CN114580171B - Method for identifying basin flood type and analyzing influence factors of basin flood type - Google Patents
Method for identifying basin flood type and analyzing influence factors of basin flood type Download PDFInfo
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Abstract
The invention discloses a method for identifying the type of flood in a drainage basin and analyzing the influence factors of the flood, which comprises the following steps: collecting geographic factors of weather, human activity factors, social and economic indexes and runoff data of a hydrological site in a research area; secondly, preprocessing the data; constructing a flood event dividing method for coupling the ultra-quantitative threshold sampling and the flow sequence moving variance, and dividing flood events from hydrologic site daily runoff data; step four, constructing a flood behavior characteristic index system to depict a complete flood process; step five, clustering the flood events obtained by division by using a clustering method, and identifying the main types and the flood characteristics of the flood in the drainage basin; step six, statistically analyzing the main flood types of each hydrological station, and identifying regional characteristics of the watershed flood process; and seventhly, analyzing the influence of the spatial heterogeneity of the influence factors on the flood process of the drainage basin based on the geographic detector. The method can accurately divide flood events from long-sequence runoff data, identify the main flood types and main influence factors of the watershed, and provide support for characteristic information mining, rain flood resource utilization and water resource scientific management of the watershed flood process.
Description
Technical Field
The invention relates to the technical field of flood type identification, in particular to a method for identifying a basin flood type and analyzing an influence factor of the basin flood type.
Background
Flood is one of the most frequent and most harmful natural disasters in the world, and seriously threatens the life safety and property safety of human beings. Affected by regional climate, landform and social economic development difference, water resources are unevenly distributed in space and time, so that regional difference in the flood process is obvious. Under the background of global change and high-speed development of social economy, flood damage is more serious, and scientific support can be provided for river basin water resource management, flood control and disaster reduction by quickly and effectively identifying main flood types and influence factors of the main flood types in the river basin.
The flood type identification needs a large amount of flood event samples, and the accurate division of the flood process from the continuous runoff sequence is of great importance. At present, the method for dividing the flood process from the continuous runoff sequence is mainly used for artificially judging the flood process by combining rainfall data and expert experience, the method is mainly used for judging rising and falling points by depending on the expert experience, the ratio of subjective factors is large, and the efficiency is lower in the process of screening the flood in large samples in batches. Therefore, a reasonable method needs to be constructed to quickly and accurately divide flood events from the continuous runoff sequence, and the method has important significance for identifying the types of the flood. In addition, the identification of the main flood types and the regional characteristics of the watershed is of great significance for guiding flood management. The core of identifying the flood type and the regional characteristics of the flood is to describe the watershed flood by constructing a comprehensive characteristic index system, and perform clustering analysis on the watershed flood process by using a clustering method based on the comprehensive characteristic index system to identify the flood type with a similarity process. At present, flood behavior characteristic index systems can be divided into three categories: hydrometeorological indices, hydrological indices, and flood process-based indices. The hydrological and hydrological indicators are mainly factors for inducing flood, including hydrological indicators such as cyclone path, atmospheric circulation mode and dynamic characteristics of weather system, and hydrological indicators such as precipitation, air temperature, evaporation, snow depth and soil humidity. The flood process-based indexes are calculated according to the behavior characteristics of the truly-occurring flood events, and compared with the hydrological meteorological indexes and the hydrological indexes, the flood with the same flood behavior is guaranteed to have hydrological similarity and is increasingly applied in the world. However, the indexes based on the flood process adopted in the current research are mainly concentrated on the magnitude characteristics of the flood, including the flood peak, the flood volume, the runoff depth and the like, and the characteristic elements of the line change process of the flood process are ignored, so that the river basin flood characteristic information is compressed and lost to different degrees. Therefore, future flood behavior characteristic index systems need to be further developed and perfected, and relevant indexes of flood magnitude, time domain characteristics, change rate and flood process line form characteristics are considered emphatically. Meanwhile, factors influencing the flood process are numerous, and the influence explanation degree of each factor on the flood process is different. Therefore, it is also critical to accurately identify the dominant factor among many factors.
