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- All Subjects: Hydrologic sciences
- Creators: Mascaro, Giuseppe
- Creators: Kavazanjian, Edward
- Resource Type: Text
Description
This dissertation details an attempt to experimentally evaluate the Giroud et al. (1995) concentration factors for geomembranes loaded in tension perpendicular to a seam by laboratory measurement. Field observations of the performance of geomembrane liner systems indicates that tears occur at average strains well below the yield criteria. These observations have been attributed, in part, to localized strain concentrations in the geomembrane loaded in tension in a direction perpendicular to the seam. Giroud et al. (1995) has presented theoretical strain concentration factors for geomembrane seams loaded in tension when the seam is perpendicular to the applied tensile strain. However, these factors have never been verified. This dissertation was prepared in fulfillment of the requirements for graduation from Barrett, the Honors College at Arizona State University. The work described herein was sponsored by the National Science Foundation as a part of a larger research project entitled "NEESR: Performance Based Design of Geomembrane Liner Systems Subject to Extreme Loading." The work is motivated by geomembrane tears observed at the Chiquita Canyon landfill following the 1994 Northridge earthquake. Numerical analysis of the strains in the Chiquita Canyon landfill liner induced by the earthquake indicated that the tensile strains, were well below the yield strain of the geomembrane material. In order to explain why the membrane did fail, strain concentration factors due to bending at seams perpendicular to the load in the model proposed by Giroud et al. (1995) had to be applied to the geomembrane (Arab, 2011). Due to the localized nature of seam strain concentrations, digital image correlation (DIC) was used. The high resolution attained with DIC had a sufficient resolution to capture the localized strain concentrations. High density polyethylene (HDPE) geomembrane samples prepared by a leading geomembrane manufacturer were used in the testing described herein. The samples included both extrusion fillet and dual hot wedge fusion seams. The samples were loaded in tension in a standard triaxial test apparatus. to the seams in the samples including both extrusion fillet and dual hot wedge seams. DIC was used to capture the deformation field and strain fields were subsequently created by computer analysis.
ContributorsAndresen, Jake Austin (Author) / Kavazanjian, Edward (Thesis director) / Gutierrez, Angel (Committee member) / Civil, Environmental and Sustainable Engineering Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
There is a considerable need for improved understanding of the outcome and amounts of water used to manage urban landscapes in arid and semiarid cities. Outdoor irrigation in urban parks consists of a large fraction of water demands in Phoenix, Arizona. Hence, ecohydrological processes need to be considered to improve outdoor irrigation management. With the goal of reducing outdoor water use and advancing the general knowledge of water fluxes in urban parks, this study explores water conservation opportunities in an arid city through observations and modeling.Most urban parks in Phoenix consist of a mosaic of turfgrass and trees which receive scheduled maintenance, fertilization and watering through sprinkler or flood irrigation. In this study, the effects that different watering practices, turfgrass management and soil conditions have on soil moisture observations in urban parks are evaluated. Soil moisture stations were deployed at three parks with stations at control plots with no compost application and compost treated sites with either a once or twice per year application instead of traditional fertilizer. An eddy covariance system was installed at a park to help quantify water losses and water, energy and carbon fluxes between the turfgrass and atmosphere. Additional meteorological observations are provided through a network of weather stations. The assessment covers over one year of observations, including the period of turfgrass growth in the warm season, and a period of dormancy during the cool season. The observations were used to setup and test a plot-scale soil water balance model to simulate changes in daily soil moisture in response to irrigation, precipitation and evapotranspiration demand for each park.
Combining modeling and observations of climate-soil-vegetation processes, I provide guidance on irrigation schedules and management that could help minimize water losses while supporting turfgrass health in desert urban parks. The irrigation scenarios suggest that water savings of at least 18% can be achieved at the three sites. While the application of compost treatment to study plots did not show clear improvements in soil water retention when compared to the control plots, this study shows that water conservation can be promoted while maintaining low plant water stress.
