Hai V. Pham
Groundwater Modeler
ResearchGate | Github | Google Scholar
Education
2011-2015, Ph.D. in Civil Engineering, Louisiana State University, Baton Rouge, USA
2009-2011, Master of Engineering, Dongguk University, Republic of Korea
2001-2006, Bachelor of Engineering, Water Resources University, Vietnam
2009-2011, Master of Engineering, Dongguk University, Republic of Korea
2001-2006, Bachelor of Engineering, Water Resources University, Vietnam
Research Interest
- Groundwater flow and contaminant transport modeling in porous and fractured media, optimization, sensitivity and uncertainty analysis;
- Data analytics, GIS analysis, Interferometric Synthetic Aperture Radar (InSAR), machine learning algorithms and high-performance/cloud computing;
- Subsurface characterization, saltwater intrusion, 3-D printing technology, optimal experimental designs,
- Bayesian model selection and model averaging, and information theory
Experience
2016 - 2020: Postdoctoral Fellow, Desert Research Institute, Las Vegas, NV, USA
- Developing hydrogeologic conceptual models and calculating water budgets for the groundwater system in Niger.
- Combining machine learning, big data, and Interferometric Synthetic Aperture Radar (InSAR) to predict land subsidence and groundwater levels.
- Characterizing hydraulic properties and improve understanding of flow and transport mechanism in fractured rocks by the development of integrated modeling approaches that combine insights gained from experimental studies using 3D printers with novel numerical/theoretical techniques.
- Quantifying land loss and its effects on agricultural production in the Vietnamese Mekong Delta (VMD) under climate change.
- Developed groundwater models for the Pahrump Valley in Nevada, conducting sensitivity and uncertainty analysis, and making predictions.
- Investigated the interplay between groundwater and wildfire in the United States using a data-driven approach.
- Developed computer models to study groundwater and contaminants transport through discrete fracture networks and forecast radionuclide contaminant boundaries at the Western Pahute Mesa area of the Nevada National Security Site (NNSS).
- Assessed the effects on surface water depletion caused by groundwater pumping for the upper Humboldt River basin in Nevada. Evaluating the impacts of model nonlinearity
and parameter uncertainty on predicted results to improve prediction reliability. - Developed capture maps to assess the effects on surface water by groundwater pumping for the Tahoe Valley South Groundwater Basin in Nevada.
- Developed groundwater models to investigate primary flow paths in the Western Pahute Mesa which account for the influence of regional stress on fault permeability.
- Developed two computer programs to detect water level trends and to determine the groundwater flow directions and gradients for the Frenchman Flat Corrective Action Unit (CAU) 98 of the Nevada National Security Site (NNSS).
- Introduced a grid generation technique that automatically maps water-containing sand layers of a subsurface media and converts it into computational grids for groundwater modeling.
- Developed an efficient and powerful method to calibrate complex groundwater models and quantify prediction uncertainty based on the covariance matrix-adaptation evolution strategy (CMA-ES). Paralleled the code to run in high-performance computing systems to shorten model calibration time.
- Reconstructed a highly complex faulted hydrostratigraphy of the Baton Rouge Aquifer System, converted the complex hydrostratigraphy into groundwater modeling grids and built a high-resolution three-dimensional numerical model to study groundwater flow and saltwater intrusion.
- Designed hydraulic barries to protect pumping wells from salt water intrusion.
- Conducted optimal experimental designs for model discrimination and conceptual model uncertainty reduction using Bayesian model averaging and information theory. A new γ-identifiable model discrimination criterion was introduced to distinguish groundwater models and identify a reliable groundwater model for future applications.
- Derived a new model discrimination function to guide through data collection in optimal experimental designs for groundwater model discrimination. The function combines the Bayesian model averaging (BMA) framework and information theory to evaluate the worth of additional data collection under various sources of uncertainty and in the presence of data correlation.
- Introduced the concept of firm information gain in optimal experimental design for conceptual model discrimination.
- Developed groundwater models and assessed impacts of sea-level rise and groundwater extraction on seawater intrusion.
- Assisted in grading homework and final exams.
- Simulated river hydrodynamic and water quality, basin water allocation, coastal sediment transport and ocean oil-spill pollution.
- Prepared final reports and published 9 papers in domestic proceedings; trained 2 new employees and 1 intern student
Skills
Modeling: MODFLOW, MODPATH, FloPy, MT3DMS, SEAWAT, PEST, GMS, dfnWorks, PFLOTRAN, Rockworks.
Programming: Python, Matlab, FORTRAN, C++, Bash, Git.
Software: ArcMap, ParaView, Tecplot, Surfer, AutoCAD, Illustrator/InkScape, Latex, Microsoft Word, Excel, & PowerPoint.
Interferometric Synthetic Aperture Radar (InSAR): ISCE, GIANT, MintPy
Cloud Computing: Amazon Web Services (AWS).
Programming: Python, Matlab, FORTRAN, C++, Bash, Git.
Software: ArcMap, ParaView, Tecplot, Surfer, AutoCAD, Illustrator/InkScape, Latex, Microsoft Word, Excel, & PowerPoint.
Interferometric Synthetic Aperture Radar (InSAR): ISCE, GIANT, MintPy
Cloud Computing: Amazon Web Services (AWS).
Contact Information
Hai Pham
Groundwater Modeler
INTERA Incorporated
9600 Great Hills Trail, Suite 300W
Austin, TX 78759
Email: ___hpham___at___intera.com___
www.intera.com
ResearchGate | Github | Google Scholar
Groundwater Modeler
INTERA Incorporated
9600 Great Hills Trail, Suite 300W
Austin, TX 78759
Email: ___hpham___at___intera.com___
www.intera.com
ResearchGate | Github | Google Scholar