Method for extending a series of land water storage change data and determining an impact index

Abstract

The present application relates to the technical field of hydrology analysis, and especially relates to a method for prolonging land water storage change data series and determining influence indexes, which comprises the following steps: obtaining shape parameters and scale parameters of a monthly average precipitation sequence through a gamma distribution; obtaining shape parameters and scale parameters of a remote sensing surface monthly average precipitation sequence through the gamma distribution, and performing secondary correction; obtaining a monthly reference evapotranspiration sequence according to the monthly average precipitation sequence after secondary correction, land water storage change amount and monthly runoff of an outlet section, and obtaining corresponding shape parameters and scale parameters of the monthly reference evapotranspiration sequence through the gamma distribution; obtaining corresponding shape parameters and scale parameters of a monthly evapotranspiration average value sequence through the gamma distribution, and performing secondary correction; and calculating the contribution of each factor to the land water storage change. The present application can fuse remote sensing monitoring data and ground station monitoring data, and accurately obtain main influence indexes of the land water storage change.

Description

Technical Field

[0001] This invention relates to the field of hydrological analysis technology, and in particular to a method for extending a data series on changes in terrestrial water storage and determining its influencing indicators. Background Technology

[0002] Due to global warming, the terrestrial water storage on the Qinghai-Tibet Plateau has undergone significant changes in recent years. Climate warming and humidification have led to the melting of glaciers and permafrost on the plateau, altering the ratio of solid to liquid water storage, and causing over 80% of lakes to expand. Continued warming has reduced the area of ​​permafrost and thickened the active layer; the area of ​​seasonally frozen ground has increased, while the maximum frost depth has decreased. Influenced by precipitation and glacial / permafrost melting, the overall river runoff and groundwater resources on the Qinghai-Tibet Plateau have increased. Climate and vegetation changes also affect the actual evapotranspiration of the basin. Traditional runoff evolution analysis methods typically do not consider interannual variations in terrestrial water storage, only the impact of climate and underlying surfaces on runoff. Some articles use statistical methods or hydrological models to quantitatively distinguish the contributions of climate and underlying surfaces to runoff changes. In reality, hydrological elements such as cryosphere melting, soil moisture changes, groundwater changes, and river runoff are all closely related to changes in terrestrial water storage.

[0003] The starting year for terrestrial water storage changes retrieved by gravity satellites is usually relatively late, and its data sequence length is shorter than that of remote sensing evapotranspiration data sequences, as well as the year length of precipitation reanalysis data and river runoff data. On the other hand, meteorological monitoring stations on the Tibetan Plateau are extremely sparse, and remote sensing meteorological products also contain systematic errors. How to integrate remote sensing monitoring data and ground station monitoring data, extend the terrestrial water storage change data sequence backward based on the principle of water balance, analyze the relationship between factors such as precipitation, evapotranspiration, and river runoff and terrestrial water storage changes, and determine the main influencing indicators of terrestrial water storage changes is an urgent problem to be solved. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method for extending the data series on changes in terrestrial water storage and determining its influencing indicators. This method integrates remote sensing monitoring data with ground station monitoring data to accurately obtain the main influencing indicators of changes in terrestrial water storage.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A method for extending a data series on changes in terrestrial water storage and determining its influencing indicators includes the following steps:

[0007] S1. Collect data from all surface precipitation monitoring stations within the catchment area above the designated river cross-section, calculate the average precipitation of each surface precipitation monitoring station monthly to obtain a monthly average precipitation sequence, and obtain the shape and scale parameters of the monthly average precipitation sequence through gamma distribution.

[0008] S2. Collect all grid point data from the ground monitoring stations, calculate the precipitation at each grid point of the ground precipitation monitoring station monthly to obtain the average monthly precipitation sequence of the remote sensing surface, obtain the shape parameters and scale parameters of the average monthly precipitation sequence of the remote sensing surface through gamma distribution, perform secondary correction on the average monthly precipitation sequence of the remote sensing surface, and obtain the first corrected monthly average precipitation sequence and the second corrected monthly average precipitation sequence in sequence.

[0009] S3. Based on the second corrected monthly average precipitation sequence, the change in land water storage, and the monthly runoff at the outlet section, calculate the monthly reference evapotranspiration over the catchment area in the most recent N years to obtain the monthly reference evapotranspiration sequence, and obtain the shape parameters and scale parameters corresponding to the monthly reference evapotranspiration sequence through gamma distribution.

