the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Jul 2024
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
Abstract. Advances in Earth Observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water land-surface models with the capability to simultaneously assimilate several of such data streams. The present article discusses the requirements for such a model and presents one such model based on the combination of the existing DALEC land vegetation carbon cycle model with the BETHY land-surface and terrestrial vegetation scheme. The resulting D&B model, made available as a community model, is presented together with a comprehensive evaluation for two selected study sites of widely varying climate. We then demonstrate the concept of land surface modelling aided by data streams that are available from satellite remote sensing. Here we present D&B with four observation operators that translate model-derived variables into measurements available from such data streams, namely: fraction of photosynthetically active radiation (FAPAR), solar-induced chlorophyll fluorescence (SIF), vegetation optical depth (VOD) at microwave frequencies, and near-surface soil moisture, also available from microwave measurements. As a first step, we evaluate the combined model system using local observations, and finally discuss the potential of the system presented for multi-stream data assimilation in the context of Earth Observation systems.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.-
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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1534', Anonymous Referee #1, 15 Aug 2024
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AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
We would like to express our gratitude for the thorough evaluation and very useful as well as thoughtful comments from the two anonymous reviewers as well as the handling editor.
We understand that the present manuscript is a long one, given the subject of model documentation, presentation and evaluation. We also appreciate the critical remarks about (a) the novelty of the combined D&B model or the need for such a model, and (b) what the place is of data assimilation in the current manuscript given that no example is given.
For this reason, we have now included a thorough evaluation and comparison three other models that have also been used extensively in a data assimilation framework. We believe that this clarifies why D&B offers some important advantages because of its particular position between simplicity and complexity.
We have tried to strengthen the discussion around data assimilation, pointing out in particular the Bayesian approach we follow, and the openness of the system to different approaches to data assimilation. We have also added some elements to the discussion about machine learning approaches and why we believe that the use of process based models is important.
Other points of discussion that that we tried to strengthen were the simulation of the Solar Induced Fluorescence (SIF) signal, which is still a field of active research. And we are grateful for a suggestion be Reviewer 2 to look more closely into soil evaporation, which we plan to do in a later study, as we currently lack direct observations.
We have added a point-by-point response to each of the two reviewers and very much hope that the revisions and clarification will make the manuscript suitable for publication in GMD.
For all of the authors,
Wolfgang Knorr
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC1 -
AC2: 'Reply on RC1', Wolfgang Knorr, 17 Oct 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1534/egusphere-2024-1534-AC2-supplement.pdf
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AC4: 'Corrections to AC2', Wolfgang Knorr, 03 Nov 2024
We would like to report some typos in AC2:
Under Major Comment 3, the correct added text is
“... it can be used to find the most reliable model and data based estimates of quantities of interest, e.g. carbon fluxes, ...”
Under Minor Comment 16, the reference to the Section is missing. The reply should read:
We have kept switching between both, as this is explained in Section 4.1 ("Study Sites").
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC4
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AC4: 'Corrections to AC2', Wolfgang Knorr, 03 Nov 2024
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AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
-
RC2: 'Comment on egusphere-2024-1534', Anonymous Referee #2, 24 Sep 2024
Review of
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
by Knorr et al.
General comments:
This paper sounds like a well written technical report and needs major changes before it can be published in GMD as a model description paper. A number of models, not all very complex, are mature enough to do the same job (and more) as the one proposed by the authors. Why is this new model needed? In terms of process representation, I could not see any innovation in the proposed modeling framework, except for the simulation of SIF and VOD. I assume that the innovation is in the data assimilation part, but no example is shown. Since data assimilation is not demonstrated here, "data assimilation" should be removed from the title. Similarly, data assimilation is mentioned frequently throughout the paper, but no example is provided. Data assimilation could be mentioned briefly in the Introduction and as a perspective in the Discussion section. No more. A clear definition of data assimilation is also lacking. Data assimilation can be done in many different ways. As far as I could understand, in this paper data assimilation is equivalent to "model parameter tuning". This is quite different from the variational or sequential Kalman filtering methods used in meteorology and in some land modeling frameworks to initialize initial conditions (e.g. of root-zone soil moisture) at a given time. This should be clearly explained. It should also be explained why the authors do not trust their default model parameter values. I assume that these default model parameter values come from the scientific literature. Why not trust them? Finally, a critical risk of parameter tuning is that the tuned model might be good for bad reasons, which is unacceptable for a model that aims to explicitly represent the main biophysical processes ("process-based modeling system"). This is acknowledged by the authors on L. 546. But how do the authors ensure that this does not happen? This is not clear. Finally, the rationale for "parameter tuning" is that improved static model parameter values are needed for the surface component of climate models and for climate change impact models. I do not believe that the current model is designed for such applications. What is the real purpose of the model? For monitoring and reanalysis, sequential assimilation would be preferable to model tuning. This work combines the BETHY and DALEC models. A comparison of the model simulations is presented over two contrasting sites (Spain and Finland). Why these two sites in particular? Results from the comparison are not good, which tends to show that this new model is not a good model. Or maybe these sites are particularly difficult to represent? Could you indicate score values from other models over these sites?
