index, 90-day weight cumulative precipitation standardized index and 180-day precipitation evaporation standardized index.Determine the drought and flood grade intensity standard according to the typical recurrence period threshold value of DFI, carry out real-time daily drought and flood grade intensity monitoring.The application can realize the real-time monitoring of the grade intensity of drought and flood occurrence, and can also evaluate the historical drought and flood intensity, which is of great significance to improve the ability to prevent and respond to drought and flood disasters.

Description

Technical Field

[0001] This invention belongs to the field of meteorological and hydrological disaster monitoring and assessment, and relates to the real-time monitoring and assessment of drought and flood disaster levels and intensities. Specifically, it involves a method that uses daily precipitation and potential evaporation data to construct a multi-scale, multi-index comprehensive drought and flood index, and establishes a historical comprehensive drought and flood index intensity sample sequence to classify drought and flood levels and intensities through a return period threshold. This method can monitor the occurrence of drought and flood in real time, comprehensively determine the level and intensity of drought and flood, and improve the timeliness and accuracy of real-time drought and flood monitoring and assessment. Background Technology

[0002] Drought and flood are two typical hydro-meteorological disasters that have a wide-ranging impact on agricultural production, the ecological environment, infrastructure, and human life. Meteorological conditions are the primary factor in the occurrence of droughts and floods. Constructing comprehensive indicators based on meteorological elements to monitor the occurrence and determine the intensity of droughts and floods in real time is not only a scientific research issue but also directly affects the accuracy of drought and flood disaster monitoring and assessment. Therefore, establishing methods suitable for real-time monitoring of drought and flood occurrence and determination of their intensity is of great significance.

[0003] Existing technologies for monitoring drought and flood using meteorological elements mainly include precipitation anomaly percentage, standardized precipitation index, standardized precipitation-evaporation index, and relative humidity. These indices all face the challenge of determining the time scale. While short-term time scales like ten-day periods or months can effectively monitor short-term torrential rain and floods and sudden droughts, they are inaccurate for long-term droughts. Using time scales longer than a season is more effective for monitoring prolonged droughts, but less effective for short-term torrential rain and floods or sudden droughts. The existing Comprehensive Meteorological Drought Index (MCI), although derived from multiple scales, is applicable to meteorological drought monitoring, but its effectiveness in monitoring torrential rain and floods is unsatisfactory. Furthermore, when using indices such as precipitation anomaly percentage and relative humidity, comparisons are generally made directly with multi-year climate averages or cumulative potential evaporation, neglecting the seasonal and regional differences in the severity of droughts and floods. If the different thresholds for the impact of precipitation and evaporation on actual droughts and floods in different seasons and climate zones (arid, humid, etc.) are not considered, false drought or flood phenomena may be detected. Additionally, determining the intensity of droughts and floods requires consideration of standards such as typical return periods commonly used in disaster prevention. Therefore, using current conventional drought and flood index methods to simultaneously monitor the occurrence of droughts and floods, and to classify their intensity, has certain shortcomings and limitations.

[0004] In summary, existing methods for determining drought and flood monitoring indices and their intensity levels generally suffer from problems such as a single time scale, a single index composition, index thresholds that do not consider the impact of precipitation and evaporation on actual drought and flood conditions, and unscientific and unreasonable intensity level classifications. These shortcomings make it difficult to accurately and comprehensively reflect drought and flood conditions in different regions and at different time scales, and fail to meet the current needs for refined monitoring and assessment of drought and flood disasters. Therefore, how to objectively select "precipitation thresholds," "evaporation thresholds," and index value corrections based on local climate characteristics and differences in index scales, and how to construct new drought and flood indices by weighting multiple scales and indicators, and then objectively and quantitatively determine their intensity levels based on the return period of drought and floods, in order to improve the timeliness of drought and flood monitoring and the accuracy of intensity level classification, thereby providing scientific support for drought and flood disaster prevention and mitigation, are urgent technical problems that need to be solved. Summary of the Invention

[0005] (a) Purpose of the invention

[0006] To address the aforementioned deficiencies and shortcomings of existing technologies, and to resolve at least one of the technical problems mentioned above and others, the present invention aims to provide a method, medium, and program product for real-time monitoring and assessment of drought and flood disaster intensity based on a multi-scale, multi-index DFI (Flood-Drought Index). This method dynamically selects the "10-day annual precipitation average" and the "30-day potential evaporation threshold" according to the climatic characteristics of different seasons and regions. It innovatively constructs a multi-scale, multi-index weighted comprehensive drought-flood index (DFI) based on the "annual precipitation average," a 10-day (ten-day scale) precipitation anomaly index, a 30-day (monthly scale) new relative humidity index based on the "evaporation threshold," a 90-day (seasonal scale) weighted cumulative precipitation standardized index, and a 180-day (semi-annual scale) precipitation evaporation standardized index, and objectively determines the weight of each index. Finally, it quantifies and determines the DFI drought-flood intensity standard based on the typical return period threshold for drought and flood prevention. This enables real-time monitoring of drought and flood occurrence intensity and can also assess historical drought and flood intensity, providing scientific and accurate drought and flood monitoring and assessment information for flood control and drought relief.

