| NSF Org: |
EAR Division Of Earth Sciences |
| Recipient: |
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| Initial Amendment Date: | June 26, 2019 |
| Latest Amendment Date: | August 4, 2021 |
| Award Number: | 1907555 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Laura Lautz
llautz@nsf.gov (703)292-7775 EAR Division Of Earth Sciences GEO Directorate for Geosciences |
| Start Date: | July 15, 2019 |
| End Date: | June 30, 2022 (Estimated) |
| Total Intended Award Amount: | $299,515.00 |
| Total Awarded Amount to Date: | $299,515.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
520 LEE ENTRANCE STE 211 AMHERST NY US 14228-2577 (716)645-2634 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
126 Cooke Hall Buffalo NY US 14260-0001 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Hydrologic Sciences |
| Primary Program Source: |
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| Program Reference Code(s): | |
| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Groundwater constitutes a significant component of fresh water supplies in the United States. With changing climate, the dependence on groundwater is only expected to grow, especially in areas with limited surface water supplies. Because of their susceptibility to contamination, assertive management and cleanup efforts of groundwater systems require proper understanding of how contaminants move in these systems. Field-scale prediction of contaminants transport in complex groundwater systems is a long-standing research challenge. This project combines numerical modeling of contaminant transport with geophysical methods to advance understanding and enhance the ability to monitor and predict field-scale contaminant transport in highly complex groundwater systems. The project also contributes to the development of a diverse scientific workforce and benefits society by supporting an early career faculty member and postdoctoral researcher, providing research opportunities to undergraduate students, engaging students from underrepresented groups in STEM, and exposing middle-school students to geoscience education and opportunities.
Proper management of groundwater systems and mitigation of health risks posed by contaminated aquifers requires an understanding of field-scale solute migration, particularly small-scale transport processes in highly heterogeneous aquifers. The clasic advection-dispersion model, commonly used to predict solute migration, often fails to reproduce field measurements due to the lack of knowledge and uncertainty of solute transport parameters (STPs). Traditional well-based sampling methods employed to gain insights into field-scale transport parameters provide spatially limited information. Their invasive nature may also disturb the natural small-scale transport behavior that needs to be understood. Hydrogeophysics provides opportunities to rapidly characterize spatially continuous, field-scale solute plume migration for quantitative evaluation of transport parameters using minimally-invasive methods. Hydrogeophysical estimation requires prior information about the spatial distribution of the target solute plume for computational stability. The conventional prior constraints applied in hydrogeophysics, however, lack information about the physics of the target transport process (e.g., advection-dispersion) that is driving the evolution of the contaminant plume, resulting in inaccurate estimation of the transport parameters. Given the complex heterogeneity and uncertainty in hydrogeological systems, stochastic methods are well suited for solute transport prediction in these systems. Standard stochastic sampling methods can, however, become computationally intractable in spatially-distributed, high-dimensional hydrological problems. The goal of this project is to develop and test novel stochastic estimation strategies that: 1) incorporates prior physics-based constraints of the solute transport process (i.e., accounts for multiple scales of plume dispersion and complexity) to improve velocity, plume-dispersion, and mass estimations; and 2) performs stochastic estimation in the reduced-hydrologic-process parameter space to improve computational efficiency and enable field-scale characterization of small-scale transport processes in highly heterogeneous aquifers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
This research aimed to develop a new statistical method to predict the movement of contamination in groundwater that better predicts the mass and location/shape of the contamination plume. These numerical methods were to be further constrained with field data collected using electrical resistivity tomography. Stochastic inversion of the solute transport experiment at the Massachusetts Military Reservation in Cape Cod was conducted by an MS student at Buffalo. These results were processed using the proposed basis-constrained inversion and compared to the more traditional Tikhonov inversion method. It was projected that a new basis-constrained inversion method would better predict total mass and plume morphology by matching field results by Singha and Gorelick (2005) compared to results produced using the Tikhonov method. Results showed that this new basis-constrained inversion had a poorer fit for both mass and plume morphology. Results to date have confirmed the null hypothesis. This may have been due to the need for a more extensive library of training images or the inversion code favoring some basis vectors at the expense of model realism. Personal changes in the Principal Investigator prevented further exploration of these results and caused the research to end early. While the null hypothesis was confirmed, this research did support the partial training of four students and one postdoctoral scholar. All five participants are currently in the STEM fields, two are continuing their graduate education, two work in industry, and one is a high school science teacher.
Last Modified: 09/21/2022
Modified by: Christopher S Lowry
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