[0010] S4. Collect evapotranspiration data from the ground precipitation monitoring stations, calculate the average evapotranspiration of each ground precipitation monitoring station monthly to obtain a monthly evapotranspiration average sequence, obtain the shape parameters and scale parameters corresponding to the monthly evapotranspiration average sequence through gamma distribution, and perform secondary correction on the monthly evapotranspiration average sequence to obtain a first corrected monthly evapotranspiration sequence and a second corrected monthly evapotranspiration sequence in sequence.

[0011] S5. The second corrected monthly average precipitation sequence, the monthly runoff at the outlet section, and the second corrected monthly evapotranspiration sequence are extended using the water balance equation to calculate the contribution of precipitation variation factors, evapotranspiration variation factors, and runoff variation factors to the change in terrestrial water storage.

[0012] Further, in step S1, the monthly average precipitation sequence is: ,in, For year serial number, For years, The month number is used; the first shape parameter and the first scale parameter of the monthly average precipitation sequence are obtained through gamma distribution, and the first shape parameter is: The second scale parameter .

[0013] Further, in step S2, the remotely sensed monthly precipitation average sequence is: ,in For year serial number, For years, The month number is used as the reference number. By fitting the gamma distribution to the average monthly precipitation sequence of the remote sensing surface, the second shape parameter and the second scale parameter of the average monthly precipitation sequence of the remote sensing surface are obtained. The second shape parameter is... The second scale parameter is .

[0014] Further, in step S2, the monthly evapotranspiration average sequence is corrected for the first time:

[0015] Formula (1)

[0016] in, This is the first corrected monthly evapotranspiration sequence. For year serial number, For years, The month number; and These are the distribution function and inverse function of the gamma distribution, respectively;

[0017] Based on the difference between the monthly average precipitation sequence of remote sensing precipitation products over the catchment area and the grid point data, a second correction is performed on the first corrected monthly evapotranspiration sequence:

[0018] Formula (2)

[0019] in, This is the second corrected monthly evapotranspiration sequence; This is a sequence of monthly average precipitation values ​​based on remote sensing precipitation products over the catchment area.

[0020] Furthermore, in step S3, the method for calculating the monthly reference evapotranspiration over the catchment area in the most recent N years is as follows:

[0021] Formula (3)

[0022] in, This represents the monthly reference evapotranspiration over the catchment area for the past N years. This is the second corrected monthly evapotranspiration sequence; This represents the monthly runoff at the outlet section; This represents the change in terrestrial water storage over the catchment area, in mm. For year serial number; Number of years; The month number represents the starting monitoring year number; M-N+1 represents the starting monitoring year number.

[0023] The third shape parameter and third scale parameter corresponding to the monthly reference evapotranspiration sequence were obtained through gamma distribution. The third shape parameter is: The third scale parameter is .

[0024] Further, in step S4, the monthly evapotranspiration average sequence is: By fitting the monthly evapotranspiration average sequence with a gamma distribution, the fourth shape parameter and the fourth scale parameter corresponding to the monthly evapotranspiration average sequence are obtained. The fourth shape parameter is... The fourth scale parameter is .

[0025] Further, in step S4, the monthly evapotranspiration average sequence is corrected for the first time using the fourth shape parameter and the fourth scale parameter:

[0026] Formula (4)

[0027] in, This is the first corrected monthly evapotranspiration sequence; and These are the distribution function and inverse function of the gamma distribution, respectively;

[0028] A second correction was performed on the first corrected monthly evapotranspiration sequence:

[0029] Formula (5)

[0030] in, The second correction is required for the monthly evapotranspiration sequence; evapotranspiration values ​​prior to the starting year of the monitoring of changes in terrestrial water storage do not require secondary correction.

[0031] Further, in step S5, the second corrected monthly average precipitation sequence, the monthly runoff at the outlet section, and the second corrected monthly evapotranspiration sequence are extended using the water balance equation to obtain the monthly changes in land water storage over the previous MN years:

[0032] Formula (6)

[0033] in, This represents the monthly changes in terrestrial water storage over the preceding MN years.

[0034] The Pettitt test was used to identify abrupt changes in the annual series of terrestrial water storage changes. The total number of years was divided into two stages: before and after the abrupt change. The contributions of precipitation change, evapotranspiration change, and runoff change to the changes in terrestrial water storage were calculated using the monthly terrestrial water storage changes for the first MN years, the second-corrected monthly average precipitation series, the monthly runoff at the outlet section, and the second-corrected monthly evapotranspiration series.