[0007] (II) Technical Solution

[0008] To achieve the objective of this invention and solve its technical problems, the present invention adopts the following technical solution:

[0009] The first objective of this invention is to provide a real-time monitoring and assessment method for drought and flood disaster intensity based on the multi-scale, multi-index DFI index. This method enables more real-time monitoring of drought and flood occurrence using the same index, improving the monitoring, early warning, and prevention capabilities for drought and flood disasters, and providing a scientific basis for mitigating disaster losses. The method, when implemented, includes at least the following steps:

[0010] S100. Collection and preprocessing of station precipitation and potential evaporation data:

[0011] Collect topographic and environmental information of target sites and daily precipitation and potential evaporation observation data for more than 30 years. After preprocessing the raw data, establish a long-term precipitation database.

[0012] S200. Determine the annual precipitation average and evaporation threshold for the target station:

[0013] The multi-year climatic average of the daily rolling 10-day precipitation at the calculation station is used as the annual precipitation value P. av If it is less than 50mm, then take P. av =50mm; then, the maximum potential daily evaporation over the years is calculated, and the minimum value is taken as the evaporation threshold E for that station. pt ;

[0014] S300. Daily rolling 10-day relative precipitation anomaly index for statistical stations:

[0015] Calculate the daily rolling 10-day precipitation R 10 According to PAI 10 =(R 10 -P av ) / P av Calculate the PRI index of daily 10-day rolling relative precipitation anomalies. 10 For humid regions, if PRI 10 <0, correction ;

[0016] S400. Daily 30-day rolling relative humidity index for statistical stations:

[0017] Calculate the daily rolling 30-day precipitation R 30 and daily rolling 30-day potential evaporation E 30 If E 30 <30E pt Take E 30 =30E pt Then press NMI. 30 =(R 30 -E 30 ) / E 30 Obtain the daily 30-day rolling relative humidity index (NMI). 30 For humid regions, if NMI 30 <0, correction ;

[0018] S500. Daily 90-day rolling standardized weighted precipitation index for statistical stations:

[0019] The daily 90-day rolling weighted cumulative precipitation was calculated, with a weighted decay coefficient of 0.9. Then, based on its historical series, the daily 90-day rolling standardized weighted precipitation index (SPI) was calculated. 90w ;

[0020] S600. Daily 180-day rolling standardized precipitation-evaporation index for statistical stations:

[0021] The daily 180-day rolling precipitation and potential evaporation, as well as the difference between them, are calculated. Based on the historical series of this difference, the daily rolling 180-day standardized precipitation-evaporation index (SPEI) is calculated. 180 ;

[0022] S700. Constructing a multi-scale, multi-index comprehensive drought and flood index (DFI):

[0023] based on A multi-scale, multi-index comprehensive drought and flood index (DFI) was constructed, and the weight coefficients a, b, c, and d were optimized and determined based on correlation analysis with historical drought and flood disaster data.

[0024] S800. Determine the criteria for classifying the intensity of drought and flood at the stations:

[0025] By statistically analyzing daily DFI data over the years and using a standardized normal distribution function fitting statistical method, the DFI thresholds corresponding to typical drought return periods and flood return periods are calculated, and the drought and flood level classification standards are determined accordingly.

[0026] S900. Real-time monitoring of drought and flood occurrence levels, intensity, and impacts:

[0027] The daily DFI value of the station is calculated based on real-time meteorological data, and the corresponding drought and flood intensity is determined according to the classification standard, so as to realize the unified real-time monitoring and assessment of drought and flood disasters.

[0028] The second objective of this invention is to provide a computer program product, including computer instructions, which are used to execute the above-mentioned method for real-time monitoring and assessment of drought and flood disaster intensity based on the multi-scale, multi-index DFI index.

[0029] The third objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for real-time monitoring and assessment of drought and flood disaster intensity based on the multi-scale, multi-index DFI index.

[0030] (III) Technical Effects

[0031] Compared with existing technologies, the method, medium, and program product for real-time monitoring and assessment of drought and flood disaster intensity based on multi-scale and multi-index DFI index of the present invention have the following beneficial and significant technical effects:

[0032] In constructing the new drought and flood index, this invention utilizes multiple scales (ten-day, monthly, seasonal, and semi-annual) and four different indices, and performs correlation analysis with actual drought and flood disasters to determine weights for comprehensive analysis. When constructing the precipitation anomaly index and the new humidity index, the "annual precipitation" and "evaporation threshold" are first determined based on the climate characteristics of the stations, then the indices are calculated, and further enhanced and corrected according to climate zones. Finally, drought and flood intensity threshold standards are determined based on the typical return periods commonly used in disaster prevention, and drought and flood intensity levels are classified. This solves the problems of traditional methods, such as the single or unreasonable scale of drought and flood indices, the inability to simultaneously monitor drought and flood, the neglect of seasonal and regional differences in drought and flood occurrence, and the failure to consider the impact of actual disaster prevention standards in determining intensity levels. Therefore, the newly constructed drought and flood index can objectively integrate multiple scales and indicators, and the intensity levels reflect the combined effects of actual prevention and disaster impact. The constructed comprehensive drought and flood index and the determined intensity levels can simultaneously monitor drought and flood occurrence. The correlation between the annual drought and flood index intensity and the actual drought and flood affected area is higher than existing methods, and it has passed the significance level test (…). α =0.01), which can more accurately reflect the impact of drought and flood disasters. Attached Figure Description

[0033] Figure 1 The diagram shows the implementation process of the real-time monitoring and assessment method for drought and flood disaster intensity based on the multi-scale, multi-index DFI index of the present invention.

[0034] Figure 2 The diagram shows a comparison of the standardized changes in the intensity of drought-inducing factors (DFI) and the drought-affected area in Hubei Province, southern China, calculated using the method of this invention.

