the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Apr 2025
Spatiotemporal variation of growth-stage specific concurrent climate extremes and their yield impacts for rice in southern China
Abstract. Increasing evidence highlights the disruptive effects of compound climate extremes on global crop yields under climate change. Existing studies predominantly rely on the whole growing-season scale and relative thresholds, and hamper the capture of crop physiological sensitivities and yield responses that vary critically across growth stages. Here, we analyzed the spatiotemporal variations, dominant drivers, and potential impacts on the yields of concurrent heat-drought and chilling-rainy events for single- and late-rice in southern China from 1981 to 2018. Specifically, we carefully distinguished three sensitive growth stages of rice, and used growth-stage-specific physiological thresholds. Temporally, single-rice experienced a significant increase in concurrent heat-drought events, while late-rice experienced a modest rise in chilling-rainy events. Hotspots of concurrent heat-drought events in single-rice systems moved upstream in the Yangtze Basin during the growing season, and the concurrent chilling-rainy events of late-rice were widespread within the planting regions, with a higher incidence in certain areas. These spatial characteristics were primarily driven by spatial differences in phenology rather than the occurrence of extreme events. Path analysis identified heat stress as the primary driver of heat-drought impacts (particularly in jointing-booting and heading-flowering stages), whereas chilling and rainy stress exerted comparable effects for late-rice. Our assessment of compound event impacts and sensitivity to rice yield revealed significant growth-stage-specific differences, with comparable yield losses from both concurrent heat-drought and chilling-rainy events. Single-rice showed the highest sensitivity to heat-drought events during the grain filling stage, whereas the late-rice exhibited greater sensitivity during the heading-flowering stage. The historical yield impact diverged markedly across growth stages, with the largest having occurred in the grain filling stage, particularly for heat-drought events. Our study provided important information on compound agroclimatic extremes, in the context of southern China’s rice production system, and the results provide important information for risk management and adaptation strategies under climate change.
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Notice on discussion status
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
(1649 KB)
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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.
- Preprint
(1649 KB) - Metadata XML
- BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-1393', Anonymous Referee #1, 20 May 2025
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AC1: 'Reply on RC1', Tao Ye, 30 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1393/egusphere-2025-1393-AC1-supplement.pdf
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AC1: 'Reply on RC1', Tao Ye, 30 Jun 2025
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RC2: 'Comment on egusphere-2025-1393', Anonymous Referee #2, 03 Jun 2025
The paper has significantly improved compared to the earlier version. I thank the authors for taking the revision process seriously and applying the requested modifications.
In my view, the paper still requires more clarifications, particularly in the methods section and in how the results are contextualized within the broader literature:-
Copulas are introduced but never mentioned in the results. Is the KDE introduced in Fig. 2 equivalent to the copula CDF? If so, the terminology needs to be harmonized. If the KDE represents something else, this should be clearly introduced in the methods section.
In the copula section, the purpose of Lines 204–207 and Equation 6 is unclear. Isn’t the joint probability (i.e., P(x > X, y > Y)) the main quantity of interest? If so, why not introduce Equation 7 directly? You may refer to this article for inspiration on copula methods and joint return periods: https://wires.onlinelibrary.wiley.com/doi/10.1002/wat2.1579. -
Section 2.6 is rather generic. What are B1 and B2? Please introduce them properly. If B1 refers to climatic conditions and B2 to non-climatic factors, then from Line 416 onwards, a direct inference about the impact of infrastructure on yields cannot be made.
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Discussion section: Please revise the text to reflect the broader implications of your findings and include only points that can be directly deduced from your analysis.
Specific Comments:
- L14: "Hamper" doesn’t sound right.
- L116: Briefly introduce the two datasets at the end of this sentence before discussing them individually.
- L121 (and repeated elsewhere, e.g., L163): What is "QX/T 468–2018"? This terminology is unclear. If it refers to internal coding, it may be unnecessary to mention.
- L248: Use "The impact of … on yield" instead of "yield impact."
- Figure 4: I am not sure I understand what DC refers to. If it represents correlation, shouldn’t the boxplot range be limited to 1? Why does it go up to 1.2 in panel d1 C2r2 for DCtot?
