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.

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.

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.

Featured Publications

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

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, Nathaniel W. Chaney, Ming Pan, Justin Sheffield, Hylke E. Beck, Craig R. Ferguson, Laura Torres-Rojas, Sara Sadri, Eric F. Wood

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

HydroBlocks v0.2: enabling a field-scale two-way coupling between the 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 decades, 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 tiling schemes and river networks in the HydroBlocks 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

HydroBlocks v0.2: enabling a field-scale two-way coupling between the land surface and river networks in Earth system models

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

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

Drought Diagnosis: What the Medical Sciences Can Teach Us

Drought management is currently informed by a variety of approaches, mostly responding to drought crisis when it happens. Toward more …

David W. Walker, Louise Cavalcante, Sarra Kchouk, Germano G. Ribeiro Neto, Art Dewulf, Rubens S. Gondim, Eduardo S. Passos Rodrigues Martins, Lieke A. Melsen, Francisco de Assis de Souza Filho, Noemi Vergopolan, Pieter R. Van Oel

Drought Diagnosis: What the Medical Sciences Can Teach Us

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 …

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

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°) …

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

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

See all publications