[0035] Figure 3 The diagram shows a comparison of the standardized changes in the intensity of drought-inducing factors (DFI) and the drought-affected area in Shanxi Province, northern China, calculated using the method of this invention.

[0036] Figure 4 The diagram shows a comparison of the standardized changes in the intensity of the DFI flood-causing factor and the flood-affected area in Hubei Province, southern China, calculated using the method of this invention.

[0037] Figure 5 The diagram shows a comparison of the standardized changes over the years in the intensity of the DFI flood-causing factor and the flood-affected area in Shanxi Province, northern China, calculated using the method of this invention. Detailed Implementation

[0038] This invention aims to provide a method, medium, and program product for real-time monitoring and assessment of drought and flood disaster intensity based on the multi-scale, multi-index DFI index. To better understand this invention, the following embodiments further illustrate its content. The technical solutions in the embodiments of this invention will be described in more detail below with reference to the accompanying drawings. The described embodiments are only some, not all, of the embodiments of this invention. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0039] Example 1: Real-time monitoring and assessment method for drought and flood disaster severity

[0040] like Figure 1 As shown, this invention utilizes a real-time monitoring and assessment method for drought and flood disaster intensity based on the multi-scale, multi-index DFI index. First, it collects pre-processed data on precipitation and potential evaporation from various stations to determine the average annual precipitation value and evaporation threshold. Then, it constructs a multi-scale, multi-index comprehensive drought and flood index using four different scale indices, and determines the weighted comprehensive coefficient through correlation analysis with drought and flood disaster conditions. Finally, it determines the comprehensive drought and flood index threshold based on the typical return period to classify drought and flood intensity levels. The method includes the following steps in its implementation:

[0041] S100. Collection and preprocessing of station topography and daily precipitation data:

[0042] Collect station topographic and environmental data, including at least the station's longitude, latitude, altitude, and the required daily precipitation data for the past 30 years and above, as well as potential evaporation. If no potential evaporation data is available for a station, average temperature and other factors are collected to calculate evapotranspiration as a substitute. Then, perform quality control and preprocessing on the raw data, removing spurious values ​​exceeding climate thresholds and dead values ​​identified during spatiotemporal consistency checks, to establish a reliable long-term precipitation series database.

[0043] Preferably, the collection and preprocessing of precipitation and potential evaporation data at the station includes at least:

[0044] S101. Collect topographic and environmental information of the target site and daily precipitation and potential evaporation observation data, including at least: the longitude, latitude, altitude of the target site and daily precipitation and potential evaporation data for the past 30 years or more;

[0045] S102. The collected daily precipitation and potential evaporation observation data of the stations are preprocessed. The climate threshold check method is used to remove false values ​​in the raw data that exceed the climate threshold. The spatiotemporal consistency check is used to identify and remove dead or wrong values. At the same time, missing data is filled in by interpolation method. The climate threshold is determined by statistical analysis of historical data of the proposed station and its surrounding meteorological stations and dynamically adjusted according to geographical and climatic characteristics.

[0046] S103. If there is no potential evaporation observation data at the target site, the potential evaporation is replaced by evapotranspiration by collecting factors such as average temperature. The evapotranspiration is calculated using methods such as Thornthwaite or FAO Penman-Monteith, referring to the calculation method in Appendix C of the national standard "Meteorological Drought Class" (GB / T 20481-2017).

[0047] More preferably, in step S102 above, the climate threshold is determined as follows: select all long-sequence meteorological observation stations within a 100km radius of the station and with an altitude difference of no more than 100m, calculate the variance of daily precipitation and daily potential evaporation for the station and the selected surrounding long-sequence stations, and take three times the maximum variance of daily precipitation and potential evaporation for all stations plus the climate average as the climate threshold for the station; check the original meteorological data of the station based on the determined climate threshold, and remove false values ​​in the data that exceed the climate threshold.

[0048] S200. Determine the annual precipitation average and evaporation threshold for the station:

[0049] First, based on the preprocessed precipitation observation data, the daily rolling 10-day precipitation for each station is calculated. Then, the climate average over the past 30 years is statistically analyzed to form the daily rolling 10-day precipitation climate average as the annual 10-day precipitation value (i.e., the annual precipitation value P). av If the average precipitation is less than 50 mm, then the annual precipitation value P shall be used. av =50mm.

[0050] Subsequently, based on the preprocessed potential evaporation observation data or the potential evaporation calculated using factors such as average temperature, the daily maximum potential evaporation values ​​that are greater than zero over the past 30 years are sorted from smallest to largest according to the local climate characteristics of the station. The annual daily maximum potential evaporation value with the smallest value is selected as the daily potential evaporation threshold (i.e., evaporation threshold E) for that station. pt Table 1 shows the minimum, maximum, and 30-day evaporation thresholds of the annual maximum potential daily evaporation for representative stations in each region, obtained through statistical analysis.

[0051] Table 1. Historical maximum daily potential evaporation series and 30-day evaporation threshold for representative stations in each region.

