Investigating the potential of Morris algorithm for improving the computational constraints of global sensitivity analysis.

Abstract

Sensitivity analysis (SA) is widely acknowledged as advantageous and worthwhile in recognizing parameters for model calibration and optimization, especially in complex hydrological models. Although Sobol global SA is an efficient way to evaluate the sensitivity indices, the computational cost is a constraint. This study analyzes the potential of Morris global SA to achieve results tantamount to Sobol SA, at a much cheaper computational expense, using a new approach of increasing the number of replications for the Morris algorithm. SA for two catchments is performed on a coupled hydrological model using Morris and Sobol algorithms. Two target functions are used for each of the algorithms. Sobol SA required 660000 model simulations accounting for about 400 computing hours, whereas increasing the replications from 1000 to 3000, the Morris method called for 63000 runs and 06 computing hours to produce significantly similar results. The Morris parameter ranking improved 50% with respect to Sobol SA by a three-fold increase in replications with a small 5-h increase in the computational expense. The results also suggest that target functions and catchments influence parameter sensitivity. The new approach to employ the Morris method of SA shows promising results for highly parameterized hydrological models without compromising the quality of SA, specifically if there are time constraints. The study encourages the use of SA, which is mainly skipped because of higher computational demands.