the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 09 Aug 2023
Multi-fold increase in rainforests tipping risk beyond 1.5–2 °C warming
Abstract. Tropical rainforests invest in their root systems to store moisture in their root zone from water-rich periods for use in water-scarce periods. An inadequate root-zone soil moisture storage predisposes or forces these forest ecosystems to transition to a savanna-like state, devoid of their native structure and functions. Yet changes in soil moisture storage and its influence on the rainforest ecosystems under future climate change remain uncertain. Using the (mass-balance-based) empirical understanding of root zone storage capacity, we assess the future state of the rainforests and the forest-to-savanna transition risk in South America and Africa under four different shared socioeconomic pathway scenarios. We find that under the end-of-the-21st-century climate, nearly one-third of the total forest area will be influenced by climate change. Furthermore, beyond 1.5–2 °C warming, ecosystem recovery reduces gradually, whereas the forest-savanna transition risk increases several folds. For Amazon, this risk can grow by about 1.5–6 times compared to its immediate lower warming scenario, whereas for Congo, this risk growth is not substantial (0.7–1.65 times). The insight from this study underscores the urgent need to limit global surface temperatures below the Paris Agreement.
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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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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
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RC1: 'Comment on egusphere-2023-1486', Anonymous Referee #1, 27 Sep 2023
General comments:
Singh et al. classified the tropical terrestrial ecosystems under current climate and future climate by calculating the hydroclimate-derived root zone storage capacity. Then they assessed the potential rainforests tipping risk with the global warming. They found that the forest-savanna transition risk would largely increase if the climate warming is beyond 1.5-2 degrees. The topic is meaningful and interesting since the land cover change used in current ESMs of CMIP6 is lacked of the consideration of the effects of hydroclimate.
However, readers could be hard to follow and even confused in the main text, because some introduction of method and discussions are not easy to understand. More importantly, the main findings are not clearly shown in the main text. For example, in the Abstract, the “1.5-6 times” growth is the key finding for this study (also corresponding to the title), but how these values are derived is not shown.
In this study, >20% of model convergence are regarded as ‘moderate mode agreement’ or ‘moderate-high model agreement’. Given that the findings with >20% of model convergence are important in this research, I doubt whether the 20% is too low to hardly help obtain the robust results.
It is interesting to compare the prescribed future land-use in IAMs with the projected transitions in this study. But it is not clear for readers which results are more robust. Readers cannot figure it out from the discussions of the authors. For example, on the one hand, the author said the extent of forest-savanna transitions is often underestimated in prescribed land-use compared to those projected in their study. In this case, it seems that results from this study are regarded as more robust. However, on the other hand, the authors said forests that revert to a ‘less water-stressed state’ is overestimated in their analysis. It seems that results from the prescribed future land-use in IAMs are more robust.
Specific comments:
Line 28: which scenario for this growth by about 1.5-6 times.
Lines 98-100: please explain why the hydroclimate and ecosystem can be regarded as in equilibrium. The hydroclimate and ecosystem are projected by ESM in SSP scenario simulations, which are apparently not in equilibrium because of the continued warming.
Lines 130-131: The spatial resolutions of most of ESMs output are close to 0.25 degree? I suppose that the spatial resolutions of most of ESMs are much lower than 0.25 degree.
Line 162: “to reduce loss of root zone moisture storage”?
Line 183: “the actual state of the ecosystems” includes many aspects of ecosystems. “this model can capture the dynamics of actual soil moisture availability for the ecosystems” would be better.
Line 380-381: please add the references of related figure(s).
Lines 590-592: But as shown in Figure 3, even in SSP1-2.6, there are still many regions belonging to “Transition to a more water-stressed state”.
Citation: https://doi.org/10.5194/egusphere-2023-1486-RC1 -
AC1: 'Reply on RC1', Chandrakant Singh, 05 Mar 2024
Dear Reviewer,
Thank you for the time and effort you've invested in reviewing our manuscript. We appreciate your insights and are committed to integrating your suggestions into the revised version. Please refer to the attached response sheet for detailed information on the changes we plan to make.
