CUAHSI CyberSeminar: Assessing Snow and Snowmelt Runoff in Remote Mountain Ranges
Event Type: Webinars and Virtual Events
When: 14 February 2014
Where: Online: 3:00PM EST
The presenters will be Dr. Jeff Dozier from the University of California, Santa Barbara, Bren School of Environmental Science & Management in conjunction with Dr. Anne Nolin - Oregon State University, Department of Geosciences.
Our objective is to estimate seasonal snow volumes, relative to historical trends and extremes, in snow-dominated mountains that have austere infrastructure, sparse gauging, challenges of accessibility, and emerging or enduring insecurity related to water resources. The world's mountains accumulate substantial snow and, in some areas, produce the bulk of the runoff. In ranges like Afghanistan's Hindu Kush, availability of water resources affects US policy, military and humanitarian operations, and national security. The rugged terrain makes surface measurements difficult and also affects the analysis of remotely sensed data. To judge feasibility, we consider two regions, a validation case and a case representing inaccessible mountains. For the validation case, we use the Sierra Nevada of California, a mountain range of extensive historical study, emerging scientific innovation, and conflicting priorities in managing water for agriculture, urban areas, hydropower, recreation, habitat, and flood control. For the austere regional focus, we use the Hindu Kush, where some of the most persistent drought in the world causes food insecurity and combines with political instability, and occasional flooding. Our approach uses a mix of satellite data and spare modeling to present information essential for planning and decision making, ranging from optimization of proposed infrastructure projects to assessment of water resources stored as snow for seasonal forecasts. We combine optical imagery (MODIS on Terra/Aqua), passive microwave data (SSM/I and AMSR-E), retrospective reconstruction with energy balance calculations, and a snowmelt model to establish the retrospective context. With the passive microwave data we bracket the historical range in snow cover volume.