Kate Hale, PhD
Postdoctoral Scholar | Institute of Northern Engineering | University of Alaska Fairbanks
Becoming a snow scientist
Throughout the early stages of my career, I have been involved in a number of projects across the snow community. From modeling the intricacies of catchment and continental water balance to instilling fundamentals in the classroom and propelling satellite missions, I strive to continually expand my snow skills and snow stories, becoming an expert in my field. Both my research projects and teaching yield the opportunities and challenges to further my experience as a snow scientist, effective communicator, and environmental professional.
Recent decreases in snow water storage in western North America
Publication linked here: Mountain snowpacks act as natural water towers, storing winter precipitation until summer months when downstream water demand is greatest. While previous studies have explored snow water equivalent trends, quantifying snowpack water storage in terms of its’ magnitude and duration has largely been ignored despite its importance to water availability for society and ecosystems. Across western North America, we introduce a Snow Storage Index (SSI), which represents the annual temporal phase difference between daily precipitation and surface water inputs – representing the sum of rainfall and snowmelt – weighted by relative volumes. Annual SSI values have decreased (p < 0.05) from 1950-2013 in over 25% of mountainous areas. The SSI decline is a result of significantly earlier snowmelt and rainfall in spring months, with additional declines in winter precipitation. Different from trends in snow water equivalent or snow fraction, the SSI represents the degree to which snow is delaying the timing and magnitude of surface water inputs relative to precipitation. The lag in timing between when precipitation falls and the delivery of that water is a fundamental component of the hydrologic cycle in snow-affected regions. The SSI offers a new perspective on changes in water delivery and related climatic sensitivities for hydrological and ecological applications (Hale et al., 2023).
Original high-resolution figures available for download linked here.
Annual average Snow Storage Index compared to the Budyko anomaly per grid-cell, per eco-region across the western United States. All relationships are statistically significant (linear and Spearman rank p < 0.05). Shading represents the 95% confidence interval, and Variable Infiltration Capacity grid cells are colored by average annual aridity (). Corresponding slope, linear r2 values and Spearman rank correlation values (Rs) are listed in each panel. Positive Budyko anomalies indicate overproduction of streamflow, whereas negative Budyko anomalies indicate underproduction of streamflow. Black open circles indicate the Catchment Attributes and Meteorology for Large-Sample Studies data within each eco-region.
Effects of Snow Water Storage on Hydrologic Partitioning Across the Mountainous, Western United States
Publication linked here: In the montane western United States, where the majority of downstream water resources are derived from snowmelt, a warming climate threatens the timing and amount of future water availability. It is expected that the fraction of precipitation falling as snow will continue decreasing and the timing of snowmelt will continue shifting earlier in the year with unknown impacts on partitioning between evapotranspiration and streamflow. To assess this, we employ a Snow Storage Index (SSI) to represent the annual temporal phase difference between daily precipitation and daily modeled surface water inputs (SWI, the sum of rainfall and snowmelt), weighted by the respective amounts. We coupled the SSI metric with a Budyko-based framework to determine the effect of snow water storage on relative hydrologic partitioning across snow-influenced watersheds in the western U.S.
NASA SnowEx Campaign: Time Series Site Lead, Niwot Ridge, CO
SnowEx is a long-term NASA THP funded program to address the most important gaps in snow remote sensing knowledge. SnowEx focuses on airborne campaigns and field work, and on comparing the various sensing technologies, from those more mature to more experimental, in globally-representative types of snow. SnowEx thus lays the groundwork for a future snow satellite mission. My responsibilities each snow season have included: digging and analyzing snow pit profiles and completing snow depth transects, completing ground penetrating radar (GPR) grids and terrestrial LiDAR scans (TLS), analyzing raw data for public distribution and complementary research, maintaining snow sensor sites at designated field locations. These efforts take place across the mountainous western United States, including Niwot Ridge of the Front Range, Colorado and the North Slope of Alaska.
Changes in hydrologic partitioning in the Colorado upper montane forest
Publication linked here: As the climate warms, the fraction of precipitation falling as snow is expected to decrease and the timing of snowmelt is expected to shift earlier in spring. In snow-dominated mountainous regions that rely on snowpack and snowmelt for water supply, this change in snow accumulation and melt prompts us to examine downstream changes in streamflow. The objective of this study is to understand how changes in precipitation phase and snowmelt timing alter the timing of surface water inputs (i.e. rainfall and snowmelt) and the partitioning of these inputs between evapotranspiration and streamflow. Increased streamflow generation efficiency during winter months effectively buffered the net annual streamflow decline associated with warming. This cold season buffering effect is unique to snow influenced systems, as the magnitude and timing of water released from snowpacks, and input to the surface, is sensitive to warming whereas the timing and magnitude of surface water inputs in rain-dominated systems are insensitive to warming. Seasonal streamflow buffering may diminish as warming drives many systems toward rain-dominance and may have important implications for hydrological and ecological processes and for water resource management across Earth’s snow-influenced regions (Hale et al., 2022a).
Drivers of spatiotemporal patterns of surface water inputs in a catchment at the rain-snow transition zone of the water-limited western United States
Publication linked here: Spatial and temporal dynamics of rainfall and snowmelt (i.e., surface water inputs, SWI) control soil moisture, groundwater recharge and streamflow at annual, seasonal, and event scales. In the rain-snow transition zone, comprising a large portion of the mountainous western United States, there is limited understanding of the sensitivity of spatiotemporal SWI dynamics across hydrologically variable water years (WYs). We modeled rainfall and snowpack dynamics in a small headwater catchment (1.8 km2) spanning the rain-snow transition in southwestern Idaho, USA, for two hydrologically distinct WYs (2011 and 2014). In wet WY 2011 and dry WY 2014, total precipitation drove spatial variability in annual SWI. Snow drifts generated more SWI (901-2080 mm) than high-elevation scour zones (442-640 mm), which generated less SWI than mid-elevation, non-drift locations (452-784 mm). Seasonally, energy fluxes differed most during the snowmelt period, where higher net radiation at lower elevations and south-facing slopes drove SWI production. At the rain-on-snow (ROS) event scale, higher elevations and north-facing slopes generated 15-20% of annual SWI, due mainly to higher turbulent fluxes. The most productive ROS events occurred after peak snow water equivalent (SWE), when rainfall fell onto ripe snowpacks. Snow drift locations were less susceptible to melt during ROS events, offset by the larger cold content and snowpack mass. Thus, catchment water resources depend on SWI magnitude, location, and timing, which are moderated by drift persistence at all temporal scales. As the climate warms, shifts in spatiotemporal SWI distribution are expected with declines in snowfall and snowfall redistribution in this area (Hale et al., 2022b).