Extreme hydrological sequence reconstruction method

Abstract

The invention discloses an extreme hydrological sequence reconstruction method. The method comprises the following steps: S1, acquiring a natural runoff sequence; S2, acquiring a segmented research period and a segmented natural runoff sequence; S3, acquiring a segmented extreme hydrological sequence; according to the method, a multi-type natural runoff sequence acquisition method is introduced and improved, and the problem of hydrological sequence inconsistency is solved; the annual period is divided into 24 segmented research periods according to 24 solar terms, and the problem that the annual extremum method sequence is relatively short is solved; the sequence distribution linetype is determined through multiple ways, the problem that the sample error is large during parameter estimation through an annual extreme value method is solved, and the problem that the threshold value is difficult to determine is also solved; according to the method, the evaluation standard of the extreme hydrological event is broadened, lossless and accurate analysis of the extreme hydrological design value is realized, scientific data support is provided for hydrological analysis and calculation, and the purpose of better estimation effect of the extreme hydrological event is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological analysis, and in particular to an extreme hydrological sequence reconstruction method. Background Art

[0002] Extreme hydrological events refer to events with extremely low probability of occurrence of hydrological elements (flow or water level) at a specific location and time, usually accounting for only 10% or less of such hydrological phenomena. Accurate prediction of extreme hydrological events plays an important role in ensuring regional water resources security, protecting regional ecological environment, and sustainable development of regional social economy. However, extreme hydrological events are different from flood or drought events, and also different from flood or drought disasters. Flood or drought events are the result of extreme hydrological events accumulating over a period of time, while flood or drought disasters are the result of flood or drought events affecting the social and economic system and the ecological and environmental system. Therefore, the prediction of extreme hydrological events cannot simply adopt the prediction method of drought or flood.

[0003] To accurately predict extreme hydrological events, we must first obtain extreme hydrological sequence samples. In the prior art, the methods for obtaining consistent hydrological sequence samples mainly include the annual extreme value method and the super-threshold method. The annual extreme value method, that is, taking the annual maximum or minimum value of the hydrological element, is widely used in engineering design practice, but its sequence is relatively short and the sample error is large when estimating parameters. The super-threshold method, that is, taking all samples above or below a certain threshold, can more completely describe the formation process of hydrological events and expand the use of hydrological information, but the determination of the threshold value is more difficult. With the deepening of the impact of climate change and the intensification of the impact of human activities, the hydrological sequence has shown inconsistency. For the acquisition of inconsistent hydrological sequence samples, it is usually first restored, restored, etc., and then the annual extreme value method or super-threshold method is used. However, the above-mentioned method for obtaining hydrological sequence samples cannot completely obtain hydrological sequences with a probability of occurrence equal to or less than 10%. At the same time, no extreme hydrological sequence reconstruction method has been found in the prior art. Therefore, an extreme hydrological sequence reconstruction method is urgently needed to solve the above problems. Summary of the invention

[0004] In order to achieve the above object, the technical solution adopted by the present invention is: a method for reconstructing extreme hydrological sequences, comprising the following steps: S1. Obtain natural runoff sequence; S2. Obtain segmented study periods and segmented natural runoff sequences; S3. Obtain segmented extreme hydrological series; S4. Obtain extreme hydrological design values.

[0005] Furthermore, the S1 step includes: S11. Collecting long-sequence daily flow data measured at hydrological stations in the target basin; wherein the long sequence refers to a sequence length greater than 30 years; S12. Obtain the natural runoff sequence X1; the obtaining of the natural runoff sequence X1 comprises: firstly establishing a distributed hydrological model, and verifying and validating the model using the measured daily flow data of S11; then performing natural runoff restoration calculation on the target watershed to obtain the daily natural runoff sequence X1={X11, X12, …, X1 n}; S13. Obtain the natural runoff sequence X2; the obtaining of the natural runoff sequence X2 includes: using the inconsistency recognition method to test the mutation of the measured daily flow data of S11 to obtain the mutation time point; based on the mutation time point, dividing the measured daily flow data of S11 into a period T1 affected by natural rhythm and a period T2 affected by environmental changes; calculating the water production coefficient a of T1; transplanting the water production coefficient a to T2 to calculate the natural runoff sequence of T2; combining the natural runoff sequences of T1 and T2 to obtain the daily natural runoff sequence X2={X21, X22, …, X2 n}; Wherein, the water yield coefficient a refers to the annual average runoff of T1 divided by the annual average precipitation; S14. Obtain the natural runoff sequence X3; the obtaining of the natural runoff sequence X3 comprises: firstly using multi-source remote sensing data and measured meteorological data, adopting a causal analysis method, to determine the static characteristic factors and dynamic characteristic factors of the deep machine learning model; then establishing a deep neural network model, using the measured daily flow data of S11 to train, adjust and verify the model; then calculating the daily natural runoff sequence X3={X31, X32, …, X3 n}; wherein the causal analysis method includes one or more combinations of explainable machine learning methods and geospatial analysis methods.

