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Summary: Globally, spring phenology and abiotic processes are shifting earlier with warming. Differences in the magnitudes of these shifts may decouple the timing of plant resource requirements from resource availability. In riparian forests across the northern hemisphere, warming could decouple seed release from snowmelt peak streamflow, thus reducing moisture and safe sites for dominant tree recruitment.We combined field observations with climate, hydrology, and phenology models to simulate future change in synchrony of seed release and snowmelt peaks in the South Platte River Basin, Colorado, for three Salicaceae species that dominate western USA riparian forests.Chilling requirements for overcoming winter endodormancy were strongest in Salix exigua, moderately supported for Populus deltoides, and indiscernible in Salix amygdaloides. Ensemble mean projected warming of 3.5°C shifted snowmelt peaks 10–19 d earlier relative to S. exigua and P. deltoides seed release, because decreased winter chilling combined with increased spring forcing limited change in their phenology. By contrast, warming shifted both snowmelt peaks and S. amygdaloides seed release 21 d earlier, maintaining their synchrony.Decoupling of snowmelt from seed release for Salicaceae with strong chilling requirements is likely to reduce resources critical for recruitment of these foundational riparian forests, although the magnitude of future decoupling remains uncertain. [ABSTRACT FROM AUTHOR]

Highlights • Uncertainty in simulated streamflow and runoff was quantified for the United States. • Upper and lower uncertainty Intervals were derived at gaged and ungaged locations. • Three metrics evaluated the reliability, sharpness, and skill of the intervals. • The intervals adequately captured uncertainty at 74% of locations. • The metrics can help supplement information from model goodness-of-fit measures. Abstract One important component of continental-scale hydrologic modeling is quantifying the level of uncertainty in long-term hydrologic simulations and providing a range of possible simulated streamflow and/or runoff values for gaged and ungaged locations. In this paper, uncertainty was quantified for simulated streamflow and runoff generated from a monthly water balance model (MWBM) at 1575 streamgages and 109,951 hydrologic response units (HRUs), which span the conterminous United States (CONUS). A stochastic-approach, which incorporated the properties of modeled streamflow residuals back into the simulated model output, was used to create time series of upper and lower uncertainty intervals (UIs) around the simulated monthly time series. This approach was applied to an existing hydrologic regionalization implementation. Metrics used to evaluate the UIs across the CONUS (the coverage ratio, average width index, and interval skill score) indicated that on average the UIs were reliable, skillful, and sharp in being able to both contain measured streamflow observations and reduce estimates of uncertainty based on expected model predictions. These uncertainty evaluation metrics can complement each other in characterizing model skill and uncertainty over large-scale domains. [ABSTRACT FROM AUTHOR]

Assessment of water resources at a national scale is critical for understanding their vulnerability to future change in policy and climate. Representation of the spatiotemporal variability in snowmelt processes in continental-scale hydrologic models is critical for assessment of water resource response to continued climate change. Continental-extent hydrologic models such as the U.S. Geological Survey National Hydrologic Model ( NHM) represent snowmelt processes through the application of snow depletion curves ( SDCs). SDCs relate normalized snow water equivalent ( SWE) to normalized snow covered area ( SCA) over a snowmelt season for a given modeling unit. SDCs were derived using output from the operational Snow Data Assimilation System ( SNODAS) snow model as daily 1-km gridded SWE over the conterminous United States. Daily SNODAS output were aggregated to a predefined watershed-scale geospatial fabric and used to also calculate SCA from October 1, 2004 to September 30, 2013. The spatiotemporal variability in SNODAS output at the watershed scale was evaluated through the spatial distribution of the median and standard deviation for the time period. Representative SDCs for each watershed-scale modeling unit over the conterminous United States ( n = 54,104) were selected using a consistent methodology and used to create categories of snowmelt based on SDC shape. The relation of SDC categories to the topographic and climatic variables allow for national-scale categorization of snowmelt processes. [ABSTRACT FROM AUTHOR]