[0035] Formula (7)

[0036] Formula (8)

[0037] Formula (9)

[0038] in, The contribution of factors affecting precipitation variation; The contribution of factors affecting changes in evapotranspiration; The contribution of runoff variation factors; Total number of years This represents the number of years before the mutation. The number of years before the mutation, and .

[0039] The beneficial effects of this invention are:

[0040] By using gamma distribution to determine the shape and scale parameters of the monthly precipitation series, a secondary correction is performed on the monthly remote-sensed areal precipitation within the catchment area, resulting in a secondary-corrected monthly precipitation series for the catchment area. This allows for the fusion of remote-sensed monthly precipitation data with ground-based monitoring data, ensuring the accuracy of the fused monthly precipitation data and obtaining a second-corrected monthly average precipitation sequence. Since the monitoring of land water storage changes started relatively late, the monthly reference evapotranspiration over the catchment area is calculated based on the secondary-corrected monthly areal precipitation and monthly runoff at the outlet section during the same period. The gamma distribution is then used to fit this monthly reference evapotranspiration data series to obtain the corresponding shape and scale parameters. Through secondary correction, the monthly evapotranspiration of the catchment area after remote-sensing product correction is obtained, thus achieving the fusion of remote-sensed evapotranspiration data with ground-based monitoring data. The data are fused, and a second correction is used to ensure the accuracy of the fused evapotranspiration data, while obtaining a second-corrected monthly evapotranspiration sequence. The second-corrected monthly average precipitation sequence monitored by gravity satellites, the monthly runoff at the outlet section, and the second-corrected monthly evapotranspiration sequence are extended using the water balance equation. This allows the time series to be extended further back, making it possible to identify the evolution patterns of water storage over several decades, avoiding the bias of conclusions due to short data. By identifying abrupt change points in the terrestrial water storage change data sequence, the year of the abrupt change can be accurately pinpointed, helping to clarify the combined impact of climate change and human activities. The contribution of changes in different driving factors such as precipitation, evapotranspiration, and runoff on the annual scale before and after the abrupt change point to the change in terrestrial water storage can be calculated, determining the factors causing the change in terrestrial water storage and providing a scientific and quantitative basis for decision-making regarding the sustainable use of regional water resources. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating a preferred embodiment of the present invention regarding the extension of a land water storage change data series and the determination of influencing indicators. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0044] Please see Figure 1 The method for extending the terrestrial water storage change data series and determining the influencing indicators according to a preferred embodiment of the present invention includes the following steps:

[0045] S1. Collect data from all surface precipitation monitoring stations within the catchment area above the designated river cross-section, calculate the average precipitation of each surface precipitation monitoring station monthly to obtain the monthly average precipitation sequence, and obtain the shape and scale parameters of the monthly average precipitation sequence through gamma distribution.

[0046] In step S1, the monthly average precipitation sequence is as follows: ,in, For year serial number, For years, The month number is used; the first shape parameter and first scale parameter of the monthly average precipitation sequence are obtained through gamma distribution. The first shape parameter is... Second scale parameter .

[0047] In this embodiment, the first shape parameter and the second scale parameter of the monthly precipitation series are determined by calculating the average precipitation of each station month by month and using the gamma distribution.

[0048] S2. Collect all grid point data from ground monitoring stations, calculate the monthly precipitation at each grid point of the ground precipitation monitoring station to obtain the average monthly precipitation sequence of the remote sensing surface, obtain the shape and scale parameters of the average monthly precipitation sequence of the remote sensing surface through gamma distribution, perform secondary correction on the average monthly precipitation sequence of the remote sensing surface to obtain the first corrected monthly average precipitation sequence and the second corrected monthly average precipitation sequence in sequence.

[0049] In step S2, the remote sensing surface monthly precipitation average sequence is: ,in For year serial number, For years, The month number is used as the reference number. By fitting the gamma distribution to the average monthly precipitation sequence of the remote sensing surface, the second shape parameter and second scale parameter of the average monthly precipitation sequence of the remote sensing surface are obtained. The second shape parameter is... The second scale parameter is .

