the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Jul 2024
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis
Abstract. Long-term time series of transpiration, evaporation, plant photosynthesis, and soil respiration are essential for addressing numerous research questions related to ecosystem functioning. However, quantifying these fluxes is challenging due to the lack of reliable and direct measurement techniques, which has left gaps in the understanding of their temporal cycles and spatial variability. To help address this open challenge, we generated a dataset of these four components by implementing five (conventional and novel) approaches to partition total ET and CO2 fluxes into plant and soil fluxes across 47 NEON sites. The final dataset (https://doi.org/10.5281/zenodo.12191876) spans a five-year period and covers various ecosystems, including forests, grasslands, and agricultural terrain. This is the first comprehensive dataset covering such a wide spatial and temporal distribution. Overall, we observed good agreement across most methods for ET components, increasing the reliability of these estimates. Partitioning of CO2 components was found to be less robust and more dependent on prior knowledge of water-use efficiency. This dataset has several potential future applications, such as addressing critical questions regarding the response of ecosystems to extreme weather events, which are expected to become more severe and frequent with climate change.
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RC1: 'Comment on essd-2024-272', Anonymous Referee #1, 28 Aug 2024
This article describes a data set on water and CO2 fluxes that are partitioned into the underlying processes evaporation(E)/transpiration(T) and assimilation(GPP)/respiration(R), respectively. It covers a wide range of ecosystems across North America making use of the measurements at 47 NEON (National Ecological Observatory Network) sites over a period of five years. These data are relevant for the evaluation and calibration of earth system models and other local- to regional-scale models that predict the evolution of ecosystems, weather and climate. The study area and input data are described in sufficient detail. The methods section describes the partitioning algorithm in order to disentangle the net fluxes from eddy-covariance, the pre- and post-processing of the data, including gap-filling. The final data set is made available on zenodo and the structure of this dataset is also described in this article, including the variable names and units. It has a substantial size of 2.2 GB. The source code used for the application of the flux partitioning is available on a github repository. This rather descriptive part of the manuscript is followed by a comparative analysis (section 5), an exploration of potential research opportunities (section 6) and conclusions, which highlight the relevance and value of these data but also indicate the limitations and uncertainty.
Overall, this article describes highly relevant and unique data that may be used in many ways in the field of earth system sciences. The article itself is appropriated and of very high quality and is recommended for publication in ESSD.
Citation: https://doi.org/10.5194/essd-2024-272-RC1 -
AC1: 'Reply on RC1', Einara Zahn, 08 Oct 2024
We thank the overall positive assessment of this paper by the reviewer. We believe this dataset offers valuable applications and hope it will contribute to advancing research in flux partitioning and related areas.
Following the suggestions from the second reviewer, we have revised the paper to improve language and more clearly convey the experimental nature of the dataset and its limitations.
Citation: https://doi.org/10.5194/essd-2024-272-AC1
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AC1: 'Reply on RC1', Einara Zahn, 08 Oct 2024
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RC2: 'Comment on essd-2024-272', Anonymous Referee #2, 06 Sep 2024
This paper provides a dataset of partitioned water and carbon fluxes from NEON tower datasets. This is a unique dataset providing estimates of partitioning using multiple approaches, with estimates of uncertainty across a wide domain of land covers. The data could support a number studies on controlling factors of water and carbon fluxes that are hard to glean from bulk (NEE and ET) flux data. The products are provided as easy to use csv files with sufficient metadata. I have a few comments and suggestions below but my main concerns are:
- There is a long history of partitioning of NEE based on models such as nighttime respiration. Many of these partitioning approaches are regularly operationalized for ecosystems studies on GPP and respiration. I am surprised that data for these more typical methods (not based on flux theory) are not included in the dataset.
- This is very much an experimental dataset and while the authors acknowledge this, I am concerned that users might grab the data and use it with insufficient caution. I would consider a name for the paper that notes this is “experimental” and clearly distinguished this product from other NEE partitioning approaches (mentioned above).
25: There are a wider arrange of partitioning approaches such as isotopes (carbon and water), carbonyl sulfide, solar induced fluorescence, nighttime respiration etc…. A deeper acknowledgment of these approaches and how the approach her is independent is needed. Also, can you partitioning ET with a lysimeter or is this just an estimate of ET?
67: It was confusing to me why soil moisture was excluded from the predictor set. What does it mean to say it was “noisy”. I would assume soil moisture data would not be so noisy.
82: Should say “CO2 flux”
125: Was canopy height adjusted for grasslands during the growing season or is that considered a constant in the model?
150: Was stomatal saturation estimated with IR/skin temperature or air temperature?
414: I am a little concerned about the use of the term “remarkable” especially when the diurnal cycle of T/ET with FVS seems to be totally inverted relative to the other approaches. I agree that the consistency is generally quite impressive but some core differences are present.
Citation: https://doi.org/10.5194/essd-2024-272-RC2 -
AC2: 'Reply on RC2', Einara Zahn, 08 Oct 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-272/essd-2024-272-AC2-supplement.pdf
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EC1: 'Comment on essd-2024-272', Tobias Gerken, 08 Oct 2024
I would like to thank the authors for their replies.
I have a small technical correction, which I would like the authors to do while they are preparing the revised submission.
The authors are citing the github for the code and making the code public is commendable. However, github is not a suitable long-term archive for code. The authors should create a release of the final code and then archive that using zenodo as well (see for example here: https://coderefinery.github.io/github-without-command-line/doi/)
Citation: https://doi.org/10.5194/essd-2024-272-EC1 -
AC3: 'Reply on EC1', Einara Zahn, 08 Oct 2024
We appreciate the editor for bringing this issue to our attention. The code is already available on Zenodo, and we will ensure that the correct link and citation are included in the next revision.