[0006] Furthermore, the S2 step includes: S21. Obtaining segmented research periods; the obtaining segmented research periods is to divide the annual cycle into 24 segmented research periods according to the 24 solar terms; wherein the 24 solar terms include: Beginning of Spring, Rain Water, Waking of Insects, Spring Equinox, Tomb-Sweeping Day, Grain Rain, Beginning of Summer, Grain Full, Grain in Ear, Summer Solstice, Minor Heat, Major Heat, Beginning of Autumn, End of Heat, White Dew, Autumnal Equinox, Cold Dew, Frost Descent, Beginning of Winter, Minor Snow, Major Snow, Winter Solstice, Minor Cold, Major Cold; the 24 solar terms are determined according to specific time points each year; S22. Obtain the segmented natural runoff sequence X11, X12, ..., X124 of the natural runoff sequence X1; the segmented natural runoff sequence X11, X12, ..., X124 of the natural runoff sequence X1 is obtained by dividing the natural runoff sequence X1 into 24 segmented natural runoff sequences X11={X111, X112, ..., X114} according to the 24 segmented research periods of S21. n},X12={X121,X122,…,X12 n},…,X124={X1241,X1242,…,X124 n}; S23. Obtain the segmented natural runoff sequence X21, X22, ..., X224 of the natural runoff sequence X2; the segmented natural runoff sequence X21, X22, ..., X224 of the natural runoff sequence X2 is obtained by dividing the natural runoff sequence X2 into 24 segmented natural runoff sequences X21={X211, X212, ..., X214} according to the 24 segmented research periods of S21. n},X22={X221,X222,…,X22 n},…,X224={X2241,X2242,…,X224 n}; S24. Obtain the segmented natural runoff sequence X31, X32, ..., X324 of the natural runoff sequence X3; the segmented natural runoff sequence X31, X32, ..., X324 of the natural runoff sequence X3 is obtained by dividing the natural runoff sequence X3 into 24 segmented natural runoff sequences X31={X311, X312, ..., X31} according to the 24 segmented research periods of S21. n},X32={X321,X322,…,X32 n},…,X324={X3241,X3242,…,X324 n}.

[0007] Furthermore, the S3 step includes: S31. Obtain the segmented extreme hydrological sequence E11, E12, ..., E124 of the segmented natural runoff sequence X11, X12, ..., X124; the obtaining of the segmented extreme hydrological sequence E11, E12, ..., E124 of the segmented natural runoff sequence X11, X12, ..., X124 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X11, X12, ..., X124 of S22, and then respectively determining the distribution line type of the segmented natural runoff sequence X11, X12, ..., X124, and then respectively determining the extreme hydrological sequences of different degrees of the 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequence X11, X12, ..., X124, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E11={E111, E112, ..., E11 10},E12={E121,E122,…,E12 10},…,E124={E1241,E1242,…,E124 10}; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E11={E11 90 ,E11 91 ,…,E11 99}, E12={E12 90 ,E12 91 , …, E12 99}, …, E124={E124 90 ,E124 91 , ..., E124 99}; wherein the distribution line type includes one or a combination of Pearson III distribution, generalized extreme value distribution, generalized Pareto distribution, and Copula joint distribution; the percentile threshold includes the 1st to 10th percentile value or the 90th to 99th percentile value; S32. Obtain the segmented extreme hydrological sequence E21, E22, ..., E224 of the segmented natural runoff sequence X21, X22, ..., X224; the obtaining of the segmented extreme hydrological sequence E21, E22, ..., E224 of the segmented natural runoff sequence X21, X22, ..., X224 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X21, X22, ..., X224 of S23, and then respectively determining the distribution line type of the segmented natural runoff sequence X21, X22, ..., X224, and then respectively determining the extreme hydrological events of different degrees of the 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequence X21, X22, ..., X224, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E21={E211, E212, ..., E21 10},E22={E221,E222,…,E2210},…,E224={E2241,E2242,…,E224 10}; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E21={E21 90 , E21 91 , …, E21 99}, E22={E22 90 , E22 91 , …, E22 99}, …, E224={E224 90 ,E224 91 , ..., E224 99}; wherein the distribution line type includes one or a combination of Pearson III distribution, generalized extreme value distribution, generalized Pareto distribution, and Copula joint distribution; the percentile threshold includes the 1st to 10th percentile value or the 90th to 99th percentile value; S33. Obtain the segmented extreme hydrological sequence E31, E32, ..., E324 of the segmented natural runoff sequence X31, X32, ..., X324; the obtaining of the segmented extreme hydrological sequence E31, E32, ..., E324 of the segmented natural runoff sequence X31, X32, ..., X324 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X31, X32, ..., X324 of S24, and then respectively determining the distribution line type of the segmented natural runoff sequence X31, X32, ..., X324, and then respectively determining the extreme hydrological events of different degrees of the 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequence X31, X32, ..., X324, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E31={E311, E312, ..., E31 10},E32={E321,E322,…,E32 10},…,E324={E3241,E3242,…,E324 10}; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E31={E31 90 ,E31 91 , …, E31 99}, E32={E32 90 , E32 91 , ..., E32 99}, ..., E324 =}E324 90 ,E324 91 ,…,E324 99}; wherein the distribution line type includes one or more combinations of Pearson III distribution, generalized extreme value distribution, generalized Pareto distribution, and Copula joint distribution; the percentile threshold includes the 1st to 10th percentile values ​​or the 90th to 99th percentile values.

