Development of an Integrated Snow and Glacier Melt Prediction Module

Globally, rapid climate change is increasing the complexity of response planning and preparedness for local communities, as well as for regional and national government agencies. These changes are creating unanticipated operational challenges for organizations world-wide, including for the United States (US) Department of Defense, among others.

This is especially true in the Arctic, where rising temperatures are challenging situational awareness of key environmental parameters like snowmelt, glacier melt, and resultant overland flow. In the Arctic, runoff is largely the result of snow and glacier melt. However, trusted Earth-system models used by US agency operational centers (e.g., US Army, US National Oceanic and Atmospheric Administration) do not incorporate glacier melt as part of the snowmelt overland flow budget. Instead, glaciers are typically treated as permanent land-ice features.

Therefore, this research seeks to resolve this gap by developing a more holistic prediction capability that incorporates snow and glacier melt into overland flow. Our snow and glacier melt model enables effective planning and risk assessment in cold regions through improved representation of Arctic meltwater processes. This effort adapts snow and ice melt energy-balance functions from the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) to create a stand-alone overland flow module that has the potential to be integrated into larger, more comprehensive systematic climate and land surface model frameworks.

Model validation is via local field data from Alaska, USA. We utilize snow and ice cover, hydrology, and streamflow data from the Eklutna Glacier, near Anchorage, Alaska, leveraging a long-term data collection program run by Alaska Pacific University. Predictions by our new melt module will also be compared to a widely used glacier mass-balance and discharge model, the Distributed Enhanced Temperature Index Model (DETIM).

Future research efforts will also consider local tribal stakeholder knowledge through incorporation of published, indigenous-led best practices for data collection at Eklutna Glacier. This will be done through a partnership with the Alaska Native community of Eklutna, including outreach efforts with local stakeholders, as well as with researchers and students at Alaska Pacific University in Anchorage.