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

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Title: MATLAB algorithm to implement soil water data assimilation with the Ensemble Kalman Filter using HYDRUS
Authors: Valdes-Abellan, Javier | Pachepsky, Yakov | Martinez, Gonzalo
Research Group/s: Ingeniería Hidráulica y Ambiental (IngHA)
Center, Department or Service: Universidad de Alicante. Departamento de Ingeniería Civil
Keywords: Hydrus | EnKF | Soil water flux modelling
Knowledge Area: Ingeniería Hidráulica
Issue Date: 2018
Publisher: Elsevier
Citation: MethodsX. 2018, 5: 184-203. doi:10.1016/j.mex.2018.02.008
Abstract: 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.
Sponsor: This study forms part of the CGL2013-48802-C3-3-R project financed by the Spanish Ministry of Science and Innovation, the FPDI-2013-16742 from the Spanish Ministry of Economics, and GRE15-19 financed by the University of Alicante. A post-doctoral research fellowship (CAS 15/00244) funded by the Spanish Ministry of Science and Innovation was awarded to J. Valdes-Abellan for this project.
URI: http://hdl.handle.net/10045/74204
ISSN: 2215-0161
DOI: 10.1016/j.mex.2018.02.008
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2018 Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: http://dx.doi.org/10.1016/j.mex.2018.02.008
Appears in Collections:INV - IngHA - Artículos de Revistas

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