-
AC1: 'Reply on RC1', Chandrakant Singh, 05 Mar 2024
-
RC2: 'Comment on egusphere-2023-1486', Anonymous Referee #2, 02 Jan 2024
Singh et al. compare estimates of the plant-accessible root-zone water storage capacity (Sr) to the expected amount of water needed to supply ET during a 20-year return drought length across the Amazon and Congo rainforests. They classify forests with Sr smaller than the amount of storage needed to withstand a drought of this magnitude as water-stressed and compare the current extent of water-stressed forests to the projected extent based on simulated future ET and P used to generate future Sr estimates. By using thresholds of water limitation associated with the transition of ecosystems from forest to savanna from a previous publication, they identify areas that might experience forest-savanna transition.
This work is important because of our limited understanding of climate change-induced ecosystem transition.
The figures are well made and clear, with excellent explanations both in the figures themselves and in the captions. I also appreciate the attention to subsurface moisture availability as a driving factor of landcover transition and water stress. However, I had difficulty following the methods in this paper. I am also concerned with the interpretation of the root-zone water storage capacity metric.
I am confused by the authors’ method of calculating Sr as well as their conversion of Sr to an indication of water stress. For the Sr calculation, I believe that they are calculating the maximum deficit of each year at each pixel, then choosing the 20-year return period value to represent the ‘Sr’ for the pixel? Figuring out what they were doing took me quite some time and involved reading their previous paper on this topic [1]. I am still unsure if I understand their methods and believe other readers would also have difficulty following. I would recommend improving the clarity of the terminology used in the method (for example, differentiating between ‘maximum deficit’ and ‘Sr’) as well as incorporating more of the “Calculating root zone storage capacity” section of the SI into the main text.
If I am understanding the authors’ calculation of Sr correctly, then I am skeptical about their interpretation of it. This confusion starts for me in the first sentence of the abstract. Forests themselves don’t “store moisture” - the subsurface may store moisture (abiotically) and rainforests can access this moisture via roots. There are many places in the manuscript (for example, line 47) where the authors do not fully articulate the abiotic influence on Sr, and I think this may have large consequences for their interpretation of Sr as an indication of water stress.
For example, in my understanding, having a large deficit does not necessarily translate to water stress. Instead, it is a reflection of ET from storage, and therefore, of an ecosystem having a high Sr. Whether this high Sr confers resilience or risk to an ecosystem cannot be known from the Sr estimate itself. For example, “excessive short-term water deficits” (line 53-60) can be rephrased as ‘a lot of ET and not a lot of P’ which may mean that vegetation has a lot of access to subsurface water, not that it is at risk of mortality, as the authors write. I think this paper may be conflating drought and low subsurface moisture levels with the deficit when in my understanding the deficit and Sr are very difficult to convert directly to mortality or stress metrics (see [2] as an example of how complex using deficit-based methods to understand landcover can be). If my understanding of the methods of this paper is correct, this conflation would be a fundamental misuse of Sr.
At present it is difficult to tell how much Sr is being misinterpreted because it is difficult to follow the methods of the paper. These issues could possibly be improved by (1) improving the clarity of the methods so it is possible for a reader to follow exactly how the metrics are calculated and compared to one another to derive the categories plotted in the figures, (2) more discussion on the abiotic controls of the deficit and how that might conflate interpretations of low vs high Sr as indicative of water stress and landcover transition, including careful examination and explanation of the logic outlined in Figure 2a (describing the relationship between Sr and transition) and (3) more information on the authors’ previous thresholds for forest-savanna transition, which are critical to accepting their main results here.
Other comments:
Line 137: Using a threshold of 50% to determine if a pixel should be classified as “forest” seems generous, especially as other ecosystems might be expected to have very different ET (e.g. crops) and would therefore alter estimates of Sr to a large extent. It might be helpful to see the distribution of fractional forest cover present in your “forest” pixels or to otherwise show that the most of the area you are analyzing is more fully forested than 50% coverage.
The model agreement threshold of 20% seems too low for gaining a robust understanding of likely future transitions. This is especially concerning as the area with >50% model agreement is so small (for example in Figure 2, the forest-savanna transition in Africa).
Line 48: The ‘-’ is a typo? This sentence doesn’t make sense.
Typo in Figure 2 panel a, under ‘transition to a more water-stressed state” (hydraulic failures).
[1] Singh, C., Wang-Erlandsson, L., Fetzer, I., Rockström, J., & Van Der Ent, R. (2020). Rootzone storage capacity reveals drought coping strategies along rainforest-savanna transitions. Environmental Research Letters, 15(12), 124021.
[2] Hahm, W. Jesse, et al. "Lithologically controlled subsurface critical zone thickness and water storage capacity determine regional plant community composition." Water Resources Research 55.4 (2019): 3028-3055.
