A STRATEGY FOR CALIBRATING THE HSPF MODEL
| dc.contributor.advisor | McCuen, Richard H | en_US |
| dc.contributor.author | Gutierrez-Magness, Angelica Lucia | en_US |
| dc.contributor.department | Civil Engineering | en_US |
| dc.contributor.publisher | Digital Repository at the University of Maryland | en_US |
| dc.contributor.publisher | University of Maryland (College Park, Md.) | en_US |
| dc.date.accessioned | 2005-08-03T14:29:18Z | |
| dc.date.available | 2005-08-03T14:29:18Z | |
| dc.date.issued | 2005-04-29 | en_US |
| dc.description.abstract | The development of Total Maximum Daily Loads (TMDLs) and environmental policies rely on the application of mathematical models, both empiric and deterministic. The Hydrologic Simulation Program-FORTRAN (HSPF) is the most comprehensive model, and it is frequently applied in the development of TMDLs for nonpoint sources control. Despite the wide use of HSPF, a documented strategy for its calibration is not available. Furthermore, the most common calibration approach uses subjective fitting and focuses on the attainment of statistical goodness of fit, ignoring in many cases the rationality of the model. The goal of this research was to develop a strategy for calibrating the HSPF model in combination with the model-independent-parameter estimator (PEST). PEST is an objective parameter estimator that should eliminate some of the subjectivity from the calibration process and reduce the repetitive effort associated with subjective fitting. The strategy was established through a series of analyses, which included the development of a weighted multi-component objective function used as the criterion for calibration. The weights are a function of the flow components of the measured runoff. The use of this new weighting procedure improves model and prediction accuracy. Methods of rainfall disaggregation and their effect on the prediction accuracy were studied. The results indicated that methods based on analyses of actual storm frequency data provided the most accurate daily-disaggregated values and thus, the best conditions to achieve accurate predictions with the HSPF. Analyses showed that the HSPF model requires a start-up period of about a year to allow the predicted discharges to become insensitive to erroneous estimates of the initial storages. The predictions during the start-up period should not be used for either calibration or the analysis of the goodness of fit. Analyses also showed that using HSPF as a lumped model can reduce the prediction accuracy of discharges from a watershed with an inhomogeneous land use distribution. The fulfillment of the research objectives provides a systematic procedure that improves the hydrologic calibration of the HSPF model. | en_US |
| dc.format.extent | 1448355 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1903/2497 | |
| dc.language.iso | en_US | |
| dc.subject.pqcontrolled | Engineering, Civil | en_US |
| dc.subject.pqcontrolled | Hydrology | en_US |
| dc.subject.pquncontrolled | HSPF | en_US |
| dc.subject.pquncontrolled | Calibration | en_US |
| dc.subject.pquncontrolled | PEST | en_US |
| dc.subject.pquncontrolled | Hydrologic models | en_US |
| dc.title | A STRATEGY FOR CALIBRATING THE HSPF MODEL | en_US |
| dc.type | Dissertation | en_US |
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