[0050] In step S2, the monthly evapotranspiration average sequence is corrected for the first time:

[0051] Formula (1)

[0052] in, This is the first corrected monthly evapotranspiration sequence. For year serial number, For years, The month number; and These are the distribution function and inverse function of the gamma distribution, respectively;

[0053] Based on the difference between the monthly average precipitation sequence of remote sensing precipitation products over the catchment area and the grid point data, a second correction is performed on the first-corrected monthly evapotranspiration sequence:

[0054] Formula (2)

[0055] in, This is the second corrected monthly evapotranspiration sequence; This is a sequence of monthly average precipitation values ​​based on remote sensing precipitation products over the catchment area.

[0056] This embodiment performs a secondary correction on the monthly remote sensing surface precipitation of the catchment area above a specified river cross-section, obtaining a series of monthly precipitation data for the catchment area after secondary correction. To avoid the influence of annual bias on the data, the average monthly precipitation sequence of the remote sensing precipitation product over the catchment area A is also considered. Compared with the aforementioned monthly precipitation average of some grid points The differences are corrected by formula (3) to improve the accuracy of the merged monthly precipitation data.

[0057] S3. Based on the second corrected monthly average precipitation sequence, the change in terrestrial water storage, and the monthly runoff at the outlet section, calculate the monthly reference evapotranspiration over the catchment area for the most recent N years to obtain the monthly reference evapotranspiration sequence, and obtain the shape parameters and scale parameters corresponding to the monthly reference evapotranspiration sequence through gamma distribution.

[0058] In step S3, the method for calculating the monthly reference evapotranspiration over the catchment area in the most recent N years is as follows:

[0059] Formula (3)

[0060] in, This represents the monthly reference evapotranspiration over the catchment area for the past N years. This is the second corrected monthly evapotranspiration sequence; This represents the monthly runoff at the outlet section; This represents the change in terrestrial water storage over the catchment area, in mm. For year serial number; Number of years; The month number represents the starting monitoring year number; M-N+1 represents the starting monitoring year number.

[0061] The third shape parameter and third scale parameter corresponding to the monthly reference evapotranspiration sequence were obtained through gamma distribution. The third shape parameter is: The third scale parameter is .

[0062] In this embodiment, since the starting year for monitoring the change in terrestrial water storage is relatively late, M-N+1 is used to represent the starting year number. First, based on the monthly surface precipitation, the change in terrestrial water storage, and the monthly runoff at the outlet section of the catchment area after secondary correction in the last N years, the monthly reference evapotranspiration of the catchment area in the last N years is calculated.

[0063] S4. Collect evapotranspiration data from ground precipitation monitoring stations, calculate the average evapotranspiration of each ground precipitation monitoring station monthly to obtain a monthly evapotranspiration average sequence, obtain the shape and scale parameters corresponding to the monthly evapotranspiration average sequence through gamma distribution, and perform secondary correction on the monthly evapotranspiration average sequence to obtain the first corrected monthly evapotranspiration sequence and the second corrected monthly evapotranspiration sequence in sequence.

[0064] In step S4, the monthly evapotranspiration average sequence is: By fitting the monthly evapotranspiration average sequence with a gamma distribution, the fourth shape parameter and fourth scale parameter corresponding to the monthly evapotranspiration average sequence are obtained. The fourth shape parameter is... The fourth scale parameter is .

[0065] In step S4, the monthly evapotranspiration average sequence is corrected for the first time using the fourth shape parameter and the fourth scale parameter:

[0066] Formula (4)

[0067] in, This is the first corrected monthly evapotranspiration sequence; and These are the distribution function and inverse function of the gamma distribution, respectively;

[0068] A second correction was performed on the first corrected monthly evapotranspiration sequence:

[0069] Formula (5)

[0070] in, The second correction is required for the monthly evapotranspiration sequence; evapotranspiration values ​​prior to the starting year of the monitoring of changes in terrestrial water storage do not require secondary correction.

[0071] In this embodiment, the third shape parameter and third scale parameter of the gamma distribution are used, and the monthly evapotranspiration of the catchment area after correction of the remote sensing product is obtained through secondary correction. To avoid the influence of annual bias on the data, the monthly evapotranspiration values ​​after correction for the most recent N years are used, while the evapotranspiration values ​​before the starting year of the land water storage change data monitoring do not need to be corrected.

[0072] In steps S1-S4 of this embodiment, the probability density function of the gamma distribution used is:

[0073] Formula (10)

[0074] in, For shape parameters; Scale parameters; For random variables, representing the waiting time for an event to occur or other continuous variables; This is the gamma function.

