Recent water shortages, particularly evident in the state of California, are calling for better predictive capabilities, and
improved management techniques for existing water distribution infrastructure. One particular example involves large-scale
water distribution systems (on the scale of reservoirs and dams) in the Sierra Nevada, where the majority of the
state's water is obtained from melting snow. Current control strategies at this scale rely on sparse data sets, and are often
based on statistical predictions of snowmelt. Sudden, or unexpected, snowmelt can thus often lead to dam-overtopping,
or downstream flooding.
This paper assesses the feasibility of employing real-time hydrologic data, acquired by large-scale wireless sensor
networks (WSNs), to improve current water management strategies. A sixty node WSN, spanning a square kilometer,
was deployed in the Kings River Experimental Watershed, a research site in the Southern Sierra Nevada, at an elevation
of 1,600-2,000 m. The network provides real time information on a number of hydrologic variables, with a particular
emphasis on parameters pertaining to snowmelt processes. We lay out a system architecture that describes how this real-time
data could be coupled with hydrologic models, estimation-, optimization-, and control-techniques to develop an
automated water management infrastructure. We also investigate how data obtained by such networks could be used to
improve predictions of water quantities at nearby reservoirs.