Introduction
This presents the key results of the Probable Maximum Precipitation (PMP) Study for the State of New Jersey, completed in 2024. The study updates and replaces PMP values derived from Hydrometeorological Report No. 51 (HMR 51), originally developed in the late 1970s, which no longer reflected current data availability or the state of atmospheric science. The purpose of this analysis was to provide modern, physically based estimates of extreme precipitation for a wide range of storm durations, ensuring that the design and evaluation of dams, reservoirs, and other high-hazard infrastructure are based on the most scientifically defensible information. In addition to the deterministic PMP development, AWA calculated Annual Exceedance Probabilities (AEP) for the 6- and 24-hour durations out to 10-10 and evaluated how the PMP and AEP may change through time by evaluating climate change projections.
The study was using advanced, storm-based hydrometeorological techniques supported by regional climatology, rainfall observations, and detailed climatological data. The results define the upper-bound precipitation values expected across New Jersey for durations from one hour to three days. These PMP values, along with AEP and climate-change sensitivity analyses, provide a comprehensive foundation for hydrologic modeling, dam safety and dam rehab, risk assessment, and long-term infrastructure resilience.
Objectives
The principal objectives of the New Jersey PMP Study were to:
1. Develop updated, site-specific PMP values using physically consistent storm-based methods that incorporate modern meteorological observations and terrain effects.
2. Evaluate historical extreme precipitation events that influence the Mid-Atlantic region, including both tropical and extratropical systems, and determine their applicability to New Jersey.
3. Calculate AEP through 10-10 for key durations and suitable for direct use in hydrologic and dam-safety models.
4. Compare results with HMR 51 values to identify magnitude or spatial differences attributable to improved data and analytical methods.
5. Assess sensitivity to climate change to determine whether projected increases in atmospheric moisture or storm intensity could materially alter PMP magnitudes.
6. Provide recommendations for integrating PMP and AEP results into design, operational, and regulatory frameworks that govern dam-safety and flood-risk management in New Jersey.
Methodology
The PMP for New Jersey was derived through AWA’s storm-based, physically constrained approach, which differs fundamentally from the generalized map-based techniques used in HMR 51. The process integrates meteorological analysis, numerical modeling, and GIS-based spatial tools to reconstruct and maximize representative storms that define the upper limit of precipitation potential.
Storm Reconstruction and Selection
Extreme rainfall events were compiled from AWA’s SPAs storm database, including hurricanes, post-tropical cyclones, thunderstorms, and mid-latitude frontal systems that have historically affected the Mid-Atlantic region. Data sources included rain-gauge networks, Doppler radar mosaics, satellite precipitation estimates, and NCEP/ERA5 reanalysis fields. Each event was reconstructed to determine temporal evolution, moisture flux, and efficiency.
Moisture Maximization and Transposition
For each selected event, the observed precipitable-water content was increased to the theoretical maximum available at the time of year, derived from long-term dew-point and sea-surface-temperature climatologies. The storms were then transposed within meteorologically and topographically similar regions—typically along the Appalachian and Coastal Plain corridors—to represent the most severe precipitation that could occur at New Jersey locations. Geographic Transposition Factors (GTFs) were applied to adjust rainfall totals for orographic and synoptic similarity.
Storm Precipitation Analysis System (SPAS)
All rainfall reconstructions were processed in AWA’s SPAS software, which produces high-resolution spatial rainfall fields (1/3rd square mile) with hourly and sub hourly temporal patterns. SPAS captures all aspects related to a given storm events and quantifies the magnitude in rainfall at the highest accuracy possible. SPAS produces critical outputs needed for PMP analysis and hydrologic modeling.
AEP Extension and Frequency Analysis
In parallel with the deterministic PMP development, regional AEP analyses were performed using L-moment statistics and bias-corrected NOAA Atlas 14 data. This probabilistic extension bridges conventional frequency rainfall (1 %–0.01 % AEP) to the PMP envelope (10⁻⁷–10⁻⁸ AEP), supporting risk-informed design.
