Abstract
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.
Highlights
Water quality models are usually developed to simulate a large number of variables, such as water temperature, dissolved and particulate nutrients, phytoplankton and dissolved oxygen (DO)
The main conclusions are as follows: 1. The original Morris method is capable of ranking the parameters in order of sensitivity in affecting a model output variable
Sensitive parameters can be determined according to their statistics of elementary effects on model output
Summary
Water quality models are usually developed to simulate a large number of variables, such as water temperature, dissolved and particulate nutrients, phytoplankton and dissolved oxygen (DO). Such models often contain many parameters ( called coefficients) associated with the many processes and state variables simulated. Local sensitivity methods (such as differential analysis) calculate local gradients of the model output with respect to infinitesimal variations of a factor (such as a model coefficient). They are simple to implement and are not computationally demanding
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