the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Dec 2025
Asymmetric Decline in Hydrological Efficiency of China's Natural and Planted Forests
Abstract. The vegetation transpiration fraction (TF) is a key parameter linking terrestrial water and carbon cycles. Against the backdrop of global greening and climate change, the response of TF sensitivity to Leaf Area Index (LAI) changes (θ), and the relative roles of soil moisture (SM) and atmospheric drought (VPD), remain unclear, especially lacking a systematic comparison between China's Natural Forests (NF) and Planted Forests (PF). This study utilized multi-source datasets from 1990–2020 (including forest types, GLEAM, and ERA5-Land) and employed methods including sliding windows, partial correlation, ridge regression, and mediation effect models to systematically analyze the spatiotemporal dynamics of θ in NF and PF, and to quantify the independent contributions and dynamic shifts of SM and VPD. Results show: 1) Forest θ spatially increases from humid to semi-arid regions (NF > PF); temporally, θ shows a widespread significant decline, with PF declining more (mean of −0.262 %∙m−2 · m² · decade⁻¹) than NF, especially in semi-arid/semi-humid transition zones. 2) θ exhibits a "hump-shaped" nonlinear response to the joint SM-VPD gradient, peaking at moderate SM and medium-high VPD. 3) The key hydrological drivers of θ are undergoing a dynamic shift from "atmospheric demand" (VPD) to "soil supply" (SM); the independent control of SM (βSM) has significantly strengthened over time, while that of VPD (βVPD) has weakened. 4) The two forest types show distinct response mechanisms: NF is more sensitive to VPD, while PF is more sensitive to SM stress. Overall, China's forests are shifting towards a more "conservative" water-use strategy, and the enhancing effect of LAI on TF has significantly weakened under strengthening SM constraints and VPD stress. The differentiated high sensitivity – NF to atmospheric drought and PF to soil drought – provides critical insights for forest water resource management and afforestation planning under future climate change scenarios.
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Status: final response (author comments only)
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RC1: 'Comment on egusphere-2025-5821', Anonymous Referee #1, 09 Jan 2026
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AC1: 'Reply on RC1', Jia Guodong, 13 Feb 2026
Publisher’s note: the content of this comment was removed on 13 February 2026 since the comment was posted by mistake.
Citation: https://doi.org/10.5194/egusphere-2025-5821-AC1 -
AC2: 'Reply on RC1', Jia Guodong, 13 Feb 2026
Publisher’s note: the content of this comment was removed on 13 February 2026 since the comment was posted by mistake.
Citation: https://doi.org/10.5194/egusphere-2025-5821-AC2 -
AC4: 'Final Response to Referee #1', Jia Guodong, 13 Feb 2026
We sincerely thank the referee for the critical and insightful comments on our manuscript. We particularly appreciate the referee’s emphasis on the inherent nonlinearity and saturation in the TF–LAI relationship and the potential bias introduced by imposing a linear sensitivity. This feedback has motivated us to re-evaluate and strengthen both our methodological framework and the interpretation of θ in the revised manuscript.
Major Comment 1:
Response: We will comprehensively improve figure readability by increasing font sizes (axes, legends, titles, annotations), exporting higher-resolution figures, and optimizing multi-panel layouts and spacing to ensure clarity in the journal format.
Major Comment 2:
Response: We agree this is a key point. To avoid bias from forcing a nonlinear TF–LAI relation into a linear form, and to better reflect biophysical constraints, we will revise both the methodology and interpretation:
- Method: we will extend θ estimation from a simple linear sensitivity to a bounded nonlinear framework, applying a logit transform to keep TF within (0,1) and including a quadratic term to capture curvature/saturation. We will retain the linear approach as a benchmark and provide robustness checks comparing both approaches.
- Interpretation: we will explicitly define θ as an emergent marginal sensitivity/elasticity of TF to LAI, whose long-term changes can be driven by component saturation and energy/water constraints, and thus should not be over-interpreted as a direct shift in plant water-use strategy. We will clarify that VPD/SM effects on θ are largely indirect via constraints on absolute transpiration and partitioning.
Major Comment 3:
Response: We agree and will strengthen the manuscript in two ways:
- Method clarification: we will state explicitly that analyses are based mainly on growing-season aggregated annual series and sliding-window low-frequency variability to reduce synoptic noise, and that partial correlation/regression quantify statistical independent contributions.
- Discussion: we will add a dedicated discussion that SM–VPD feedbacks under drought can limit strict causal separability; thus we will interpret results as converging statistical evidence across methods and explicitly state the boundary in the limitations section.