Disclosure of Invention
The invention aims to provide a method for identifying the flood type of a drainage basin and analyzing influence factors of the flood type, which is used for constructing a flood event dividing method for coupling the over-quantitative threshold sampling and the flow sequence moving variance, so as to quickly and effectively accurately divide flood events from hydrological station daily runoff data, identify the main flood type and the representative characteristics of the drainage basin, analyze the influence of the spatial heterogeneity of the influence factors on a flood process, and provide support for the characteristic information mining of the drainage basin flood process, the utilization of rain flood resources and the scientific management of water resources.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
step 1) collecting continuous daily-scale radial flow data of hydrological stations in a downloading research area; collecting and downloading meteorological geographic factors, human activity factors and socioeconomic factors which influence the flood process of the drainage basin, wherein the method comprises the following steps: precipitation, temperature, wind speed, DEM, NDVI, gradient, urbanization rate, population density, GDP, carbon emission and the like;
step 2) data preprocessing, including one or more items of data space interpolation, sampling, reclassification, data format conversion and region statistics; the spatial interpolation of the data adopts an inverse distance weighted average method; sampling, generating uniform sampling points in space according to 1km resolution by a Fishnet function in GIS software, and acquiring various data at the positions of the sampling points; re-classifying by adopting a natural breakpoint method; the data format conversion mainly refers to the mutual conversion of vector and raster data according to research needs; the area statistics adopts the zonal statistics function in GIS software, and the average value, the maximum value, the minimum value and the like of each influence factor in each area are counted;
step 3) constructing a flood event partitioning method coupling the over-quantitative threshold sampling and the flow sequence moving variance, and partitioning flood events from daily runoff data: 3-1, judging the flood peak by using a super quantitative threshold sampling method; 3-2, determining an initial rise point and a water withdrawal point of the flood process based on the flow sequence moving variance, wherein a one-time flood process is performed between the initial rise point and the water withdrawal point;
step 4), constructing a flood behavior characteristic index system to describe a complete flood process: 4-1, constructing a flood behavior characteristic index system capable of completely describing a flood process, wherein the flood behavior characteristic indexes comprise relevant indexes of flood magnitude, time domain, change rate and flood process line shape characteristics; 4-2, calculating flood behavior characteristic indexes of the flood events obtained by dividing in the step 3);
step 5) clustering the flood events obtained by dividing by using a clustering method, and identifying the main types and flood characteristics of the flood in the drainage basin: clustering flood events of the divided research areas by using a k-means clustering algorithm to obtain the number of types of the drainage basin, and identifying the main types and the process characteristics of the flood of the drainage basin;
step 6), carrying out statistical analysis on main flood types of all hydrological stations in the research area, and identifying regional characteristics of the flood process of the drainage basin: calculating the occupation ratio of different flood types of each hydrological station, representing the flood type of the station by the type with the largest occupation ratio, dividing the station with the same flood type into the same area, and identifying the area characteristics of the watershed flood process;
step 7) analyzing the influence of the spatial heterogeneity of the influence factors on the flood process of the drainage area based on the geographic detector: and detecting the regional distribution characteristics of the flood process of the drainage basin by using a factor detection module in the geographic detector, and analyzing the influence of the spatial heterogeneity of the influence factors on the regional characteristics of the flood.
Further, the formula for determining the independent peak of the superstoichiometric threshold sampling method in step 3) -1 is as follows:
in the formula, theta is the time interval between two adjacent flood peaks, and the unit is day; a is the area of the drainage basin and the unit is km 2 ；Q min For two adjacent peaks Q 1 And Q 2 Minimum flow rate of m 3 /s。
Further, the adaptive threshold of the superscalar sampling method in step 3) is determined by the following six steps:
i, taking the median of the annual maximum sequence as an initial threshold value u 0 And forming a threshold space: u. of i ＝u 0 +(i-1)/5×σ x Where i is the number of threshold samples, σ x Standard deviation of daily mean flow; taking 5+ ln (A) days as sampling block time, A as river basin area (km) 2 ) And forming i overrun peak sequences. The number of super-threshold peaks should obey poisson distribution:
P(x＝k)＝e -λ λ k /k！ (2)
where k is 0,1,2, … and λ is the number of over-thresholds that occur on average per year.
And II, performing chi-square test on the sample number of each transfinite flood peak sequence, wherein the significance level is 0.05.
