A method for quantitative analysis of driving factors of meteorological drought

Abstract

The present invention discloses a method for quantitatively analyzing the driving factors of meteorological drought. By performing an inconsistency test on the standardized rainfall index (SPI) sequence and combining it with an analysis of the impact of human activities, the watershed type is identified. The entire study period is divided into a baseline period and a human activity period. The meteorological drought index of the two periods is analyzed and calculated to obtain the impact of human activities on meteorological drought and the impact of climate factors on meteorological drought, thereby quantitatively distinguishing the impact of the two factors on meteorological drought.

Description

Technical Field

[0001] The present invention relates to the field of hydrological drought prediction, and in particular to a method for quantitatively analyzing meteorological drought driving factors. Background Art

[0002] Meteorological droughts are primarily influenced by changes in natural climate conditions and human activities (such as the greenhouse effect). Analyzing the drivers of meteorological droughts can help mitigate their socioeconomic impacts. Currently, some spatiotemporal analysis methods, such as sensitivity analysis, are used to analyze drought drivers. While simple and convenient, these methods do not quantify the impact of human activities on meteorological droughts or distinguish between the contributions of these two factors. Quantifying the impacts of climate change and human activities on meteorological droughts is a key focus of current hydrological research. Summary of the Invention

[0003] Purpose of the invention: The purpose of the present invention is to provide a method for quantitatively analyzing the driving factors of meteorological drought, which can quantitatively distinguish the impact of climate change and human activities on meteorological drought.

[0004] Technical solution: The method for quantitatively analyzing the driving factors of meteorological drought described in the present invention specifically comprises the following steps:

[0005] S1: Divide the sub-basin and calculate the cumulative probability F(x) of the cumulative rainfall sequence x at different time scales based on the rainfall data of each sub-basin;

[0006] S2: Perform normalized distribution on the cumulative probability F(x) of the cumulative rainfall sequence of each sub-basin on the time scale to obtain the standardized rainfall index SPI sequence of each sub-basin x=(x1,x2,…x n );

[0007] S3: For the standardized rainfall index SPI sequence x=(x1,x2,…x n ) to conduct inconsistency tests. The Pettitt test and the Mann-Kendall test were used to conduct univariate inconsistency tests on the cumulative rainfall series x. If the inconsistency in the test results was significant, it was considered that the watershed was greatly affected by human activities. The test results were used as the first indicator for identifying natural sub-watersheds.

[0008] S4: Calculate the ratio of urban construction land to cultivated land within each sub-basin. This ratio is used as the second indicator for identifying natural sub-basins. The calculation formula is as follows:

[0009]

[0010] Where γ represents the scale factor, A c For urban construction land, Aa is the cultivated land area;

[0011] S5: Calculate the reservoir capacity coefficient within the sub-basin The calculation formula is as follows:

[0012]

[0013] Where V represents the beneficial storage capacity of the reservoir, W is the average runoff into the reservoir over many years, and the existence of a reservoir in the sub-basin and the reservoir capacity coefficient are taken into account. as a third indicator for identifying natural sub-watersheds;

[0014] S6: Population density, nighttime light density, and GDP within each sub-basin are used as the fourth indicator to identify natural sub-basins;

[0015] S7: judging whether the sub-basin is a natural sub-basin or a human-interferenced sub-basin based on the first indicator of the natural sub-basin, the second indicator of the natural sub-basin, the third indicator of the natural sub-basin, and the fourth indicator of the natural sub-basin, and then classifying the sub-basin as a natural sub-basin or a human-interferenced sub-basin at a spatial scale;

[0016] S8: The study period of the sub-basin is divided into a baseline period with climate change as the dominant factor and a human activity period with human activity as the dominant factor. The sub-basin is then divided into a sub-basin in the baseline period and a sub-basin in the human activity period on a temporal scale.

[0017] S9: Analyze the Standardized Precipitation Index (SPI) of each sub-basin to quantitatively distinguish the impacts of human activities and climate change on meteorological drought. Specifically:

[0018] Option 1: Subtract the standardized rainfall index (SPI) of the natural sub-basin and the human-disturbed sub-basin during the baseline period to obtain the direct impact of human activities and climate factors on meteorological drought during the baseline period;

[0019] Option 2: Subtract the standardized rainfall index (SPI) of the natural sub-basin during the human activity period from that of the human-disturbed sub-basin to obtain the direct impact of human activities and climate factors on meteorological drought during the human activity period;

[0020] Option 3: Subtract the standardized rainfall index (SPI) of the natural sub-basin during the baseline period and the human activity period to obtain the climate change during the human activity period and the direct impact of human activities on meteorological drought;

[0021] Option 4: Subtract the standardized rainfall index (SPI) of the baseline period and the human activity period of the human interference sub-basin to obtain the direct and indirect impacts of human activities on meteorological drought during the human activity period.

