Streamlining Hydrological Models with Improved Parameter Learning Techniques
To assess the impact of observation period and gauge location, model parameters were learned on scenarios using different chunks of streamflow observations.
Exploring Soil Respiration Spatial Patterns in Coastal Ecosystems using Remote Sensing and Machine Learning
This study characterized above- and below-ground properties to explore the spatial heterogeneity of the terrestrial aquatic interface ecosystem within the Chesapeake Bay area and evaluate the major drivers of soil respiration.
Bridging the Data Gap in Southern Hemisphere Aerosol Research
Extensive in situ and remote sensing measurements were collected to address data gaps and better understand the interactions of convective clouds and the surrounding environment.
Coastal Collaborators: Water, Plants, and Ecosystems
Due to their inherent variability and complexity over space and time, scientists are challenged to understand the complex interactions among soil, vegetation, and water along coastal terrestrial-aquatic interfaces.
Establishing Open Access to Near Real-Time Environmental Sensor Data in Two Distinct, Coastal Regions
The Coastal Observations, Mechanisms, and Predictions Across Systems and Scales: Field, Measurements, and Experiments project established a network of observational field sites across Chesapeake Bay and western Lake Erie.
Characterizing Giant Storm Precipitation in the Central Amazon
The prediction of rainfall over the Amazon rainforest by weather and climate models is highly uncertain, particularly for large rainstorms which are commonly seen during the wet season, from March to May.
Spatial Heterogeneity of Clouds Impacts Rain Initiation in Global Models
The rate of conversion of cloud droplets to precipitation, known as the autoconversion rate, remains a major source of uncertainty in characterizing aerosol’s cloud lifetime effects and precipitation in global and regional models.
Water Quality Impacts from the Largest Wildfire in New Mexico’s History
This study used historical data, remote sensing, and aquatic sensors to measure how far wildfire impacts propagated through the watershed after the 2022 Hermit’s Peak/Calf Canyon fire, New Mexico’s largest wildfire in history.
Improving the Representation of Methane Dynamics in Global Lake Models
Lakes are important sentinels of climate change and contribute significantly to the emissions of the second most important greenhouse gas—methane.
Drivers and Impacts of Sediment Deposition in Amazonian Floodplains
High-resolution hydrodynamic-sediment modeling shows that inundation, suspended sediment concentration in the Amazon River, and floodplain hydrodynamics drive sediment deposition in Amazonian floodplains.