Articles | Volume 24, issue 2
https://doi.org/10.5194/hess-24-615-2020
© Author(s) 2020. 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-24-615-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Feb 2020
Research article |
| 13 Feb 2020
| 13 Feb 2020
Dual state/rainfall correction via soil moisture assimilation for improved streamflow simulation: evaluation of a large-scale implementation with Soil Moisture Active Passive (SMAP) satellite data
Yixin Mao
Department of Civil & Environmental Engineering, University of
Washington, Seattle, WA, USA
Wade T. Crow
Hydrology and Remote Sensing Laboratory, Agricultural Research Service, USDA, Beltsville, MD, USA
Department of Civil & Environmental Engineering, University of
Washington, Seattle, WA, USA
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Cited
18 citations as recorded by crossref.
- Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China Y. Wen et al. https://doi.org/10.3390/w13202875
- Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. https://doi.org/10.23939/mmc2022.02.203
- Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions L. Lackoóvá et al. https://doi.org/10.3390/rs15133316
- Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China M. Cheng et al. https://doi.org/10.1016/j.agwat.2021.107298
- Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al. https://doi.org/10.1038/s41598-024-77050-0
- Runoff simulation of Mengjiang River Basin based on distributed hydrological model X. Chen et al. https://doi.org/10.1051/e3sconf/202562802007
- Combining physical models and machine learning for enhanced soil moisture estimation M. Li et al. https://doi.org/10.1016/j.compag.2026.111480
- Hydrologically relevant bias correction of GPM-IMERG: Performance across quantiles in upland tropical watershed E. Suhartanto et al. https://doi.org/10.1016/j.rineng.2026.110760
- On the relation between antecedent basin conditions and runoff coefficient for European floods C. Massari et al. https://doi.org/10.1016/j.jhydrol.2023.130012
- Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information J. Zhou et al. https://doi.org/10.1016/j.rse.2022.113161
- Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations R. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3457941
- Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning M. Cheng et al. https://doi.org/10.1016/j.agwat.2022.107530
- Advances in remote sensing techniques for surface soil moisture estimation: A systematic review of recent developments (2019–2024) M. Rawat et al. https://doi.org/10.1016/j.compag.2026.111983
- Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model C. Meng et al. https://doi.org/10.3103/S1068373924060037
- Earth data assimilation in hydrologic models: recent advances S. Jeyalakshmi et al. https://doi.org/10.1080/00207233.2021.1875303
- Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. https://doi.org/10.3389/frwa.2022.981745
- Soil Moisture Data Assimilation in MISDc for Improved Hydrological Simulation in Upper Huai River Basin, China Z. Ding et al. https://doi.org/10.3390/w14213476
- Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. https://doi.org/10.1016/j.jhydrol.2023.130574
18 citations as recorded by crossref.
- Evaluation of the RF-Based Downscaled SMAP and SMOS Products Using Multi-Source Data over an Alpine Mountains Basin, Northwest China Y. Wen et al. https://doi.org/10.3390/w13202875
- Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm O. Kozhushko et al. https://doi.org/10.23939/mmc2022.02.203
- Unlocking the Potential of Remote Sensing in Wind Erosion Studies: A Review and Outlook for Future Directions L. Lackoóvá et al. https://doi.org/10.3390/rs15133316
- Using multimodal remote sensing data to estimate regional-scale soil moisture content: A case study of Beijing, China M. Cheng et al. https://doi.org/10.1016/j.agwat.2021.107298
- Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques M. Tahmouresi et al. https://doi.org/10.1038/s41598-024-77050-0
- Runoff simulation of Mengjiang River Basin based on distributed hydrological model X. Chen et al. https://doi.org/10.1051/e3sconf/202562802007
- Combining physical models and machine learning for enhanced soil moisture estimation M. Li et al. https://doi.org/10.1016/j.compag.2026.111480
- Hydrologically relevant bias correction of GPM-IMERG: Performance across quantiles in upland tropical watershed E. Suhartanto et al. https://doi.org/10.1016/j.rineng.2026.110760
- On the relation between antecedent basin conditions and runoff coefficient for European floods C. Massari et al. https://doi.org/10.1016/j.jhydrol.2023.130012
- Improving soil moisture assimilation efficiency via model calibration using SMAP surface soil moisture climatology information J. Zhou et al. https://doi.org/10.1016/j.rse.2022.113161
- Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations R. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3457941
- Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning M. Cheng et al. https://doi.org/10.1016/j.agwat.2022.107530
- Advances in remote sensing techniques for surface soil moisture estimation: A systematic review of recent developments (2019–2024) M. Rawat et al. https://doi.org/10.1016/j.compag.2026.111983
- Variational Assimilation of the SMAP Surface Soil Moisture Retrievals into an Integrated Urban Land Model C. Meng et al. https://doi.org/10.3103/S1068373924060037
- Earth data assimilation in hydrologic models: recent advances S. Jeyalakshmi et al. https://doi.org/10.1080/00207233.2021.1875303
- Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. https://doi.org/10.3389/frwa.2022.981745
- Soil Moisture Data Assimilation in MISDc for Improved Hydrological Simulation in Upper Huai River Basin, China Z. Ding et al. https://doi.org/10.3390/w14213476
- Assimilation of remotely sensed evapotranspiration products for streamflow simulation based on the CAMELS data sets C. Deng et al. https://doi.org/10.1016/j.jhydrol.2023.130574
Saved (final revised paper)
Latest update: 24 Jul 2026
Short summary
The new generation of satellite soil moisture observations are used to correct the streamflow in a regional-scale river basin simulated by a mathematical model. The correction is done via both the direct updating of soil moisture and correction of rainfall input. Results show some streamflow improvement, but the magnitude is small. A larger improvement will need future generations of even higher-quality satellite soil moisture data and better process representation in the mathematical model.
The new generation of satellite soil moisture observations are used to correct the streamflow in...
