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dc.contributor.advisorSorooshian, Sorooshen
dc.contributor.authorHogue, Terri Sue
dc.creatorHogue, Terri Sueen
dc.date.accessioned2018-02-28T16:24:37Z
dc.date.available2018-02-28T16:24:37Z
dc.date.issued1998
dc.identifier.urihttp://hdl.handle.net/10150/626876
dc.description.abstractExtensive flooding occurred throughout the northeastern United States during January of 1996. The flood event cost the lives of 33 people and over a billion dollars in flood damage. Following the "Blizzard of '96", a warm front moved into the Mid Atlantic region bringing extensive rainfall and causing significant melting and flooding to occur. Flood forecasting is a vital part of the National Weather Service (NWS) hydrologic responsibilities. Currently, the NWS River Forecast Centers use either the Antecedent Precipitation Index (API) or the Sacramento Soil-Moisture Accounting Model (SAC-SMA). This study evaluates the API and SAC-SMA models for their effectiveness in flood forecasting during this rain-on-snow event. The SAC-SMA, in conjunction with the SNOW-17 model, is calibrated for five basins in the Mid-Atlantic region using the Shuffled Complex Evolution (SCE-UA) automatic algorithm developed at the University of Arizona. Nash-Sutcliffe forecasting efficiencies (Er) for the calibration period range from 0. 79 to 0.87, with verification values from 0.42 to 0.95. Flood simulations were performed on the five basins using the API and calibrated SAC SMA model. The SAC-SMA model does a better job of estimating observed flood discharge on three of the five study basins, while two of the basins experience flood simulation problems with both models. Study results indicate the SAC-SMA has the potential for better flood forecasting during complex rain-on-snow events such as during the January 1996 floods in the Northeast.
dc.language.isoen_USen
dc.publisherThe University of Arizona.en
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en
dc.titleAnalysis of the National Weather Service soil moisture accounting models for flood prediction in the northeast floods of January 1996en_US
dc.typetexten
dc.typeThesis-Reproduction (electronic)en
thesis.degree.grantorUniversity of Arizonaen
thesis.degree.levelmastersen
dc.contributor.committeememberSorooshian, Sorooshen
thesis.degree.disciplineGraduate Collegeen
thesis.degree.disciplineHydrology and Water Resourcesen
thesis.degree.nameM.S.en
dc.description.noteDigitized from paper copies provided by the Department of Hydrology & Atmospheric Sciences.en
refterms.dateFOA2018-09-13T18:09:35Z
html.description.abstractExtensive flooding occurred throughout the northeastern United States during January of 1996. The flood event cost the lives of 33 people and over a billion dollars in flood damage. Following the "Blizzard of '96", a warm front moved into the Mid Atlantic region bringing extensive rainfall and causing significant melting and flooding to occur. Flood forecasting is a vital part of the National Weather Service (NWS) hydrologic responsibilities. Currently, the NWS River Forecast Centers use either the Antecedent Precipitation Index (API) or the Sacramento Soil-Moisture Accounting Model (SAC-SMA). This study evaluates the API and SAC-SMA models for their effectiveness in flood forecasting during this rain-on-snow event. The SAC-SMA, in conjunction with the SNOW-17 model, is calibrated for five basins in the Mid-Atlantic region using the Shuffled Complex Evolution (SCE-UA) automatic algorithm developed at the University of Arizona. Nash-Sutcliffe forecasting efficiencies (Er) for the calibration period range from 0. 79 to 0.87, with verification values from 0.42 to 0.95. Flood simulations were performed on the five basins using the API and calibrated SAC SMA model. The SAC-SMA model does a better job of estimating observed flood discharge on three of the five study basins, while two of the basins experience flood simulation problems with both models. Study results indicate the SAC-SMA has the potential for better flood forecasting during complex rain-on-snow events such as during the January 1996 floods in the Northeast.


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