Abstract
Hydrologic Simulation Program-Fortran (HSPF) model calibration is typically done manually due to the lack of an automated calibration tool as well as the difficulty of balancing objective functions to be considered. This paper discusses the development and demonstration of an automated calibration tool for HSPF (HSPF-SCE). HSPF-SCE was developed using the open source software “R”. The tool employs the Shuffled Complex Evolution optimization algorithm (SCE-UA) to produce a pool of qualified calibration parameter sets from which the modeler chooses a single set of calibrated parameters. Six calibration criteria specified in the Expert System for the Calibration of HSPF (HSPEXP) decision support tool were combined to develop a single, composite objective function for HSPF-SCE. The HSPF-SCE tool was demonstrated, and automated and manually calibrated model performance were compared using three Virginia watersheds, where HSPF models had been previously prepared for bacteria total daily maximum load (TMDL) development. The example applications demonstrate that HSPF-SCE can be an effective tool for calibrating HSPF.
Highlights
While some hydrologic model parameters are measurable, others are either difficult to measure or represent some system process in such a way that physically determining the parameter value is not possible
This paper presents a detailed description of the newly developed Hydrological Simulation Program-FORTRAN (HSPF)-SCE tool and exhibits its capability with example applications
The HSPF model is a process-based, continuous, spatially lumped-parameter model that is capable of describing the movement of water and a variety of water quality constituents on pervious and impervious surfaces, in soil profiles, and within streams and well-mixed reservoirs [37,38]
Summary
While some hydrologic model parameters are measurable, others are either difficult to measure or represent some system process in such a way that physically determining the parameter value is not possible. Often, those parameters that are not directly physically based are calibrated. Calibration is the process of adjusting selected model parameters to minimize the difference between the simulated and observed variables of interest [1,2]. Model calibration may be performed manually, or the processes can be automated using an optimization algorithm [5,6]. An automatic model parameter calibration has the potential to be quicker and less labor intensive [5,7,8,9,10,11]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