[0075] S5. The monthly average precipitation sequence, monthly runoff at the outlet section, and monthly evapotranspiration sequence of the second correction are extended using the water balance equation to calculate the contribution of precipitation variation factors, evapotranspiration variation factors, and runoff variation factors to the change in terrestrial water storage.

[0076] In step S5, the second corrected monthly average precipitation series, the monthly runoff at the outlet section, and the second corrected monthly evapotranspiration series are extended using the water balance equation to obtain the monthly changes in land water storage over the previous MN years:

[0077] Formula (6)

[0078] in, This represents the monthly changes in terrestrial water storage over the preceding MN years.

[0079] The Pettitt test was used to identify abrupt changes in the annual series of terrestrial water storage changes. The total number of years was divided into two stages: before and after the abrupt change. The contributions of precipitation change, evapotranspiration change, and runoff change to the changes in terrestrial water storage were calculated using the monthly terrestrial water storage changes for the first MN years, the second-corrected monthly average precipitation series, the monthly runoff at the outlet section, and the second-corrected monthly evapotranspiration series.

[0080] Formula (7)

[0081] Formula (8)

[0082] Formula (9)

[0083] in, The contribution of factors affecting precipitation variation; The contribution of factors affecting changes in evapotranspiration; The contribution of runoff variation factors; Total number of years This represents the number of years before the mutation. The number of years before the mutation, and .

[0084] In this embodiment, the second-corrected monthly average precipitation sequence, monthly runoff at the outlet section, and second-corrected monthly evapotranspiration sequence monitored by gravity satellites are extended using the water balance equation. This allows the time series to be extended further back, making it possible to identify the evolution patterns of water storage over several decades, thus avoiding the bias of conclusions due to short data. By identifying abrupt change points in the terrestrial water storage change data series, the year in which the abrupt change occurred can be accurately pinpointed. This helps to clarify the combined impact of climate change and human activities, and to calculate the contribution of changes in different driving factors such as precipitation, evapotranspiration, and runoff on the annual scale before and after the abrupt change point to the changes in terrestrial water storage. This determines the factors that cause the changes in terrestrial water storage, providing a scientific and quantitative basis for decision-making on the sustainable use of regional water resources.

Claims

1. A method for extending a data series on changes in terrestrial water storage and determining its influencing indicators, characterized in that, Includes the following steps: S1. Collect data from all surface precipitation monitoring stations within the catchment area above the designated river cross-section, calculate the average precipitation of each surface precipitation monitoring station monthly to obtain a monthly average precipitation sequence, and obtain the shape and scale parameters of the monthly average precipitation sequence through gamma distribution. S2. Collect all grid point data from the ground monitoring stations, calculate the precipitation at each grid point of the ground precipitation monitoring station monthly to obtain the average monthly precipitation sequence of the remote sensing surface, obtain the shape parameters and scale parameters of the average monthly precipitation sequence of the remote sensing surface through gamma distribution, perform secondary correction on the average monthly precipitation sequence of the remote sensing surface, and obtain the first corrected monthly average precipitation sequence and the second corrected monthly average precipitation sequence in sequence. S3. Based on the second corrected monthly average precipitation sequence, the change in land water storage, and the monthly runoff at the outlet section, calculate the monthly reference evapotranspiration over the catchment area in the most recent N years to obtain the monthly reference evapotranspiration sequence, and obtain the shape parameters and scale parameters corresponding to the monthly reference evapotranspiration sequence through gamma distribution. S4. Collect evapotranspiration data from the ground precipitation monitoring stations, calculate the average evapotranspiration of each ground precipitation monitoring station monthly to obtain a monthly evapotranspiration average sequence, obtain the shape parameters and scale parameters corresponding to the monthly evapotranspiration average sequence through gamma distribution, and perform secondary correction on the monthly evapotranspiration average sequence to obtain a first corrected monthly evapotranspiration sequence and a second corrected monthly evapotranspiration sequence in sequence. S5. The second corrected monthly average precipitation sequence, the monthly runoff at the outlet section, and the second corrected monthly evapotranspiration sequence are extended using the water balance equation to calculate the contribution of precipitation variation factors, evapotranspiration variation factors, and runoff variation factors to the change in terrestrial water storage.

