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  • 1
    Publication Date: 2024-03-18
    Description: Various environmental variables drive gross primary productivity (GPP) and light use efficiency (LUE) of forest ecosystems. However, due to their intertwined nature and the complexity of measuring absorbed photosynthetically active radiation (APAR) of forest canopies, the assessment of LUE and the importance of its environmental drivers are difficult. Here, we present a unique combination of measurements during the 2021 growing season including eddy covariance derived GPP, sap flow, Sentinel‐2 derived canopy chlorophyll content and in situ measured APAR. The importance of environmental variables for GPP models is quantified with state‐of‐the‐art machine learning techniques. A special focus is put on photosynthesis‐limiting conditions, which are identified by a comparison of GPP and sap flow hysteretic responses to Vapor pressure deficit (VPD) and APAR. Results demonstrate that (a) LUE of the canopy's green part was on average 4.0% ± 2.3%, (b) canopy chlorophyll content as a seasonal variable for photosynthetic capacity was important for GPP predictions, and (c) on days with high VPD, tree‐scale sap flow and ecosystem‐scale GPP both shift to a clockwise hysteretic response to APAR. We demonstrate that the onset of such a clockwise hysteretic pattern of sap flow to APAR is a good indicator of stomatal closure related to water‐limiting conditions at the ecosystem‐scale.
    Description: Plain Language Summary: The efficiency by which a forest uses sunlight to perform photosynthesis is an important feature for climate and ecosystem modeling. However, the light that is actually captured by forests and is useable for photosynthesis is difficult to assess. Here, we show a sophisticated approach to estimate the light use efficiency of a spruce forest in Germany and analyze environmental influences on it and on photosynthesis. Our results indicate that about 4% of the light useable for photosynthesis was actually used by the forest during the 2021 growing season and that seasonal variations of chlorophyll in the canopy are a good indicator for carbon capture.
    Description: Key Points: A seasonal variable such as canopy chlorophyll content was useful to predict gross primary productivity with machine learning models. A clockwise hysteretic pattern of sap flow to radiation is a good indicator of water‐related stomatal closure. The light use efficiency of green parts of a spruce forest was 4.0% with a standard deviation of 2.3% during the 2021 growing season.
    Description: RWTH Aachen University http://dx.doi.org/10.13039/501100007210
    Description: University of Alberta http://dx.doi.org/10.13039/501100000190
    Description: Excellence Strategy of the Federal Government and the Länder
    Description: Waldklimafonds http://dx.doi.org/10.13039/501100010297
    Description: German Federal Ministry of Food and Agriculture
    Description: German Federal Ministry for the Environment, Nature Conservation, Nuclear Safety and Consumer Protection
    Description: TERENO project
    Description: Helmholtz research infrastructure Modular Observation Solutions for Earth Systems
    Description: http://doi.org/10.5281/zenodo.7014604
    Description: https://www.opengeodata.nrw.de/produkte/geobasis/hm/3dm_l_las/3dm_l_las/
    Description: https://scihub.copernicus.eu/
    Keywords: ddc:634.9 ; photosynthetically active radiation ; canopy chlorophyll content ; hysteresis ; sap flow ; variable importance ; Picea abies
    Language: English
    Type: doc-type:article
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  • 2
    Publication Date: 2015-03-12
    Description: Cosmic-ray probes are an emerging technology to continuously monitor soil water content at a scale significant to land surface processes. However, the application of this method is hampered by its susceptibility to the presence of aboveground biomass. Here, we present a simple empirical framework to account for moderation of fast neutrons by aboveground biomass in the calibration. The method extends the N 0 -calibration function and was developed using an extensive data set from a network of ten cosmic-ray probes located in the Rur catchment, Germany. The results suggest a 0.9% reduction in fast neutron intensity per 1kg of dry aboveground biomass per m 2 or per 2kg of biomass water equivalent per m 2 . We successfully tested the novel vegetation correction using temporary cosmic-ray probe measurements along a strong gradient in biomass due to deforestation, and using the COSMIC, and the hmf-method as independent soil water content retrieval algorithms. The extended N 0 -calibration function was able to explain 95% of the overall variability in fast neutron intensity. This article is protected by copyright. All rights reserved.
