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In the western United States, water supplies largely originate as snowmelt from forested land. Forests impact the water balance of these headwater streams, yet most predictive runoff models do not explicitly account for changing snow‐vegetation dynamics. Here, we present a case study showing how warmer temperatures and changing forests in the Henrys Fork of the Snake River, a seasonally snow‐covered headwater basin in the Greater Yellowstone Ecosystem, have altered the relationship between April 1st snow water equivalent (SWE) and summer streamflow. Since the onset and recovery of severe drought in the early 2000s, predictive models based on pre‐drought relationships over‐predict summer runoff in all three headwater tributaries of the Henrys Fork, despite minimal changes in precipitation or snow accumulation. Compared with the pre‐drought period, late springs and summers (May–September) are warmer and vegetation is greener with denser forests due to recovery from multiple historical disturbances. Shifts in the alignment of snowmelt and energy availability due to warmer temperatures may reduce runoff efficiency by changing the amount of precipitation that goes to evapotranspiration versus runoff and recharge. To quantify the alignment between snowmelt and energy on a timeframe needed for predictive models, we propose a new metric, the Vegetation‐Water Alignment Index (VWA), to characterize the synchrony of vegetation greenness and snowmelt and rain inputs. New predictive models show that in addition to April 1st SWE, the previous year's VWA and summer reference evapotranspiration are the most significant predictors of runoff in each watershed and provide more predictive power than traditionally used metrics. These results suggest that the timing of snowmelt relative to the start of the growing season affects not only annual partitioning of streamflow, but can also determine the groundwater storage state that dictates runoff efficiency the following spring. 

As drought and wildfire frequency increase across the western United States, our ability to predict how water resources will respond to these disturbances depends on our understanding of the feedbacks that maintain watershed function and streamflow. Previous studies of non‐perennial headwater streams have ranked drivers of low‐flow conditions; however, there is a limited understanding of the interactions between these drivers and the processes through which these interactions affect streamflow. Here, we use stream water level, soil moisture, sap flow, and vapor pressure deficit data to investigate ecohydrological interactions along a mountainous headwater stream. Correlation and cross‐correlation analyses of these variables show that ecohydrological interactions are (a) nonlinear and (b) interconnected, suggesting that analyses assuming linearity and independence of each driver are inadequate for quantifying these interactions. To account for these issues and investigate causal linkages, we use convergent cross‐mapping (CCM) to characterize the feedbacks that influence non‐perennial streamflow. CCM is a nonlinear, dynamic method that has only recently been applied to hydrologic systems. CCM results reveal that atmospheric losses associated with local sap flow and vapor pressure deficit are driving changes in soil moisture and streamflow (p < 0.01) and that atmospheric losses influence stream water more directly than shallow soil moisture. These results also demonstrate that riparian processes continue to affect subsurface flows in the channel corridor even after stream drying. This study proposes a nonlinear framework for quantifying the ecohydrologic interactions that may determine how headwater streams respond to disturbance.