Recommendation: major revisions.
Particular comments:
- L. 54-59 (list of requirements): Is it something that other models could not do?
- L. 97 (daily time step): I believe that the daily time step is not sufficient to represent snow processes. Especially when snow melt occurs.
- L. 154 (potential photosynthesis): It is not clear whether potential photosynthesis varies from one day to another according to solar radiation and leaf temperature. Could you clarify?
- L. 275: The ICOS data portal contains a large number of sites. Why have you selected these two sites in particular?
- L. 324 (overestimate of GPP): Over the Boreal site, GPP is much more than "overestimated". There is nearly a factor of two at summertime. Why is the model that bad?
- L. 433: On L. 214, Ssif = 1 and on L. 434 Ssif = 10. Could you explain why?
- L. 481: "in the simulations, soil moisture decreases to near zero": why? Could this be caused by the overestimation of soil evaporation (a classical modeling problem)?
- L. 486-487 (carbon fluxes [...] are simulated reasonably well): You cannot say that for the Boreal site.
- L. 529: The large uncertainty on Ssif shows that the biophysical basis for SIF is very weak in the proposed model. Why not using machine learning to build an observation operator for SIF?
Citation: https://doi.org/10.5194/egusphere-2024-1534-RC2 -
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
We would like to express our gratitude for the thorough evaluation and very useful as well as thoughtful comments from the two anonymous reviewers as well as the handling editor.
We understand that the present manuscript is a long one, given the subject of model documentation, presentation and evaluation. We also appreciate the critical remarks about (a) the novelty of the combined D&B model or the need for such a model, and (b) what the place is of data assimilation in the current manuscript given that no example is given.
For this reason, we have now included a thorough evaluation and comparison three other models that have also been used extensively in a data assimilation framework. We believe that this clarifies why D&B offers some important advantages because of its particular position between simplicity and complexity.
We have tried to strengthen the discussion around data assimilation, pointing out in particular the Bayesian approach we follow, and the openness of the system to different approaches to data assimilation. We have also added some elements to the discussion about machine learning approaches and why we believe that the use of process based models is important.
Other points of discussion that that we tried to strengthen were the simulation of the Solar Induced Fluorescence (SIF) signal, which is still a field of active research. And we are grateful for a suggestion be Reviewer 2 to look more closely into soil evaporation, which we plan to do in a later study, as we currently lack direct observations.
We have added a point-by-point response to each of the two reviewers and very much hope that the revisions and clarification will make the manuscript suitable for publication in GMD.
For all of the authors,
Wolfgang Knorr
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC1 -
AC3: 'Reply on RC2', Wolfgang Knorr, 17 Oct 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1534/egusphere-2024-1534-AC3-supplement.pdf
-
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
-
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
We would like to express our gratitude for the thorough evaluation and very useful as well as thoughtful comments from the two anonymous reviewers as well as the handling editor.
We understand that the present manuscript is a long one, given the subject of model documentation, presentation and evaluation. We also appreciate the critical remarks about (a) the novelty of the combined D&B model or the need for such a model, and (b) what the place is of data assimilation in the current manuscript given that no example is given.
For this reason, we have now included a thorough evaluation and comparison three other models that have also been used extensively in a data assimilation framework. We believe that this clarifies why D&B offers some important advantages because of its particular position between simplicity and complexity.
We have tried to strengthen the discussion around data assimilation, pointing out in particular the Bayesian approach we follow, and the openness of the system to different approaches to data assimilation. We have also added some elements to the discussion about machine learning approaches and why we believe that the use of process based models is important.
Other points of discussion that that we tried to strengthen were the simulation of the Solar Induced Fluorescence (SIF) signal, which is still a field of active research. And we are grateful for a suggestion be Reviewer 2 to look more closely into soil evaporation, which we plan to do in a later study, as we currently lack direct observations.
We have added a point-by-point response to each of the two reviewers and very much hope that the revisions and clarification will make the manuscript suitable for publication in GMD.
For all of the authors,
Wolfgang Knorr
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC1
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1534', Anonymous Referee #1, 15 Aug 2024
-
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
We would like to express our gratitude for the thorough evaluation and very useful as well as thoughtful comments from the two anonymous reviewers as well as the handling editor.
