Katherine Hale | Geology Reports

USGS2025

Warmer winters drive declines in snowpack and consequent increases in annual and seasonal runoff in a headwater region of the northeastern United States

In montane, snow-affected regions of the United States (US), a warming climate threatens the timing and amount of future water delivery. It is expected that winter precipitation falling as snow will continue decreasing and the frequency of winter snowmelt events will continue increasing, with unknown impacts on partitioning of water between evapotranspiration and runoff, water quality, flooding, and drought. The Northeastern US represents a humid environment with relatively uniform precipitation seasonality and a transient snowpack, and the region has been largely overlooked with respect to changing winter conditions and water availability, in part due to scarce observations. An observational network has been recently established to span a Summit-to-Shore (S2S) continuum in Vermont for improved understanding and characterization of snowpack variability across the landscape. We leverage the S2S network alongside available multi-decade records of meteorology, snow depth, and runoff to relate long-term snowpack characteristics in the Northeast to seasonal and annual runoff within the high-elevation headwater Ranch Brook watershed (9.6 km2). In the last 57 years, average winter temperatures have increased by 2.6oC, snow season length has decreased by almost 3 weeks, average snow water equivalent has decreased by 13%, and winter season rain-on-snow (ROS) event frequency has increased from 1 to 3.5 per year. In response, average daily winter season runoff has increased, which is strongly related to increased annual runoff ratios (R2 = 0.70). Separating the 22-year runoff record into years with more versus less winter season runoff, years with more winter runoff corresponded to significantly increased winter temperatures, 15% smaller snowpack, two times more ROS events, 52% more winter runoff, 31% larger annual runoff ratio, and increased summer rainfall variance. A steady decline in the regional snowpack and related impacts on downstream water resources implications for ecosystems and agricultural, industrial, and domestic water supply.

Vermont↗

USGS2023

Snowpack relative permittivity and density derived from near-coincident lidar and ground-penetrating radar

Depth-based and radar-based remote sensing methods (e.g., lidar, synthetic aperture radar) are promising approaches for remotely measuring snow water equivalent (SWE) at high spatial resolution. These approaches require snow density estimates, obtained from in-situ measurements or density models, to calculate SWE. However, in-situ measurements are operationally limited, and few density models have seen extensive evaluation. Here, we combine near-coincident, lidar-measured snow depths with ground-penetrating radar (GPR) two-way travel times ( twt ) of snowpack thickness to derive >20 km of relative permittivity estimates from nine dry and two wet snow surveys at Grand Mesa, Cameron Pass, and Ranch Creek, Colorado. We tested three equations for converting dry snow relative permittivity to snow density and found the Kovacs et al. (1995) equation to yield the best comparison with in-situ measurements (RMSE = 54 kg m −3 ). Variogram analyses revealed a 19 m median correlation length for relative permittivity and snow density in dry snow, which increased to >30 m in wet conditions. We compared derived densities with estimated densities from several empirical models, the Snow Data Assimilation System (SNODAS), and the physically based iSnobal model. Estimated and derived densities were combined with snow depths and twt to evaluate density model performance within SWE remote sensing methods. The Jonas et al. (2009) empirical model yielded the most accurate SWE from lidar snow depths (RMSE = 51 mm), whereas SNODAS yielded the most accurate SWE from GPR twt (RMSE = 41 mm). Densities from both models generated SWE estimates within ±10% of derived SWE when SWE averaged >400 mm, however, model uncertainty increased to >20% when SWE averaged <300 mm. The development and refinement of density models, particularly in lower SWE conditions, is a high priority to fully realize the potential of SWE remote sensing methods.

Colorado↗