ARS | Publication request: Upscaling with data assimilation in soil hydrology

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: February 16, 2012
Publication Date: May 12, 2012
Citation: Pachepsky, Y.A., Gish, T.J., Daughtry, C.S., Jacques, D., Cady, R., Nicholson, T. 2012. Upscaling with data assimilation in soil hydrology. [abstract]Proceedings of PEDOFRACT. p.13.

Technical Abstract: Most of measurements in soil hydrology are point-based, and methods are needed to use the point-based data for estimating soil water contents at larger societally-important scales, such as field, hillslope or watershed. One group of appropriate methods involves data assimilation which is a methodology to correct simulation results with monitoring of simulated variables. It has been demonstrated in atmospheric and ocean applications that assimilation based corrections can rely on the number of observation locations that is very small with the number of locations where corrections will be made. This creates the much needed opportunity of efficient upscaling of soil moisture from small number of monitoring points. The efficient soil water data assimilation requires reliable estimation of the uncertainty in water contents at both coarse and fine scales. This can be achieved by using (a) ensemble Kalman filter with pedotransfer functions to build a realistic ensemble for estimating the uncertainty of simulated soil water content values at the coarse scale, (b) using a spatiotemporal model of the temporal stability in soil water contents to provide the correct estimate of the noise in the components of the average soil water content, and (c) introducing the site specific corrections for soil water sensors. This lecture will elaborate these ideas and illustrate it with examples from soil water monitoring and measurements at two field sites – one in Beltsville Agricultural research center, Maryland, and another in Bekkevoort, Belgium. We demonstrate that while the saturated hydraulic conductivity provides the dominant local control of spatiotemporal dynamics of soil water contents at field and pedon scales, its spatial variability does not preclude the efficient data assimilation and upscaling of soil moisture.