Watershed similarity discrimination method based on watershed attribute distance

Abstract

The invention relates to a hydrological analysis technology in water conservancy and hydropower engineering, and discloses a watershed similarity discrimination method based on watershed attribute distance to improve comprehensiveness, objectivity and rationality of reference watershed selection. The method comprises the following steps: firstly, performing nested watershed division and watershedattribute optimization based on high-resolution multi-source massive satellite remote sensing attribute raster data; secondly, extracting attribute raster data of each nested watershed according to the mask layer of each nested watershed; calculating a basin attribute distance of each nested basin through a generalized distance capable of comprehensively reflecting a basin attribute factor and a basin nested structure; and finally, calculating the basin similarity of each nested basin based on the basin attribute distance, and performing basin similarity judgment according to similarity grading. According to the method, the accuracy and accuracy of basin similarity judgment are effectively improved, important technical support is provided for reasonably selecting the reference basin in hydrological analysis of water conservancy and hydropower engineering in regions lack of data, and the selection of the reference basin is more comprehensive, objective and reasonable.

Description

technical field [0001] The invention relates to hydrological analysis technology in water conservancy and hydropower engineering, in particular to a watershed similarity discrimination method based on watershed attribute distance. Background technique [0002] Basic hydrological data are the basis of hydrological analysis and calculation of water conservancy and hydropower projects, and their importance is self-evident. Today, the overall development of water conservancy and hydropower in the basin is showing a development trend from areas with data to areas without data; and the local area often lacks long series of hydrological data that can meet the requirements of the "Code for Hydrological Calculation of Water Conservancy and Hydropower Engineering (SL 278-2002)" (the length of the series should be within over 30 years). Therefore, the shortage or scarcity of basic hydrological data is a common problem faced by hydrological analysis and calculation of water conservancy...

AI Technical Summary

Problems solved by technology

[0006] (1) The selection of watershed attribute factors usually relies on the subjective experience of researchers for qualitative analysis and selection, artificially selecting one or several elements (such as topography, landform, soil, vegetation, etc.) as watershed attributes, lacking a complete set of watershed attributes Optimum system, there is a lot of subjective arbitrariness and low efficiency

[0007] (2) "Euclidean distance" (that is, Euclidean measure) is often used to describe the distance between two or more watersheds, which is often ineffective in measuring the distance between nested watersheds, especially Nested watersheds with large differences in upstream and downstream watershed attributes

[0008] (3) In practice, the classification of the similarity degree in the watershed similarity judgment is not completely consistent, and most of them take fixed values ​​based on the experience of researchers, and there is a certain degree of uncertainty

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