CN111985389B - Basin similarity discrimination method based on basin attribute distance - Google Patents
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Abstract
The invention relates to a hydrologic analysis technology in water conservancy and hydropower engineering, and discloses a basin similarity discrimination method based on basin attribute distance, which improves comprehensiveness, objectivity and rationality of parameter basin selection. Firstly, carrying out nested drainage basin division and drainage basin attribute optimization based on high-resolution multi-source mass satellite remote sensing attribute raster data; secondly, extracting attribute grid data of each nested drainage basin according to a mask layer of each nested drainage basin; then calculating the basin attribute distance of each nested basin through a generalized distance capable of comprehensively reflecting the basin attribute factors and the basin nesting structure; and finally, calculating the basin similarity of each nested basin based on the basin attribute distance, and judging the basin similarity according to the similarity classification level. The method effectively improves accuracy and precision of similarity discrimination of the drainage basins, provides important technical support for reasonably selecting the reference drainage basins in hydrologic analysis of water conservancy and hydropower engineering in the data-missing area, and enables the selection of the reference drainage basins to be more comprehensive, objective and reasonable.
Description
Technical Field
The invention relates to a hydrologic analysis technology in water conservancy and hydropower engineering, in particular to a basin similarity discrimination method based on basin attribute distance.
Background
The hydrologic basic data is the basis of hydrologic analysis and calculation work of hydraulic and hydroelectric engineering, and the importance of the hydrologic basic data is self-evident. At present, the water conservancy and hydropower development of the river basin integrally presents a development situation from a data area to a data-lacking area; the local area often lacks a long-series hydrologic data (the series length is more than 30 years) which can meet the requirements of the hydrologic calculation Specification of Water conservancy and hydropower engineering (SL 278-2002). Therefore, hydrologic basic data shortage or scarcity is a common problem faced by hydrologic analysis and calculation of water conservancy and hydropower engineering in the current data-shortage area, and has become a bottleneck for limiting comprehensive development and utilization of local water resources. Considering the complexity of the hydrologic circulation system of the river basin and the space-time variability of hydrologic elements, the traditional engineering hydrologic analysis and calculation method still has certain uncertainty in the area of lack of data, and is one of the problems which have long plagued the international hydrologic community. Hydrologic analysis research in the data-missing area has become the leading edge and hot spot problem of international hydrologic and water resource field research. The international hydrologic science association (International Association of Hydrological Sciences, IAHS) started the international hydrologic ten-year program in 2003-the "non-data area hydrologic research" (Prediction in Ungauged Basins, PUB) aiming at the ten-year time to develop the non-data area hydrologic research, which greatly promotes the development of the research in the hydrologic analysis and calculation field of the domestic and foreign non-data areas; in 2013, a new international hydrologic ten-year plan of Panta Rhei is started, namely, the rows are unusual and everything is constantly changed, which shows that an innovative hydrologic research method is used for solving the long-term and difficult problems of hydrologic analysis and calculation in the data-deficient area.
At present, one accepted and feasible method for solving the problem of hydrologic analysis and calculation in the data-missing area is a hydrologic similarity method. The method is based on a watershed hydrologic cycle basic principle, and assumes that the hydrologic behavior of a watershed is jointly determined by characteristics of the watershed such as climate, weather, underlying surface, river network structure and the like; it is believed that when the watershed is similar in physical properties, its hydrologic information can be transferred to each other. The method mainly takes key factors affecting hydrologic cycle process of the drainage basin as precondition, and searches for a data drainage basin similar to the research drainage basin as a reference drainage basin; establishing a hydrological response relation between hydrological information (such as runoff quantity, runoff depth, runoff modulus, runoff coefficient, rainfall runoff related graph and the like) and basin attributes (such as climate type, soil type, vegetation coverage, topography and topography features and the like) on the ginseng and evidence basin; the hydrologic information is transplanted from the data-bearing area to the data-lack area under the similar framework, and the hydrologic information transmission between similar waterbasins is realized, so that the hydrologic analysis and calculation result of the water conservancy and hydropower engineering in the data-lack area is completed. Therefore, the method is most critical to properly select the adjacent drainage basins which are similar to the hydrologic cycle characteristics of the research drainage basins and have longer actual measurement hydrologic basic data as reference drainage basins, namely judging whether the drainage basins have similarity and the similarity degree, wherein the accuracy depends on the similarity degree of the research drainage basins and the reference drainage basins in the aspect of hydrologic response characteristics, in particular to the space distribution condition under the drainage basins. Therefore, the accuracy of the watershed similarity discrimination directly influences the reliability of the hydrologic analysis and calculation result of the water conservancy and hydropower engineering in the data-lack area.
In recent years, hydrologists at home and abroad sequentially put forward a plurality of quantitative analysis methods for screening and evaluating similar waterbasins. Most of the methods adopt related evaluation methods (such as fuzzy optimization method, cluster analysis method, projection pursuit method, neural network and the like) to classify similar watershed or select reference watershed by manually setting the characteristic indexes of the watershed. Some hydrologists try to directly prove the watershed similarity by establishing a generic, uniform physical equation or mathematical formula describing the watershed hydrologic response process through mathematical physical equation derivation approaches. Because the theory system is imperfect, the complexity of the hydrologic cycle rule of the river basin is not fully considered, and the effect of the approach in practical application is not ideal. Therefore, an objective and effective method is provided to judge the similarity of the watershed, solve the problem of objective selection of the reference watershed, and is a primary task for water conservancy and hydropower engineering hydrologic analysis and calculation in the area with data deficiency.
