Interested in all aspects of hydrogeology, Allan is especially interested in pairing data analytics and hydrogeology knowledge to inform water resource management including implementing facets of Machine Learning (ML) and Artificial Intelligence (AI) into groundwater models for real-time decision-making and analysis. He is also interested in developing and integrating his data analysis skills into contiminant fate and transport modeling studies.

Google Scholar Profile

Current Projects:

• MODFLOW groundwater modeling of East St. Louis area
• Develop automated, integrated, real-time groundwater modeling with FloPy
• High performance computing to enhance modeling ensembles

Publications:

1. Krasowski, M. P., Gulsen, E., Jones, A. E., & Abrams, D. B. (2024). Modelling Seasonal Variability in Parameters Defining Volumetric Water Content in a Low Permeability Soil in Central Illinois: An Application of MODFLOW-6 and the Unsaturated Zone Flow Package. Hydrological Processes, 38(11), e70007. doi:10.1002/hyp.70007.
2. Wei, H., Xu, X., Jones, A. E., Hardison, A. K., Moffett, K. B., and McClelland, J. W. (2022). Tidal Freshwater Zones Modify the Forms and Timing of Nitrogen Export from Rivers to Estuaries. Estuaries and Coasts. doi:10.1007/s12237-022-01112-7.
   
3. Jones, A. E., A. K. Hardison, B. R. Hodges, J. W. McClelland, and K. B. Moffett (2020), Defining a Riverine Tidal Freshwater Zone and Its Spatiotemporal Dynamics, Water Resources Research, V56 I4, doi:10.1029/2019WR026619.
4. Jones, A. E., Hardison, A. K., Hodges, B. R., McClelland, J. W., & Moffett, K. B. (2019). An expanded rating curve model to estimate river discharge during tidal influences across the progressive-mixed-standing wave systems. PLOS ONE, 14(12), e0225758. doi.org/10.1371/journal.pone.0225758
5. Jones, A. E., B. R. Hodges, J. W. McClelland, A. K. Hardison, and K. B. Moffett (2017), Residence-time-based classification of surface water systems, Water Resources Research, 53, doi:10.1002/2016WR019928.
6. Jones, A. E. (2017), Where the River Meets the Sea: An Initial Investigation into the Riverine Tidal Freshwater Zone, The University of Texas at Austin Dissertation, Accepted: Dec. 2017. (UTexas Repository).

Useful coding products:

• Jones, A. E. Web-scraping module to download data from the USGS NWIS. GitHub repository – Python.
  URL: https://github.com/allanejones/GIT-Download-NWIS
  • Python web-scraping module that downloaded ~8 years of data from 15 United States Geological Survey (USGS) National Water Information System (NWIS) gauging stations across the Texas Gulf Coast, which resulted in ~561,024 data points.
• Jones, A. E. Web-scraping module to download data from the TCOON Tidal Network. GitHub repository – Python.
  URL: https://github.com/allanejones/GIT-Download-TCOON
  • Python web-scraping module that downloaded data from the no-longer funded Texas Coastal Oceanic Observatory Network (TCOON). The script garnered ~140, 256 data points from nine tidal sites.
• Jones, A. E. Web-scraping module to download data from Weather Underground. GitHub repository – Python.
  URL: https://github.com/allanejones/GIT-Download-Wunderground
  • Python web-scraping module that downloaded data from public weather stations hosted by Weather Underground for seasonal records of precipitation, barometric pressure, and temperature to investigate municipal (Portland, OR, USA) water- and energy-balances.