C011-0002
COVID-19 impacts on snow albedo in the Indus River Basin

Tuesday, 8 December 2020
Poster
Ned Bair1, Timbo Stillinger1, McKenzie Skiles2 and Karl Rittger3, (1)University of California Santa Barbara, Santa Barbara, CA, United States, (2)University of Utah, Geography, Salt Lake City, UT, United States, (3)University of Colorado at Boulder, Institute for Arctic and Alpine Research, Boulder, CO, United States
Abstract:
In a 2016 survey of 1,650 global cities, the World Health Organization found that Delhi, India had the highest average PM10 levels for cities with 14M or more inhabitants. Studies indicate that pollution from Delhi and other south Asian cities causes observable darkening on snowpacks across High Mountain Asia. On March 25, 2020, India issued strict stay at home orders for its 1.3B citizens to reduce the spread of COVID-19. Effects on air pollution were dramatic. A NASA analysis of aerosol optical depth measurements from MODIS Terra over Northern India shows the lowest sustained levels recorded in April over the entire 20 year MODIS-era record.

We hypothesize that there will be observable increases in snow albedo caused by a reduction in light absorbing particles (LAP, i.e. dust and soot) due to unprecedented changes in travel and economic activity. Such an increase in snow and ice albedo would have significant effects on snow and ice melt for the Indus River, which supplies water for 200M people, and more broadly impacts the delicate snow ice albedo feedback. Early analysis over the Nandi Devi region of India show some of the lowest dust values on record in April 2020.

We will present results from new as well as mature spectral unmixing models developed by the authors to examine and bound estimates for snow and ice albedo in the Indus Basin. Estimates using both moderate (MODIS) and higher resolution sensors (Landsat 8 OLI) will be made during the COVID-19 pandemic lockdowns and compared to baseline values over the past 20 years. Use of multiple models will provide a range of albedo estimates, crucial for assessing confidence in results.