NSF requires disclosure of AI tool usage in proposal preparation. Ensure you disclose the use of FindGrants' AI drafting in your application.
NSF
Water that evaporates from ground surfaces cannot contribute to plant growth, streamflow or groundwater. Understanding the factors that control evaporation is therefore critical for predicting water availability and related natural resources. Plant litter—the leaves and twigs that accumulate on forest floors—plays a complex and poorly understood role. Some research shows plant litter acts like mulch in a garden bed, covering and shading soils to reduce evaporation. However, prior research also shows that plant litter can capture and store rainfall, causing much of it to evaporate before reaching soils. This study will quantify the relative influences of these competing effects: the mulching effect that reduces soil evaporation versus the interception effect that enhances evaporation. This research will combine observations in field sites across diverse United States (US) ecosystems, controlled laboratory and field experiments with collected samples, and development of a predictive simulation model. This integrated approach will advance understanding and capabilities to predict how litter properties, climate conditions, and ecosystem characteristics interact to influence evaporation across US landscapes. The project will train graduate students at University of Nevada Reno and Cleveland State University, develop new experiential learning modules, create openly available datasets, and support engagement with various audiences including land managers and other stakeholders. This project will pursue four key research tasks to advance understanding of water fluxes at the forest floor and their variation with weather, climate, and ecosystem traits. Litter-specific storage capacities and drainage parameters will be quantified using an in-lab precipitation generator and litter collected from 32 sites of the National Ecological Observatory Network (NEON). Soil and litter traits as well as surface evaporation rates will be measured in-situ at a subset of NEON sites. A replicated field experiment using soil plots with litter layers collected from NEON sites will test litter effects on soil water storages and fluxes. Evaporation modeling parameters will be developed from the collected laboratory and field data and used in a soil water-balance model to test how evaporation is affected by the interplay of meteorology and litter traits across a range of realistic climate scenarios. By using a systematic macro-scale approach and multiple prongs of investigation, this study will enhance mechanistic understanding of how litter enhances precipitation interception versus resisting soil evaporation, and reveal site-specific variations to develop generalizable conclusions that can ultimately support future applications. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Up to $426K
2028-12-31
Detailed requirements not yet analyzed
Have the NOFO? Paste it below for AI-powered requirement analysis.
One-time $49 fee · Includes AI drafting + templates + PDF export
Global Affairs Canada — International Development Grants
Global Affairs Canada — up to $20M
A Shallow Drilling Campaign to Assess the Pleistocene Hydrogeology, Geomicrobiology, Nutrient Fluxes, and Fresh Water Resources of the Atlantic Continental Shelf, New England
NSF — up to $5.0M
Sustainable Development Technology Canada (SDTC)
Sustainable Development Technology Canada — up to $5M
Collaborative Research: Overturning in the Subpolar North Atlantic Program
NSF — up to $4.9M
BII: Predicting the global host-virus network from molecular foundations
NSF — up to $4.8M
E-CORE RII: Technology for Innovative Visualization, Aggregation & Training in Environmental Preparedness and Resilience for Kentucky
NSF — up to $4.1M