Title: Exploring Critical Attributes of 3D Channels for Enhanced Probabilistic Flood Inundation Mapping
 
Project Lead: Rebecca Diehl

Research Team: David Baude

University of Vermont Research Plan: Flood inundation mapping (FIM) represents an essential planning and risk assessment tool produced by the National Water Center and a key product from the National Water Model (NWM). During forecasted high flow events, FIM is generated parsimoniously through the use of the Height Above Nearest Datum and relies on a deterministic synthetic rating curve (SRC) to map discharge to stage. This 2-year project is in collaboration with Colin Phillips and Belize Lane at Utah State University and will enhance the accuracy of FIM through the incorporation of hydraulic and river corridor terrain variability through the development of a probabilistic SRC. The probabilistic SRC will form the hydraulic basis for generating probabilistic FIM within the NextGen NWM. The incorporation of variability and uncertainty into FIM visualizations can highlight the risk within forecasted FIM and potentially reduce the risk of natural flood hazards. 

Title: Developing and Integrating a Social Water Use Model to Improve the Predictive Capacity of Water Resources Models to Account for Water Uses and Sector Tradeoffs
 
Project Lead: Asim Zia

Research Team: Scott Turnbull, Patrick, Clemins, Carina Manitius, Kevin Andrew, Rakhshinda Bano 

University of Vermont Research Plan: Urbanization and intensification of agriculture alter water resources with respect to quality, quantity, and demand. Increasing frequency and intensity of floods and droughts exacerbates water management challenges. Predictive water models are used to support management decisions, but without accounting for human decision-making and subsequent actions, model outputs can be unreliable. Agent based models represent a way to incorporate agency and decision-making into models, both at the individual and institutional level. The goal of this project is to develop a prototype coupled natural and human systems (CNHS) model that simulates community- and sector-level decision-making regarding municipal and agricultural water use at watershed and basin scales. A behavioral theory-agnostic Agent Based Model (ABM) will be integrated with the next-generation national water model (Next-Gen) in this novel CNHS approach to generate realistic scenarios of water use forecasts and hindcasts at sub-watershed and watershed scales. This coupled model will be piloted to simulate and project future water use scenarios under alternate climate change induced extreme event projections for three focal watersheds: Lake Champlain/Richelieu basin; Milk River basin; and San Jacinto River basin. Using a genetic algorithm as a wrapper, the resulting CNHS model will be interrogated with various user-defined scenarios to explore sensitivity and robustness as a basis for transition to a process-based operational model development pathway for informing the configuration of the Next-Gen. This project's outcomes are critical to advance adaptive management of water resources across sectors and communities in the face of increasing frequencies and intensities of extreme events (floods, droughts, heatwaves etc.). Integration of US/Canadian expertise in complementary ways also serves to incorporate transboundary contexts.