Estimation of in situ hydraulic conductivity function from nonlinear filtering theory

Abstract

A method based on an optimal nonlinear filtering technique is proposed and tested for the determination of the hydraulic conductivity function from a field drainage experiment. Simplifications to Richards's equation lead to a Langevin type differential equation to describe the redistribution of stored water as a function of drainage flux excited by a random initial condition and state forcing. The derived equation is then utilized in an optimal estimation scheme that explicitly accounts for the formulation and observation uncertainty in determining the hydraulic conductivity parameters. A field drainage experiment was carried out to study the usefulness of the proposed method for routine in situ hydraulic conductivity function estimation.

State-space approach to evaluate the relation between soil physical and chemical properties

“…In contrast, these newly applied methods in the field of soil science take advantage of spatial dependence by making use of the characteristics of each observation location. Statistical tools like autocorrelation function, semivariograms, and state-space, have been used recently to define the structure of spatial distributions of soil properties (Wendroth et al, 1992;Katul et al, 1993;Wendroth et al, 1997;Hui et al, 1998;Dourado-Neto et al, 1999;Timm et al, 2000Timm et al, , 2003a. According to Bresler et al (1981), research during the last two decades has focused the study of soil spatial variability on an improved understanding of processes that influence crop production variability.…”

Scaling xylem sap flux and soil water balance and calculating variance: a method for partitioning water flux in forests

“…[2], or by spatial variation in rooting volume and soil moisture availability [27,28], this is not always the case [54]. [33,66] …”

Land, Water and Energy Data Assimilation

“…Surface hydrology data assimilation is based primarily on soil moisture information from surface observations or remote sensing. There are several soil moisture estimation data assimilation techniques that use a one-dimensional optimal estimation approach, including studies by Milly (1986), Katul et al (1993), Parlange et al, (1993), Entekhabi et al (1994), Galantowicz et al (1999), Calvet et al (1998) and Castelli et al (1999). Several additional studies have used low-level atmospheric observations to infer soil moisture using one-dimensional optimal variation assimilation approaches (Mahfouf, 1991;Bouttier et al, 1993;Hu et al, 1999;Callies et al, 1998;Rhodin, et al, 1999).…”