Mountain snowpacks provide essential water for socio-economic systems around the world, with nearly two billion people living in snow sensitive regions. Therefore, methods for characterizing the snowpack-streamflow dynamics at the watershed level are essential for understanding how changes in temperature and precipitation due to climate change will affect the water supply in these regions. However, in-situ snowpack measurements, such as snow water equivalent (SWE), are often unavailable or insufficient due to the financial and logistical constraints of installing snowpack monitoring systems. Remotely-sensed snow cover extent (SCE), or the proportion of a watershed that is covered in snow, has been previously integrated into snowmelt models and used to assess the relationships between snowmelt and streamflow. However, no research was found that provided a comprehensive analysis of the ability of SCE to characterize snowpack-streamflow dynamics in a way that supports analysis between and within watersheds. This study develops and tests a methodology for characterizing the snowpack-streamflow dynamics of a watershed using SCE-based metrics that capture the shape and key temporal inflections in the SCE curve – start of snow season, start of snow melt, end of snow season, and average SCE. The results demonstrate that SCE and streamflow sufficiently characterize snowpack-streamflow dynamics to allow for inter-watershed comparison and intra-watershed pattern recognition. The techniques developed and tested in this study allow for the characterization of snowpack-streamflow dynamics in remote and unmonitored watersheds to support future research into how those dynamics may change under future climate change scenarios.
remote sensing, snow cover extent, hysteresis, snowpack-streamflow dynamics, snow water equivalent, MODIS
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Van Osdel, Jennifer N.. "Assessing the power of remotely-sensed snow cover extent to improve understanding of snowpack-streamflow dynamics: an application of MODIS snow cover in Western U.S. mountain watersheds." (2018). https://digitalrepository.unm.edu/ce_etds/202