[0022] Preferably, the calculation method of the cumulative probability F(x) of the cumulative rainfall sequence x in S1 is as follows:

[0023]

[0024] where λ and β are the scale and shape parameters respectively, x > 0, λ > 0, and β > 0, and the maximum likelihood method is used to estimate λ and β.

[0025] Preferably, the calculation method of the standardized precipitation index SPI sequence of each sub-basin in S2 is as follows:

[0026] When 0 ≤ F(x) ≤ 0.5:

[0027]

[0028]

[0029] When 0.5 < F(x) ≤ 1:

[0030]

[0031] where c0 = 2.515517; c1 = 0.802853; c2 = 0.010328; d1 = 1.4132788; d2 = 0.189269; d3 = 0.001308; k represents the time scale of the drought index.

[0032] Preferably, the Pettitt test method and the Mann-Kendall test method are used for the non-consistency test of the SPI sequence in S3. Specifically:

[0033] The formula of the Pettitt test method is as follows:

[0034]

[0035] where U t,n is the test statistic, representing the cumulative number of times the value at the t-th moment is greater than or less than the value at the i-th moment. If at the t0-th moment:

[0036] K t0,n = max|U t,n | (t = 1, 2,..., n)

[0037] Then it is considered that there is a mutation point at t0. Combining with the Mann-Kendall test method, the trend characteristics of the sample sequence can be obtained:

[0038] First, determine the number x of the paired numbers of the sequence i (the number of times the value at the i-th moment is greater than the value at the j-th moment), and then calculate the sequence variance and the statistic, specifically as follows:

[0039]

[0040]

[0041] Given a significance level α, if |U n |>U α , it indicates that there is an obvious trend change in the sequence, that is, the basin is greatly affected by human activities.

[0042] Preferably, when the area ratio γ of urban construction land to cultivated land in S4 is less than 1%, it means that human activities are very weak, and the sub-basin is identified as a natural sub-basin.

[0043] As a preference, the reservoir capacity coefficient in S5 is The larger it is, the stronger the reservoir's ability to regulate hydrological processes is, which means the heavier the human activities are, and the sub-basin is identified as a human interference sub-basin.

[0044] Beneficial effects: This application identifies watershed types based on the inconsistency test of the standardized rainfall index (SPI) sequence combined with the analysis of the impact of human activities, and divides the entire study period into a baseline period and a human activity period. The meteorological drought index analysis and calculation of the two periods can obtain the impact of human activities on meteorological drought and the impact of climate factors on meteorological drought, thereby quantitatively distinguishing the impact of the two factors on meteorological drought. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a flowchart of this application;

[0046] Figure 2 This is a schematic diagram of how the study period is divided in this application;

[0047] Figure 3 This is a schematic diagram for quantitatively distinguishing the impact of human activity factors and climate change factors on meteorological drought in this application. DETAILED DESCRIPTION

[0048] The present application will be further described below with reference to specific embodiments.

[0049] like Figure 1 The flowchart of this application is shown in FIG. , which specifically includes the following steps:

[0050] S1: Divide the sub-basins and calculate the cumulative probability F(x) of the accumulated rainfall sequence x at different time scales based on the rainfall data of each sub-basin. The specific calculation method is as follows:

[0051]

[0052] where λ and β are the scale and shape parameters respectively, x > 0, λ > 0, and β > 0, and the maximum likelihood method is used to estimate λ and β;

[0053] S2: Perform a normal standard distribution on the cumulative probability F(x) of the cumulative rainfall sequence of each sub-basin at the time scale to obtain the standardized rainfall index SPI sequence x = (x1, x2, … x n ), and the specific calculation method is as follows:

[0054] When 0 ≤ F(x) ≤ 0.5:

[0055]

[0056]

[0057] When 0.5 < F(x) ≤ 1:

[0058]

[0059] where c0 = 2.515517; c1 = 0.802853; c2 = 0.010328; d1 = 1.4132788; d2 = 0.189269; d3 = 0.001308; k represents the time scale of the drought index;

