the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Jun 2026
Dynamic Satellite-Derived Vegetation and Radiation Inputs Advance Continental-Scale Hydrological Simulation Across China
Abstract. Global vegetation greening is reshaping water and energy cycles, challenging land surface hydrological modeling. Satellite remote sensing provides dynamic observations of vegetation and radiation, offering a pathway to improve simulations. However, models often rely on static parameters, failing to capture critical transient biogeophysical feedbacks. This study quantifies the impact of integrating remote sensing data—leaf area index, fractional vegetation cover, albedo, and downward radiation—into the Variable Infiltration Capacity (VIC) model across China. We evaluated simulations against observed runoff from 50 stations, evapotranspiration (ET) from >40 flux sites, and satellite ET products. The dynamic data-driven VIC model accurately simulated runoff and ET. In ungauged basins, simple parameter transfer achieved Nash-Sutcliffe efficiency >0.6 for runoff. Using static vegetation parameters induced substantial biases: a national-scale ET underestimation of 5 % (20 mm yr−1) and runoff overestimation of 14 % (29 mm yr−1). In the rapidly greening basin (i.e., Pearl River Basin), dynamic vegetation data corrected ET and runoff biases by ~70 mm yr−1. Remote sensing radiation data offered limited improvement, likely due to the model's inherent radiation estimation capability. This work provides conclusive evidence that dynamic remote sensing data, particularly vegetation parameters, are crucial for accurate large-scale hydrological simulation in changing environments, offering a practical framework for data-sparse regions.
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Status: open (until 09 Sep 2026)
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CC1: 'Comment on egusphere-2026-2505', Nima Zafarmomen, 05 Jul 2026
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CC2: 'Reply on CC1', Dawei Peng, 23 Jul 2026
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Dear Reviewer,
We sincerely thank the commenter for the constructive and insightful comments. We appreciate the positive assessment of the relevance and timeliness of our study, and we fully agree that several aspects of the manuscript should be clarified and strengthened in the revised version. Our detailed responses are provided below.
Comment 1:
The manuscript should more clearly state its main novelty. The study includes dynamic vegetation inputs, dynamic radiation inputs, VIC calibration, parameter transfer to ungauged basins, and multi-source ET validation. The authors should clarify whether the primary contribution is the dynamic vegetation–radiation forcing framework, the continental-scale evaluation, or the parameter-transfer approach.
Response 1:
We thank the reviewer for this helpful comment. The primary novelty of our study is the first continental‑scale demonstration that incorporating satellite‑derived dynamic vegetation and radiation forcings significantly improves VIC model simulations of evapotranspiration and runoff, with China as a critical case due to its global hotspot status for vegetation greening. The VIC calibration, parameter transfer, and multi‑source ET validation are supporting components that enable this core attribution analysis, rather than being the main contribution themselves. We will revise the Introduction and Conclusions to clearly state this hierarchy.
Comment 2:
The treatment of dynamic vegetation parameters should be described more explicitly. Since LAI, FVC, and albedo directly affect canopy interception, transpiration, soil evaporation, and runoff partitioning, the authors should explain more clearly how each variable influences the VIC model equations and which parameter contributes most to the final improvement.
Response 2:
We appreciate the suggestion to describe the treatment of dynamic vegetation parameters in more detail. Specifically, LAI directly influences canopy interception, canopy resistance, transpiration, and vegetation shading; FVC controls the partitioning between vegetated and bare-soil fractions, thereby affecting transpiration and soil evaporation; while albedo regulates the absorption of incoming shortwave radiation and consequently influences the available surface energy for evapotranspiration. In the revised manuscript, we will expand the methodological description of how LAI, FVC, and albedo are incorporated into VIC and how they affect key hydrological processes. We will briefly discuss the relative roles of LAI, FVC, and albedo in contributing to the simulated improvements.
Comment 3:
The parameter-transfer method for ungauged basins is promising, but the multiple linear regression approach should be justified in more detail. The authors should discuss whether nonlinear or machine-learning regionalization methods were considered, and why MLR is sufficient for transferring VIC parameters across diverse hydroclimatic regions.
Response 3:
Thank you for this helpful suggestion. In the revised manuscript, we will provide a clearer explanation for adopting multiple linear regression (MLR) as a parsimonious, interpretable, and computationally efficient regionalization approach for large-scale hydrological applications. Given the limited number of calibrated basins, more flexible nonlinear or machine-learning-based methods may be prone to overfitting, although they could offer improved transfer performance when larger calibration datasets are available. We also acknowledge that such approaches may be particularly advantageous in highly heterogeneous hydroclimatic regions. However, a systematic comparison of different regionalization methods is beyond the scope of the present study. We will also clarify the limitations of the current MLR-based transfer method and discuss possible future extensions using nonlinear regionalization approaches.