We understand that the present manuscript is a long one, given the subject of model documentation, presentation and evaluation. We also appreciate the critical remarks about (a) the novelty of the combined D&B model or the need for such a model, and (b) what the place is of data assimilation in the current manuscript given that no example is given.
For this reason, we have now included a thorough evaluation and comparison three other models that have also been used extensively in a data assimilation framework. We believe that this clarifies why D&B offers some important advantages because of its particular position between simplicity and complexity.
We have tried to strengthen the discussion around data assimilation, pointing out in particular the Bayesian approach we follow, and the openness of the system to different approaches to data assimilation. We have also added some elements to the discussion about machine learning approaches and why we believe that the use of process based models is important.
Other points of discussion that that we tried to strengthen were the simulation of the Solar Induced Fluorescence (SIF) signal, which is still a field of active research. And we are grateful for a suggestion be Reviewer 2 to look more closely into soil evaporation, which we plan to do in a later study, as we currently lack direct observations.
We have added a point-by-point response to each of the two reviewers and very much hope that the revisions and clarification will make the manuscript suitable for publication in GMD.
For all of the authors,
Wolfgang Knorr
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC1 -
AC2: 'Reply on RC1', Wolfgang Knorr, 17 Oct 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1534/egusphere-2024-1534-AC2-supplement.pdf
-
AC4: 'Corrections to AC2', Wolfgang Knorr, 03 Nov 2024
We would like to report some typos in AC2:
Under Major Comment 3, the correct added text is
“... it can be used to find the most reliable model and data based estimates of quantities of interest, e.g. carbon fluxes, ...”
Under Minor Comment 16, the reference to the Section is missing. The reply should read:
We have kept switching between both, as this is explained in Section 4.1 ("Study Sites").
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC4
-
AC4: 'Corrections to AC2', Wolfgang Knorr, 03 Nov 2024
-
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
-
RC2: 'Comment on egusphere-2024-1534', Anonymous Referee #2, 24 Sep 2024
Review of
A comprehensive land surface vegetation model for multi-stream data assimilation, D&B v1.0
by Knorr et al.
General comments:
This paper sounds like a well written technical report and needs major changes before it can be published in GMD as a model description paper. A number of models, not all very complex, are mature enough to do the same job (and more) as the one proposed by the authors. Why is this new model needed? In terms of process representation, I could not see any innovation in the proposed modeling framework, except for the simulation of SIF and VOD. I assume that the innovation is in the data assimilation part, but no example is shown. Since data assimilation is not demonstrated here, "data assimilation" should be removed from the title. Similarly, data assimilation is mentioned frequently throughout the paper, but no example is provided. Data assimilation could be mentioned briefly in the Introduction and as a perspective in the Discussion section. No more. A clear definition of data assimilation is also lacking. Data assimilation can be done in many different ways. As far as I could understand, in this paper data assimilation is equivalent to "model parameter tuning". This is quite different from the variational or sequential Kalman filtering methods used in meteorology and in some land modeling frameworks to initialize initial conditions (e.g. of root-zone soil moisture) at a given time. This should be clearly explained. It should also be explained why the authors do not trust their default model parameter values. I assume that these default model parameter values come from the scientific literature. Why not trust them? Finally, a critical risk of parameter tuning is that the tuned model might be good for bad reasons, which is unacceptable for a model that aims to explicitly represent the main biophysical processes ("process-based modeling system"). This is acknowledged by the authors on L. 546. But how do the authors ensure that this does not happen? This is not clear. Finally, the rationale for "parameter tuning" is that improved static model parameter values are needed for the surface component of climate models and for climate change impact models. I do not believe that the current model is designed for such applications. What is the real purpose of the model? For monitoring and reanalysis, sequential assimilation would be preferable to model tuning. This work combines the BETHY and DALEC models. A comparison of the model simulations is presented over two contrasting sites (Spain and Finland). Why these two sites in particular? Results from the comparison are not good, which tends to show that this new model is not a good model. Or maybe these sites are particularly difficult to represent? Could you indicate score values from other models over these sites?
Recommendation: major revisions.
Particular comments:
- L. 54-59 (list of requirements): Is it something that other models could not do?
- L. 97 (daily time step): I believe that the daily time step is not sufficient to represent snow processes. Especially when snow melt occurs.
- L. 154 (potential photosynthesis): It is not clear whether potential photosynthesis varies from one day to another according to solar radiation and leaf temperature. Could you clarify?
- L. 275: The ICOS data portal contains a large number of sites. Why have you selected these two sites in particular?
