Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Ecology
Description
Population growth within drylands is occurring faster than growth in any other ecologic zone, putting pressure on already stressed water resources. Because the availability of surface water supplies in drylands tends to be highly variable, many of these populations rely on groundwater. A critical process contributing to groundwater recharge is the interaction between ephemeral channels and groundwater aquifers. Generally, it has been found that ephemeral channels contribute to groundwater recharge when streamflow infiltrates into the sandy bottoms of channels. This process has traditionally been studied in channels that drain large areas (10s to 100s km2). In this dissertation, I study the interactions between surface water and groundwater via ephemeral channels in a first-order watershed located on an arid piedmont slope within the Jornada Experimental Range (JER) in the Chihuahuan Desert. To achieve this, I utilize a combination of high-resolution observations and computer simulations using a modified hydrologic model to quantify groundwater recharge and shed light on the geomorphic and ecologic processes that affect the rate of recharge. Observational results indicate that runoff generated within the piedmont slope contributes significantly to deep percolation. During the short-term (6 yr) study period, we estimated 385 mm of total percolation, 62 mm/year, or a ratio of percolation to rainfall of 0.25. Based on the instrument network, we identified that percolation occurs inside channel areas when these receive overland sheetflow from hillslopes. By utilizing a modified version of the hydrologic model, TIN-based Real-time Integrated Basin Simulator (tRIBS), that was calibrated and validated using the observational dataset, I quantified the effects of changing watershed properties on groundwater recharge. Distributed model simulations quantify how deep percolation is produced during the streamflow generation process, and indicate that it plays a significant role in moderating the production of streamflow. Sensitivity analyses reveal that hillslope properties control the amount of rainfall necessary to initiate percolation while channel properties control the partitioning of hillslope runoff into streamflow and deep percolation. Synthetic vegetation experiments show that woody plant encroachment leads to increases in both deep percolation and streamflow. Further woody plant encroachment may result in the unexpected enhancement of dryland aquifer sustainability.
ContributorsSchreiner-McGraw, Adam P (Author) / Vivoni, Enrique R. (Thesis advisor) / Whipple, Kelin X. (Committee member) / Mascaro, Giuseppe (Committee member) / Throop, Heather L. (Committee member) / Sala, Osvaldo E. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Integrated water resources management for flood control, water distribution, conservation, and food security require understanding hydrological spatial and temporal trends. Proliferation of monitoring and sensor data has boosted data-driven simulation and evaluation. Developing data-driven models for such physical process-related phenomena, and meaningful interpretability therein, necessitates an inventive methodology. In this dissertation, I developed time series and deep learning model that connected rainfall, runoff, and fish species abundances. I also investigated the underlying explainabilty for hydrological processes and impacts on fish species. First, I created a streamflow simulation model using computer vision and natural language processing as an alternative to physical-based routing. I tested it on seven US river network sections and showed it outperformed time series models, deep learning baselines, and novel variants. In addition, my model explained flow routing without physical parameter input or time-consuming calibration. On the basis of this model, I expanded it from accepting dispersed spatial inputs to adopting comprehensive 2D grid data. I constructed a spatial-temporal deep leaning model for rainfall-runoff simulation. I tested it against a semi-distributed hydrological model and found superior results. Furthermore, I investigated the potential interpretability for rainfall-runoff process in both space and time. To understand impacts of flow variation on fish species, I applied a frequency based model framework for long term time series data simulation. First, I discovered that timing of hydrological anomalies was as crucial as their size. Flooding and drought, when properly timed, were both linked with excellent fishing productivity. To identify responses of various fish trait groups, I used this model to assess mitigated hydrological variation by fish attributes. Longitudinal migratory fish species were more impacted by flow variance, whereas migratory strategy species reacted in the same direction but to various degrees. Finally, I investigated future fish population changes under alternative design flow scenarios and showed that a protracted low flow with a powerful, on-time flood pulse would benefit fish. In my dissertation, I constructed three data-driven models that link the hydrological cycle to the stream environment and give insight into the underlying physical process, which is vital for quantitative, efficient, and integrated water resource management.
ContributorsDeng, Qi (Author) / Sabo, John (Thesis advisor) / Grimm, Nancy (Thesis advisor) / Ganguly, Auroop (Committee member) / Li, Wenwen (Committee member) / Mascaro, Giuseppe (Committee member) / Arizona State University (Publisher)
Created2022