And III, drawing an average over function graph of each over-limit peak sequence, and judging a reasonable threshold value u range. Where the average over-run function is a linear function of the threshold u, which can be expressed as:
in the formula, X is a random variable, u is a threshold, ξ is a morphological parameter, and σ is a scale parameter.
Empirical mean of samples exceeding function e n (u) can be estimated using the following equation:
wherein n is the number of samples.
And IV, performing Anderson-Darling test on the over-limit peak sequences, and selecting a proper threshold value u by combining the average over-function graph result. The statistics of the Anderson-Darling test for k samples can be expressed as:
in the formula, n i The number of samples; f (x) is the cumulative distribution function of sample x, N ═ Σ N i Counting all samples; h N (x) A distribution function for all N samples; b is N ＝{x∈R:H N (x)<1}. Anderson-Darling statistics can be obtained through interpolation extrapolation in actual calculation
P of AD Value when P AD >And alpha, accepting the original hypothesis, and rejecting the original hypothesis if not. In the calculation, empirical distribution and theoretical cumulative distribution of successive transfinite flood peak sequences are used as two independent sample calculation statistics, and P is calculated AD The value is obtained.
And V, performing parameter estimation of Generalized Pareto Distribution (Generalized Pareto Distribution) on the selected threshold u and the overrun flood peak sequence corresponding to the threshold by using a maximum likelihood method. The generalized pareto distribution can be expressed as:
where ξ is the morphological parameter, σ is the scale parameter, u is the position parameter, and u is the threshold in this method. When the morphological parameter xi is zero, the generalized pareto distribution corresponds to an exponential distribution; when the morphological parameter xi is less than zero, the parameter is a normal Pareto distribution; when the morphological parameter xi is larger than zero, the particle is in Pareto-II type distribution.
And VI, selecting a flow value corresponding to the suitable recurrence period as a threshold value of the over-quantitative threshold value sampling method according to research requirements.
Further, the 3-2 in the step 3) of determining the rising point and the falling point of the flood process based on the flow sequence moving variance includes the following three steps: A. selecting proper mobile window days, and calculating the mobile variance Var of the flow sequence; B. threshold TH for determining flow sequence moving variance var (ii) a C. And comparing the moving variance of the flow sequence with a threshold value, and determining the rising point and the falling point of the flood.
Further, in step 3), 3-2, determining a rising point and a falling point of the flood process based on the flow sequence moving variance, wherein the flow sequence moving variance and a calculation formula of a threshold thereof are respectively as follows:
Var(i)＝Var(Q i ,Q i+1 ,…,Q i+n ) (7)
wherein Q is the flow sequence value, n is the number of days of the moving window,
is the mean value of the flow sequence moving variance, σ (Var) is the variance of the flow sequence moving variance, θ is a coefficient for controlling the sensitivity of the algorithm to identify the flood process, and the smaller θ, the more flood processes can be identified.
Further, the flood behavior characteristic indexes 4-1 in the step 4) include variation coefficients, skewness coefficients, kurtosis, flood rise time ratio, high pulse time ratio, standardized flood peak, flood rise rate, flood fall rate, flood peak, total flood amount, peak present time, flood occurrence time, duration, flood peak number and flood peak modulus.
Further, in step 4), the step 4-2 of calculating the flood behavior characteristic index of the flood event partitioned in step 3 may be represented as:
X＝[x 1 ,x 2 ,…,x n ] (9)
in the formula, X is a behavior characteristic index matrix of a flood event; x is a flood behavior characteristic index; n is the number of indexes.
Further, the distance used by the k-means clustering algorithm in the step 5) is an euclidean distance, and a calculation formula thereof is as follows:
wherein d is the Euclidean distance between two points X and Y in the n-dimensional Euclidean space, and the X coordinate is (X) 1 ,x 2 ,…,x n ) The Y coordinate is (Y) 1 ,y 2 ,…,y n )。
Further, the criterion for judging whether the clustering reaches the optimal result in the step 5) is that the Davies-Bouldin Index (DBI) reaches the minimum, and a calculation formula of the Index is as follows:
wherein n is the number of classes divided by the clustering algorithm, c i Is the cluster center of the ith class, σ i Is the distance of all flood events in the ith class from its cluster center, d is the distance between the class center of the ith class and the class center of the jth class, c j Is the cluster center of class j, σ j Is the distance of all flood events in class j from its cluster center.
