From space-borne snow observation to seamless snow water resource information: making the most of cutting-edge snow measurements from space
Abstract
Snow is a critical water resource worldwide. However, estimating snow water equivalent (SWE) from space, which is arguably the only viable way to consistently quantify the snow water resource globally, remains elusive. A recent review of remote sensing in hydrology thus noted that among all areas of hydrologic remote sensing, SWE is the most in need of new strategic thinking from the hydrology community. In recent years, the new thinking to make inroads into snow remote sensing has gradually converged to measuring snow depth (SD) first, and subsequently infer SWE from the measured SD, mainly because the technological breakthroughs have enabled accurate measurements of SD and that SD entails most of the SWE variation. The 2017 Decadal Survey for Earth Science and Applications from Space (ESAS 2017) thus categorizes SD and SWE among the candidate variables with the highest priority for space mission planning in the coming decade. Due to the importance of snow as a water resource, the most significant utility and value of future snow satellite missions can only be attained when the observed SD is transformed to accurate information of SWE and streamflow, which are most indicative of the snow water resource availability. However, there are two challenges involve in this transformation: (1) Satellite SD observations will likely be collected along tracks (rather than images) and thus the observations will be spatiotemporally discontinuous. In contrast, SWE distribution and streamflow generation are space-time continuous, so the between-track and between-overpass gaps in the observations will need to be filled; (2) SD observations will need to be converted to SWE and streamflow. Here, we present our ongoing work that aims to develop a data assimilation (DA) system that will optimally convert space-based SD observations to seamless SWE and streamflow information. The DA system consists of two major components; it first transforms track-based SD measurements to space-time continuous SWE, and then directly inserts the posterior SWE to re-initialize a model-based ensemble streamflow forecasting. We review recent work in our group and by others that addresses the above two challenges, and provide an outlook of near- and longer-term challenges.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2021
- Bibcode:
- 2021AGUFM.H54E..01L