[0008] Furthermore, the S4 step includes: S41. Determine the segmented extreme low water design value sequence Min1, Min2, ..., Min24; the determination of the segmented extreme low water design value sequence Min1, Min2, ..., Min24 includes: first taking the percentile threshold 10, for the first segmented study period, taking E11 of S31 10 、S32 E21 10 、S33 E31 10 The minimum value of the 10th percentile value is obtained as the first segment extreme low water design value Min1 10 Similarly, the other 23 segmented study periods of the 10th percentile value take the minimum value of the segmented extreme hydrological sequence in S31, S32, and S33 respectively, and obtain the 23 segmented extreme low water design values ​​Min2 10 , Min3 10 , …, Min24 10 ; Then take other percentile thresholds less than 10 percentile values ​​to determine the segmented extreme low water design values ​​of other percentile thresholds; Finally, the segmented extreme low water design value sequence Min1={Min11, Min12, …, Min1 10}, Min2={Min21, Min22,…,Min2 10},…,Min24={Min241,Min242,…,Min24 10}; S42. Determine the segmented extreme flood design value sequence Max1, Max2, ..., Max24; the determination of the segmented extreme flood design value sequence Max1, Max2, ..., Max24 includes: first taking the percentile threshold 90, for the first segmented study period, taking E11 of S31 90 、S32 E21 90 、S33 E31 90 The maximum value of the 90th percentile value is obtained as the first segment extreme flood design value Max1 90 Similarly, the maximum values ​​of the segmented extreme hydrological sequences in S31, S32, and S33 are taken for the other 23 segmented study periods of the 90th percentile value, respectively, to obtain the 23 segmented extreme flood design values ​​Max2 90 , Max3 90 , …, Max24 90; Then take other percentile thresholds greater than the 90th percentile value to determine the segmented extreme flood design values ​​of other percentile thresholds; Finally, the segmented extreme flood design value sequence Max1={Max1 90 , Max1 91 , …, Max1 99},Max2={Max2 90 , Max2 91 , …, Max2 99},…,Max24={Max24 90 , Max24 91 , …, Max24 99}; S43. Determine the extreme low water design value sequence MIN; the determination of the extreme low water design value sequence MIN includes: first taking a percentile threshold i less than 10 percentile values, and then taking the segmented extreme low water design value sequence {Min1 i , Min2 i , …, Min24 i}, and obtain the extreme low water design value Min corresponding to percentile threshold i i , and then combine the 1st to 10th percentile values ​​to obtain the extreme low water design value sequence MIN={Min1, Min2, …, Min 10}; S44. Determine the extreme flood design value sequence MAX; the determination of the extreme flood design value sequence MAX includes: first taking a percentile threshold i greater than the 90th percentile value, and then taking the segmented extreme hydrological design value sequence {Max1 i , Max2 i , …, Max24 i}, and obtain the extreme flood design value Max corresponding to percentile threshold i i , and then combine the 90th to 99th percentile values ​​to get the extreme flood design value MAX={Max 90 , Max 91 , …, Max 99}; S45. Comprehensive extreme low water design value sequence {Min1, Min2, …, Min 10} and extreme flood design value series {Max 90 , Max 91 , …, Max 99}, and obtain extreme hydrological design values ​​of different degrees in the target basin.

[0009] The working principle of the present invention is as follows: The present invention provides an extreme hydrological sequence reconstruction method, including: obtaining a natural runoff sequence, obtaining a segmented research period and a segmented natural runoff sequence, obtaining a segmented extreme hydrological sequence, and obtaining an extreme hydrological design value. First, the natural runoff sequence is obtained by improving three types of technologies, namely, a distributed hydrological model, a water yield coefficient reduction method, and a deep machine learning model; second, the annual cycle is divided into 24 segmented research periods according to the 24 solar terms, and the natural runoff sequences obtained based on the above three types of technologies are respectively divided into segmented natural runoff sequences according to the 24 segmented research periods; third, the distribution line types of the above three types of segmented natural runoff sequences are respectively determined, and segmented extreme hydrological sequences of different degrees are respectively determined according to the percentile threshold of the distribution; finally, the maximum or minimum value of different segmented extreme hydrological sequences corresponding to different percentile thresholds is taken to comprehensively obtain the extreme hydrological design value.

