Program Overview

The CLaSH Small Grant Program is open to the wider community and aims to provide flexible, seed-level funding to:

• Support creative, high-risk/high-reward projects that broaden community participation by bringing new investigators and new expertise into the Center
• Advance understanding of interacting land-surface hazards, complementing the scope of current Center activities
• Foster cross-cutting science that connects observations, modeling, and societal relevance
• Open the door to new approaches to land surface hazard cascade research, via pilot studies that may grow into larger collaborative projects, development of new field sites, and application of novel modeling frameworks
• Contribute to the Center's goals of education, workforce development, and community engagement

2025–2026 Priority Areas

The 2025–2026 Small Grant Request for Proposals seeks to catalyze integrative studies that couple new data acquisition, process-based modeling, or AI-driven analyses to predict how multi-hazard cascades evolve. During 2025-2026, we plan to prioritize the following research areas, but welcome all proposals that advance hazard cascade science, along with aligned community engagement and workforce development activities:

1. Site-Specific Investigations at CLaSH's Hazard Observatories

Located in Alaska, Appalachia, Puerto Rico, and Southern California, including proposals that:

• Apply geophysical, geochemical, or remote-sensing approaches to investigate site-specific hazard processes
• Integrate new tools and datasets across CLaSH Hazard Observatories, and/or
• Extend ongoing or post-event data collection in fire, landslide, or flood-impacted sites

2. Modeling of Weather-Related Forcing and Phenomena

Relevant to CLaSH's current Hazard Observatories, including proposals that:

• Quantify how storms, rainfall, winds, or wildfire alter hazard frequency and magnitude
• Investigate novel couplings of meteorological, hydrological, geomorphic, and/or hazard models, and/or
• Produce datasets and frameworks for scalable hazard prediction

3. Applications of Machine Learning and Development of AI-Ready Datasets

To increase the understanding of land surface hazards, including proposals that:

• Build FAIR-compliant, AI-ready datasets to enable model training, validation, and cross-hazard benchmarking
• Develop novel AI algorithms for event detection, forecasting, and early warning of geomorphic hazards, and/or
• Apply and optimize machine learning models to simulate cascading hazard sequences linking landslides, erosion, and sediment transport dynamics