Hydrologic Calibration Strategies for the HSPF Watershed Model: Identifying Effective Objective Functions for Use with the Parameter Estimation (PEST) Program

Hydrologic Simulation Program FORTRAN (HSPF) is a lumped-parameter watershed model implemented in EPA's Better Assessment Science Integrating Point and Nonpoint Sources (BASINS) system. In order to perform water quality simulations in specific study areas, HSPF's hydrology must first be calibrated to observed streamflow data. PEST is a model-independent parameter optimization program which minimizes an "objective function" quantifying the misfit between model outputs and corresponding field measurements. PEST also performs nonlinear predictive confidence interval estimation in which PEST determines maximum and minimum limits on key model predictions. A PEST/HSPF Windows interface is being developed for incorporation in the BASINS GIS-based watershed and water quality modeling system. This study seeks to identify successful PEST/HSPF calibration protocols that successfully minimize key model predictions for incorporation in the interface design. We present specific details of PEST implementation for HSPF, including model setup, identification of sensitive parameters, and parameter transformations required to facilitate the parameter estimation process. Calibration parameter set nonuniqueness is demonstrated. PEST utilizes this non-uniqueness to define nonlinear predictive confidence interval estimates. PEST varies parameters, while maintaining the objective function at or below a user-defined threshold, to define the range of predictions that satisfy calibration constraints of the objective function.