CN117370939A - Basin rainfall runoff response relation evolution analysis method and system considering soil moisture content - Google Patents
Basin rainfall runoff response relation evolution analysis method and system considering soil moisture content Download PDFInfo
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Abstract
The invention discloses a drainage basin rainfall runoff response relation evolution analysis method and system considering soil moisture content, comprising the steps of calling a preconfigured hydrologic variation diagnosis method set and dividing a benchmark period and a change period; respectively adopting generalized extremum distribution functions to construct edge distribution of each research data, and analyzing change characteristics of a reference period and a change period; based on the edge distribution of the research data, constructing two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff; fitting precipitation distribution and two-dimensional conditional joint distribution of precipitation and soil moisture content based on conditional runoff to form conditional probability distribution; and obtaining the drainage basin rainfall runoff response relation evolution characteristics, and pre-storing the characteristics for hydrologic forecasting. The main characteristics of different elements in each distribution type are combined, so that the basin rainfall runoff response relation evolution rule is obtained, and the method is comprehensive and reliable.
Description
Technical Field
The invention belongs to the technical field of hydrology, in particular to a basin rainfall runoff response relation evolution analysis method considering the water content of soil.
Background
The rainfall runoff response relationship of the river basin is important content of hydrology, reflects the runoff generating capacity of the river basin on rainfall and the runoff forming process, and is a foundation of hydrologic simulation, flood forecast, water resource evaluation, water and soil conservation and the like. As climate change and human activity affect, the basin rainfall runoff response relationship may change, affecting basin hydrologic cycle and water resource management. Therefore, the method for analyzing the evolution characteristics and the mechanism of the rainfall runoff response relationship of the drainage basin has important theoretical significance and practical value for revealing the hydrologic variation rule of the drainage basin, evaluating the influence of hydrologic variation, improving the accuracy of hydrologic forecasting.
For this problem, existing research main methods include a statistical analysis-based method, a hydrologic model-based method, and a neural network method. The method based on statistical analysis cannot reveal the inherent mechanism of the river basin rainfall runoff response relationship and cannot predict the future change condition, and the method based on the hydrologic model has uncertainty and greater sensitivity.
Problems with the prior art mainly include: the influence factors of the rainfall runoff response relationship of the river basin are more, such as rainfall, rainfall intensity, rainfall space-time distribution, soil water content, land utilization, vegetation coverage, topography and the like, the interaction and the comprehensive effect among the factors are difficult to quantify, and the change of the rainfall runoff response relationship of the river basin is complex and various. The change of the rainfall runoff response relationship has space-time heterogeneity, and the change characteristics and mechanisms of different watercourses, different scales and different time periods can be different, so that the specificity and regionality of the watercourses need to be considered in the analysis and prediction of the rainfall runoff response relationship of the watercourses, and a general rule and method are difficult to form. The change of the river basin rainfall runoff response relationship is influenced by the climate change and the human activity, the relative contribution and interaction of the two effects are difficult to distinguish, the reason for the change of the river basin rainfall runoff response relationship is difficult to determine, and the influence of the change of the river basin rainfall runoff response relationship on water resources and water environment is difficult to evaluate.
In summary, the evolution analysis of the water circulation elements in the changing environment is not deep enough, the systematicness and the reliability are lacking, the analysis is often carried out between single elements or limited elements, and the connection between different elements is lacking. The water circulation process is a whole, the interaction among elements is inseparable, the analysis between limited elements often induces the effect of more or less than a certain driving factor to the elements, and the knowledge of the evolution of the water circulation elements is insufficient, and even incorrect knowledge exists.
Disclosure of Invention
The invention aims to provide a basin rainfall runoff response relation evolution analysis method and system considering the water content of soil so as to solve the problems in the prior art.
According to one aspect of the application, a method for analyzing the evolution of the rainfall runoff response relationship of a river basin in consideration of the water content of soil is provided, and comprises the following steps:
step S1, determining a range of a research river basin and acquiring research data, wherein the research data comprises soil moisture content, rainfall data and runoff data; calling a preconfigured hydrologic variation diagnosis method set, searching for mutation points through research data, and dividing the surface average precipitation process into a reference period and a change period according to the mutation points;
S2, respectively adopting generalized extremum distribution functions to construct edge distribution of each research data aiming at a reference period and a change period, and analyzing change characteristics of the reference period and the change period;
step S3, constructing two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff by using Gaussian Copula functions of an elliptic Copula family based on edge distribution of research data, and obtaining Kendall coefficients tau and Copula parameters alpha;
s4, fitting precipitation distribution and two-dimensional condition joint distribution of the precipitation and the soil water content based on conditional runoff aiming at a reference period and a change period to form conditional probability distribution;
and S5, obtaining drainage basin rainfall runoff response relation evolution characteristics based on the edge distribution, the joint distribution and the condition distribution obtained in the steps S2 to S4, and pre-storing the drainage basin rainfall runoff response relation evolution characteristics for hydrologic forecasting.
According to one aspect of the application, the step S1 is further:
s11, obtaining geographic data including a digital elevation model of a research river basin range, extracting a river network and selecting at least one runoff section;
s12, calling a preconfigured water collecting region dividing module to divide the range of the research river basin into N water collecting regions, and establishing a mapping relation between the water collecting regions and the runoff sections, wherein N is a natural number;
S13, collecting research data of each water collecting area and pre-analyzing the characteristics of the research data, wherein the characteristics comprise soil moisture content, rainfall data and runoff data;
s14, constructing a hydrologic variation diagnosis method set, wherein the hydrologic variation diagnosis method comprises an MK method, an ITA method, a Pettitt method, a Cramer' S method, a Yamamoto method and a sliding T test method, and the MK method comprises a basic MK method, a trending preset white MK, a variance correction MK and a bootstrap method MK;
and S15, calling at least two hydrologic variation diagnosis methods according to the characteristics of the research data to form a hydrologic variation diagnosis unit, searching mutation points in the research data, and dividing the surface average precipitation process into a reference period and a change period according to the mutation points.
According to one aspect of the present application, the step S2 is further:
s21, respectively acquiring research data of a reference period and a change period, and sequentially extracting annual sequence data of the reference period and the change period and annual flood period sequence data;
s22, selecting at least one generalized extremum distribution function from a preconfigured generalized extremum distribution function library, and determining undetermined parameters of the generalized extremum distribution function according to the distribution characteristics of research data, wherein the undetermined parameters comprise position parameters, scale parameters and shape parameters;
S23, respectively adopting generalized extremum distribution functions to perform edge distribution fitting on the research data aiming at a reference period and a change period of each runoff section, and calculating the fitting goodness and the fitting effect to obtain the edge distribution of the research data of each period of each runoff section;
and step S24, calculating analysis indexes one by one aiming at the edge distribution of the research data, and comparing the change characteristics of the reference period and the change period of each section.
According to one aspect of the present application, the step S3 is further:
step S31, selecting at least two Copula functions from a preconfigured Copula function library; analyzing the correlation of the research data and determining the type of the Copula function to obtain a pending Copula function;
s32, constructing joint distribution aiming at each research data by adopting a undetermined Copula function, wherein the construction of two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff by utilizing Gaussian Copula functions of an elliptical Copula group, and calculating construction parameters including Kendall coefficient tau and Copula parameter alpha to represent the correlation degree and correlation structure of each research data;
and S33, analyzing each research data based on the Copula function after the parameters are determined to perform joint distribution, calculating analysis indexes, and comparing the change characteristics of the reference period and the change period.
