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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2021
Research article |
| 24 Aug 2021
| 24 Aug 2021
Satellite soil moisture data assimilation for improved operational continental water balance prediction
Fenner School of Environment & Society, Australian National
University, Canberra, 2601, Australia
Luigi J. Renzullo
Fenner School of Environment & Society, Australian National
University, Canberra, 2601, Australia
Robert C. Pipunic
Bureau of Meteorology, Melbourne, 3000, Australia
Julien Lerat
Bureau of Meteorology, Melbourne, 3000, Australia
Wendy Sharples
Bureau of Meteorology, Melbourne, 3000, Australia
Chantal Donnelly
Bureau of Meteorology, Melbourne, 3000, Australia
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Cited
14 citations as recorded by crossref.
- Global evaluation of terrestrial evaporation trend from diagnostic products F. Ruan et al. 10.1016/j.jhydrol.2025.132979
- An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation P. Deb et al. 10.1016/j.agrformet.2022.108982
- Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture L. Zhu et al. 10.3390/rs16071225
- High spatio-temporal resolution soil moisture nowcasting at multiple depths with data-driven approaches Y. Zhang et al. 10.1016/j.agwat.2025.109457
- Flood Hazard Assessment in Australian Tropical Cyclone-Prone Regions M. Kaspi & Y. Kuleshov 10.3390/cli11110229
- Detecting groundwater dependence and woody vegetation restoration with NDVI and moisture trend analyses in an Indonesian karst savanna P. Godwin et al. 10.3389/frsen.2024.1280712
- Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation K. Mohammed et al. 10.3390/w15020321
- A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series R. Qin et al. 10.3390/app12157414
- Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale X. Xing et al. 10.3390/rs15030676
- Geodetic first order data assimilation using an extended Kalman filtering technique I. Kalu et al. 10.1007/s12145-022-00869-6
- Improving continental hydrological models for future climate conditions via multi-objective optimisation W. Sharples et al. 10.1016/j.envsoft.2024.106018
- Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. 10.1029/2021WR031649
- Development and preliminary validation of a land surface image assimilation system based on the Common Land Model W. Shen et al. 10.5194/gmd-17-3447-2024
- The effects of satellite soil moisture data on the parametrization of topsoil and root zone soil moisture in a conceptual hydrological model M. Kuban et al. 10.2478/johh-2022-0021
14 citations as recorded by crossref.
- Global evaluation of terrestrial evaporation trend from diagnostic products F. Ruan et al. 10.1016/j.jhydrol.2025.132979
- An ensemble data assimilation approach to improve farm-scale actual evapotranspiration estimation P. Deb et al. 10.1016/j.agrformet.2022.108982
- Regional Assessment of Soil Moisture Active Passive Enhanced L3 Soil Moisture Product and Its Application in Agriculture L. Zhu et al. 10.3390/rs16071225
- High spatio-temporal resolution soil moisture nowcasting at multiple depths with data-driven approaches Y. Zhang et al. 10.1016/j.agwat.2025.109457
- Flood Hazard Assessment in Australian Tropical Cyclone-Prone Regions M. Kaspi & Y. Kuleshov 10.3390/cli11110229
- Detecting groundwater dependence and woody vegetation restoration with NDVI and moisture trend analyses in an Indonesian karst savanna P. Godwin et al. 10.3389/frsen.2024.1280712
- Impacts of Spatiotemporal Gaps in Satellite Soil Moisture Data on Hydrological Data Assimilation K. Mohammed et al. 10.3390/w15020321
- A Kalman Filter-Based Method for Reconstructing GMS-5 Land Surface Temperature Time Series R. Qin et al. 10.3390/app12157414
- Soil Moisture Assimilation Improves Terrestrial Biosphere Model GPP Responses to Sub-Annual Drought at Continental Scale X. Xing et al. 10.3390/rs15030676
- Geodetic first order data assimilation using an extended Kalman filtering technique I. Kalu et al. 10.1007/s12145-022-00869-6
- Improving continental hydrological models for future climate conditions via multi-objective optimisation W. Sharples et al. 10.1016/j.envsoft.2024.106018
- Spatio‐Temporal Model Variance‐Covariance Approach to Assimilating Streamflow Observations Into a Distributed Landscape Water Balance Model S. Tian et al. 10.1029/2021WR031649
- Development and preliminary validation of a land surface image assimilation system based on the Common Land Model W. Shen et al. 10.5194/gmd-17-3447-2024
- The effects of satellite soil moisture data on the parametrization of topsoil and root zone soil moisture in a conceptual hydrological model M. Kuban et al. 10.2478/johh-2022-0021
Latest update: 23 Apr 2025
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.
Accurate daily continental water balance predictions are valuable in monitoring and forecasting...
