Reducing Hydrological Uncertainty: A Multi-Variable SWAT Calibration Using Global AET Products

Parameter equifinality remains a central challenge in hydrological modeling, limiting the reliability of process-based tools such as the Soil and Water Assessment Tool (SWAT). This study evaluates how multi-variable calibration strategies that combine in-situ streamflow with five remote sensing global actual evapotranspiration (RSAET) products (GLEAM v3.6, GLEAM v4.2, ETMonitor, PML, and SSEBop) can reduce equifinality, using the Essaouira watershed (Morocco) as a study case. A total of 10,000 Monte Carlo simulations were performed, from which the 100 best-performing parameter sets were selected for posterior uncertainty assessment. A Composite Identifiability Score (CIS) was developed by integrating normalized metrics of standard deviation, entropy, peak-to-width ratio, and Kullback-Leibler divergence to quantify parameter identifiability.

Results show that streamflow-only calibration (S0) yields the highest CIS, confirming the strong constraining power of discharge on routing and runoff parameters. However, multi-variable calibration further reduces equifinality for several soil–plant–atmosphere parameters, with the Streamflow + GLEAM v3.6 configuration achieving the highest multi-source CIS, followed by SSEBop and GLEAM v4.2. In terms of performance, streamflow-only scenarios achieve the highest NSE and lowest PBIAS, while hybrid streamflow–AET calibrations maintain strong predictive skill and improve the physical consistency of ET-related processes. In contrast, AET-only calibrations exhibit poor runoff-volume accuracy and large water-balance inconsistencies.

Overall, integrating complementary AET datasets with discharge observations enhances parameter identifiability, constrains key hydrological processes, and mitigates equifinality. This demonstrates a practical pathway to strengthen SWAT model robustness in data-scarce regions, as is the case for many African basins. These results are preliminary, and ongoing work aims to consolidate them by extending the calibration and identifiability framework to include soil-moisture remote-sensing products, with the goal of further constraining soil-water dynamics and reducing remaining model uncertainties.