According to one aspect of the present application, the step S5 is further:
s51, calculating a characterization index of a rainfall runoff response relationship of the river basin by using edge distribution, joint distribution and conditional distribution; the characterization indexes comprise a rainfall elastic coefficient, a soil moisture content elastic coefficient and a rainfall-soil moisture content-runoff three-dimensional elastic coefficient;
step S52, calling the difference analysis of the edge distribution, the joint distribution and the condition distribution on the reference period and the change period, and judging the reason of each difference;
and step S53, obtaining the drainage basin rainfall runoff response relation evolution characteristics, and pre-storing the characteristics for hydrologic forecasting.
According to one aspect of the application, the process of collecting the moisture content of the soil and pre-analyzing in the step S13 further includes:
step S13a, sequentially acquiring a soil moisture content sequence and acquisition point position information of each acquisition point aiming at each water collecting area in the range of the research area;
step S13b, calling the pre-constructed multi-source remote sensing data, extracting the remote sensing index, and fusing the remote sensing index and the soil moisture content data of the acquisition points by adopting a GIS module to obtain the spatial distribution of the soil moisture content of each water collecting area; the remote sensing indexes comprise a microwave bright temperature, a normalized vegetation index and a soil humidity index;
Step S13c, constructing a distribution map gradient of the soil moisture content based on the spatial distribution of the soil moisture content, dividing each water collecting area into at least one calculation unit according to a soil moisture content threshold value, and calculating an average value of the soil moisture content of the water collecting areas according to the soil moisture content of each calculation unit;
and step S13d, calculating an annual average value of the soil moisture content of the water collection area corresponding to each runoff section and a flood season average value in each year according to the mapping relation between each runoff section and the water collection area.
According to one aspect of the present application, the process of calculating the construction parameter in the step S32 is further:
adopting a Goodless-of-fit test or a Bootstrap test to test the two-dimensional and three-dimensional joint distribution, and evaluating the construction Goodness;
step S32a, reading at least part of research data as a data set;
step S32b, calling a Goodness-of-fit test or a Bootstrap test according to the type of the joint distribution to perform calculation of a Goodness-of-fit test or a Bootstrap test, so as to obtain corresponding test statistics and reject domains, and constructing an evaluation index of Goodness;
and step S32c, evaluating and analyzing according to the calculation result, comparing the effects of different methods and parameters, and evaluating whether the construction goodness meets the requirement.
The step S4 is further: according to the edge distribution and the joint distribution, the precipitation distribution based on the conditional runoff and the joint distribution of the precipitation and the soil water content condition are calculated according to the definition of the conditional probability and aiming at the reference period and the change period.
According to one aspect of the present application, the step S5 further includes step S50:
step S50a, according to the two-dimensional condition joint distribution of precipitation and soil moisture content, a condition cumulative distribution function is obtained;
step S50b, a conditional probability density function is obtained according to the conditional cumulative distribution function;
and step S50c, a conditional probability density function obtains the median and the uncertainty interval of the parameters.
According to an aspect of the present application, the step S51 further includes:
step S51a, calculating to obtain a characterization index, and reading at least part of research data as verification data;
step S51b, calculating root mean square errors of measured values and calculated values in the research data respectively by adopting edge distribution, joint distribution and conditional distribution; judging whether a nonlinear response relation exists according to the threshold value;
step S51c, if the calculated characterization index does not exist, outputting the calculated characterization index; if the characteristic index exists, correcting the characteristic index by adopting a nuclear regression or local weighted regression method.
According to another aspect of the present application, there is provided a basin rainfall runoff response relationship evolution analysis system considering the water content of soil, including: at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,
the memory stores instructions executable by the processor for execution by the processor to implement the basin rainfall runoff response relationship evolution analysis method taking the soil moisture content into consideration according to any one of the above technical schemes.
The method has the beneficial effects that the method is developed according to the progressive angles of edge distribution, joint distribution and conditional distribution, the basic period and the change period are divided by key element precipitation, and the main characteristics of different elements in each distribution type are combined, so that the basin rainfall runoff response relation evolution rule is obtained. Compared with the traditional method, the method has the advantage that the conclusion is more comprehensive and reliable. The related advantages will be described in detail below.
Drawings
Fig. 1 is a flow chart of the present invention.
Fig. 2 is a flowchart of step S1 of the present invention.
Fig. 3 is a flow chart of step S2 of the present invention.
Fig. 4 is a flowchart of step S3 of the present invention.
Fig. 5 is a flowchart of step S5 of the present invention.
Detailed Description
As shown in fig. 1, according to an aspect of the present application, there is provided a basin rainfall runoff response relation evolution analysis method considering the water content of soil, including the steps of:
step S1, determining a range of a research river basin and acquiring research data, wherein the research data comprises soil moisture content, rainfall data and runoff data; calling a preconfigured hydrologic variation diagnosis method set, searching for mutation points through research data, and dividing the surface average precipitation process into a reference period and a change period according to the mutation points;
s2, respectively adopting generalized extremum distribution functions to construct edge distribution of each research data aiming at a reference period and a change period, and analyzing change characteristics of the reference period and the change period;
step S3, constructing two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff by using Gaussian Copula functions of an elliptic Copula family based on edge distribution of research data, and obtaining Kendall coefficients tau and Copula parameters alpha;
s4, fitting precipitation distribution and two-dimensional condition joint distribution of the precipitation and the soil water content based on conditional runoff aiming at a reference period and a change period to form conditional probability distribution;
And S5, obtaining drainage basin rainfall runoff response relation evolution characteristics based on the edge distribution, the joint distribution and the condition distribution obtained in the steps S2 to S4, and pre-storing the drainage basin rainfall runoff response relation evolution characteristics for hydrologic forecasting.
In the embodiment, mutation points of the watershed hydrologic variation can be effectively identified, edge distribution, joint distribution and conditional distribution of the watershed rainfall runoff response relationship are constructed, evolution characteristics of the watershed rainfall runoff response relationship are evaluated, and accuracy and reliability of hydrologic analysis are improved. Specifically, by adopting a preconfigured hydrologic variation diagnosis method set, comprising Mann-Kendall test, pettitt test, buishand test and the like, the advantages and disadvantages and applicable conditions of different test methods are comprehensively considered, and the accuracy and reliability of mutation point identification are improved. By adopting a generalized extremum distribution function and a Gaussian copula function, the edge distribution and the joint distribution of each hydrologic variable are respectively fitted, the extreme value and the nonlinear correlation of the hydrologic variable are considered, and the accuracy and the sensitivity of the distribution fitting are improved. By adopting the condition runoff to the precipitation distribution and the two-dimensional condition joint distribution of the precipitation and the soil moisture content, the difference and the change rate of the reference period and the change period are analyzed, the influence of the soil moisture content on the rainfall runoff response relationship is considered, and the comprehensiveness and the practicability of the evolution analysis are improved.
As shown in fig. 2, according to an aspect of the present application, the step S1 is further:
s11, obtaining geographic data including a digital elevation model of a research river basin range, extracting a river network and selecting at least one runoff section; specifically, digital Elevation Model (DEM) data of the scope of the study basin, such as SRTM, ASTER, etc., is downloaded, and the appropriate resolution and format, such as 30m, TIFF, etc., is selected. And (3) importing DEM data by using Geographic Information System (GIS) software such as ArcGIS, QGIS and the like, and performing preprocessing operations such as projection conversion, resampling and the like so as to ensure the accuracy and consistency of the data. A hydrological analysis tool such as ArcHydro, tauDEM using GIS software extracts a river network, selects at least one runoff section such as a river basin outlet, a main tributary and the like according to river basin characteristics and research purposes, and records position and attribute information such as longitude and latitude, flow, area and the like.
