Week 8- Interpolation

I chose to interpolate the rainfall data using the Inverse Distance Weighted (IDW) and Kriging methods. It was very interesting mapping the season to date rainfall and the normal amount of rainfall in a given season. This provides for a very telling visual, giving us an idea of where we are in the season, relatively how much rainfall we are recieving, and the places throughout the county that receive more or less rainfall than others. Seeing such data visualized, there are clearly regions of the county that receive more precipitation than others.

This year we have had a very high amount of rain. This is clear on the east side of the difference images, as the greens and yellows represent negative values, meaning that the average seasonal amount of rainfall has already been surpassed during this season. I included a hillshade model in my maps, and it is fairly clear how the rainfall patterns coincide with elevation areas. The eastern part of the county that receives the most rain is in the mountains and the driest part is in the north, above the Grapevine, forming the lower portion of the Central Valley.

Kriging seems like the best way to interpolate this kind of data. It shows very general trends and gradations, which is what is needed for portraying a continuous feature like rainfall across a given area. IDW is good too, but the data is not dense enough for it to be as accurate. It portrays a lot more concentrated areas of rainfall around particular gauging stations, which is not necessarily the realistic case.