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Temporal compounding increases economic impacts of atmospheric rivers in California

A table with two rows and five columns are shown. Each location in the table includes a map of California with purple and blue circles to display data for a grid of locations. The columns represent atmospheric river intensity ranks AR1 to AR5. The top row represents the probability of an atmospheric river occurring within 5 days before or after a given event, referred to as an adjacent case. The bottom row represents the probability of an atmospheric river occurring within 5 days both before and after a given event, referred to as a sandwich case. The probability of an atmospheric river being adjacent and/or sandwiched increases moving from south to north and moving from smaller to larger magnitudes. This indicates the more intense atmospheric rivers are more likely to occur in close temporal proximity to other atmospheric river events.

SERAFIN – Temporal compounding increases economic impacts of atmospheric rivers in California

Corinne Bowers, Jack W. Baker, Katherine A. Serafin

Article first published online: 19 January 2024

DOI: https://doi.org/10.1029/2023EF003536

ABSTRACT: Temporally compounding atmospheric river (AR) events cause severe flooding and damage in California. However, the contribution of temporal compounding to AR-induced loss has yet to be systematically quantified. We show that the strongest ARs are more likely to be part of sequences, which are periods of elevated hydrologic hazard associated with temporally clustered ARs. Sequences increase the likelihood of flood-related impacts by 8.3% on AR days and 5.4% on non-AR days, and across two independent loss datasets, we find that ARs within sequences have over three times higher expected losses compared to ARs outside of sequences. Expected losses also increase when the preceding AR is higher intensity, when time since the preceding AR is shorter, and when an AR is the second or later event within a sequence. We conclude that temporal compounding is a critical source of information for predicting an AR’s potential consequences.

Read the full publication in Science Advances.