Phenolic acid supplier rainfall patterns, Figure eight maps the relative goodness of six procedures in estimating the precipitation spatial pattern beneath diverse climatic conditions. The ideal technique is marked in red. For the integrated numerous rainfall magnitudes, the C-values of six solutions have been mapped to one pie chart, quantitatively assessing the relative validity involving the six procedures for estimating precipitation spatial pattern in Chongqing. As outlined by Figure 8, primarily based on integrated various rainfall magnitudes, KIB would 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 will be the model with all the lowest estimated accuracy, which is consistent with the aforementioned evaluation. Also, the rank of interpolation methods in estimating precipitation spatial pattern in Chongqing inside the order of KIB EBK OK RBF DIB IDW, the pie chart quantitatively manifests the relative effectiveness with the six procedures evaluated by TOPSIS evaluation.(a) Mean 7-Hydroxymethotrexate Data Sheet annual precipitation(b) Rainy-season precipitationFigure eight. Cont.Atmosphere 2021, 12,21 of(c) Dry-season precipitation(d) Integrated several rainfall scenarioFigure 8. Relative goodness of six techniques based on both diverse rainfall magnitudes and integrated various rainfall magnitudes5. Conclusions and Discussion This paper compared the functionality of distinct interpolation strategies (IDW, RBF, DIB, KIB, OK, EBK) in predicting the spatial distribution pattern of precipitation primarily based on GIS technologies applied to 3 rainfall patterns, i.e., annual mean, rainy-season, and dry-season precipitation. Multi-year averages calculated from everyday precipitation data from 34 meteorological stations were utilised, spanning the period 1991019. Leaveone-out cross-validation was adopted to evaluate the estimation error and accuracy with the six approaches based on different rainfall magnitudes and integrating multiple rainfall magnitudes. Entropy-Weighted TOPSIS was introduced to rank the performance of the six interpolation solutions beneath distinctive climatic circumstances. The principle conclusions is often summarized as follows. (1) The estimation performance of six interpolation methods in the dry-season precipitation pattern is larger than that within the rainy season and annual mean precipitation pattern. For that reason, the interpolators might have higher accuracy in predicting spatial patterns for periods with low precipitation than for periods with higher precipitation. (2) Cross-validation shows that the very best 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 most effective interpolator for dry-season precipitation pattern is KIB, followed by EBK. The performance of interpolation methods replicating the precipitation spatial distribution of rainy season shows significant variations, which might be attributed towards the truth that summer precipitation in Chongqing is drastically influenced by western Pacific subtropical high stress [53], low spatial autocorrelation, plus the inability to carry out great spatial pattern analysis using the interpolation strategies. Alternatively, it could be attributed for the directional anisotropy of spatial variability in precipitation [28], or both. (three) The Entropy-Weighted TOPSIS final results show that the six interpolation procedures primarily based on integrated various rainfall magnitudes are ranked in order of superiority for estimating the spati.
