Noemi Vergopolan

Assistant Professor of Earth, Environmental and Planetary Sciences

Rice 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 prediction. My group develops scalable computational approaches combining satellite remote sensing, land surface modeling, machine learning, data fusion, and high-performance computing to monitor and forecast hydrological extremes, such as floods and droughts, and their impacts on water and food security at the local scales where decisions are made.

My work has been recognized with the NSF CAREER Award (2026), the AGU Science for Solutions Award (2022), and the AAEES Paul F. Boulos Excellence in Computational Hydrology Award (2022). I hold a Ph.D. from Princeton University, was a research scientist at the NOAA Geophysical Fluid Dynamics Laboratory, and previously worked in water resources engineering consulting.

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Research & Portfolio

Integrating Earth Observations through AI and Physical Models

SMAP-HydroBlocks is the first 30-m resolution satellite-based surface soil moisture dataset over the continental United States, integrating multi-scale data with ML and physical modeling.

Hyper-Resolution Hydrologic and Land Surface Modeling

HydroBlocks is a field-scale resolving land surface model for computationally efficient hydrologic applications over continental extents. Learn about model development & applications here.

Field-scale Crop Yield Prediction

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.

From Hydroclimate Data to Science-Informed Decisions

Integrating computational models and hydroclimate data for actionable agriculture decision-making. Towards resilience and adaptability in a changing climate.

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.

SMAP-HydroBlocks, the first 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.

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.

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

Assistant Professor of Earth, Environmental and Planetary Sciences

Rice University

About Me

Research & Portfolio

Featured Publications

Recent Publications

Contact

Noemi Vergopolan

Assistant Professor of Earth, Environmental and Planetary Sciences

Rice University

About Me

Research & Portfolio

Featured Publications

Recent Publications

News & Social

Contact