Sensitivity and uncertainty analysis for streamflow prediction using multiple optimization algorithms and objective functions: San Joaquin Watershed, California

Abstract

Uncertainty analysis prior to the model calibration is key to the effective implementation of the hydrologic models. The major application of sensitivity analysis is to indicate the uncertainties in the input parameters of the model, which affects the model performance. There are different optimization algorithms developed and applied in the hydrologic model, which can be performed with different objective functions to calibrate and quantify the uncertainties in the system. The purpose of this study was to evaluate the model calibration performance and sensitivity of parameters using three optimization algorithms and five objective functions for predicting monthly streamflow. Sequential Uncertainty Fitting (SUFI-2), Generalized Likelihood Uncertainty Estimation (GLUE), and Parameter Solution (ParaSol) were used to calibrate the monthly streamflow for the semi-arid San Joaquin Watershed in California by using Soil and Water Assessment Tool (SWAT) model. The best performance metrics (R2, NSE, PBIAS, P-factor, and R-factor) were obtained by SUFI-2 while using NSE as the objective function. The coefficient of determination (R2), Nash–Sutcliffe Efficiency (NSE), the percentage of bias (PBIAS), Kling-Gupta efficiency (KGE) and Ratio of the standard deviation of observations to root mean square error (RSR) were used as an objective function to assess the monthly calibration performance. KGE was found to be a suitable objective function to calibrate this complex and snowmelt-dominated watershed. The findings from this study will serve as a guideline for hydro-ecological researchers to achieve further watershed management goals.