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Determination Method of Optimal Resolution for Spatial Interpolation of Hydrometeorological Elements
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A technology of optimal resolution and spatial interpolation, applied in the field of hydrology and water resources research, can solve problems such as large interpolation error and increased covariance between pixels
Active Publication Date: 2019-07-12
CHINA INST OF WATER RESOURCES & HYDROPOWER RES
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Gottschalk et al (Gottschalk L., Leblois, E and J.Correlation and covariance of runoff revisited.Journal of Hydrology, 398(2011)76-90, doi:10.1016 / j.jhydrol.2010.12.011) when studying the correlation of geographical objects, began to notice the grid scale change ( Spatial resolution change) has an impact on the reliability of interpolation results. Under the condition that other conditions remain unchanged, increase the resolution and reduce the grid size. Although the amount of information available in the image becomes larger, the covariance between pixels increases. Correspondingly, according to the Kriging interpolation algorithm, the interpolation error of a single unit is greater
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Embodiment 1
[0069] Example 1: Rainfall.
[0070] (1) In the same study area, select rainfall data with the same number of observation stations, and use the Kriging interpolation method to carry out spatial interpolation and distribution of rainfall observation data at different resolutions. Among them, the number of observation stations is 145, and the output resolutions are respectively Select 5km, 10km, 15km, 20km, 25km, 30km, 35km, 40km, 45km, 50km, and output the spatial interpolation results of different resolutions of rainfall as shown in image 3 It can be clearly seen from the figure that as the grid size decreases, the spatial characteristics of the rainfall spatial interpolation results have obvious changes.
[0071] (2) Calculate the signal-to-noise ratio of rainfall spatial interpolation.
[0072] Based on the interpolation result images of rainfall at different resolutions output above, the Kriging interpolation method and the signal-to-noise ratio formula are used to calcul...
[0077] (1) In the same study area, select the actual evaporation data with the same number of observation stations, and use the Kriging interpolation method to carry out spatial interpolation and distribution of the actual evaporationobservation data at different resolutions. Among them, the number of observation stations is 13, and the output The resolutions are 20km, 40km, 60km, 80km, 100km, 120km, 140km, 160km, 180km, 200km, and output the spatial interpolation results of different resolutions of the actual evaporation, such as Figure 7 It can be clearly seen from the figure that as the grid size decreases, the spatial characteristics of the actual evaporation spatial interpolation results have obvious changes.
[0078] (2) Calculate the signal-to-noise ratio of the actual evaporation space interpolation.
[0079] Based on the spatial interpolation results of different resolutions of the actual evaporation output above, the Kr...
Embodiment 3
[0083] Embodiment three: deep runoff.
[0084] (1) In the same study area, select the runoff depth data with the same number of observation stations, and use the Kriging interpolation method to carry out spatial interpolation and distribution of the runoff depth observation data at different resolutions. Among them, the number of observation stations is 20, and the output resolution Select 5km, 10km, 15km, 20km, 25km, 30km, 35km, 40km, 45km, 50km respectively, and output the spatial interpolation result map of different resolutions of runoff depth, as shown in Figure 11 It can be clearly seen from the figure that as the grid size decreases, the spatial characteristics of the runoff deep spatial interpolation results have obvious changes.
[0085] (2) Calculate the signal-to-noise ratio of deep spatial interpolation of real runoff.
[0086] Based on the spatial interpolation results of different resolutions of runoff depth output above, with the help of Kriging interpolation ...
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Abstract
The invention discloses a method for determining an optimal hydrometeorological element spatial interpolation resolution. The method comprises steps that on the basis of same observation station quantities of one area, a Kriging interpolation method is employed to carry out spatial interpolation distribution of the hydrometeorological element observation data on the basis of different resolutions, and a spatial interpolation result image is acquired; spatial interpolation information amount, spatial interpolation noise amount and spatial interpolation signal to noise ratios of the spatial interpolation result image on the basis of each resolution are calculated; the spatial interpolation signal to noise ratios on the basis of each resolution is ordered, and the resolution with the largest spatial interpolation signal to noise ratio is taken as the optimal spatial interpolation resolution. According to the method, through defining conception of the spatial interpolation signal to noise ratio as an index for balancing information quality of the spatial interpolation result, spatial interpolation quality is made to realize concrete quantification, through calculating the spatial interpolation signal to noise ratio on the basis of each resolution, the optimal resolution is acquired; on the basis of present sample data sources, through selecting the optimal resolution, the effective data included by the acquired spatial interpolation result image is made to realize maximization, and more reference use values are realized.
Description
technical field [0001] The invention relates to the fields of hydrology and water resources research, in particular to a method for determining the optimum resolution of hydrometeorological element space interpolation. Background technique [0002] Spatial interpolation is an important way to obtain the spatial distribution information of hydrometeorological elements based on observation sites. This information method based on thematic images can provide us with continuous spatial information that cannot be provided by observation sites. The accuracy of the information contained in the image obtained by spatial interpolation is determined by the number of original observation sites and the spatial resolution of the image. [0003] Usually, when people pay attention to the interpolation results, they mainly focus on the spatial sampling characteristics of the original observation site data, that is, the influence of site sample size, site location, data spatial density, data ...
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