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  • 1
    In: Biogeosciences, Copernicus GmbH, Vol. 17, No. 4 ( 2020-02-26), p. 1033-1061
    Abstract: Abstract. The dynamics of biochemical processes in terrestrial ecosystems are tightly coupled to local meteorological conditions. Understanding these interactions is an essential prerequisite for predicting, e.g. the response of the terrestrial carbon cycle to climate change. However, many empirical studies in this field rely on correlative approaches and only very few studies apply causal discovery methods. Here we explore the potential for a recently proposed causal graph discovery algorithm to reconstruct the causal dependency structure underlying biosphere–atmosphere interactions. Using artificial time series with known dependencies that mimic real-world biosphere–atmosphere interactions we address the influence of non-stationarities, i.e. periodicity and heteroscedasticity, on the estimation of causal networks. We then investigate the interpretability of the method in two case studies. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem. Secondly, we explore global Normalised Difference Vegetation Index time series (GIMMS 3g), along with gridded climate data to study large-scale climatic drivers of vegetation greenness. We compare the retrieved causal graphs to simple cross-correlation-based approaches to test whether causal graphs are considerably more informative. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. For example, we find a complete decoupling of the net ecosystem exchange from meteorological variability during summer in the Mediterranean ecosystem. However, cautious interpretations are needed, as the violation of the method's assumptions due to non-stationarities increases the likelihood to detect false links. Overall, estimating directed biosphere–atmosphere networks helps unravel complex multidirectional process interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful sets of relations, which can be powerful insights for the evaluation of terrestrial ecosystem models.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2158181-2
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  • 2
    In: Biogeosciences, Copernicus GmbH, Vol. 16, No. 19 ( 2019-10-01), p. 3747-3775
    Abstract: Abstract. Evaporation (E) and transpiration (T) respond differently to ongoing changes in climate, atmospheric composition, and land use. It is difficult to partition ecosystem-scale evapotranspiration (ET) measurements into E and T, which makes it difficult to validate satellite data and land surface models. Here, we review current progress in partitioning E and T and provide a prospectus for how to improve theory and observations going forward. Recent advancements in analytical techniques create new opportunities for partitioning E and T at the ecosystem scale, but their assumptions have yet to be fully tested. For example, many approaches to partition E and T rely on the notion that plant canopy conductance and ecosystem water use efficiency exhibit optimal responses to atmospheric vapor pressure deficit (D). We use observations from 240 eddy covariance flux towers to demonstrate that optimal ecosystem response to D is a reasonable assumption, in agreement with recent studies, but more analysis is necessary to determine the conditions for which this assumption holds. Another critical assumption for many partitioning approaches is that ET can be approximated as T during ideal transpiring conditions, which has been challenged by observational studies. We demonstrate that T can exceed 95 % of ET from certain ecosystems, but other ecosystems do not appear to reach this value, which suggests that this assumption is ecosystem-dependent with implications for partitioning. It is important to further improve approaches for partitioning E and T, yet few multi-method comparisons have been undertaken to date. Advances in our understanding of carbon–water coupling at the stomatal, leaf, and canopy level open new perspectives on how to quantify T via its strong coupling with photosynthesis. Photosynthesis can be constrained at the ecosystem and global scales with emerging data sources including solar-induced fluorescence, carbonyl sulfide flux measurements, thermography, and more. Such comparisons would improve our mechanistic understanding of ecosystem water fluxes and provide the observations necessary to validate remote sensing algorithms and land surface models to understand the changing global water cycle.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2158181-2
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  • 3
    Online Resource
    Online Resource
    Copernicus GmbH ; 2020
    In:  Geoscientific Instrumentation, Methods and Data Systems Vol. 9, No. 1 ( 2020-05-29), p. 239-254
    In: Geoscientific Instrumentation, Methods and Data Systems, Copernicus GmbH, Vol. 9, No. 1 ( 2020-05-29), p. 239-254
    Abstract: Abstract. Soil CO2 efflux is the second-largest carbon flux in terrestrial ecosystems. Its feedback to climate determines model predictions of the land carbon sink, which is crucial to understanding the future of the earth system. For understanding and quantification, however, observations by the most widely applied chamber measurement method need to be aggregated to larger temporal and spatial scales. The aggregation is hampered by random error that is characterized by occasionally large fluxes and variance heterogeneity that is not properly accounted for under the typical assumption of normally distributed fluxes. Therefore, we explored the effect of different distributional assumptions on the aggregated fluxes. We tested the alternative assumption of lognormally distributed random error in observed fluxes by aggregating 1 year of data of four neighboring automatic chambers at a Mediterranean savanna-type site. With the lognormal assumption, problems with error structure diminished, and more reasonable prediction intervals were obtained. While the differences between distributional assumptions diminished when aggregating data of single chambers to an annual value, differences were important on short timescales and were especially pronounced when aggregating across chambers to plot level. Hence we recommend as a good practice that researchers report plot-level fluxes with uncertainties based on the lognormal assumption. Model data integration studies should compare predictions and observations of soil CO2 efflux on a log scale. This study provides methodology and guidance that will improve the analysis of soil CO2 efflux observations and hence improve understanding of soil carbon cycling and climate feedbacks.
    Type of Medium: Online Resource
    ISSN: 2193-0864
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2690575-9
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  • 4
    In: Earth System Science Data, Copernicus GmbH, Vol. 13, No. 6 ( 2021-06-14), p. 2607-2649
    Abstract: Abstract. Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.
    Type of Medium: Online Resource
    ISSN: 1866-3516
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2475469-9
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  • 5
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 26, No. 23 ( 2022-12-13), p. 6263-6287
    Abstract: Abstract. The input of liquid water to terrestrial ecosystems is composed of rain and non-rainfall water (NRW). The latter comprises dew, fog, and the adsorption of atmospheric vapor on soil particle surfaces. Although NRW inputs can be relevant to support ecosystem functioning in seasonally dry ecosystems, they are understudied, being relatively small, and therefore hard to measure. In this study, we apply a partitioning routine focusing on NRW inputs over 1 year of data from large, high-precision weighing lysimeters at a semi-arid Mediterranean site. NRW inputs occur for at least 3 h on 297 d (81 % of the year), with a mean diel duration of 6 h. They reflect a pronounced seasonality as modulated by environmental conditions (i.e., temperature and net radiation). During the wet season, both dew and fog dominate NRW, while during the dry season it is mostly the soil adsorption of atmospheric water vapor. Although NRW contributes only 7.4 % to the annual water input, NRW is the only water input to the ecosystem during 15 weeks, mainly in the dry season. Benefitting from the comprehensive set of measurements at our experimental site, we show that our findings are in line with (i) independent measurements and (ii) independent model simulations forced with (near-) surface energy and moisture measurements. Furthermore, we discuss the simultaneous occurrence of soil vapor adsorption and negative eddy-covariance-derived latent heat fluxes. This study shows that NRW inputs can be reliably detected through high-resolution weighing lysimeters and a few additional measurements. Their main occurrence during nighttime underlines the necessity to consider ecosystem water fluxes at a high temporal resolution and with 24 h coverage.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2100610-6
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