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  • 1
    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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  • 2
    Publication Date: 2023-01-26
    Description: Cosmic‐ray neutron sensors (CRNS) enable noninvasive determination of field‐scale soil moisture content by exploiting the dependence of the intensity of aboveground epithermal neutrons on the hydrogen contained in soil moisture. However, there are other hydrogen pools besides soil moisture (e.g., biomass). Therefore, these hydrogen pools should be considered for accurate soil moisture content measurements, especially when they are changing dynamically (e.g., arable crops, deforestation, and reforestation). In this study, we test four approaches for the correction of biomass effects on soil moisture content measurements with CRNS using experiments with three crops (sugar beet, winter wheat, and maize) based on high‐quality reference soil moisture: (a) site‐specific functions based on in‐situ measured biomass, (b) a generic approach, (c) the thermal‐to‐epithermal neutron ratio (Nr), and (d) the thermal neutron intensity. Bare soil calibration of the CRNS resulted in high root mean square errors (RMSEs) of 0.097, 0.041, and 0.019 m³/m³ between estimated and reference soil moisture content for sugar beet, winter wheat, and maize, respectively. Considering in‐situ measured biomass for correction reduced the RMSE to 0.015, 0.018, and 0.009 m³/m³. The consideration of thermal neutron intensity for correction was similarly accurate. We also explored the use of CRNS for biomass estimation and found that Nr only provided accurate biomass estimates for sugar beet. In contrast, we found significant site‐specific relationships between biomass and thermal neutron intensity for all three crops, suggesting that thermal neutron intensity can be used both to improve CRNS‐based soil moisture content measurements and to quantify crop biomass.
    Description: Plain Language Summary: Water availability is a key challenge in agriculture, especially given the expected increase of droughts related to climate change. A promising noninvasive technique to monitor soil moisture content is cosmic‐ray neutron sensing (CRNS), which is based on the negative correlation between the number of near‐surface fast neutrons originating from cosmic radiation and the amount of hydrogen stored as soil moisture. However, hydrogen is also stored in other pools, such as biomass. These additional pools of hydrogen must be considered to accurately determine soil moisture content with CRNS. In this study, we used data from three experiments with different crops for comparing four methods for the correction of biomass effects on the measurement of soil moisture content with CRNS. We found that soil moisture content measurements were most accurate when locally measured biomass was considered for correction. We also found that changes in the amount of biomass of different crops can be quantified using thermal neutrons additionally detected by CRNS, that is, neutrons from cosmic rays that have a lower energy than fast neutrons. A correction of biomass effects using thermal neutron measurements also provided accurate soil moisture content measurements.
    Description: Key Points: Cosmic ray soil moisture measurements were most accurate when corrected with in‐situ biomass measurements or thermal neutron intensity. The effect of biomass on epithermal and thermal neutron intensity is plant‐specific. Biomass could be estimated from thermal neutron intensity for three crops, but not with the thermal‐to‐epithermal neutron ratio.
    Description: Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
    Description: EU‐FP7
    Description: https://doi.org/10.34731/qb7h-6287
    Keywords: ddc:631.4 ; soil moisture ; cosmic ray neutron sensing ; biomass influence ; biomass estimation ; thermal neutrons
    Language: English
    Type: doc-type:article
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