Predicting snowmelt runoff using a deterministic watershed model with stochastic precipitation inputs

Persistent Link:
http://hdl.handle.net/10150/191620
Title:
Predicting snowmelt runoff using a deterministic watershed model with stochastic precipitation inputs
Author:
Hanes, William Toby,1951-
Issue Date:
1975
Publisher:
The University of Arizona.
Rights:
Copyright © 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.
Abstract:
The accuracy of currently used long-term runoff forecasting techniques, such as used by the Soil Conservation Service, are limited because of their inability to deal with the uncertainty in the amount of precipitation expected to fall after the forecast date. The basis for a simulation-based, long-term runoff forecasting technique is developed to overcome this problem by simulating future precipitation events. The technique utilizes a deterministic watershed snowmelt model and a sequence, event-based stochastic precipitation model to provide daily precipitation data inputs for the watershed model. A number of sets of inputs are run through the watershed model to produce an equal number of predictions of total seasonal runoff. A relative frequency distribution of total seasonal runoff is then plotted to which a PDF may be fitted. Various criteria were used to test the precipitation model. The majority showed no significant differences between the observed and simulated data. The lack of data prevented reasonable watershed model optimization and testing. Taking into consideration the poor watershed model response the forecasting technique responded well to the uncertainty in future precipitation and to abnormal monthly precipitation.
Type:
Thesis-Reproduction (electronic); text
LCSH Subjects:
Hydrology.; Snow.; Runoff Arizona -- Mathematical models.; Watersheds -- Mathematical models.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Renewable Natural Resources; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Fogel, Martin M.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titlePredicting snowmelt runoff using a deterministic watershed model with stochastic precipitation inputsen_US
dc.creatorHanes, William Toby,1951-en_US
dc.contributor.authorHanes, William Toby,1951-en_US
dc.date.issued1975en_US
dc.publisherThe University of Arizona.en_US
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_US
dc.description.abstractThe accuracy of currently used long-term runoff forecasting techniques, such as used by the Soil Conservation Service, are limited because of their inability to deal with the uncertainty in the amount of precipitation expected to fall after the forecast date. The basis for a simulation-based, long-term runoff forecasting technique is developed to overcome this problem by simulating future precipitation events. The technique utilizes a deterministic watershed snowmelt model and a sequence, event-based stochastic precipitation model to provide daily precipitation data inputs for the watershed model. A number of sets of inputs are run through the watershed model to produce an equal number of predictions of total seasonal runoff. A relative frequency distribution of total seasonal runoff is then plotted to which a PDF may be fitted. Various criteria were used to test the precipitation model. The majority showed no significant differences between the observed and simulated data. The lack of data prevented reasonable watershed model optimization and testing. Taking into consideration the poor watershed model response the forecasting technique responded well to the uncertainty in future precipitation and to abnormal monthly precipitation.en_US
dc.description.notehydrology collectionen_US
dc.typeThesis-Reproduction (electronic)en_US
dc.typetexten_US
dc.subject.lcshHydrology.en_US
dc.subject.lcshSnow.en_US
dc.subject.lcshRunoff Arizona -- Mathematical models.en_US
dc.subject.lcshWatersheds -- Mathematical models.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineRenewable Natural Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairFogel, Martin M.en_US
dc.contributor.committeememberThames, John L.en_US
dc.contributor.committeememberRasmussen, Williamen_US
dc.contributor.committeememberDavis, Donalden_US
dc.contributor.committeememberDuckstein, Lucienen_US
dc.identifier.oclc212886557en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.