A Physically Constrained Model-Based Moisture Amplification Approach for Probable Maximum Precipitation (PMP) Estimation

Abstract The flood that would result from the greatest depth of precipitation “meteorologically possible” or probable maximum precipitation (PMP) is used in the design of dam spillways and other high-risk structures. Historically, PMP has been estimated by scaling precipitation totals obtained from severe historical storms, assuming more moisture could have been available. Over the last decade, numerical weather prediction models have been used to instead predict precipitation resulting from the addition of moisture in the simulations [called relative humidity maximization (RHM)]. Despite the major improvement they represent, two important barriers limit the applicability of model-based methods: first, the existence of different moisture amplification approaches that produce different estimates, and second, the need for a regional implementation of those techniques that were developed for individual basins. Taking Oregon’s mountainous coastal watersheds affected by atmospheric river storms as a case study, we develop a moisture amplification approach, which we call relative humidity perturbation (RHP) ratio that is physically constrained by historical maximum moisture. We find that both the magnitude and location of moisture increase matter and that RHP ratio produces lower amplified precipitation totals but storms that are more consistent with observed events than other methods such as RHM. We additionally find that it is possible to position a storm near-optimally over several basins in a homogeneous area, enabling the production of regional PMP estimates. The understanding we develop of the control moisture exerts on PMP-magnitude precipitation totals allows us to develop a more physically based methodology for the development of reliable storm amplification guidance.