[0010] The beneficial effects of the present invention are as follows: The present invention introduces and improves the method for obtaining multiple types of natural runoff sequences, solves the problem of inconsistency in hydrological sequences; divides the annual cycle into 24 segmented research periods according to the 24 solar terms, solves the problem of relatively short sequences of the annual extreme value method; uses multiple approaches to determine the sequence distribution line type, solves the problem of large sample errors in parameter estimation of the annual extreme value method, and also solves the problem of difficulty in determining threshold values; the present invention broadens the evaluation criteria for extreme hydrological events, realizes non-destructive and accurate analysis of extreme hydrological design values, provides scientific data support for hydrological analysis and calculation, and achieves the purpose of better prediction of extreme hydrological events. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0012] Figure 1 is a flow chart of an extreme hydrological sequence reconstruction method provided by an embodiment of the present invention; Figure 2 is a flow chart of obtaining a natural runoff sequence S1 provided by an embodiment of the present invention; Figure 3 is a flow chart for obtaining segmented research periods and segmented natural runoff sequences S2 provided by an embodiment of the present invention; Figure 4 is a flowchart of obtaining a segmented extreme hydrological sequence S3 provided by an embodiment of the present invention; Figure 5 It is a flow chart of obtaining extreme hydrological design value S4 provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] In addition, the described features, structures or characteristics may be combined in one or more example embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the example embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0015] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown, an embodiment of the present invention provides an extreme hydrological sequence reconstruction method, including obtaining a natural runoff sequence S1, obtaining a segmented study period and a segmented natural runoff sequence S2, obtaining a segmented extreme hydrological sequence S3, and obtaining an extreme hydrological design value S4.

[0017] Further, the natural runoff sequence S1 is obtained, such as Figure 2 As shown, including: S11. Collect long-series daily flow data measured at hydrological stations in the target basin; S12. Obtain natural runoff sequence X1; S13. Obtain natural runoff sequence X2; S14. Obtain the natural runoff sequence X3.

[0018] Specifically, the S11 long sequence refers to a sequence length greater than 30 years.

[0019] Specifically, the S12. Obtaining the natural runoff sequence X1 includes: firstly establishing a distributed hydrological model, and verifying and validating the model using the daily flow data measured in the S11; then performing natural runoff restoration calculation on the target watershed to obtain the daily natural runoff sequence X1={X11, X12, …, X1 n}.

[0020] Specifically, the S13. Obtaining the natural runoff sequence X2 includes: using an inconsistency recognition method to test the mutation of the daily flow data measured in S11 to obtain the mutation time point; based on the mutation time point, dividing the daily flow data measured in S11 into a period T1 affected by natural rhythms and a period T2 affected by environmental changes; calculating the water production coefficient a of T1; transplanting the water production coefficient a to T2 to calculate the natural runoff sequence of T2; combining the natural runoff sequences of T1 and T2 to obtain the daily natural runoff sequence X2={X21, X22, …, X2 n}; Wherein, the water production coefficient a refers to the annual average runoff of T1 divided by the annual average precipitation.

[0021] Specifically, the S14. Obtaining the natural runoff sequence X3 includes: firstly using multi-source remote sensing data and measured meteorological data, adopting a causal analysis method, to determine the static characteristic factors and dynamic characteristic factors of the deep machine learning model; then establishing a deep neural network model, using the S11 measured daily flow data to train, adjust and verify the model; and then calculating the daily natural runoff sequence X3={X31, X32, …, X3 n}; wherein the causal analysis method includes one or more combinations of explainable machine learning methods and geospatial analysis methods.

[0022] Furthermore, the acquisition of segmented study periods and segmented natural runoff sequences S2 is as follows: Figure 3 As shown, including: S21. Get segmented research period; S22. Obtaining the segmented natural runoff sequence X11, X12, ..., X124 of the natural runoff sequence X1; S23. Obtaining the segmented natural runoff sequence X21, X22, ..., X224 of the natural runoff sequence X2; S24. Obtain the segmented natural runoff sequence X31, X32, ..., X324 of the natural runoff sequence X3.

[0023] Specifically, the S21. obtaining segmented research periods is to divide the annual cycle into 24 segmented research periods according to the time points of the 24 solar terms; wherein the 24 solar terms include: Beginning of Spring, Rain Water, Waking of Insects, Vernal Equinox, Pure Brightness, Grain Rain, Beginning of Summer, Grain Full, Grain in Ear, Summer Solstice, Lesser Heat, Greater Heat, Beginning of Autumn, End of Heat, White Dew, Autumnal Equinox, Cold Dew, Frost Descent, Beginning of Winter, Light Snow, Heavy Snow, Winter Solstice, Lesser Cold, Greater Cold; the time points of the 24 solar terms are determined according to the specific time points of each year.

[0024] Specifically, the step S22. Obtaining the segmented natural runoff sequences X11, X12, ..., X124 of the natural runoff sequence X1 is to divide the natural runoff sequence X1 into 24 segmented natural runoff sequences X11={X111, X112, ..., X114} according to the 24 segmented research periods of the step S21. n},X12={X121,X122,…,X12 n},…,X124={X1241,X1242,…,X124 n}.