Comment 4:
The manuscript reports relatively strong runoff performance, but ET validation against flux towers shows notable bias at some sites. The authors should discuss the possible causes of this bias, including scale mismatch between flux-tower footprints and model grids, uncertainty in satellite vegetation products, and limitations in VIC’s ET partitioning.
Response 4:
Thank you for this insightful comment. We appreciate the comment regarding the ET validation against flux-tower observations and acknowledge that the notable biases observed at some sites were not sufficiently discussed in the current manuscript. These may include the spatial scale mismatch between flux-tower footprints and the VIC grid cells, uncertainties in satellite-derived vegetation products, energy-balance closure issues in eddy-covariance observations, and limitations in VIC’s representation of ET partitioning, irrigation, groundwater access, root-zone processes, and local vegetation physiological responses. We will incorporate a more comprehensive discussion of these factors to better explain the remaining ET biases and the limitations of the current modeling framework.
Comment 5:
The comparison among VIC-simulated ET and satellite ET products is useful, but the authors should avoid treating satellite products as fully independent truth. Since several ET products also rely on remote sensing vegetation information, some agreement may partly reflect shared input data or similar assumptions.
Response 5:
Thank you for this valuable comment. We agree that satellite-based evapotranspiration products should not be treated as fully independent ground truth. In the revised manuscript, we will adjust the wording accordingly and refer to these datasets as satellite-based ET reference products. The comparison with these products is intended only as a reginal-scale evaluation to complement the validation based on runoff observations and eddy covariance measurements, rather than as independent validation. We will explicitly acknowledge that some ET products rely on remotely sensed vegetation information and related energy-balance or empirical assumptions, so the agreement between VIC simulations and satellite ET products may partly reflect shared input information or similar modeling assumptions. Nevertheless, the overall consistency still provides complementary evidence supporting the reliability of the simulated spatial patterns. We will therefore interpret these comparisons mainly as assessments of spatial consistency and product-level agreement, while recognizing their limitations.
Comment 6:
Some figures are information-rich but visually dense, particularly Figures 3, 6, 7, 9, and 12. The authors should improve readability by increasing font sizes, simplifying legends where possible, and ensuring that all color scales, units, and scenario labels are clearly interpretable.
Response 6:
Thank you for this helpful suggestion. In the revised manuscript, we will improve these figures by increasing font sizes, simplifying legends where appropriate, clarifying color scales, units, and experiment/scenario labels, and enhancing the overall visual presentation. We will also consider moving overly detailed information to the Supplement where appropriate to improve the clarity of the main figures.
Comment 7:
The authors are strongly recommended to cite recent work on assimilating satellite-based vegetation information into coupled surface water–groundwater modeling. In particular, Zafarmomen, N., Alizadeh, H., Bayat, M., Ehtiat, M., and Moradkhani, H.: Assimilation of sentinel-based leaf area index for modeling surface-ground water interactions in irrigation districts, Water Resources Research, 60(10), e2023WR036080, 2024, is highly relevant. That study directly addresses the assimilation of Sentinel-based LAI into a hydrological modeling framework and demonstrates the value of high-resolution vegetation information for improving water-cycle simulations, including evapotranspiration, irrigation-related processes, groundwater recharge, and surface water–groundwater interactions.
Response 7:
Thanks for suggesting the valuable work by Zafarmomen et al. (2024). We agree that this work is highly relevant to the broader topic of incorporating satellite-based vegetation information into hydrological modeling. In the revised manuscript, we will cite this study and other relevant papers to better position our work within the recent literature. Moreover, we will clarify the distinction between their Sentinel-based LAI data assimilation framework for coupled surface water–groundwater modeling and our use of satellite-derived vegetation variables as dynamic forcings in VIC for evaluating continental-scale impacts on runoff and evapotranspiration simulations across China.
Overall, we are grateful for these helpful suggestions. We believe they will help us improve the clarity, methodological transparency, and positioning of the manuscript in the revised version.
Citation: https://doi.org/10.5194/egusphere-2026-2505-CC2
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CC2: 'Reply on CC1', Dawei Peng, 23 Jul 2026
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The manuscript presents a continental-scale hydrological modeling study that evaluates how dynamic satellite-derived vegetation and radiation inputs affect VIC model simulations across China. The authors incorporate remote sensing products for LAI, fractional vegetation cover, albedo, and radiation into VIC and compare model outputs against runoff observations from 50 stations, ET observations from 41 flux sites, and multiple satellite-based ET products. The main finding is that dynamic vegetation inputs have a much stronger effect than dynamic radiation inputs: using static vegetation leads to ET underestimation of about 5.11% and runoff overestimation of about 14.13%, while dynamic radiation has a comparatively smaller influence. The manuscript is relevant and timely because it addresses the need to represent vegetation dynamics in land surface and hydrological models under changing environmental conditions.