Introduction
Extreme precipitation estimation is fundamental to the safe design and operation of high-hazard and critical infrastructure such as dams, tailings storage facilities, and nuclear sites.
Probable Maximum Precipitation (PMP) defines the upper bound of meteorologically possible rainfall for a given duration and area, forming the cornerstone for deriving the Probable Maximum Flood (PMF) and related hydrologic design events. This study updated PMP, developed Annual Exceedance Probability (AEP), and checked outcomes against climate change analyses for the Faro Mine Basin in the Yukon territory of northern Canada. The goal was to ensure resilient design using updated data, evolving climate understanding, and improved analytical tools.
The analysis was performed using state-of-the-science approaches developed and refined by Applied Weather Associates (AWA), incorporating more than two decades of advancements in hydrometeorology, numerical modeling, and geographic information systems. The study replaces older Hydrometeorological Reports (HMRs) and regional estimates that no longer capture the spatial or temporal detail required for robust infrastructure design.
Objectives
The primary objectives of this study were to:
1. Develop site-specific PMP values representative of the basin’s meteorology, topography, and storm climatology. This included development of Snow Water Equivalent data up to and including the Probable Maximum Snow Accumulation.
2. Derive AEP-based rainfall estimates that extend through the PMP level to support risk-informed hydrologic assessments.
3. Evaluate climate change influences on extreme precipitation and identify potential trends affecting storm intensity and duration.
4. Produce datasets, depth-duration relationships, and temporal storm distributions suitable for PMF and hydrologic modeling applications.
5. Provide recommendations for incorporating these findings into long-term infrastructure resilience and design criteria.
Methodology
AWA’s methodologies combined several core components:
1. Storm Precipitation Analysis System (SPAS): A proprietary, GIS-based hydrometeorological tool was used to reconstruct and analyze historical extreme storms at high spatial and temporal resolution. SPAS provides storm-specific reconstructions of precipitation magnitude, spatial variability, and temporal accumulation pattern.
2. Probable Maximum Precipitation (PMP) Development:
· Identification of extreme historical storms across the overall domain relevant for a given basin using modern reanalysis data, satellite products, and surface observations.
· Maximization of storm moisture content to climatological limits, based on 100-year dew point and sea surface temperature (SST) climatologies.
· Transposition of storms across meteorologically and topographically similar regions to derive the highest physically plausible rainfall estimates for each grid cell.
· Application of In-Place Maximization Factors (IPMF) and Geographic Transposition Factors (GTF) within a gridded GIS environment to quantify spatial variability.
· Calculation of PMP depths over relevant area sizes and durations need for accurate hydrologic analyses and engineering design.
3. Annual Exceedance Probability (AEP) Analysis: Regional frequency analysis was performed using L-moment statistics, which minimize bias from extreme outliers and yield more stable precipitation frequency relationships. The method produced 24-hour, 48-hour, and 72-hour AEPs up to 10⁻⁸ (approaching the PMP limit).
4. Climate Change Assessment: Using downscaled CMIP6 model outputs (NEX-GDDP-CMIP6), projected changes in precipitation and temperature were evaluated under SSP2-4.5 and SSP5-8.5 scenarios. Mann-Kendall trend analyses were performed for 1-day, 3-day, monthly, seasonal, and annual durations to identify statistically significant trends.
5. Hydrologic Integration: Derived PMP and AEP datasets were formatted for direct use in hydrologic models
supporting PMF estimation and design evaluation. Spatial and temporal patterns were analyzed to inform flood routing and storage design sensitivity.
Key Findings
1. PMP Magnitudes:
· The updated PMP values showed no major increase relative to previous estimates, with some localized refinements reflecting improved storm representation and orographic detail.
2. AEP Estimates:
· Basin-average AEPs corresponding to PMP levels were typically in the range of 10⁻⁸, consistent with theoretical expectations.
· The probabilistic extension of rainfall frequency up to PMP improves the ability to conduct risk-based dam safety assessments.
3. Climate Change Impacts:
· Climate projections indicate moderate increases (5–15%) in 30- to 90-day rainfall totals, particularly under higher emissions scenarios (SSP5-8.5).
· No statistically significant changes were found in short-duration (1–3 day) extreme precipitation magnitudes, suggesting PMP estimates remain stable under projected mid-century conditions.
· Seasonal shifts indicate slightly earlier onset of intense precipitation events in some basins, relevant for reservoir operation and snowmelt interaction.
4. Hydrologic and Operational Implications:
· PMP events remain physically plausible but extremely rare, reinforcing their suitability for PMF-based safety design.
Conclusions and Recommendations
The study demonstrates that modern, storm-based PMP and AEP methodologies yield consistent and defendable results that reflect the latest meteorological understanding and data availability. While climate change does not significantly alter PMP magnitudes, its influence on antecedent snowfall and snowmelt should be considered in operational planning.
Recommendations include:
1. Adopt the updated PMP and AEP datasets for all hydrologic modeling, PMF development, and dam safety evaluations.
2. Incorporate climate change sensitivity testing into long-term water management and emergency planning scenarios.
3. Periodically revisit the analysis (every 10–15 years) to integrate emerging data, updated climate projections, and observed changes in regional hydrometeorology.
4. Use PMP and AEP results in risk-informed design approaches, aligning with evolving regulatory and international best practices.
5. Maintain a data-driven resilience strategy, leveraging basin-scale hydrometeorological modeling to guide infrastructure design and adaptation.
The results of this study support a high degree of confidence in existing hydrologic design bases while providing modernized datasets and context for resilient infrastructure management under changing climatic conditions.
