Erpolation approaches for estimating the mean annual precipitation are KIB and EBK. For the estimation in the rainy season, RBF and EBK attain superior benefits. For estimating precipitation in the dry season, the KIB method achieves the most effective interpolation result with all the optimal values of all five evaluation indicators. Therefore, even with the similar model, the interpolating performances have been dissimilar below distinct climatic situations. By contrasting the assessment indexes of six interpolation techniques below the identical rainfall magnitudes, its evident that 4 error indexes (MSE, MAE, MAPE, SMAPE) of IDW will be the maximum, and accuracy index (NSE) will be the minimum. Hence, IDW has the relative worst overall performance in estimating the spatial distribution of precipitation among the six interpolation approaches, plus the accuracy of your obtained precipitation surface is low. Nonetheless, the method using the optimal performance under unique climatic conditions is disparate, and additional study in accordance with this situation is carried out inside the next section. For the sake of displaying the fitting degree on the estimated and observed values, scatterplots of six interpolation methods in replicating distinctive rainfall magnitudes are drawn in Figure six, in which Spearman coefficients describe the correlation between the two datasets, and p-values denote substantial degree of correlation.Atmosphere 2021, 12,17 ofFigure 6. Correlation test and Spearman coefficients involving estimated and observed values determined by six interpolation strategies (IDW, RBF, DIB, KIB, OK, EBK): (a) mean annual; (b) rainy season; and (c) dry season.Scatterplots and correlation coefficients involving the two N-Acetyl-L-cysteine ethyl ester custom synthesis datasets (estimated and observed values) validate the preceding evaluation. For every single method, the Spearman coefficient is greater for the dry season than for the rainy season and annual mean precipitation patterns. The interpolation tactics have superior functionality in estimating the spatial distribution for the duration of periods of low precipitation. The identical process also exhibits various performances in estimating the spatial distribution below diverse climatic conditions, showing the uncertainty in the interpolation algorithms to some extent.Atmosphere 2021, 12,18 ofThe above-mentioned final results are only a separate evaluation of every single interpolation system under distinct climate conditions. To additional analyze the accuracy of diverse interpolation procedures, a comprehensive evaluation of each and every system determined by the integrated various rainfall magnitudes was carried out. To comprehensively evaluate the effectiveness of six approaches in estimating the spatial patterns beneath integrated several rainfall magnitudes, i.e., without having Direct Red 80 In Vivo regard for the influence of rainfall magnitude on interpolation accuracy, the estimated and observed values of 34 stations were analyzed by error measures under unique climatic conditions. 4 error indicators (MSE, MAE, MAPE, SMAPE) of every station within the six methods below integrated many rainfall magnitudes have been calculated and Figure 7 was drawn for manifesting the performance of interpolation procedures in estimating the spatial patterns based on integrated various rainfall magnitudes.Figure 7. Cross-validation error indicators values (MSE, MAE, MAPE, SMAPE) of six interpolation methods based on integrated multiple rainfall magnitudes.Atmosphere 2021, 12,19 ofHorizontal coordinates denote 34 meteorological stations; vertical coordinates denote the six spatial interpol.
