Bigleaf—An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data

Knauer, Jürgen; El-Madany, Tarek S.; Zaehle, Sönke; Migliavacca, Mirco

doi:10.1371/journal.pone.0201114

Cited by 93 publications

(102 citation statements)

References 97 publications

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“…However, when we apply linear interpolation to derive the forcing for the TIME-12h experiment, we obtain poor SWIN estimates, with a large difference between the estimated and CTL average SWIN fluxes (+97 W m −2 ). In order to better estimate the SWIN forcing for the TIME-12h experiment, we employ a method based on the potential (clear-sky) radiation at 30 min temporal resolution (Knauer et al, 2018) at the site of Torgnon. For each day of the year, the 48 values of potential radiation are rescaled according to the observed SWIN at 12:00 UTC+1.…”

Section: Experimental Designmentioning

confidence: 99%

“…The first method is a basic linear interpolation of the available measurements. The second method is slightly more sophisticated and employs the potential (clear-sky) incoming shortwave radiation (Knauer et al, 2018) at 30 min temporal resolution and at the coordinates of the Torgnon station and the SWIN station measurements at 12:00 UTC+1. For each day of the year, the 48 daily values of potential radiation are rescaled according to the observed SWIN value at 12:00, to obtain an "estimated SWIN" (Fig.…”

Section: Appendix B: Spatial Interpolation Of Meteorological Forcingsmentioning

confidence: 99%

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Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments

Terzago

Andreoli

Arduini

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Snow models are usually evaluated at sites providing high-quality meteorological data, so that the uncertainty in the meteorological input data can be neglected when assessing model performances. However, high-quality input data are rarely available in mountain areas and, in practical applications, the meteorological forcing used to drive snow models is typically derived from spatial interpolation of the available in situ data or from reanalyses, whose accuracy can be considerably lower. In order to fully characterize the performances of a snow model, the model sensitivity to errors in the input data should be quantified. In this study we test the ability of six snow models to reproduce snow water equivalent, snow density and snow depth when they are forced by meteorological input data with gradually lower accuracy. The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a.s.l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolution, obtained by (i) sampling the original Torgnon 30 min time series at 3, 6, and 12 h, (ii) spatially interpolating neighbouring in situ station measurements and (iii) extracting information from GLDAS, ERA5 and ERA-Interim reanalyses at the grid point closest to the Torgnon site. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity. The results show that, when forced by accurate 30 min resolution weather station data, the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills to the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower-complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills to the control run, while the use of 6- and 12-hourly temporal resolution forcings may lead to a reduction in model performances if the incoming shortwave radiation is not properly represented. The SMASH model generally shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from neighbouring stations and reanalyses are found to be adequate forcings, provided that temperature and precipitation variables are not affected by large biases over the considered period. However, a simple bias-adjustment technique applied to ERA-Interim temperatures allowed all models to achieve similar performances to the control run. Regardless of their complexity, all models show weaknesses in the representation of the snow density.

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Section: Experimental Designmentioning

confidence: 99%

Section: Appendix B: Spatial Interpolation Of Meteorological Forcingsmentioning

confidence: 99%

Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments

Terzago

Andreoli

Arduini

et al. 2020

Hydrol. Earth Syst. Sci.

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“…soil and canopy evaporation) and account for changes in atmospheric demand. New tools are now available to facilitate the derivation of G a and G s from the eddy‐covariance flux observations (Knauer, El‐Madany, El‐Madany, Zaehle, & Migliavacca, )—but all methods to date necessitate a “big‐leaf” assumption in the inversion of G s , which introduces additional assumptions.…”

Section: Estimating Historical Trends In Water‐use Efficiency At the mentioning

confidence: 99%

Observed and modelled historical trends in the water‐use efficiency of plants and ecosystems

Lavergne

Graven

Kauwe

et al. 2019

Global Change Biology

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Plant water‐use efficiency (WUE, the carbon gained through photosynthesis per unit of water lost through transpiration) is a tracer of the plant physiological controls on the exchange of water and carbon dioxide between terrestrial ecosystems and the atmosphere. At the leaf level, rising CO2 concentrations tend to increase carbon uptake (in the absence of other limitations) and to reduce stomatal conductance, both effects leading to an increase in leaf WUE. At the ecosystem level, indirect effects (e.g. increased leaf area index, soil water savings) may amplify or dampen the direct effect of CO2. Thus, the extent to which changes in leaf WUE translate to changes at the ecosystem scale remains unclear. The differences in the magnitude of increase in leaf versus ecosystem WUE as reported by several studies are much larger than would be expected with current understanding of tree physiology and scaling, indicating unresolved issues. Moreover, current vegetation models produce inconsistent and often unrealistic magnitudes and patterns of variability in leaf and ecosystem WUE, calling for a better assessment of the underlying approaches. Here, we review the causes of variations in observed and modelled historical trends in WUE over the continuum of scales from leaf to ecosystem, including methodological issues, with the aim of elucidating the reasons for discrepancies observed within and across spatial scales. We emphasize that even though physiological responses to changing environmental drivers should be interpreted differently depending on the observational scale, there are large uncertainties in each data set which are often underestimated. Assumptions made by the vegetation models about the main processes influencing WUE strongly impact the modelled historical trends. We provide recommendations for improving long‐term observation‐based estimates of WUE that will better inform the representation of WUE in vegetation models.

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“…Negative H and inaccurate ra modelling sometimes yielded negative rhs or values greater than one, especially at nighttime. In these cases, rhs was assigned the value of one following the approach described in the bigleaf R package 33 . We then estimated LEQ and LEG from Eq.…”

Section: Discussionmentioning

confidence: 99%

“…(9) instead of Eq. (7) in order to explicitly include atmospheric stability and canopy height dynamics33 .…”

mentioning

confidence: 99%

Relative humidity gradients as a key constraint on terrestrial water and energy fluxes

Kim¹,

García²,

Morillas³

et al. 2020

Preprint

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Bigleaf—An R package for the calculation of physical and physiological ecosystem properties from eddy covariance data

Cited by 93 publications

References 97 publications

Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments

Sensitivity of snow models to the accuracy of meteorological forcings in mountain environments

Observed and modelled historical trends in the water‐use efficiency of plants and ecosystems

Relative humidity gradients as a key constraint on terrestrial water and energy fluxes

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