Land Surface Processes In Natural and Artificial Tropical Ecosystems

Persistent Link:
http://hdl.handle.net/10150/194510
Title:
Land Surface Processes In Natural and Artificial Tropical Ecosystems
Author:
Rosolem, Rafael
Issue Date:
2010
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:
Land Surface Parameterization (LSP) schemes have evolved from simple tipping-bucket models to fully interactive models, including parameterizations which account for exchanges of momentum, energy, mass, and biogeochemistry. As the demand for greater realism has increased, so has the complexity of LSPs which now includes some parameters that may not be universally relevant to all regions of the globe. The performance of LSP schemes depends on the magnitude of structural, data-related (input and output), and parameter uncertainties in the model. Parameter estimation uncertainty can be reduced by calibrating LSPs against measurements available at field sites. Given the multiple outputs of the models, multi-objective optimization approaches are performed. Some of the parameter values used in LSPs have originally obtained from laboratory studies which analyzed plant behavior under a range of conditions in enclosed chambers. The research described in this dissertation takes advantage of currently available data from several eddy covariance flux towers located mainly in the Brazilian Amazon basin to estimate parameter values of a widely-used LSP scheme, version 3 of the Simple Biosphere model (SiB3). Background climatological data was used to assess the representativeness of the data collection period that might have affected model calibration. Variance-based sensitivity analysis was then used to investigate potential structural deficiencies in SiB3 and to reduce the dimensionality of the subsequent optimization by identifying those model parameters that merit calibration. Finally, some structural and conceptual aspects of SiB3 were tested inside Biosphere 2 Tropical Rain Forest biome (B2-TRF) under meteorological conditions that resemble those predicted in future climate scenarios for the Amazon basin.
Type:
text; Electronic Dissertation
Keywords:
Amazon; Biosphere2; Hydrometeorology; Land Surface Modeling; Optimization; Sensitivity Analysis
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Hydrology; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Shuttleworth, William J
Committee Chair:
Shuttleworth, William J

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleLand Surface Processes In Natural and Artificial Tropical Ecosystemsen_US
dc.creatorRosolem, Rafaelen_US
dc.contributor.authorRosolem, Rafaelen_US
dc.date.issued2010en_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.abstractLand Surface Parameterization (LSP) schemes have evolved from simple tipping-bucket models to fully interactive models, including parameterizations which account for exchanges of momentum, energy, mass, and biogeochemistry. As the demand for greater realism has increased, so has the complexity of LSPs which now includes some parameters that may not be universally relevant to all regions of the globe. The performance of LSP schemes depends on the magnitude of structural, data-related (input and output), and parameter uncertainties in the model. Parameter estimation uncertainty can be reduced by calibrating LSPs against measurements available at field sites. Given the multiple outputs of the models, multi-objective optimization approaches are performed. Some of the parameter values used in LSPs have originally obtained from laboratory studies which analyzed plant behavior under a range of conditions in enclosed chambers. The research described in this dissertation takes advantage of currently available data from several eddy covariance flux towers located mainly in the Brazilian Amazon basin to estimate parameter values of a widely-used LSP scheme, version 3 of the Simple Biosphere model (SiB3). Background climatological data was used to assess the representativeness of the data collection period that might have affected model calibration. Variance-based sensitivity analysis was then used to investigate potential structural deficiencies in SiB3 and to reduce the dimensionality of the subsequent optimization by identifying those model parameters that merit calibration. Finally, some structural and conceptual aspects of SiB3 were tested inside Biosphere 2 Tropical Rain Forest biome (B2-TRF) under meteorological conditions that resemble those predicted in future climate scenarios for the Amazon basin.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectAmazonen_US
dc.subjectBiosphere2en_US
dc.subjectHydrometeorologyen_US
dc.subjectLand Surface Modelingen_US
dc.subjectOptimizationen_US
dc.subjectSensitivity Analysisen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineHydrologyen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorShuttleworth, William Jen_US
dc.contributor.chairShuttleworth, William Jen_US
dc.contributor.committeememberShuttleworth, William Jen_US
dc.contributor.committeememberGupta, Hoshin Ven_US
dc.contributor.committeememberZeng, Xubinen_US
dc.contributor.committeememberde Goncalves, Luis G Gen_US
dc.identifier.proquest11285en_US
dc.identifier.oclc752261128en_US
All Items in UA Campus Repository are protected by copyright, with all rights reserved, unless otherwise indicated.