[0052]

[0053] S300. Daily rolling 10-day relative precipitation anomaly index for statistical stations:

[0054] The 10-day relative precipitation anomaly index (PAI) is rolled daily at statistical stations. 10 ,include:

[0055] S301. First, based on the preprocessed meteorological observation data, calculate the daily rolling 10-day precipitation. For example, the rolling 10-day precipitation for day j is calculated by adding the precipitation for day j, day j-1, day j-2, ..., day j-9, a total of 10 days. The calculation formula is as follows: In the formula R nj10 R represents the precipitation over the previous 10 days on day j in year n; n is a given year sequence; R d This represents daily precipitation. After calculating the rolling 10-day daily precipitation over the years, the climatic average (multi-year average) of the rolling 10-day daily precipitation is then calculated. The formula is as follows: In the formula R avj Let be the average precipitation over the 10 days preceding day j in year m, where m is greater than or equal to 30 (at least 30 years); n is the year number. Then determine R. avj The magnitude of the value, such as R avj If the precipitation is less than 50.0 mm, then the climatological average precipitation value P for the 10 days preceding day j is the annual average precipitation value. avj =50.0mm, otherwise take P avj =R avj ;

[0056] S302. PAI Based on Mathematical Formulas 10 =(R nj10 -P avj ) / P avj Calculate the rolling 10-day relative precipitation anomaly index (PAI) for day j in a given year. 10 In the formula, PAI 10 R is the 10-day relative precipitation anomaly index for year n and day j; n is the year number; nj10 P represents the precipitation over the previous 10 days on day j in year n. avj This is the climatic average of precipitation over the 10 days preceding day j, i.e., the annual precipitation value.

[0057] S303. For humid regions with a multi-year average annual precipitation ≥800 mm, when the calculated PAI 10 A negative value, i.e., PRI 10 When < 0, an exponentially enhanced correction is applied, i.e.: .

[0058] S400. Daily 30-day rolling new relative humidity index for statistical stations:

[0059] Calculate the daily rolling 30-day precipitation R 30 and daily rolling 30-day potential evaporation E 30 If E 30 <30E pt Take E 30 =30E pt Then press NMI. 30 =(R 30 -E 30 ) / E 30 Calculate the 30-day rolling relative humidity index (NMI). 30 For humid regions, if NMI 30 <0, correction Specifically, when calculating the daily 30-day rolling relative humidity index for statistical sites, at least the following should be included:

[0060] S401. Based on preprocessed meteorological observation data, calculate the daily rolling 30-day precipitation and potential evaporation, using the following formulas: and In the formula, R nj30 E represents the precipitation over the 30 days preceding day j in year n. nj30 R represents the potential evaporation over the 30 days preceding day j in year n, where n is a given year sequence. d E represents daily precipitation. d Daily potential evaporation;

[0061] S402. Based on the evaporation threshold E obtained in step S200 pt Multiply by 30 to get the 30-day evaporation threshold;

[0062] S403. Discrimination E nj30 The value is such that it is less than 30 times the evaporation threshold E. pt Then the potential evaporation E in the 30 days prior to day j nj30 =30*E pt ;

[0063] S404. NMI Based on Mathematical Formula 30 =(R nj30 -E nj30 ) / E nj30 Calculate the 30-day rolling new relative humidity index (NMI) for day j of a certain year. 30 ;

[0064] S405. For humid regions with an average annual precipitation ≥800 mm, when the calculated NMI 10 A negative value, i.e., NMI 30 When < 0, an exponentially enhanced correction is applied, i.e. .

[0065] S500. Daily 90-day rolling standardized weighted precipitation index for statistical stations:

[0066] Calculate the daily 90-day rolling weighted cumulative precipitation, and then calculate the daily 90-day rolling standardized weighted precipitation index (SPI) based on its historical time series. 90w Specifically, when calculating the daily 90-day rolling standardized weighted precipitation index for statistical stations, at least the following should be included:

[0067] S501. Based on the preprocessed precipitation data, first calculate the daily 90-day rolling weighted cumulative precipitation. For example, the 90-day rolling weighted cumulative precipitation for day j is the precipitation for day j multiplied by a. 0 Daily precipitation multiplied by a (j-1) 1 ,j-2 day precipitation multiplied by a 2 ..., J-89 day precipitation multiplied by a 89 Then, the precipitation amounts with the aforementioned weighted attenuation are summed, and the calculation formula is as follows: R nj90w Let R be the cumulative precipitation over 90 days, with daily weight decay over n years and j days, where a is the weight decay coefficient (taken as 0.9). d Daily precipitation;

[0068] S502. After obtaining the historical series of daily 90-day rolling weighted cumulative precipitation, the 90-day rolling standardized weighted precipitation index (SPI) can be calculated by referring to the calculation method in Appendix D of the national standard "Meteorological Drought Classification" (GB / T 20481-2017). 90w .

[0069] S600. Daily 180-day rolling standardized precipitation-evaporation index for statistical stations:

[0070] Calculate the daily 180-day rolling precipitation and potential evaporation, and the difference between them. Based on the historical series of this difference, calculate the daily rolling 180-day standardized precipitation-evaporation index (SPEI). 180 Specifically, when calculating the 180-day standardized precipitation-evaporation index for statistical stations on a daily rolling basis, at least the following should be included:

[0071] S601. Based on the preprocessed precipitation data, calculate the daily rolling 180-day precipitation and potential evaporation, using the following formulas: and R nj180 E represents the precipitation over the previous 180 days, starting from day j in year n. nj180 R represents the potential evaporation over the 180 days preceding day j in year n, where n is the year number. d E represents daily precipitation. dDaily potential evaporation;

[0072] S602. After obtaining the daily rolling 180-day precipitation and potential evaporation, calculate the difference between the daily rolling 180-day precipitation and the daily rolling 180-day precipitation potential evaporation, and establish a historical series.

[0073] S603. Based on the formed calendar year series, and referring to the calculation method in Appendix E of the national standard "Meteorological Drought Classification" (GB / T 20481-2017), the daily rolling 180-day standardized precipitation-evaporation index (SPEI) can be calculated. 180 .

