ID:
publications-1607
Type:
PEER REVIEWED ARTICLE
Year:
2018
Authors:
A. Gruber , W. T. Crow , W. A. Dorigo
Title:
Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain
Venue/Journal:
DOI:
10.1002/2017wr021277
Research type:
Simulation & Modeling
Water System:
Water Distribution Networks
Technical Focus:
Abstract:
AbstractGrowth in the availability of nearârealâtime soil moisture observations from groundâbased networks has spurred interest in the assimilation of these observations into land surface models via a twoâdimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and modelâbased soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a twoâdimensional (2âD) data assimilation system designed to assimilate spatially sparse observations acquired from existing groundâbased soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2âD system is compared to that obtained from the 1âD assimilation of remote sensing retrievals to assess the value of groundâbased observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold 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 satelliteâbased surface soil moisture retrievals.
Link with Projects:
603608
Link with Tools:
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