In general, the drainage basin similarity discrimination method in the conventional technology is studied mainly with the following defects:
(1) In the aspect of the selection of the basin attribute factors, the subjective experience qualitative analysis selection of researchers is generally relied on, one or more elements (such as topography, landform, soil, vegetation and the like) are manually selected as the basin attributes, a complete basin attribute optimization system is lacking, and the subjective randomness is high and the efficiency is low.
(2) "Euclidean distance" (i.e., euclidean metric) is often employed in describing how far and near between two or more watershed, which tends to be ineffective in measuring how far and near nested watershed are, especially nested watershed that differ greatly in upstream and downstream watershed attributes.
(3) In the basin similarity discrimination, the degree of similarity classification is not completely consistent in practice, and a fixed value is mostly obtained according to the experience of researchers, so that certain uncertainty exists.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: the basin similarity judging method based on the basin attribute distance is provided, and comprehensiveness, objectivity and rationality of parameter basin selection are improved.
The invention solves the technical problems by adopting the following scheme:
a basin similarity discrimination method based on basin attribute distance comprises the following steps:
a. performing nested drainage basin division;
b. performing basin attribute optimization;
c. extracting satellite remote sensing attribute grid data of each nested drainage basin;
d. calculating the basin attribute distance of each nested basin;
e. and calculating the similarity of each nested drainage basin, and judging the similarity of the drainage basins.
As a further optimization, in the step a, the nested watershed division specifically includes:
a1, collecting and arranging DEM (Digital Elevation Model) data which can be obtained at home and abroad and cover the boundary range of a research river basin;
a2, performing operations such as splicing, cutting, projection and extraction on the acquired DEM data by using a hydrological analysis tool of ArcGIS software to acquire the DEM data of a rectangular area where a research river basin is located;
a3, combining a given drainage basin water outlet, and extracting and researching drainage basin boundaries and river network water systems through operations such as depression filling, flow direction analysis, confluence accumulation, water flow length, river network, water collection drainage basins, grid rotation vectors and the like;
and a4, dividing the nested drainage basins according to the hydrologic stations of the research drainage basins and the geographic positions of the engineering sections, and sequentially encoding to obtain mask layers of all the nested drainage basins in the research drainage basins.
As a further optimization, in the step b, the drainage basin attribute preferably specifically includes:
b1, collecting and arranging various satellite remote sensing attribute raster data which can be acquired at home and abroad and cover the boundary range of a research river basin, wherein the data mainly comprises runoff, precipitation, evaporation, topography, soil, vegetation and the like, and the information such as start-stop time, coverage, time resolution, spatial resolution and the like of various data sources is defined;
b2, splicing, cutting, projecting, extracting and other operations are carried out on the data by utilizing ArcGIS software, and satellite remote sensing attribute grid data of a rectangular area where a research river basin is located are obtained; calculating the average value of the satellite remote sensing attribute data with the time attribute to obtain satellite remote sensing attribute grid data with an average time scale for many years;
b3, analyzing and extracting attribute grid data such as longitude, latitude, gradient, slope direction, topography index, topography opening width, surface roughness, topography relief and the like of each grid of the research river basin by utilizing ArcGIS software based on DEM data of the region where the research river basin is located;
b4, aiming at satellite remote sensing attribute raster data with different spatial resolutions, adopting a raster data aggregation method to aggregate corresponding grid data of the satellite remote sensing attribute raster data with each resolution into attribute raster data with the same spatial resolution;
and b5, optimizing the key satellite remote sensing attribute grid data closely related to the local runoff forming process by adopting a river basin attribute optimizing method based on a geographic detector based on the runoff attribute grid data of the region where the research river basin is and other satellite remote sensing attribute grid data.
As a further optimization, in the step c, the extracting satellite remote sensing attribute grid data of each nested drainage basin specifically includes:
c1, searching and recording attribute data of grids corresponding to each code by grids by adopting a grid data extraction method based on codes and mask layers of nested drainage basins, and extracting satellite remote sensing attribute grid data of all the nested drainage basins;
and c2, converting the extracted satellite remote sensing attribute grid data of all nested watersheds into network universal format data by using ArcGIS software for storage.
As a further optimization, step c1 specifically includes:
in the grid data of the remote sensing attribute of each key satellite, analyzing and extracting a grid center coordinate value cor corresponding to each grid through a coordinate origin corresponding to the lower left corner 1 ；
In a mask layer of the nested drainage basin, analyzing and extracting a grid center coordinate value cor corresponding to each grid through a coordinate origin corresponding to the lower left corner 2 ；
By matching Coor 1 And Coor 2 The corresponding grids between the two grids are used for searching and recording the attribute data of the grids corresponding to each code grid by grid, and the satellite remote sensing attribute grid data of all nested drainage basins are extracted.
As a further optimization, in the step d, the calculating the basin attribute distance of each nested basin specifically includes:
based on satellite remote sensing attribute grid data of nested watersheds, a new generalized distance which can comprehensively reflect the degree of distance between the watershed attribute factors and the descriptive watersheds of the watershed nested structures is adopted, and the watershed attribute distance of each nested watershed is analyzed and calculated.
