|Kumar, S - SSAI|
|Reichle, R - NASA GSFC|
|Koster, R - NASA GSFC|
|Peters-Lidard, C - NASA GSFC|
Submitted to: Journal of Hydrometeorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: May 15, 2009
Publication Date: December 1, 2009
Repository URL: http://handle.nal.usda.gov/10113/42601
Citation: Kumar, S.V., Reichle, R.H., Koster, R.D., Crow, W.T., Peters-Lidard, C.D. 2009. Role of subsurface physics in the assimilation of surface soil moisture observations. Journal of Hydrometeorology. dx.doi.org/10.1175/2009JHM1134.1. Interpretive Summary: Data assimilation is a mathematic process by which information gleaned from independent sources is optimally merged to yield the best single estimate of an unknown variable. In the hydrologic sciences such techniques are commonly applied to minimize errors in soil moisture, stream flow and land surface evaporation estimates which are, in turn, used for agricultural applications including: crop yield forecasting, the optimization of fertilizer application, water quality monitoring and irrigation scheduling. This manuscript examines a specific technical aspect of such approaches when applied to the remote sensing retrievals of surface soil moisture. In particular, it looks at the impact of typical model differences on the marginal value of assimilating soil moisture for the very common case in which the correct model specification is unknown. Dealing with such modeling uncertainty is an important source of error in many data assimilation systems and hampers the development of important agricultural applications. The results presented here are an important first step towards developing improved strategies for dealing with model structural uncertainty which, in turn, will allow us to better monitoring hydrologic conditions over large scales and address key agricultural applications requiring information about such conditions.
Technical Abstract: Soil moisture controls the exchange of water and energy between the land surface and the atmosphere and exhibits memory that may be useful for climate prediction at monthly time scales. Though spatially distributed observations of soil moisture are increasingly becoming available from remotely sensed platforms, similar large-scale observations of root zone soil moisture are not routinely available. Assimilation of surface soil moisture observations into a land surface model (LSM) is an effective way to generate estimates of root zone soil moisture stores. Land surface models, however, differ significantly in their representation of subsurface soil moisture processes. Therefore, the propagation of surface information into deeper soil layers depends on the LSM that is used in the assimilation system. In a suite of experiments we assimilate synthetic surface soil moisture observations into four different LSMs (Catchment, Mosaic, Noah and CLM) using the Ensemble Kalman Filter and investigate the impact of subsurface physics on the skill of soil moisture assimilation products. The analysis presented in the article provides two key results. The first result indicates that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. The second result concerns the optimal choice of LSM for data assimilation when the true subsurface physics is unknown. The results indicate that models with strong surface to root zone coupling representation (Catchment and Mosaic) provide greater skill improvements in the root zone compared to models with weaker surface to root zone coupling (Noah and CLM). The sensitivity of the data assimilation system in response to different observations was also found to be less in case of Catchment compared to the other LSMs. Finally, the skill improvements through assimilation were found to be sensitive to the geographic location and also the vegetation and soil types used in the land surface models.