Comparison of several heuristic approaches to calibration of WATCLASS hydrologic model1☆

Abstract

The purpose of this study is to find a computationally inexpensive calibration method of a hydrologic model for predicting river flows. The approach is called the surrogate model optimization (SMO), which relies on optimizing a surrogate model instead of the original model that requires significantly more computing time. The proposed SMO method combines the Latin hypercube sampling (LHS) method and a statistical approach called the “Design and Analysis of Computer Experiments (DACE)”. To investigate the performance of this approach, the Monte Carlo sampling and LHS results are compared with the results of the proposed SMO. As the case study, the prediction results of WATCLASS hydrologic model over Smokey river watershed using MC, LHS and SMO are presented. The proposed SMO is shown to be significantly faster than traditional calibration methods based on Monte Carlo simulation or other global optimization methods.