ContributorsKindler, Mercedes (Author) / Vivoni, Enrique R (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Garcia, Margaret (Committee member) / Arizona State University (Publisher)
Created2021
Description
Observational evidence is mounting on the reduction of winter precipitation and an earlier snowmelt in the southwestern United States. It is unclear, however, how these changes, along with forest thinning, will impact water supplies due to complexities in the precipitation-streamflow transformation. In this study, I use the Triangulated Irregular Network-based Real-time Integrated Basin Simulator (tRIBS) to provide insight into the independent and combined effects of climate change and forest cover reduction on the hydrologic response in the Beaver Creek (~1100 km2) of central Arizona. Prior to these experiments, confidence in the hydrologic model is established using snow observations at two stations, two nested streamflow gauges, and estimates of spatially-distributed snow water equivalent over a long-term period (water years 2003-2018). Model forcings were prepared using station observations and radar rainfall estimates in combination with downscaling and bias correction techniques that account for the orographic controls on air temperature and precipitation. Model confidence building showed that tRIBS is able to capture well the variation in snow cover and streamflow during wet and dry years in the 16 year simulation period. The results from this study show that the climate change experiments increased average annual streamflow by 1.5% at +1°C of warming. However, a 28% decrease in streamflow occurs by +6°C of warming as evapotranspiration (ET) increases by 10%. Forest thinning shifted the warming threshold where ET increases reduce streamflow yield until +4°C of warming as compared to no forest thinning when this threshold occurs at +2°C. An average increase in streamflow of 12% occurs after forest thinning across all climate scenarios. While the snow covered area is unaffected by thinning, the volume of snowmelt increases and is linked to the higher water yield. These findings indicate that water managers can expect decreases in streamflow due to climate change but may be able to offset these impacts up to a warming threshold by thinning forested areas within the Beaver Creek.
ContributorsCederstrom, Charles Joshua (Author) / Vivoni, Enrique R (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Svoma, Bohumil (Committee member) / Arizona State University (Publisher)
Created2021
Description
Accelerated climate and land use land cover (LULC) changes are anticipated to significantly impact water resources in the Colorado River Basin (CRB), a major freshwater source in the southwestern U.S. The need for actionable information from hydrologic research is growing rapidly, given considerable uncertainties. For instance, it is unclear if the predicted high degree of interannual precipitation variability across the basin could overwhelm the impacts of future warming and how this might vary in space. Climate change will also intensify forest disturbances (wildfire, mortality, thinning), which can significantly impact water resources. These impacts are not constrained, given findings of mixed post-disturbance hydrologic responses. Process-based models like the Variable Infiltration Capacity (VIC) platform can quantitatively predict hydrologic behaviors of these complex systems. However, barriers limit their effectiveness to inform decision making: (1) simulations generate enormous data volumes, (2) outputs are inaccessible to managers, and (3) modeling is not transparent. I designed a stakeholder engagement and VIC modeling process to overcome these challenges, and developed a web-based tool, VIC-Explorer, to “open the black box” of my efforts. Meteorological data was from downscaled historical (1950-2005) and future projections (2006-2099) of eight climate models that best represent climatology under low- and high- emissions. I used two modeling methods: (1) a “top-down” approach to assess an “envelope of hydrologic possibility” under the 16 climate futures; and (2) a “bottom-up” evaluation of hydrology in two climates from the ensemble representing “Hot/Dry” and “Warm/Wet” futures. For the latter assessment, I modified land cover using projections of a LULC model and applied more drastic forest disturbances. I consulted water managers to expand the legitimacy of the research. Results showed Far-Future (2066-2095) basin-wide mean annual streamflow decline (relative to 1976-2005; ensemble median trends of -5% to -25%), attributed to warming that diminished spring snowfall and melt and year-round increased soil evaporation from the Upper Basin, and overall precipitation declines in the Lower Basin. Forest disturbances partially offset warming effects (basin-wide mean annual streamflow up to 12% larger than without disturbance). Results are available via VIC-Explorer, which includes documentation and guided analyses to ensure findings are interpreted appropriately for decision-making.
ContributorsWhitney, Kristen Marie (Author) / Vivoni, Enrique R (Thesis advisor) / Mascaro, Giuseppe (Committee member) / Whipple, Kelin X (Committee member) / White, Dave D (Committee member) / Xu, Tianfang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Weather radars provide quantitative precipitation estimates (QPEs) with seamless spatial coverage that can complement limitations of sparse rain gage measurements, including those affecting intensity-duration-frequency (IDF) relations used for infrastructure design. The goal of this M.S. thesis is to assess the ability of 4-km, 1-h QPEs from the Stage IV analysis of the Next-Generation Radar (NEXRAD) network to reproduce the statistics of extreme precipitation (P) in central Arizona, USA, using a dense network of 257 rain gages as reference. The generalized extreme value (GEV) distribution is used to model the frequency of annual P maximum series observed at gages and radar pixels for durations, d, from 1 to 24 h. Estimates of P quantiles from radar QPEs are negatively biased (-20% – -30%) for d = 1 h. The bias tends to 0 and errors are small for d ≥ 6 h, independently of the return period. The presence of scaling for the GEV location and scale parameters, needed to apply IDF scaling models, was found for both radar and gage products. Regional frequency analysis methods combined with bias correction of the GEV shape parameter allow reducing the statistical uncertainty and providing seamless spatial distribution of P quantiles at daily and subdaily durations that address limitations of current IDF relations in southwestern U.S. based on NOAA Atlas 14.