Citation: https://doi.org/10.5194/essd-2024-272-AC3
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AC3: 'Reply on EC1', Einara Zahn, 08 Oct 2024
Status: closed
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RC1: 'Comment on essd-2024-272', Anonymous Referee #1, 28 Aug 2024
This article describes a data set on water and CO2 fluxes that are partitioned into the underlying processes evaporation(E)/transpiration(T) and assimilation(GPP)/respiration(R), respectively. It covers a wide range of ecosystems across North America making use of the measurements at 47 NEON (National Ecological Observatory Network) sites over a period of five years. These data are relevant for the evaluation and calibration of earth system models and other local- to regional-scale models that predict the evolution of ecosystems, weather and climate. The study area and input data are described in sufficient detail. The methods section describes the partitioning algorithm in order to disentangle the net fluxes from eddy-covariance, the pre- and post-processing of the data, including gap-filling. The final data set is made available on zenodo and the structure of this dataset is also described in this article, including the variable names and units. It has a substantial size of 2.2 GB. The source code used for the application of the flux partitioning is available on a github repository. This rather descriptive part of the manuscript is followed by a comparative analysis (section 5), an exploration of potential research opportunities (section 6) and conclusions, which highlight the relevance and value of these data but also indicate the limitations and uncertainty.
Overall, this article describes highly relevant and unique data that may be used in many ways in the field of earth system sciences. The article itself is appropriated and of very high quality and is recommended for publication in ESSD.
Citation: https://doi.org/10.5194/essd-2024-272-RC1 -
AC1: 'Reply on RC1', Einara Zahn, 08 Oct 2024
We thank the overall positive assessment of this paper by the reviewer. We believe this dataset offers valuable applications and hope it will contribute to advancing research in flux partitioning and related areas.
Following the suggestions from the second reviewer, we have revised the paper to improve language and more clearly convey the experimental nature of the dataset and its limitations.
Citation: https://doi.org/10.5194/essd-2024-272-AC1
-
AC1: 'Reply on RC1', Einara Zahn, 08 Oct 2024
-
RC2: 'Comment on essd-2024-272', Anonymous Referee #2, 06 Sep 2024
This paper provides a dataset of partitioned water and carbon fluxes from NEON tower datasets. This is a unique dataset providing estimates of partitioning using multiple approaches, with estimates of uncertainty across a wide domain of land covers. The data could support a number studies on controlling factors of water and carbon fluxes that are hard to glean from bulk (NEE and ET) flux data. The products are provided as easy to use csv files with sufficient metadata. I have a few comments and suggestions below but my main concerns are:
- There is a long history of partitioning of NEE based on models such as nighttime respiration. Many of these partitioning approaches are regularly operationalized for ecosystems studies on GPP and respiration. I am surprised that data for these more typical methods (not based on flux theory) are not included in the dataset.
- This is very much an experimental dataset and while the authors acknowledge this, I am concerned that users might grab the data and use it with insufficient caution. I would consider a name for the paper that notes this is “experimental” and clearly distinguished this product from other NEE partitioning approaches (mentioned above).
25: There are a wider arrange of partitioning approaches such as isotopes (carbon and water), carbonyl sulfide, solar induced fluorescence, nighttime respiration etc…. A deeper acknowledgment of these approaches and how the approach her is independent is needed. Also, can you partitioning ET with a lysimeter or is this just an estimate of ET?
67: It was confusing to me why soil moisture was excluded from the predictor set. What does it mean to say it was “noisy”. I would assume soil moisture data would not be so noisy.
82: Should say “CO2 flux”
125: Was canopy height adjusted for grasslands during the growing season or is that considered a constant in the model?
150: Was stomatal saturation estimated with IR/skin temperature or air temperature?
414: I am a little concerned about the use of the term “remarkable” especially when the diurnal cycle of T/ET with FVS seems to be totally inverted relative to the other approaches. I agree that the consistency is generally quite impressive but some core differences are present.
Citation: https://doi.org/10.5194/essd-2024-272-RC2 -
AC2: 'Reply on RC2', Einara Zahn, 08 Oct 2024
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2024-272/essd-2024-272-AC2-supplement.pdf
-
EC1: 'Comment on essd-2024-272', Tobias Gerken, 08 Oct 2024
I would like to thank the authors for their replies.
I have a small technical correction, which I would like the authors to do while they are preparing the revised submission.
The authors are citing the github for the code and making the code public is commendable. However, github is not a suitable long-term archive for code. The authors should create a release of the final code and then archive that using zenodo as well (see for example here: https://coderefinery.github.io/github-without-command-line/doi/)
Citation: https://doi.org/10.5194/essd-2024-272-EC1 -
AC3: 'Reply on EC1', Einara Zahn, 08 Oct 2024
We appreciate the editor for bringing this issue to our attention. The code is already available on Zenodo, and we will ensure that the correct link and citation are included in the next revision.
Citation: https://doi.org/10.5194/essd-2024-272-AC3
-
AC3: 'Reply on EC1', Einara Zahn, 08 Oct 2024
Data sets
Partitioning of water and CO2 fluxes at NEON sites into soil and plant components: a five-year dataset for spatial and temporal analysis Einara Zahn and Elie Bou-zeid https://doi.org/10.5281/zenodo.12191876
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