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In Situ Soil Moisture Sensors in Undisturbed Soils
Published on: November 18, 2022
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Late-fall satellite-based soil moisture observations show clear connections to subsequent spring streamflow
Randal D Koster1, Qing Liu2,3, Wade T Crow4
1Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA. randal.d.koster@nasa.gov.
Nature Communications
|June 15, 2023
Summary
Satellite soil moisture data can improve seasonal streamflow forecasts. Late-fall surface soil moisture estimates from the Soil Moisture Active Passive satellite show a strong connection to springtime river flow, offering a new prediction tool.
Area of Science:
- Hydrology
- Remote Sensing
- Environmental Science
Background:
- Runoff generation is more efficient on wetter soils.
- Soil moisture exhibits memory, influencing hydrological processes.
- Accurate streamflow prediction is crucial for water resource management.
Purpose of the Study:
- To assess the potential of satellite-derived soil moisture for seasonal streamflow prediction.
- To investigate the relationship between late-fall surface soil moisture and subsequent springtime streamflow.
- To compare satellite soil moisture data with reanalysis products for predictive skill.
Main Methods:
- Utilized surface (0-5 cm) soil moisture retrievals from NASA's Soil Moisture Active Passive (SMAP) satellite.
- Analyzed streamflow data from 236 unregulated river basins in the conterminous United States.
- Correlated late-fall SMAP soil moisture estimates with subsequent springtime streamflow measurements.
Main Results:
- A strong correlation was found between late-fall satellite-based surface soil moisture and springtime streamflow.
- Satellite soil moisture retrievals alone demonstrated the potential for skillful seasonal streamflow predictions.
- In data-scarce regions, satellite data may outperform reanalysis soil moisture products for streamflow forecasting.
Conclusions:
- Satellite-derived surface soil moisture is a valuable predictor of seasonal streamflow.
- SMAP data offers a promising, independent method for advancing hydrological forecasting capabilities.
- This approach can enhance streamflow prediction accuracy, especially in areas with limited ground-based instrumentation.
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