ContributorsSrivastava, Nehal Ansh (Author) / Mascaro, Giuseppe (Thesis advisor) / Chester, Mikhail (Committee member) / Garcia, Margaret (Committee member) / Papalexiou, Simon Michael (Committee member) / Arizona State University (Publisher)
Created2022
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
Description
The Water-Energy Nexus (WEN) is a concept that recognizes the interdependence of water and energy systems. The Phoenix metropolitan region (PMA) in Arizona has significant and potentially vulnerable WEN interactions. Future projections indicate that the population will increase and, with it, energy needs, while changes in future water demand are more uncertain. Climate change will also likely cause a reduction in surface water supply sources. Under these constraints, the expansion of renewable energy technology has the potential to benefit both water and energy systems and increase environmental sustainability by meeting future energy demands while lowering water use and CO2 emissions. However, the WEN synergies generated by renewables have not yet been thoroughly quantified, nor have the related costs been studied and compared to alternative options.Quantifying WEN intercations using numerical models is key to assessing renewable energy synergy. Despite recent advances, WEN models are still in their infancy, and research is needed to improve their accuracy and identify their limitations. Here, I highlight three research needs. First, most modeling efforts have been conducted for large-scale domains (e.g., states), while smaller scales, like metropolitan regions, have received less attention. Second, impacts of adopting different temporal (e.g., monthly, annual) and spatial (network granularity) resolutions on simulation accuracy have not been quantified. Third, the importance of simulating feedbacks between water and energy components has not been analyzed.
This dissertation fills these major research gaps by focusing on long-term water allocations and energy dispatch in the metropolitan region of Phoenix. An energy model is developed using the Low Emissions Analysis Platform (LEAP) platform and is subsequently coupled with a water management model based on the Water Evaluation and Planning (WEAP) platform. Analyses are conducted to quantify (1) the value of adopting coupled models instead of single models that are externally coupled, and (2) the accuracy of simulations based on different temporal resolutions of supply and demand and spatial granularity of the water and energy networks. The WEAP-LEAP integrated model is then employed under future climate scenarios to quantify the potential of renewable energy technologies to develop synergies between the PMA's water and energy systems.
ContributorsMounir, Adil (Author) / Mascaro, Giuseppe (Thesis advisor) / White, Dave (Committee member) / Garcia, Margaret (Committee member) / Xu, Tianfang (Committee member) / Chester, Mikhail (Committee member) / Arizona State University (Publisher)
Created2022
Description
The hydrologic cycle in drylands is complex with large spatiotemporal variationsacross scales and is particularly vulnerable to changes in climate and land cover. To
address the challenges posed by hydrologic changes, a synergistic approach that
combines numerical models, ground and remotely sensed observations, and data analysis
is crucial. This dissertation uses innovative detection and modeling techniques to assess
key hydrologic variables in drylands, including irrigated water use, streamflow, and
snowpack conditions, answering following research questions that also have broad
societal implications: (1) What are the individual and combined effects of future climate
and land use change on irrigation water use (IWU) in the Phoenix Metropolitan Area
(PMA)?; (2) How can temporal changes in streamflow and the impacts of flash flooding
be detected in dryland rivers?; and (3) What are the impacts of rainfall-snow partitioning
on future snowpack and streamflow in the Colorado River Basin (CRB)?
Firstly, I conducted a scenario modeling using the Variable Infiltration Capacity
(VIC) model under future climate and land use change scenarios. Results showed that
future IWU will change from -0.5% to +6.8% in the far future (2071-2100) relative to the
historical period (1981-2010). Secondly, I employed CubeSat imagery to map streamflow
presence in the Hassayampa River of Arizona, finding that the imaging capacity of
CubeSats enabled the detection of ephemeral flow events using the surface reflectance of
the near-infrared (NIR) band. Results showed that 12% of reaches were classified as
intermittent, with the remaining as ephemeral. Finally, I implemented a physically-based
rainfall-snow partitioning scheme in the VIC model that estimates snowfall fraction from
the wet-bulb temperature using a sigmoid function. The new scheme predicts more
significant declines in snowfall (-8 to -11%) and streamflow (-14 to -27%) by the end of
the 21st century over the CRB, relative to historical conditions.
Overall, this dissertation demonstrates how innovative technologies can enhance
the understanding of dryland hydrologic changes and inform decision-making of water
resources management. The findings offer important insights for policymakers, water
managers, and researchers who seek to ensure water resources sustainability under the
effects of climate and land use change.
