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Streamflow is one of the key variables in the hydrological cycle. Simulation and forecasting of streamflow are challenging tasks for hydrologists, especially in sparsely gauged areas. Coarse spatial resolution remote sensing soil moisture products (equal to or larger than 9 km) are often assimilated into hydrological models to improve streamflow simulation in large catchments. This study uses the Ensemble Kalman Filter (EnKF) technique to assimilate SMAP soil moisture products at the coarse spatial resolution of 9 km (SMAP 9 km), and downscaled SMAP soil moisture product at the higher spatial resolution of 1 km (SMAP 1 km), into the Soil and Water Assessment Tool (SWAT) to investigate the usefulness of different spatial and temporal resolutions of remotely sensed soil moisture products in streamflow simulation and forecasting. The experiment was set up for eight catchments across the tropical climate of Vietnam, with varying catchment areas from 267 to 6430 km2 during the period 2017–2019. We comprehensively evaluated the EnKF-based SWAT model in simulating streamflow at low, average, and high flow. Our results indicated that high-spatial resolution of downscaled SMAP 1 km is more beneficial in the data assimilation framework in aiding the accuracy of streamflow simulation, as compared to that of SMAP 9 km, especially for the small catchments. Our analysis on the impact of observation resolution also indicates that the improvement in the streamflow simulation with data assimilation is more significant at catchments where downscaled SMAP 1 km has fewer missing observations. This study is helpful for adding more understanding of performances of soil moisture data assimilation based hydrological modelling over the tropical climate region, and exhibits the potential use of remote sensing data assimilation in hydrology. [ABSTRACT FROM AUTHOR]

Highlights • Coupled SMAR model with EnKF to update root zone soil moisture for improving streamflow. • Study involves both synthetic and real data experiments. • SMOS and ASCAT soil moisture retrievals are used for assimilation. • Proposed approach enhances the assimilation efficiency for streamflow simulations. • Streamflow simulations are improved but only to a moderate level. Abstract The present study investigates the potential of coupled Soil Moisture Analytical Relationship (SMAR) and Ensemble Kalman Filter (EnKF) based surface soil moisture data assimilation for improving the streamflow simulations. For this purpose, synthetic and real data assimilation experiments were carried out using Soil and Water Assessment Tool (SWAT) hydrological model in two different sub-catchments lying in the Krishna River basin, India. Here, the satellite-based surface soil moisture estimates from Soil Moisture and Ocean Salinity (SMOS) and Advanced Scatterometer (ASCAT) are used for assimilation. Results of the synthetic experiment show that the use of physically based SMAR scheme coupled with EnkF for updating profile soil moisture has better ability to improve the surface flow, groundwater flow and consequently streamflow over the covariance-based updates using EnKF only. Likewise, the real data assimilation experiment also shows SMAR-EnKF assimilation strategy performs better than the EnKF only updates for simulating streamflow. However, in both the synthetic as well as real data experiment, the improvements are only moderate. This restricted success in improving streamflow simulations indicate that updating only the soil moisture through any updating scheme adopted here is not sufficient to reduce the effect of the errors in model forcing on subsequent simulation days. [ABSTRACT FROM AUTHOR]

This article presents a study carried out using EnKF based assimilation of coarser-scale SMOS soil moisture retrievals to improve the streamflow simulations and forecasting performance of SWAT model in a large catchment. This study has been carried out in Munneru river catchment, India, which is about 10,156 km 2 . In this study, an EnkF based new approach is proposed for improving the inherent vertical coupling of soil layers of SWAT hydrological model during soil moisture data assimilation. Evaluation of the vertical error correlation obtained between surface and subsurface layers indicates that the vertical coupling can be improved significantly using ensemble of soil storages compared to the traditional static soil storages based EnKF approach. However, the improvements in the simulated streamflow are moderate, which is due to the limitations in SWAT model in reflecting the profile soil moisture updates in surface runoff computations. Further, it is observed that the durability of streamflow improvements is longer when the assimilation system effectively updates the subsurface flow component. Overall, the results of the present study indicate that the passive microwave-based coarser-scale soil moisture products like SMOS hold significant potential to improve the streamflow estimates when assimilating into large-scale distributed hydrological models operating at a daily time step. [ABSTRACT FROM AUTHOR]