the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Apr 2024
Exploring drought hazard, vulnerability, and related impacts to agriculture in Brandenburg
Abstract. Adaptation to an increasingly dry regional climate requires spatially explicit information about current and future risks. Existing drought risk studies often rely on expert-weighted composite indicators, while empirical evidence on impact-relevant factors is still scarce. The aim of this study is to investigate to what extent hazard and vulnerability indicators can explain observed agricultural drought impacts via data-driven methods. We focus on the German federal state of Brandenburg, 2013–2022, including several consecutive drought years. As impact indicators we use thermal-spectral anomalies (LST/NDVI) on field level, and empirical yield gaps from reported statistics on county level. Empirical associations to the impact indicators on both spatial levels are compared. Non-linear models explain up to about 60 % variance in the yield gap data, with lumped models for all crops being more stable than models for individual crops, and models for the drought years performing better than for the pre-drought years. Meteorological drought in June and soil quality are selected as strongest impact-relevant factors. Rye is found less vulnerable than wheat, despite growing on poorer soils. LST/NDVI only weakly relates to our empirical yield gaps. We recommend comparing different impact indicators on multiple scales to proceed with the development of empirically grounded risk maps.
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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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Preprint
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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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Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1149', Marthe Wens, 08 Jun 2024
The manuscript and research behind are highly interesting, innovative and relevant to the journal.
I have only very few comments, except that I miss a discussion section (I don't find a deep gap analysis, recommendations for future research nor a comprehensive summary of findings with a thorough comparison with existing research outputs in the current version - parts of it are covered in other chapters but I don't think that is clear enough). Besides, While the results are written down neatly with some informative figures, it is hard to follow for people not working with similar models. I think the manuscript could benefit from a sentence here and there saying "meaning that..." where the result is explained in an easily interpretable way (especially in the parts where SHAP is used).
Here some more detailed other comments:
There is a clear justification of the research and methodological choices made. While referenced once, the method is quite like the study of Naumann et al 2021 and the European Drought atlas - the differences can be highlighted better.
I like that multiples ways of looking at (quantifying) impact are tested, that you compare empirical and modelled impact on production. The general workflow figure is very clear.
In line 141, I would disagree with the definition of vulnerability (or the phrasing thereof) as a characteristic of exposure. Maybe as an internal characteristic of the exposed items? At least the IPCC would not describe it that way.
I wonder why a groundwater and/or streamflow indicator was not considered as potential hazard/predictor? And I like the calculating of the magnitude of deficit, I wonder how sensitive the results are to the choice of -0.5 as threshold for these?
The detrending of the impact data is done with a moving window: were there no sharp agrotech jumps in the yield over time?
L193: "we refer to..." this sentence is a bit unclear.
Paragraph starting at L203: it is a bit unclear whether you take modelled or empirical yield gaps as closer to 'the reality'. Also, starting from line 209, this alinea is fuzzy. i think this is the first time there is a reference to a reference period? I don't fully understand what is conveyed there - maybe rephrase?Looking into the list of indicators, I would miss some related to irrigation and general farm management, county rules on when crops can be planted/harvested, use of fertiliser, market prices etc. Some of this info might be available?
(most of) the socio economic vulnerability indicators will barely have an effect on the hazard impact link (if impacts are yield deficits) but will influence how this drought loss cascades through society. A critical reflection could be good here.
l324: this paragraph is raises some questions. how does it relate to the previous paragraphs? Why is this relevant / what is the key take away from it?
The R2 scores are not high. It is explained in the manuscript, but some figures showing time series of obs/pred could help explaining why that is not considered problematically low. and add in the discussion how htis could potentially be improved.
The paragraph starting at l403 is a nice and critical piece. also the conclusion is concise, comprehensive and clear
Some observations I am wondering whether the authors considered (and could thus address in the discussion):
The use of XGBoost, rather than random forests, does limit the amount of variability in between trees. that is a pity as different trees can give different potential pathways to impact and thus account for different drought types.
The impact variable is continuous, rather that categorized or made boolean. that might have an influence on which types of nonlinearity the models can capture.