As a further optimization, step d specifically includes:
d1, assuming two nested watersheds A and B, wherein the number of grids is M and N respectively, and the number of the optimized key satellite remote sensing attribute grid data is K, a certain point a in the watershed A i Is the coordinates of (a)
Some point B in basin B j Is +.>
Where i=1, 2, …, M, j=1, 2, …, N. Mid point a of basin a (i.e., point a in a i ) To a point B in the basin B (i.e. a point B in B j ) Taking the minimum value of Euclidean distances from the point a to all points in the drainage basin B as the distance from the point a to the drainage basin B, and marking as h (a, B); the distances from all points in the river basin A to the river basin B are calculated, the average value is taken as the distance from the river basin A to the river basin B, the distance is marked as h (A, B), and the calculation formula is as follows:
h(A,B)＝mean(a∈A)min(b∈B||a-b||；
in the formula, coordinate elements of points a and B in nested watershed A and B all adopt attribute grid data (x 1 ,…,x k ,…,x K ) Wherein x is k K are the kth basin attributes corresponding to the grid points.
d2, midpoint B of basin B (i.e. point B in B j ) To a certain point in the river basin Aa (i.e. a point a in A i ) Taking the minimum value of Euclidean distances from the point b to all points in the river basin A as the distance from the point b to the river basin A, and marking as h (b, A); the distances from all points in the drainage basin B to the drainage basin A are calculated, the average value is taken as the distance from the drainage basin B to the drainage basin A, the distance is marked as h (B, A), and the calculation formula is as follows:
h(B,A)＝mean(b∈B)min(a∈A||b-a||；
d3, taking the maximum value of the distance H (A, B) from the basin A to the basin B and the distance H (B, A) from the basin B to the basin A as the basin attribute distance between the basin A and the basin B, and marking as H (A, B), wherein the calculation formula is as follows:
H(A,B)＝H(B,A)＝max(h(A,B),h(B,A))；
wherein H (a, B) and H (B, a) represent the distance from the basin a to the basin B and the distance from the basin B to the basin a, respectively.
In step e, the similarity of each nested drainage basin is calculated, and the drainage basin similarity is judged, which specifically includes:
based on the basin attribute distance of each nested basin, a basin similarity index is constructed, and the similarity degree between the nested basins is quantized to judge the similarity between the nested basins.
As a further optimization, step e specifically includes:
e1, defining similarity (A, B) between the watershed A and the watershed B based on the watershed attribute distance of each nested watershed, wherein the calculation formula is as follows:
wherein the value of Similar (A, B) is generally [0,1], the larger the value is, the higher the similarity between two watershed is; the smaller the value, the more dissimilar the two watershed are; in particular, a value of 1 indicates that the two basins are identical.
e2, determining the classification according to the value range of the similarity (A, B) of the drainage basin and the equal interval area method 1 And S is 2 It is divided into dissimilar and oneGenerally similar, substantially similar, identical, in total four cases:
if Similar (A, B) is E [0,S ] 1 ]Watershed a and B are considered dissimilar;
if Similar (A, B) E (S) 1 ,S 2 ]Watershed a and B are considered generally similar;
if Similar (A, B) E (S) 2 1), then watershed a and B are considered substantially similar;
if Similar (a, B) =1, then watershed a and B are considered identical.
As a further optimization, in step e2, the classification level S 1 、S 2 The value-taking mode of (2) comprises:
according to the total area F magnitude of the research drainage basin, nested drainage basin division of the research drainage basin is sequentially carried out from small to large according to an equidistant area method to obtain a plurality of nested drainage basins with different area grades, then the similarity of each nested drainage basin is analyzed and calculated, and according to the overall distribution condition of the similarity, the division grade S is analyzed and determined 1 And S is 2 Numerical values.
The beneficial effects of the invention are as follows:
based on high-resolution multisource massive satellite remote sensing attribute raster data, the data sources are stable and reliable, and the river basin attribute optimization method based on the geographic detector is provided, so that key satellite remote sensing attribute raster data are optimized, and the river basin runoff space distribution rule is reflected more objectively; in addition, the provided 'basin attribute distance' comprehensively considers basin attribute factors and a basin nesting structure, and ensures objective rationality of the distance between the basins; in addition, an equal interval area method is constructed to reasonably determine the similarity classification level, so that the difference of the runoff space distribution rules of each area of the river basin is reflected more effectively. Based on the scheme of the invention, a more effective technical means can be provided for reasonably selecting the reference drainage basin in the hydrologic analysis and calculation of the water conservancy and hydropower engineering in the data-missing area, the hydrologic information is transplanted from the data-missing area to the data-missing area under a similar framework, and the method has important practical significance and popularization and application value.
Drawings
FIG. 1 is a flow chart of a basin similarity determination method in the present invention;
FIG. 2 is a schematic diagram of a nested watershed according to the present invention (A and B, C and B are nested watersheds);
fig. 3 is a schematic diagram of a basin attribute distance calculation in the present invention.
Detailed Description
The invention aims to provide a basin similarity judging method based on basin attribute distance, which improves comprehensiveness, objectivity and rationality of parameter basin selection. The core idea is as follows: based on multi-source mass satellite remote sensing attribute raster data of precipitation, evaporation, topography, soil, vegetation and the like with high resolution, nested drainage basin division and drainage basin attribute optimization are carried out; extracting satellite remote sensing attribute grid data of each nested drainage basin according to the mask layer of each nested drainage basin; and a new generalized distance which can comprehensively reflect the basin attribute factors and the basin nesting structure is provided, and the basin attribute distance of each nesting basin is calculated; the basin similarity of each nested basin is calculated based on the basin attribute distance, basin similarity discrimination is carried out according to the similarity classification, and important technical support is provided for reasonably selecting the reference basin in hydrologic analysis and calculation of the water conservancy and hydropower engineering in the data-missing area, so that the selection of the reference basin is more comprehensive, objective and reasonable, and the method has a better application prospect.