S12, calling a preconfigured water collecting region dividing module to divide the range of the research river basin into N water collecting regions, and establishing a mapping relation between the water collecting regions and the runoff sections, wherein N is a natural number;
the hydrologic analysis tool using GIS software divides the range of the research river basin into N water collecting areas according to river network and DEM data, N is a natural number, and can be adjusted according to the scale and complexity of the river basin, and the proposal is generally 10-100. And establishing a mapping relation between the water collecting areas and the runoff sections, namely determining the runoff sections corresponding to the outlets of each water collecting area and the upstream water collecting area of each runoff section, wherein space analysis tools of GIS software such as space connection, space selection and the like can be used for operation.
S13, collecting research data of each water collecting area and pre-analyzing the characteristics of the research data, wherein the characteristics comprise soil moisture content, rainfall data and runoff data;
the research data of each water collecting area, including soil moisture content, rainfall data and runoff data, can be acquired from different data sources, such as ground observation stations, satellite remote sensing, meteorological modes and the like, and proper time scales and space scales, such as day, month, year, point, face and the like, are selected. The features of the pre-analytical study data, such as data integrity, consistency, reliability, distribution type, trend of variation, periodicity, variability, etc., can be manipulated using different statistical analysis tools, such as Excel, R, matlab, etc.
S14, constructing a hydrologic variation diagnosis method set, wherein the hydrologic variation diagnosis method comprises an MK method, an ITA method, a Pettitt method, a Cramer' S method, a Yamamoto method and a sliding T test method, and the MK method comprises a basic MK method, a trending preset white MK, a variance correction MK and a bootstrap method MK;
the method is characterized by comprising the steps of constructing a hydrologic variation diagnosis method set, wherein the hydrologic variation diagnosis method set comprises an MK method, an ITA method, a Pettitt method, a Cramer's method, a Yamamoto method and a sliding T test method, the methods are variation diagnosis methods based on non-parameter assumptions, the method can be suitable for different types of hydrologic data, at least two hydrologic variation diagnosis methods are called according to the characteristics of research data to form a hydrologic variation diagnosis unit, variation diagnosis is carried out on the research data, mutation points in the research data are searched, and different programming languages and software such as Python, R, matlab, excel and the like can be used for realizing algorithms. According to the mutation point, the surface average precipitation process is divided into a reference period and a change period, the mutation point is generally taken as a boundary, the data before the mutation point is taken as the reference period, the data after the mutation point is taken as the change period, and the mutation points can be combined or split according to the number and the positions of the mutation points to cluster so as to ensure that the data volume of the reference period and the change period is enough and representative.
And S15, calling at least two hydrologic variation diagnosis methods according to the characteristics of the research data to form a hydrologic variation diagnosis unit, searching mutation points in the research data, and dividing the surface average precipitation process into a reference period and a change period according to the mutation points. In this example, MK-EMD, MK-ITA, MK-PCA and other combinations may be used.
If MK-ITA is used, the data processing procedure is specifically:
and (3) carrying out Mann-Kendall test on the time sequence of the research data to judge whether monotonic trend exists, and if so, calculating the significance level and the slope of the trend. And (3) carrying out an ITA method on the time sequence of the research data, judging whether mutation points exist, and if so, calculating the positions and the number of the mutation points, and the mean value and the variance before and after mutation. And (3) carrying out segmentation processing on the time sequence of the research data, dividing the time sequence into a plurality of subsequences according to mutation points, and respectively carrying out Mann-Kendall test and ITA method on each subsequence to obtain the trend and mutation characteristics of each subsequence. And comprehensively analyzing the time sequence of the research data, and obtaining the overall trend and mutation characteristics of the time sequence of the research data, and the influence degree and action mechanism of each factor according to the trend and mutation characteristics of each subsequence.
If MK-EMD is used, the data processing flow may be: EMD method is carried out on the time sequence of the research data, the time sequence of the research data is decomposed into a plurality of Intrinsic Mode Functions (IMFs) and residual terms, wherein the IMFs represent high-frequency components of the time sequence, and the residual terms represent low-frequency components of the time sequence. And carrying out Mann-Kendall test on residual items of the time sequence of the research data, judging whether monotonic trend exists, and if so, calculating the significance level and the slope of the trend. And (3) carrying out Mann-Kendall test on each IMF of the time sequence of the research data, judging whether mutation points exist, and if so, calculating the positions and the number of the mutation points, and the mean value and the variance before and after mutation. And comprehensively analyzing the time sequence of the research data, and obtaining the overall trend and mutation characteristic of the time sequence, and the influence degree and action mechanism of each frequency component according to the trend of the residual error item and the mutation characteristic of each IMF.
If MK-PCA is used, this may be: and (3) performing a PCA method on the multi-element time sequence of the research data to convert the multi-element time sequence into a plurality of main components, wherein the main components represent the main change direction of the multi-element time sequence of the research data, and the variance of the main components represents the change degree of the multi-element time sequence. And carrying out Mann-Kendall test on the first main component of the multivariate time sequence of the research data to judge whether monotonic trend exists, and if so, calculating the significance level and the slope of the trend. And (3) carrying out Mann-Kendall test on other main components of the multivariate time sequence of the research data, judging whether mutation points exist, and if so, calculating the positions and the number of the mutation points, and the mean value and the variance before and after mutation. And comprehensively analyzing the multi-element time sequence of the research data, and obtaining the overall trend and mutation characteristics of the multi-element time sequence, and the influence degree and action mechanism of each variable according to the trend and mutation characteristics of each main component.
In this embodiment, spatial interpolation is performed on the soil moisture content, rainfall data and runoff data of each water collection area to obtain the surface average soil moisture content, surface average rainfall and surface average runoff of each water collection area, and different spatial interpolation methods, such as an inverse distance weighting method, a kriging method, a spline method and the like, may be used. The surface average soil moisture content, the surface average precipitation and the surface average runoff of each water collecting area are subjected to time division to obtain the surface average soil moisture content, the surface average precipitation and the surface average runoff of each water collecting area in a reference period and a change period, and different time division methods such as year by year, month by month, season by season can be used for operation. The surface average soil moisture content, the surface average precipitation and the surface average runoff of the upstream water collecting area of each runoff section are weighted and averaged to obtain the surface average soil moisture content, the surface average precipitation and the surface average runoff of the reference period and the change period of each runoff section, and different weighted average methods such as area-wise, distance-wise, gradient-wise and the like can be used.
The research is conducted from the aspect of characteristic recognition of hydrological response relation of a river basin, and the benchmark period (1960-1982) and the change period (1983-2016) are divided based on natural runoff change points. The characteristic identification part of the watershed hydrologic response relation is developed from three aspects of precipitation, soil moisture content (integrated according to a certain proportion according to the relation between the layered soil moisture content and the ground) and natural runoff, and developed according to independent edge distribution of elements, two-dimensional combined distribution of precipitation-runoff, three-dimensional combined distribution of precipitation-soil moisture content and runoff considering the soil moisture content, and two-dimensional conditional combined distribution of precipitation condition distribution and precipitation-soil moisture content based on conditional runoff.
In a certain embodiment, a moment estimation method can be used for carrying out parameter fitting on the total annual precipitation and the 7-month and 8-month total precipitation edge distribution of a typical section of a river basin. The fitting result shows that the shape parameters of the annual total precipitation edge distribution are negative values, the change period is relatively longer than the reference period, and the cumulative probability rising speed of the change period is accelerated; there is a significant drop in both the shape parameter and the position parameter. 7. The shape parameters of the rest sections except the section A of the reservoir are changed from negative values to positive values in the edge distribution parameters of the total precipitation for 8 months, so that the variation is large; and the variation of the scale parameter and the position parameter of the section of the reservoir B is the largest.
