Assimilation of SSM into hydrological models: comparing assimilation in higher spatial resolution (1km, few-daily) vs. higher temporal resolution (25km, daily)

Surface Soil Moisture (SSM) is a key variable in terrestrial hydrology, governing land–atmosphere exchanges of water and energy, influencing runoff generation, and mediating interactions with deeper soil layers and groundwater recharge. Accurate representation of SSM within land surface and hydrological models is critical for simulating these processes realistically. To achieve this, operational hydrometeorological systems assimilate satellite-derived SSM observations into models.

Looking ahead, next-generation hydrological modeling aims to develop “digital twins” of Earth’s water cycle, high-resolution (≤1 km), physically consistent systems that integrate advanced models with spaceborne observations through Data Assimilation (DA). However, implementing SSM DA at such fine spatial scales raises fundamental questions. For instance, the benefits and limitations of assimilating high-resolution (1 km) SSM products remain poorly understood. Furthermore, it is unclear how DA performance compares when using high-resolution but temporally sparse observations (e.g., every few days) versus coarser-resolution data available daily.

This study addresses these gaps by conducting two DA experiments: (i) assimilation of ASCAT-derived SSM at 25 km resolution with daily availability, and (ii) assimilation of Sentinel-1-derived SSM at 1 km resolution with a few-day revisit. Both experiments employ the World Wide Water Resources Assessment (W3RA) hydrological model, downscaled to operate at 1 km daily resolution. The Ebro River basin (Iberian Peninsula) serves as the testbed, chosen for its availability of 1 km precipitation forcing, in-situ discharge observations, and significant irrigation activity, which is an anthropogenic factor not explicitly represented in W3RA but potentially captured through SSM DA. Assimilation is implemented via a localized Ensemble Kalman Filter (EnKF).

Evaluation is carried out in three stages: (1) comparison of DA outputs against assimilated observations to assess assimilation skill; (2) analysis of spatial and temporal variability in SSM estimates to quantify DA method’s downscaling capability; and (3) validation of simulated river runoff against independent discharge measurements. Through this comparative framework, the study aims to elucidate the trade-offs, benefits, and challenges of high-resolution SSM DA for operational hydrological modeling.