Noemi Vergopolan

Noemi Vergopolan

Computational Hydrology

Princeton University

About Me

I am a computational hydrologist, engineer, and scientist working on solutions for water resources and climate. My research aims to aid actionable decision-making by improving hydrological information for monitoring and forecasting of hydrological extremes and their impacts. To this end, I develop scalable computational approaches for high-resolution hydrological prediction by leveraging satellite remote sensing, land surface modeling, machine learning, data fusion, and high-performance computing.

I obtained a PhD in Civil and Environmental Engineering from Princeton University, working with Prof. Eric Wood and Justin Sheffield. Currently, I am a postdoctoral research associate in the Atmospheric and Ocean Science Program at Princeton University and the NOAA Geophysical Fluid Dynamics Laboratory working with Dr. Elena Shevliakova on Earth System Modeling and satellite data assimilation. Prior, I worked on water resources and environmental engineering consulting.

Learn more about my interests in research and publications, and by following my updates on Twitter.

Interests

  • Hydrology
  • Agriculture
  • Floods and Droughts
  • Satellite Remote Sensing
  • Artificial Intelligence
  • High Performance Computing
  • Big Data

Education

  • PhD in Civil & Environmental Engineering

    Princeton University, 2021

  • Statistics & Machine Learning Certificate

    Princeton University, 2021

  • Computational Science & Engineering Certificate

    Princeton University, 2019

  • MA in Civil & Environmental Engineering

    Princeton University, 2017

  • BS Environmental Engineering

    Federal University of Paraná, 2014

Data & Models

SMAP-HydroBlocks
SMAP-HydroBlocks is the first field-scale hyper-resolution satellite-based surface soil moisture dataset at 30-m resolution over the continental United States. Learn more about it here.
SMAP-HydroBlocks
HydroBlocks
HydroBlocks is a field-scale resolving land surface model for computationally efficient applications over continental extents. Learn more on the model development & applications here.
HydroBlocks
Field-scale Crop Yield Prediction
Field-scale satellite observations, physical models, and machine learning combined can enable crop yield prediction at high spatial resolution at data-scarse regions. Learn more about it here.
Field-scale Crop Yield Prediction

Recent Publications

PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018
We introduce the Precipitation Probability DISTribution (PPDIST) dataset, a collection of global high-resolution (0.1°) …
PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

Social