Further, the input of the k-means clustering algorithm in the step 5) is a flood behavior characteristic index of flood events of all stations in the drainage basin, which can be expressed as:
for a drainage basin with m observation sites, a matrix W formed by flood behavior characteristic indexes of flood events of all sites in the drainage basin can be represented as:
W＝[Y 1 ,Y 2 ,…,Y m ] (12)
in the formula, Y m The subscript 1,2, …, m represents the 1 st to the mth sites, which is a matrix composed of flood behavior characteristic indexes of all flood events of the mth site in the drainage basin.
For a site Y with k flood events, a matrix formed by all flood event behavior characteristic indexes can be represented as follows:
Y＝[X 1 ,X 2 ,…,X k ] (13)
in the formula, X is the behavior characteristic index matrix of a flood event in the above formula (9). And equation (13) can be further expressed as:
according to equation (14), then W can be further expressed as:
wherein the first subscript 1,2, …, m represents the 1 st to the m th sites; the second subscript 1,2, …, k represents the 1 st through kth flood events; the third subscript 1,2, …, n denotes the 1 st to nth flood behavior characteristic indices.
Further, the interpretation degree of the influence factors in the factor detection and interaction detection module of the geographic detector in step 7) on the characteristics of the flood area can be expressed as:
where h is 1, …, L, is a partition that affects factor X; n is a radical of h And N is the number of units of the h-th region and the whole region, respectively;
and σ 2 The variance of the flood area characteristic Y in the h area and the whole area is respectively.
The invention has the beneficial effects that:
the method for identifying the flood type and analyzing the influence factors of the flood in the drainage basin can quickly and effectively accurately divide flood events from hydrological station daily runoff data, identify the main flood type and the representative characteristics of the drainage basin and analyze the influence of the spatial heterogeneity of the influence factors on the flood process by constructing a flood event division method coupling the super-quantitative threshold sampling and the flow sequence moving variance. The method can accurately divide flood events from long-sequence runoff data, identify the main flood types and main influence factors of the watershed, and provide support for characteristic information mining of the watershed flood process, rain flood resource utilization and water resource scientific management.
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Drawings
FIG. 1 is a flow chart of a method for identifying flood types and analyzing influence factors thereof in a drainage basin according to the present invention;
FIG. 2 is a flow diagram of a flood event classification method coupling ultraquantitative threshold sampling and flow sequence mobility variance;
FIG. 3 is a schematic diagram of an average overrun function for overrun samples;
FIG. 4 is a schematic diagram of a method for determining a rising point and a falling point of a flood process based on a flow sequence moving variance;
FIG. 5 is a schematic diagram of flood event behavior characteristic indicators;
fig. 6 is a schematic diagram of a process of identifying flood types and distribution characteristics of the flood types.
Detailed Description
Example 1
The invention provides a method for identifying flood types of drainage basins and analyzing influence factors of the flood types, and the specific application of the technical scheme of the invention is further explained by taking a certain drainage basin in China as a case area, wherein the specific application comprises the following steps:
step 1) collecting geographic factors of the gas images, human activity factors, social and economic indexes and runoff data of hydrological sites in a research area:
the daily runoff data of hydrological sites in the research area is from hydrological annual book of the people's republic of China, and the sites are uniformly distributed in the whole watershed; the day-by-day rainfall data of the meteorological site comes from the national meteorological science data center (http:// data. cma.cn); DEM (90m) and NDVI (1km) were obtained from the resource environmental sciences and data center of Chinese academy of sciences (https:// www.resdc.cn /); the social and economic data comprise urbanization rate, population density, GDP and carbon emission, which are respectively extracted from Chinese City statistics yearbook and Chinese carbon accounting databases (CEADs); the data of the influence factors are shown in Table 1:
table 1 impact factor data case
| Serial number | Influencing factor | For short | Unit of | |
| 1 | Annual cumulative precipitation | P total | mm | Site |
| 2 | Maximum daily precipitation per year | P max | mm | Site |
| 3 | Maximum 3 days of precipitation per year | P max3 | mm | Site |
| 4 | Slope of slope | Slope | — | Grid (C) |
| 5 | Elevation | Elev | m | Grid (C) |
| 6 | NDVI | NDVI | — | Grid (C) |
| 7 | Urbanization rate | CR | ％ | Grid (C) |
| 8 | Population density | HD | People/km 2 | Grid (C) |
| 9 | GDP | GDP | Billion yuan | Grid (C) |
| 10 | Intensity of human activity | IHA | Million tons | Grid (C) |
And 2) carrying out one or more items of spatial interpolation, sampling, reclassification, data format conversion and regional statistics on the precipitation, the DEM, the NDVI and the social and economic data.
