UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
More information is available at Theses and Dissertations at University of Maryland Libraries.
Browse
2 results
Search Results
Item Response of hydrologic calibration to replacing gauge-based with NEXRAD-based precipitation data in the USEPA Chesapeake Bay Watershed model(2012) Kim, Sunghee; Brubaker, Kaye L; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This study investigated the response of hydrologic calibration to replacing gauge-based with radar-based precipitation data in the USEPA Chesapeake Bay Program (CBP) Watershed (CBW) model over the Potomac River Basin. Specific objectives were to (1) compare gauge-based and NEXRAD radar-based (Multisensor Precipitation Estimator, MPE) data at the (a) point-pixel and (b) spatially aggregated level; (2) evaluate the model's calibration accuracy using the different precipitation data sets; and (3) examine the response of model hydrology. Hourly gauge-point and MPE-pixel data were compared at 80 locations. The CBP's interpolated and aggregated precipitation data at the model unit (county) level were compared with MPE data aggregated to the same 114 county-based spatial segments. The model calibration followed the CBP's automated approach, using observed streamflow at 37 gauge stations. Model performance was evaluated using calibration and hydrologic statistics, and GIS-aided spatial information. Calibrated parameters and model hydrologic fluxes were compared. The average annual gauge-point and MPE-pixel values (excluding hours when either was missing) agreed well. Differences in average annual values between the spatially aggregated data sets were, however, significant in parts of the study area. When parameter constraints were relaxed to allow calibration to adjust to the smaller volume of precipitation, the model using MPE outperformed the model calibrated to CBP precipitation data at 65% of the 37calibration sites. The model response was controlled largely by the seasonal difference in precipitation inputs: (1) calibration process could not compensate for large differences in seasonal flow bias caused by the seasonal volume of precipitation; (2) seasonal flow bias affected the lower zone nominal soil moisture storage parameter (LZSN), mainly affecting interflow and groundwater flow. The surface flow component was generally the same for the different precipitation inputs. The two precipitation data types can be used interchangeably to simulate surface-flow dominated processes, but care must be taken in simulations where subsurface pathways and residence times are important. MPE is a strong alternative to gauge-based precipitation data because of its spatiotemporal coverage and rare missing records. Using MPE in hydrologic modeling is appealing because of the improved calibration accuracy of the CBW model demonstrated in this study.Item A STRATEGY FOR CALIBRATING THE HSPF MODEL(2005-04-29) Gutierrez-Magness, Angelica Lucia; McCuen, Richard H; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)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.