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.

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

High-Resolution Soil Moisture Data Reveal Complex Multi-Scale Spatial Variability Across the United States

Soil moisture (SM) space and time variability critically influences freshwater resources, agriculture, ecosystem dynamics, climate and land-atmosphere interactions, and it can also trigger hazards such as droughts, floods, landslides, and aggravate wildfires. Here, we present the first continental assessment of how SM varies at the local scales using SMAP-HydroBlocks. This study maps the SM spatial variability, characterizes the landscape drivers, and quantifies how this variability persists across larger spatial scales. Results revealed striking SM spatial variability across the United States. However, this SM variability does not persist at coarser spatial scales resulting in extensive information loss. This information loss implicates inaccuracies when predicting non-linear SM-dependent hydrological, ecological, and biogeochemical processes using coarse-scale models and satellite estimates.

Noemi Vergopolan, Justin Sheffield, Nathaniel W. Chaney, Ming Pan, Hylke E. Beck, Craig R. Ferguson, Laura Torres-Rojas, Felix Eigenbrod, Wade Crow, Eric F. Wood

High-Resolution Soil Moisture Data Reveal Complex Multi-Scale Spatial Variability Across the United States

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

We introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. Its largest benefit of SMAP-HB is the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products.

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

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

Satellite missions measuring soil moisture from space continue to improve the availability of soil moisture information. However, the utility of these satellite products is limited by the large footprint of the microwave sensors. This study presents a merging framework that combines a hyper-resolution land surface model (LSM), a radiative transfer model (RTM), and a Bayesian scheme to merge and downscale coarse resolution remotely sensed hydrological variables to a 30-m spatial resolution. The framework is based on HydroBlocks, an LSM that solves the field-scale spatial heterogeneity of land surface processes through interacting hydrologic response units (HRUs). Our approach was demonstrated for soil moisture by coupling HydroBlocks with the Tau-Omega RTM used in the Soil Moisture Active Passive (SMAP) mission. The brightness temperature from the HydroBlocks-RTM and SMAP L3 were merged to obtain updated 30-m resolution soil moisture estimates.

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 effective and integrated drought management, we introduce a conceptual drought diagnosis framework inspired by diagnostic concepts from the field of medicine. This framework comprises five steps: 1. Initial diagnostic assessment; 2. Diagnostic testing; 3. Consultation; 4. Communication of the diagnosis; and 5. Treatment and prognosis. This framework can help drought managers to be more proactive in enabling drought-affected regions to become more drought resilient in the future.

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

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

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

We evaluated the largest and most diverse set of surface soil moisture products ever evaluated in a single study. We found pronounced differences in performance among individual products and product groups. Our results provide guidance to choose the most suitable product for a particular application.

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