Flood Frequency Analysis (FFA) typically relies on fitting a probability distribution to Annual Maximum Peak flows (AMPs) to estimate the frequency of various flood magnitudes. It is generally assumed that longer observational records enhance the reliability of FFA. In this study, we challenge this assumption by examining the Kickapoo watershed in the north-central United States where at-site FFA is susceptible to significant sampling errors despite a relatively long record of observations (90 years). We demonstrate that three exceptionally large events, with only a 1.7% chance to occur in a single watershed, significantly affect extreme quantiles and their associated confidence intervals. We argue that FFA using a weighted skewness coefficient, recommended by Bulletin 17C FFA guidelines, can yield more reliable flood frequency estimates than at-site methods by combining both local and regional characteristics. We also leverage a process-driven FFA approach, which integrates Stochastic Storm Transposition (SST) with Monte Carlo (MC) physics-based hydrologic modeling (SST-MC), to gain additional insights into flood frequency. We employed the WRF-Hydro hydrologic model and a process-based calibration approach with Fusion, a new high-resolution forcing dataset over the continental United States. By expanding the sample size and incorporating watershed-scale and regional information, SST-MC can effectively reduce the sensitivity of FFA to individual extreme events and provide more reliable frequency estimates. The SST-MC method also adds physical interpretations by quantifying internal variability in flood frequency. Our study highlights the benefits of integrating regional analysis and advanced physic-based hydrologic modeling techniques into traditional FFA.

Extratropical cyclones are major contributors to consequential weather in the mid-latitudes and tend to develop in regions of enhanced cyclogenesis and progress along climatological storm tracks. Numerous studies have noted the influence that terrestrial snow cover exerts on atmospheric baroclinicity which is critical to the formation and trajectories of such cyclones. Fewer studies have examined the explicit role which continental snow cover extent has in determining cyclones intensities, trajectories, and precipitation characteristics. While several examinations of climate model projections have generally shown a poleward shift in storm tracks by the late 21st century, none have determined the degree to which the coincident poleward shift in snow extent is responsible. A method of imposing 10th , 50th , and 90th percentile values of snow retreat between the late 20th and 21st centuries as projected by 14 Coupled Model Intercomparison Project Phase Five (CMIP5) models is used to alter 20 historical cold season cyclones which tracked over or adjacent to the North American Great Plains. Simulations by the Advanced Research version of the Weather Research and Forecast Model (WRF-ARW) are initialized at 0 to 4 days prior to cyclogenesis. Cyclone trajectories and their central sea level pressure did not change substantially, but followed consistent spatial trends. Near-surface wind speed generally increased, as did precipitation with preferred phase change from solid to liquid state. Cyclone-associated precipitation often shifted poleward as snow was removed. Variable responses were dependent on the month in which cyclones occurred, with stronger responses in the midwinter than the shoulder months.

The term “weather whiplash” was recently coined to describe abrupt swings in weather conditions from one extreme to another, such as from a frigid cold spell to anomalous warmth or from drought to prolonged precipitation. These events are often highly disruptive to agriculture, ecosystems, and daily activities. In this study we propose and demonstrate a novel metric to identify weather whiplash events (WWEs) and track their frequency over time. We define a WWE as a transition from one persistent large-scale circulation regime to another distinctly different one, as determined using an objective pattern cluster analysis called self-organizing maps (SOMs). We focus on the domain spanning North America and the eastern N. Pacific Ocean. A matrix of representative atmospheric patterns in 500-hPa geopotential height anomalies is created. We analyze the occurrence of WWEs originating with long-duration events (defined as lasting 4 or more days) in each pattern, as well as the associated extremes in temperature and precipitation. A WWE is detected when the pattern two days following a long-duration event is substantially different, measured using internal matrix distances and thresholds. Changes in WWE frequency are assessed objectively based on reanalysis and climate model output, and in the future with climate model projections. Temporal changes in the future under RCP 8.5 forcing are more robust than in recent decades, with consistent increases (decreases) in WWEs originating in patterns with an anomalously warm (cold) Arctic.

The term “weather whiplash” was recently coined to describe abrupt swings in weather conditions from one extreme to another, such as from a frigid cold spell to anomalous warmth or from drought to prolonged precipitation. These events are often highly disruptive to agriculture, ecosystems, and daily activities. In this study we propose and demonstrate a novel metric to identify weather whiplash events (WWEs) and track their frequency over time. We define a WWE as a transition from one persistent large-scale circulation regime to another distinctly different one, as determined using an objective pattern cluster analysis called self-organizing maps (SOMs). We focus on the domain spanning North America and the eastern N. Pacific Ocean. A matrix of representative atmospheric patterns in 500-hPa geopotential height anomalies is created. We analyze the occurrence of WWEs originating with long-duration events (defined as lasting 4 or more days) in each pattern, as well as the associated extremes in temperature and precipitation. A WWE is detected when the pattern two days following a long-duration event is substantially different, measured using internal matrix distances and thresholds. Changes in WWE frequency are assessed objectively based on reanalysis and climate model output, and in the future with climate model projections. Temporal changes in the future under RCP 8.5 forcing are more robust than those during recent decades, with consistent increases (decreases) in WWEs originating in patterns with an anomalously warm (cold) Arctic.