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.
Highlights
In past decades, numerous hydrological models have been developed and implemented in the field of flood forecasting and water resources management
To account for the spatial heterogeneity of topography and its impact on hydrological process, distributed rainfall-runoff model (DDRM) assumes that the soil moisture capacity (SMC) of each cell is related to the corresponding topographic index, TIi
This study incorporated the Soil Moisture Active Passive (SMAP) soil moisture and streamflow data into the joint calibration of the DEM-based distributed rainfall-runoff model (DDRM) and analyzed the simulation performance of soil moisture and streamflow under 10 different calibration schemes, where the SMAP soil moisture data are assigned to different weights in the objective function
Summary
Numerous hydrological models have been developed and implemented in the field of flood forecasting and water resources management. For most conceptual hydrological models, one or more of their parameters are designed to represent mechanisms that are either poorly understood or too computationally expensive to resolve [3]. These model parameters are generally unmeasurable from catchment conditions and usually obtained from calibration procedures. As the observed outlet hydrograph is the result of interactions of numerous complex hydrological processes within a catchment, several parameter combinations that produce reasonable simulation results are possible during calibration (i.e., equifinality [4]). Other hydrologic variables (e.g., surface flow and soil moisture) may be inaccurately reproduced with model parameters calibrated against only the observed streamflow hydrograph
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