In one embodiment, according to an aspect of the present application, the process of collecting the moisture content of the soil and pre-analyzing in the step S13 further includes:
step S13a, sequentially acquiring a soil moisture content sequence and acquisition point position information of each acquisition point aiming at each water collecting area in the range of the research area;
step S13b, calling the pre-constructed multi-source remote sensing data, extracting the remote sensing index, and fusing the remote sensing index and the soil moisture content data of the acquisition points by adopting a GIS module to obtain the spatial distribution of the soil moisture content of each water collecting area; the remote sensing indexes comprise a microwave bright temperature, a normalized vegetation index and a soil humidity index;
Step S13c, constructing a distribution map gradient of the soil moisture content based on the spatial distribution of the soil moisture content, dividing each water collecting area into at least one calculation unit according to a soil moisture content threshold value, and calculating an average value of the soil moisture content of the water collecting areas according to the soil moisture content of each calculation unit;
and step S13d, calculating an annual average value of the soil moisture content of the water collection area corresponding to each runoff section and a flood season average value in each year according to the mapping relation between each runoff section and the water collection area.
The embodiment can effectively reflect the space-time distribution and change characteristics of the soil moisture of the river basin, and provides effective input parameters for the construction of the edge distribution, the joint distribution and the conditional distribution of the hydrologic variables of the river basin. Through multisource remote sensing data and a GIS module, the coverage area and the precision of the soil moisture content data are improved by combining the soil moisture content data of the ground observation station, meanwhile, the correlation between the soil moisture content and the remote sensing index is considered, and the reliability and the stability of the soil moisture content data are improved. According to the spatial distribution of the soil moisture content, each water collecting area is divided into at least one calculation unit, so that the spatial resolution and the spatial representativeness of the soil moisture content data are improved, meanwhile, the spatial heterogeneity and the spatial correlation of the soil moisture content are considered, and the spatial continuity and the spatial consistency of the soil moisture content data are improved. The average value of the soil moisture content of the water collecting area is calculated according to the soil moisture content of each calculation unit, so that the time resolution and the time representativeness of the soil moisture content data are improved, the time change and the time lag of the soil moisture content are considered, and the time continuity and the time consistency of the soil moisture content data are improved.
In a certain embodiment, step S13e is further included, whether a gap exists between adjacent computing units is calculated, if yes, a center point of the gap is calculated, a soil moisture content detecting unit is arranged at the center point, and the center point is a circle center of a circumscribed circle of a polygon formed by centers of all computing units around.
In the embodiment, by optimizing the setting of the soil moisture content distribution points, the calculation accuracy of the soil moisture content is improved, and the accuracy of subsequent detection is improved.
In a further embodiment, further comprising:
s13i, constructing a soil moisture content vertical distribution model, calibrating parameters of the soil moisture content vertical distribution model by adopting remote sensing data, and calculating to obtain a vertical distribution curve of the soil moisture content of each calculation unit;
and step S13ii, calculating the effective soil moisture content of each calculation unit according to the vertical distribution curve of the soil moisture content, namely, carrying out weighted average on the vertical distribution curve of the soil moisture content to obtain the effective soil moisture content of each calculation unit.
And S13iii, calculating an annual average value of the effective soil water content of the water collecting area corresponding to each runoff section and a flood season average value in each year according to the mapping relation between each runoff section and the water collecting area.
In this embodiment, not only the planar distribution of the water content of the soil but also the vertical distribution are analyzed and calculated, so that the three-dimensional water content of the soil in the water collection area is calculated more accurately. The method solves the problem that the accuracy is not high because the prior art only uses the soil moisture content of the detection point as fitting input data.
As shown in fig. 3, according to an aspect of the present application, the step S2 is further:
s21, respectively acquiring research data of a reference period and a change period, and sequentially extracting annual sequence data of the reference period and the change period and annual flood period sequence data; research data may be acquired from different data sources and data platforms, such as remote sensing satellites, ground observation stations, weather stations, hydrologic stations, national hydrologic information centers, and the like. And selects proper time range and time resolution, such as year, month, day, etc., and proper space range and space resolution, such as drainage basin, water collecting area, grid, etc., to ensure comparability and consistency of data. Different data processing and analysis software is used for screening, sorting, counting and other operations on the research data so as to extract annual sequence data and annual flood season sequence data. In some embodiments, the relevant data may be: the annual sequence data refer to total rainfall, maximum daily runoff and maximum daily soil moisture content of each year, and the annual flood period sequence data refer to daily rainfall, daily runoff and daily soil moisture content of each year (6-9 months).
S22, selecting at least one generalized extremum distribution function from a preconfigured generalized extremum distribution function library, and determining undetermined parameters of the generalized extremum distribution function according to the distribution characteristics of research data, wherein the undetermined parameters comprise position parameters, scale parameters and shape parameters;
the generalized extremum distribution function library comprises three types of generalized extremum distribution functions, namely Gumbel distribution, frenchet distribution and Weibull distribution, and can be used for fitting asymptotically upper unbounded extremum data, asymptotically lower unbounded extremum data and asymptotically lower unbounded extremum data. Generally, a suitable generalized extremum distribution function is selected based on the distribution characteristics of the study data, such as skewness, kurtosis, tail behavior, and the like. The position parameter represents the central position of the extremum data, the scale parameter represents the variation amplitude of the extremum data, and the shape parameter represents the distribution form of the extremum data. In general, various methods may be used to determine the undetermined parameters of the generalized extremum distribution function, such as graphical methods, moment methods, maximum likelihood methods, and the like. The graph method intuitively estimates the value of the parameter by drawing a cumulative distribution function or a probability density function of the extremum data and comparing the cumulative distribution function or the probability density function with a curve of the generalized extremum distribution function. The moment method is used for solving an equation by calculating the sample moment of the extremum data, such as the mean value, the variance, the skewness and the like, and the theoretical moment of the generalized extremum distribution function, such as the expected value, the variance, the skewness and the like, so as to obtain the value of the parameter. The maximum likelihood method obtains the value of the parameter by maximizing the likelihood function of the extremum data, namely, the continuous multiplication of the probability density function of the generalized extremum distribution function.
S23, respectively adopting generalized extremum distribution functions to perform edge distribution fitting on the research data aiming at a reference period and a change period of each runoff section, and calculating the fitting goodness and the fitting effect to obtain the edge distribution of the research data of each period of each runoff section; edge distribution fitting was performed on the study data using different data processing and analysis software. Parameters of the study data and generalized extremum distribution function are input, and then a functional expression and graphical representation of the edge distribution are output. Different indices and methods are used to evaluate goodness of fit and fit effects, such as the Kolmogorov-Smirnov test, akaike information criterion, mean square error, etc. The Kolmogorov-Smirnov test determines if the fit is significant by comparing the maximum difference of the cumulative distribution functions. The Akaike information criterion judges whether the fitting is reasonable or not by considering the complexity of the fitting and the error of the fitting. And the mean square error is used for judging whether the fitting is accurate or not by calculating the mean value of the mean square of the observed value and the fitting value.
And step S24, calculating analysis indexes one by one aiming at the edge distribution of the research data, and comparing the change characteristics of the reference period and the change period of each section. The analysis indexes comprise the mean value, variance, skewness, kurtosis, variation coefficient and the like of the extremum, and the change rate, change trend, change significance and the like of the extremum. Different methods can be used to calculate and compare the analysis index for the edge distribution of the study data.
