Samuel Tuttle

Visiting Assistant Professor of Data Science

As a hydrologist and data scientist, I study how water (in all of its phases) moves around the Earth, and how changes in water distribution and water-related processes affect weather, climate, the biosphere, water resources, and human activities.  I am particularly interested in interrelationships between hydrological, atmospheric, and land surface processes, especially using satellite remote sensing. My research mainly consists of using statistical methods to learn from observational data, modeling, and simulation studies. Some recent projects include identification of feedbacks between soil moisture and rainfall, and improvement of spring snowmelt flood prediction in the north central U.S. using satellite observations of snow and soil moisture.

Please contact me ( if you would like a copy of my CV, or would like to learn more about what I am working on.


Current projects:

  • Using passive microwave satellite data and air temperature data to identify snow melt and refreeze events
  • Identification of lake ice cover timing on Lake Linné, Svalbard, Norway using satellite data from Google Earth Engine
  • Evaluating the role of soil moisture on land surface model air temperature biases
  • Using relationships between soil moisture and precipitation to learn about soil and climate conditions


Courses taught:

  • GEOL 131 - Introduction to Hydrology: A Data Perspective
  • GEOL 247 - Environmental Modeling & Statistics
  • GEOL 326 - Global Climate Change


Selected Publications:

Tuttle, S. E., & Salvucci, G. D. (2016). Empirical evidence of contrasting soil moisture-precipitation feedbacks across the United States. Science352(6287), 825-828, doi: 10.1126/science.aaa7185.

Tuttle, S. E., & Salvucci, G. D. (2017). Confounding factors in determining causal soil moisture-precipitation feedback. Water Resources Research, 53, doi:10.1002/2016WR019869.

Tuttle, S. E., & Salvucci, G. D. (2012). A new method for calibrating a simple, watershed scale model of evapotranspiration: Maximizing the correlation between observed streamflow and model inferred storage. Water Resources Research48(5), doi:10.1029/2011WR011189.

Tuttle, S. E., Cho, E., Restrepo, P., Jia, X., Vuyovich, C. M., Cosh, M. H., & Jacobs, J. M. (2016). Chapter 2: Remote sensing of drivers of spring snowmelt flooding in the North Central U.S. InV. Lakshmi (ed.), Remote Sensing of Hydrological Extremes, Springer Remote Sensing/Photogrammetry, Switzerland, p. 21-45.


Media Links:

"Soil moisture alters next-day rainfall in the United States" Science Magazine News: ScienceShots, May 12, 2016

"When Earth Speaks to Sky" Boston University Research, August 4, 2016