Original User: Eric6346
I talked this over with a few people, and none of us are completely sure exactly why this is happening, but we have a few ideas.
First, our semivariogram estimation algorithms implicitly assume that the data can, in fact, be accurately modeled with a semivariogram. When that is true, it does a good job of estimating the parameters. Unfortunately, when the data cannot be accurately modeled with a semivariogram, the calculations can produce unintuitive results. Even small changes in the input data can manifest in big changes in the semivariogram parameters because it's trying to fit something that fundamentally doesn't fit.
Second, your two datasets aren't as similar as you might think. The line graphs do look similar and mostly honor the same highs and lows, but their variances are quite different (Day 2 has twice the variance of Day 1). This explains the big differences in the sill estimation.
I wish I could give more helpful feedback on this issue, but you may want to rethink how you're quantifying the spatial structure of this phenomenon because it looks like comparing estimated semivariogram parameters is not going to work well.