Abstract: SWAT model calibrated by streamflow data from gauging stations often provides a relatively high accuracy of hydrologic simulation, nevertheless those calibrated model parameters are still with uncertainty, particularly in the area with sparse gauging stations where the compensation between parameters would make SWAT model deeply disturbed by the phenomenon of equifinality. In this paper, a multi-objective calibration method is proposed to calibrate SWAT model and improve modeling quality by using leaf area index (LAI) from remote sensing data. Generally, vegetation has a crucial impact on eco-hydrological process that is also the core component of hydrological process. Therefore, the calibration of vegetation module facilitates to increase the precision of hydrologic model output. In-situ measured streamflow data is usually collected at gauging points, while remote sensed data is a snapshot over a large space. So LAI data may presents much more spatial details in the model other than streamflow data. The test was carried out with streamflow, crop yield yearbook, and MODIS LAI datasets in the Meichuan River basin, and the results showed that the multi-objective method significantly reduced the impact of equifinality on model parameters and improved the simulation accuracy as well as the robustness of the SWAT model because it took full advantages of vegetation remote sensing and concerned the great role of vegetation in hydrological process.

Key words: SWAT model, equifinality, multi-objective optimization, leaf area index, crop yield