A network of sensors for spatially representative water‐balance measurements was developed and deployed across the 2000 km2 snow‐dominated portion of the upper American River basin, primarily to measure changes in snowpack and soil‐water storage, air temperature, and humidity. This wireless sensor network (WSN) consists of 14 sensor clusters, each with 10 measurement nodes that were strategically placed within a 1 km2 area, across different elevations, aspects, slopes, and canopy covers. Compared to existing operational sensor installations, the WSN reduces hydrologic uncertainty in at least three ways. First, redundant measurements improved estimation of lapse rates for air and dew‐point temperature. Second, distributed measurements captured local variability and constrained uncertainty in air and dew‐point temperature, snow accumulation, and derived hydrologic attributes important for modeling and prediction. Third, the distributed relative‐humidity measurements offer a unique capability to monitor upper‐basin patterns in dew‐point temperature and characterize elevation gradient of water vapor‐pressure deficit across steep, variable topography. Network statistics during the first year of operation demonstrated that the WSN was robust for cold, wet, and windy conditions in the basin. The electronic technology used in the WSN‐reduced adverse effects, such as high current consumption, multipath signal fading, and clock drift, seen in previous remote WSNs.

A spatially distributed wireless‐sensor network, installed across the 2154 km2 portion of the 5311 km2 American River basin above 1500 m elevation, provided spatial measurements of temperature, relative humidity, and snow depth in the Sierra Nevada, California. The network consisted of 10 sensor clusters, each with 10 measurement nodes, distributed to capture the variability in topography and vegetation cover. The sensor network captured significant spatial heterogeneity in rain versus snow precipitation for water‐year 2014, variability that was not apparent in the more limited operational data. Using daily dew‐point temperature to track temporal elevational changes in the rain‐snow transition, the amount of snow accumulation at each node was used to estimate the fraction of rain versus snow. This resulted in an underestimate of total precipitation below the 0°C dew‐point elevation, which averaged 1730 m across 10 precipitation events, indicating that measuring snow does not capture total precipitation. We suggest blending lower elevation rain gauge data with higher‐elevation sensor‐node data for each event to estimate total precipitation. Blended estimates were on average 15–30% higher than using either set of measurements alone. Using data from the current operational snow‐pillow sites gives even lower estimates of basin‐wide precipitation. Given the increasing importance of liquid precipitation in a warming climate, a strategy that blends distributed measurements of both liquid and solid precipitation will provide more accurate basin‐wide precipitation estimates, plus spatial and temporal patters of snow accumulation and melt in a basin.

Historically, the study of mountain hydrology and the water cycle has been largely observational, with meteorological forcing and hydrological variables extrapolated from a few infrequent manual measurements. Recent developments in Internet of Things (IoT) technology are revolutionizing the field of mountain hydrology. Low-power wireless sensor networks can now generate denser data in real-time and for a fraction of the cost of labor-intensive manual measurement campaigns. The American River Hydrological Observatory (ARHO) project has deployed thirteen low-power wireless IoT networks throughout the American River basin to monitor California's snowpack. The networks feature a total of 945 environmental sensors, each reporting a reading every 15 minutes. The data reported is made available to the scientific community minutes after it is generated. This paper provides an in-depth technical description of the ARHO project. It details the requirements and different technical options, describes the technology deployed today, and discusses the challenges associated with large-scale environmental monitoring in extreme conditions.