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MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS

Data assimilation is becoming a promising technique in hydrologic modelling to update not only model states but also to infer model parameters, specifically to infer soil hydraulic properties in Richard-equation-based soil water models. The Ensemble Kalman Filter method is one of the most widely emp...

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Detalles Bibliográficos
Autores principales: Valdes-Abellan, Javier, Pachepsky, Yakov, Martinez, Gonzalo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945924/
https://www.ncbi.nlm.nih.gov/pubmed/29755950
http://dx.doi.org/10.1016/j.mex.2018.02.008
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author Valdes-Abellan, Javier
Pachepsky, Yakov
Martinez, Gonzalo
author_facet Valdes-Abellan, Javier
Pachepsky, Yakov
Martinez, Gonzalo
author_sort Valdes-Abellan, Javier
collection PubMed
description Data assimilation is becoming a promising technique in hydrologic modelling to update not only model states but also to infer model parameters, specifically to infer soil hydraulic properties in Richard-equation-based soil water models. The Ensemble Kalman Filter method is one of the most widely employed method among the different data assimilation alternatives. In this study the complete Matlab© code used to study soil data assimilation efficiency under different soil and climatic conditions is shown. The code shows the method how data assimilation through EnKF was implemented. Richards equation was solved by the used of Hydrus-1D software which was run from Matlab. • MATLAB routines are released to be used/modified without restrictions for other researchers; • Data assimilation Ensemble Kalman Filter method code. • Soil water Richard equation flow solved by Hydrus-1D.
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spelling pubmed-59459242018-05-11 MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS Valdes-Abellan, Javier Pachepsky, Yakov Martinez, Gonzalo MethodsX Agricultural and Biological Science Data assimilation is becoming a promising technique in hydrologic modelling to update not only model states but also to infer model parameters, specifically to infer soil hydraulic properties in Richard-equation-based soil water models. The Ensemble Kalman Filter method is one of the most widely employed method among the different data assimilation alternatives. In this study the complete Matlab© code used to study soil data assimilation efficiency under different soil and climatic conditions is shown. The code shows the method how data assimilation through EnKF was implemented. Richards equation was solved by the used of Hydrus-1D software which was run from Matlab. • MATLAB routines are released to be used/modified without restrictions for other researchers; • Data assimilation Ensemble Kalman Filter method code. • Soil water Richard equation flow solved by Hydrus-1D. Elsevier 2018-03-07 /pmc/articles/PMC5945924/ /pubmed/29755950 http://dx.doi.org/10.1016/j.mex.2018.02.008 Text en http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Agricultural and Biological Science
Valdes-Abellan, Javier
Pachepsky, Yakov
Martinez, Gonzalo
MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS
title MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS
title_full MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS
title_fullStr MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS
title_full_unstemmed MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS
title_short MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS
title_sort matlab algorithm to implement soil water data assimilation with the ensemble kalman filter using hydrus
topic Agricultural and Biological Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5945924/
https://www.ncbi.nlm.nih.gov/pubmed/29755950
http://dx.doi.org/10.1016/j.mex.2018.02.008
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