Multi-scale assimilation of surface soil moisture data for robust root zone moisture predictions

✍ Scribed by Nicola Montaldo; John D Albertson

• Book ID: 104326820
• Publisher: Elsevier Science
• Year: 2003
• Tongue: English
• Weight: 501 KB
• Volume: 26
• Category: Article
• ISSN: 0309-1708
• DOI: 10.1016/s0309-1708(02)00103-3

✦ Synopsis

In the presence of uncertain initial conditions and soil hydraulic properties land surface model performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the near surface soil moisture as observed from a remote platform. Recently, Montaldo et al. [Water Resour Res 37 (2001) 2889] derived a framework that uses biases between observed and modeled time rates of change of surface soil moisture to quantify biases between modeled and actual root-zoneaverage soil moisture contents. For very large errors in the saturated conductivity the soil moisture assimilation procedure is continuously working against the drainage errors, resulting in a persistent bias in its predictions. In this paper, we adopt this persistent (directional) bias in soil moisture as evidence of an error in the saturated conductivity. From manipulations of soil water balance equations we derived an expression that quantitatively relates the persistent bias in soil moisture to the estimated error in the saturated hydraulic conductivity. We combined this result with the approach of Montaldo et al. [Water Resour Res 37 (2001) 2889] to form a multi-scale assimilation approach. The multi-scale assimilation system is shown to provide marked improvements in the prediction of root zone soil moisture for a case study using data taken from an experimental catchment near Cork, Ireland. In effect, the root zone moisture is updated to provide a temporal trajectory of the near surface moisture that follows the trajectory of the observed surface moisture, and the hydraulic conductivity is adjusted on the basis of the time averaged corrections applied to the root zone water content. It is anticipated that this approach would be useful in operational forecasting models over large domains, where system parameters would be uncertain and occasional distributed observations would be limited to the near surface zone.