[0074] S700. Constructing a multi-scale, multi-index comprehensive drought and flood index (DFI):

[0075] Based on the daily rolling 10-day relative precipitation anomaly index (PAI) obtained in step S300 10 The daily rolling 30-day new relative humidity index (NMI) obtained in step S400 30 The daily rolling 90-day standardized weighted precipitation index (SPI) obtained in step S500 90w And the daily rolling 180-day standardized precipitation-evaporation index (SPEI) obtained from S700. 180 By weighting and summing the above four indices, a multi-scale, multi-index comprehensive drought and flood index (DFI) is constructed, and its calculation formula is as follows:

[0076]

[0077] In the formula, the weight coefficients a, b, c, and d of each index are obtained through optimal correlation analysis between the regional intensity of DFI in each region and historical drought and flood disaster information, and are respectively taken as a=0.26, b=0.27, c=0.23, and d=0.24. In arid areas with annual precipitation less than 200 mm, drought and flood monitoring is not carried out, the DFI monitoring level is normal, its DFI=0, and the drought and flood intensity is assigned to zero.

[0078] Calculate the drought-inducing factor intensity. Using the method described above, the daily drought index (DFI) for a given station can be calculated. Then, the cumulative drought index values ​​for a given year (i.e., DFI ≤ -0.5) and the absolute value of these values ​​are taken to obtain the drought-inducing factor intensity for that year. From this, the average annual drought-inducing factor intensity for a province (region) can be statistically obtained. Figure 2 , Figure 3 These are schematic diagrams comparing the annual average drought-causing factor intensity and drought-affected area in Hubei Province (southern China) and Shanxi Province (northern China), respectively, after standardization. It can be seen that their changes are basically consistent. The correlation coefficients between the DFI drought-causing factor intensity and the drought-affected area are 0.59 and 0.58, respectively, both passing the significance level. αThe significance test was performed at 0.001 (see Table 3 below).

[0079] Calculate the intensity of flood-causing factors. Using the same method described above, calculate the daily DFI (Flood Index Index) for a given station over the years. Then, the cumulative value of the flood index values ​​(i.e., DFI ≥ 0.5) for a given year is the intensity of the flood-causing factors for that year. From this, the average historical intensity of flood-causing factors for a province (region) can be statistically obtained. Figure 4 , Figure 5 These are schematic diagrams comparing the annual average intensity of flood-causing factors and the standardized changes in flood-affected area in Hubei Province (southern China) and Shanxi Province (northern China). It can be seen that their changes are basically consistent; the correlation coefficients between the DFI flood-causing factor intensity and the flood-affected area are 0.72 and 0.49, respectively, both passing the significance level. α The significance test was performed at 0.001 (see Table 4 below).

[0080] S800. Determine the criteria for classifying the intensity of drought and flood at the stations:

[0081] Historical data of the daily comprehensive drought and flood index (DFI) were compiled. Referring to the return period threshold standard commonly used by flood control and drought relief departments, and employing a standardized normal distribution function fitting statistical method, the typical drought return period (T) was calculated. d =3, 5, 10, 20, 50 years) and flood recurrence interval (T f The comprehensive drought and flood index thresholds corresponding to 3, 5, 10, 20, and 50 years are shown in Table 2 below. Then, drought and flood levels are divided into 11 levels: extreme drought I, severe drought II, moderate drought III, mild drought IV, slight drought V, slight flood V, mild flood IV, moderate flood III, severe flood II, extreme flood I, and normal. The severity of drought and flood is reflected by the magnitude of the DFI value. The intensity of each drought and flood level is assigned an integer value from -5 to 5 as the drought and flood level intensity. The negative value is the drought level, and the smaller the value, the more severe the drought. The positive value is the flood level, and the larger the value, the more severe the flood. Zero is the normal level, i.e., no drought or flood.

[0082] S900. Real-time monitoring of drought and flood occurrence levels, intensity, and impacts:

[0083] Based on preprocessed meteorological data and the constructed multi-scale, multi-index comprehensive drought and flood index (DFI), the daily DFI values ​​for each station are calculated in real time. Then, using the aforementioned drought and flood intensity classification standards, the daily drought and flood intensity of each station can be objectively determined in real time, thereby enabling real-time monitoring of drought and flood occurrence intensity and assessment of their potential impact.

[0084] Table 2. Classification of Comprehensive Drought and Flood Levels and Intensities and Potential Impacts

[0085]

[0086] Example 2: Effect Verification

[0087] Representative provinces (regions) from different regions across China were selected: Heilongjiang in Northeast China, Shanxi in North China, Ningxia in Northwest China, Jiangsu in East China, Hubei in Central China, Yunnan in Southwest China, and Guangxi in South China. Historical data on various commonly used drought and flood indices, including the 10-day relative precipitation anomaly (PA), were statistically analyzed for all stations in each province (region). 10 30-day relative humidity (MI) 30 90-day Standardized Weighted Precipitation Index (SPI) 90w 180-day Standardized Precipitation Evaporation Index (SPEI) 180 And the newly constructed 10-day relative precipitation anomaly index (PAI) 10 30-day New Relative Humidity Index (NMI) 30 The annual drought intensity I of the multi-scale, multi-index comprehensive drought and flood index (DFI) d and annual flood intensity I f Then, the average values ​​of drought-causing factor intensity and flood-causing factor intensity for each province (region) over the years were calculated, and the correlation coefficients between drought-causing factor intensity and drought-affected area in the province (region) were statistically analyzed (see Table 3), as well as the correlation coefficients between flood-causing intensity and flood-affected area in the province (region). The annual drought-causing factor intensity refers to the absolute value of the cumulative values ​​of drought / flood index values ​​≤ -0.5 in a given year; the higher the value, the stronger the drought-causing intensity. The annual flood-causing intensity refers to the cumulative value of drought / flood index values ​​≥ 0.5 in a given year; the higher the value, the stronger the flood-causing intensity.

