Evaluation of Multi- and Many-Objective Optimization Techniques to Improve the Performance of a Hydrologic Model Using Evapotranspiration Remote-Sensing Data

In this study, we explore the use of different multi- and many-objective calibration approaches in hydrological modeling when considering both observed streamflow and remotely sensed actual evapotranspiration (ETa). Eight remotely sensed ETa and an Ensemble products were used in a watershed in Michigan. Regarding the calibration process, the Unified-Non-dominated Sorting Genetic Algorithm III was integrated with the soil and water assessment tool (SWAT). The first nine calibrations used a multi-objective approach with two variables, one being streamflow and the other being a remotely sensed ETa products/Ensemble. The tenth calibration was a many-objective calibration with nine objective functions that represented observed streamflow and all eight of the remotely sensed evapotranspiration datasets. Results showed that the multi-objective calibrations were able to successfully calibrate both streamflow and ETa. However, the highest model performances were achieved using the Ensemble ETa product. Meanwhile, the required computational time for the many-objective calibration is significantly higher than the multi-objective calibration. In addition, the overall performance of many-objective method can be improved by considering weighting factors and constraining the search space.