No accumulation times nor lag times were tested. This could also potentially improve the models to reflect diversity of drought types.
For crop losses in economic terms, were price shocks accounted for?The piece could end with some key take aways for farmers, for agricultural ministries and for drought disaster managers. now the suggestions are not very specific, but there are quite some learnings in the paper that could be translated into specific policy advises.
Citation: https://doi.org/10.5194/egusphere-2024-1149-RC1 - AC1: 'Reply on RC1', Fabio Brill, 18 Jul 2024
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RC2: 'Comment on egusphere-2024-1149', Anonymous Referee #2, 03 Jul 2024
Reviewer's comments: manuscript egusphere-2024-1149
In this study, the authors used a multi-index approach (exposure, risk, vulnerability) to model the impacts of drought events on agricultural systems in the German federal state of Brandenburg, considering the LST/NDVI ratio as the response variable. The scientific approach used is valid. It reflects the multifactorial complexity of the implications of drought for the productivity of the region's farming systems. However, there are a few points of clarification, particularly in the methodology section. My comments and remarks are as follows:
Comment 1
« Empirical associations to the impact indicators on both spatial levels are compared. Non-linear models explain up to about 60% variance in the yield gap data, with lumped models for all crops being more stable than models for individual crops». This is imprecise, you must specify the names of the nonlinear models as well as for the grouped models. It is also important to include in the abstract the performance statistics of the models used.
Comment 2
“Rye is found less vulnerable than wheat, despite growing on poorer soils”. The fact that rye grows on poorer soils is a proof that it is more resilient and less vulnerable than wheat, so I do not see why the conjunction of subordination although? The sentence was rephrased.
Comment 3
In introduction, « This has implications for modelling and Monitoring ». You mean implications in the modelling and monitoring of agricultural drought. If so, the sentence should be completed.
Comment 4
Overall, the introduction is well written and argued. However, the application of artificial intelligence models in modelling drought impacts, risk, and vulnerability has been limited. It is worth adding a paragraph on the advantages and limitations of intelligence models in modelling the impacts of drought given that in your methodology you have used the extreme gradient boosting algorithm (XGBoost).
Comment 5
Line 250 «To retain as much information about the hazard distributions, we computed the relative affected area (non-)exceeding specified thresholds (in regular intervals of 0.5 for SPEI, 0.25 for LST/NDVI-anom., 0.05 for SMI, 5 for SMI-Total, and using the LBG class limits for AZL). A total of 68 features were created this way on county level».
On what criterion were these thresholds considered? This deserves to be clarified. The different classification thresholds for these indices and their meanings should be provided in a table in the methodology section.
Comment 6
The principle of the calculation of the LST/NDVI anomaly has not been sufficiently described. There should be a separate section to better describe and justify the choice of this anomaly to represent the impacts of drought when there are various other anomalies or indices that can better reflect the impacts of drought on agricultural systems. In this sense, the normalization indicated in Table 1 concerns only the LST values and/or the LST/NDVI values. If so, considering the max and min values or mean and standard deviation (SD)?
Comment 7
Ligne 255-260 « In 2013 and 2014 the SMI-Total is close to 0, observed vegetation health is at its maximum (i.e. negative LST/NDVI-anom.), essentially no impact-related statements…..» Similarly, to better assess the consistency of these statements, the formula and principle of the calculation of the IMS and IMS-Total must be clearly described in the methodological section with the different classification thresholds.
Comment 8
In Table 1, you mentioned that the monthly SPEI used has a resolution of 10 km and the source is the reference Zhang et al. (2024). However, in this reference, the SPEI used has a 1 km resolution. It is a bit ambiguous. Has the SPEI been calculated? or was the same database from the Zhang et al. (2024) study used? If this is the case, the spatial resolution of 10 km should be rectified because in the source reference mentioned it is rather 1 km that is mentioned.
Comment 9
The algorithm used to calculate the Landsat LST was not explained in the methodology.