[0088] Table 3. Correlation coefficients between drought-causing factor intensity and drought-affected area for each index in representative provinces (regions).

[0089]

[0090] Table 3 shows the correlation coefficients between drought intensity and drought-affected area for each index representing the provinces (regions). It can be seen that, using the usual method, the 10-day relative precipitation anomaly PA... 10 and 30-day relative humidity MI 30 The calculated correlation coefficient between drought-causing factor intensity and drought-affected area is compared with the 10-day relative precipitation anomaly index (PAI) of the improved isoscale index of this invention. 10 and 30-day new relative humidity NMI 30 There was no significant difference compared to the previous method; the correlation coefficients between the intensity of drought-inducing factors and the drought-affected area constructed in this invention were all higher than those of the single-scale index (PA). 10 PAI 10 MI 30、 NMI 30 SPI 90w SPEI 180Compared with the Comprehensive Meteorological Drought Index (MCI) recommended by national standards, although the correlation coefficients of Heilongjiang, Jiangsu, Hubei, and Guangxi provinces (regions) were lower than those of the MCI, the correlation coefficients of others were higher. The correlation coefficients of all provinces (regions) in the DFI passed the significance level. α The significance level was 0.001, while MCI only passed the significance test at the 0.001 level in Ningxia. α =0.01 test, therefore the difference between provinces (regions) in DFI is smaller than that in MCI.

[0091] Table 4. Correlation coefficients between flood-causing intensity and flood-affected area for each index in representative provinces (regions).

[0092]

[0093] From Table 4, the correlation coefficients between the intensity of flood-causing factors and the flood-affected area for each index representing the provinces (regions) show that: using the usual method, the 10-day relative precipitation anomaly PA 10 and 30-day relative humidity MI 30 The calculated correlation coefficient between the intensity of flood-causing factors and the flood-affected area is compared with the 10-day relative precipitation anomaly index (PAI) of the improved isoscale index of this invention. 10 and 30-day new relative humidity NMI 30 Compared to the original index, the correlation coefficients of the new index for all provinces (regions) are significantly higher. The correlation coefficients between the intensity of the DFI flood-causing factor and the flood-affected area constructed in this invention, except for Ningxia (0.40), are significant at the statistical level. α= Except for the 0.01 test, all other provinces (regions) passed the significance level. α The correlation coefficient (C=0.001) shows that the DFI is significantly higher than the composite index (MCI). The DFI also correlates with other single-scale indices (PA). 10 PAI 10 MI 30、 NMI 30 SPI 90w SPEI 180 Compared to other regions, the regional PAI varies significantly across provinces (regions), with some exceeding or falling below it, indicating poor stability. For Ningxia in the Northwest region, the PAI... 10 The correlation coefficient was 0.38, and the DFI was 0.40. Only these two indices passed the significance level. α =0.01, and the other indices were all poor. In Hubei, Central China, except for MI... 30 Apart from the extremely poor performance of the MCI and MI indices, all other indices performed very well, passing the significant level. α =0.001 test. Improved NMI in Guangxi, South China. 30 Better than DFI, through a significant level α =0.001 test, all other indices are very poor.

[0094] In summary, the DFI constructed in this invention is superior to other indices in terms of comprehensively reflecting the intensity of drought and flood and the correlation between representative provinces (regions) and drought and flood disasters. It also has good regional stability and can be used to monitor the occurrence of drought and flood in various regions simultaneously.

[0095] The above embodiments are merely the main technical ideas and specific implementations of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for real-time monitoring and evaluation of the intensity of drought and flood disaster levels based on multi-scale multi-index DFI index, characterized in that, Comprise: S100. Collecting daily precipitation and potential evaporation observation data of the target station for more than 30 years, preprocessing the original data, and establishing a long-time sequence precipitation database; S200. Calculate the multi-year average of the 10-day precipitation at the site for each day of the year, as the precipitation climatology P av . If P av < 50 mm, then take P av = 50 mm. Then calculate the maximum potential evaporation for each day of the year, and take the minimum value as the evaporation threshold E pt for the site. S300. Calculate the 10-day rolling daily precipitation R 10 and based on PAI 10 = (R 10 - P av ) / P av calculate its anomaly index PAI 10 For humid regions, if PAI 10 < 0, correct S400. Calculate the daily rolling 30-day precipitation R. 30 and daily rolling 30-day potential evaporation E 30 If E 30 <30E pt Take E 30 =30E pt Then press NMI. 30 =(R 30 -E 30 ) / E 30 Calculate the 30-day rolling relative humidity index (NMI). 30 For humid regions, if NMI 30 <0, correction S500. Calculate the daily 90-day rolling weighted-decay cumulative precipitation, then based on its annual sequence, calculate the daily 90-day rolling standardized-weighted precipitation index SPI 90w ; S600. Calculate daily 180-day rolling precipitation and potential evapotranspiration and the difference between the two, and based on the sequence of the difference over the years, calculate the daily rolling 180-day standardized precipitation evapotranspiration index SPEI 180 ; S700. Based on DFI = a*PAI 10 + b*NMI 30 + c*SPI 90w + d*SPEI 180 The DFI index is constructed, and each weight coefficient a, b, c, and d is determined based on correlation analysis and optimization related to historical drought and flood data. S800. Statistics of DFI daily data of all years, using normal distribution function fitting statistical method to calculate typical drought recurrence period and flood recurrence period and corresponding DFI threshold, and determining the drought and flood grade division standard accordingly; S900. Based on real-time meteorological data, calculate the daily DFI value of the station, and determine the corresponding drought and flood grade intensity according to the grade division standard, realize the real-time monitoring and evaluation of drought and flood disasters.