In the embodiment, the edge distribution of the rainfall runoff response relationship of the river basin can be effectively constructed, and the statistical characteristics and the change characteristics of the hydrologic variables of the river basin are reflected.
In a certain embodiment, the cumulative probability distribution curve of annual precipitation changes significantly between the reference period and the change period, and is mainly represented by the increase of annual precipitation in the change period at low frequency (p=0.1), the decrease of annual precipitation in the change period at high frequency (P > 0.4), the overall appearance is that the phenomenon of annual precipitation homogenization is very obvious, and the high-order precipitation becomes more difficult to occur. Table 43 shows the structural change of the annual total precipitation edge distribution probability of a typical section of a river basin, and when P=0.1, 0.5 and 0.9 are taken as an example of a section of a certain reservoir, the annual precipitation in the reference period is 278.7mm, 398.2mm and 533.1mm, the annual precipitation in the change period is 297.3mm, 374.1mm and 464.6mm, the response change amount is 18.6mm, -24.1mm and-68.5 mm, and the annual precipitation reduction in the change period is more than-50 mm compared with that in the reference period when P is more than 0.7. Similarly, the ABCD reservoir profile varies 15.3mm, -1.4mm, 7.5mm at p=0.1, and this variation gradually decreases to-69.6 mm, -37.6mm, -55.4mm at p=0.9.
The rainfall in the flood season is the most important source of runoff, and the related analysis of runoff shows that the runoff with the maximum specific gravity in the month scale is 7 months and 8 months. According to the edge distribution function of the total precipitation of 7 and 8 months in the standard period and the change period of the typical section of a river basin, the change amplitude of the cumulative probability distribution curve of 7 and 8 months of precipitation is larger relative to annual precipitation. The precipitation amount of the change period is smaller than the reference period on all probabilities. When P=0.1, the total precipitation amount of the ABCD reservoir section in the reference period 7 and 8 months is 132.0mm, 133.0mm, 129.0mm and 134.1mm respectively, the variation period is 114.7mm, 117.6mm, 108.9mm and 117.3mm, and the variation amount is-17.3 mm, -15.4mm, -20.1mm and-16.8 mm respectively; at p=0.5, the variation was-36.3 mm, -42.5mm, -47.7mm, -49.2mm; whereas at = 0.9 the variation was-65.5 mm, -66.8mm, -62.4mm, -63.5mm. It is known that the precipitation amount in the flood season in the change period is reduced in all directions, and the reduction of the precipitation amount in the flood season is one of the important reasons for the reduction of the runoff in the flood season.
In one embodiment, the ERA5-Land data product divides the soil into four layers, one for each: SWV-1 (0 cm-7 cm), SWV-2 (7 cm-28 cm), SWV-3 (28 cm-100 cm), SWV-4 (100 cm-289 cm), the layering provides a soil volume moisture content (m 3/m 3), and the post-treatment conditioning is a percent soil volume moisture content (100 x m3/m 3). Considering the difference of difficulty in exchanging 4 layers of soil water with the atmosphere and the like, the water content of the soil is weighted in layers to represent the overall situation: swv=40% > SWV-1+30% > SWV-2+20% > SWV-3+10% > SWV-4. And estimating results according to the annual average of typical sections of a river basin and the average soil moisture content edge distribution parameters of 7 months and 8 months. The shape parameters, the scale parameters and the position parameters all vary to different extents. The shape parameters of the average soil moisture content of 7 and 8 months are more changed than the average soil moisture content of the year.
The annual average soil moisture content of the 4 typical sections is obviously changed in the change period, and the left shift phenomenon is obvious, wherein the left shift range of the sections of certain two reservoirs is larger, which accords with the change condition of certain market in the main area of the water collecting area of the section of certain reservoir. Taking a certain reservoir section as an example, the average annual soil moisture contents in the reference period are respectively 27.9%, 31.1% and 33.5% at the P=0.1, 0.5 and 0.9, and are respectively 26.2%, 28.8% and 31.4% in the change period, and the drop amplitude is respectively-1.8%, 2.3% and-2.1%. For ABCD reservoir sections, p=0.1, 0.5, 0.9 drop ranges were-1.9%, -2.6%, -2.4%, -3.2%, -4.0%, -3.3%, -5.3%, -6.0%, -4.4%, respectively. From these results, it is clear that the annual average soil moisture content is reduced in all directions.
From the results, the average soil moisture content of 7 and 8 months of the change period of 4 typical sections is obviously shifted to the left, but the left shift range of only a certain reservoir section is larger than the average soil moisture content of the year. The drop width of the reservoir a at p=0.1, 0.5, 0.9 was-2.5%, -3.1%, -2.8%, respectively. The drop width of the reservoir B is-2.5%, 3.4% and 3.0% when P=0.1, 0.5 and 0.9. The decrease width of the C reservoir section at P=0.1, 0.5 and 0.9 is-3.4%, 3.6% and 4.2%, respectively. The drop width of the reservoir D at p=0.1, 0.5, 0.9 was-4.9%, -5.8%, -4.0%, respectively. It can be concluded that the annual average and the average soil moisture content of 7 and 8 months in the change period are obviously changed, and the soil moisture content is developed towards the declining trend.
In a certain embodiment, the annual average of a river basin and the average natural runoff quantity edge distribution parameter estimation results of 7 and 8 months are given. It is clear from this that the parameters of the reference period and the change period change significantly, and the position parameters still fall mainly.
The natural runoff quantity is larger in change amplitude relative to the precipitation and the soil moisture content, and the natural runoff quantity in the change period is far smaller than the reference period.
The natural runoff of the reference period P=0.1, 0.5 and 0.9 of the section of the reservoir is 16.3m 3 /s、20.7m 3 /s、28.7m 3 /s, the natural runoff of the change period is 10.7m 3 /s、13.3m 3 /s、17.2m 3 /s, variation of-5.6 m 3 /s、-7.4m 3 /s、-11.5m 3 And/s. The natural runoff variable amounts of the reference period and the variable period of the ABCD reservoir section are respectively-11.4 m 3 /s、-13.5m 3 /s、-18.1m 3 /s，-11.1m 3 /s、-12.0m 3 /s、-13.4m 3 /s，-22.6m 3 /s、-27.2m 3 /s, -30.2m3/s. The above analysis shows that the change in annual average natural runoff amount is an omnidirectional decrease.
And (3) analyzing the natural runoff accumulation probability in the flood season: the natural runoff variable amounts of the reference period and the variable period of the ABCD reservoir section are respectively-20.3 m 3 /s、-27.2m 3 /s、-47.4m 3 /s，-10.2m 3 /s、-17.8m 3 /s、-35.5m 3 /s，-37.1m 3 /s、-54.7m 3 /s、-91.4m 3 And/s. The natural runoff quantity in the flood period is larger than the natural runoff quantity in the average year, and the natural runoff quantity is reduced in the change period, and each accumulation is carried out on a reservoir section of a river basin main flow monitoring sectionThe natural runoff reduction ratio of the probability is more than 50%.
As shown in fig. 4, according to an aspect of the present application, the step S3 is further:
step S31, selecting at least two Copula functions from a preconfigured Copula function library; analyzing the correlation of the research data and determining the type of the Copula function to obtain a pending Copula function;
s32, constructing joint distribution aiming at each research data by adopting a undetermined Copula function, wherein the construction of two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff by utilizing Gaussian Copula functions of an elliptical Copula group, and calculating construction parameters including Kendall coefficient tau and Copula parameter alpha to represent the correlation degree and correlation structure of each research data;
And S33, analyzing each research data based on the Copula function after the parameters are determined to perform joint distribution, calculating analysis indexes, and comparing the change characteristics of the reference period and the change period.
