Model-Based Probable Maximum Precipitation Estimation: How to Estimate the Worst-Case Scenario Induced by Atmospheric Rivers?
Abstract
The concept of probable maximum precipitation (PMP) is widely used for the design and risk assessment of water resource infrastructure. Despite its importance, past attempts to estimate PMP have not investigated the realism of design maximum storms from a meteorological perspective. This study investigates estimating PMP with realistically maximized storms in a Pacific Northwest region dominated by atmospheric rivers (ARs) using numerical weather models (NWMs). The moisture maximization and storm transposition methods used in NWM-based PMP estimates are examined. We use integrated water vapor transport as a criterion to modify water vapor only at the modeling boundary crossing the path of ARs, whereas existing methods maximize relative humidity at all initial/boundary conditions. It is found that saturation of the entire modeling boundaries can produce unrealistic atmospheric conditions and does not necessarily maximize precipitation over a watershed due to storm structure, stability, and topography. The proposed method creates more realistic atmospheric fields and more severe precipitation. The simultaneous optimization of moisture content and location of storms is also considered to rigorously estimate the most extreme precipitation. Among the 20 most severe storms during 1980–2016, the AR event during 5–9 February 1996 produces the largest 72-h basin-average precipitation when maximized with our method (defined as PMP of this study), in which precipitation is intensified by 1.9 times with a 0.7° shift south and a 30% increase in AR moisture. The 24-, 48-, and 72-h PMP estimates are found to be at least 70 mm lower than the Hydrometeorological Reports estimates regardless of duration.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2019
- Source ID
- 10.1175/jhm-d-19-0039.1
Entities
People
- Angela M. Duren
- Chris D. Frans
- John F England
- Kinya Toride
- M. Levent Kavvas
- Michael D. Warner
- Yoshihiko Iseri
Organizations
- United States Army Corps of Engineers
- University of California
- University of Tokyo