I have a dataset with the low-water and high-water surface area of lakes/ponds within a delta for each year. These lakes can undergo substantial change from year to year, and sometimes can dry out completely. As such, surface area can have values of 0 during the low-water period. I'm trying to quantify the magnitude of flooding in the spring on the surface areas of these lakes. Given the high inter annual variations in surface area, I need to compare the low-water value from the previous year to the high-water value of the following year to quantify this magnitude; comparing to a mean isn't sensitive enough. However, given the low water surface area of 0 for some lakes, I cannot quantify percent change.
My current idea is to do an "inverse" of percent change (don't know how else to describe it), where I divide the low-water value by the high-water value. This gives me a scale where large change will equal 0 and little change will equal 1. However, again small changes from a surface area of 0 will be over represented. Any idea how I could accurately compare the magnitude of flooding in such a case?