Location: Hydrology and Remote Sensing LaboratoryTitle: Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain
|GRUBER, A. - Collaborator
|DORIGO, W.A. - Vienna University Of Technology
Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/15/2017
Publication Date: 2/16/2018
Citation: Gruber, A., Crow, W.T., Dorigo, W. 2018. Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain. Water Resources Research. 54:1353-1367. https://doi.org/10.1002/2017WR021277.
Interpretive Summary: Agricultural drought has enormous implications for domestic economic interests and international food security concerns. However, the negative impact of drought can be minimized through early detection and rapid adoption of drought mitigating management techniques. Land data assimilation systems are increasingly being tasked with optimally combining hydrologic information acquired from a variety of sources (e.g., hydrologic modeling, ground-based observations and remotely-sensed observations) into integrated predictions of root-zone soil moisture availability. These predictions, in turn, form the basis of agricultural drought monitoring systems used for early detection. The performance of these systems requires detailed statistical information about errors in various sources of soil moisture information (since accurate estimates should be weighted more than inaccurate ones). This paper describes a new mathematical technique for obtaining such information. This advance allows us, for the first time, to objectively measure the relative contribution of various observation types (e.g., ground-based versus remote sensing-based soil moisture products) to large-scale agricultural drought monitoring. Results from this paper will be used to develop priorities for the next-generation of earth observing systems for agricultural drought monitoring.
Technical Abstract: Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here, we apply newly-developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially-continuous land surface model. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2D system is compared to that obtained from the 1D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis within the CONUS. Results demonstrate that a tenfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.