[0060] S3: Perform a non - consistency test on the standardized rainfall index SPI sequence x = (x1, x2, … x n ), and use the Pettitt test method and the Mann - Kendall test method to perform a univariate non - consistency test on the cumulative rainfall sequence x respectively. If the non - consistency in the test result is relatively significant, it is considered that the basin is greatly affected by human activities, and the test result is used as the first index for identifying natural sub - basins, specifically:

[0061] The formula of the Pettitt test method is as follows:

[0062]

[0063] where U t,n is the test statistic, representing the cumulative number of times the value at the t - th moment is greater than or less than the value at the i - th moment. If at the t0 moment:

[0064] K t0,n = max|U t,n |(t = 1, 2, …, n)

[0065] Then it is considered that there is a mutation point at t0. Combining with the Mann - Kendall test method, the trend characteristics of the sample sequence can be obtained:

[0066] First, determine the number x of the paired numbers of the sequencei (the number of times the value at time i is greater than the value at time j), and then calculate the sequence variance and statistics, as follows:

[0067]

[0068]

[0069] Given a significance level α, if |U n |>U α , it indicates that there is an obvious trend change in the sequence, that is, the basin is greatly affected by human activities;

[0070] S4: Calculate the ratio of urban construction land to cultivated land within each sub-basin. This ratio is used as the second indicator for identifying natural sub-basins. The calculation formula is as follows:

[0071]

[0072] Where γ represents the scale factor, A c For urban construction land, A a is the cultivated land area. When the ratio γ between urban construction land and cultivated land area is less than 1%, it means that human activities are very weak, and the sub-basin is identified as a natural sub-basin.

[0073] S5: Calculate the reservoir capacity coefficient within the sub-basin The calculation formula is as follows:

[0074]

[0075] Where V represents the beneficial storage capacity of the reservoir, W is the average runoff into the reservoir over many years, and the existence of a reservoir in the sub-basin and the reservoir capacity coefficient are taken into account. As the third indicator for identifying natural sub-basins, when the reservoir capacity coefficient When the value is larger, it indicates that the reservoir has a stronger regulating capacity on the hydrological process, which means that human activities are heavier, and the sub-basin is identified as a human interference sub-basin;

[0076] S6: Population density, nighttime light density, and GDP within each sub-basin are used as the fourth indicator to identify natural sub-basins;

[0077] S7: judging whether the sub-basin is a natural sub-basin or a human-interferenced sub-basin based on the first indicator of the natural sub-basin, the second indicator of the natural sub-basin, the third indicator of the natural sub-basin, and the fourth indicator of the natural sub-basin, and then classifying the sub-basin as a natural sub-basin or a human-interferenced sub-basin at a spatial scale;

[0078] S8: The study period of the sub-basin is divided into a baseline period with climate change as the dominant factor and a human activity period with human activity as the dominant factor. The sub-basin is then divided into a sub-basin in the baseline period and a sub-basin in the human activity period on a temporal scale.

[0079] S9: Analyze the Standardized Precipitation Index (SPI) of each sub-basin to quantitatively distinguish the impacts of human activities and climate change on meteorological drought. Specifically:

[0080] Option 1: Subtract the standardized rainfall index (SPI) of the natural sub-basin and the human-disturbed sub-basin during the baseline period to obtain the direct impact of human activities and climate factors on meteorological drought during the baseline period;

[0081] Option 2: Subtract the standardized rainfall index (SPI) of the natural sub-basin during the human activity period from that of the human-disturbed sub-basin to obtain the direct impact of human activities and climate factors on meteorological drought during the human activity period;

[0082] Option 3: Subtract the standardized rainfall index (SPI) of the natural sub-basin during the baseline period and the human activity period to obtain the climate change during the human activity period and the direct impact of human activities on meteorological drought;

[0083] Option 4: Subtract the standardized rainfall index (SPI) of the baseline period and the human activity period of the human interference sub-basin to obtain the direct and indirect impacts of human activities on meteorological drought during the human activity period.

