Next Article in Journal
Water–Energy–Food Nexus Simulation: An Optimization Approach for Resource Security
Previous Article in Journal
An Analysis of the Factors Affecting Hyporheic Exchange based on Numerical Modeling
Previous Article in Special Issue
Optimal Energy Recovery from Water Distribution Systems Using Smart Operation Scheduling
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle

Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation

State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
Author to whom correspondence should be addressed.
Water 2019, 11(4), 666;
Received: 18 February 2019 / Revised: 27 March 2019 / Accepted: 28 March 2019 / Published: 31 March 2019
PDF [5351 KB, uploaded 31 March 2019]


With the increased availability of remote sensing products, more hydrological variables (e.g., soil moisture and evapotranspiration) other than streamflow data are introduced into the calibration procedure of a hydrological model. However, how the incorporation of these hydrological variables influences the calibration results remains unclear. This study aims to analyze the impact of remote sensing soil moisture data in the joint calibration of a distributed hydrological model. The investigation was carried out in Qujiang and Ganjiang catchments in southern China, where the Dem-based Distributed Rainfall-runoff Model (DDRM) was calibrated under different calibration schemes where the streamflow data and the remote sensing soil moisture are assigned to different weights in the objective function. The remote sensing soil moisture data are from the SMAP L3 soil moisture product. The results show that different weights of soil moisture in the objective function can lead to very slight differences in simulation performance of soil moisture and streamflow. Besides, the joint calibration shows no apparent advantages in terms of streamflow simulation over the traditional calibration using streamflow data only. More studies including various remote sensing soil moisture products are necessary to access their effect on the joint calibration. View Full-Text
Keywords: SMAP; remote sensing; distributed hydrological model; joint calibration SMAP; remote sensing; distributed hydrological model; joint calibration

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Xiong, L.; Zeng, L. Impacts of Introducing Remote Sensing Soil Moisture in Calibrating a Distributed Hydrological Model for Streamflow Simulation. Water 2019, 11, 666.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top