- AC2: 'Reply on RC2', Fabio Brill, 18 Jul 2024
-
CC1: 'Comment on egusphere-2024-1149', Tobia Lakes, 12 Jul 2024
Dear Kathrin, dear authors,
Thank you very much for providing very helpful comments and for giving us the chance to improve our earlier version of the manuscript. Since we received the comments on July 3rd, we would appreciate an extension until July 19th, to be able to carefully address these and have the feedback from all co-authors.
Kind regards,
Tobia Lakes (on behalf of all co-authors)
Citation: https://doi.org/10.5194/egusphere-2024-1149-CC1
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1149', Marthe Wens, 08 Jun 2024
The manuscript and research behind are highly interesting, innovative and relevant to the journal.
I have only very few comments, except that I miss a discussion section (I don't find a deep gap analysis, recommendations for future research nor a comprehensive summary of findings with a thorough comparison with existing research outputs in the current version - parts of it are covered in other chapters but I don't think that is clear enough). Besides, While the results are written down neatly with some informative figures, it is hard to follow for people not working with similar models. I think the manuscript could benefit from a sentence here and there saying "meaning that..." where the result is explained in an easily interpretable way (especially in the parts where SHAP is used).
Here some more detailed other comments:
There is a clear justification of the research and methodological choices made. While referenced once, the method is quite like the study of Naumann et al 2021 and the European Drought atlas - the differences can be highlighted better.
I like that multiples ways of looking at (quantifying) impact are tested, that you compare empirical and modelled impact on production. The general workflow figure is very clear.
In line 141, I would disagree with the definition of vulnerability (or the phrasing thereof) as a characteristic of exposure. Maybe as an internal characteristic of the exposed items? At least the IPCC would not describe it that way.
I wonder why a groundwater and/or streamflow indicator was not considered as potential hazard/predictor? And I like the calculating of the magnitude of deficit, I wonder how sensitive the results are to the choice of -0.5 as threshold for these?
The detrending of the impact data is done with a moving window: were there no sharp agrotech jumps in the yield over time?
L193: "we refer to..." this sentence is a bit unclear.
Paragraph starting at L203: it is a bit unclear whether you take modelled or empirical yield gaps as closer to 'the reality'. Also, starting from line 209, this alinea is fuzzy. i think this is the first time there is a reference to a reference period? I don't fully understand what is conveyed there - maybe rephrase?Looking into the list of indicators, I would miss some related to irrigation and general farm management, county rules on when crops can be planted/harvested, use of fertiliser, market prices etc. Some of this info might be available?
(most of) the socio economic vulnerability indicators will barely have an effect on the hazard impact link (if impacts are yield deficits) but will influence how this drought loss cascades through society. A critical reflection could be good here.
l324: this paragraph is raises some questions. how does it relate to the previous paragraphs? Why is this relevant / what is the key take away from it?
The R2 scores are not high. It is explained in the manuscript, but some figures showing time series of obs/pred could help explaining why that is not considered problematically low. and add in the discussion how htis could potentially be improved.
The paragraph starting at l403 is a nice and critical piece. also the conclusion is concise, comprehensive and clear
Some observations I am wondering whether the authors considered (and could thus address in the discussion):
The use of XGBoost, rather than random forests, does limit the amount of variability in between trees. that is a pity as different trees can give different potential pathways to impact and thus account for different drought types.
The impact variable is continuous, rather that categorized or made boolean. that might have an influence on which types of nonlinearity the models can capture.
No accumulation times nor lag times were tested. This could also potentially improve the models to reflect diversity of drought types.
For crop losses in economic terms, were price shocks accounted for?The piece could end with some key take aways for farmers, for agricultural ministries and for drought disaster managers. now the suggestions are not very specific, but there are quite some learnings in the paper that could be translated into specific policy advises.
