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  • 1
    Publication Date: 2021-07-04
    Description: In their study, Dong and Ochsner (2018, https://doi.org/10.1002/2017WR021692) used an extensive data set of 18 cosmic‐ray neutron rover surveys along a 150 km long transect on unpaved roads to assess the influence of precipitation and soil texture on mesoscale soil moisture patterns. Based on their analysis, they concluded that soil texture, represented by sand content, exerted a stronger influence on mesoscale soil moisture variability than precipitation, represented by the antecedent precipitation index, on 17 of the 18 survey days. However, we found that Dong and Ochsner (2018) made a mistake in their calculation of volumetric soil moisture. After correction, the validity of the original conclusions of Dong and Ochsner (2018) was considerably weakened, as soil texture exerted a stronger influence on soil moisture than precipitation on 12 of the 18 survey days only.
    Description: Key Points: Dong and Ochsner (2018) concluded that soil texture exerted a stronger influence on mesoscale soil moisture variability than precipitation. Dong and Ochsner (2018) made a mistake in their calculation of volumetric soil moisture. We found that correlations between soil moisture and soil texture and precipitation were significantly different in only 8 of 18 surveys.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Keywords: 631.4 ; Cosmic‐Ray Neutron (CRN) Sensing ; CRN Rover ; mesoscale soil moisture ; soil moisture patterns
    Type: article
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  • 2
    Publication Date: 2022-03-29
    Description: A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
    Description: Plain Language Summary: A reanalysis is a unique set of continuous variables produced by optimally merging a numerical model and observed data. The data are merged with the model using available uncertainty estimates to generate the best possible estimate of the target variables. The framework for generating a reanalysis consists of the model, the data, and the model‐data‐fusion algorithm. The very specific requirements of reanalysis frameworks have led to the development of Earth‐compartment specific reanalysis for the atmosphere, the ocean and land. Here, we review atmospheric and oceanic reanalyzes, and in more detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic, and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Based on a review of existing achievements, we identify five major steps required to develop reanalysis for terrestrial ecosystem to shed more light on biotic and abiotic interactions. In the future, terrestrial ecosystem reanalysis will deliver continuous data streams on the state and the development of terrestrial ecosystems.
    Description: Key Points: Reanalyzes provide decades‐long model‐data‐driven harmonized and continuous data sets for new scientific discoveries. Novel global scale reanalyzes quantify the biogeochemical ocean cycle, terrestrial carbon cycle, land surface, and hydrologic processes. New observation technology and modeling capabilities allow in the near future production of advanced terrestrial ecosystem reanalysis.
    Description: European Union's Horizon 2020 research and innovation programme
    Description: Deutsche Forschungsgemeinschaft
    Description: U.S. Department of Energy
    Description: Emory University's Halle Institute for Global Research and the Halle Foundation Collaborative Research
    Description: NSF
    Description: NASA
    Description: Natural Environment Research Council
    Description: European Union'’s Horizon 2020 research and innovation programme
    Description: NSERC Discovery program, the Ocean Frontier Institute, and MEOPAR
    Description: Research Foundation Flanders (FWO)
    Description: Helmholtz Association
    Description: NASA Terrestrial Ecosystems
    Keywords: ddc:550
    Language: English
    Type: doc-type:article
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