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Colin Gleason
University of Massachusetts AmherstMeeting roles in:
Using the SWOT Satellite to Assess Global Models of Individual Rivers
Global River Suspended Sediment Flux from Space using Highly Scalable Deep Learning Models
RiverScope: High-Resolution River Masking Dataset
Combining climate reanalysis and remote sensing data to predict river discharge using a generalizable, distributed, deep learning model.
How reliable are global river discharge simulations?
Quantifying Riverine Sediment Transport due to Tropical Cyclones in the Eastern United States
An Assessment of the Value of SWOT River and Lake Data for Hydrologic Modeling Using Physics-embedded Learning
Improving Streamflow Predictions in Reservoir-Influenced Basins Using Machine Learning
Understanding SWOT discharge accuracy: Exploring typological drivers of SWOT discharge performance
Global hydropower potential of world rivers revealed by the SWOT Mission
Improving Machine Learning Framework for SWOT-Based Discharge Estimation Using Integrated Static and Dynamic Features
Assessing and Comparing SWOT River Discharge Performance Between Fast Sampling and Science Orbits
Exploring the Effect of River Spatial Variability on SWOT Discharge Accuracy
Narrow river detection with SWOT High Rate hydrology products: An evaluation over the conterminous United States
SWOT Discharge performance, error budget, timeline and science use cases
Evaluating Machine Learning Hydrologic Modeling under Future Climate Scenarios
Global Reach Scale Hydrology: Progress and Challenges
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