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dc.contributor.authorGharari, S.
dc.contributor.authorGupta, H.V.
dc.contributor.authorClark, M.P.
dc.contributor.authorHrachowitz, M.
dc.contributor.authorFenicia, F.
dc.contributor.authorMatgen, P.
dc.contributor.authorSavenije, H.H.G.
dc.date.accessioned2021-07-20T00:11:22Z
dc.date.available2021-07-20T00:11:22Z
dc.date.issued2021
dc.identifier.citationGharari, S., Gupta, H. V., Clark, M. P., Hrachowitz, M., Fenicia, F., Matgen, P., & Savenije, H. H. G. (2021). Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data. Water Resources Research, 57(6).
dc.identifier.issn0043-1397
dc.identifier.doi10.1029/2020WR027948
dc.identifier.urihttp://hdl.handle.net/10150/660830
dc.description.abstractProcess-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of “fidelius” process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of “minimally restrictive process parameterization equations—MR-PPEs,” which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over “parameter spaces” to a search over “function spaces.”. © 2021. The Authors.
dc.language.isoen
dc.publisherBlackwell Publishing Ltd
dc.rightsCopyright © 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectcatchment
dc.subjectgeneral or miscellaneous
dc.subjectmodel calibration
dc.subjectmodeling
dc.subjectuncertainty assessment
dc.titleUnderstanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Hydrology and Atmospheric Sciences, The University of Arizona
dc.identifier.journalWater Resources Research
dc.description.noteOpen access article
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal published version
dc.source.journaltitleWater Resources Research
refterms.dateFOA2021-07-20T00:11:22Z


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Copyright © 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution License.
Except where otherwise noted, this item's license is described as Copyright © 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution License.