In a specific implementation, as shown in fig. 1, the drainage basin similarity discrimination method based on the drainage basin attribute distance in the invention comprises the following implementation steps:
1. nested watershed partitioning
And collecting and arranging digital elevation model (Digital Elevation Model, DEM) data which can be acquired at home and abroad and cover the boundary range of the research river basin. And (3) performing operations such as splicing, cutting, projection and extraction on the DEM data by using a hydrological analysis tool of ArcGIS software to generate a study basin boundary and a river network water system, performing nested basin division, and sequentially encoding to obtain mask layers of all nested basins in the study basin. As shown in fig. 2, the drainage basins a and B and the drainage basins C and B in the figure are the nested drainage basins.
The specific implementation means of the step comprises:
(1) And collecting and arranging digital elevation model (Digital Elevation Model, DEM) data which can be acquired at home and abroad and cover the boundary range of the research river basin.
(2) And (3) performing operations such as splicing, cutting, projection, extraction and the like on the DEM data by using a hydrological analysis tool of ArcGIS software to obtain the DEM data (a minimum rectangular grid covering the vector boundary of the research river basin) of the rectangular region where the research river basin is located.
(3) And combining a given drainage basin water outlet, and extracting and researching drainage basin boundaries and river network water systems of the drainage basin through operations such as depression filling, flow direction analysis, confluence accumulation, water flow length, river network, water collecting drainage basin, grid rotation vector and the like.
(4) And (3) dividing the nested drainage basins according to the hydrologic stations of the research drainage basins and the geographic positions of the engineering sections, and sequentially encoding to obtain mask layers (including vector files and raster files) of all the nested drainage basins in the research drainage basins.
2. Basin properties are preferably
And collecting and arranging various satellite remote sensing attribute grid data which can be acquired at home and abroad and cover the boundary range of the research river basin, including runoff, precipitation, evaporation, topography, soil, vegetation and the like. Critical satellite remote sensing attribute grid data closely related to a local runoff forming process is optimized through a river basin attribute optimizing method based on a geographic detector.
The specific implementation means of the step comprises:
(1) And collecting and arranging various satellite remote sensing attribute grid data which can be acquired at home and abroad and cover the boundary range of the research river basin, including runoff, precipitation, evaporation, topography, soil, vegetation and the like, and determining the information of the start and stop time, coverage, time resolution, spatial resolution and the like of each data source.
The satellite remote sensing precipitation data which are representative mainly comprise: global multisource Weighted aggregate precipitation product (MSWEP), united states climate predicted precipitation center fusion technology precipitation product (Climate prediction center morphing technique, CMORPH), global satellite mapping precipitation plan (Global satellite mapping of precipitation, GSMaP), tropical rainfall observation plan (Tropical rainfall measuring mission, TRMM), global precipitation measurement plan (Global Precipitation Measurement, GPM); the representative satellite remote sensing attribute grid data are various land standard products generated by a medium resolution imaging spectrometer (MODIS) of remote sensing satellites Terra and Aqua transmitted by the National Aviation Space Agency (NASA), and the maximum spatial resolution of the land standard products can reach 250m; such as normalized vegetation Index (Normalized Difference Vegetation Index, NDVI), daytime surface temperature (Land Surface Temperature Day, LSTD), night surface temperature (Land Surface Temperature Night, LSTN), leaf Area Index (LAI), surface reflectivity (Surface Reflectance, SR), etc., with data spatial resolutions of 250m, 500m, 1000m, etc.
(2) Splicing, cutting, projecting, extracting and other operations are carried out on the data by utilizing ArcGIS software, so as to obtain satellite remote sensing attribute grid data (minimum rectangular grid covering the vector boundary of the research river basin) of the rectangular area where the research river basin is located; calculating the average value of the satellite remote sensing attribute data with the time attribute to obtain satellite remote sensing attribute grid data with an average time scale for many years;
(3) Based on the digital elevation model data of the research river basin, analyzing and extracting attribute grid data such as Longitude (Longitude), latitude (Latitude), slope (Slope), slope direction (Aspect), topography index (Tin), topography width (Top), surface Roughness (Roughness), topography Relief (Relief) and the like of each grid of the research river basin by ArcGIS software.
(4) For satellite remote sensing attribute raster data with different spatial resolutions, a raster data aggregation method is adopted to aggregate the satellite remote sensing attribute raster data with each resolution into corresponding grid data (for example, a window takes 10 multiplied by 10 grids), and the corresponding grid data is converted into attribute raster data with the same spatial resolution (for example, the spatial resolution of longitude and latitude takes 0.1 multiplied by 0.1 ℃).
Because the spatial resolution of the satellite remote sensing attribute raster data of various sources is high, most of the spatial resolution is 0.01 degrees; if the spatial resolution of the satellite remote sensing attribute raster data is smaller than 0.01 degrees, directly adopting a spatial interpolation method to convert the satellite remote sensing attribute raster data into attribute raster data with the spatial resolution of 0.01 degrees multiplied by 0.01 degrees; and then, calculating the average value of the attribute data corresponding to the 0.01 degree by 0.01 degree grids according to the attribute data corresponding to the 0.1 degree by 0.1 degree grids contained in the 0.1 degree by 0.1 degree grids by adopting a raster data aggregation method to obtain the attribute raster data with the spatial resolution of 0.1 degree by 0.1 degree.
(5) Based on the high-resolution runoff raster data of the area where the research river basin is located and other satellite remote sensing attribute raster data (such as precipitation, evaporation, topography, soil, vegetation and the like), a river basin attribute optimization method based on a geographic detector is adopted to optimize key satellite remote sensing attribute raster data closely related to the local runoff forming process.
