the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 May 2025
Enhanced understanding of dominant drivers of Water Yield change across China through the improved coupled carbon and water model
Abstract. The rapid environmental changes, including climate change, escalating atmospheric CO2 concentration ([CO2]), and vegetation dynamics, have been significantly impacting hydrological processes. Accurately quantifying their contribution to water yield (WY) has become a significant challenge in water resource management and climate adaptation studies. Therefore, this study improved the coupled carbon and water (CCW) model integrating dynamic water use efficiency (WUE) to quantify the CO2-physiological feedback; furthermore systematically investigated the causes for WY change during 1982–2017 in China using a scenario analysis method based on the improved CCW model. The results showed that the effects on WY from changes in climate, vegetation, and [CO2] exhibited a significant regional variability. Climate change (especially precipitation change) emerged as the dominant driver, directly affecting over 70 % of China's land area. The vegetation change was the second largest factor, especially in central China, where vegetation change led to a general decrease in runoff. The effect of the escalating [CO2], which reduced transpiration by inducing stomatal closure, was relatively small. Spatial analysis aligned with isohyetal lines further revealed that vegetation change and [CO2] exerted greater influence within the 400–1600 mm precipitation range. In addition, the elasticity analysis showed that the sensitivity ranking of impact factors is precipitation (εP = 1.55) > [CO2] (εCO2= 0.55) > NDVI (εNDVI = -0.44) for the whole China. Historically, NDVI change has exceeded precipitation and [CO2] impacts on runoff in some regions due to its higher relative change; however, CMIP6 SSP585 projections indicate that accelerating [CO2] rise (2.34 % yr⁻¹) will amplify its hydrological effect to a +1.29 % annual WY increase by 2100, surpassing vegetation influences. This study provided theoretical support for water resource management and offered new perspectives for climate change adaptation strategies, vegetation restoration, and water resource management.
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RC1: 'Comment on egusphere-2025-2152', Anonymous Referee #1, 25 Jun 2025
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AC2: 'Reply on RC1', Huilan Shen, 18 Nov 2025
We sincerely appreciate the reviewer’s thorough and constructive evaluation of our manuscript. Your summary accurately captured the core objectives and contributions of our work, and we are grateful for the recognition of its methodological advancement and relevance to ecohydrological modeling. We have studied your and the other reviewers’ comments carefully and have made corrections/revisions as suggested.
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AC2: 'Reply on RC1', Huilan Shen, 18 Nov 2025
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CC1: 'Comment on egusphere-2025-2152', Xingguo Mo, 01 Jul 2025
The topic is interesting and worthy for published. But the current version is not clearly written in the model parameters, model implementation and attribution analysis. So it is hard to confirm whether the results are reseasonable or believable. Suggest the authors resubmit after revision.
Citation: https://doi.org/10.5194/egusphere-2025-2152-CC1 -
AC1: 'Reply on CC1', Huilan Shen, 18 Nov 2025
Thank you for your constructive comment. We have substantially revised the Methods section to improve clarity regarding model parameters, model implementation, and the attribution framework. In particular, we now provide a clearer description of the attribution procedure, including the computation of long-term trends and how these trends are incorporated into the relative-contribution analysis. We hope these revisions address the concerns about transparency and enhance the credibility of the presented results.
Citation: https://doi.org/10.5194/egusphere-2025-2152-AC1
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AC1: 'Reply on CC1', Huilan Shen, 18 Nov 2025
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RC2: 'Comment on egusphere-2025-2152', Anonymous Referee #2, 09 Oct 2025
General comments：
This manuscript presents an improved Coupled Carbon and Water (CCW) model incorporating dynamic water use efficiency (WUE) to disentangle the effects of climate change, vegetation dynamics, and atmospheric CO₂ on water yield (WY) across China during 1982–2017. The study addresses an important research gap by explicitly accounting for CO2 effects and providing a robust attribution analysis at both national and regional scales. The integration of scenario-based attribution and elasticity analysis is innovative and valuable for water resource management and climate adaptation strategies.
The study is generally well-structured, with clear objectives, methods, and results. However, there are a few areas where further clarity, elaboration, and enhancement could improve the overall impact and rigor of the paper. I provide specific comments and suggestions below:
Specific Comments
- Abstract:
* The abstract effectively summarizes the study, but it is rather dense with technical terms and numerical results. Consider slightly rebalancing it by adding one or two sentences that emphasize the practical implications (e.g., relevance for water resource management and ecological restoration) so that non-specialist readers can more easily grasp the significance.
- Introduction and Background:
* While the introduction clearly outlines the motivation, it would be useful to more explicitly highlight what distinguishes this study from other model applications. For example, a brief comparison with previous studies could emphasize the novelty of incorporating CO₂-induced WUE changes.
* The introduction mainly focuses on China, but since similar issues of climate–vegetation–CO₂ interactions exist globally, it may help to briefly situate this work in a broader international context. For example, mentioning comparable studies in other semi-arid or monsoon-influenced regions would show the wider relevance of the improved CCW model.
- Methods:
* The improved CCW model assumes interception evaporation factor fi equals zero. Since this simplification is acknowledged in the Discussion, please provide a short justification earlier in the Methods section so that readers can immediately understand this limitation.
* The attribution analysis is based on “trends” in WY under different scenarios, but the exact method of calculating these trends (e.g., linear regression, Mann–Kendall test, or another approach) is not clearly described. Providing details on the trend detection method, as well as the statistical significance criteria, would help readers better assess the robustness of the results.
- Results:
* Figures 5 and 6 demonstrate spatial heterogeneity in WY drivers. It would help if the authors could provide a more policy-relevant interpretation, e.g., what the findings imply for water resource planning in regions where vegetation dominates versus where climate dominates.
* The elasticity analysis provides valuable insights into the sensitivity of WY to different drivers. However, the discussion could be enhanced by more explicitly linking the elasticity results to the relative contributions of each driver. For instance, why does CO₂ have a higher elasticity than NDVI yet a smaller overall contribution? Clarifying how elasticity and the magnitude of change jointly determine the net impact would strengthen the interpretation.
- Discussion:
* While the study focuses on climate, vegetation, and CO₂ drivers, other human activities such as reservoir regulation, irrigation, and groundwater extraction can also significantly affect water yield in China. Since these processes are briefly mentioned as limitations, it would strengthen the discussion if the authors could add a short paragraph acknowledging how such anthropogenic factors may interact with the modeled drivers, and whether the improved CCW framework could potentially incorporate them in future work.
Citation: https://doi.org/10.5194/egusphere-2025-2152-RC2 -
AC3: 'Reply on RC2', Huilan Shen, 18 Nov 2025
Thank you very much for your thorough and constructive evaluation of our manuscript. We sincerely appreciate the time and effort you have devoted to assessing our work. Your positive recognition of the study’s innovation—particularly the improved Coupled Carbon and Water (CCW) model with dynamic water use efficiency (WUE) and the integration of scenario-based attribution with elasticity analysis—encourages us greatly. We have carefully considered all your valuable comments and those from the other reviewers, and we have revised the manuscript accordingly to enhance its clarity, depth, and scientific rigor.
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Please refer to the attached pdf.