In this embodiment, an appropriate Copula function can be selected according to the characteristics of the research data, so as to construct a joint distribution conforming to the data distribution, quantify the degree of correlation and the correlation structure between variables, compare the differences of the joint distribution in the reference period and the change period, and analyze the evolution characteristics and mechanisms of the rainfall runoff response relationship of the river basin. The method can analyze the change characteristics and mechanisms of the river basin on the rainfall capacity and the runoff forming process, and provides scientific basis for hydrologic simulation, flood forecast, water resource evaluation, water and soil conservation and other aspects. By considering the drainage basin rainfall runoff response relation evolution analysis of the soil moisture content, the limitation of the traditional combined distribution method is broken through, and the accuracy and reliability of hydrologic analysis are improved.
In a certain embodiment, the gaussian Copula function of the elliptical Copula family is utilized as a combined precipitation-natural runoff distribution constructor based on edge distribution. Kendall coefficients τ and Copula parameters α are estimated. And (5) carrying out joint parameter estimation on 7-month and 8-month precipitation and 7-month average natural runoff of a typical section of a river basin. The Kendall correlation coefficient tau and Copula parameter alpha in the change period are smaller than those in the reference period, and the rainfall-runoff correlation coefficient in the flood period is slightly larger than the annual scale.
Taking a section of a reservoir as an example, a reference periodP=0.1, precipitation and natural runoff were 291.4mm and 20.3m, respectively 3 /s, change times of 329.3mm and 12.3m 3 /s, variation of 37.9mm and-7.9 m 3 S, annual precipitation increases while natural runoff decreases; at the position ofP=At 0.5, the variation was-11.3 mm and-12.5 m 3 At that time, both annual precipitation and natural runoff are reduced; at the position ofP=At 0.9, the variation was-58.9 mm and-17.3 m 3 And/s, annual precipitation and natural runoff decrease more. The ABCD reservoir section rule is similar to that, when the joint probability is smaller, the annual precipitation in the change period can be increased slightly compared with the annual precipitation in the reference period, but the annual precipitation is changed into a decrease along with the increase of the joint accumulation probability; the natural runoffs in the change period always show a decreasing trend compared with the natural runoffs in the reference period, and the decreasing proportion becomes larger and larger along with the increase of the joint accumulation probability.
During flood period and reference periodP=At 0.1, precipitation and natural runoff were 140.1mm and 31.5m, respectively 3 /s, with a period of change of 139.1mm and 16.2m 3 S, the variation is-1.1 mm and-15.4 m 3 S; at the position ofP=At 0.5, the variation was-12.6 mm and-31.3 m 3 S; at the position ofP=At 0.9, the variation was-51.5 mm and-61.5 m 3 S; of a change period relative to a reference periodP=0.1~P=0.9 average precipitation change of-17.8 mm and average natural runoff change of-34.0 m 3 And/s. The ABCD reservoir section is similar to the ABCD reservoir section, and the average rainfall variation and the average natural runoff variation are-21.5 mm and-44.2 m respectively 3 /s, -38.0mm and-27.5 m 3 /s, -30.3mm and-82.1 m 3 And/s. The annual-scale precipitation-natural runoff combined distribution is larger in the combined distribution change range of the flood season, the precipitation homogenization phenomenon is more obvious, and the natural runoff is remarkably reduced.
Based on three-dimensional joint distribution probability of precipitation, soil moisture content and natural runoff, the precipitation amount in the reference period and the precipitation amount in the change period are respectively approximately 300-600 mm and 300-550 mm, the soil moisture content in the reference period and the soil moisture content in the change period are respectively approximately 28-24% and 28-32%, and the natural runoffs in the reference period and the change period are respectively approximatelyBetween 15m 3 /s~40m 3 /s、10m 3 /s~22m 3 And/s. Precipitation mainly shows up as upper bound shrinkage, soil moisture content mainly shows up as downward translation, and natural runoff mainly shows up as omnibearing decline. The ABCD reservoir section is basically the same as a certain reservoir section. It should be noted that the natural runoff lower boundary of the reference period of the ABCD reservoir section is very close to the natural runoff upper boundary of the change period, and the condition of natural runoff change is very obvious.
Taking a section of a reservoir as an example, a reference periodP=Precipitation under 0.1, soil water content and natural runoff of 335.8mm, 29.1% and 23.2m respectively 3 Per s, with a period of change of 352.7mm, 27.4%, 13.4m 3 The variation period is 16.9mm, -1.7%, -9.8m compared with the reference period 3 S; at the position ofP=Under the condition of 0.5, the change period is-43.3 mm, -2.1 percent and-15.2 m compared with the reference period 3 S; at the position ofP=Under the condition of 0.9, the variation period is-74.0 mm, -1.9 percent and-18.9 m compared with the reference period 3 And/s. When the joint probability of the section of a certain reservoir is smaller, precipitation is increased slightly, and then the precipitation is reduced along with the increase of the joint probability. The water content of the soil and the natural runoff are always reduced, wherein the water content of the soil is not changed along with the increase of the joint probability and is always kept at about-2%; natural runoff decreases with increasing joint probability. The law of the ABCD reservoir section is basically the same as that of a certain reservoir section, but the variation of the soil moisture content in the variation period is different from that in the reference period, the A reservoir section and the B reservoir section are about-2%, the C reservoir section is about-3.5%, the D reservoir section is reduced along with the increase of the joint probability, and the average value is-4.4%.
In another embodiment of the present application, according to an aspect of the present application, the process of calculating the construction parameter in the step S32 is further:
adopting a Goodless-of-fit test or a Bootstrap test to test the two-dimensional and three-dimensional joint distribution, and evaluating the construction Goodness;
Step S32a, reading at least part of research data as a data set;
step S32b, calling a Goodness-of-fit test or a Bootstrap test according to the type of the joint distribution to perform calculation of a Goodness-of-fit test or a Bootstrap test, so as to obtain corresponding test statistics and reject domains, and constructing an evaluation index of Goodness;
and step S32c, evaluating and analyzing according to the calculation result, comparing the effects of different methods and parameters, and evaluating whether the construction goodness meets the requirement.
In this example, in the last substep, two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff have been constructed using Copula functions, but it is also necessary to check whether these joint distributions fit the actual data well and whether the Copula functions and parameters selected are appropriate. The specific implementation process can be as follows: 1000 samples were randomly drawn from the study data as a dataset for testing the fit of the joint distribution. Suitable test methods, such as Cramer-von Mises test, kolmogorov-Smirnov test, anderson-Darling test, etc., may be selected based on the type of Copula function selected, computing test statistics, such as CvM statistics, KS statistics, AD statistics, etc., and corresponding reject fields, such as CvM reject field, KS reject field, AD reject field, etc. It is also possible to calculate an evaluation index of the construction goodness, such as a goodness-of-fit coefficient R2, a root mean square error RMSE, an average absolute error MAE, and the like.
Judging whether the constructed joint distribution has a significant difference with the actual data according to the value of the test statistic and the reject domain, if the value of the test statistic is larger than the value of the reject domain, indicating that the original assumption is rejected, namely the joint distribution has a significant difference with the actual data, otherwise, indicating that the original assumption is accepted, namely the joint distribution has no significant difference with the actual data. And judging whether the constructed joint distribution has a good fitting effect with the actual data according to the evaluation index of the construction goodness, if the value of the fitting goodness coefficient R2 is close to 1, the constructed joint distribution can well explain the change of the actual data, and if the value of the root mean square error RMSE and the average absolute error MAE is smaller, the error between the constructed joint distribution and the actual data is smaller.
