A Wireless Sensor Network (WSN) for Structural Health Monitoring (SHM) is designed, implemented, deployed and tested on the 4200 ft long main span and the south tower of the Golden Gate Bridge (GGB). Ambient structural vibrations are reliably measured at a low cost and without interfering with the operation of the bridge. Requirements that SHM imposes on WSN are identified and new solutions to meet these requirements are proposed and implemented. In the GGB deployment, 64 nodes are distributed over the main span and the tower, collecting ambient vibrations synchronously at 1 kHz rate, with less than 10 mus jitter, and with an accuracy of 30 muG. The sampled data is collected reliably over a 46-hop network, with a bandwidth of 441 B/s at the 46th hop. The collected data agrees with theoretical models and previous studies of the bridge. The deployment is the largest WSN for SHM.

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.

Monitoring the snow pack is crucial for many stakeholders, whether for hydro-power optimization, water management or flood control. Traditional forecasting relies on regression methods, which often results in snow melt runoff predictions of low accuracy in non-average years. Existing ground-based real-time measurement systems do not cover enough physiographic variability and are mostly installed at low elevations. We present the hardware and software design of a state-of-the-art distributed Wireless Sensor Network (WSN)-based autonomous measurement system with real-time remote data transmission that gathers data of snow depth, air temperature, air relative humidity, soil moisture, soil temperature, and solar radiation in physiographically representative locations. Elevation, aspect, slope and vegetation are used to select network locations, and distribute sensors throughout a given network location, since they govern snow pack variability at various scales. Three WSNs were installed in the Sierra Nevada of Northern California throughout the North Fork of the Feather River, upstream of the Oroville dam and multiple powerhouses along the river. The WSNs gathered hydrologic variables and network health statistics throughout the 2017 water year, one of northern Sierra’s wettest years on record. These networks leverage an ultra-low-power wireless technology to interconnect their components and offer recovery features, resilience to data loss due to weather and wildlife disturbances and real-time topological visualizations of the network health. Data show considerable spatial variability of snow depth, even within a 1 km ${}^2$ network location. Combined with existing systems, these WSNs can better detect precipitation timing and phase in, monitor sub-daily dynamics of infiltration and surface runoff during precipitation or snow melt, and inform hydro power managers about actual ablation and end-of-season date across the landscape.

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.

Spatial variations in frictional properties on natural faults are believed to be a factor influencing the presence of slow slip events (SSEs). This effect was tested on a laboratory frictional interface between two polymethyl methacrylate (PMMA) bodies. We studied the evolution of slip and slip rates that varied systematically based on the application of both high and low normal stress (σ₀=0.8 or 0.4 MPa) and the far‐field loading rate (V_LP). A spontaneous, frictional rupture expanded from the central, weaker, and more compliant section of the fault that had fewer asperities. Slow rupture propagated at speeds V_slow∼0.8 to 26 mm s⁻¹ with slip rates from 0.01 to 0.2 μm s⁻¹, resulting in stress drops around 100 kPa. During certain nucleation sequences, the fault experienced a partial stress drop, referred to as precursor detachment fronts in tribology. Only at the higher level of normal stress did these fronts exist, and the slip and slip rates mimicked the moment and moment release rates during the 2013–2014 Boso SSE in Japan. The laboratory detachment fronts showed rupture propagation speeds V_slow/V_R∈ (5 to 172) × 10⁻⁷ and stress drops ∼ 100 kPa, which both scaled to the aforementioned SSE. Distributions of asperities, measured using a pressure sensitive film, increased in complexity with additional normal stress—an increase in normal stress caused added complexity by increasing both the mean size and standard deviation of asperity distributions, and this appeared to control the presence of the detachment front.