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. Currently, I am a Postdoctoral Research Associate at the Atmospheric and Ocean Science Program at Princeton University working on Earth System Modeling at the NOAA Geophysical Fluid Dynamics Laboratory.

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

Research

SMAP-HydroBlocks

The first hyper-resolution satellite-based surface soil moisture dataset at 30-m resolution over the continental United States

HydroBlocks

Development & applications of a field-scale resolving land surface model over continental extents

Crop Yield Prediction

Combining hyper-resolution land surface modeling and machine learning for predicting crop yields at field scale

Featured Publications

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

Drought monitoring and yield prediction often rely on coarse-scale hydroclimate data or (infrequent) vegetation indexes that do not always indicate the conditions farmers face in the field. Consequently, decision-making based on these indices can often be disconnected from the farmer reality. Our study focuses on smallholder farming systems in data-sparse developing countries, and it shows how field-scale soil moisture can leverage and improve crop yield prediction and drought impact assessment.

Noemi Vergopolan, Sitian Xiong, Lyndon Estes, Niko Wanders, Nathaniel W. Chaney, Eric F. Wood, Megan Konar, Kelly Caylor, Hylke E. Beck, Nicolas Gatti, Tom Evans, Justin Sheffield

Field-scale soil moisture bridges the spatial-scale gap between drought monitoring and agricultural yields

Two-way coupling between the sub-grid land surface and river networks in Earth system models

Although there have been significant advances in river routing and sub-grid heterogeneity (i.e., tiling) schemes in Earth system models over the past decade, there has yet to be a concerted effort to couple these two concepts. This paper aims to bridge this gap through the development of a two-way coupling between sub-grid tiles and river networks in a field-scale resolving land surface model. The scheme is implemented and tested over a 1 arc degree domain in Oklahoma, United States.

Nathaniel W. Chaney, Laura Torres-Rojas, Noemi Vergopolan, Colby K. Fisher

Two-way coupling between the sub-grid land surface and river networks in Earth system models

Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates

Accurate and detailed soil moisture information is essential for, among other things, irrigation, drought and flood prediction, water …

Noemi Vergopolan, Nathaniel W. Chaney, Hylke E. Beck, Ming Pan, Justin Sheffield, Steven Chan, Eric F. Wood

Combining hyper-resolution land surface modeling with SMAP brightness temperatures to obtain 30-m soil moisture estimates

Recent Publications

Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

Hylke E. Beck, Ming Pan, Diego G. Miralles, Rolf H. Reichle, Wouter A. Dorigo, Sebastian Hahn, Justin Sheffield, Lanka Karthikeyan, Gianpaolo Balsamo, Robert M. Parinussa, Albert I. J. M. van Dijk, Jinyang Du, John S. Kimball, Noemi Vergopolan, Eric F. Wood

PPDIST, global 0.1° daily and 3-hourly precipitation probability distribution climatologies for 1979–2018

Hylke E. Beck, Seth Westra, Jackson Tan, Florian Pappenberger, George J. Huffman, Tim R. McVicar, Gaby J. Gründemann, Noemi Vergopolan, Hayley J. Fowler, Elizabeth Lewis, Koen Verbist, Eric F. Wood

A global near-real-time soil moisture index monitor for food security using integrated SMOS and SMAP

Sara Sadri, Ming Pan, Yoshihide Wada, Noemi Vergopolan, Justin Sheffield, James S. Famiglietti, Yann Kerr, Eric F. Wood

See all publications