Multivariate calibration of the SWAT model using remotely sensed datasets

Authors: DANGOL, S. - University Of Maryland; Zhang, Xuesong; LIANG, XIN-ZHONG - George Mason University; Anderson, Martha; Crow, Wade; LEE, S. - University Of Seoul; Moglen, Glenn; McCarty, Gregory

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/3/2023
Publication Date: 5/5/2023
Citation: Dangol, S., Zhang, X., Liang, X., Anderson, M.C., Crow, W.T., Lee, S., Moglen, G.E., McCarty, G.W. 2023. Multivariate calibration of the SWAT model using remotely sensed datasets. Remote Sensing. 15(9):2417. https://doi.org/10.3390/rs15092417.
DOI: https://doi.org/10.3390/rs15092417

Interpretive Summary: The value of remote sensing data products for improving hydrologic models receives wide attention. Here, we used a computer model called the Soil and Water Assessment Tool (SWAT) to test different combinations of remotely sensed evapotranspiration and soil moisture to improve hydrologic modeling. We found that using the extra remotely sensed evapotranspiration and soil moisture for model calibration could change the results of the computer model (e.g., runoff and groundwater), but using remotely sensed data didn't always result in better calibration outcomes as compared with calibration using streamflow data alone. When we used the evapotranspiration or soil moisture data on their own, the model didn't work as well as when we used only the streamflow data. We also compared two versions of the SWAT models and found that they worked differently depending on the type of data used for calibration. We recommend caution when using additional remote sensed data and exploring different ways of setting up the computer model to ensure the results are accurate.

Technical 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 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) carefully examining the quality of remotely sensed data before using them to complement the traditionally used streamflow data for model calibration and (2) different model structures should be considered in the model calibration process to support robust hydrologic modeling.