And 3) dividing flood events, and compiling a corresponding program by utilizing matlab software. The specific algorithm is as follows:
3-1. determining flood peaks of flood events according to the over-quantitative threshold sampling method (fig. 2):
3-1-1 determining the flood threshold by taking a hydrologic station downstream of the basin as an example, the median of the annual maximum sequence is 32800m 3 S as initial threshold u 0 The threshold value space of the composition is 32800-51556 m 3 And(s) in the presence of a catalyst. And taking 24 days as sampling blocking time to form 15 overrun flood peak sequences. The threshold and number of overrun for each sequence are shown in table 2:
TABLE 2 threshold and parameter estimation results of generalized pareto distribution
3-1-2 chi-square test is carried out on the sample number of each overrun flood peak sequence, and the significance level is 0.05. The results show that 13 sequences in the 15 overrun flood peak sequences exceed the overrun flood peak sample number and accord with Poisson distribution (see H in Table 1) 0 One row, H 0 A value of 0 indicates that the original hypothesis is accepted, i.e. that the sample obeys a poisson distribution; h 0 A value of 1 indicates rejection of the original hypothesis).
3-1-3, drawing an average over function graph of each over-limit peak sequence, and judging a reasonable threshold value u range. From FIG. 3, it can be found that the average over-function plot shows that the linear trend appears at 43500m 3 Around/s. 43500m was selected in consideration of the convergence of the overrun data amount and the overrun distribution 3 The/s is used as a reference for determining the threshold.
3-1-4 Anderson-Darling test is carried out on the transfinite flood peak sequences, the result is shown in the table 2, and the result of the average excess function graph is combined, so that the threshold value is determined to be 43518m 3 /s。
3-1-5 pairs of selected threshold values 43518m 3 The parameter estimation of Generalized Pareto Distribution (Generalized Pareto Distribution) is carried out by using a maximum likelihood method according to/s and an overrun peak sequence corresponding to the threshold, and the result is shown in table 1;
3-1-6 according to research requirements, selecting a flow value corresponding to a recurrence period of 5 years as a threshold value of the over-quantitative threshold value sampling method.
3-1-7 calculating the maximum value in the runoff sequence by using a findpeaks function, writing corresponding judgment sentences according to the formula (1) and the threshold value, and screening flood event flood peak T meeting conditions pk 。
And 3-2, determining the rising point and the falling point of the flood process based on the flow sequence moving variance. The specific implementation process of determining the rising point and the falling point of the flood process based on the flow sequence moving variance is shown in fig. 4, the moving variance and the threshold of the flow sequence are calculated according to the above formula (7) and formula (8), and the rising point and the falling point of the flood process are determined by comparing the moving variance and the threshold of the flow sequence. A flood process is carried out between the rising point and the water withdrawal point. This division, the hydrological station divided 30 flood events.
Step 4), calculating flood behavior characteristic indexes:
4-1, screening indexes for describing flood behavior characteristics, wherein the flood behavior characteristic indexes comprise relevant indexes of flood magnitude, time domain, change rate and flood process line form characteristics, and the number of the flood behavior characteristic indexes adopted in the case area is 15, as shown in table 3:
TABLE 3 flood behavior characteristic index
Note: q t Is the flow (m) of the t-th period 3 S); a is the basin area (km) 2 )；t start And t end Are the rise and fall points of a flood event,
σ、μ 3 、μ 4 respectively, average flow (m) of flood events 3 (s), variance, third-order central moment and fourth-order central moment; t is t 0.75pk The time for the flow to be more than 0.75 time of the peak flood in the flood process is long.
4-2, according to the flood behavior characteristic indexes of the flood events divided in the step 3): the process of calculating the flood behavior feature index according to the flood event flow process line is shown in fig. 5.
