Evaluation of the Utility of Satellite Rainfall Estimates for Water Resource Applications using Sub-Basin Areal Averages and Pixel-to-Pixel Comparisons.

Persistent Link:
http://hdl.handle.net/10150/191295
Title:
Evaluation of the Utility of Satellite Rainfall Estimates for Water Resource Applications using Sub-Basin Areal Averages and Pixel-to-Pixel Comparisons.
Author:
Claggett, Seton Paul.
Issue Date:
2001
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:
Remotely sensed data from satellites has the potential to provide spatially and temporally relevant hydrologic information. This data has been used in the development of the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system. At a global scale, the results of this system can be used at coarser 1 0 x 1 0 resolution, which allows for greater accuracy in daily precipitation values. However, at regional and watershed levels, which are customary to most hydrologic applications, higher spatial resolutions are required (4 km x 4 km). The accuracy at this spatial resolution is investigated at the 0.25° x 0.25° grid scale and is accomplished by comparing precipitation gauges and ground-based radar (NEXRAD) to the PERSIANN output at both scales. More importantly, watershed average precipitation, obtained from NEXRAD and Thiessen polygon interpolation of gauges is, for the first time, compared against satellite precipitation estimates. A robust methodology for both of these types of estimation is presented along with other factors influencing the data assimilation process including the relative performance measures of the corresponding data and seasonal variability in data platform implementation.
Type:
Thesis-Reproduction (electronic); text
LCSH Subjects:
Hydrology.; Satellite meteorology.; Water resources development.
Degree Name:
M.S.
Degree Level:
masters
Degree Program:
Hydrology and Water Resources; Graduate College
Degree Grantor:
University of Arizona
Committee Chair:
Sorooshian, Soroosh; Imam, Bisher

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleEvaluation of the Utility of Satellite Rainfall Estimates for Water Resource Applications using Sub-Basin Areal Averages and Pixel-to-Pixel Comparisons.en_US
dc.creatorClaggett, Seton Paul.en_US
dc.contributor.authorClaggett, Seton Paul.en_US
dc.date.issued2001en_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.abstractRemotely sensed data from satellites has the potential to provide spatially and temporally relevant hydrologic information. This data has been used in the development of the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) system. At a global scale, the results of this system can be used at coarser 1 0 x 1 0 resolution, which allows for greater accuracy in daily precipitation values. However, at regional and watershed levels, which are customary to most hydrologic applications, higher spatial resolutions are required (4 km x 4 km). The accuracy at this spatial resolution is investigated at the 0.25° x 0.25° grid scale and is accomplished by comparing precipitation gauges and ground-based radar (NEXRAD) to the PERSIANN output at both scales. More importantly, watershed average precipitation, obtained from NEXRAD and Thiessen polygon interpolation of gauges is, for the first time, compared against satellite precipitation estimates. A robust methodology for both of these types of estimation is presented along with other factors influencing the data assimilation process including the relative performance measures of the corresponding data and seasonal variability in data platform implementation.en_US
dc.description.notehydrology collectionen_US
dc.typeThesis-Reproduction (electronic)en_US
dc.typetexten_US
dc.subject.lcshHydrology.en_US
dc.subject.lcshSatellite meteorology.en_US
dc.subject.lcshWater resources development.en_US
thesis.degree.nameM.S.en_US
thesis.degree.levelmastersen_US
thesis.degree.disciplineHydrology and Water Resourcesen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.chairSorooshian, Sorooshen_US
dc.contributor.chairImam, Bisheren_US
dc.identifier.oclc214124861en_US
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