Dr. Ryan Smith, Assistant Professor in Geological Engineering

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Research Opportunities

If you are interested in joining the remote sensing hydrology research group, see the link below

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Ongoing and Past Projects

  • Project 1
  • Project 2
  • Project 3
  • Project 4
  • Additional Research

Project 1

Integrating Remote Sensing Datasets to Estimate Groundwater Pumping and Subsidence

Multiple satellite datasets are sensitive to different aspects of the water balance, but are traditionally not integrated because they are challenging to relate to each other. In this project, we develop a machine learning framework to integrate these datasets to predict groundwater pumping and land subsidence at large spatial scales.

Link >

Project 2

Estimating Plant Water Stress with Remote Sensing Data

Plant water stress is challenging to characterize without in-situ data. In this project, we combine satellite data (NDWI and ET) and in-situ measures of plant water stress (water content and potential), as well as yield and total water availability to develop estimates for plant water stress.

Project 3

Groundwater Modeling with InSAR and AEM Data in California

This project, in collaboration with Stanford University and the USGS, is currently being funded by NASA. The goal of this project is to improve groundwater model development by integrating high-resolution hydrostratigraphic data from airborne electromagnetics (AEM), and high-resolution hydrologic data from interferometric synthetic aperture radar (InSAR). InSAR has the highest signal over areas with high groundwater declines and high clay content. AEM has the highest signal over areas with high clay content. Because both datasets are sensitive to the presence of subsurface clay, they can complement each other and improve the accuracy and unique-ness of model inversions.
 

Project 4

Modeling Subsidence Using InSAR and Hydraulic Head

Land subsidence is linked to changes in hydraulic head due to groundwater extraction, but this relationship is challenging to model because it is non-linear. When the hydraulic head drops below its previously lowest level (preconsolidation head), inelastic, or plastic, deformation occurs. However, when the hydraulic head stays above the preconsolidation head, the deformation is elastic. It is challenging to estimate the preconsolidation head, because it varies spatially and over time. To further complicate the problem, subsidence is heavily influenced by the amount and thickness of clays, which is challenging to estimate.

In this project, we are developing approaches to model land subsidence using only hydraulic head and InSAR data. This model solves for the clay content, hydrologic properties, and spatio-temporal evolution of the preconsolidation head.

Link >

Additional Research

Integration of InSAR with Airborne Geophysical Data for the Development of Groundwater Models
7/01/2019 - 6/30/2021
Budget: $35,229.00
Sponsor: Stanford University
 
Monitoring Groundwater Extraction Using Automated Assessment of Land Subsidence
11/03/2020 - 11/02/2022
Budget: $94,635.00
Sponsor: Natl Geospatial Intelligence Agency
  • Past Project 1
  • Past Project 2
  • Past Project 3

Past Project 1

Joint Inversion of Subsidence and Electromagnetic Data

Modeling deformation data related to groundwater pumping is challenging, because it is a function of the subsurface geology, which is typically poorly known. In this study, we combined InSAR data, which is used to estimate deformation , with AEM data, which is used to estimate geology. We combined these two datasets in a joint inversion framework, solving for the subsurface geology and hydrologic properties of the geologic layers. This joint inversion resulted in a model that successfully predicted land deformation on our validation dataset. 

Past Project 2

Quantifying the Link between Groundwater Over-Pumping and Contamination

In the San Joaquin Valley, California, arsenic contamination affects over 10% of wells drilled in the San Joaquin Valley. Arsenic levels as low as 10 parts per billion have been linked to increased risk of cancer and numerous other health problems. Some have hypothesized that arsenic contamination is linked to groundwater pumping, but this had never been explored or quantified in California.

We processed subsidence data from satellites (interferometric synthetic aperture radar, or InSAR), which are a proxy for groundwater pumping. We then ran a machine learning model to explore the relationship between groundwater pumping (inferred by subsidence), geochemical conditions, and arsenic concentrations.

Our findings indicate that groundwater pumping is a significant factor in arsenic concentrations. This suggests that continued unsustainable pumping of groundwater in the San Joaquin Valley could raise arsenic levels throughout the Valley. However, our results also indicate that reductions in pumping are likely to improve water quality.

Link >

Past Project 3

Estimating Permanent Groundwater Storage Loss Using Satellites

In the southern portion of the San Joaquin Valley, California, agricultural demands are so high that they account for roughly 10% of the pumped groundwater in the United States. Because of the high water demand of the crops that are grown, large volumes of groundwater are required to irrigate the crops. Groundwater pumping in this area has caused significant declines in groundwater levels over the past century, leading to subsidence as high as 10 m over this time period. Water managers are concerned that the subsidence represents a permanent loss of storage.

Using Interferometric Synthetic Aperture Radar (InSAR), we can measure subsidence with cm-scale accuracy over large areas. We have processed InSAR data over the study area from 2007-2010. Using groundwater level data, geologic models, and knowledge of the geomechanical properties of sediments in the area, in conjunction with InSAR, we estimated the permanent loss of groundwater storage.

Our results show that the vast majority (98%) of subsidence in our study area is related to the permanent loss of groundwater storage. By volume this is m3 of groundwater storage, or roughly 9% of the volume of groundwater used from 2007-2010.

Link >

Publications


Dr. Ryan Smith

dr_ryan_smith
The Remote Sensing Hydrology Research Group uses hydrogeophysical tools, including satellite, airborne and ground-based geophysical methods, to address water resource problems, including groundwater depletion, subsidence, and aquifer contamination. The group is led by Ryan Smith, an Assistant Professor in Missouri University of Science and Technology's Geological Engineering program.
 
By combining geophysical datasets--primarily interferometric synthetic aperture radar (InSAR) and electromagnetic (EM) data--with hydrologic time series, we are able to quantify changes in groundwater storage and quality. A sample of some of the research we do is shown in the figure below.
 
Key tools and methods used by this research group include:
 
InSAR processing, time-domain electromagnetics, remote sensing data fusion, hydrogeophysics, groundwater modeling, subsidence and deformation modeling, machine learning
 
To learn more about specific research projects, check out the research page or send me an email.
 
Sanaz Vejedian
Sayantan Majumdar
Jiawei Li
Rahel Pommerenke
Fahim Hasan
Dawit Asfaw
Lindi Oyler

Sanaz Vajedian - Postdoctoral Scholar

Sayantan Majumdar (he/him/his) - PhD Student

Jiawei Li - PhD Student

Rahel Pommerenke - MS Student

Fahim Hasan - MS Student

Dawit Asfaw - PhD Student

Lindi Oyler (they/them/theirs) - MS Student Lab Alumni