Step 5) clustering the flood events obtained by dividing by using a clustering method, and identifying the main types and flood characteristics of the flood in the drainage basin: the flow of identifying the type of the basin flood is shown in the upper part of fig. 6, the flood events of the divided research areas are clustered by using a k-means clustering algorithm, the number of the basin categories is obtained, and the main type and the flood process characteristics of the basin flood are identified:
and inputting a matrix formed by the behavior characteristics of the flood events into matlab, and clustering the site flood events by using a kmeans function. And (4) selecting the DBI index as a clustering effect judgment standard, wherein a DBI index calculation formula is shown as a formula (10). The smaller the DBI index value is, the larger the difference between categories is, and the better the clustering effect is. In this case, site flood events are classified into 3 categories.
Step 6), carrying out statistical analysis on main flood types of all hydrological stations in the research area, and identifying regional characteristics of the flood process of the drainage basin: the process of identifying the regional distribution characteristics of the watershed flood process is shown in the lower half of fig. 6, and the regional distribution characteristics of the watershed flood process are identified by calculating the occupation ratio of different flood types of each hydrological station, representing the flood type of the station by the type with the largest occupation ratio, dividing the station with the same flood type into the same region, and identifying the regional distribution characteristics of the watershed flood process.
Step 7) analyzing the influence of the spatial heterogeneity of the influence factors on the distribution characteristics of the flood type areas of the drainage basin by using a geographic detector method:
the influence factors are classified by adopting a natural breakpoint method, meteorological factors such as annual accumulated precipitation, annual maximum daily precipitation and annual maximum 3-day precipitation are classified into 10 grades, geographic factors such as gradient and elevation are classified into 12 grades, NDVI factors are classified into 6 grades, and urbanization rate, population density, GDP and human activity intensity factors are classified into 8 grades.
Detecting the influence factors of the characteristics of the flood area of the drainage basin by using a factor detection module in the geographic detector, and determining the influence degree of each influence factor on the characteristics of the flood area, wherein the result is shown in table 4:
table 4 geo-detector results
The above described geo-detector results indicate that the influence and interaction of the basin meteorological conditions, geographical factors, land use and human activities are important causes of flood area characteristics. For a single factor, meteorological conditions (q 0.40-0.57), geographic factors (q 0.08-0.54), and human activities (q 0.24-0.48) are the main drivers of flood area characteristics, while vegetation coverage (q 0.15) has less impact. For multifactorial co-action, P max3 The combined effect of Elev and Elev has the greatest effect on the characteristics of the flood area (q 0.79), followed by P total And P max In a mixture ofWork together (q ═ 0.72).
The above description is only for the implementation of the present invention, and is not intended to limit the present invention, and the conversion of data and the selection of the clustering algorithm in the present invention can be set according to the needs and the specific research area. All changes, equivalents, modifications and the like which come within the scope of the invention as defined by the appended claims are intended to be embraced therein.
Claims (5)
1. A method for identifying the flood type of a drainage basin and analyzing the influence factors of the flood type is characterized in that: the method comprises the following steps:
step 1) collecting continuous daily-scale radial flow data of hydrological stations in a downloading research area; collecting and downloading meteorological geographic factors, human activity factors and social and economic factors which influence the flood process of the drainage basin;
step 2), data preprocessing, including one or more items of data spatial interpolation, sampling, reclassification, data format conversion and region statistics;
step 3) constructing a flood event partitioning method coupling the over-quantitative threshold sampling and the flow sequence moving variance, and partitioning flood events from daily runoff data: 3-1, judging the flood peak by using a super-quantitative threshold sampling method; the threshold value of the super quantitative threshold value sampling method is determined by the following six steps: i, taking the median of the annual maximum sequence as an initial threshold value u 0 And forming a threshold space: u. of i ＝u 0 +(i-1)/5×σ x Where i is the number of threshold samples, σ x Standard deviation of daily average flow; taking 5+ ln (A) days as sampling block time, A is drainage basin area and unit is km 2 I overrun peak sequences are formed; II, performing chi-square test on the sample number of each overrun flood peak sequence, wherein the significance level is 0.05; III, drawing an average over function graph of each over-limit peak sequence, and judging the range of the threshold value u; IV, performing Anderson-Darling test on the i transfinite flood peak