In February 2017, a 5‐day sequence of atmospheric river storms in California, USA, resulted in extreme inflows to Lake Oroville, the state's second‐largest reservoir. Damage to the reservoir's spillway infrastructure necessitated evacuation of 188,000 people; subsequent infrastructure repairs cost $1 billion. We assess the atmospheric conditions, snowmelt, and runoff against major historical events. The event generated exceptional runoff volumes (second largest in a 30‐yr record) partially at odds with the event precipitation totals (ninth largest). We explain the discrepancy with observed record melt of deep antecedent snowpack, heavy rainfall extending to unusually high elevations, and high water vapor transport during the atmospheric river storms. An analysis of distributed snow water equivalent indicates that snowmelt increased water available for runoff watershed‐wide by 37% (25–52% at 90% confidence). The results highlight potential threats to public safety and infrastructure associated with a warmer and more variable climate.

We used multiple sources of remotely sensed and ground based information to evaluate the spatiotemporal variability of snowpack accumulation, potential evapotranspiration (PET), and Normalized Difference Vegetation Index (NDVI) throughout the Southern Rocky Mountain ecoregion, USA. Relationships between these variables were used to establish baseline values of expected forest productivity given water and energy inputs. Although both the snow water equivalent (SWE) and a snow aridity index (SAI), which used SWE to normalize PET, were significant predictors of the long-term (1989-2012) NDVI, SAI explained 11% more NDVI variability than SWE. Deviations from these relationships were subsequently explored in the context of widespread forest mortality due to bark beetles. Over the entire study area, NDVI was lower per unit SAI in beetle-disturbed compared to undisturbed areas during snow-related drought; however, both SAI and NDVI were spatially heterogeneous within this domain. As a result, we selected three focus areas inside the larger study area within which to isolate the relative impacts of SAI and disturbance on NDVI using multivariate linear regression. These models explained 66%-85% of the NDVI and further suggested that both SAI and disturbance effects were significant, although the disturbance effect was generally greater. These results establish the utility of SAI as a measure of moisture limitation in snow-dominated systems and demonstrate a reduction in forest productivity due to bark beetle disturbance that is particularly evident during drought conditions resultant from low snow accumulation during the winter.

No abstract