2. The method for real-time monitoring and evaluation of drought-flood disaster grade intensity based on multi-scale multi-index DFI index according to claim 1, characterized in that, In the above step S100, the collection and preprocessing of precipitation and potential evaporation data of the target station at least includes: S101. Collecting the topographic environment information and daily precipitation and potential evaporation observation data of the target station, including at least: the longitude, latitude, altitude of the target station, and the daily precipitation and potential evaporation data of the target station for more than 30 years; S102. Preprocessing the collected daily precipitation and potential evaporation observation data of the target station, using climate threshold checking method to eliminate false values in the original data exceeding the climate threshold, identifying and eliminating the dead values or wrong values in the data through space-time consistency checking, and completing the missing data by interpolation method, wherein the climate threshold is determined by statistical analysis of the historical data of the target station and its surrounding meteorological stations, and is dynamically adjusted according to geographical and climatic characteristics; S103. If the target station has no potential evaporation observation data, collect average temperature data and calculate the evapotranspiration instead of potential evaporation based on Thornthwaite or other methods.

3. The method for real-time monitoring and evaluation of drought-flood disaster grade intensity based on multi-scale multi-index DFI index according to claim 2, characterized in that, In step S102, the determination method of the climate threshold is: Select all long sequence meteorological observation stations within 100km range of the target station and with altitude difference not more than 100m, calculate the variance of daily precipitation and daily potential evaporation of the target station and the selected surrounding long sequence stations, take 3 times of the maximum variance of daily precipitation and potential evaporation of all stations plus the climate average value as the climate threshold of the station; Based on the determined climate threshold, check the original meteorological data of the station and eliminate the false values in the data exceeding the climate threshold.

4. The method for real-time monitoring and evaluation of drought-flood disaster grade intensity based on multi-scale multi-index DFI index according to claim 1, characterized in that, In the above step S200, when determining the precipitation annual value and evaporation threshold of the target station, at least includes: S201. Based on the long-time sequence precipitation database, statistics of the 10-day rolling daily precipitation of the target station for the past 30 years; S202. Based on the daily rolling 10-day precipitation in previous years, the climate average value in the past 30 years is statistically calculated as the annual precipitation value P of the site av ; S203. Determine the annual precipitation value P of the obtained station av whether it is less than 50 mm, if it is less than 50 mm, then 50 mm is taken as the annual precipitation value P of the station av ; S204. Based on the preprocessed potential evaporation data, statistics of the maximum daily potential evaporation of the target station for the past 30 years and the annual extreme value sequence; S205. The annual extreme value sequence of the station's maximum daily potential evaporation over the years is sorted from small to large, and the historical minimum annual maximum daily potential evaporation is selected as the evaporation threshold E of the station pt .

5. The method for real-time monitoring and evaluation of drought-flood disaster grade intensity based on multi-scale multi-index DFI index according to claim 1, characterized in that, In the step S300, the statistical station calculates the 10-day relative precipitation anomaly index PAI day by day 10 at least comprises: S301. Based on the pre-processed daily precipitation data, the 10-day accumulated precipitation R of each day is calculated in a rolling manner 10 and the corresponding annual precipitation P is obtained based on the statistics of step S200 av ; S302. Calculate the relative precipitation anomaly index PAI based on the mathematical formula 10 = (R 10 -P av ) / P av 10 ;​ S303. For humid regions with a multi-year average annual precipitation ≥ 800 mm, if its PAI 10 is negative, an e exponential enhancement correction is made using the mathematical formula .

6. The multi-scale multi-index DFI index-based real-time monitoring and assessment method for flood and drought disaster grade intensity according to claim 1, characterized in that, In the above step S400, when statistics of the daily 30-day rolling relative humidity index of the target station, at least includes: S401. Based on the pre-processed precipitation and potential evaporation data, calculate the daily rolling 30-day cumulative precipitation R 30 and the daily rolling 30-day cumulative potential evaporation E 30 ; S402. Based on the evaporation threshold E obtained in step S200 pt Multiplication by 30 gives the 30-day evaporation threshold; S403. Determine the daily rolling 30-day cumulative potential evapotranspiration E 30 If E 30 <30E pt then the daily rolling 30-day cumulative potential evapotranspiration E 30 is replaced with the 30-day evaporation threshold 30E pt ; S404. Based on mathematical formula NMI 30 = (R 30 -E 30 ) / E 30 Statistical calculation site daily 30-day rolling relative humidity index NMI 30 ; S405. For humid regions with a multi-year average annual precipitation ≥ 800 mm, if its NMI 30 is negative, the e exponential enhancement correction is made using the mathematical formula .

7. The multi-scale multi-index DFI index-based real-time monitoring and assessment method for flood and drought disaster grade intensity according to claim 1, characterized in that, In the above step S500, when statistics of the daily 90-day rolling standardized weight precipitation index of the target station, at least includes: S501. Based on the preprocessed precipitation data, calculate the daily 90-day rolling weight decay cumulative precipitation, and the weight decay coefficient is 0.9; S502. Calculate the rolling 90-day standardized precipitation index (SPI) using the rolling 90-day 90-day weight-decay cumulative precipitation time series. 90w .