ContributorsWang, Zhaocheng (Author) / Vivoni, Enrique R (Thesis advisor) / White, Dave D (Committee member) / Mascaro, Giuseppe (Committee member) / Huang, Huei-Ping (Committee member) / Wang, Zhihua (Committee member) / Arizona State University (Publisher)
Created2023
Description
This dissertation critically evaluated methodologies and devices for assessing and protecting the health of human populations, with particular emphasis on groundwater remediation and the use of wastewater-based epidemiology (WBE) to inform population health. A meta-analysis and assessment of laboratory-scale treatability studies for removing chlorinated solvents from groundwater found that sediment microcosms operated as continuous-flow columns are preferable to batch bottles when seeking to emulate with high fidelity the complex conditions prevailing in the subsurface in contaminated aquifers (Chapter 2). Compared to monitoring at the field-scale, use of column microcosms also showed (i) improved chemical speciation, and (ii) qualitative predictability of field parameters (Chapter 3). Monitoring of glucocorticoid hormones in wastewater of a university campus showed (i) elevated stress levels particularly at the start of the semester, (ii) on weekdays relative to weekend days (p = 0.05) (161 ± 42 μg d-1 per person, 122 ± 54 μg d-1 per person; p ≤ 0.05), and (iii) a positive association between levels of stress hormones and nicotine (rs: 0.49) and caffeine (0.63) consumption in this student population (Chapter 4). Also, (i) alcohol consumption determined by WBE was in line with literature estimates for this young sub-population (11.3 ± 7.5 g d-1 per person vs. 10.1 ± 0.8 g d-1 per person), whereas caffeine and nicotine uses were below (114 ± 49 g d-1 per person, 178 ± 19 g d-1 per person; 627 ± 219 g d-1 per person, 927 ± 243 g d-1 per person). The introduction of a novel continuous in situ sampler to WBE brought noted benefits relative to traditional time-integrated sampling, including (i) a higher sample coverage (93% vs. 3%), (ii) an ability to captured short-term analyte pulses (e.g., heroin, fentanyl, norbuprenorphine, and methadone), and (iii) an overall higher mass capture for drugs of abuse like morphine, fentanyl, methamphetamine, amphetamine, and the opioid antagonist metabolite norbuprenorphine (p ≤ 0.01). Methods and devices developed in this work are poised to find applications in the remediation sector and in human health assessments.
ContributorsDriver, Erin Michelle (Author) / Halden, Rolf (Thesis advisor) / Conroy-Ben, Otakuye (Committee member) / Kavazanjian, Edward (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Arizona State University (Publisher)
Created2018
Description
Soil moisture (θ) is a fundamental variable controlling the exchange of water and energy at the land surface. As a result, the characterization of the statistical properties of θ across multiple scales is essential for many applications including flood prediction, drought monitoring, and weather forecasting. Empirical evidences have demonstrated the existence of emergent relationships and scale invariance properties in θ fields collected from the ground and airborne sensors during intensive field campaigns, mostly in natural landscapes. This dissertation advances the characterization of these relations and statistical properties of θ by (1) analyzing the role of irrigation, and (2) investigating how these properties change in time and across different landscape conditions through θ outputs of a distributed hydrologic model. First, θ observations from two field campaigns in Australia are used to explore how the presence of irrigated fields modifies the spatial distribution of θ and the associated scale invariance properties. Results reveal that the impact of irrigation is larger in drier regions or conditions, where irrigation creates a drastic contrast with the surrounding areas. Second, a physically-based distributed hydrologic model is applied in a regional basin in northern Mexico to generate hyperresolution θ fields, which are useful to conduct analyses in regions and times where θ has not been monitored. For this aim, strategies are proposed to address data, model validation, and computational challenges associated with hyperresolution hydrologic simulations. Third, analyses are carried out to investigate whether the hyperresolution simulated θ fields reproduce the statistical and scaling properties observed from the ground or remote sensors. Results confirm that (i) the relations between spatial mean and standard deviation of θ derived from the model outputs are very similar to those observed in other areas, and (ii) simulated θ fields exhibit the scale invariance properties that are consistent with those analyzed from aircraft-derived estimates. The simulated θ fields are then used to explore the influence of physical controls on the statistical properties, finding that soil properties significantly affect spatial variability and multifractality. The knowledge acquired through this dissertation provides insights on θ statistical properties in regions and landscape conditions that were never investigated before; supports the refinement of the calibration of multifractal downscaling models; and contributes to the improvement of hyperresolution hydrologic modeling.
ContributorsKo, Ara (Author) / Mascaro, Giuseppe (Thesis advisor) / Vivoni, Enrique R. (Thesis advisor) / Myint, Soe (Committee member) / Wang, Zhihua (Committee member) / Muenich, Rebecca (Committee member) / Arizona State University (Publisher)
Created2018