Pplied for the imply annual precipitation, rainy-season precipitation and dry-season precipitation patterns in Chongqing city to generate continuous precipitation surfaces within GIS environment, and spatial variability maps of three rainfall scenarios are shown in Figure three. The colored Isophorone Autophagy dividing lines in Figure three are precipitation contours. Statistical evaluation shows that roughly 75 of annual precipitation in Chongqing is concentrated in the rainy season (May ctober), even though approximately 25 is distributed in dry season (November pril). The intra-annual distribution of precipitation is extremely uneven, Phenolic acid Description manifesting substantial seasonal variations. Spatially, the western and central regions are low-value precipitation areas, followed by the northeastern locations. The southeastern region could be the area of higher precipitation values, followed by components from the northwestern area. The spatial and temporal distribution of precipitation in Chongqing is inhomogeneous.Atmosphere 2021, 12,12 ofFigure 3. Cont.Atmosphere 2021, 12,13 ofFigure three. Precipitation spatial patterns in Chongqing under diverse climatic conditions according to six interpolation methods (IDW, RBF, DIB, KIB, OK, EBK): (a) mean annual; (b) rainy season; and (c) dry season.4.two. Overall performance of Unique Spatial Interpolation Approaches Comparison of Interpolation Approaches beneath Different Climatic Circumstances For the sake of visualizing the error distribution in unique spatial interpolation approaches in replicating varying rainfall magnitudes, error degree in each and every meteorological station from each method is drawn depending on the corresponding spatial distribution maps of precipitation, that are given in Figure four. Amongst them, a constructive error signifies that the interpolator overestimates precipitation and is marked in red; a unfavorable error represents an underestimate that is marked in green. The relative size on the marked graph represented the relative size on the error worth. As shown in Figure 4, it’s evident that some interpolation techniques estimated higher errors, most notably IDW, indicating that the accuracy of this method is relatively low and not applicable towards the study region. Generally, a high degree of constructive errors is observed in the low-precipitation areas, when unfavorable errors are mainly observed inside the highprecipitation locations, which indicates to some extent that the interpolation strategies are largely close for the average on the observed values for the estimation of the areas with unhomogeneous precipitation.Figure four. Cont.Atmosphere 2021, 12,14 ofFigure four. Spatial distribution of estimated errors under diverse climatic situations depending on six interpolation methods (IDW, RBF, DIB, KIB, OK, EBK): (a) mean annual; (b) rainy season; and (c) dry season.To additional determine the efficiency of six interpolation strategies in replicating rainfall magnitudes beneath distinct climatic situations, the absolute error distributions of diverse procedures are presented as box plots in Figure five. Red lines inside the box represent the median worth of the absolute errors. Black dotted lines display the imply worth. Red dots indicate outliers. The center represents the middle 50 , or 50th percentile, of your data set and was derived making use of the reduce and upper quartile values [11]. The upper and decrease whiskers of each box are drawn towards the 90th and 10th percentiles [6], along with the upper and lower edges from the rectangle (i.e., box) are defined as the 75th and 25th percentile with the information set, respectively [5,46.