Citation: https://doi.org/10.5194/egusphere-2024-1149-RC1 - AC1: 'Reply on RC1', Fabio Brill, 18 Jul 2024
-
RC2: 'Comment on egusphere-2024-1149', Anonymous Referee #2, 03 Jul 2024
Reviewer's comments: manuscript egusphere-2024-1149
In this study, the authors used a multi-index approach (exposure, risk, vulnerability) to model the impacts of drought events on agricultural systems in the German federal state of Brandenburg, considering the LST/NDVI ratio as the response variable. The scientific approach used is valid. It reflects the multifactorial complexity of the implications of drought for the productivity of the region's farming systems. However, there are a few points of clarification, particularly in the methodology section. My comments and remarks are as follows:
Comment 1
« Empirical associations to the impact indicators on both spatial levels are compared. Non-linear models explain up to about 60% variance in the yield gap data, with lumped models for all crops being more stable than models for individual crops». This is imprecise, you must specify the names of the nonlinear models as well as for the grouped models. It is also important to include in the abstract the performance statistics of the models used.
Comment 2
“Rye is found less vulnerable than wheat, despite growing on poorer soils”. The fact that rye grows on poorer soils is a proof that it is more resilient and less vulnerable than wheat, so I do not see why the conjunction of subordination although? The sentence was rephrased.
Comment 3
In introduction, « This has implications for modelling and Monitoring ». You mean implications in the modelling and monitoring of agricultural drought. If so, the sentence should be completed.
Comment 4
Overall, the introduction is well written and argued. However, the application of artificial intelligence models in modelling drought impacts, risk, and vulnerability has been limited. It is worth adding a paragraph on the advantages and limitations of intelligence models in modelling the impacts of drought given that in your methodology you have used the extreme gradient boosting algorithm (XGBoost).
Comment 5
Line 250 «To retain as much information about the hazard distributions, we computed the relative affected area (non-)exceeding specified thresholds (in regular intervals of 0.5 for SPEI, 0.25 for LST/NDVI-anom., 0.05 for SMI, 5 for SMI-Total, and using the LBG class limits for AZL). A total of 68 features were created this way on county level».
On what criterion were these thresholds considered? This deserves to be clarified. The different classification thresholds for these indices and their meanings should be provided in a table in the methodology section.
Comment 6
The principle of the calculation of the LST/NDVI anomaly has not been sufficiently described. There should be a separate section to better describe and justify the choice of this anomaly to represent the impacts of drought when there are various other anomalies or indices that can better reflect the impacts of drought on agricultural systems. In this sense, the normalization indicated in Table 1 concerns only the LST values and/or the LST/NDVI values. If so, considering the max and min values or mean and standard deviation (SD)?
Comment 7
Ligne 255-260 « In 2013 and 2014 the SMI-Total is close to 0, observed vegetation health is at its maximum (i.e. negative LST/NDVI-anom.), essentially no impact-related statements…..» Similarly, to better assess the consistency of these statements, the formula and principle of the calculation of the IMS and IMS-Total must be clearly described in the methodological section with the different classification thresholds.
Comment 8
In Table 1, you mentioned that the monthly SPEI used has a resolution of 10 km and the source is the reference Zhang et al. (2024). However, in this reference, the SPEI used has a 1 km resolution. It is a bit ambiguous. Has the SPEI been calculated? or was the same database from the Zhang et al. (2024) study used? If this is the case, the spatial resolution of 10 km should be rectified because in the source reference mentioned it is rather 1 km that is mentioned.
Comment 9
The algorithm used to calculate the Landsat LST was not explained in the methodology.
- AC2: 'Reply on RC2', Fabio Brill, 18 Jul 2024
-
CC1: 'Comment on egusphere-2024-1149', Tobia Lakes, 12 Jul 2024
Dear Kathrin, dear authors,
Thank you very much for providing very helpful comments and for giving us the chance to improve our earlier version of the manuscript. Since we received the comments on July 3rd, we would appreciate an extension until July 19th, to be able to carefully address these and have the feedback from all co-authors.
Kind regards,
Tobia Lakes (on behalf of all co-authors)
Citation: https://doi.org/10.5194/egusphere-2024-1149-CC1
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Pedro Henrique Lima Alencar
Huihui Zhang
Friedrich Boeing
Silke Hüttel
Tobia Lakes
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(6245 KB) - Metadata XML