Quantifying magnitude of change in dataset containing base values of 0?

I have a dataset with the low-water and high-water surface area of lakes/ponds within a delta for each year obtained from satellite imagery. These lakes vary substantially in surface area and can experience important changes from year to year, sometimes drying 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 hasn't shown enough sensitivity to change. However, given the low water surface area of 0 for some lakes, I cannot quantify percent change. Unfortunately we don't have volume and elevation data for the area as it is quite remote, but I'm hoping to interpolate the results using kriging to see flood water influence over the delta.

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? I confess to have little background in statistics so I may be missing something important.

• You could scale the absolute difference by the mean of the two values, ie. $$\frac{\textrm{high}_{t+1} - \textrm{low}_t}{(\textrm{high}_{t+1} + \textrm{low}_{t})/2}$$ which would avoid the problems you've seen so far (the only problem would be lakes that had zero surface for both measurements, but you might be OK with throwing those out). But statistically this would be no more or less correct than any of your other choices ...