Citation: https://doi.org/10.5194/egusphere-2023-1486-RC2 -
AC2: 'Reply on RC2', Chandrakant Singh, 05 Mar 2024
Dear Reviewer,
Thank you for the time and effort you've invested in reviewing our manuscript. We appreciate your insights and are committed to integrating your suggestions into the revised version. Please refer to the attached response sheet for detailed information on the changes we plan to make.
-
AC2: 'Reply on RC2', Chandrakant Singh, 05 Mar 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-1486', Anonymous Referee #1, 27 Sep 2023
General comments:
Singh et al. classified the tropical terrestrial ecosystems under current climate and future climate by calculating the hydroclimate-derived root zone storage capacity. Then they assessed the potential rainforests tipping risk with the global warming. They found that the forest-savanna transition risk would largely increase if the climate warming is beyond 1.5-2 degrees. The topic is meaningful and interesting since the land cover change used in current ESMs of CMIP6 is lacked of the consideration of the effects of hydroclimate.
However, readers could be hard to follow and even confused in the main text, because some introduction of method and discussions are not easy to understand. More importantly, the main findings are not clearly shown in the main text. For example, in the Abstract, the “1.5-6 times” growth is the key finding for this study (also corresponding to the title), but how these values are derived is not shown.
In this study, >20% of model convergence are regarded as ‘moderate mode agreement’ or ‘moderate-high model agreement’. Given that the findings with >20% of model convergence are important in this research, I doubt whether the 20% is too low to hardly help obtain the robust results.
It is interesting to compare the prescribed future land-use in IAMs with the projected transitions in this study. But it is not clear for readers which results are more robust. Readers cannot figure it out from the discussions of the authors. For example, on the one hand, the author said the extent of forest-savanna transitions is often underestimated in prescribed land-use compared to those projected in their study. In this case, it seems that results from this study are regarded as more robust. However, on the other hand, the authors said forests that revert to a ‘less water-stressed state’ is overestimated in their analysis. It seems that results from the prescribed future land-use in IAMs are more robust.
Specific comments:
Line 28: which scenario for this growth by about 1.5-6 times.
Lines 98-100: please explain why the hydroclimate and ecosystem can be regarded as in equilibrium. The hydroclimate and ecosystem are projected by ESM in SSP scenario simulations, which are apparently not in equilibrium because of the continued warming.
Lines 130-131: The spatial resolutions of most of ESMs output are close to 0.25 degree? I suppose that the spatial resolutions of most of ESMs are much lower than 0.25 degree.
Line 162: “to reduce loss of root zone moisture storage”?
Line 183: “the actual state of the ecosystems” includes many aspects of ecosystems. “this model can capture the dynamics of actual soil moisture availability for the ecosystems” would be better.
Line 380-381: please add the references of related figure(s).
Lines 590-592: But as shown in Figure 3, even in SSP1-2.6, there are still many regions belonging to “Transition to a more water-stressed state”.
Citation: https://doi.org/10.5194/egusphere-2023-1486-RC1 -
AC1: 'Reply on RC1', Chandrakant Singh, 05 Mar 2024
Dear Reviewer,
Thank you for the time and effort you've invested in reviewing our manuscript. We appreciate your insights and are committed to integrating your suggestions into the revised version. Please refer to the attached response sheet for detailed information on the changes we plan to make.
-
AC1: 'Reply on RC1', Chandrakant Singh, 05 Mar 2024
-
RC2: 'Comment on egusphere-2023-1486', Anonymous Referee #2, 02 Jan 2024
Singh et al. compare estimates of the plant-accessible root-zone water storage capacity (Sr) to the expected amount of water needed to supply ET during a 20-year return drought length across the Amazon and Congo rainforests. They classify forests with Sr smaller than the amount of storage needed to withstand a drought of this magnitude as water-stressed and compare the current extent of water-stressed forests to the projected extent based on simulated future ET and P used to generate future Sr estimates. By using thresholds of water limitation associated with the transition of ecosystems from forest to savanna from a previous publication, they identify areas that might experience forest-savanna transition.
This work is important because of our limited understanding of climate change-induced ecosystem transition.
The figures are well made and clear, with excellent explanations both in the figures themselves and in the captions. I also appreciate the attention to subsurface moisture availability as a driving factor of landcover transition and water stress. However, I had difficulty following the methods in this paper. I am also concerned with the interpretation of the root-zone water storage capacity metric.
