For sheer destruction, extreme floods are among the most worst natural disasters, causing widespread damage to communities, infrastructure, and the environment. Severe flood events are often tied to specific weather events, such as tropical systems and atmospheric rivers. In “Benchmark Dataset of Historical Annual Peak Floods Classified by Causal Mechanisms for Select U.S. River Basins,” Scott D. Hamshaw and a team of coauthors explore the factors that drive major floods and introduce a new benchmark dataset that helps connect historic flood events with their underlying causes. The dataset consists of manual, expert-determined classifications of flood events from multiple river basins across the continental United States and is paired with a flexible framework for categorizing floods from both hydrologic and weather-related perspectives. The work was developed in response to a growing need for reliable reference data, as many existing large-scale flood classification methods have not been thoroughly validated against expert-determined flood causes.
The value of this research extends well beyond academic study. By creating a trusted dataset and a consistent classification approach, the authors provide a resource that can help improve future flood forecasting, risk assessment, and infrastructure planning efforts. The study examines how experts identified the primary drivers behind historic flood events, such as rainfall, snowmelt, or combinations of both, and lays the groundwork for developing automated tools that can classify floods across large geographic regions. For professionals involved in water resources, transportation, public works, and community resilience, this research highlights the importance of understanding not only how often floods occur, but also why they occur. Readers interested in the future of flood analysis and management can get insight from the full article in the Journal of Hydrologic Engineering at https://ascelibrary.org/doi/10.1061/JHYEFF.HEENG-6758. The abstract is below.
Abstract
Considering the causal mechanisms of floods can improve estimates of flood recurrence intervals given that certain flood types can be associated with higher magnitude and more damaging floods. However, few verified datasets of flood types are available to validate the semiautomated and automated classification algorithms needed to apply flood-typing across large hydrologically diverse regions. To address this gap, a benchmark dataset of manually classified flood types was compiled for 1,763 annual maximum flood peaks from 18 stream gauges in six different river basins across the conterminous United States from 1851 to 2022. Within each basin, three representative stream gauges were selected for manual flood typing. A flexible classification framework is introduced that facilitates flood typing across hydrologically diverse regions and accommodates unique combinations of weather and antecedent watershed conditions specific to each region. Floods were manually typed by domain experts using multiple lines of evidence to identify a primary surface water input of each flood (rainfall, snowmelt, or both) and, if relevant, associated storm type and secondary causal mechanisms characterizing antecedent watershed conditions. Across all the study basins, 49% of historical annual maximum flood peaks were attributable to rainfall, 28% to snowmelt, 22% to mixed precipitation, and 1% could not be assigned to a mechanism due to missing or incomplete data. The proposed flood-typing schema supports varying levels of flood typing specificity required for mixed population flood-frequency analysis, flood-type-specific design hydrographs, water quality response studies, and additional applications. This detailed, manually determined benchmark dataset serves as a resource that can be used developing and validating automated or machine learning-based algorithms capable of operationalizing expanded flood peak information.
Learn more about what the new dataset reveals about extreme floods, and how to apply it to make your community better prepared, in the ASCE Library: https://ascelibrary.org/doi/10.1061/JHYEFF.HEENG-6758.