The method is based on satellite remote sensing runoff raster data (0.1 degree multiplied by 0.1 degree) and other key satellite remote sensing attribute raster data (0.1 degree multiplied by 0.1 degree), a factor detection method in a geographic detector is adopted, the runoff raster data is used as a dependent variable, the other key satellite remote sensing attribute raster data is used as an independent variable, and whether the influence of each variable on the dependent variable is obviously different is detected.
The basic principle of factor detection in the geographic detector is that when the spatial diversity pattern of an explained variable and an influence factor is converged, the two are explained to have statistical correlation so as to detect the spatial diversity of the dependent variable and how much an independent variable explains the spatial diversity of the dependent variable; the q value is used for measurement, the larger the q value is, the more obvious the space diversity of the dependent variable is, and the calculation formula is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
SST＝Nσ 2 ；
where h=1, …, L is the stratification, i.e. classification or partitioning, of the dependent variable Y or factor X; n (N) h And N is the number of units of layer h and the full area respectively;
sum sigma 2 The variance of the Y values of layer h and full region, respectively; SSW (secure Signal processing)SST is the sum of intra-layer variances and the total variance of the full region, respectively.
3. Drainage basin attribute extraction
Based on the key satellite remote sensing attribute raster data, according to codes and mask layers of each nested drainage basin in a research drainage basin, a raster data extraction method is adopted to search and record precipitation data of grids corresponding to each code grid by grids, and satellite remote sensing attribute raster data of all nested drainage basins is extracted.
The specific implementation means of the step comprises:
(1) According to the codes of the nested drainage basins and the mask layer, searching and recording attribute data of grids corresponding to each code by grids by adopting a grid data extraction method, and extracting satellite remote sensing attribute grid data of all the nested drainage basins;
specifically, in the remote sensing attribute grid data of each key satellite, the coordinate value cor of the grid center corresponding to each grid is analyzed and extracted through the coordinate origin corresponding to the lower left corner 1 The method comprises the steps of carrying out a first treatment on the surface of the In a mask layer of the nested drainage basin, analyzing and extracting a grid center coordinate value cor corresponding to each grid through a coordinate origin corresponding to the lower left corner 2 The method comprises the steps of carrying out a first treatment on the surface of the By matching Coor 1 And Coor 2 And searching grids between the two grids, recording attribute data of grids corresponding to each code, and extracting satellite remote sensing attribute grid data of all nested drainage domains.
(2) And (3) extracting satellite remote sensing attribute raster data of all nested watersheds in the step (1), and converting all data formats into a network universal data format (Network Common Data Form, netCDF) by means of ArcGIS software for storage.
4. Basin attribute distance calculation
Based on the attribute raster data of each nested drainage basin in the research drainage basin, the invention provides a new generalized distance which can comprehensively reflect the drainage basin attribute factors and the drainage basin nesting structure, and the drainage basin attribute distance of each nested drainage basin is analyzed and calculated. As shown in fig. 3, there are two nested watershed a and B, there are two points a, c in the watershed a, there are three points a ', c', B in the watershed B, and a=a ', c=c', then the distance from the point a to the watershed B is the minimum value of euclidean distances from the point a to all points in the watershed B; the distance from point b to basin a is the minimum of the euclidean distances from point b to all points of basin a.
In the area of lack of data, searching for a data-bearing basin similar to the research basin and having long-term measured hydrologic data as a reference basin, generally adopting a distance similarity method, and searching for a neighboring basin closest to the research basin in geographic distance in the research range as the reference basin. However, the distance between the watercourses in the geographic distance does not necessarily fully reflect the distance between the watercourses in the physical attribute characteristics of the watercourses, and the difference of the hydrologic response process between the two watercourses cannot be fully reflected, so that the real distance degree between the watercourses in the aspect of the hydrologic cycle process response mechanism cannot be effectively described.
The "distance" reflecting the distance of the river basin is generally euclidean distance (i.e. "euclidean distance") and its coordinate element is the geographic coordinate (i.e. longitude and latitude coordinate values) where the center of gravity of the river basin is located, which is called "geographic distance". It can be seen that the traditional geographic distance adopts Euclidean distance between the gravity center geographic coordinates of the watershed to describe the distance between the watersheds, and the distance on the characteristic of the watershed attribute cannot be expressed. In addition, the traditional geographic distance only emphasizes the distance of the geographic position of the gravity center of the watershed, and does not consider the influence of the nested structure of the watershed on the distance, so that the watershed which is nested with each other can be mistakenly identified as the most similar watershed. For example, in two domains where the geographic positions of the points in the domains are far apart, it is also possible to calculate the geographic positions (i.e., latitude and longitude coordinate values) where the respective centers of gravity are located at the same geographic position.
Assuming two nested basins A and B, the areas are F respectively A And F B The "euclidean distance" between them, i.e. the euclidean distance between the expected values of all grid point attribute raster data in the basin, is calculated by the following formula:
in the formula, the ith point a of the river basin A i Is (x) i ,y i ) Basin B jth point B j Is (x) j ,y j ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein i is the ith grid (i=1, 2, …, M) of the river basin a, and the number of the i is M; j is the j-th grid (j=1, 2, …, N) of the drainage basin B, and the number of j is N; x, y are the geographic coordinates (i.e., longitude and latitude) of a point in the basin.
For the special case of calculating the basin distance between polygons consisting of points or blocks, the scholars such as Gottschalk improve the geographical distance between the past 'basin gravity center' and 'basin gravity center' into the average value of Euclidean distances between the geographical positions of all point pairs of two basins, and put forward a calculation method for measuring the relative distance between nested basins, namely 'Ghosh distance'. The 'Ghosh distance' considers the influence of the nested structure of the drainage basin water system on the 'distance', eliminates the area scale effect of the drainage basin water collecting area, and can better reflect the effect of the drainage basin nested structure in the 'distance'. When there is no spatial inclusion relationship between the two watershed a and B, i.e. not the watershed and sub-watershed, the "Ghosh" distance and the "Euclid distance" are equal in value.
