Three complementary methods for sensitivity analysis of a water quality model

• Published in: Environmental modelling & software : with environment data news Vol. 37; pp. 19 - 29
• Main Authors: Sun, X.Y., Newham, L.T.H., Croke, B.F.W., Norton, J.P.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2012
• Subjects: computer software; environmental models; evapotranspiration; Hydrological modelling; model uncertainty; Monte Carlo; Morris Method; nitrogen; One-at-A-Time; plant stress; pollution load; Regional sensitivity analysis; rivers; sediments; time series analysis; uncertainty analysis; Water quality; watersheds; Australia; Hydrological modelling; Morris Method; Monte Carlo; Regional sensitivity analysis; Water quality; One-at-A-Time
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2012.04.010

In this paper, sensitivity analysis (SA) has been used to assess model sensitivities to input parameter values in a water quality model. The water quality model incorporates a rainfall-runoff sub-model and a sediment load estimation sub-model, and is calibrated against hydrologic and water quality data from the Moruya River catchment in southeast Australia. The tested methods, One-at-A-Time (OAT), Morris Method (MM) and Regional SA (RSA) are found to be complementary, and help to characterise the behaviour of the water quality model. The most important parameters are plant stress threshold (f), coefficient of evapotranspiration (e), catchment moisture threshold (d), in decreasing order, indicating that sediment and nutrient loads are more sensitive to parameters that affect the magnitude of flows than those (vs, τq, τs) that control the timing and shape of the peak in a time series. But this application shows a need to be flexible in the use of different SA techniques. RSA is more appropriate for complex models where system nonlinearities and parameter interactions are more likely to be important. The RSA suggests that f and vs have strong interactions in the influence on nitrogen estimation. This study is also valuable for future uncertainty analysis, by separating the source of uncertainty of model parameters from the uncertainty in the model inputs.