Detailed and accurate hydrological information is essential to monitor and predict changes in freshwater dynamics, irrigation water demands, hydroclimate extremes (e.g., droughts and floods), natural geohazards (e.g., wildfires and landslides), biogeochemistry, and ecosystem dynamics. While in-situ observations can provide precise information locally, networks of sensors are often not widely available. Hyperspectral satellite observations offer global coverage, but measurements can be infrequent or too coarse to capture the local extremes. This observation data gap limits the use of hydrologic information for locally relevant decision-making and policy implementation. To bridge this gap, recent advances in hyper-resolution land surface and Earth system models (operating at the ~100m to 1km resolution) and the ever-increasing availability of satellite remote sensing, big environmental datasets, machine learning, and high-performance computing provide a pathway forward.
This presentation highlights recent advances in monitoring freshwater systems through improved realism in hyper-resolution land surface models via novel sub-grid tiling schemes and satellite land data assimilation. These approaches enabled hydrological information at unprecedented scales and over continental domains, such as SMAP-HydroBlocks – the first 30m resolution satellite-based surface soil moisture dataset in the United States. More importantly, these advances established the foundation for understanding, modeling, and predicting soil-water-plant-climate interactions at the spatial scales required for many water and food security applications. These include, for instance, improved capabilities to model floodplain interactions, detect droughts and their impacts on crop yields, and predict vegetation carbon storage and their interactions with climate at the ~100s meters scale. In a fast-changing climate, creative approaches bridging such information data gaps and harnessing the ever-increasing potentials of satellite observation and Earth system models will continue to play a critical role in supporting terrestrial water and food security efforts at scales relevant to stakeholder interventions and policy implementation.
Dr. Noemi Vergopolan is a computational hydrologist working on data-driven solutions for water resources and climate. Her 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, she develops 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.
Dr. Vergopolan holds an M.A. and Ph.D. in Civil and Environmental Engineering from Princeton University in the United States. Currently, she is a postdoctoral research associate in the Atmospheric and Ocean Science Program at Princeton University and the NOAA Geophysical Fluid Dynamics Laboratory working on Earth System Modeling and satellite land data assimilation. In July, Dr. Vergopolan will transition to Rice University in Texas as a faculty in the Earth, Environmental, and Planetary Sciences Department.
For her recent contributions to science, Noemi was awarded the 2022 Paul F. Boulos Excellence in Computational Hydrology Award by the American Academy of Environmental Engineers and Scientists, and the 2022 AGU Science for Solutions Award for “outstanding contributions to water and food security through advances in hyper-resolution land surface modeling and satellite remote sensing.”
Postdoctoral Research Associate, Atmospheric and Ocean Science Program, Princeton University