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dc.contributor.author
Zhang, Qiuru
dc.contributor.author
Shi, Liangsheng
dc.contributor.author
Holzman, Mauro Ezequiel
dc.contributor.author
Ye, Ming
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Wang, Yakun
dc.contributor.author
Carmona, Facundo
dc.contributor.author
Zha, Yuanyuan
dc.date.available
2020-08-14T15:14:45Z
dc.date.issued
2019-10
dc.identifier.citation
Zhang, Qiuru; Shi, Liangsheng; Holzman, Mauro Ezequiel; Ye, Ming; Wang, Yakun; et al.; A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation; Elsevier; Advances in Water Resources; 132; 103407; 10-2019; 1-17
dc.identifier.issn
0309-1708
dc.identifier.uri
http://hdl.handle.net/11336/111757
dc.description.abstract
Attributing to the flexibility in considering various types of observation error and model error, data assimilation has been increasingly applied to dynamically improve soil moisture modeling in many hydrological practices. However, accurate characterization of model error, especially the part caused by defective model structure, presents a significant challenge to the successful implementation of data assimilation. Model structural error has received limited attention relative to parameter and input errors, mainly due to our poor understanding of structural inadequacy and the difficulties in parameterizing structural error. In this paper, we present a dynamic data-driven approach to estimate the model structural error in soil moisture data assimilation without the need for identifying error generation mechanism or specifying particular form for the error model. The error model is based on the Gaussian process regression and then integrated into the ensemble Kalman filter (EnKF) to form a hybrid method for dealing with multi-source model errors. Two variants of the hybrid method in terms of two different error correction manners are proposed. The effectiveness of the proposed method is tested through a suit of synthetic cases and a real-world case. Results demonstrate the potential of the proposed hybrid method for estimating model structural error and providing improved model predictions. Compared to the traditional EnKF without explicitly considering the model structural error, parameter compensation issue is obviously reduced and soil moisture retrieval is substantially improved.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DATA ASSIMILATION
dc.subject
DATA-DRIVING
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MACHINE LEARNING
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MODEL STRUCTURAL ERROR
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SOIL MOISTURE
dc.subject.classification
Oceanografía, Hidrología, Recursos Hídricos
dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.subject.classification
Otras Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-04-24T17:49:43Z
dc.journal.volume
132
dc.journal.number
103407
dc.journal.pagination
1-17
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Zhang, Qiuru. Wuhan University; China
dc.description.fil
Fil: Shi, Liangsheng. Wuhan University; China
dc.description.fil
Fil: Holzman, Mauro Ezequiel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; Argentina
dc.description.fil
Fil: Ye, Ming. Florida State University; Estados Unidos
dc.description.fil
Fil: Wang, Yakun. Wuhan University; China
dc.description.fil
Fil: Carmona, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión de Investigaciones Científicas de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff". - Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto de Hidrología de Llanuras "Dr. Eduardo Jorge Usunoff"; Argentina
dc.description.fil
Fil: Zha, Yuanyuan. Wuhan University; China
dc.journal.title
Advances in Water Resources
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.advwatres.2019.103407
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/abs/pii/S0309170818310170
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