Major Comment 4:
Response：
We agree and will temper the physiological interpretation. In the revision, we will avoid equating θ with a directly observed physiological parameter and instead present it as an emergent ecohydrological sensitivity metric within the gridded product framework. We will add a dedicated limitations paragraph on dataset uncertainty and interpretational boundaries and revise wording to be more cautious (e.g., “may suggest/indicate” rather than “demonstrate”).Major Comment 5:
Response: Thank you. We will correct the GLEAM4 description following official documentation: assimilation is primarily applied to surface soil moisture, and the hybrid ML component is implemented mainly within the vegetation transpiration module rather than across the entire model.
Major Comment 6:
Response: We agree and will add explicit collinearity diagnostics. In the revision, we will:
- compute VIF (or tolerance) to quantify redundancy between SM and VPD and report distributions by climate zone and NF/PF in the Supplement;
- clarify that ridge regression is adopted specifically to stabilize coefficient estimation under collinearity, and use relative contribution (RC) to compare the roles of VPD and SM;
- emphasize that conclusions are supported by convergence across multiple methods rather than a single model.
Major Comment 7:
Response: We will streamline repetitive result summaries and refocus the Discussion on mechanisms, comparison with prior studies, and possible sources of discrepancies. We will also add a dedicated “Methodological limitations” subsection addressing dataset uncertainty, SM–VPD coupling/feedback, scale mismatch, and interpretational boundaries of θ.
Specific Comments Line 15–16:
Response: We will revise the text to clearly separate datasets (forest-type maps and gridded products) from analytical frameworks (sliding window for temporal evolution) and inference methods (partial correlation, ridge regression, and mediation analysis).
Specific Comments Line 75–79:
Response: We will add appropriate references to support these statements and ensure that each key claim is properly cited.
Specific Comments Line 123:
Response: We apologize for the grammatical error. We will correct this sentence in the revised manuscript to read: "This dataset ... was produced by a machine learning algorithm ...".
Specific Comments Line 188:
Response: We will update the legend of Figure 2b to explicitly state that the left bars represent Natural Forests and the right bars represent Planted Forests.
Specific Comments Line 214–216:
Response: We will clarify that TF and θ are computed from growing-season aggregated annual series (growing-season accumulated values), and that an 11-year sliding window is applied on the annual series to generate a θ time series for trend analysis.
Specific Comments Line 273–279:
Response: We will integrate the “Trend Analysis and Testing” content into Section 2.3.4 so that trend estimation/testing and attribution steps are described coherently in one place.
Specific Comments Line 320:
Response: We appreciate this observation. We will revise the description in the revised manuscript to more accurately characterize the actual distribution pattern shown in Figure 4a. Specifically, we will modify the text to strictly align with the observed data trends.
Specific Comments Line 458-459:
Response: We agree that the phrase is ambiguous. We will replace “non-linear trend of increasing” with a precise description consistent with the results, e.g., “a significant increasing trend with pronounced interannual variability” (or other wording strictly aligned with the observed pattern).
Citation: https://doi.org/10.5194/egusphere-2025-5821-AC4
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AC1: 'Reply on RC1', Jia Guodong, 13 Feb 2026
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RC2: 'Comment on egusphere-2025-5821', Anonymous Referee #2, 24 Jan 2026
The present manuscript explores the spatiotemporal distribution patterns and climatic mechanisms of transpiration sensitivity in both natural and planted forests in China. The subject matter is interesting, and the obtained results are expected to deepen the understanding of forest-water relations in the context of climate change. Nevertheless, there are still a series of substantive concerns, which must be adequately addressed prior to the manuscript’s publication. Detailed comments are outlined as follows.
(1) One of my main concerns is that whether in situ observations could be used to calculate the transpiration sensitivity and examine their temporal evolution. Then, the declining trend of transpiration sensitivity estimated could be validated using in situ observations.
(2) Another main concern pertains to the tight correlation between the two key variables: atmospheric demand (vapor pressure deficit, VPD) and soil demand (soil moisture, SM). An explicit clarification is needed regarding how to quantitatively separate their respective contributions. The descriptions in Section 2.3.3 and Section 2.3.4 are still not clear.
(3) The other concern is that this study did not consider the effect of forest age, which should be addressed. In addition, rising atmospheric CO2 concentrations have an important effect on forest water use efficiency (Section 4.2), but it is not considered in the present manuscript.
(4) Section 2.1: Please clarify the soil depth for the soil moisture data used in this study.
(5) As shown in Eq. 1-4, VPD is mainly affected by air temperature and relative humidity, why was relative humidity not considered as one of climate factors in the analysis?
(6) Line 102: The aridity index framework was used to divide the study area to three climatic zones. But there are four climatic zones (Lines 182-184, Fig. 2) although the arid zone was excluded from the analysis (Lines 185-187).
(7) Figure 2: It is better to add the fraction of NF and PF for different climatic zones in Fig. 2a.
(8) Line 550: the wrong variable “$\beta_{VPD}$”.
(9) Proofreading of the manuscript needs to be polished by a native English speaker before the submission.
