Introduction
Probable Maximum Precipitation (PMP) analysis performed for the State of Maryland. The objective of this study was to inform infrastructure design, flood hazard assessment, and resilience planning by providing updated, physically based estimates of extreme precipitation over a range of durations. Given evolving meteorological data, enhanced computational tools, and a shifting climate background, this effort was commissioned to replace legacy PMP values and associated design criteria with a scientifically rigorous, transparent, and regionally appropriate foundation.
Modern infrastructure—particularly dams, stormwater systems, critical transportation corridors, and protective levee systems—requires that extreme precipitation inputs reflect current understanding of storm physics, moisture maximization, and orographic/topographic influences. This study draws on newly compiled observational datasets, high-resolution gridded fields, and state-of-the-art modeling methods to deliver site- and basin-specific PMP values tailored for Maryland’s hydrologic and topographic context.
Objectives
The primary objectives of the PMP Study for Maryland were to:
1. Develop updated, site- and basin-specific PMP values across durations ranging from one hour to ten days, using a storm-based, physically informed methodology.
2. Identify representative extreme rainfall events from regional and global records, maximize their moisture and spatial attributes, transpose them to Maryland basins with comparable meteorology/topography, and derive depth-duration-area (DDA) estimates and spatial rainfall fields.
3. Produce a set of depth-duration relationships, grids, and summary tables appropriate for hydrologic modeling, flood design, and dam-/levee-safety applications across the state.
4. Develop Annual Exceedance Probabilities through 10-10 recurrence interval for key durations over the entire study domain.
5. Evaluate the influence of recent climate trends and projections on extreme precipitation to determine whether the updated PMP values remain conservative and applicable under future conditions.
6. Provide a clear technical foundation enabling engineers, planners, and decision-makers to use the updated PMP values in flood-risk evaluation, resilient design, and infrastructure adaptation efforts.
Methodology The methodology applied in this study integrates hydrometeorology, statistical analysis, and geographic information system (GIS) tools in a systematic process:
· Storm Reconstruction and Selection: A catalogue of extreme rainfall events was assembled, focusing on convective thunderstorms, mesoscale convective systems, and synoptic‐scale rainfall episodes with high moisture availability. Meteorological data sources included rain gauges, radar reflectivity records, satellite rainfall estimates, and upper‐air soundings. Storms were selected on the basis of maximum rainfall intensity, spatial extent, atmospheric moisture content, and basin comparability.
· Moisture Maximization & Transposition: For each selected event, the available moisture was maximized (considering dew point, precipitable water, and sea surface temperature analogues) to represent a theoretical “worst‐case” storm for Maryland. The storms were then transposed, adjusting for orographic enhancement and basin characteristics via Geographic Transposition Factors (GTF).
· PMP Derivation: PMP values were defined as the depth corresponding to the maximized storm for each duration and area class, with conservative rounding and engineering judgment applied to ensure defensibility.
· Climate Influence Assessment: Using downscaled global climate model (GCM) outputs, the study examined trends in extreme precipitation, moisture availability, and storm duration. Statistical tests (e.g., Mann–Kendall) and percentile change analyses were applied to determine whether future conditions might exceed current PMP boundaries.
Key Findings
· The storm-based PMP methodology provides enhanced realism in spatial and temporal representation of extreme rainfall, improving upon previous statistical‐extrapolation methods and producing greater confidence in upper‐bound rainfall inputs for hydrologic modeling.
· The probabilistic AEP analysis extended to recurrence intervals through PMP (10-10), producing probabilistic estimates of rainfall for key durations. This enables engineers to evaluate both deterministic (PMP) and probabilistic (AEP) design paths.
· Analysis of climate change projections indicates no statistically significant upward trend in short‐duration (1–3 day) extreme precipitation through mid‐century. However, moderate increases (5–10%) in aggregated 30- to 90-day rainfall
totals are projected under higher emissions scenarios. These increases could influence water‐balance and reservoir‐management issues but do not necessitate fundamental adjustment to the PMP envelope for design flood purposes.
· PMP values retain a margin of safety. They remain suitable for underpinning inflow design floods (IDFs), dam safety reviews, emergency spillway evaluation, and flood-risk planning for critical infrastructure across Maryland.
Conclusions
This PMP Study for Maryland delivers a modernized, scientifically robust foundation for extreme precipitation evaluation. By employing storm‐based reconstruction, moisture maximization, and spatial grid generation, the results present an updated set of PMP values tailored for Maryland’s hydrometeorological context and suitable for engineering and regulatory application.
The findings support the continued use of PMP‐based design standards for high-hazard infrastructure, with the assurance that the updated values are conservative, regionally consistent, and aligned with current climate projections. The modest increases in longer‐duration PMP reflect enhanced analytical fidelity rather than radical shifts in storm potential.
In summary, this study reinforces the technical foundation for decision-makers and engineers alike, enabling informed design, investment, and operational decisions for Maryland’s water resource and dam safety portfolio under both present-day and future conditions.
