Status of Automatic Calibration for Hydrologic Models: Comparison with Multilevel Expert Calibration

TLDR

Hydrologic model performance depends on careful calibration, yet manual procedures are time‑consuming; a global optimization algorithm called shuffled complex evolution has proven effective at locating optimal parameters. This study compares the capability of the shuffled complex evolution automatic calibration with an interactive multilevel, multistage semi‑automated method for the Sacramento soil moisture accounting streamflow forecasting model. The comparison is conducted on the Sacramento model of the U.S. National Weather Service, evaluating the shuffled complex evolution algorithm against the multilevel calibration procedure.

Abstract

The usefulness of a hydrologic model depends on how well the model is calibrated. Therefore, the calibration procedure must be conducted carefully to maximize the reliability of the model. In general, manual procedures for calibration can be extremely time-consuming and frustrating, and this has been a major factor inhibiting the widespread use of the more sophisticated and complex hydrologic models. A global optimization algorithm entitled shuffled complex evolution recently was developed that has proved to be consistent, effective, and efficient in locating the globally optimal model parameters of a hydrologic model. In this paper, the capability of the shuffled complex evolution automatic procedure is compared with the interactive multilevel calibration multistage semiautomated method developed for calibration of the Sacramento soil moisture accounting streamflow forecasting model of the U.S. National Weather Service. The results suggest that the state of the art in automatic calibration now can be expected to perform with a level of skill approaching that of a well-trained hydrologist. This enables the hydrologist to take advantage of the power of automated methods to obtain good parameter estimates that are consistent with the historical data and to then use personal judgment to refine these estimates and account for other factors and knowledge not incorporated easily into the automated procedure. The analysis also suggests that simple split-sample testing of model performance is not capable of reliably indicating the existence of model divergence and that more robust performance evaluation criteria are needed.