Citation: https://doi.org/10.5194/egusphere-2025-5821-RC2 -
AC3: 'Reply on RC2', Jia Guodong, 13 Feb 2026
We are grateful to the referee for the positive evaluation of our work and the recognition of its significance in understanding forest-water relations. The constructive suggestions, particularly regarding the validation of transpiration sensitivity and the separation of climatic drivers, have been very helpful in improving the quality of this study.
Comment 1:
Response: We agree that in situ validation is important. In the revision, we will conduct a feasible “minimum validation” using available eddy-covariance and/or sap-flow sites by constructing TF and θ consistently with our definitions and evaluating whether the sign/magnitude of θ variability aligns with our gridded results and climate drivers. If site coverage is limited, we will explicitly state the sampling limitations, treat the validation as qualitative/semi-quantitative consistency checks, and outline future expansions for systematic validation.
Comment 2:
Response: We will provide an operationally clear separation strategy and add diagnostics/robustness evidence:
- Spatial gradients: use 2D binning with a control-variable logic (compare θ across VPD groups within similar SM bins, and vice versa) to estimate net effects;
- Temporal dynamics: use partial correlation (net association controlling the other variable) alongside ridge regression (stabilized coefficients and contribution decomposition under collinearity);
- Diagnostics: add VIF/tolerance to quantify collinearity and demonstrate that results are not methodological artefacts. We will rewrite Sections 2.3.3–2.3.4 as clear step-by-step procedures and include equations/flowcharts if needed (possibly in the Supplement).
Comment 3:
Response: We will examine the potential role of rising CO₂ by adding a time-varying CO₂ covariate and conducting sensitivity analyses, and we will explicitly discuss the interpretational limits (correlation vs causation).
We will explore available gridded forest-age products and, where feasible, perform stratified analyses; otherwise, we will clearly state this as a limitation and outline it as future work.Comment 4:
Response：We will explicitly clarify in the revised manuscript that the Root-Zone Soil Moisture (SMrz) from GLEAM 4.2 is a vegetation-specific variable. According to the GLEAM formulation (Martens et al., 2017), SMrz represents the integrated soil moisture over the effective rooting depth, which is 0–100 cm for low vegetation and 0–250 cm for tall vegetation (forests).
Since our study focuses on forest ecosystems, the GLEAM SMrz data effectively characterizes the water status of the entire soil profile (0–250 cm) accessible to tree roots. This captures the hydraulic conditions more comprehensively than surface-only metrics. We have added this definition and the relevant citation to Section 2.1 to ensure methodological precision.
Comment 5:
Response: We will clarify that RH is already explicitly incorporated through the VPD calculation as an integrated metric of atmospheric demand. Including RH as an additional independent driver alongside VPD would introduce redundancy and stronger collinearity, undermining attribution. We will add this rationale in the revision and, if helpful, provide a sensitivity analysis in the Supplement by examining RH–θ relationships in a model setup that avoids simultaneously using RH and VPD.
Comment 6:
Response: We will harmonize the description: the AI framework yields four zones (humid, semi-humid, semi-arid, arid), but the arid zone is excluded from core analyses due to insufficient forest samples for robust statistics. We will state this consistently in the Methods, Results, and figure captions (definition vs included zones).
Comment 7:
Response: We will incorporate the NF and PF fractions for each climate zone into Fig. 2a
Comment 8:
Response: Thank you. We will correct the typo and carefully check notation throughout the manuscript to ensure consistency between all β symbols and their definitions (e.g., standardized regression coefficients).
Comment 9:
Response: We will comprehensively polish the manuscript for English clarity and consistency and will have the revised version proofread by a native English speaker before resubmission.
Citation: https://doi.org/10.5194/egusphere-2025-5821-AC3
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AC3: 'Reply on RC2', Jia Guodong, 13 Feb 2026
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- 1
The paper by Zhang et al. calculated the transpiration fraction (TF), estimated its sensitivity to leaf area index (denoted as θ), examined how the mean and trend of are influenced by vapor pressure deficit (VPD) and soil moisture (SM), and quantified the contribution of climate change to changes in θ through alterations in SM and VPD. These analyses are performed separately for natural and plantation forests in China, with direct comparisons made between the two ecosystem types.
Although this study seeks to address an important question—specifically, the relative roles of VPD and SM in mediating the coupling between plant physiological processes (transpiration) and structural attributes (leaf area), as well as potential differences in these roles between natural and plantation forests—I find that neither the methodology nor the presented results are sufficiently convincing. Several major concerns remain unaddressed. Unless these are satisfactorily resolved in a revision, I do not consider the manuscript suitable for publication in Hydrology and Earth System Sciences.
Major comments
Comment 1: The text in all figures is excessively small, making it difficult to read. This significantly impairs the clarity and accessibility of the data presentation. I strongly recommend enlarging the font sizes (including axis labels, legends, titles, and annotations) and improving the overall figure resolution to enhance readability!
