Title: A New Snow Observing Strategy in Support of Hydrological Science and Applications
Presenting Author: Carrie Vuyovich
Organization: NASA Goddard Space Flight Center
Co-Author(s): Sujay V. Kumar, Ethan Gutmann, Kwo-Sen Kuo, Paul Grogan, Batuhan Osmanoglu, Mark Carroll, Melissa L. Wrzesien, Michael Bauer, Dai-Hai Ton That, Niklas Griessbaum, Bob Rosenberg, Josue Tapia, Hadis Banafsheh

Abstract:
Snow accumulation is a seasonally evolving process that results in a reflective, insulating cover over the Earth’s land mass, provides water supply to billions of people and supports numerous ecosystems. Snow melt also contributes to short-term and long-term disasters. Snow is a critical storage component of the global water cycle, yet no satellite currently provides global snow water equivalent (SWE) data, the essential information to address hydrologic science questions, at the frequency, resolution and accuracy needed. While snow contributes water resources to a large portion of the Earth’s terrestrial area, its coverage and role evolves throughout the season, affecting different regions, elevations and latitudes at different times of the year. The data needs also shift throughout the year. Seasonal snow is an ideal candidate for an optimized observational strategy that leverages existing sensors and focuses future mission concepts on monitoring the most critical areas to provide cost-effective and robust information. In this project we merge multiple technologies to develop a hypothetical experiment and demonstrate a potential snow observing strategy that utilizes multiple diverse data to improve basin-wide SWE and streamflow forecasts. We developed metrics to trigger an additional and/or taskable observations response when observations meet a certain threshold in comparison to the climatology. We assess the value of new potential sensors, such as from commercial smallsats to fill observing gaps and provide higher frequency observation during critical time periods. We also evaluate the potential for focusing higher density observations in regions where concerns for flood, drought or wildfires will benefit from early warning. These dynamic observations could inform use of ground, UAS or airborne observations in regions showing snow volumes outside the normal range or experiencing unexpected snowpack conditions.