Assimilation versus optimization for SWAT calibration: accuracy, uncertainty, and computational burden analysis

Abstract The accurate estimation of runoff by hydrological models depends on proper model calibration. Sequential Data Assimilation (DA), as an online method, is used to estimate complex models' states and parameters simultaneously. Although DA was applied for estimating the Soil Water Assessment Tool (SWAT) model's state and/or parameter, previous research did not pay attention calibration or comparison between popular SWAT methods. This paper compares Ensemble Kalman Filter (EnKF), a well-known with Uncertainty Fitting (SUFI2), calibrate model. We test impact selected objective function in SUFI2 application. evaluate results based multiple deterministic uncertainty-based Goodness Fit (GOF) measures compare all scenarios simulation accuracy, computational burden, uncertainty assessment, parameter ranges. Results show that under application, some GOFs might be located unsatisfactory ranges while algorithm obtains (very) good concerning functions. On other hand, EnKF simultaneously locates most ratings. Moreover, we found selection SUFI2's specification uncertainty's error have significant effects results.