A Better Ground-Based Sensor Network

Two-thirds of the Sierra Nevada precipitation is snow, much of which falls when the temperature is just below 0°C. Therefore, a few degrees increase in temperature will turn this snow into rain and also cause an earlier snowmelt. UC Merced professor Roger Bales using blended satellite and ground-based data to estimate the amount of mountain snow and thus, accurately predict the availability of water.

The current operational water measurement system in the Sierra Nevada does not allow for quantitative, real-time estimates of snowpack and downstream water quantities. The lack of these accurate estimates for water managers underscores deficiencies in the current measurement network and provides an impetus for designing a representative measurement network across mountain basins.

In addition, Bales and colleagues are building prototypes of a better ground-based sensor network whose data can be blended with satellite information to increase its utility to water managers.  Given the physiographic and vegetation variability of the mountain landscape, developing strategies for deciding where to measure also involves research; but this is a challenge that Bales and colleagues are meeting by strategically placing the instrument clusters across latitudinal and elevation gradients.

Ongoing deployment of snow and water-balance instrument clusters in the Sierra Nevada is designed to overcome current deficiencies and provide low-cost, spatial information on snowpack, soil moisture, evapotranspiration, streamflow and energy balance.