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Grad student seminar: Estimating the spatial distribution of snow water equivalent in the California

Thursday, October 17, 2019, 12:30PM - 1:30PM

Speaker

Kehan Yang

Location:

SEEC room S249

Full title

Estimating the spatial distribution of snow water equivalent in the California Sierra Nevada using in situ and remote sensing observations

Abstract

Seasonal snowpack water storage variability is of crucial importance for assessing water availability and water supply forecasting under the changing climate and growing water demand. About 1/6 of the global population relies on the glacier and seasonal snowpack derived runoff as their primary water resources. Mountain snowpack functions as a natural ‘water tower', holding water in the cold winter months and releasing it gradually throughout spring and summer when water demand is higher. Under recent climate warming, decreased snowpack volume and earlier snowmelt are observed in many dry regions, especially in California. These changes have caused deficits in agricultural, industrial and domestic water supply, leading to higher frequency of severe drought conditions and tremendous economic loss. Therefore, improved knowledge of the spatial and temporal distributions of the snowpack is crucial for understanding snowmelt runoff and timing, and associated sensitivities to climate variability and change. This knowledge is essential for water management in snow-dominated regions, especially in forecasting runoff and reducing vulnerability to droughts and floods. The proposed research aims to improve our understanding of the snow water equivalent (SWE) distribution and its relationship with snowmelt streamflow. Specifically, the following three research questions will be answered:

  1. How accurately can existing methods estimate SWE in the mountainous area?
  2. How can existing satellite, airborne, and ground observations be optimally blended to improve near real-time SWE estimates?
  3. How can future airborne campaign and satellite mission be leveraged to improve near real-time SWE estimates?