[0025] Specifically, the step S23. Obtaining the segmented natural runoff sequences X21, X22, ..., X224 of the natural runoff sequence X2 is to divide the natural runoff sequence X2 into 24 segmented natural runoff sequences X21={X211, X212, ..., X214} according to the 24 segmented research periods of the step S21. n},X22={X221,X222,…,X22 n},…,X224={X2241,X2242,…,X224 n}.

[0026] Specifically, the step S24. Obtaining the segmented natural runoff sequence X31, X32, ..., X324 of the natural runoff sequence X3 is to divide the natural runoff sequence X3 into 24 segmented natural runoff sequences X31={X311, X312, ..., X314} according to the 24 segmented research periods of the step S21. n},X32={X321,X322,…,X32 n},…,X324={X3241,X3242,…,X324 n}.

[0027] Furthermore, the segmented extreme hydrological sequence S3 is obtained, such as Figure 4 As shown, including: S31. Obtaining the segmented extreme hydrological sequence E11, E12, ..., E124 of the segmented natural runoff sequence X11, X12, ..., X124; S32. Obtaining the segmented extreme hydrological sequence E21, E22, ..., E224 of the segmented natural runoff sequence X21, X22, ..., X224; S33. Obtain the segmented extreme hydrological sequence E31, E32, ..., E324 of the segmented natural runoff sequence X31, X32, ..., X324.

[0028] Specifically, the S31. Obtaining the segmented extreme hydrological sequence E11, E12, ..., E124 of the segmented natural runoff sequence X11, X12, ..., X124 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X11, X12, ..., X124 of the S22, and then respectively determining the distribution line types of the segmented natural runoff sequences X11, X12, ..., X124, and then respectively determining the extreme hydrological sequences of different degrees of the 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequences X11, X12, ..., X124, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E11={E111, E112, ..., E11 10},E12={E121,E122,…,E12 10},…,E124={E1241,E1242,…,E124 10}; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E11={E11 90 ,E11 91 ,…,E11 99}, E12={E12 90 ,E12 91 , …, E12 99}, …, E124={E124 90 ,E124 91 , ..., E124 99}.

[0029] Specifically, the S32. Obtaining the segmented extreme hydrological sequence E21, E22, ..., E224 of the segmented natural runoff sequence X21, X22, ..., X224 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X21, X22, ..., X224 of the S23, and then respectively determining the distribution line types of the segmented natural runoff sequences X21, X22, ..., X224, and then respectively determining the extreme hydrological events of different degrees in 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequences X21, X22, ..., X224, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E21={E211, E212, ..., E21 10},E22={E221,E222,…,E22 10},…,E224={E2241,E2242,…,E224 10}; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E21={E21 90 , E21 91 , …, E21 99}, E22={E22 90 , E2291 , …, E22 99}, …, E224={E224 90 ,E224 91 , ..., E224 99}.

[0030] Specifically, the S33. Obtaining the segmented extreme hydrological sequence E31, E32, ..., E324 of the segmented natural runoff sequence X31, X32, ..., X324 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X31, X32, ..., X324 of the S24, and then respectively determining the distribution line types of the segmented natural runoff sequences X31, X32, ..., X324, and then respectively determining the extreme hydrological events of different degrees in 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequences X31, X32, ..., X324, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E31={E311, E312, ..., E31 10},E32={E321,E322,…,E32 10},…,E324={E3241,E3242,…,E324 10}; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E31={E31 90 ,E31 91 , …, E31 99}, E32={E32 90 , E32 91 , ..., E32 99}, ..., E324 =}E324 90 ,E324 91 ,…,E324 99}.

[0031] Specifically, the distribution line type includes one or a combination of Pearson III distribution, generalized extreme value distribution, generalized Pareto distribution, and Copula joint distribution.

[0032] Specifically, the percentile threshold includes the 1st to 10th percentile values ​​or the 90th to 99th percentile values. In this embodiment, taking X11 as an example, the percentile threshold has the following two calculation methods: First, in ascending order, X11={X111, X112, …, X11 n Arrange the n natural runoff data in} to obtain {x1, x2, …, x n}, then the percentile is x=(1-a)x i +ax i+1 , i=[p(n+1)], a=p(n+1)-i, where i is the serial number after being sorted in ascending order; p is the probability corresponding to the percentile value; square brackets indicate rounding of values; Second, in ascending order, X11={X111, X112, …, X11 n Arrange the n natural runoff data in} to obtain {x1, x2, …, x m , …, x n}, less than or equal to x m The probability of p=(m-0.31) / (n+0.38) , if there are 100 observations, then the 95th percentile value is x 95 ( p =94.3%) and x 96 ( p =95.3%) of linear interpolation.

[0033] Furthermore, the extreme hydrological design value S4 is obtained, such as Figure 5 As shown, including: S41. Determine the segmented extreme low water design value sequence Min1, Min2, ..., Min24; S42. Determine the segmented extreme flood design value sequence Max1, Max2, ..., Max24; S43. Determine the extreme low water design value sequence MIN; S44. Determine the extreme flood design value sequence MAX; S45. Obtain extreme hydrological design values ​​of different degrees for the target watershed.

