UCLA

A body of work over the last several decades has demonstrated that most major floods along the U.S. West Coast are attributable to atmospheric rivers (ARs). Recent studies suggest that observed changes in extreme precipitation associated with a general warming have not necessarily lead to corresponding changes in floods, and changes in antecedent hydrological conditions could be a primary causal mechanism. This study examines climate change impacts on AR-related floods and their modulation by antecedent soil moisture (ASM) conditions in three watersheds that form a transect along the U.S. Pacific Coast: the Chehalis River basin in Washington State, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. All three basins are rain-dominant and frequented by ARs. I used the Distributed Hydrology-Soil-Vegetation Model (DHSVM), a spatially distributed hydrological model, to reconstruct floods in all three basins.

For historical AR flooding, based on a combination of observed and model-simulated soil moisture in the Russian River basin, my results show that the storm runoff-precipitation ratio during extreme precipitation events is much more strongly related to ASM than to storm total precipitation. If ASM is low, extreme precipitation may not lead to extreme discharge. When I used Global Climate Model (GCM)-projected future atmospheric forcings (primarily precipitation and temperature) in the three basins, my results show that the projected fraction of AR-related extreme discharge events slightly decreases in the Chehalis basin, but increases in the Russian and Santa Margarita River basins. These changes in California are driven by increases in AR-related extreme precipitation events, as well as projected increases in year-to-year volatility of annual precipitation, which increases the likelihood of concurrent occurrence of large storms and wet ASM. I also investigated the subseasonal forecast skill of AR-related flooding using the NOAA/Climate Testbed Subseasonal Experiment (SubX) database, applied to both AR- and non-AR related floods. I found that flood forecast skill drops quickly after week 1. There is some probabilistic forecast skill in week 2, but only a hint of skill in weeks 3-4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in weeks 3-4.