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Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

Tijdschriftbijdrage - Tijdschriftartikel

© 2018 The Authors. 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 (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based 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 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based 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 satellite-based surface soil moisture retrievals.
Tijdschrift: Water Resources Research
ISSN: 0043-1397
Issue: 2
Volume: 54
Pagina's: 1353 - 1367
Jaar van publicatie:2018
BOF-keylabel:ja
IOF-keylabel:ja
BOF-publication weight:10
CSS-citation score:2
Auteurs:International
Authors from:Higher Education
Toegankelijkheid:Open