Noemi Vergopolan

Noemi Vergopolan

Computational Hydrology

Princeton University

About Me

My research aims to aid water resource decision-making by improving monitoring and forecasting of hydrological hazards and their impacts from global to local spatial scales. To this aim, I work developing scalable solutions for high-resolution hydrological predictions 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 F. Wood and Prof. Justin Sheffield. Currently, I am a Postdoctoral Research Associate at the Princeton University Atmospheric and Ocean Science Program and NOAA Geophysical Fluid Dynamics Laboratory working with Dr. Elena Shevliakova on Earth System Modeling and satellite data assimilation.

Learn more about my interests in research and publications.

Interests

  • Hydrology
  • Agriculture
  • 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

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors
Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic …
Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors
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

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