Identifying parameter sensitivity in a water quality model of a reservoir

Sensitivity analysis can provide useful insights into how a model responds to the variations in its parameter values (i.e. coefficients). The results can be very helpful for model calibration, refinement and application. A one-dimensional model has been set up to simulate the hydrothermal and water quality conditions of Cannonsville Reservoir, which provides water supply for New York City. This paper aims at identifying the most influential parameters in the model through sensitivity analysis. Firstly, the Morris method (a screening method) is used to identify influential parameters. It is found that 18 parameters are important in simulations of variables that include temperature, dissolved oxygen (DO), total phosphorus (TP) and chlorophyll a (Chla). Secondly, the method is enhanced to investigate the global sensitivity of the parameters. It highlights 20 parameters that are sensitive in the simulations of the above-mentioned variables. The 18 parameters identified by the original Morris method are among the 20 parameters and the other two parameters are not very sensitive. The results show that similar results can be obtained through the original and enhanced Morris methods, although they each have their own strengths and weaknesses.