Satellite soil moisture data assimilation for improved  operational continental water balance prediction

Tian, Siyuan; Renzullo, Luigi J.; Pipunic, Robert C.; Lerat, Julien; Sharples, Wendy; Donnelly, Chantal

doi:https://doi.org/10.5194/hess-25-4567-2021

Articles | Volume 25, issue 8

https://doi.org/10.5194/hess-25-4567-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-4567-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 8

Research article

|

24 Aug 2021

Research article |

| 24 Aug 2021

Satellite soil moisture data assimilation for improved operational continental water balance prediction

Siyuan Tian, Luigi J. Renzullo, Robert C. Pipunic, Julien Lerat, Wendy Sharples, and Chantal Donnelly

Data sets

SMAP Enhanced L2 Radiometer Half-Orbit 9 km EASE-Grid Soil Moisture, Version 4 O'Neill, P. E., S. Chan, E. G. Njoku, T. Jackson, R. Bindlish, and J. Chaubell https://doi.org/10.5067/Q8J8E3A89923

MODIS/Aqua Vegetation Indices Monthly L3 Global 0.05Deg CMG V061 K. Didan https://doi.org/10.5067/MODIS/MYD13C2.061

Dynamic Land Cover Dataset Version 2.1 L. Lymburner, P. Tan, A. McIntyre, M. Thankappan, and J. Sixsmith http://pid.geoscience.gov.au/dataset/ga/83868

Model code and software

Community Modelling System (Frost et al. 2018) AWRA-CMS - The Australian Water Resources Assessment https://github.com/awracms/awra_cms

Short summary

Accurate daily continental water balance predictions are valuable in monitoring and forecasting water availability and land surface conditions. A simple and robust method was developed for an operational water balance model to constrain model predictions temporally and spatially with satellite soil moisture observations. The improved soil water storage prediction can provide constraints in model forecasts that persist for several weeks.