Assuming two nested basins A and B, the areas are F respectively A And F B The "Ghosh distance" between them, i.e. the expected value of Euclidean distance of all grid points in the river basin to geographic coordinates (i.e. longitude and latitude), is calculated as:
in which the point pair a i 、b j The geographic distance between the two is "Euclid distance", and the element is the geographic coordinate (namely longitude and latitude) of a point in the river basin.
For distance computation between polygons composed of points or blocks, hausdorff et al give a distance measurement method between two polygons, i.e. the maximum distance from one polygon (set of points) to another polygon (set of points), called the "Hausdorff distance". The distance is an effective method for describing the similarity between two point setsA broad form of "Euclid distance" for all pairs of points between a set of points. Assume that there are two sets of points, a= { a 1 ,a 2 ,…,a m Sum b= { B 1 ,b 2 ,…,b n And the Hausdorff distance between the two point sets is calculated as follows:
H(A,B)＝max(h(A,B),h(B,A))；
wherein h (A, B) =max (a εA) min (B εB) ||a-b||,
h(B,A)＝max(b∈B)min(a∈A)||b-a||；
in the formula, the geographic distance between the point pairs a and b is equal to the 'Euclid distance', and the element is the geographic coordinate (namely longitude and latitude) of a certain point in the river basin.
H (A, B) is called a bidirectional Hausdorff distance, and is the most basic form of the Hausdorff distance; h (A, B) and h (B, A) are referred to as unidirectional Hausdorff distances from A set to B set and from B set to A set, respectively; i.e. h (A, B) actually first counts each point a in the set of points A i To a distance of this point a i Nearest point B of the set B j Distance between the two i -b j Ranking the I, and taking the maximum value in the distance as the value of h (A, B); h (B, A) is likewise obtainable. The bi-directional Hausdorff distance H (a, B) is the greater of the uni-directional distances H (a, B) and H (B, a), measuring the maximum degree of mismatch between the two point sets.
By comprehensively comparing and analyzing the advantages and disadvantages of the Euclidean distance, the Ghosh distance and the Hausdorff distance, the influence of the area scale of the drainage basin on the calculation of the distance can be eliminated by calculating the average value of the Euclidean distance between all the point pairs between two drainage basins. However, the effect of the distance between the pairs of points in the final "distance" value is exaggerated too much for the distance between the pairs of points that are not close together, so that the degree of closeness expresses distortion. The Hausdorff distance can better solve the defect of the Ghosh distance. However, this distance is applicable to the case where the properties of the two polygons are relatively uniform, i.e., the properties of points within the basin are relatively close in character. When the attribute factor of a certain point in the river basin is particularly prominent and the difference between other areas is large, the Hausdorff distance calculation result is distorted. For example, a mountain exists in a certain area in a flow field, and the Hausdorff distance between the flow field and other nested flow fields is determined by the attribute characteristics of the mountain regardless of the attribute factors of other areas.
By comparing the advantages and disadvantages of the Euclid distance, the Ghosh distance and the Hausdorff distance, the invention provides a new generalized distance which can comprehensively reflect the basin attribute factors and the basin nested structure, and is called the basin attribute distance. The elements in the calculation formula of the 'basin attribute distance' adopt key attribute raster data selected by optimizing basin attributes, and do not adopt geographic coordinates (namely longitude and latitude), so that the calculation formula has good representativeness for the hydrologic cycle characteristics of the basins, can fully reflect the similarity or heterogeneity of the hydrologic cycle rules among the basins on the attribute characteristics, and can better express the degree of similarity among the basins in the hydrologic cycle process.
Assuming that two nested watersheds A and B comprise M and N grids respectively and K grid data of the watershed attribute, a certain point a in the watershed A i Is the coordinates of (a)
Some point B in basin B j Is the coordinates of (a)
Where i=1, 2, …, M, j=1, 2, …, N. Mid point a of basin a (i.e., point a in a i ) To a point B in the basin B (i.e. a point B in B j ) Taking the minimum value of Euclidean distances from the point a to all points in the drainage basin B as the distance from the point a to the drainage basin B, and marking as h (a, B); the distances from all points in the river basin A to the river basin B are calculated, the average value is taken as the distance from the river basin A to the river basin B, the distance is marked as h (A, B), and the calculation formula is as follows:
h(A,B)＝mean(a∈A)min(b∈B||a-b||；
in the formula, coordinate elements of points a and B in nested watershed A and B all adopt attribute grid data (x 1 ,…,x k ,…,x K ) Wherein x is k For the kth basin attribute corresponding to the grid point, co-existK.
Midpoint B of basin B (i.e., B j ) To a certain point a in the river basin A (i.e. a i ) Taking the minimum value of Euclidean distances from the point b to all points in the river basin A as the distance from the point b to the river basin A, and marking as h (b, A); the distances from all points in the drainage basin B to the drainage basin A are calculated, the average value is taken as the distance from the drainage basin B to the drainage basin A, the distance is marked as h (B, A), and the calculation formula is as follows:
h(B,A)＝mean(b∈B)min(a∈A||b-a||；
in the formula, coordinate elements of points a and B in nested watershed A and B all adopt attribute grid data (x 1 ,…,x k ,…,x K ) Wherein x is k K are the kth basin attributes corresponding to the grid points.
