A newly developed algorithm not only automates discovery of useful data points, but accurately predicts water flow between them. The new system isn't specifically designed with California in mind, but with an estimated 30-percent of the state's water coming from such aquifers, and a recent five-year drought, understanding this potentially valuable, but delicate source of H20 is more important than ever.

It's no secret that increased temperatures, and low snow fall has made water supply in Stanford's home state a hot topic. This made the use of groundwater more important than ever. The problem is, pumping too much water -- known as overdrafting -- is expensive, and can even stop the aquifer from naturally replenishing. What makes this new technique so tantalizing, is that as well as discovering more groundwater, it could help us get much more out of existing sources too.

The new algorithm, developed by Jingyi "Ann" Chen, a Stanford postdoctoral researcher on the team, allows them to automate the analysis of the InSAR data, which is a huge time saver in itself. Perhaps the more significant part, is that Chen was also able to figure out a way to accurately "fill in" data in what would otherwise be blind spots. The team tested the algorithm on satellite images of Colorado's San Luis Valley between 2007 and 2011 and the predictions closely matched the real life measurements.

While California's immediate water situation might have taken a step back from the precipice, the Stanford team will continue to incorporate data from other sources to improve and refine the algorithm. The ultimate goal would be to give geologists the tools to be able to accurately estimate the full water "budget" of anywhere that has the available satellite imagery. Knowing what's flowing underground, and how to get the most yield from it, will have both environmental, and economical benefits, the full implications of which are hard to measure.