More than half of the water used in California for farming and drinking and other everyday uses comes by way of runoff from the Sierra Nevada, and gauging the amount of snow there and predicting how much runoff there will be is an annual rite that has a major trickledown effect of its own.
Currently, water managers physically measure the snow depth at a series of index sites, and they compare the results to those from past years to predict water availability and thus determine how much water will be allocated to farmers and communities. The data are limited and the predictions can have a high uncertainty, which leads to costly inefficiencies in the process.
That could all be changing soon, as professor Roger Bales of the University of California, Merced — with help from UC Berkeley and the UC’s Center for Information Technology Research in the Interest of Society (CITRIS) — has designed a system that will use clusters of wireless sensors to provide more accurate measurements of snowpack depth, water storage in soil, stream flow and other important factors, and make that data available to the public in real time.
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