Evaluation of the subseasonal forecast skill of floods associated with atmospheric rivers in coastal Western U.S. watersheds

Abstract

AbstractAtmospheric rivers (ARs) are responsible for up to 90% of major flood events along the U.S. West Coast. The timescale of subseasonal forecasting (two weeks to one month) is a critical lead time for proactive mitigation of flood disasters. The NOAA/Climate Testbed Subseasonal Experiment (SubX) is a research-to-operations project with almost immediate availability of forecasts. It has produced a reforecast database that facilitates evaluation of flood forecasts at these subseasonal lead times. Here, we examine the SubX driven forecast skill of AR-related flooding out to 4-week lead using the Distributed Hydrology Soil Vegetation Model (DHSVM), with particular attention to the role of antecedent soil moisture (ASM), which modulates the relationship between meteorological and hydrological forecast skill. We study three watersheds along a transect of the U.S. West Coast: the Chehalis River basin in Washington, the Russian River basin in Northern California, and the Santa Margarita River basin in Southern California. We find that the SubX driven flood forecast skill drops quickly after week 1, during which there is relatively high deterministic forecast skill. We find some probabilistic forecast skill relative to climatology as well as ensemble streamflow prediction (ESP) in week 2, but minimal skill in weeks 3-4, especially for annual maximum floods, notwithstanding some probabilistic skill for smaller floods in week 3. Using ESP and reverse-ESP experiments to consider the relative influence of ASM and SubX reforecast skill, we find that ASM dominates probabilistic forecast skill only for small flood events at week 1, while SubX reforecast skill dominates for large flood events at all lead times.