Climate Change Sensitivity
A detailed climate-sensitivity evaluation was completed which used downscaled CMIP6 GCM projections under moderate (SSP 2-4.5) and high (SSP 5-8.5) emission scenarios. Changes in temperatures, precipitable, and atmospheric moisture were quantified through 2100 and compared to past data and variability. Statistical testing determined whether these shifts materially affect PMP magnitudes.
Quality Assurance and Comparison
All computed PMP values were cross-checked against regional PMP studies in adjacent states (Maryland, Pennsylvania, and Virginia) to ensure spatial continuity and consistency with observed storm climatology. Internal QA/QC included verification of moisture-maximization factors, terrain adjustments, and depth-duration coherence.
Key Findings
1. Consistency with HMR 51 Magnitudes The updated PMP values generally show lower magnitudes to those in HMR 51 for most durations and areas. Improvements in data quality and storm representation have reduced previous over-generalization, particularly for coastal and northern interior basins. The reductions—typically 5 % to 25 %—reflect more realistic moisture limits and refined spatial analysis rather than any decrease in flood risk.
2. Improved Spatial Resolution The gridded PMP datasets capture gradients across New Jersey, with higher values along the northwestern Highlands and coastal zones where topography and onshore moisture flux amplify rainfall. This spatial variability was not depicted in the generalized HMR 51 isohyetal patterns.
3. Duration-Dependent Behavior Short-duration (1–6 hour) PMP values remain controlled by convective systems, while longer durations (24–120 hour) are governed by tropical and frontal storm
interactions. The resulting depth-duration curves exhibit smooth transitions between mechanisms, improving hydrologic modeling stability.
4. AEP Relationship and Risk Continuum The corresponding AEP curves demonstrate convergence toward PMP magnitudes at probabilities between 10⁻⁷ and 10⁻⁸, confirming internal consistency between deterministic and probabilistic approaches. This allows agencies to employ either traditional PMP-based design or risk-based frameworks without loss of defensibility.
5. Climate Change Sensitivity The climate analysis indicates no statistically significant change in short-duration extreme rainfall intensity through mid-century. Model ensembles project modest increases (5–10 %) in multi-week rainfall accumulation, primarily influencing antecedent soil moisture and reservoir inflow sequencing. These effects are well within the conservative margin inherent in PMP design criteria.
6. Hydrologic Application and Resilience The updated PMP values provide consistent inputs for Probable Maximum Flood (PMF) and dam-safety studies statewide. Their spatial and temporal detail supports modern hydrologic and hydraulic modeling platforms, offering improved confidence in spillway adequacy assessments and emergency-action planning.
Conclusions
The New Jersey PMP Study provides a scientifically advanced and regionally consistent basis for evaluating extreme precipitation risk across the state. By leveraging physically based storm reconstruction, improved data analyses, and GIS-enabled databases, the study corrects the limitations of HMR 51 and delivers realistic, defendable PMP values for engineering and regulatory use.
Key conclusions include:
· The updated PMP magnitudes represent more accurate depths and reflect appropriately conservative depths and continue to represent the upper bound of meteorological potential.
· Climate-change influences are small relative to existing PMP uncertainty, and current PMP values remain suitable for design through at least mid-century.
· Integration of these PMP and AEP datasets into hydrologic modeling frameworks will enhance design resilience, facilitate risk-informed decision-making, and align New Jersey dam-safety practice with modern national standards.
Recommendations
1. Adopt the updated PMP and AEP datasets for all dam-safety and flood-hazard evaluations statewide.
2. Apply PMP within risk-informed design frameworks that consider both deterministic and probabilistic perspectives.
3. Use basin-specific PMP grids to evaluate spatially distributed hydrologic responses, ensuring robust design against extreme but physically plausible events.