I am confused by the authors’ method of calculating Sr as well as their conversion of Sr to an indication of water stress. For the Sr calculation, I believe that they are calculating the maximum deficit of each year at each pixel, then choosing the 20-year return period value to represent the ‘Sr’ for the pixel? Figuring out what they were doing took me quite some time and involved reading their previous paper on this topic [1]. I am still unsure if I understand their methods and believe other readers would also have difficulty following. I would recommend improving the clarity of the terminology used in the method (for example, differentiating between ‘maximum deficit’ and ‘Sr’) as well as incorporating more of the “Calculating root zone storage capacity” section of the SI into the main text.
If I am understanding the authors’ calculation of Sr correctly, then I am skeptical about their interpretation of it. This confusion starts for me in the first sentence of the abstract. Forests themselves don’t “store moisture” - the subsurface may store moisture (abiotically) and rainforests can access this moisture via roots. There are many places in the manuscript (for example, line 47) where the authors do not fully articulate the abiotic influence on Sr, and I think this may have large consequences for their interpretation of Sr as an indication of water stress.
For example, in my understanding, having a large deficit does not necessarily translate to water stress. Instead, it is a reflection of ET from storage, and therefore, of an ecosystem having a high Sr. Whether this high Sr confers resilience or risk to an ecosystem cannot be known from the Sr estimate itself. For example, “excessive short-term water deficits” (line 53-60) can be rephrased as ‘a lot of ET and not a lot of P’ which may mean that vegetation has a lot of access to subsurface water, not that it is at risk of mortality, as the authors write. I think this paper may be conflating drought and low subsurface moisture levels with the deficit when in my understanding the deficit and Sr are very difficult to convert directly to mortality or stress metrics (see [2] as an example of how complex using deficit-based methods to understand landcover can be). If my understanding of the methods of this paper is correct, this conflation would be a fundamental misuse of Sr.
At present it is difficult to tell how much Sr is being misinterpreted because it is difficult to follow the methods of the paper. These issues could possibly be improved by (1) improving the clarity of the methods so it is possible for a reader to follow exactly how the metrics are calculated and compared to one another to derive the categories plotted in the figures, (2) more discussion on the abiotic controls of the deficit and how that might conflate interpretations of low vs high Sr as indicative of water stress and landcover transition, including careful examination and explanation of the logic outlined in Figure 2a (describing the relationship between Sr and transition) and (3) more information on the authors’ previous thresholds for forest-savanna transition, which are critical to accepting their main results here.
Other comments:
Line 137: Using a threshold of 50% to determine if a pixel should be classified as “forest” seems generous, especially as other ecosystems might be expected to have very different ET (e.g. crops) and would therefore alter estimates of Sr to a large extent. It might be helpful to see the distribution of fractional forest cover present in your “forest” pixels or to otherwise show that the most of the area you are analyzing is more fully forested than 50% coverage.
The model agreement threshold of 20% seems too low for gaining a robust understanding of likely future transitions. This is especially concerning as the area with >50% model agreement is so small (for example in Figure 2, the forest-savanna transition in Africa).
Line 48: The ‘-’ is a typo? This sentence doesn’t make sense.
Typo in Figure 2 panel a, under ‘transition to a more water-stressed state” (hydraulic failures).
[1] Singh, C., Wang-Erlandsson, L., Fetzer, I., Rockström, J., & Van Der Ent, R. (2020). Rootzone storage capacity reveals drought coping strategies along rainforest-savanna transitions. Environmental Research Letters, 15(12), 124021.
[2] Hahm, W. Jesse, et al. "Lithologically controlled subsurface critical zone thickness and water storage capacity determine regional plant community composition." Water Resources Research 55.4 (2019): 3028-3055.
Citation: https://doi.org/10.5194/egusphere-2023-1486-RC2 -
AC2: 'Reply on RC2', Chandrakant Singh, 05 Mar 2024
Dear Reviewer,
Thank you for the time and effort you've invested in reviewing our manuscript. We appreciate your insights and are committed to integrating your suggestions into the revised version. Please refer to the attached response sheet for detailed information on the changes we plan to make.
-
AC2: 'Reply on RC2', Chandrakant Singh, 05 Mar 2024
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Cited
Chandrakant Singh
Ruud van der Ent
Ingo Fetzer
Lan Wang-Erlandsson
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(8817 KB) - Metadata XML
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Supplement
(15038 KB) - BibTeX
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- Final revised paper