Rainfall data shows evidence of low-dimensional chaos, but streamflow
data does not, which is surprising for streamflows that come from
rainfall. However, there are problems with the analysis of streamflow
data. One problem is that the data sets are so small that finding
the correlation dimension is not an easy matter, and even finding an appropriate
can be problematic. Thus, we developed the C-C method for dealing with such small data sets. This method does a good job of finding an appropriate delay time, as well as an appropriate delay time window. I will discuss this
method and show that the use of the delay time window, rather than the delay time, makes it easier to find the correlation dimension for small data sets. While this method leads to improved analyses of rainfall data, streamflow data still fail to how evidence of low-dimensional chaos. The problem may lie in the aggregation and sampling processes involved in streamflow data. I will show how such aggregation and sampling can obscure the nonlinear determinism in a low-dimensional chaotic system.