Automatic Calibration of Hydrological Model by Shuffled Complex Evolution Metropolis Algorithm

An accurate forecast of runoff is very significant so that there is ample time for the pertinent authority to issue a forewarning of the impending flood. Due to the highly dimension and nonlinear, the calibration of hydrological model become very complex, so the unique “best” parameter set can not be obtained easily. In this study, an MCMC sampler entitled the Shuffled Complex Evolution Metropolis algorithm(SCEM-UA) is presented, which is well suited to infer the posterior distribution of hydrologic model parameters. This algorithm operates by merging the strengths of the Metropolis algorithm, controlled random search, competitive evolution, and complex shuffling in order to continuously update the proposal distribution and evolve the sampler to the posterior target distribution. Therefore, SCEM-UA is applied in this study to calibrate a lumped Xinanjiang hydrological model having 16 parameters. Three types of data were used for Xinanjiang model: rainfall, evaporation and discharge. One years´ data were used for calibration and one years´ data were used for testing. The criterion used to measure the fitness of the calculated against the observed discharges was the Deterministic Coefficient (DC) and Root Mean Square Error(RMSE). The calibration processes included first of all defining the feasible domains of the model parameters, and initialize the parameters in the feasible domains, then the model parameters were iteratively evaluated and updated, until the terminal condition was satisfied. In order to test the efficiency of the SCEM-UA, Genetic algorithm (GA) is also employed for comparison. The results showed that both calibration and testing results are satisfactory: the DC values of SCEM-UA for the calibration period is 0.79, which is much higher than that of GA, 0.73, and the DC for the testing period is 0.81, which is also better than GA, the same as the RMSE. Visual examinations shows in the high peak flood event, the simulated runoff by SCE- - M-UA is much better than that by GA.