Data assimilation: bridging the gap between Earth observations and hydrologic modelling

Date & time

4–5pm 27 March 2018


Jaeger 1 Seminar Room, RSES


Ms Siyuan Tian (RSES)

Event series


 Antony Burnham

Water availability is critical in planning agricultural decisions, forecasting droughts and land management. With accurate knowledge of root-zone soil water dynamics, skilful forecast of vegetation conditions can be made months in advance for most of the world’s dryland. However, the forecast potential remains largely unexplored due to the lack of observations at large scale and the low fidelity of hydrological model. For the first time, contrasting satellite observations of water presence over different vertical domains have been assimilated into a water balance model and provide unprecedented accuracy of soil moisture profile and groundwater storage estimates.


The water availability at different depths observed from soil moisture (SMOS) and surface gravity (GRACE) missions provides an opportunity to separate total water storage vertically into different layers through data assimilation. However, combining these two data sets is challenging due to the disparity in temporal and spatial resolution at both vertical and horizontal scale. SMOS provides global high spatial (i.e. 40 km2) and temporal resolution soil moisture estimates from microwave brightness temperature observations. In contrast with microwave-based satellite missions that are only sensitive to near-surface soil moisture (0 - 5 cm), the GRACE mission provides accurate measurements of the entire vertically integrated terrestrial water storage column, but it is characterized by low spatial (i.e. 300 km2) and temporal resolutions. The use of data assimilation integrates these two measurements to effectively constrain model simulations and accurately characterizing vertical distribution of water storage.

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