Assimilation of SMAP Products for Improving Streamflow Simulations over Tropical Climate Region—Is Spatial Information More Important Than Temporal Information?

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