Uncertainty in Estimating the Relative Change of Design Floods under Climate Change: a Stylized Experiment with Process-Based, Deep Learning, and Hybrid Models
The resilience of water systems to future hydrology depends on reliable projections of hydrological change. Process-based and, more recently, machine learning-based hydrological models are commonly used for such projections. To account for model uncer
Authors
- Scott Steinschneider (ORCID: https://orcid.org/0000-0002-8882-1908)
- Nasser Najibi (ORCID: https://orcid.org/0000-0002-0515-7192)
- Sandeep Poudel (ORCID: https://orcid.org/0009-0005-8342-7935)
Institutions
- Cornell University (US)
- University of Florida (US)
- Florida Agricultural and Mechanical University (US)
Publication Details
- Published
- 2025-07-18
- DOI
- https://doi.org/10.22541/essoar.175069522.21075713/v2
- Primary Topic
- Flood Risk Assessment and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00