- L. 324 (overestimate of GPP): Over the Boreal site, GPP is much more than "overestimated". There is nearly a factor of two at summertime. Why is the model that bad?
- L. 433: On L. 214, Ssif = 1 and on L. 434 Ssif = 10. Could you explain why?
- L. 481: "in the simulations, soil moisture decreases to near zero": why? Could this be caused by the overestimation of soil evaporation (a classical modeling problem)?
- L. 486-487 (carbon fluxes [...] are simulated reasonably well): You cannot say that for the Boreal site.
- L. 529: The large uncertainty on Ssif shows that the biophysical basis for SIF is very weak in the proposed model. Why not using machine learning to build an observation operator for SIF?
Citation: https://doi.org/10.5194/egusphere-2024-1534-RC2 -
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
We would like to express our gratitude for the thorough evaluation and very useful as well as thoughtful comments from the two anonymous reviewers as well as the handling editor.
We understand that the present manuscript is a long one, given the subject of model documentation, presentation and evaluation. We also appreciate the critical remarks about (a) the novelty of the combined D&B model or the need for such a model, and (b) what the place is of data assimilation in the current manuscript given that no example is given.
For this reason, we have now included a thorough evaluation and comparison three other models that have also been used extensively in a data assimilation framework. We believe that this clarifies why D&B offers some important advantages because of its particular position between simplicity and complexity.
We have tried to strengthen the discussion around data assimilation, pointing out in particular the Bayesian approach we follow, and the openness of the system to different approaches to data assimilation. We have also added some elements to the discussion about machine learning approaches and why we believe that the use of process based models is important.
Other points of discussion that that we tried to strengthen were the simulation of the Solar Induced Fluorescence (SIF) signal, which is still a field of active research. And we are grateful for a suggestion be Reviewer 2 to look more closely into soil evaporation, which we plan to do in a later study, as we currently lack direct observations.
We have added a point-by-point response to each of the two reviewers and very much hope that the revisions and clarification will make the manuscript suitable for publication in GMD.
For all of the authors,
Wolfgang Knorr
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC1 -
AC3: 'Reply on RC2', Wolfgang Knorr, 17 Oct 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1534/egusphere-2024-1534-AC3-supplement.pdf
-
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
-
AC1: 'Comment on egusphere-2024-1534', Wolfgang Knorr, 17 Oct 2024
We would like to express our gratitude for the thorough evaluation and very useful as well as thoughtful comments from the two anonymous reviewers as well as the handling editor.
We understand that the present manuscript is a long one, given the subject of model documentation, presentation and evaluation. We also appreciate the critical remarks about (a) the novelty of the combined D&B model or the need for such a model, and (b) what the place is of data assimilation in the current manuscript given that no example is given.
For this reason, we have now included a thorough evaluation and comparison three other models that have also been used extensively in a data assimilation framework. We believe that this clarifies why D&B offers some important advantages because of its particular position between simplicity and complexity.
We have tried to strengthen the discussion around data assimilation, pointing out in particular the Bayesian approach we follow, and the openness of the system to different approaches to data assimilation. We have also added some elements to the discussion about machine learning approaches and why we believe that the use of process based models is important.
Other points of discussion that that we tried to strengthen were the simulation of the Solar Induced Fluorescence (SIF) signal, which is still a field of active research. And we are grateful for a suggestion be Reviewer 2 to look more closely into soil evaporation, which we plan to do in a later study, as we currently lack direct observations.
We have added a point-by-point response to each of the two reviewers and very much hope that the revisions and clarification will make the manuscript suitable for publication in GMD.
For all of the authors,
Wolfgang Knorr
Citation: https://doi.org/10.5194/egusphere-2024-1534-AC1
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Wolfgang Knorr
Matthew Williams
Thomas Kaminski
Michael Voßbeck
Marko Scholze
Tristan Quaife
Luke Smallmann
Susan Steele-Dunne
Mariette Vreugdenhil
Tim Green
Sönke Zähle
Mika Aurela
Alexandre Bouvet
Emanuel Bueechi
Wouter Dorigo
Tarek El-Madany
Mirco Migliavacca
Marika Honkanen
Yann Kerr
Anna Kontu
Juha Lemmetyinen
Hannakaisa Lindqvist
Arnaud Mialon
Tuuli Miinalainen
Gaetan Pique
Amanda Ojasalo
Shaun Quegan
Peter Rayner
Pablo Reyes-Muñoz
Nemesio Rodríguez-Fernández
Mike Schwank
Jochem Verrelst
Songyan Zhu
Dirk Schüttemeyer
Matthias Drusch
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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