Hypothetical experiment to test new snow observing strategy

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 study we developed a hypothetical experiment to 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 a targeted observational strategy where concerns for flood, drought or wildfires will benefit from early warning. We present results of the hypothetical experiment over a test domain in California which has experienced record snowfall and drought conditions in recent years.