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

Manh-Hung Le; Binh Quang Nguyen; Hung T. Pham; Amol Patil; Hong Xuan Do; RAAJ Ramsankaran; John D. Bolten; Venkataraman Lakshmi

doi:10.3390/rs14071607

Remote Sensing (Mar 2022)

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

Manh-Hung Le,
Binh Quang Nguyen,
Hung T. Pham,
Amol Patil,
Hong Xuan Do,
RAAJ Ramsankaran,
John D. Bolten,
Venkataraman Lakshmi

Affiliations

Manh-Hung Le: Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA
Binh Quang Nguyen: Faculty of Water Resources Engineering, The University of Danang—University of Science and Technology, Da Nang 550000, Vietnam
Hung T. Pham: Faculty of Water Resources Engineering, The University of Danang—University of Science and Technology, Da Nang 550000, Vietnam
Amol Patil: Chair of Regional Climate and Hydrology, Institute of Geography, University of Augsburg, 86159 Augsburg, Germany
Hong Xuan Do: Faculty of Environment and Natural Resources, Nong Lam University, Ho Chi Minh City 700000, Vietnam
RAAJ Ramsankaran: Hydro-Remote Sensing Applications (H-RSA) Group, Department of Civil Engineering, Indian Institute of Technology, Mumbai 400076, Maharashtra, India
John D. Bolten: Hydrological Sciences Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
Venkataraman Lakshmi: Department of Engineering Systems and Environment, University of Virginia, Charlottesville, VA 22904, USA

DOI: https://doi.org/10.3390/rs14071607
Journal volume & issue: Vol. 14, no. 7
p. 1607

Abstract

Read online

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.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

About the journal

Abstract

Keywords