Based on a process‐level modeling of the rain‐on‐snow (ROS) events in the period of 1950 to 2013 and in a warmer climate, we quantify the historical and future runoff contribution from ROS to extreme floods and the source of runoff and snowmelt in large ROS events within the conterminous United States (CONUS). We find that the regions impacted most heavily by ROS include the West Coast, the major mountain ranges of the western interior, the Upper Midwest, the Northeast, and the lower Appalachians. While 70% of extreme (upper 0.1%) runoff events in these regions have some contribution from ROS, the runoff generated during these ROS events accounts for less than 10% of the total extreme flood runoff; the much larger fraction of extreme runoff is from either intense rainfall or clear‐sky snowmelt. Rainfall is the dominant source of runoff in ROS events along the West Coast and over the west‐facing slopes of the Cascades and Sierra Nevada, while snowmelt dominates ROS runoff in the other regions in the CONUS. Net radiation dominates the snowmelt during ROS in the high mountains in the West, while net radiation and turbulent heat flux are equally dominant in the rest of CONUS. Historically, the role of ROS in streamflow extremes is most significant in midelevation areas, but this “significant influence zone” will shift to higher elevations in a warmer future. The future ROS frequency changes exert a first order control on the future change of the runoff contribution from ROS to extreme floods.

Snow water equivalent (SWE), particularly in mountains regions, has been an elusive hydrologic measurement. We examine the utility of a data assimilation approach to generate space‐time continuous estimates of SWE from more readily available snow depth (SD) measurements. A multitemporal lidar data set provides a unique opportunity to assimilate single SD images and verify posterior estimates against SD images at nonassimilation times. Application over three water years shows significant improvement in the posterior estimates with an average correlation between estimated and measured SD fields of 0.88 compared to 0.52 for prior estimates that do not benefit from the assimilated SD data. We also show that posterior estimates are consistent with independent in situ SWE and streamflow measurements. This work demonstrates that using high‐resolution/high‐accuracy, but infrequent, SD measurements combined with a data assimilation framework could make significant inroads toward the goal of spatially distributed SWE and snowmelt estimates at the global scale.

Analysis of the Sierra Nevada (USA) snowpack using a new spatially distributed snow reanalysis data set, in combination with longer term in situ data, indicates that water year 2015 was a truly extreme (dry) year. The range‐wide peak snow volume was characterized by a return period of over 600 years (95% confidence interval between 100 and 4400 years) having a strong elevational gradient with a return period at lower elevations over an order of magnitude larger than those at higher elevations. The 2015 conditions, occurring on top of three previous drought years, led to an accumulated (multiyear) snowpack deficit of ~ −22 km³, the highest over the 65 years analyzed. Early estimates based on 1 April snow course data indicate that the snowpack drought deficit will not be overcome in 2016, despite historically strong El Niño conditions. Results based on a probabilistic Monte Carlo simulation show that recovery from the snowpack drought will likely take about 4 years.

Climate change is linked to changing precipitation, yet uncertainty remains as to whether and how hydroclimatic changes will influence flood frequency, magnitude, and timing. Part of this uncertainty is because few regional‐scale studies have analyzed changes in floods produced by different mechanisms. We identify six flood generating mechanisms (FGMs) based on prominent meteorological processes and moisture supply to the soil column in the Western U.S.; these include large‐scale frontal storms (atmospheric and non‐atmospheric rivers), monsoons, convective storms, snowmelt, and rain‐on‐snow. We assess trends in the frequency, magnitude, and timing of floods produced by different FGMs across 119 basins during 1960–2018. Overall, we find decreased frequency and magnitude of rain‐on‐snow‐driven floods, increased frequency and magnitude of convective‐storm‐driven floods, and a temporal shift to earlier in the year for snowmelt‐driven floods. The flood characteristics are generally stable for floods produced by other FGMs.