[0034] Specifically, the step S41. Determine the segmented extreme low water design value sequence Min1, Min2, ..., Min24, including: firstly taking the percentile threshold 10, for the first segmented study period, taking E11 of the step S31 10 、S32 E21 10 、S33 E31 10 The minimum value of the 10th percentile value is obtained as the first segment extreme low water design value Min1 10 Similarly, the other 23 segmented study periods of the 10th percentile value take the minimum value of the segmented extreme hydrological sequence in S31, S32, and S33 respectively, and obtain the 23 segmented extreme low water design values ​​Min2 10 , Min3 10 , …, Min24 10 ; Then take other percentile thresholds less than 10 percentile values ​​to determine the segmented extreme low water design values ​​of other percentile thresholds; Finally, the segmented extreme low water design value sequence Min1={Min11, Min12, …, Min1 10}, Min2={Min21, Min22,…,Min2 10},…,Min24={Min241,Min242,…,Min24 10}.

[0035] Specifically, the step S42. Determine the segmented extreme flood design value sequence Max1, Max2, ..., Max24, including: firstly, take the percentile threshold 90, for the first segmented study period, take E11 of the step S31 90 、S32 E21 90 、S33 E31 90 The maximum value of the 90th percentile value is obtained as the first segment extreme flood design value Max1 90 Similarly, the maximum values ​​of the segmented extreme hydrological sequences in S31, S32, and S33 are taken for the other 23 segmented study periods of the 90th percentile value, respectively, to obtain the 23 segmented extreme flood design values ​​Max2 90 , Max3 90 , …, Max24 90 ; Then take other percentile thresholds greater than the 90th percentile value to determine the segmented extreme flood design values ​​of other percentile thresholds; Finally, the segmented extreme flood design value sequence Max1={Max1 90 , Max1 91 , …, Max1 99},Max2={Max2 90 , Max2 91 , …, Max2 99},…,Max24={Max24 90 , Max24 91 , …, Max24 99}.

[0036] Specifically, the S43. determining the extreme low water design value sequence MIN includes: first taking a percentile threshold i less than 10 percentile values, and then taking the segmented extreme low water design value sequence {Min1 i , Min2 i , …, Min24 i}, and obtain the extreme low water design value Min corresponding to percentile threshold i i , and then combine the 1st to 10th percentile values ​​to obtain the extreme low water design value sequence MIN={Min1, Min2, …, Min 10}.

[0037] Specifically, the step S44. determining the extreme flood design value sequence MAX includes: first taking a percentile threshold i greater than the 90th percentile value, and then taking the segmented extreme hydrological design value sequence {Max1 i , Max2 i , …, Max24 i}, and obtain the extreme flood design value Max corresponding to percentile threshold i i , and then combine the 90th to 99th percentile values ​​to get the extreme flood design value MAX={Max 90 , Max 91 , …, Max 99}.

[0038] Specifically, the S45. obtains the extreme hydrological design values ​​of different degrees in the target basin, which is a comprehensive extreme low-flow design value sequence {Min1, Min2, ..., Min 10} and extreme flood design value series {Max 90 , Max 91 , …, Max 99}, and obtain extreme hydrological design values ​​of different degrees in the target basin.

[0039] The embodiments in this specification are described in a progressive manner, and the embodiments focus on the differences from other embodiments. The same or similar parts between the embodiments can be referenced to each other.

[0040] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0041] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0042] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0043] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for reconstructing extreme hydrological sequences, characterized in that: include: S1. Obtain natural runoff sequence; S2. Obtain segmented study periods and segmented natural runoff sequences; S3. Obtain segmented extreme hydrological series; S4. Obtain extreme hydrological design values.

2. The extreme hydrological sequence reconstruction method according to claim 1, characterized in that: S1. Obtaining natural runoff sequences, including: S11. Collecting long-sequence daily flow data measured at hydrological stations in the target basin; wherein the long sequence refers to a sequence length greater than 30 years; S12. Obtain the natural runoff sequence X1; the obtaining of the natural runoff sequence X1 comprises: firstly establishing a distributed hydrological model, and verifying and validating the model using the measured daily flow data of S11; then performing natural runoff restoration calculation on the target watershed to obtain the daily natural runoff sequence X1={X11, X12, …, X1 n }; S13. Obtain the natural runoff sequence X2; the obtaining of the natural runoff sequence X2 includes: using the inconsistency recognition method to test the mutation of the measured daily flow data of S11 to obtain the mutation time point; based on the mutation time point, dividing the measured daily flow data of S11 into a period T1 affected by natural rhythm and a period T2 affected by environmental changes; calculating the water production coefficient a of T1; transplanting the water production coefficient a to T2 to calculate the natural runoff sequence of T2; combining the natural runoff sequences of T1 and T2 to obtain the daily natural runoff sequence X2={X21, X22, …, X2 n }; Wherein, the water yield coefficient a refers to the annual average runoff of T1 divided by the annual average precipitation; S14. Obtain the natural runoff sequence X3; the obtaining of the natural runoff sequence X3 comprises: firstly using multi-source remote sensing data and measured meteorological data, adopting a causal analysis method, to determine the static characteristic factors and dynamic characteristic factors of the deep machine learning model; then establishing a deep neural network model, using the measured daily flow data of S11 to train, adjust and verify the model; then calculating the daily natural runoff sequence X3={X31, X32, …, X3 n }; wherein the causal analysis method includes one or more combinations of explainable machine learning methods and geospatial analysis methods.

