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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2018
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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 |
Sumario: | 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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