Rainfall patterns, Figure 8 maps the relative goodness of six strategies in estimating the precipitation spatial Loracarbef Bacterial pattern beneath diverse climatic circumstances. The most beneficial method is marked in red. For the integrated several rainfall magnitudes, the C-values of six solutions were mapped to 1 pie chart, quantitatively assessing the relative validity in between the six solutions for estimating precipitation spatial pattern in Chongqing. According to Figure eight, primarily based on integrated numerous rainfall magnitudes, KIB may be the optimal model for estimating the precipitation spatial pattern in Chongqing, with all the C-value is the highest to 0.954, followed by EBK. Meanwhile, IDW would be the model with the lowest estimated accuracy, that is constant using the aforementioned evaluation. In addition, the rank of interpolation methods in estimating precipitation spatial pattern in Chongqing within the order of KIB EBK OK RBF DIB IDW, the pie chart quantitatively manifests the relative effectiveness of the six approaches evaluated by TOPSIS evaluation.(a) Mean annual precipitation(b) Rainy-season precipitationFigure 8. Cont.Atmosphere 2021, 12,21 of(c) Dry-season precipitation(d) Integrated a number of rainfall scenarioFigure 8. Relative goodness of six procedures primarily based on both distinct rainfall magnitudes and integrated several rainfall magnitudes5. Conclusions and Discussion This paper compared the efficiency of different interpolation solutions (IDW, RBF, DIB, KIB, OK, EBK) in predicting the spatial distribution pattern of precipitation primarily based on GIS technologies applied to three rainfall patterns, i.e., annual mean, rainy-season, and dry-season precipitation. Multi-year averages calculated from day-to-day precipitation information from 34 meteorological stations had been utilized, spanning the period 1991019. Leaveone-out cross-validation was adopted to evaluate the estimation error and accuracy from the six techniques based on various rainfall magnitudes and integrating various rainfall magnitudes. Entropy-Weighted TOPSIS was introduced to rank the performance from the six interpolation approaches under different climatic circumstances. The main conclusions may be summarized as follows. (1) The estimation performance of six interpolation approaches in the dry-season precipitation pattern is larger than that within the rainy season and annual mean precipitation pattern. Therefore, the interpolators could have higher accuracy in predicting spatial patterns for periods with low precipitation than for periods with high precipitation. (2) Cross-validation shows that the top interpolator for annual mean precipitation pattern in Chongqing is KIB, followed by EBK. The very best interpolator for rainy-season patterns is RBF, followed by KIB. The best interpolator for dry-season precipitation pattern is KIB, followed by EBK. The performance of interpolation approaches replicating the precipitation spatial distribution of rainy season shows substantial variations, which could be attributed for the reality that summer season precipitation in Chongqing is considerably influenced by western Pacific subtropical high pressure [53], low spatial autocorrelation, and also the inability to perform excellent spatial pattern analysis working with the interpolation approaches. Alternatively, it may be attributed for the directional anisotropy of spatial variability in precipitation [28], or each. (three) The Entropy-Weighted TOPSIS outcomes show that the six interpolation techniques primarily based on integrated many rainfall magnitudes are ranked in order of superiority for estimating the spati.
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