In another embodiment of the present application, further comprising:
s34, constructing a multi-dimensional extremum distribution function of precipitation, soil moisture content and runoff, and obtaining extremum probability and extremum correlation of each variable;
and step S35, calculating an extremum analysis index based on the multidimensional extremum distribution function, and comparing the change characteristics of the reference period and the change period. The extremum analysis index comprises an extremum elasticity coefficient, an extremum correlation coefficient and an extremum uncertainty interval.
The step S4 is further: according to the edge distribution and the joint distribution, the precipitation distribution based on the conditional runoff and the joint distribution of the precipitation and the soil water content condition are calculated according to the definition of the conditional probability and aiming at the reference period and the change period.
As shown in fig. 5, according to an aspect of the present application, the step S5 is further:
step S50:
step S50a, according to the two-dimensional condition joint distribution of precipitation and soil moisture content, a condition cumulative distribution function is obtained;
step S50b, a conditional probability density function is obtained according to the conditional cumulative distribution function;
and step S50c, a conditional probability density function obtains the median and the uncertainty interval of the parameters.
And according to a Copula function and an edge distribution function of the two-dimensional condition joint distribution of precipitation and soil moisture content, a condition cumulative distribution function is obtained by utilizing the definition of conditional probability. And then solving a conditional probability density function according to the conditional cumulative distribution function. And finally, according to the conditional probability density function, the median and the uncertainty interval of the parameters are obtained.
S51, calculating a characterization index of a rainfall runoff response relationship of the river basin by using edge distribution, joint distribution and conditional distribution; the characterization indexes comprise a rainfall elastic coefficient, a soil moisture content elastic coefficient and a rainfall-soil moisture content-runoff three-dimensional elastic coefficient;
Step S51a, calculating to obtain a characterization index, and reading at least part of research data as verification data;
step S51b, calculating root mean square errors of measured values and calculated values in the research data respectively by adopting edge distribution, joint distribution and conditional distribution; judging whether a nonlinear response relation exists according to the threshold value;
step S51c, if the calculated characterization index does not exist, outputting the calculated characterization index; if the characteristic index exists, correcting the characteristic index by adopting a nuclear regression or local weighted regression method.
In this step, according to the edge distribution, the joint distribution and the conditional distribution, the definition of the elastic coefficient is utilized to calculate the characterization indexes of the rainfall runoff response relationship of the river basin, such as epsilon P= (dR/R)/(dP/P), epsilon S= (dR/R)/(dS/S) and epsilon PS= (dR/R)/(dP/p+ds/S), wherein epsilon P represents the rainfall elastic coefficient, namely the relative change rate of natural runoff to the rainfall, epsilon S represents the soil moisture content elastic coefficient, namely the relative change rate of natural runoff to the soil moisture content, epsilon PS represents the three-dimensional elastic coefficient of rainfall-soil moisture content-runoff amount, namely the joint relative change rate of natural runoff to the rainfall and the soil moisture content. These indicators may reflect the sensitivity and elasticity of the basin to the runoff producing capacity of rainfall and the runoff forming process.
Specifically, 1000 samples are randomly extracted from the study data as check data for checking the accuracy and reliability of the characterization index. And respectively calculating the actually measured natural runoff in the research data and the root mean square error of the natural runoff, such as RMSE, according to the edge distribution, the joint distribution and the conditional distribution. And judging whether a nonlinear response relationship exists or not according to a threshold value, such as 0.1, namely judging whether the difference between the actual measured natural runoff and the calculated natural runoff exceeds the threshold value or not, if so, judging that the nonlinear response relationship exists, otherwise, judging that the nonlinear response relationship does not exist. If the nonlinear response relation does not exist, directly outputting calculated characterization indexes such as epsilon P, epsilon S and epsilon PS to obtain a result; if nonlinear response relation exists, a nuclear regression or local weighted regression method is adopted, and the relation between actual measured natural runoff and calculated natural runoff is utilized to correct the characterization index, so as to obtain corrected characterization indexes such as epsilon P and epsilon SAnd epsilonps.
Step S52, calling the difference analysis of the edge distribution, the joint distribution and the condition distribution on the reference period and the change period, and judging the reason of each difference;
the change rate of the precipitation amount, the change rate of the soil moisture content, the change rate of the runoff amount, and the like, and the correlation coefficient therebetween, for example, the correlation coefficient of the precipitation amount and the runoff amount, the correlation coefficient of the precipitation amount and the soil moisture content, the correlation coefficient of the soil moisture content and the runoff amount, and the like are calculated. And a mathematical model of evolution reasons of the river basin rainfall runoff response relationship can be established by adopting a multiple linear regression or structural equation model method, and the influence of climate change and human activity on hydrologic variables and the relative contribution and interaction of the climate change and the human activity are analyzed to obtain indexes such as regression coefficients, significance level, fitting goodness and the like of each factor.
And step S53, obtaining the drainage basin rainfall runoff response relation evolution characteristics, and pre-storing the characteristics for hydrologic forecasting.
For example, if the precipitation elasticity factor, the soil moisture content elasticity factor, and the precipitation-soil moisture content-runoff volume three-dimensional elasticity factor are all increased, this indicates that the drainage basin has an increased capacity for producing rainfall, and vice versa, this indicates that the drainage basin has a decreased capacity for producing rainfall. The extreme frequency and intensity of the rainfall and runoff are both increased significantly if the shape parameters and the scale parameters of the edge distribution of the rainfall and the runoff are both increased significantly, the Kendall coefficient tau and the Copula parameter alpha of the combined distribution of the rainfall and the runoff are both increased significantly, the correlation and the dependence of the rainfall and the runoff are both increased significantly, and the conditional probability and the uncertainty of the rainfall and the runoff are both increased significantly if the median and the uncertainty of the conditional distribution of the rainfall and the runoff are both increased significantly. The evolution characteristics of the drainage basin rainfall runoff response relationship can be stored in a database in the form of a table or graph, each drainage basin or section corresponds to a record, and each evolution characteristic corresponds to a field, so that subsequent query and analysis are facilitated.
According to another aspect of the present application, there is provided a basin rainfall runoff response relationship evolution analysis system considering the water content of soil, including: at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,
the memory stores instructions executable by the processor for execution by the processor to implement the basin rainfall runoff response relationship evolution analysis method taking the soil moisture content into consideration according to any one of the above technical schemes.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.
Claims (10)
1. The method for analyzing the evolution of the drainage basin rainfall runoff response relation by considering the water content of the soil is characterized by comprising the following steps of:
step S1, determining a range of a research river basin and acquiring research data, wherein the research data comprises soil moisture content, rainfall data and runoff data; calling a preconfigured hydrologic variation diagnosis method set, searching for mutation points through research data, and dividing the surface average precipitation process into a reference period and a change period according to the mutation points;
S2, respectively adopting generalized extremum distribution functions to construct edge distribution of each research data aiming at a reference period and a change period, and analyzing change characteristics of the reference period and the change period;
step S3, constructing two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff by using Gaussian Copula functions of an elliptic Copula family based on edge distribution of research data, and obtaining Kendall coefficients tau and Copula parameters alpha;
s4, fitting precipitation distribution and two-dimensional condition joint distribution of the precipitation and the soil water content based on conditional runoff aiming at a reference period and a change period to form conditional probability distribution;
and S5, obtaining drainage basin rainfall runoff response relation evolution characteristics based on the edge distribution, the joint distribution and the condition distribution obtained in the steps S2 to S4, and pre-storing the drainage basin rainfall runoff response relation evolution characteristics for hydrologic forecasting.
