DOI: 10.5194/hess-18-2503-2014
Scopus记录号: 2-s2.0-84903830169
论文题名: Kalman filters for assimilating near-surface observations into the Richards equation - Part 1: Retrieving state profiles with linear and nonlinear numerical schemes
作者: Chirico G ; B ; , Medina H ; , Romano N
刊名: Hydrology and Earth System Sciences
ISSN: 10275606
出版年: 2014
卷: 18, 期: 7 起始页码: 2503
结束页码: 2520
语种: 英语
Scopus关键词: Control nonlinearities
; Finite difference method
; Kalman filters
; Nonlinear equations
; Soil moisture
; Solute transport
; Backward Euler scheme
; Ensemble Kalman Filter
; Explicit finite differences
; Finite difference scheme
; Kalman filter algorithms
; One-dimensional problem
; Operational assimilation
; Standard Kalman filters
; Algorithms
; algorithm
; Eulerian analysis
; Kalman filter
; nonlinearity
; Richards equation
; soil water
英文摘要: This paper examines the potential of different algorithms, based on the Kalman filtering approach, for assimilating near-surface observations into a one-dimensional Richards equation governing soil water flow in soil. Our specific objectives are: (i) to compare the efficiency of different Kalman filter algorithms in retrieving matric pressure head profiles when they are implemented with different numerical schemes of the Richards equation; (ii) to evaluate the performance of these algorithms when nonlinearities arise from the nonlinearity of the observation equation, i.e. when surface soil water content observations are assimilated to retrieve matric pressure head values. The study is based on a synthetic simulation of an evaporation process from a homogeneous soil column. Our first objective is achieved by implementing a Standard Kalman Filter (SKF) algorithm with both an explicit finite difference scheme (EX) and a Crank-Nicolson (CN) linear finite difference scheme of the Richards equation. The Unscented (UKF) and Ensemble Kalman Filters (EnKF) are applied to handle the nonlinearity of a backward Euler finite difference scheme. To accomplish the second objective, an analogous framework is applied, with the exception of replacing SKF with the Extended Kalman Filter (EKF) in combination with a CN numerical scheme, so as to handle the nonlinearity of the observation equation. While the EX scheme is computationally too inefficient to be implemented in an operational assimilation scheme, the retrieval algorithm implemented with a CN scheme is found to be computationally more feasible and accurate than those implemented with the backward Euler scheme, at least for the examined one-dimensional problem. The UKF appears to be as feasible as the EnKF when one has to handle nonlinear numerical schemes or additional nonlinearities arising from the observation equation, at least for systems of small dimensionality as the one examined in this study. © Author(s) 2014. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/78199
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Agricultural Engineering, University of Naples Federico II, Naples, Italy; Department of Basic Sciences, Agrarian University of Havana, Havana, Cuba
Recommended Citation:
Chirico G,B,, Medina H,et al. Kalman filters for assimilating near-surface observations into the Richards equation - Part 1: Retrieving state profiles with linear and nonlinear numerical schemes[J]. Hydrology and Earth System Sciences,2014-01-01,18(7)