Investigation of the national weather service soil moisture accounting models for flood prediction in the northeast floods of january 1996

Persistent Link:
http://hdl.handle.net/10150/615796
Title:
Investigation of the national weather service soil moisture accounting models for flood prediction in the northeast floods of january 1996
Author:
Hogue, Terri S.; Sorooshian, Soroosh
Affiliation:
Department of Hydrology & Water Resources, The University of Arizona
Publisher:
Department of Hydrology and Water Resources, University of Arizona (Tucson, AZ)
Issue Date:
1999-10
Rights:
Copyright © Arizona Board of Regents
Collection Information:
This title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.
Abstract:
Extensive 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 (Ef) 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.
Series/Report no.:
Technical Reports on Hydrology and Water Resources, No. 99-030
Sponsors:
This research was partially supported by grants from the National Science Foundation Graduate Research Trainee Program (DGE-9355029 #3), the NASA Space Grant Graduate Fellowship Program, and a NOAA National Weather Service Cooperative Agreement with the Hydrologic Research Laboratory of the Office of Hydrology (NA77WHO425). The support provided by these programs is greatly appreciated.

Full metadata record

DC FieldValue Language
dc.contributor.authorHogue, Terri S.en
dc.contributor.authorSorooshian, Sorooshen
dc.date.accessioned2016-07-07T23:57:30Z-
dc.date.available2016-07-07T23:57:30Z-
dc.date.issued1999-10-
dc.identifier.urihttp://hdl.handle.net/10150/615796-
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 (Ef) 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.en
dc.description.sponsorshipThis research was partially supported by grants from the National Science Foundation Graduate Research Trainee Program (DGE-9355029 #3), the NASA Space Grant Graduate Fellowship Program, and a NOAA National Weather Service Cooperative Agreement with the Hydrologic Research Laboratory of the Office of Hydrology (NA77WHO425). The support provided by these programs is greatly appreciated.en
dc.language.isoen_USen
dc.publisherDepartment of Hydrology and Water Resources, University of Arizona (Tucson, AZ)en
dc.relation.ispartofseriesTechnical Reports on Hydrology and Water Resources, No. 99-030en
dc.rightsCopyright © Arizona Board of Regentsen
dc.sourceProvided by the Department of Hydrology and Water Resources.en
dc.titleInvestigation of the national weather service soil moisture accounting models for flood prediction in the northeast floods of january 1996en_US
dc.typetexten
dc.typeTechnical Reporten
dc.contributor.departmentDepartment of Hydrology & Water Resources, The University of Arizonaen
dc.description.collectioninformationThis title from the Hydrology & Water Resources Technical Reports collection is made available by the Department of Hydrology & Atmospheric Sciences and the University Libraries, University of Arizona. If you have questions about titles in this collection, please contact repository@u.library.arizona.edu.en
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