Taking the maximum value of the distance H (A, B) from the basin A to the basin B and the distance H (B, A) from the basin B to the basin A as the basin attribute distance between the basin A and the basin B, and marking as H (A, B), wherein the calculation formula is as follows:
H(A,B)＝H(B,A)＝max(h(A,B),h(B,A))；
wherein H (a, B) and H (B, a) represent the distance from the basin a to the basin B and the distance from the basin B to the basin a, respectively.
5. Basin similarity discrimination
The similarity between hydrologic cycle systems of two or more watershed can be measured from multiple aspects to yield a comprehensive similarity measure. A basin similarity calculation method is constructed based on basin attribute distance to effectively characterize the distance degree between nested basins, and the method is called 'basin similarity'. In general, basin similarity is defined as a function Similar (a, B), where a, B represent two different basins; the value is usually [0,1], which indicates the degree of similarity between two watershed; the larger the value is, the higher the similarity degree between the two watershed is; the smaller the value, the more dissimilar the two basins. The two waterbasins with higher similarity can be represented by the same hydrological response relation, and under a similar framework, the hydrological response relation of the area with data is reasonably extrapolated to a control section or the area without data, and then the hydrological design method parameters or results are obtained.
Similarity between basin a and basin B (a, B) can be defined as:
wherein the value of Similar (A, B) is generally [0,1], the larger the value is, the higher the similarity between two watershed is; the smaller the value, the more dissimilar the two watershed are; in particular, a value of 1 indicates that the two basins are identical; h (a, B) is the basin attribute distance between basins a and B.
Determining the classification grade S according to the value range of the basin similarity (A, B) and the equal interval area method 1 And S is 2 Totally dividing the two cases into four cases of dissimilarity, general similarity, basically similarity and complete identity; the method comprises the following steps: if Similar (A, B) is E [0,S ] 1 ]Watershed a and B are considered dissimilar; if Similar (A, B) E (S) 1 ,S 2 ]Watershed a and B are considered generally similar; if Similar (A, B) E (S) 2 1), then watershed a and B are considered substantially similar; if Similar (a, B) =1, then watershed a and B are considered identical.
Grade of grade S 1 And S is 2 The values are generally fixed at 0.75 and 0.90 according to the experience of the researchers. We consider a grading of S for a particular research basin 1 And S is 2 The values are not completely consistent and need to be reasonably analyzed and determined according to the overall distribution condition of similarity in the research flow domain. According to the F magnitude of the total area of the research drainage basin, nested drainage basins are divided from small to large in sequence according to an equal interval area method, a plurality of nested drainage basins with different area grades are obtained, and then the similarity of each nested drainage basin is analyzed and calculated. Reasonably determining the classification grade S according to the overall distribution condition of similarity in the research flow domain 1 And S is 2 Numerical values.
The "equidistant area method" assumes that the number of area levels is U (which is an integer), and the area calculation formula of each level is:
wherein F is the total area of the research drainage basin; u is the number of area grades (integer); i is the order, i=1, 2, …, U are taken sequentially.
Based on the scheme provided by the invention, the intelligent identification of similar watershed can be rapidly and accurately completed, an important technical support is provided for reasonably selecting the reference watershed in hydrologic analysis and calculation of the water conservancy and hydropower engineering in the data-deficient area, so that the selection of the reference watershed is more comprehensive, objective and reasonable, and the method has a better application prospect.
Claims (5)
1. The basin similarity judging method based on the basin attribute distance is characterized by comprising the following steps of:
a. performing nested drainage basin division;
b. performing basin attribute optimization;
c. extracting satellite remote sensing attribute grid data of each nested drainage basin;
d. calculating the basin attribute distance of each nested basin: based on satellite remote sensing attribute grid data of nested drainage basins, adopting generalized distance capable of comprehensively reflecting drainage basin attribute factors and the degree of depth between drainage basins described by the drainage basin nesting structure, analyzing and calculating the drainage basin attribute distance of each nested drainage basin, and specifically comprising the following steps of d1-d3:
d1, assuming two nested watersheds A and B, wherein the number of grids is M and N respectively, and the number of the optimized key satellite remote sensing attribute grid data is K, a certain point a in the watershed A i Is the coordinates of (a)
Some point B in basin B j Is +.>
Where i=1, 2, …, M, j=1, 2, …, N; the Euclidean distance from the point a in the river basin A to a certain point B in the river basin B is the minimum value of the Euclidean distances from the point a to all points in the river basin B as the distance from the point a to the river basin B,denoted as h (a, B); calculating the distances from all points in the river basin A to the river basin B, taking the average value of the distances as the distances from the river basin A to the river basin B, and marking the distances as h (A, B); the calculation formula is as follows:
h(A,B)＝mean(a∈A)min(b∈B)||a-b||；
in the formula, coordinate elements of points a and B in nested watershed A and B all adopt attribute grid data (x 1 ,…,x k ,…,x K ) Wherein x is k K drainage basin attributes corresponding to grid points are provided;
d2, taking the minimum value of Euclidean distances from the point B to all points in the drainage basin A as the distance from the point B to the drainage basin A, and recording as h (B, A); calculating the distances from all points in the drainage basin B to the drainage basin A, taking the average value of the distances as the distances from the drainage basin B to the drainage basin A, and marking the distances as h (B, A); the calculation formula is as follows:
h(B,A)＝mean(b∈B)min(a∈A)||b-a||；
d3, taking the maximum value of the distance H (A, B) from the river basin A to the river basin B and the distance H (B, A) from the river basin B to the river basin A as the river basin attribute distance between the river basin A and the river basin B, and marking as H (A, B); the calculation formula is as follows:
H(A,B)＝H(B,A)＝max(h(A,B),h(B,A))；
