Probable Maximum Precipitation (PMP) estimates were last updated for the eastern half of the United States by the federal government in 1978. Due to the many nuclear infrastructure projects along rivers and reservoirs in the eastern United States, the Nuclear Regulatory Commission (NRC) provided funding to the Bureau of Reclamation Flood Hydrology, Meteorology, and Consequences Group to investigate multiple facets of the historical PMP process and to explore modernization through use of quantitative precipitation estimates (QPE; i.e., radar-gage blended rainfall).
After completing a review of the Hydrometeorological Reports (HMRs) series, the project focused on analysis of major tropical storms (e.g., Floyd and Fran) that impacted the Carolinas – which was the initial testbed for automated QPE projects at the then National Climatic Data Center, now NCEI. Results indicated that PMP had been exceeded at short-durations and area sizes during Fran and for larger area sizes and durations during Floyd over portions of North Carolina. These results were presented at the NRC’s Probabilistic Flood Hazard Workshop multiple times by Dr. Caldwell of Weather & Water. The practice of applying these methods to PMP, precipitation frequency, and hydrologic hazards studies continues today based on this important work!
In 2014, while establishing a Hydrometeorology & Climate Change program at Leonard Rice Engineers, Jason led a significant project under subcontract to GZA for Entergy Corporation for a nuclear plant in northwestern Arkansas (followed by another smaller project in the New Orleans area). This project evaluated the estimated return period with uncertainty bounds for the Probable Maximum Precipitation in the Arkansas River Basin. In conjuction with MGS Engineering, he developed novel methods for extrapolation of NOAA Atlas 14 frequency estimates coupled with stochastic storm transposition approaches to constrain extrapolation and reduce uncertainty in the tails of the distribution through space-for-time substitution and using actual observations over a wide range of climate regimes.
Modelers at GZA ingested the inputs to evaluate hydrologic uncertainty – though Jason lef a statistical approach using the Australian Rainfall Runoff (ARR) method and PEAK-FQ software applied at federal agencies like Reclamation. Weather & Water continues to utilize similar methodologies, but has advanced the methods through use of reanalysis products and resampling techniques to produce a superior nationally consistent gridded frequency dataset. Look to the original implementation folks who continue to advance the science for your PMP and probabilistic PMP needs.
Altus Dam is located in Oklahoma but spans from the eastern Texas panhandle eastward to near the western Arkansas border. Across this diverse climate regime, the annual precipitation ranges from less than 15 inches to nearly 40 inches, resulting in a complicated problem for regional frequency analysis – where the mechanism for extreme rainfall in the west is convective supercell thunderstorms of short duration, while eastern areas receive tropical influences (e.g., TS Erin 1997) and minor orographic enhancement from proximity to the Ouachita Mountains. As such, the watershed was delineated into multiple sections based on climatology and individual precipitation-frequency estimates were designed for each segment for application in the Stochastic Event Flood Model (or SEFM) from MGS Engineering.
Storm patterns, therefore, also had to represent the gamut of storm types with different durations and forcing mechanisms at play. In retrospect, this project would have benefitted from the storm-type specific methodology applied in the Colorado-New Mexico Regional Extreme Precipitation Study. Since those studies, automated machine learning has been applied to discern the type of storms occurring during major precipitation events for attribution and use in frequency-based projects. Contact Weather & Water today to learn more!
Atmospheric rivers dominate the hydrologic landscape in central California. These firehoses of moisture from the tropical Pacific Ocean impact the rapid uplift generated by the 10,000-foot plus terrain increase over only a few hundred miles in the Sierra Nevada Mountains. In collaboration with MGS Engineering, the Flood Hydrology Group at Reclamation developed precipitation and temperature time series, along with climatological information (snow water equivalent, freezing level heights) to support stochastic modeling of hydrologic risks for the Friant Dam along the San Joaquin River.
Jason’s role took the disaggregation and basin-averaging from Excel spreadsheets to scripted automated algorithms for implementation in SEFM, including reformatting these inputs for HEC-1 templates within the modeling system. In addition, L-Moments packages in R Statistical Software were applied, along with custom-crafted and semi-automated QA-QC algorithms to clean time series prior to use in both gridded storm (i.e., QPE) precipitation patterns and frequency analyses. These advances in automated data conversion (i.e., improving processing time from weeks to single day) serve as impetus for DAART and CRAFTEA now available through Weather & Water.