Member Since 2016
Noemi Vergopolan
Assistant Professor , Rice University
My research aims to aid actionable decision-making by improving hydrological information for monitoring and forecasting hydrological extremes and their impacts at the local scales. To this end, I develop scalable computational approaches for high-resolution hydrological prediction by leveraging advances in satellite remote sensing, land surface modeling, machine learning, data fusion, and high-performance computing.
Professional Experience
Rice University
Assistant Professor
2024 - Present
Princeton University
Postdoctoral Researcher
2021 - 2024
Education
Princeton University
Doctorate
2021
Honors & Awards
Science for Solutions Award
Received December 2022
Citation
Noemi is an exceptionally talented early-career researcher whose
research on computational hydrology has provided outstanding
contributions to solving water and food security challenges. Noemi
develops scalable approaches for hydrological prediction by advancing
realism in hyperresolution (tens of meters) land surface models through
big geospatial data, satellite land data assimilation, machine learning
and high-performance computing, including the representation of human
water management such as irrigation and groundwater pumping. These
cutting-edge computational approaches enable monitoring and
understanding of complex hydrological processes at unprecedented spatial
scales and over continental domains that otherwise would be infeasible.
Noemi’s work bridges a significant research gap and forms the basis for
solving societal problems around water and food security, targeting the
spatial scales at which impacts occur and decision-making is
implemented, through improved monitoring of crop water demands,
hydrologic extremes and other natural hazards.
She developed an assimilation framework (Vergopolan et al., 2020, Remote Sensing of Environment) to represent soil moisture variability at local scale which outperformed state-of-the-art satellite estimates and applied this approach to crop yield prediction (Vergopolan et al., 2021, Hydrology and Earth System Sciences), showing how localized estimates of food security risks are feasible across national domains. These tools are now being used for solutions on the ground, for example, forming the basis of operational flood early warning in southern Africa and quantifying maize yield gaps at field scale in Malawi to provide evidence to national food security policy. She has also pioneered the development of open-source, continental domain, hyperresolution data sets (Vergopolan et al., 2021, Scientific Data) that have potential for applications in water resources, agriculture, ecology and biogeochemistry and has enabled for the first time understanding of the scaling behavior of soil moisture at continental scales (Vergopolan et al., 2022, Geophysical Research Letters). Her current work on improving operational weather and subseasonal climate forecast systems as part of a NOAA fellowship has potential to provide early warning of hydrometeorological risks, especially for the most vulnerable of society.
Noemi’s research profile is outstanding for a researcher only 1 year since her Ph.D., with much of this a result of multidisciplinary collaborative work, which is critical to address complex societal problems around water, food and climate. As a young, female researcher from Brazil who has made great strides in the early part of her career, she is a role model for other young and talented Latin researchers and has done much to promote Earth and environmental science within and by this community.
— Justin Sheffield
University of Southampton
Southampton, United Kingdom
She developed an assimilation framework (Vergopolan et al., 2020, Remote Sensing of Environment) to represent soil moisture variability at local scale which outperformed state-of-the-art satellite estimates and applied this approach to crop yield prediction (Vergopolan et al., 2021, Hydrology and Earth System Sciences), showing how localized estimates of food security risks are feasible across national domains. These tools are now being used for solutions on the ground, for example, forming the basis of operational flood early warning in southern Africa and quantifying maize yield gaps at field scale in Malawi to provide evidence to national food security policy. She has also pioneered the development of open-source, continental domain, hyperresolution data sets (Vergopolan et al., 2021, Scientific Data) that have potential for applications in water resources, agriculture, ecology and biogeochemistry and has enabled for the first time understanding of the scaling behavior of soil moisture at continental scales (Vergopolan et al., 2022, Geophysical Research Letters). Her current work on improving operational weather and subseasonal climate forecast systems as part of a NOAA fellowship has potential to provide early warning of hydrometeorological risks, especially for the most vulnerable of society.
