Water shortages, particularly evident in the state of California, emphasize the need for a better hydrologic understanding, and improved water management techniques. The majority of the state’s water originates in the Sierra Nevada as snow, melting throughout the year to meet the needs of various stakeholders. Current measurement techniques are unable to resolve variability of the snowpack at the basin scale, and snowmelt processes are not well captured by existing hydrologic models. A system-level solution is introduced to facilitate scientific understanding and water management decisions in basins of the Sierra Nevada. The core of this thesis focuses on expanding current sensing methods at the hydrologic catchment (1-2km2) scale to develop an improved understanding of the mountain water balance. It is shown that Wireless Sensor Networks (WSNs) offer an ultra low-power, cost-effective solution to instrument catchmentscale regions. An explicit WSN deployment strategy is derived, leveraging in-situ network statistics of the Packet Delivery Ratio, and Received Signal Strength Indicator to optimize network performance. A hydrologic variability analysis is carried out on the dataset collected by the network, showing temporal stability in the variability of snowdepth at the catchment scale, and validating the use of a stratified sampling approach based on the even instrumentation of physiographic variables. To derive estimates of Snow Water Equivalent (SWE) at the larger basin scale, an estimation framework based on Gaussian Processes, and an optimal sampling strategy, which maximizes Mutual Information, is described. The sensor placement method outperforms a number of other sampling designs, reducing estimation error by up to 100mm. A computationally tractable, Hybrid System model of snow dynamics is introduced and shown to accurately reflect the various stages of snowmelt when compared to observations in the Sierra Nevada. The need for improved sensing and estimation procedures is highlighted by an analysis of the effects of improved SWE estimates on basin-scale streamflow forecasting methods. The use of historically reconstructed SWE data shows that streamflow forecasting error could be reduced by nearly 10% through improved SWE estimates.
Kerkez, B. (2012): A cyber-infrastructure for the measurement and estimation of large-scale hydrologic processes. University of California, Berkeley.
This Paper/Book acknowledges NSF CZO grant support.