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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Environmental Microbial & Food Safety Laboratory » Research » Publications at this Location » Publication #350998
Research Project: Design and Implementation of Monitoring and Modeling Methods to Evaluate Microbial Quality of Surface Water Sources Used for Irrigation

Location: Environmental Microbial & Food Safety Laboratory

Title: Matlab algorithm to implement soil water data assimilation with the ensemble kalman filter using Hydrus

Author
item VALDES-ABELLAN, JAVIER - Universidad De Alicante
item Pachepsky, Yakov
item MARTINEZ, GONZALO - Universidad De Cordoba

Submitted to: MethodsX
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/11/2018
Publication Date: 5/18/2018
Citation: Valdes-Abellan, J., Pachepsky, Y.A., Martinez, G. 2018. Matlab algorithm to implement soil water data assimilation with the ensemble kalman filter using Hydrus. MethodsX. 5:184-203.

Interpretive Summary: Hydraulic parameters of soils are of paramount importance in projects related to ability of soils to retain and conduct water and water-borne chemical and biological pollutants. A traditional way of finding these parameters is to calibrate water flow model, that is, to find parameter values allowing to this model to reproduce a long-term monitoring dataset with the highest possible accuracy. Recently it was shown that instead of waiting until a long-term dataset will be accumulated, one can gradually improve the parameter set each time as new observations become available. This methodology became known as data assimilation. The purpose of this work was to develop the computer tools to apply the data assimilation to soil water content measurements. We developed the software that is flexible and efficient to assimilate the sol water content measurement data from soil water content sensors placed at several depths in soil. The software was tested with synthetic datasets developed for two soils and four climates. This work is useful for researchers and modelers-practitioners in the field of soil hydrology and contaminant transport.

Technical Abstract: Data assimilation is becoming a promising technique in hydrologic modeling 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.