Non-stationarity (trend) Detection Algorithms: A Surgical Data Toolkit to Manage Terrestrial Arteries (rivers) and Pace-makers (dams)

Army Corps of Engineers (USACE) Risk Management Center (RMC) and the Engineer Research and Development Center (ERDC) Coastal and Hydraulics Laboratory (CHL). This innovative tool is designed to expedite flood hazard assessments within the Flood Risk Management, Planning, and Dam and Levee Safety communities. It employs a Bayesian flood frequency analysis approach that incorporates diverse hydrologic information sources, such as historical data, paleoflood indicators, regional rainfall-runoff results, and expert elicitation.
Recently, RMC-BestFit has been enhanced to include nonstationary flood frequency analysis (NSFFA) capabilities. This feature allows for the variation of distribution parameters over time, enabling users to identify evolving flood risk conditions. A case study will be presented, focusing on NSFFA for a high-hazard dam within the USACE portfolio. An example from the semi-arid western region will be featured, showcasing decreasing flood risk over time and providing an opportunity to reduce flood control storage while increasing water supply storage for climate resiliency. The study will demonstrate how to assess nonstationarity influenced by factors like land use and climate change. It will also highlight the selection of appropriate trend models for distribution parameters and the integration of historical, censored data, and Global Climate Model (GCM) projections into Bayesian NSFFA. This case study emphasizes the significant impact of NSFFA on flood risk assessments, influencing potential dam safety modifications and reallocation measures.