Comment 2: As demonstrated by Wei et al. (2017), the relationship between the transpiration fraction (TF = T/(T + Es), neglecting interception loss) and LAI is inherently non-linear. Consequently, the θ is expected to diminish with ongoing vegetation greening. Although their study omitted Ei, it robustly illustrates the non-linear response of TF to increasing LAI. This non-linearity arises from biophysical constraints rather than direct influences of VPD or SM.
Specifically, as LAI increases, Es rapidly approaches its minimum due to enhanced canopy shading, resulting in a substantial decline in the absolute value of ∂Es/∂LAI. Interception evaporation becomes largely independent of further LAI increases, as it is primarily governed by rainfall patterns (frequency and intensity). T also becomes progressively constrained by available energy or water supply, leading to a marked reduction in ∂T/∂LAI at higher LAI values.
Under these conditions, ∂T/∂LAI, ∂Es/∂LAI, and ∂Ei/∂LAI all approach zero, causing θ to decrease toward zero. These mechanisms indicate that trends in θ may not be driven by shifts in plant water-use strategies, and the effects of VPD and SM on θ are indirect—operating primarily through modulation of absolute transpiration rates rather than directly altering the sensitivity of TF to LAI.
Comment 3: This study quantifies the influences of VPD and SM on θ from both spatial gradients and temporal dynamics perspectives. However, VPD and SM operate on distinctly different timescales: VPD fluctuates relatively rapidly compared to SM, and vegetation can respond to these changes over short timescales. For instance, stomata may partially close in response to elevated VPD, leading to quick adjustments in transpiration. In contrast, vegetation responses to changes in SM are typically slower and more gradual, involving physiological acclimation and structural adjustments over longer periods. Furthermore, during the warm growing season under dry soil conditions, VPD can be amplified through land–atmosphere feedback, creating a coupled effect that makes it difficult to disentangle the independent contributions of VPD and SM.
These differences in response timescales and potential feedbacks raise questions about the interpretation of the results presented in the study. I believe this issue warrants further careful consideration and discussion to strengthen the conclusions regarding the relative roles of VPD and SM in driving changes in θ.
Comment 4: [Line 79-82] The GLEAM4 and SiTHv2 transpiration are generated using semi-empirical modelling approaches that rescale potential evapotranspiration based on a stress factor, which incorporates vegetation optical depth (VOD) and simulated root-zone SM. The only mechanisms through which physiological responses could theoretically be represented are (i) the use of VOD as a proxy for canopy characteristics and (ii) the assimilation of surface soil moisture retrieved from satellite observations (applied only in GLEAM4).
However, the relatively low signal-to-noise ratio in these datasets may introduce substantial uncertainties into the estimated transpiration values. Consequently, it remains uncertain to what extent θ reflects physiological regulation by plants. Given this ambiguity, the interpretation and wording regarding the physiological implications of θ should be considerably more restrained and carefully qualified.
Comment 5: [Line 139-143] The description of GLEAM4 is inaccurate. Its data assimilation is limited to surface soil moisture. Furthermore, the hybrid machine-learning model is implemented solely within the vegetation transpiration module.
Comment 6: The methodology employed in this study may be influenced by the strong collinearity between VPD and SM, particularly regarding the application of partial correlation and mediation effect models. To ensure that the findings are robust and not merely artefacts of the chosen methods, it is recommended to provide collinearity diagnostics (e.g., VIF or tolerance levels) to quantify the degree of redundancy between these input variables.
Comment 7: The Discussion section is currently characterised by excessive reiteration of the results. I recommend streamlining this section to enhance clarity and conciseness. Additionally, the manuscript would be significantly strengthened by a dedicated discussion on methodological limitations, which is essential for a nuanced interpretation of the findings.
Specific comments
Line 15-16: There is a conceptual mismatch in grouping forest types with reanalysis products like GLEAM and ERA5-Land. Furthermore, the sliding window approach is a temporal analysis framework, whereas partial correlation, ridge regression, and mediation models are statistical inference methods.
Line 75-79: References of these sentences?
Line 123: This dataset, …., was produced by machine learning algorithm …
Line 188: Please clarify the identities of the left and right bars in panel (b). It is currently unclear what variables or groups these bars represent.
Line 214-216: The temporal scales of transpiration and evapotranspiration used for the calculations are not specified. Please clarify whether these analyses were conducted on a daily or monthly basis.
Line 273-279: The section 'Trend Analysis and Testing' should be integrated into Section 2.3.4, as the latter mentions trend analysis but lacks a detailed explanation.
Line 320: The 'hump-shaped' pattern described by the authors is not readily apparent in Figure 4a.
Line 458-459: The phrase 'non-linear trend of increasing' is ambiguous.