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

Abstract

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.

Validation of downscaled 1‐km SMOS and SMAP soil moisture data in 2010–2021

“…streamflow simulation in tropical region using data assimilation techniques (Le et al, 2022;Tran et al, 2023), and evaluation of impact of SM on dust outbreaks in East Asia (Kim et al, 2015).…”

“…Remotely sensed SM products have been used in many studies. For instance, SM data were used for monitoring drought conditions from climatological and ecosystem aspects (Aghakouchak et al., 2015), evaluation of drought by calculating soil water deficit indices (Martínez‐Fernández et al., 2016; Fang, Lakshmi, et al., 2021), evaluation of several applications in climate model (Loew et al., 2013), improving streamflow simulation in tropical region using data assimilation techniques (Le et al., 2022; Tran et al., 2023), and evaluation of impact of SM on dust outbreaks in East Asia (Kim et al., 2015).…”

Uncertain Benefits of Using Remotely Sensed Evapotranspiration for Streamflow Estimation-Insights from a Randomized, Large-Sample Experiment

“…Therefore, instead of calibrating our models against streamflow and/or ET, we generated 10,000 random parameter sets for each catchment using the random Latin Hypercube Sampling (LHS) approach. The parameters and their ranges (Table 3) were selected based on our literature review of the most frequently used parameters for either ET or streamflow calibration (Neitsch et al, 2011;Nguyen et al, 2022a;Nguyen et al, 2020;Tobin & Bennett, 2017;Odusanya et al, 2019;Le et al, 2022). Including both ET-and streamflow-sensitive parameters allowed us to explore the uncertainty in streamflow when the models are calibrated for ET, and vice versa.…”

Quantification of Gridded Precipitation Products for the Streamflow Simulation on the Mekong River Basin Using Rainfall Assessment Framework: A Case Study for the Srepok River Subbasin, Central Highland Vietnam

“…This study provides deeper insight into using the not-yet-evaluated MSWEP and SM2RAIN products and their performances in streamflow simulations under the operation of newly built reservoirs. Studies performing assimilation of satellite soil moisture into the SWAT model [51] and Land Information System (LIS) model [52] are still in their early stages.…”