This research explores the benefits of conjunctively managed surface and groundwater resources in a volcanic aquifer system to reduce stream temperatures while valuing agricultural deliveries. The example problem involves advancing the understanding of flows, stream temperature, and groundwater dynamics in the Shasta Valley of Northern California. Three levels of interaction are explored from field data, to regional simulation, to regional management optimization. Stream temperature processes are explored using Distributed Temperature Sensing (DTS) data from the Shasta River and recalibrating an existing physically-based flow and temperature model of the Shasta River. DTS technology can collect abundant high resolution river temperature data over space and time to improve development and performance of modeled river temperatures. These data also identify and quantify thermal variability of micro-habitat that temperature modeling and standard temperature sampling do not capture. This helps bracket uncertainty of daily temperature variation in reaches, pools, side channels, and from cool or warm surface or subsurface inflows. The application highlights the influence of air temperature on stream temperatures, and indicates that physically-based numerical temperature models, using a heat balance approach as opposed to statistical models, may under-represent this important stream temperature driver. The utility of DTS to improve model performance and detailed evaluation of hydrologic processes is demonstrated.
Second, development and calibration of a numerical groundwater model of the Pluto's Cave basalt aquifer and Parks Creek valley area in the eastern portion of Shasta Valley helps quantify and organize the current conceptual model of this Cascade fracture flow dominated aquifer. Model development provides insight on system dynamics, helps identify important and influential components of the system, and highlights additional data needs. The objective of this model development is to reasonably represent regional groundwater flow and to explore the connection between Mount Shasta recharge, pumping, and Big Springs flow. The model organizes and incorporates available data from a wide variety of sources and presents approaches to quantify the major flow paths and fluxes. Major water balance components are estimated for 2008-2011. Sensitivity analysis assesses the degree to which uncertainty in boundary flow affects model results, particularly spring flow.
Finally, this work uses optimization to explore coordinated hourly surface and groundwater operations to benefit Shasta River stream temperatures upstream of its confluence with Parks Creek. The management strategy coordinates reservoir releases and diversions to irrigated pasture adjacent to the river and it supplements river flows with pumped cool groundwater from a nearby well. A basic problem formulation is presented with results, sensitivity analysis, and insights. The problem is also formulated for the Shasta River application. Optimized results for a week in July suggest daily maximum and minimum stream temperatures can be reduced with strategic operation of the water supply portfolio. These temperature benefits nevertheless have significant costs from reduced irrigation diversions. Increased irrigation efficiency would reduce warm tail water discharges to the river instead of reducing diversions. With increased efficiency, diversions increase and shortage costs decrease. Tradeoffs and sensitivity of model inputs are explored and results discussed.
|Advisor:||Lund, Jay R.|
|Commitee:||Fogg, Graham E., Harter, Thomas|
|School:||University of California, Davis|
|School Location:||United States -- California|
|Source:||DAI-B 74/10(E), Dissertation Abstracts International|
|Subjects:||Hydrologic sciences, Water Resource Management|
|Keywords:||Cascade hydrogeology, Conjunctive use, Distributed temperature sensing, Groundwater modeling, Stream temperature modeling, Water resources management|
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