Effects of Assimilating SMAP Soil Moisture Product on the Accuracy of Surface and Root Zone Soil Moisture as Represented by Noah-MP in the State of Texas

Soil moisture plays a critical role in the terrestrial water cycle, and it in many cases regulates the magnitude of flood response. As in situ soil moisture sensors are sparsely distributed, it is necessary to complement the field observations with model and remotely sensed data. In this study, we experiment with assimilating SMAP L3 soil moisture into 4-km Noah 4.0.2 Land Surface Model (LSM) over the state of Texas for the 2015-2019 window, with the ultimate aim of establishing a convincing connection between the soil moisture anomalies and runoff production for major storms in the state. We perform two sets of assimilation experiments using the NASA Land Information System in which the SMAP L3 product is assimilated prior to, and after histogram bias correction. The soil moisture analyses derived from open and closed-loop simulations for 2017-2019 are validated against in situ soil moisture observations, including those of 14 NRCS SCAN stations and 4 stations managed by the Lower Colorado River Authority. The validation results indicate that assimilation of SMAP L3 product generally improves the surface soil moisture (i.e. at 0-10 cm depth), whereas the its impacts on root-zone soil moisture is mixed. In addition, histogram matching appears to degrade the accuracy of soil moisture analysis.