2. The method for analyzing the evolution of the drainage basin rainfall runoff response relationship taking the water content of soil into consideration as set forth in claim 1, wherein the step S1 is further as follows:
s11, obtaining geographic data including a digital elevation model of a research river basin range, extracting a river network and selecting at least one runoff section;
S12, calling a preconfigured water collecting region dividing module to divide the range of the research river basin into N water collecting regions, and establishing a mapping relation between the water collecting regions and the runoff sections, wherein N is a natural number;
s13, collecting research data of each water collecting area and pre-analyzing the characteristics of the research data, wherein the characteristics comprise soil moisture content, rainfall data and runoff data;
s14, constructing a hydrologic variation diagnosis method set, wherein the hydrologic variation diagnosis method comprises an MK method, an ITA method, a Pettitt method, a Cramer' S method, a Yamamoto method and a sliding T test method, and the MK method comprises a basic MK method, a trending preset white MK, a variance correction MK and a bootstrap method MK;
and S15, calling at least two hydrologic variation diagnosis methods according to the characteristics of the research data to form a hydrologic variation diagnosis unit, searching mutation points in the research data, and dividing the surface average precipitation process into a reference period and a change period according to the mutation points.
3. The method for analyzing the evolution of the drainage basin rainfall runoff response relationship taking the water content of soil into consideration as set forth in claim 2, wherein the step S2 is further as follows:
s21, respectively acquiring research data of a reference period and a change period, and sequentially extracting annual sequence data of the reference period and the change period and annual flood period sequence data;
S22, selecting at least one generalized extremum distribution function from a preconfigured generalized extremum distribution function library, and determining undetermined parameters of the generalized extremum distribution function according to the distribution characteristics of research data, wherein the undetermined parameters comprise position parameters, scale parameters and shape parameters;
s23, respectively adopting generalized extremum distribution functions to perform edge distribution fitting on the research data aiming at a reference period and a change period of each runoff section, and calculating the fitting goodness and the fitting effect to obtain the edge distribution of the research data of each period of each runoff section;
and step S24, calculating analysis indexes one by one aiming at the edge distribution of the research data, and comparing the change characteristics of the reference period and the change period of each section.
4. The method for analyzing the evolution of the rainfall runoff response relationship in the river basin, which takes the water content of the soil into consideration, according to claim 3, wherein the step S3 is further:
step S31, selecting at least two Copula functions from a preconfigured Copula function library; analyzing the correlation of the research data and determining the type of the Copula function to obtain a pending Copula function;
s32, constructing joint distribution aiming at each research data by adopting a undetermined Copula function, wherein the construction of two-dimensional joint distribution of precipitation and natural runoff and three-dimensional joint distribution of precipitation, soil moisture content and natural runoff by utilizing Gaussian Copula functions of an elliptical Copula group, and calculating construction parameters including Kendall coefficient tau and Copula parameter alpha to represent the correlation degree and correlation structure of each research data;
And S33, analyzing each research data based on the Copula function after the parameters are determined to perform joint distribution, calculating analysis indexes, and comparing the change characteristics of the reference period and the change period.
5. The method for analyzing the evolution of the drainage basin rainfall runoff response relationship taking the water content of the soil into consideration as set forth in claim 4, wherein the step S5 is further as follows:
s51, calculating a characterization index of a rainfall runoff response relationship of the river basin by using edge distribution, joint distribution and conditional distribution; the characterization indexes comprise a rainfall elastic coefficient, a soil moisture content elastic coefficient and a rainfall-soil moisture content-runoff three-dimensional elastic coefficient;
step S52, calling the difference analysis of the edge distribution, the joint distribution and the condition distribution on the reference period and the change period, and judging the reason of each difference;
and step S53, obtaining the drainage basin rainfall runoff response relation evolution characteristics, and pre-storing the characteristics for hydrologic forecasting.
6. The method for analyzing the evolution of the drainage basin rainfall runoff response relationship taking the water content of the soil into consideration as recited in claim 5, wherein the process of collecting the water content of the soil and pre-analyzing in step S13 further comprises:
step S13a, sequentially acquiring a soil moisture content sequence and acquisition point position information of each acquisition point aiming at each water collecting area in the range of the research area;
Step S13b, calling the pre-constructed multi-source remote sensing data, extracting the remote sensing index, and fusing the remote sensing index and the soil moisture content data of the acquisition points by adopting a GIS module to obtain the spatial distribution of the soil moisture content of each water collecting area; the remote sensing indexes comprise a microwave bright temperature, a normalized vegetation index and a soil humidity index;
step S13c, constructing a distribution map gradient of the soil moisture content based on the spatial distribution of the soil moisture content, dividing each water collecting area into at least one calculation unit according to a soil moisture content threshold value, and calculating an average value of the soil moisture content of the water collecting areas according to the soil moisture content of each calculation unit;
and step S13d, calculating an annual average value of the soil moisture content of the water collection area corresponding to each runoff section and a flood season average value in each year according to the mapping relation between each runoff section and the water collection area.
7. The method for analyzing the evolution of the rainfall runoff response relationship in the river basin, which takes the water content of the soil into consideration, according to claim 5, wherein the process of calculating the construction parameters in the step S32 is further as follows:
adopting a Goodless-of-fit test or a Bootstrap test to test the two-dimensional and three-dimensional joint distribution, and evaluating the construction Goodness;
Step S32a, reading at least part of research data as a data set;
step S32b, calling a Goodness-of-fit test or a Bootstrap test according to the type of the joint distribution to perform calculation of a Goodness-of-fit test or a Bootstrap test, so as to obtain corresponding test statistics and reject domains, and constructing an evaluation index of Goodness;
step S32c, evaluating and analyzing according to the calculation result, comparing the effects of different methods and parameters, and evaluating whether the construction goodness meets the requirement;
the step S4 is further: according to the edge distribution and the joint distribution, the precipitation distribution based on the conditional runoff and the joint distribution of the precipitation and the soil water content condition are calculated according to the definition of the conditional probability and aiming at the reference period and the change period.
8. The method for analyzing the evolution of the rainfall runoff response relationship in the river basin, which considers the water content of the soil, according to claim 5, wherein the step S5 further comprises the step S50 of:
step S50a, according to the two-dimensional condition joint distribution of precipitation and soil moisture content, a condition cumulative distribution function is obtained;
step S50b, a conditional probability density function is obtained according to the conditional cumulative distribution function;
and step S50c, a conditional probability density function obtains the median and the uncertainty interval of the parameters.
9. The method for analyzing the evolution of the drainage basin rainfall runoff response relationship taking the water content of the soil into consideration as recited in claim 5, wherein the step S51 further comprises:
step S51a, calculating to obtain a characterization index, and reading at least part of research data as verification data;
step S51b, calculating root mean square errors of measured values and calculated values in the research data respectively by adopting edge distribution, joint distribution and conditional distribution; judging whether a nonlinear response relation exists according to the threshold value;
step S51c, if the calculated characterization index does not exist, outputting the calculated characterization index; if the characteristic index exists, correcting the characteristic index by adopting a nuclear regression or local weighted regression method.
10. A watershed rainfall runoff response relationship evolution analysis system taking soil moisture content into consideration, characterized by comprising: at least one processor; and
a memory communicatively coupled to at least one of the processors; wherein,
the memory stores instructions executable by the processor for implementing the basin rainfall runoff response relationship evolution analysis method taking into account the soil moisture content according to any one of claims 1 to 9.
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| KR20220080463A (en) * | 2020-12-07 | 2022-06-14 | 홍익대학교 산학협력단 | Urban flash flood forecast/warning system and method |
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