This study examined the role of participation in a year‐long school‐based group mentoring program, Project Arrive (PA), on increasing resilience during the first year of high school among students identified as being at high risk for school dropout. Participants were 114, ninth grade students taking part in one of 32 PA mentoring groups, and 71 statistically matched comparison students (53% male, 75% eligible for free/reduced‐price lunch, 62% Latinx). Using a propensity score with inverse probability of treatment weighting (IPTW) to reduce selection bias, and a multi‐level model to account for non‐independence of data within mentoring groups, we examined changes from pre‐test to program exit on seven external resilience resources (developmental supports and opportunities) and four internal resilience assets (personal strengths). At program exit, PA participants had higher adjusted means than comparisons on six external resources, including school support, school belonging, school meaningful participation, peer caring relationships, prosocial peers, and home meaningful participation. PA participants also had higher adjusted means on one internal asset, problem solving. Results point to the promise of group mentoring as an approach for increasing resilience among academically vulnerable adolescents.

Mountain snowpacks act as natural water towers, storing winter precipitation until summer months when downstream water demand is greatest. We introduce a Snow Storage Index (SSI), representing the temporal phase difference between daily precipitation and surface water inputs—sum of rainfall and snowmelt into terrestrial systems—weighted by relative magnitudes. Different from snow water equivalent or snow fraction, the SSI represents the degree to which the snowpack delays the timing and magnitude of surface water inputs relative to precipitation, a fundamental component of how snow water storage influences the hydrologic cycle. In western North America, annual SSI has decreased (p < 0.05) from 1950–2013 in over 25% of mountainous areas, as a result of substantially earlier snowmelt and rainfall in spring months, with additional declines in winter precipitation. The SSI and associated trends offer a new perspective on hydrologic sensitivity to climate change which have broad implications for water resources and ecosystems.

Abstract. Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how the spatial and temporal distribution of rainfall and snowmelt affects discharge in rain–snow transition zones. These zones experience large year-to-year variations in precipitation phase, cover a significant area of mountain catchments globally, and might extend to higher elevations under future climate change. We used observations from 11 weather stations and snow depths measured from one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semiarid, 1.8 km2 headwater catchment. We focused on surface water input (SWI; the summation of rainfall and snowmelt on the soil) for 4 years with contrasting climatological conditions (wet, dry, rainy, and snowy) and compared simulated SWI to measured discharge. A strong spatial agreement between snow depth from the lidar survey and model (r2 = 0.88) was observed, with a median Nash–Sutcliffe efficiency (NSE) of 0.65 for simulated and measured snow depths at snow depth stations for all modeled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the 4 years, with north-facing slopes producing 1.09–1.25 times more SWI than south-facing slopes, and snowdrifts producing up to 6 times more SWI than the catchment average. Annual discharge in the catchment was not significantly correlated with the fraction of precipitation falling as snow; instead, it was correlated with the magnitude of precipitation and spring snow and rain. Stream cessation depended on total and spring precipitation, as well as on the melt-out date of the snowdrifts. These results highlight the importance of the heterogeneity of SWI at the rain–snow transition zone for streamflow generation and cessation, and emphasize the need for spatially distributed modeling or monitoring of both snowpack and rainfall dynamics.