- L384–396: This section needs thorough revision. The reference to Zhang is problematic. Additionally, suggesting a dominant factor may not be valid, as these relationships are likely highly location- and case-specific. “Large” is not the right word here. Please remind the reader what "#3" refers to.
- L417: Use "Different impacts of … on yields" instead of "yield impacts."
- L421: Were these losses shown in any figures or derived from your analysis? If not, consider removing this sentence. Also, since the study does not directly assess the impact of irrigation, that discussion may not be relevant.
- L437: Replace "rainy stress" with "rain stress."
- L456: On what plots are these spatial shifts in concurrent events shown? If you refer to shifts over time, clarify this. If not, the sentence is unclear.
- L457: "Spatial difference in phenology" is unclear, please rephrase.
- L463 onwards (Conclusion): The conclusion is not the right place to introduce new references or discuss limitations. Consider revising this section and relocating these points to more appropriate sections in the manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-1393-RC2 -
AC2: 'Reply on RC2', Tao Ye, 30 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1393/egusphere-2025-1393-AC2-supplement.pdf
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Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-1393', Anonymous Referee #1, 20 May 2025
The manuscript presents a well-designed and timely study on the correlation between compound climate extremes and rice yields in southern China, with clear relevance to climate change adaptation. The authors leverage growth-stage-specific physiological thresholds, multi-source gridded data, and compound severity metrics to offer new insights into how concurrent heat-drought and chilling-rainy events affect rice production. This work makes an important contribution in the construction of metrics for compound stressors. However, several points require clarification, and improvements in structure and presentation would significantly improve the manuscript.
=== Major Comments
- Ambiguity in Drought Stress Severity Definition. The calculation of drought severity appears to exclude events shorter than 10 days, regardless of intensity. Please clarify whether severity is accumulated continuously or only calculated if a 10-day event threshold is met. For rice, a very low soil moisture period, even for a week, can be fatal. Justification for this duration cutoff should be provided, ideally based on physiological or agronomic evidence.
- Clarification of Kernel Density Estimate. Figures 2a and 2c are labeled as Kernel Density Estimates (KDEs), but the x-axis represents time (e.g., 1981–2018), which is not standard in KDE applications. It is confusing what variable is being smoothed, and how the density values should be interpreted. If these are smoothed frequencies or rolling densities over time, the figure should be relabeled or revised accordingly. I recommend providing a more detailed explanation of the construction, including the variable used, kernel type, bandwidth selection, and the interpretation of density on a time axis.
- Interpretation and Modeling. he analysis relating yield anomalies to compound severity lacks clarity. Both axes in Figure 5 are restricted to negative values, with no explanation for this truncation. Are positive yield deviations and low-stress years excluded? If so, why?
- Final Yield Model. Additionally, the use of simple scatterplots without formal statistical modeling is insufficient, given the complexity of the stress indices. I encourage the authors to fit and report a statistical model or clarify the final equation for this analysis to formally characterize the relationship between yield anomalies and compound stress severity. This would substantiate the visual patterns and improve analysis.
=== Comments on Manuscript Structure and Flow
Table 2 is referenced in the manuscript but not included.
The manuscript is generally well-organized, but there are several ways the narrative can be improved:
- Abstract: Consider simplifying and using more intuitive phrasing to improve accessibility to the general scientific audience.
- Introduction: The rationale is well-motivated, but some repetition of literature gaps can be consolidated. Move technical details to Methods.
- Methods: While comprehensive, this section is very dense. I suggest creating a labeled subsections on “Compound Severity Metrics” that put together equations and definitions. A flowchart or schematic of the data-processing pipeline would improve readability.
- Results: Avoid overuse of code-like labels (C2R2, H3D3) in narrative prose; use descriptive names. Ensure all figures are introduced with clear interpretive framing.
- Figures: Improve color bar labeling and add interpretive guidance in captions. Figures 3 and 5 in particular would benefit from better explanation of axis ranges and unit meanings.
- Discussion: While informative, the discussion can be tightened.
===Summary
This is an important and data-rich study that addresses key gaps in our understanding of compound climate extremes for analysis of crop yields. I hope the authors find these suggestions helpful in strengthening the manuscript for eventual publication.