    Print ISSN: 0043-1397
    Electronic ISSN: 1944-7973
    Topics: Architecture, Civil Engineering, Surveying , Geography
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  • 3
    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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  • 4
    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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  • 5
    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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  • 6
    Publication Date: 2020-12-10
    Description: Monitoring soil moisture is still a challenge: it varies strongly in space and time and at various scales while conventional sensors typically suffer from small spatial support. With a sensor footprint up to several hectares, cosmic-ray neutron sensing (CRNS) is a modern technology to address that challenge. So far, the CRNS method has typically been applied with single sensors or in sparse national-scale networks. This study presents, for the first time, a dense network of 24 CRNS stations that covered, from May to July 2019, an area of just 1 km2: the pre-Alpine Rott headwater catchment in Southern Germany, which is characterized by strong soil moisture gradients in a heterogeneous landscape with forests and grasslands. With substantially overlapping sensor footprints, this network was designed to study root-zone soil moisture dynamics at the catchment scale. The observations of the dense CRNS network were complemented by extensive measurements that allow users to study soil moisture variability at various spatial scales: roving (mobile) CRNS units, remotely sensed thermal images from unmanned areal systems (UASs), permanent and temporary wireless sensor networks, profile probes, and comprehensive manual soil sampling. Since neutron counts are also affected by hydrogen pools other than soil moisture, vegetation biomass was monitored in forest and grassland patches, as well as meteorological variables; discharge and groundwater tables were recorded to support hydrological modeling experiments. As a result, we provide a unique and comprehensive data set to several research communities: to those who investigate the retrieval of soil moisture from cosmic-ray neutron sensing, to those who study the variability of soil moisture at different spatiotemporal scales, and to those who intend to better understand the role of root-zone soil moisture dynamics in the context of catchment and groundwater hydrology, as well as land–atmosphere exchange processes. The data set is available through the EUDAT Collaborative Data Infrastructure and is split into two subsets: https://doi.org/10.23728/b2share.282675586fb94f44ab2fd09da0856883 (Fersch et al., 2020a) and https://doi.org/10.23728/b2share.bd89f066c26a4507ad654e994153358b (Fersch et al., 2020b).
    Language: English
    Type: info:eu-repo/semantics/article
    Format: application/pdf
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  • 7
  • 8
    Publication Date: 2021-01-04
    Description: Although the hydrological effects of land use change have been studied extensively, only few datasets are available to accurately describe, model, and predict detailed changes in spatiotemporal patterns of hydrological fluxes and states due to land use change. The Wustebach catchment within the TERENO (TERrestrial Environmental Observatories) network in Germany provides a unique monitoring setup to measure the major components of the water balance (evapotranspiration, discharge, precipitation) and the spatiotemporal distribution of soil moisture before and after a partial deforestation. Here, we present five years of measured hydrological data, including all major water budget components three years before and two years after a partial deforestation. A data-driven approach was used to understand changes and related feedback mechanisms in spatiotemporal hydrological response patterns. As expected from earlier studies, the partial deforestation caused a decrease in evapotranspiration and an increase in discharge. A closer look at the high resolution datasets revealed new insights in the intra-annual variability and relationship between the water balance components. The overall decrease in evapotranspiration caused a large increase in soil water storage in the deforested region, especially during the summer period, which in turn caused an increase in the frequency of high discharge in the same period. Although the evapotranspiration in the forested region was larger on average, the deforested region showed a higher evapotranspiration during part of the summer period. This could be related to wetter conditions in the deforested area, accompanied with the emergence of grass vegetation. At the same time, wetter soil moisture conditions in the deforested area increased the spatial variance of soil moisture in the summer and therewith altered the relationship between spatial mean and variance. Altogether, this study illustrates that detailed spatiotemporal monitoring can provide new insights into the hydrological effects of partial deforestation. (C) 2016 Elsevier B.V. All rights reserved.
    Type: info:eu-repo/semantics/article
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  • 9
    Publication Date: 2020-02-12
    Keywords: 550 - Earth sciences
    Type: info:eu-repo/semantics/article
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  • 10
    Publication Date: 2020-02-12
    Type: info:eu-repo/semantics/article
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