3. The extreme hydrological sequence reconstruction method according to claim 1, characterized in that: S2. Obtain segmented study periods and segmented natural runoff sequences, including: S21. Obtaining segmented research periods; the obtaining segmented research periods is to divide the annual cycle into 24 segmented research periods according to the 24 solar terms; wherein the 24 solar terms include: Beginning of Spring, Rain Water, Waking of Insects, Spring Equinox, Tomb-Sweeping Day, Grain Rain, Beginning of Summer, Grain Full, Grain in Ear, Summer Solstice, Minor Heat, Major Heat, Beginning of Autumn, End of Heat, White Dew, Autumnal Equinox, Cold Dew, Frost Descent, Beginning of Winter, Minor Snow, Major Snow, Winter Solstice, Minor Cold, Major Cold; the 24 solar terms are determined according to specific time points each year; S22. Obtain the segmented natural runoff sequence X11, X12, ..., X124 of the natural runoff sequence X1; the segmented natural runoff sequence X11, X12, ..., X124 of the natural runoff sequence X1 is obtained by dividing the natural runoff sequence X1 into 24 segmented natural runoff sequences X11={X111, X112, ..., X114} according to the 24 segmented research periods of S21. n },X12={X121,X122,…,X12 n },…,X124={X1241,X1242,…,X124 n }; S23. Obtain the segmented natural runoff sequence X21, X22, ..., X224 of the natural runoff sequence X2; the segmented natural runoff sequence X21, X22, ..., X224 of the natural runoff sequence X2 is obtained by dividing the natural runoff sequence X2 into 24 segmented natural runoff sequences X21={X211, X212, ..., X214} according to the 24 segmented research periods of S21. n },X22={X221,X222,…,X22 n },…,X224={X2241,X2242,…,X224 n }; S24. Obtain the segmented natural runoff sequence X31, X32, ..., X324 of the natural runoff sequence X3; the segmented natural runoff sequence X31, X32, ..., X324 of the natural runoff sequence X3 is obtained by dividing the natural runoff sequence X3 into 24 segmented natural runoff sequences X31={X311, X312, ..., X31} according to the 24 segmented research periods of S21. n },X32={X321,X322,…,X32 n },…,X324={X3241,X3242,…,X324 n }.

4. The extreme hydrological sequence reconstruction method according to claim 1, characterized in that: S3. Obtaining segmented extreme hydrological series, including: S31. Obtain the segmented extreme hydrological sequence E11, E12, ..., E124 of the segmented natural runoff sequence X11, X12, ..., X124; the obtaining of the segmented extreme hydrological sequence E11, E12, ..., E124 of the segmented natural runoff sequence X11, X12, ..., X124 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X11, X12, ..., X124 of S22, and then respectively determining the distribution line type of the segmented natural runoff sequence X11, X12, ..., X124, and then respectively determining the extreme hydrological sequences of different degrees of the 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequence X11, X12, ..., X124, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E11={E111, E112, ..., E11 10 },E12={E121,E122,…,E12 10 },…,E124={E1241,E1242,…,E124 10 }; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E11={E11 90 ,E11 91 ,…,E11 99 }, E12={E12 90 ,E12 91 , …, E12 99 }, …, E124={E124 90 ,E124 91 , ..., E124 99 }; wherein the distribution line type includes one or a combination of Pearson III distribution, generalized extreme value distribution, generalized Pareto distribution, and Copula joint distribution; the percentile threshold includes the 1st to 10th percentile value or the 90th to 99th percentile value; S32. Obtain the segmented extreme hydrological sequence E21, E22, ..., E224 of the segmented natural runoff sequence X21, X22, ..., X224; the obtaining of the segmented extreme hydrological sequence E21, E22, ..., E224 of the segmented natural runoff sequence X21, X22, ..., X224 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X21, X22, ..., X224 of S23, and then respectively determining the distribution line type of the segmented natural runoff sequence X21, X22, ..., X224, and then respectively determining the extreme hydrological events of different degrees of the 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequence X21, X22, ..., X224, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E21={E211, E212, ..., E21 10 },E22={E221,E222,…,E22 10 },…,E224={E2241,E2242,…,E224 10 }; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E21={E21 90 , E21 91 , …, E21 99 }, E22={E22 90 , E22 91 , …, E22 99 }, …, E224={E224 90 ,E224 91 , ..., E224 99 }; wherein the distribution line type includes one or a combination of Pearson III distribution, generalized extreme value distribution, generalized Pareto distribution, and Copula joint distribution; the percentile threshold includes the 1st to 10th percentile value or the 90th to 99th percentile value; S33. Obtain the segmented extreme hydrological sequence E31, E32, ..., E324 of the segmented natural runoff sequence X31, X32, ..., X324; the obtaining of the segmented extreme hydrological sequence E31, E32, ..., E324 of the segmented natural runoff sequence X31, X32, ..., X324 comprises: firstly judging the independence of the samples in the segmented natural runoff sequence X31, X32, ..., X324 of S24, and then respectively determining the distribution line type of the segmented natural runoff sequence X31, X32, ..., X324, and then respectively determining the extreme hydrological events of different degrees of the 24 segments according to the percentile threshold of the distribution of the segmented natural runoff sequence X31, X32, ..., X324, if the percentile threshold is less than 10 percentile values, then the segmented extreme hydrological sequence E31={E311, E312, ..., E31 10 },E32={E321,E322,…,E32 10 },…,E324={E3241,E3242,…,E324 10 }; If the percentile threshold is greater than the 90th percentile value, the segmented extreme hydrological series E31={E31 90 ,E31 91 , …, E31 99 }, E32={E32 90 , E32 91 , ..., E32 99 }, ..., E324 =}E324 90 ,E324 91 ,…,E324 99 }; wherein the distribution line type includes one or more combinations of Pearson III distribution, generalized extreme value distribution, generalized Pareto distribution, and Copula joint distribution; the percentile threshold includes the 1st to 10th percentile values ​​or the 90th to 99th percentile values.

