The study attempts to apply the Shuffled Complex Evolution Algorithm (SCE-UA) of Duan et al for the calibration of a Conceptual Rainfall Runoff (CRR) model. CRR modeling lies intermediate between physically based models and black box models.
An important step in application of a conceptual model to a catchment is model calibration. The objective of calibration is to determine the model parameters such that an acceptable match is obtained between the observed and the computed discharge hydrographs. Two approaches are followed for calibration of a conceptual model – manual using trial and error and automatic using an optimization algorithm.
The aspects of the conceptual models which cause problems during automatic calibration are –
- interdependence between the model parameters
- indifference of the objective function to the values of the inactive parameters
- discontinuities of the response surface, and
- the presence of local optima
The degree of complexity of a model plays a significant role in model calibration phase. The difficulties encountered during the calibration are closely connected to the number of parameters typical of the model and to the greater or lesser ease of visualizing the various parameters.
In the study, the model reported by Jain (1993) was used to simulate the response of a basin of size 820 sqkm. The results show that the algorithm is able to converge to the global optimum when the computations are started from a number of initial points.
The main conclusions of the study are as follows -
- The SCE-UA algorithm is a global optimization method and is able to converge to the global optimum parameters when different initial values of parameters are used.
- The computational requirements for calibrating a CRR model are quite reasonable and thus the algorithm is computationally efficient.
- The user has complete control on the calibration through a large number of parameters which control the execution of the algorithm.
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