sequences, and selecting a threshold u by combining the average transfinite function graph result; v, performing parameter estimation of generalized pareto distribution on the selected threshold u and the overrun flood peak sequence corresponding to the threshold by using a maximum likelihood method; VI, according to the research requirements,selecting a flow value corresponding to the suitable recurrence period as a threshold value of the over-quantitative threshold value sampling method; 3-2, determining a rising point and a water withdrawal point of a flood process based on the flow sequence moving variance, wherein a flood process is performed between the rising point and the water withdrawal point, and the rising point and the water withdrawal point of the flood process are determined based on the flow sequence moving variance, and the method comprises the following three steps: A. selecting the number of days of a moving window, and calculating the moving variance Var of the flow sequence; B. determining a threshold TH for a variance of a flow sequence movement var (ii) a C. Comparing the moving variance of the flow sequence with a threshold value, and determining a rising point and a falling point of the flood; the calculation formula of the threshold value of the flow sequence moving variance is as follows:
in the formula (I), the compound is shown in the specification,
is the mean value of the flow sequence moving variance, sigma (Var) is the variance of the flow sequence moving variance, theta is a coefficient for controlling the sensitivity of identifying the flood process, and the smaller theta is, the more flood processes can be identified;
step 4), constructing a flood behavior characteristic index system to describe a complete flood process: 4-1, constructing a flood behavior characteristic index system capable of completely depicting a flood process, wherein the flood behavior characteristic indexes comprise relevant indexes of flood magnitude, time domain, change rate and flood process line state characteristics; 4-2, calculating flood behavior characteristic indexes of the flood events obtained by dividing in the step 3);
step 5) clustering the flood events obtained by division by using a clustering method, and identifying the main types and the flood characteristics of the flood in the drainage basin: clustering flood events of the divided research areas by using a k-means clustering algorithm to obtain the number of types of the drainage basin, and identifying the main types and the process characteristics of the flood of the drainage basin;
step 6), carrying out statistical analysis on main flood types of all hydrological stations in the research area, and identifying regional characteristics of the flood process of the drainage basin: calculating the occupation ratios of different flood types of each hydrological station, representing the flood type of the station by the type with the largest occupation ratio, dividing the station with the same flood type into the same area, and identifying the area characteristics of the watershed flood process;
step 7) analyzing the influence of the spatial heterogeneity of the influence factors on the flood process of the drainage area based on the geographic detector: and detecting the influence factors of the regional distribution characteristics of the watershed flood process by using a factor detection module in the geographic detector, and analyzing the influence of the spatial heterogeneity of the influence factors on the regional characteristics of the flood.
2. The method of claim 1, wherein the method comprises the steps of: the independent flood peak in the over-quantitative threshold sampling method in the step 3) should meet the following conditions:
in the formula, theta is the time interval between two adjacent flood peaks, and the unit is day; a is the area of the drainage basin and the unit is km 2 ；Q min For two adjacent peaks Q 1 And Q 2 Minimum flow rate in m 3 /s。
3. The method of claim 1, wherein the method comprises the steps of: the flood behavior characteristic indexes in the step 4) comprise relevant indexes of flood magnitude, time domain, change rate and flood process line state characteristics, and specifically comprise variation coefficients, state bias coefficients, kurtosis, flood rise time ratio, high pulse time ratio, standardized flood peak, flood rise rate, flood fall rate, flood peak, total flood amount, peak occurrence time, flood occurrence time, duration, flood peak number and flood peak modulus.
4. The method of claim 1, wherein the method comprises the steps of: the distance adopted by the k-means clustering algorithm in the step 5) is an Euclidean distance, the standard for judging that the clustering reaches the optimal result is that the Daisenberg Digital index DBI reaches the minimum, and the calculation formula of the index is as follows:
wherein n is the number of categories divided by the clustering algorithm, c i Is the cluster center of the ith class, σ i Is the distance of all flood events in the ith class from its cluster center, d is the distance between the class center of the ith class and the class center of the jth class, c j Is the cluster center of class j, σ j Is the distance of all flood events in class j from its cluster center.
5. The method of claim 1, wherein the method comprises the steps of: and 7) detecting the interpretation degree of the influence factors on the characteristics of the flood area by adopting a factor detection module and an interaction detection module in the geographic detector.
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