8. The method for real-time monitoring and evaluation of drought-flood disaster grade intensity based on multi-scale multi-index DFI index according to claim 1, characterized in that, In the above step S600, when statistics of the daily rolling 180-day standardized precipitation evaporation index of the target station, at least includes: S601. Based on the pre-processed precipitation and potential evaporation data, calculate the daily rolling 180-day precipitation and the daily rolling 180-day potential evaporation; S602. Statistically calculate the difference between the daily rolling 180-day precipitation and the daily rolling 180-day potential evaporation, and form a daily annual sequence; S603. Based on the formed sequence of daily calendar years, a rolling 180-day standardized precipitation evapotranspiration index SPEI is calculated based on the standardized precipitation evapotranspiration index calculation method 180 .

9. The multi-scale multi-index DFI index-based real-time monitoring and assessment method for flood and drought disaster grade intensity according to claim 1, characterized in that, In the step S700, when constructing the multi-scale and multi-index comprehensive drought and flood index DFI, at least the following steps are included: S701. Based on the daily rolling 10-day relative precipitation anomaly index PAI obtained in step S300 10 ; S702. Based on the daily rolling 30-day new relative moisture index NMI obtained in step S400 30 ; S703. Deriving the daily rolling 90-day standardized precipitation index SPI based on the daily precipitation data of step S500 90w ; S704. Based on the daily rolling 180-day standardized precipitation evapotranspiration index SPEI obtained in step S600 180 ; S705. Based on DFI = a*PAI 10 + b*NMI 30 + c*SPI 90w + d*SPEI 180 The four indexes are weighted and integrated to obtain the multi-scale and multi-index comprehensive drought and flood index DFI. S706. The weight coefficients a, b, c, and d are obtained by performing optimal correlation analysis on the DFI regional intensity and the historical drought and flood disaster information of each region.

10. The method for real-time monitoring and evaluation of drought-flood disaster grade intensity based on multi-scale multi-index DFI index according to claim 1, characterized in that, In the step S800, when determining the drought and flood grade intensity division standard of the station, at least the following steps are included: S801. Based on the DFI index constructed in step S700, statistically calculate the daily DFI index historical data of the years to form a DFI index sequence of the years; S802. Calculate the typical drought return period T using the standardized normal distribution function. d =DFI threshold Z corresponding to 3, 5, 10, 20, and 50 years d3 Z d5 Z d10 Z d20 Z d50 and typical flood recurrence period T f =DFI threshold Z corresponding to 3, 5, 10, 20, and 50 years f3 Z f5 Z f10 Z f20 Z f50 ; S803. Based on the obtained critical recurrence period of drought and flood and the corresponding DFI threshold value, divide the drought and flood grade into 11 grades, which are: Special drought level I, meet T d ≥ 50 years, DFI ≤ Z d50 ; Severe drought, 50 years > T d ≥ 20 years, Z d50 < DFI ≤ Z d20 ; Moderate drought, Class III, 20 years > T d ≥ 10 years, Z d20 < DFI ≤ Z d10 ; Light drought class IV, 0 years > T d ≥ 5 years, Z d10 < DFI < Z d5 ; Micro drought class V, meet 5 years > T d ≥ 3 years, Z d5 < DFI ≤ Z d3 ; Normal level, meet Z d3 DFI < Z f3 ; Microdrought Class V, meet 3 years < T f < 5 years, Z f3 ≤ DFI < Z f5 ; Mild 4, meet 5 years < T f < 10 years, Z f5 < DFI < Z f10 ; Moderate drought level III, meet 10 years ≤ T f < 20 years, Z f10 ≤ DFI < Z f20 ; Heavy flooding class II, meet 20 years < T f < 50 years, Z f20 < DFI < Z f50 ; Class 1, meet 50 years ≤ T f , Z f50 ≤ DFI; S804. In order to facilitate data storage and drawing, according to the size of the DFI value reflecting the severity of drought and flood, the intensity of each drought and flood grade is assigned an integer value of -5 to 5, a negative value indicates drought, the smaller the value, the more serious the drought, a positive value indicates flood, the larger the value, the more serious the flood, zero indicates normal, i.e. no drought and flood; in the drought area with annual precipitation less than 200mm, no drought and flood monitoring is performed, and DFI=0.

11. The multi-scale multi-index DFI index-based real-time monitoring and assessment method for flood and drought disaster grade intensity according to claim 1, characterized in that, In the step S900, real-time monitoring of the drought and flood occurrence grade intensity and impact, at least includes: S901. Based on the pre-processed meteorological data in step S100 and the DFI index constructed in step S700, real-time statistical calculation of the comprehensive drought and flood index DFI of the station every day; S902. Using the drought and flood grade intensity division standard obtained in step S800, real-time calculation and determination of the drought and flood grade and intensity of the station every day, and drawing a drought and flood grade intensity diagram, real-time monitoring of the drought and flood occurrence grade and intensity, and evaluation of the possible impact degree.

12. A computer program product comprising computer instructions, characterized in that, The computer instructions are used to execute the drought and flood disaster grade intensity real-time monitoring and evaluation method based on the multi-scale and multi-index DFI index of any one of claims 1-11.

13. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to realize the drought and flood disaster grade intensity real-time monitoring and evaluation method based on the multi-scale and multi-index DFI index of any one of claims 1-11.

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