Noemi’s research profile is outstanding for a researcher only 1 year since her Ph.D., with much of this a result of multidisciplinary collaborative work, which is critical to address complex societal problems around water, food and climate. As a young, female researcher from Brazil who has made great strides in the early part of her career, she is a role model for other young and talented Latin researchers and has done much to promote Earth and environmental science within and by this community.
— Justin Sheffield
University of Southampton
Southampton, United Kingdom
Response
I am thrilled and honored to receive the AGU Science for Solutions Award. I
am immensely thankful to Justin Sheffield for nominating me; his pioneer
research on monitoring droughts and changes in terrestrial water
through decision-supporting tools inspired me and shaped the scientist I
am today. I also want to thank Elie Bou-Zeid, Marc Bierkens and Jay
Famiglietti for their tremendous support in my nomination and throughout
my scientific career.
As hydrologic and climate predictions depict a future in which floods and
droughts are the new normal, climate change poses an imminent threat to
water and food security worldwide. To adapt and mitigate climate change
impacts on freshwater systems, locally relevant hydrologic information
is critical to support governance and water resources decision-makers in
designing local interventions and implementing policies. We bridged
this data gap by advancing realism in hyperresolution land surface
models through big geospatial data, satellite land data assimilation,
machine learning and high-performance computing. These novel approaches
enabled hydrologic predictions at unprecedented scales and now are
forming the basis for flood early warning in southern Africa and
providing evidence to national food security policy in Zambia and
Malawi. As a scientist, very little is as rewarding as seeing the
impacts of our efforts on improving livelihoods.
As Eric Wood would say, pushing the scientific barriers requires courage.
Courage to be creative, courage to be persistent, and courage to think
in ways others aren't. As scientists, it is our duty to embrace
challenges in water and food security with courage, as the future of the
next generations depends on the scientific advances we make today. For a
positive impact on society, it has been my mission to advance
hydrological monitoring and predictions targeting the spatial scales
that can support solutions on the ground.
I am grateful to my Ph.D. advisers, Eric F. Wood and Justin Sheffield — their
research vision and commitment to scientific excellence have always
inspired me. I am thankful to Nathaniel Chaney, Niko Wanders, Elena
Shevliakova and Hylke Beck; their mentorship and encouragement nurtured
the researcher I am today. I am deeply grateful to all who supported me
along the way and to AGU for honoring me with the 2022 Science for
Solutions Award.
— Noemi Vergopolan
Princeton University
Princeton, New Jersey
NOAA Geophysical Fluid Dynamics Laboratory
Princeton, New Jersey
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Publications
The Drivers of Hydrologic Behavior in Brazil: Insights From a Catchment Classification
Despite hosting ∼16% of the global freshwater and almost 50% of water resources in South America, Brazilian catchment‐scale relationshi...
August 21, 2024
Towards an Optimal Representation of Sub‐Grid Heterogeneity ...
December 19, 2022
High‐Resolution Soil Moisture Data Reveal Complex Multi‐Scal...
August 11, 2022
Drought Diagnosis: What the Medical Sciences Can Teach Us
April 02, 2022
AGU Abstracts
Advancing Water and Food Security with AI and Hyper-Resolution Soil Moisture Data
AGU 2024
global environmental change | 13 december 2024
Noemi Vergopolan, Felipe Saavedra, Larisa Tarasova...
Effective water resource decision-making requires detailed and accurate hydrological information to understand, monitor, and predict water availabilit...
View Abstract
Climate change projections for Brazilian catchments using a regional deep learning model
AGU 2024
hydrology | 13 december 2024
André Almagro, Noemi Vergopolan, Paulo T. Oliveira
The potential impacts of climate change on catchment hydrology remain uncertain in many parts of the world, including Brazil. Conducting an integrated...
View Abstract
Advancing Urban Flood Hazard Characterization through Machine Learning: Challenges and Opportunities
AGU 2024
natural hazards | 13 december 2024
James Doss-Gollin, Arlei Silva, Antonia Sebastian,...
Urban flooding, a complex phenomenon encompassing pluvial, fluvial, and coastal processes influenced by infrastructure, poses significant challenges t...
View Abstract
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