Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets

Abstract

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.

Combining Hydrological Models and Remote Sensing to Characterize Snowpack Dynamics in High Mountains

“…In other words, it may be challenging to prove that SWE improvements directly lead to better streamflow predictions because the initial data used for validation contains uncertainties. The results of employing a multivariate calibration approach have strengthened modelling accuracy in specific cases, as noted by [38]. However, in other instances, researchers have reported either minimal improvements or even substantial declines [12,39,41].…”

“…Multi-variable calibration schemes have been proposed as useful means to address model equifinality [35,36] and reduce prediction uncertainty [37]. However, results from multivariate calibration approach have enhanced the power of model simulation in some cases [38], while others have reported poor model performance or even substantial deteriorations [39][40][41]. There could be multiple reasons for these contradictory findings such as differences in model structures, parameterization, wide variety of remote sensing datasets or case study specific.…”

Advancements in Remote Sensing for Evapotranspiration Estimation: A Comprehensive Review of Temperature-Based Models

“…Comparisons with the SWAT-Carbon model reveal significant performance differences. The study suggests evaluating multiple remote sensing options for calibration and considering different model structures for robust hydrologic modeling [74].…”

Spatial calibration and uncertainty reduction of the SWAT model using multiple remotely sensed data

“…RS-ET products are frequently combined with streamflow data to find optimal hydrologic parameter values during model calibration [ [14] , [15] , [16] , [17] , [18] , [19] ]. The concurrent utilization of streamflow and RS-ET products has been shown to effectively constrain parameter values and decrease parameter uncertainty [ [14] , [15] , [16] , [17] , [18] ].…”