Efficient Calibration of Computationally Intensive Hydrological Models

Abstract:

A new hybrid optimization algorithm for the calibration of computationally-intensive hydrological models is introduced. The calibration of hydrological models is a blackbox optimization problem where the only information available to the optimization algorithm is the objective function value. In the case of distributed hydrological models, the calibration process is often known to be hampered by computational efficiency issues. Running a single simulation may take several minutes and since the optimization process may require thousands of model evaluations, the computational time can easily expand to several hours or days.

A blackbox optimization algorithm, which can substantially improve the calibration efficiency, has been developed. It merges both the convergence analysis and robust local refinement from the Mesh Adaptive Direct Search (MADS) algorithm, and the global exploration capabilities from the heuristic strategies used by the Dynamically Dimensioned Search (DDS) algorithm. The new algorithm is applied to the calibration of the distributed and computationally-intensive HYDROTEL model on three different river basins located in the province of Quebec (Canada). Two calibration problems are considered: (1) calibration of a 10-parameter version of HYDROTEL, and (2) calibration of a 19-parameter version of the same model.

A previous study by the authors had shown that the original version of DDS was the most efficient method for the calibration of HYDROTEL, when compared to the MADS and the very well-known SCEUA algorithms. The computational efficiency of the hybrid DDS-MADS method is therefore compared with the efficiency of the DDS algorithm based on a 2000 model evaluations budget. Results show that the hybrid DDS-MADS method can reduce the total number of model evaluations by 70% for the 10-parameter version of HYDROTEL and by 40% for the 19-parameter version without compromising the quality of the final objective function value.