Citation: https://doi.org/10.5194/egusphere-2025-1393-RC1 -
AC1: 'Reply on RC1', Tao Ye, 30 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1393/egusphere-2025-1393-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2025-1393', Anonymous Referee #2, 03 Jun 2025
The paper has significantly improved compared to the earlier version. I thank the authors for taking the revision process seriously and applying the requested modifications.
In my view, the paper still requires more clarifications, particularly in the methods section and in how the results are contextualized within the broader literature:-
Copulas are introduced but never mentioned in the results. Is the KDE introduced in Fig. 2 equivalent to the copula CDF? If so, the terminology needs to be harmonized. If the KDE represents something else, this should be clearly introduced in the methods section.
In the copula section, the purpose of Lines 204–207 and Equation 6 is unclear. Isn’t the joint probability (i.e., P(x > X, y > Y)) the main quantity of interest? If so, why not introduce Equation 7 directly? You may refer to this article for inspiration on copula methods and joint return periods: https://wires.onlinelibrary.wiley.com/doi/10.1002/wat2.1579. -
Section 2.6 is rather generic. What are B1 and B2? Please introduce them properly. If B1 refers to climatic conditions and B2 to non-climatic factors, then from Line 416 onwards, a direct inference about the impact of infrastructure on yields cannot be made.
-
Discussion section: Please revise the text to reflect the broader implications of your findings and include only points that can be directly deduced from your analysis.
Specific Comments:
- L14: "Hamper" doesn’t sound right.
- L116: Briefly introduce the two datasets at the end of this sentence before discussing them individually.
- L121 (and repeated elsewhere, e.g., L163): What is "QX/T 468–2018"? This terminology is unclear. If it refers to internal coding, it may be unnecessary to mention.
- L248: Use "The impact of … on yield" instead of "yield impact."
- Figure 4: I am not sure I understand what DC refers to. If it represents correlation, shouldn’t the boxplot range be limited to 1? Why does it go up to 1.2 in panel d1 C2r2 for DCtot?
- L384–396: This section needs thorough revision. The reference to Zhang is problematic. Additionally, suggesting a dominant factor may not be valid, as these relationships are likely highly location- and case-specific. “Large” is not the right word here. Please remind the reader what "#3" refers to.
- L417: Use "Different impacts of … on yields" instead of "yield impacts."
- L421: Were these losses shown in any figures or derived from your analysis? If not, consider removing this sentence. Also, since the study does not directly assess the impact of irrigation, that discussion may not be relevant.
- L437: Replace "rainy stress" with "rain stress."
- L456: On what plots are these spatial shifts in concurrent events shown? If you refer to shifts over time, clarify this. If not, the sentence is unclear.
- L457: "Spatial difference in phenology" is unclear, please rephrase.
- L463 onwards (Conclusion): The conclusion is not the right place to introduce new references or discuss limitations. Consider revising this section and relocating these points to more appropriate sections in the manuscript.
Citation: https://doi.org/10.5194/egusphere-2025-1393-RC2 -
AC2: 'Reply on RC2', Tao Ye, 30 Jun 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-1393/egusphere-2025-1393-AC2-supplement.pdf
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Peer review completion
Journal article(s) based on this preprint
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Ran Sun
Yiqing Liu
Weihang Liu
Shuo Chen
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1649 KB) - Metadata XML
The manuscript presents a well-designed and timely study on the correlation between compound climate extremes and rice yields in southern China, with clear relevance to climate change adaptation. The authors leverage growth-stage-specific physiological thresholds, multi-source gridded data, and compound severity metrics to offer new insights into how concurrent heat-drought and chilling-rainy events affect rice production. This work makes an important contribution in the construction of metrics for compound stressors. However, several points require clarification, and improvements in structure and presentation would significantly improve the manuscript.
=== Major Comments
=== Comments on Manuscript Structure and Flow
Table 2 is referenced in the manuscript but not included.
The manuscript is generally well-organized, but there are several ways the narrative can be improved:
===Summary
This is an important and data-rich study that addresses key gaps in our understanding of compound climate extremes for analysis of crop yields. I hope the authors find these suggestions helpful in strengthening the manuscript for eventual publication.