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Precipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modeling

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dc.contributor.authorWoodson, Daviden
dc.contributor.committeechairDymond, Randel L.en
dc.contributor.committeememberYoung, Kevin D.en
dc.contributor.committeememberHodges, Clayton Christopheren
dc.contributor.departmentCivil and Environmental Engineeringen
dc.date.accessioned2019-06-20T08:01:16Zen
dc.date.available2019-06-20T08:01:16Zen
dc.date.issued2019-06-19en
dc.description.abstractQuantitative precipitation estimation (QPE) remains a key area of uncertainty in hydrological modeling, particularly in small, urban watersheds which respond rapidly to precipitation and can experience significant spatial variability in rainfall fields. Few studies have compared QPE methods in small, urban watersheds, and studies which have examined this topic only compared model results on an event basis using a small number of storms. This study sought to compare the efficacy of multiple QPE methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and QPE forcings. The Research Distributed Hydrologic Model (RDHM) was used to model a basin in Roanoke, Virginia, USA forced with QPEs from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a 6-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by root mean square error (RMSE) and peak flow relative error, despite systematic underprediction of the mean areal precipitation (MAP). Simulations forced with MFB corrected radar data consistently and significantly overpredicted discharge but had the highest accuracy in predicting the timing of peak flows.en
dc.description.abstractgeneralEstimating the amount of rain that fell during a precipitation event remains a key source of error when predicting how much stormwater runoff will be produced, particularly in small, urban watersheds which respond rapidly to precipitation and can experience significant spatial variability in rainfall distribution. Rainfall estimation in small, urban watersheds has received relatively little attention, and studies which have examined this topic have generally only examined a small number of discrete storm events. This study sought to compare the efficacy of multiple precipitation estimation methods when simulating discharge in a small, urban watershed on a continuous basis using an operational hydrologic model and precipitation inputs. The Research Distributed Hydrologic Model (RDHM), commonly used by the National Weather Service, was used to model a basin in Roanoke, Virginia, USA forced with rainfall estimates from four methods: mean field bias (MFB) correction of radar data, kriging of rain gauge data, uncorrected radar data, and a basin-uniform estimate from a single gauge inside the watershed. Based on comparisons between simulated and observed discharge at the basin outlet for a 6-month period in 2018, simulations forced with the uncorrected radar QPE had the highest accuracy, as measured by several performance statistics, despite systematic underprediction of actual precipitation. Simulations forced with MFB corrected radar data consistently and significantly overpredicted discharge but had the highest accuracy in predicting the timing of peak flows.en
dc.description.degreeMaster of Scienceen
dc.format.mediumETDen
dc.identifier.othervt_gsexam:21265en
dc.identifier.urihttp://hdl.handle.net/10919/90373en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectQPEen
dc.subjecturban hydrologyen
dc.subjectModelingen
dc.subjectprecipitationen
dc.subjectrunoffen
dc.subjectRDHMen
dc.titlePrecipitation Estimation Methods in Continuous, Distributed Urban Hydrologic Modelingen
dc.typeThesisen
thesis.degree.disciplineCivil Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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