5. The extreme hydrological sequence reconstruction method according to claim 1, characterized in that: S4. Obtaining extreme hydrological design values, including: S41. Determine the segmented extreme low water design value sequence Min1, Min2, ..., Min24; the determination of the segmented extreme low water design value sequence Min1, Min2, ..., Min24 includes: first taking the percentile threshold 10, for the first segmented study period, taking E11 of S31 10 、S32 E21 10 、S33 E31 10 The minimum value of the 10th percentile value is obtained as the first segment extreme low water design value Min1 10 Similarly, the other 23 segmented study periods of the 10th percentile value take the minimum value of the segmented extreme hydrological sequence in S31, S32, and S33 respectively, and obtain the 23 segmented extreme low water design values ​​Min2 10 , Min3 10 , …, Min24 10 ; Then take other percentile thresholds less than 10 percentile values ​​to determine the segmented extreme low water design values ​​of other percentile thresholds; Finally, the segmented extreme low water design value sequence Min1={Min11, Min12, …, Min1 10 }, Min2={Min21, Min22,…,Min2 10 },…,Min24={Min241,Min242,…,Min24 10 }; S42. Determine the segmented extreme flood design value sequence Max1, Max2, ..., Max24; the determination of the segmented extreme flood design value sequence Max1, Max2, ..., Max24 includes: first taking the percentile threshold 90, for the first segmented study period, taking E11 of S31 90 、S32 E21 90 、S33 E31 90 The maximum value of the 90th percentile value is obtained as the first segment extreme flood design value Max1 90 Similarly, the maximum values ​​of the segmented extreme hydrological sequences in S31, S32, and S33 are taken for the other 23 segmented study periods of the 90th percentile value, respectively, to obtain the 23 segmented extreme flood design values ​​Max2 90 , Max3 90 , …, Max24 90 ; Then take other percentile thresholds greater than the 90th percentile value to determine the segmented extreme flood design values ​​of other percentile thresholds; Finally, the segmented extreme flood design value sequence Max1={Max1 90 , Max1 91 , …, Max1 99 },Max2={Max2 90 , Max2 91 , …, Max2 99 },…,Max24={Max24 90 , Max24 91 , …, Max24 99 }; S43. Determine the extreme low water design value sequence MIN; the determination of the extreme low water design value sequence MIN includes: first taking a percentile threshold i less than 10 percentile values, and then taking the segmented extreme low water design value sequence {Min1 i , Min2 i , …, Min24 i }, and obtain the extreme low water design value Min corresponding to percentile threshold i i , and then combine the 1st to 10th percentile values ​​to obtain the extreme low water design value sequence MIN={Min1, Min2, …, Min 10 }; S44. Determine the extreme flood design value sequence MAX; the determination of the extreme flood design value sequence MAX includes: first taking a percentile threshold i greater than the 90th percentile value, and then taking the segmented extreme hydrological design value sequence {Max1 i , Max2 i , …, Max24 i }, and obtain the extreme flood design value Max corresponding to percentile threshold i i , and then combine the 90th to 99th percentile values ​​to get the extreme flood design value MAX={Max 90 , Max 91 , …, Max 99 }; S45. Comprehensive extreme low water design value sequence {Min1, Min2, …, Min 10 } and extreme flood design value series {Max 90 , Max 91 , …, Max 99 }, and obtain extreme hydrological design values ​​of different degrees in the target basin.

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