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Hierarchical Bayesian modeling for soil moisture
Journal of the Korean Data & Information Science Society 2019;30:713-21
Published online July 31, 2019;
© 2019 Korean Data and Information Science Society.

Yongku Kim1

1Department of Statistics, Kyungpook National University
Correspondence to: Associate professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail:
Received May 28, 2019; Revised June 5, 2019; Accepted June 8, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
oil moisture is an important hydrologic parameter that controls the various processes of the surface. It functions to connect rain water and ground water through infiltration, and directly affects the run-off characteristics according to rainfall. In order to understand the water circulation system, it is essential to study relationship between rainfall and soil moisture. In this paper, we introduced hierarchical Bayesian model for water potential or volumetric water content to assess hydraulic redistribution. We then investigate the slope change of volumetric water content before and after a rainstorm event.
Keywords : Bayesian analysis, hierarchical model, piecewise regression model, rainfall, soil moisture.