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.

Cities have historically developed close to rivers and coasts, increasing human exposure to flooding. That exposure is exacerbated by changes in climate and population, and by urban encroachment on floodplains. Although the mechanisms of how urbanization affects flooding are relatively well understood, there have been limited efforts to assess the magnitude of floodplain encroachment globally and how it has changed in both space and time. Highly resolved global datasets of both flood hazard and changes in urban area from 1985 to 2015 are now available, enabling the reconstruction of the history of floodplain encroachment at high spatial resolutions. Here we show that the urbanized area in floodplains that have an average probability of flooding of 1/100 years, has almost doubled since 1985. Further, the rate of urban expansion into these floodplains increased by a factor of 1.5 after the year 2000. We also find that urbanization rates were highest in the most hazardous areas of floodplains, with population growth in these urban floodplains suggesting an accompanying increase in population density. These results reveal the scope, trajectory and extent of global floodplain encroachment. With tangible implications for flood risk management, these data could be directly used with integrated models to assess adaptation pathways for urban flooding.

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.

The Surface Water and Ocean Topography (SWOT) satellite has the potential to transform global hydrologic science by offering simultaneous and synoptic estimates of river discharge and other hydraulic variables. Discharge is estimated from SWOT observations of water surface elevation, width, and slope. A first assessment using just the highest quality SWOT measurements, over the first 15 months (March 2023–July 2024) of the mission evaluated at 65 gauged reaches shows results consistent with pre‐launch expectations. SWOT estimates track discharge dynamics without relying on any gauge information: median correlation is 0.73, with a correlation interquartile range of 0.51–0.89. SWOT estimates capture discharge magnitude correctly in some cases but are biased (median bias is 50%) in others. There are already a total of 11,274 ungauged global locations with highest quality SWOT measurements where SWOT discharge is expected to accurately track discharge variations: this value will increase as SWOT data record length grows, algorithms are refined and SWOT measurements are reprocessed. This first look indicates that SWOT discharge is performing as expected for SWOT data that achieve performance requirements, providing observed information on discharge variations in ungauged basins globally.

Earth‐orbiting satellites provide valuable observations of upstream river conditions worldwide. These observations can be used in real‐time applications like early flood warning systems and reservoir operations, provided they are made available to users with sufficient lead time. Yet the temporal requirements for access to satellite‐based river data remain uncharacterized for time‐sensitive applications. Here we present a global approximation of flow wave travel time to assess the utility of existing and future low‐latency/near‐real‐time satellite products, with an emphasis on the forthcoming SWOT satellite mission. We apply a kinematic wave model to a global hydrography data set and find that global flow waves traveling at their maximum speed take a median travel time of 6, 4, and 3 days to reach their basin terminus, the next downstream city, and the next downstream dam, respectively. Our findings suggest that a recently proposed ≤2‐day data latency for a low‐latency SWOT product is potentially useful for real‐time river applications.