Modeling and Predicting Future Changes in Snowfall and Snow Cover in Alaska
Snow Datasets for Arctic Terrestrial Applications
Dates
Start Date
2012-09-30
End Date
2013-12-31
Release Date
2012
Summary
Snow is extremely important to a wide range of natural processes in Alaska. Snow cover helps regulate the earth’s temperature and stores water on the landscape. As it melts, snow hydrates the soil and replenishes the freshwater supplies of streams and lakes, providing water for vegetation, wildlife, and human activities such as agriculture and electricity generation. Understanding present and future snow conditions under climate change is critical for managing Alaska’s natural resources, yet many scientists, land managers, and policymakers lack this information at useful scales. Hence, the goal of this project was to produce an advanced snow modeling system for part of the Arctic that predicts a variety of factors across space and [...]
Summary
Snow is extremely important to a wide range of natural processes in Alaska. Snow cover helps regulate the earth’s temperature and stores water on the landscape. As it melts, snow hydrates the soil and replenishes the freshwater supplies of streams and lakes, providing water for vegetation, wildlife, and human activities such as agriculture and electricity generation. Understanding present and future snow conditions under climate change is critical for managing Alaska’s natural resources, yet many scientists, land managers, and policymakers lack this information at useful scales. Hence, the goal of this project was to produce an advanced snow modeling system for part of the Arctic that predicts a variety of factors across space and time, including estimates of snowfall, snow depth, and changes in snow season length. The model was developed with collaborative input from ecologists, biologists, and geophysical scientists to determine which outputs would be most useful. These datasets are presently being used by the Arctic Landscape Conservation Cooperative for continued climate, hydrologic, and ecosystem research.
Snow conditions are extremely important to a wide range of hydrologic and ecosystem components and processes, including those related to surface energy and moisture stores and fluxes, vegetation, mammals, birds, and fish. The required snow datasets currently do not exist at the required spatial and temporal scales needed by end users such as scientists, land managers, and policy makers. The goal of this project is to produce spatially distributed, time evolving, snow datasets for the Arctic LCC that can be used in a wide range of climate, hydrologic, and ecosystem applications.
The project researcher will use the MicroMet/SnowModel snow-evolution modeling system to simulate a range of snow-related variables over an Arctic domain of interest to the Arctic LCC, and will lead a workshop to be attended by ecologists, biologists, and geophysical scientists from across agencies to determine what model outputs they would find most useful so that he can custom-code his model to ingest appropriate data and to produce those outputs.