2. The method for extending the data series of changes in terrestrial water storage and determining the influencing indicators according to claim 1, characterized in that: In step S1, the monthly average precipitation sequence is: ,in, For year serial number, For years, The month number is used; the first shape parameter and the first scale parameter of the monthly average precipitation sequence are obtained through gamma distribution, and the first shape parameter is: The second scale parameter .

3. The method for extending the data series of changes in terrestrial water storage and determining the influencing indicators according to claim 2, characterized in that: In step S2, the remote sensing surface monthly precipitation average sequence is: ,in For year serial number, For years, The month number is used as the reference number. By fitting the gamma distribution to the average monthly precipitation sequence of the remote sensing surface, the second shape parameter and the second scale parameter of the average monthly precipitation sequence of the remote sensing surface are obtained. The second shape parameter is... The second scale parameter is .

4. The method for extending the data series of changes in terrestrial water storage and determining the influencing indicators according to claim 3, characterized in that: In step S2, the monthly evapotranspiration average sequence is corrected for the first time: Official (1) in, This is the first corrected monthly evapotranspiration sequence. For year serial number, For years, The month number; and These are the distribution function and inverse function of the gamma distribution, respectively; Based on the difference between the monthly average precipitation sequence of remote sensing precipitation products over the catchment area and the grid point data, a second correction is performed on the first corrected monthly evapotranspiration sequence: Official (2) in, This is the second corrected monthly evapotranspiration sequence; This is a sequence of monthly average precipitation values ​​based on remote sensing precipitation products over the catchment area.

5. The method for extending and determining the influencing indicators of a terrestrial water storage change data series according to claim 1, characterized in that: In step S3, the method for calculating the monthly reference evapotranspiration over the catchment area in the most recent N years is as follows: Official (3) in, This represents the monthly reference evapotranspiration over the catchment area for the past N years. This is the second corrected monthly evapotranspiration sequence; This represents the monthly runoff at the outlet section; This represents the change in terrestrial water storage over the catchment area, in mm. For year serial number; Number of years; The month number represents the starting monitoring year number; M-N+1 represents the starting monitoring year number. The third shape parameter and third scale parameter corresponding to the monthly reference evapotranspiration sequence were obtained through gamma distribution. The third shape parameter is: The third scale parameter is .

6. The method for extending the data series of changes in terrestrial water storage and determining the influencing indicators according to claim 5, characterized in that: In step S4, the monthly evapotranspiration average sequence is: By fitting the monthly evapotranspiration average sequence with a gamma distribution, the fourth shape parameter and the fourth scale parameter corresponding to the monthly evapotranspiration average sequence are obtained. The fourth shape parameter is... The fourth scale parameter is .

7. The method for extending the data series of changes in terrestrial water storage and determining the influencing indicators according to claim 6, characterized in that: In step S4, the monthly evapotranspiration average sequence is corrected for the first time using the fourth shape parameter and the fourth scale parameter: Official (4) in, This is the first corrected monthly evapotranspiration sequence; and These are the distribution function and inverse function of the gamma distribution, respectively; A second correction was performed on the first corrected monthly evapotranspiration sequence: Official (5) in, The second correction is required for the monthly evapotranspiration sequence; evapotranspiration values ​​prior to the starting year of the monitoring of changes in terrestrial water storage do not require secondary correction.

8. The method for extending the data series of changes in terrestrial water storage and determining the influencing indicators according to claim 5, characterized in that: In step S5, the second corrected monthly average precipitation sequence, the monthly runoff at the outlet section, and the second corrected monthly evapotranspiration sequence are extended using the water balance equation to obtain the monthly changes in land water storage over the previous MN years: Official (6) in, This represents the monthly changes in terrestrial water storage over the preceding MN years. The Pettitt test was used to identify abrupt changes in the annual series of terrestrial water storage changes. The total number of years was divided into two stages: before and after the abrupt change. The contributions of precipitation change, evapotranspiration change, and runoff change to the changes in terrestrial water storage were calculated using the monthly terrestrial water storage changes for the first MN years, the second-corrected monthly average precipitation series, the monthly runoff at the outlet section, and the second-corrected monthly evapotranspiration series. Official (7) Formula (8) Official (9) in, The contribution of factors affecting precipitation variation; The contribution of factors affecting changes in evapotranspiration; The contribution of runoff variation factors; Total number of years This represents the number of years before the mutation. The number of years before the mutation, and .

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