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, land surface and hydrologic properties can be simulated and predicted by land surface models which provide a simplified representation of physical processes. However, an accurate prediction of these components, such as SM, ET, and streamflow, is highly dependent on the quality of model forcing data, the model parameters (measured or estimated through calibration), initial and boundary conditions, and model structure. For land surface and hydrologic models, the integration of data assimilation (DA
, land surface and hydrologic properties can be simulated and predicted by land surface models which provide a simplified representation of physical processes. However, an accurate prediction of these components, such as SM, ET, and streamflow, is highly dependent on the quality of model forcing data, the model parameters (measured or estimated through calibration), initial and boundary conditions, and model structure. For land surface and hydrologic models, the integration of data assimilation (DA
-zone soil moisture from satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multisource precipitation product. The data are available from 1980 to 2018 and can be downloaded from the project website ( https://www.gleam.eu/ ). b. Definitions of the Great Plains and the Southwest The analyses in this study largely focus on the Great Plains and the Southwest regions. Many previous studies have often given their own
-zone soil moisture from satellite-observed soil moisture, vegetation optical depth and snow-water equivalent, reanalysis air temperature and radiation, and a multisource precipitation product. The data are available from 1980 to 2018 and can be downloaded from the project website ( https://www.gleam.eu/ ). b. Definitions of the Great Plains and the Southwest The analyses in this study largely focus on the Great Plains and the Southwest regions. Many previous studies have often given their own
