SWOT contribution to the understanding of global terrestrial water storage and fluxes through a multi-satellite data assimilation framework

Principle Investigator: Augusto Getirana (NASA Goddard Space Flight Center)

Co-Investigator(s): Sujay Kumar, Yeosang Yoon

Collaborator(s): Elyssa Collins, Simon Munier, Aaron Boone, Konstantinos Andreadis, Michael Durand, Wanshu Nie

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Among all the progress in surface hydrology SWOT will enable, there is a unique opportunity to integrate its data with NASA’s existing suite of Earth Observing satellite data products as an attempt to close the water cycle. It is expected that assimilating multi-satellite data into a holistic modeling framework can constrain the representation of global water storages and fluxes. This includes an improved representation of surface-subsurface water interactions, spatial and temporal water availability, and the impacts of human and climate on hydrological processes. Also, merging SWOT information with computational models’ fine temporal resolution through data assimilation (DA) techniques will enable the development of an improved, temporally continuous and spatially distributed record of hydrological products. This will allow us to monitor events, such as flash floods, that may not be detected by the satellite. Based on a multi-satellite DA framework, including SWOT and a wide range of NASA satellite missions and products, the primary objective of this proposal is to improve our understanding and representation of the impacts of surface water variability on global terrestrial water storage and fluxes, as well as its interactions with human activities and climate variability. To achieve this objective, our work is defined by three main elements: (1) integration of reach-based SWOT data into an existing DA framework; (2) optimization of localization techniques to improve SWOT DA; and (3) implementation of a pioneering multivariate DA scheme accounting for the simultaneous assimilation of SWOT data, soil moisture from the SMAP mission, terrestrial water storage (TWS) anomalies derived from the GRACE mission and leaf area index (LAI) from the MODIS mission. The hydrological model will be forced using meteorological data from NASA’s MERRA-2 and precipitation from NASA’s GPM. NASA’s Land Information System stands out as a unique tool to perform multi-model coupling and multivariate DA and will be the main tool used in this project.

We anticipate that the final hydrological modeling and multivariate DA system will provide an unprecedented representation of the terrestrial water storage variability and fluxes, and anthropogenic impacts. The primary scientific contribution expected from this proposal is a better understanding of pattern amplification and intensification of global hydroclimatic extremes and human activities. Other anticipated results from the proposed work are: (i) a refined global terrestrial water storage variability and composition (i.e., soil moisture, groundwater and snow); (ii) quantification of how reservoir operation impacts on the global water cycle; and (iii) improved understanding of the interactions between inland surface waters, soil, atmosphere and coasts.