Rainfall patterns, Figure eight maps the relative goodness of six strategies in estimating the precipitation spatial pattern under distinct climatic situations. The most effective technique is marked in red. For the Pipamperone Protocol integrated several rainfall magnitudes, the C-values of six approaches had been mapped to 1 pie chart, quantitatively assessing the relative validity involving the six strategies for estimating precipitation spatial pattern in Chongqing. According to Figure eight, primarily based on integrated several rainfall magnitudes, KIB is definitely the optimal model for estimating the precipitation spatial pattern in Chongqing, with all the C-value may be the highest to 0.954, followed by EBK. Meanwhile, IDW could be the model together with the lowest estimated accuracy, that is constant using the aforementioned analysis. Moreover, the rank of interpolation methods in estimating precipitation spatial pattern in Chongqing in the order of KIB EBK OK RBF DIB IDW, the pie chart quantitatively manifests the relative effectiveness on the six techniques evaluated by TOPSIS evaluation.(a) Imply annual precipitation(b) Rainy-season precipitationFigure 8. Cont.Atmosphere 2021, 12,21 of(c) Dry-season precipitation(d) Integrated several rainfall scenarioFigure eight. Relative goodness of six approaches primarily based on both unique rainfall magnitudes and integrated many rainfall magnitudes5. Conclusions and Discussion This paper compared the performance of diverse interpolation methods (IDW, RBF, DIB, KIB, OK, EBK) in predicting the spatial distribution pattern of precipitation based on GIS technology applied to three rainfall patterns, i.e., annual mean, rainy-season, and dry-season precipitation. Multi-year averages calculated from day-to-day precipitation data from 34 meteorological stations were employed, spanning the period 1991019. Leaveone-out cross-validation was adopted to evaluate the estimation error and accuracy on the six strategies primarily based on various rainfall magnitudes and integrating a number of rainfall magnitudes. Entropy-Weighted TOPSIS was introduced to rank the performance in the six interpolation techniques under unique climatic situations. The principle conclusions is usually summarized as follows. (1) The estimation functionality of six interpolation procedures within the dry-season precipitation pattern is larger than that in the rainy season and annual imply precipitation pattern. As a result, the interpolators may well have larger accuracy in predicting spatial patterns for periods with low precipitation than for periods with higher precipitation. (two) Cross-validation shows that the very best interpolator for annual imply precipitation pattern in Chongqing is KIB, followed by EBK. The most effective interpolator for rainy-season patterns is RBF, followed by KIB. The best interpolator for dry-season precipitation pattern is KIB, followed by EBK. The efficiency of interpolation procedures replicating the precipitation spatial distribution of rainy season shows big variations, which may well be attributed to the fact that summer season precipitation in Chongqing is drastically influenced by western Pacific subtropical higher pressure [53], low spatial autocorrelation, plus the inability to carry out excellent spatial pattern evaluation employing the interpolation solutions. Alternatively, it may be attributed for the directional anisotropy of spatial variability in precipitation [28], or both. (three) The Entropy-Weighted TOPSIS benefits show that the six interpolation procedures based on integrated multiple rainfall magnitudes are ranked in order of superiority for estimating the spati.
