Lab 5

The results of Spline and Kriging interpolation provide similar generally identified areas of high and low precipitation. In the analysis of total precipitation both methods indicate the northern area of LA County as having the lowest precipitation and a precipitation high in the mid-eastern region of the county. These same commonalities hold true for both interpolations of normal precipitation, though at initial inspection the image of the normal precipitation using Kriging looks much more similar to the total precipitation than the images for Spline interpolation. The results for analyzing the difference between normal and total also produce similar geographic distributions for Kriging compared to Spline.

From both results it is possible to conclude that this year’s precipitation is significantly above average for the county, though some small pockets of the county have experience slightly less precipitation than normal. Additionally, the regions with the largest differences in precipitation are also the areas normally experience high rainfall, and this year they have experienced the largest accumulated rainfall as well. It is also interesting to note that the middle area of the county regularly experiences this higher level of precipitation and is surround to the south, and especially the north, by regions of much lower precipitation.

One important distinction in the conclusion above is to what degree do the regions vary? This is dependent on the type of interpolation. As mentioned, they both display similar geographical patterns, however they present very different numeric scenarios. The Spline interpolation provides a much larger range of precipitation values, and also a much greater difference between the normal and total precipitation. The Kriging interpolation, however, presents a much smaller range of values that are essentially the median values of the Spline interpolation, with a larger minimum and a smaller maximum. The Kriging interpolation also presents a difference in precipitation that is about half of what the Spline interpolation provides. The reasoning for this is because the Kriging method generates a smoother result, and uses inexact method, meaning it is not bound to incorporate the exact values at the station locations. This creates a more average result, with fewer outliers and thus less difference between the normal and total precipitation. I believe, however, that despite the advantages of this more generalized approach, especially given the relatively small geographical area, the Spline interpolation is better for this data set as it is able to better identify the important detailed geographical variation that may occur in a region alternating between mountains and valleys, as demonstrated by the numerical range of the data, that wouldn’t necessarily be as visible with a Kriging interpolation.