Claims

1. A method for quantitatively analyzing the driving factors of meteorological drought, characterized by: The specific steps include: S1: Divide the sub-basin and calculate the cumulative probability F(x) of the cumulative rainfall sequence x at different time scales based on the rainfall data of each sub-basin; S2: Perform normalized distribution on the cumulative probability F(x) of the cumulative rainfall sequence of each sub-basin on the time scale to obtain the standardized rainfall index SPI sequence of each sub-basin x=(x1,x2,…x n ); S3: For the standardized rainfall index SPI sequence x=(x1,x2,…x n ) to conduct inconsistency tests. The Pettitt test and the Mann-Kendall test were used to conduct univariate inconsistency tests on the cumulative rainfall series x. If the inconsistency in the test results was significant, it was considered that the watershed was greatly affected by human activities. The test results were used as the first indicator for identifying natural sub-watersheds. The Pettitt test and Mann-Kendall test were used to test the inconsistency of the SPI sequence, specifically: The formula for the Pettitt test is as follows: Among them U t,n Is the test statistic, which indicates the cumulative number of values ​​at time t that are greater than or less than the values ​​at time i. If at time t0: K t0,n =max|U t,n |(t=1,2,…,n) It is considered that t0 is a mutation point, and combined with the Mann-Kendall test method, the trend characteristics of the sample sequence can be obtained: First determine the number of dual numbers in the sequence x i (the number of times the value at time i is greater than the value at time j), and then calculate the sequence variance and statistics, as follows: Given a significance level α, if |U n |>U α , it indicates that there is an obvious trend change in the sequence, that is, the basin is greatly affected by human activities; S4: Calculate the ratio of urban construction land to cultivated land within each sub-basin. This ratio is used as the second indicator for identifying natural sub-basins. The calculation formula is as follows: Where γ represents the scale factor, A c For urban construction land, A a is the cultivated land area; S5: Calculate the reservoir capacity coefficient within the sub-basin The calculation formula is as follows: Where V represents the beneficial storage capacity of the reservoir, W is the average runoff into the reservoir over many years, and the existence of a reservoir in the sub-basin and the reservoir capacity coefficient are taken into account. as a third indicator for identifying natural sub-watersheds; S6: Population density, nighttime light density, and GDP within each sub-basin are used as the fourth indicator to identify natural sub-basins; S7: judging whether the sub-basin is a natural sub-basin or a human-interferenced sub-basin based on the first indicator of the natural sub-basin, the second indicator of the natural sub-basin, the third indicator of the natural sub-basin, and the fourth indicator of the natural sub-basin, and then classifying the sub-basin as a natural sub-basin or a human-interferenced sub-basin at a spatial scale; S8: The study period of the sub-basin is divided into a baseline period with climate change as the dominant factor and a human activity period with human activity as the dominant factor. The sub-basin is then divided into a sub-basin in the baseline period and a sub-basin in the human activity period on a temporal scale. S9: Analyze the Standardized Precipitation Index (SPI) of each sub-basin to quantitatively distinguish the impacts of human activities and climate change on meteorological drought. Specifically: Option 1: Subtract the standardized rainfall index (SPI) of the natural sub-basin and the human-disturbed sub-basin during the baseline period to obtain the direct impact of human activities and climate factors on meteorological drought during the baseline period; Option 2: Subtract the standardized rainfall index (SPI) of the natural sub-basin during the human activity period from that of the human-disturbed sub-basin to obtain the direct impact of human activities and climate factors on meteorological drought during the human activity period; Option 3: Subtract the standardized rainfall index (SPI) of the natural sub-basin during the baseline period and the human activity period to obtain the climate change during the human activity period and the direct impact of human activities on meteorological drought; Option 4: Subtract the standardized rainfall index (SPI) of the baseline period and the human activity period of the human interference sub-basin to obtain the direct and indirect impacts of human activities on meteorological drought during the human activity period.

2. The method for quantitatively analyzing meteorological drought driving factors according to claim 1, characterized in that: The calculation method of the cumulative probability F(x) of the cumulative rainfall sequence x in S1 is as follows: where λ and β are scale and shape parameters, respectively, x>0, λ>0 and β>0, and λ and β are estimated using the maximum likelihood method.

3. The method for quantitatively analyzing meteorological drought driving factors according to claim 1, characterized in that: The calculation method of the standardized rainfall index SPI series of each sub-basin in S2 is as follows: When 0≤F(x)≤0.5: When 0.5<F(x)≤1: Where c0 = 2.515517; c1 = 0.802853; c2 = 0.010328; d1 = 1.4132788; d2 = 0.189269; d3 = 0.001308; k represents the time scale of the drought index.

4. The method for quantitatively analyzing meteorological drought driving factors according to claim 1, characterized in that: When the area ratio γ of urban construction land to cultivated land in S4 is less than 1%, it means that human activities are very weak, and the sub-basin is identified as a natural sub-basin.

5. The method for quantitatively analyzing meteorological drought driving factors according to claim 1, characterized in that: The reservoir capacity coefficient in S5 The larger it is, the stronger the reservoir's ability to regulate hydrological processes is, which means the heavier the human activities are, and the sub-basin is identified as a human interference sub-basin.

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