Publications

* Indicates student/postdoc mentee author. For a full list of publications, see my Google Scholar Profile

[22.] Vajedian, S.*, Smith, R.G., Schreüder, W. A., & Maurer, J. (2024). Aquifer system deformation in the San Luis Valley: A new framework for modeling subsidence in agricultural regions. Journal of Hydrology, 642, 131876.

[21.] Li, J.*, Smith, R., Grote, K., & Pedersen, J. B. (2024). Aquifer characterization using towed time-domain electromagnetics in a variably saturated, data-sparse region. Journal of Applied Geophysics, 228, 105440.

[20.] Majumdar, S., Smith, R. G., Hasan, M. F.*, Wilson, J. L., White, V. E., Bristow, E. L., Ribgy, J.R., Kress, W.H. & Painter, J. A., 2024, Improving crop-specific groundwater use estimation in the Mississippi Alluvial Plain: Implications for integrated remote sensing and machine learning approaches in data-scarce regions. Journal of Hydrology: Regional Studies, 52, 101674.

[19.] Grigg NS, Bailey RT, Smith, R.G. Stream-Aquifer Systems in Semi-Arid Regions: Hydrologic, Legal, and Management Issues. Hydrology. 2023; 10(12):224. https://doi.org/10.3390/hydrology10120224

[18.] Smith, R., 2023, Aquifer stress history contributes to historic shift in subsidence in the San Joaquin Valley, California. Water Resources Research, 59, e2023WR035804. https://doi.org/10.1029/2023WR035804

[17.] Hasan, F.*, Smith, R., Vajedian, S.*, Pommerenke, R.*, Majumdar, S.*, 2023, Global land subsidence mapping reveals widespread loss of aquifer storage capacity. Nature Communications, 14 (1), e2022WR034095. https://www.nature.com/articles/s41467-023-41933-z

[16.] Smith, R., Li, J.*, Grote, K., Butler, J. (2023). Estimating aquifer system storage loss with water levels, pumping and InSAR data in the Parowan Valley, Utah. Water Resources Research, 59, e2022WR034095. https://doi.org/10.1029/2022WR034095

[15.] Li, J.*, Smith, R., Grote, K. (2023). Analyzing Spatio-Temporal Mechanisms of Land Subsidence in the Parowan Valley, Utah, Hydrogeology Journal. https://link.springer.com/article/10.1007/s10040-022-02583-5

[14.] Majumdar, S.*, Smith, R., Conway, B., Lakshmi, V. (2022). Advancing Remote Sensing and Machine Learning-Driven Frameworks for Groundwater Withdrawal Estimation in Arizona: Linking Land Subsidence to Groundwater Withdrawals.  Hydrological Processes. https://onlinelibrary.wiley.com/doi/full/10.1002/hyp.14757

[13.] Adams, K. H., Reager, J. T., Rosen, P., Wiese, D. N., Farr, T. G., Rao, S., Smith, R. et al. (2022). Remote sensing of groundwater: Current capabilities and future directions. Water Resources Research, 58, e2022WR032219. https://doi.org/10.1029/2022WR032219

[12.] Lees, M., Knight, R., Smith, R., 2022, Development and Application of a 1D Compaction Model to Understand 65 Years of Subsidence in the San Joaquin Valley, Water Resources Research. [link]

[11.] Smith, R. G., Hashemi, H., Chen, J., & Knight, R. (2021). Apportioning deformation among depth intervals in an aquifer system using InSAR and head data. Hydrogeology Journal, 29(7), 2475-2486. [link]

[10.] Smith, R., Li, J.*, 2021. Modeling elastic and inelastic pumping-induced deformation with incomplete water level records in Parowan Valley, Utah. Journal of Hydrology. [link]

[9.] Smith, R.G., Oyler, L.*, Campbell, C., Woolley, E., Hopkins, B., Kerry, R., Hansen, N., 2021, A new approach for estimating and delineating within-field crop water stress zones with satellite imagery. International Journal of Remote Sensing. [link] [preprint]

[8.] Majumdar, S.*, Smith, R.G., Butler, J.J., Lakshmi, V., 2020, Groundwater Withdrawal Prediction Using Integrated Multi-Temporal Remote Sensing Datasets and Machine Learning. Water Resources Research. [link] [preprint]

[7.] Smith, R.G., Majumdar, S.*, 2020, Groundwater Storage Loss Associated with Land Subsidence in Western US Mapped Using Machine Learning. Water Resources Research. [link] [preprint]

[6. ] Smith, R.G., R. Knight, 2019, Modelling land subsidence using InSAR and Airborne Electromagnetic Data. Water Resources Research. [pdf]

[5. ] Smith, R.G., T. Mukerji, 2019, Correlating Geological and Seismic Data with Unconventional Resource Production Curves Using Machine Learning. Geophysics. [pdf]

[4. ] Smith, R.G., R. Knight, S. Fendorf, 2018, Over-Pumping Leads to California Groundwater Arsenic Threat. Nature Communications. [pdf]

[3. ] Knight, R., Smith, R.G., Asch, T., Abraham, J., Cannia, J., Viezzoli, A., Fogg, G., 2018, Mapping Aquifer Systems with Airborne Electromagnetics in the Central Valley of California. Groundwater. [link]

[2. ] Smith, R.G., R. Knight, J. Chen, J.A. Reeves, H.A. Zebker, T. Farr, and Z. Liu, 2017, Estimating the permanent loss of groundwater storage in the southern San Joaquin Valley, California. Water Resources Research [link].

[1. ] Nordin, M., Smith, R.G., Knight, R., 2016, The use of color wheels to communicate uncertainty in the interpretation of geophysical data. The Leading Edge.