wherein H (A, B) and H (B, A) respectively represent the distance from the river basin A to the river basin B and the distance from the river basin B to the river basin A;
e. calculating the similarity of each nested drainage basin, and judging the similarity of the drainage basins: based on the basin attribute distance of each nested basin, constructing a basin similarity index, quantifying the similarity degree between the nested basins, and judging the similarity between the nested basins, wherein the method specifically comprises the following steps of e1-e2:
e1, defining similarity (A, B) between the watershed A and the watershed B based on the watershed attribute distance of each nested watershed, wherein the calculation formula is as follows:
wherein, the value of the Similar (A, B) is [0,1], and the larger the value is, the higher the similarity degree between two watercourses is; the smaller the value, the more dissimilar the two watershed are; when the value is 1, the two watercourses are identical;
e2, determining the classification grade S according to the value range of the basin similarity (A, B) and the equal interval area method 1 And S is 2 They are divided into four cases of dissimilarity, general similarity, substantial similarity, and perfect identity:
if Similar (A, B) is E [0,S ] 1 ]Watershed a and B are considered dissimilar;
if Similar (A, B) E (S) 1 ,S 2 ]Watershed a and B are considered generally similar;
if Similar (A, B) E (S) 2 1), then watershed a and B are considered substantially similar;
if SimilarA, B) =1, then watershed a and B are considered identical;
in step e2, the classification level S 1 、S 2 The value-taking mode of (2) comprises:
according to the total area F magnitude of the research drainage basin, nested drainage basin division of the research drainage basin is sequentially carried out from small to large according to an equidistant area method to obtain a plurality of nested drainage basins with different area grades, then the similarity of each nested drainage basin is analyzed and calculated, and according to the overall distribution condition of the similarity, the division grade S is analyzed and determined 1 And S is 2 Numerical values.
2. The method for distinguishing the similarity of the drainage basins based on the drainage basin attribute distance according to claim 1, wherein,
in step a, the nested watershed division specifically includes:
a1, collecting and arranging DEM data which can be acquired at home and abroad and cover the boundary range of a research river basin;
a2, performing splicing, cutting, projection and extraction operations on the acquired DEM data by using a hydrological analysis tool of ArcGIS software to acquire the DEM data of a rectangular area where a research river basin is located;
a3, combining a given drainage basin water outlet, and extracting drainage basin boundaries and river network water systems of the research drainage basin after depression filling, flow direction analysis, confluence accumulation, water flow length, river network, water collection drainage basin and grid vector rotation operation;
and a4, dividing the nested drainage basins according to the hydrologic stations of the research drainage basins and the geographic positions of the engineering sections, and sequentially encoding to obtain mask layers of all the nested drainage basins in the research drainage basins.
3. The method for distinguishing the similarity of the drainage basins based on the drainage basin attribute distance according to claim 1, wherein,
in the step b, the drainage basin attribute preferably specifically includes:
b1, collecting and arranging various satellite remote sensing attribute raster data which can be acquired at home and abroad and cover the boundary range of a research river basin, wherein the data mainly comprises runoff, precipitation, evaporation, topography, soil and vegetation, and the start-stop time, coverage, time resolution and spatial resolution information of various data sources are defined;
b2, splicing, cutting, projecting and extracting the data by using ArcGIS software to obtain satellite remote sensing attribute grid data of a rectangular area where a research river basin is located; calculating the average value of the satellite remote sensing attribute data with the time attribute to obtain satellite remote sensing attribute grid data with an average time scale for many years;
b3, analyzing and extracting longitude, latitude, gradient, slope direction, topography index, topography opening width, surface roughness and topography relief attribute grid data of each grid of the research river basin by utilizing ArcGIS software based on DEM data of the region where the research river basin is located;
b4, aiming at satellite remote sensing attribute raster data with different spatial resolutions, adopting a raster data aggregation method to aggregate corresponding grid data of the satellite remote sensing attribute raster data with each resolution into attribute raster data with the same spatial resolution;
and b5, optimizing the key satellite remote sensing attribute grid data closely related to the local runoff forming process by adopting a river basin attribute optimizing method based on a geographic detector based on the runoff attribute grid data of the region where the research river basin is and other satellite remote sensing attribute grid data.
4. The method for distinguishing the similarity of the drainage basins based on the drainage basin attribute distance according to claim 1, wherein,
in step c, extracting satellite remote sensing attribute grid data of each nested drainage basin specifically includes:
c1, searching and recording attribute data of grids corresponding to each code by grids by adopting a grid data extraction method according to codes and mask layers of nested drainage basins, and extracting satellite remote sensing attribute grid data of all the nested drainage basins;
and c2, converting the extracted satellite remote sensing attribute grid data of all nested watersheds into a network general data format by using ArcGIS software for storage.
5. The method for discriminating a basin alike based on a basin attribute distance according to claim 4 wherein,
the step c1 specifically includes:
in the grid data of the remote sensing attribute of each key satellite, analyzing and extracting a grid center coordinate value cor corresponding to each grid through a coordinate origin corresponding to the lower left corner 1 ；
In a mask layer of the nested drainage basin, analyzing and extracting a grid center coordinate value cor corresponding to each grid through a coordinate origin corresponding to the lower left corner 2 ；
By matching Coor 1 And Coor 2 The corresponding grids between the two grids are used for searching and recording the attribute data of the grids corresponding to each code grid by grid, and the satellite remote sensing attribute grid data of all nested drainage basins are extracted.
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