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

Abstract

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.

Analysis of Nitrate Pollution Pathways on a Vulnerable Agricultural Plain in Slovenia: Taking the Local Approach to Balance Ecosystem Services of Food and Water

“…As the leaching of nitrate through soil is dependent on movement of soil water, a soil moisture calibration was implemented. Calibration of the model with satellite sensed soil moisture data has been getting more attention in recent years [30], but the measured data comes in quite rough resolutions; thus, we used data from time-domain reflectometry (TDR) probes (several probes in each trial location), similar to what was described before [15]. Probes were installed at 30 cm depth in the three field trial locations described earlier.…”

Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

“…Satellite-based observations have been incorporated into different model calibrations to constrain the spatially-distributed hydrologic model behavior towards reliable physics [3,6,10,12]. In this study, we sought an answer to the question whether satellite-based products can also be helpful for guiding lumped models throughout the calibration, an issue which has been rarely investigated [2].…”

“…Zink et al [10] used remotely-sensed temperature data to constrain a spatially-distributed model, and could succeed in reducing the errors in predicting actual evapotranspiration by 8%, while the model streamflow performance slightly decreased by 6%, showing the trade-off between different objectives when applied in a multi-objective calibration framework. Xiong et al [12] assessed the impact of the remotely-sensed soil moisture active passive level 3 product (SMAP L3) in a multi-objective calibration of a distributed hydrological model in the Qujiang and Ganjiang catchments in China. They documented minor improvements in the simulation performance of soil moisture and streamflow when adding the SMAP L3 product, which could be due to the short length of this high-resolution soil moisture product.…”

Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration

“…Satellite-based observations have been incorporated in different model calibrations to constrain the spatially distributed hydrologic model behaviour towards reliable physics [3,6,10,12]. In this study, we sought an answer to the question whether satellite based products can also be helpful for guiding lumped models throughout the calibration, an issue which has been rarely investigated [2].…”

“…Zink et al [10] used remotely sensed temperature data to constrain a spatially distributed model and could succeed to reduce the errors in predicting actual evapotranspiration by 8% while the model streamflow performance slightly decreased by 6% showing the trade-off between different objectives when applied in a multi-objective calibration framework. Xiong et al [12] assessed the impact of the remotely sensed soil moisture active passive level 3 product (SMAP L3) in a multi-objective calibration of a distributed hydrological model in Qujiang and Ganjiang catchments in China. They documented minor improvements in simulation performance of soil moisture and streamflow when adding the SMAP L3 product, which could be due to the short length of this high-resolution soil moisture product.…”