the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Jul 2026
Oxygen Dynamics in Intertidal Sediments: Integrating High-Resolution Data and Reaction-Transport Modeling
Abstract. Predicting the response of O₂ dynamics in intertidal sediments to changing environmental conditions is essential for understanding and forecasting the impacts of climate change on coastal biogeochemical functioning. However, key environmental controls such as light, temperature and tidal regime (immersion vs. emersion) are only partially integrated into existing sediment biogeochemical models. Additionally, inversion-based approaches, which infer reaction rates from measured O₂ profiles without explicitly representing the underlying diagenetic mechanisms, cannot be used predictively. We developed a reaction-transport model for simulating O₂ dynamics in MPB-inhabited intertidal sediments at high spatial and temporal resolution under variable environmental forcing. The model was constrained and evaluated using laboratory microsensor measurements of O₂ concentrations and gross photosynthesis in muddy sediments from the Oosterschelde tidal bay (the Netherlands). Our analysis indicates that the studied MPB community is adapted to low irradiance and responds to changing light conditions through vertical migration. Reoxidation of reduced inorganic substances dominates sediment O₂ consumption at low irradiance, whereas aerobic mineralization becomes increasingly important at higher irradiance, with photorespiration representing an additional relevant O₂ sink under high-irradiance conditions. Additionally, both the O₂ producing and consuming processes show strong immersion-emersion and temperature response. Overall, the model is released as an open-source tool, providing a framework that can be adapted and refined for interpreting and predicting O₂ dynamics in intertidal systems under a broad range of environmental conditions.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-4452', Anonymous Referee #1, 22 Aug 2026
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RC2: 'Comment on egusphere-2026-4452', Anonymous Referee #2, 06 Sep 2026
The authors constructed a high spatial resolution reaction transport model for oxygen dynamics driven by micro benthic plants (MPBs) in intertidal muddy sediments, and constrained and validated it using microsensor experimental data (including light photosynthesis curve PI, temperature gradient TR, and flooding/exposure IE experiments) from sediments in Oosterschelde tidal bay in the Netherlands. They found that the MPB community adapts to low light environments and responds to changes in light through vertical redistribution; Under low light conditions, the re oxidation of reduced inorganic substances dominates the consumption of sedimentary oxygen. As light intensity increases, the proportion of aerobic mineralization increases to about 50%. Under high light conditions, photorespiration, as an additional oxygen sink, can account for up to 20% of the total generated oxygen; Temperature exhibits different Cardinal responses to production and consumption processes, with photosynthesis reaching its peak at around 35 ° C and collapsing at 40-45 ° C, while the re oxidation process has higher heat resistance (peak at around 45 ° C); Sedimentation exposure leads to a decrease of approximately 60% in photosynthesis. The model is released in the open source R package O2DIA/O2DIAshiny, providing a transferable framework for the interpretation and prediction of intertidal sedimentary oxygen dynamics. This study is the first to couple processes such as MPB vertical migration, photorespiration, re oxidation, and aerobic mineralization into a predictable mechanistic model at the sub millimeter scale, filling the gap of previous inversion methods that could only diagnose but not predict; The fusion of high-resolution microsensor data and mechanistic models provides a paradigm for sedimentary biogeochemical research, and it is recommended to publish it after substantial modifications. Main issues and suggested modifications:
1. Insufficient discussion on parameter identifiability and calibration uncertainty (lines 280-298, section 3.2): The manuscript acknowledges that due to the fact that light field measurement and O2 measurement are not located at the same position, parameters such as kext, μ, σ, kI, w cannot be independently constrained, but are jointly estimated through O2 production rate profiles. This is a substantive model recognizability issue, but the manuscript does not provide: results of parameter sensitivity analysis; Correlation and covariance estimation between parameters; Is the objective function used for calibration the weighted least squares of the O2 concentration profile and GPP profile? How to determine the weight?)； The propagation of parameter uncertainty to the quantitative evaluation of simulation results.
2. The steady-state assumption does not match the time scale of the dynamic experiment (lines 143-148, lines 555-562): The authors have self-questioned in lines 555-562 that the "5-fold difference in light/dark re oxidation rate coefficients" observed in the PI experiment may be due to the fact that the ODU profile did not reach true steady state (ODU comes from a much thicker sedimentary layer than the oxide layer, and its response is much slower than O2). But the model calibration still assumes steady state: this assumption directly affects the estimation of r-reox, which in turn affects the quantitative reliability of the core conclusion that "re oxidation dominates low light oxygen consumption"; The manuscript does not provide any evidence to demonstrate whether the processing time of each experiment is sufficient to achieve steady state of O2, nor does it evaluate the impact of ODU quasi steady state error on parameter estimation.
3. The parameterization of MPB vertical migration is too empirical (lines 365-372, formula 15): formula 15 linearly relates σ to incident light I2: σ (I2) ≈2 × 10⁻³+2.25 × 10⁻⁷ · Ia. This relationship is only based on fitting three core samples from PI experiments, and the variation of μ with light is "statistically insignificant (p=0.357)" and lacks mechanistic basis (such as biophysical mechanisms of phototaxis/avoidance response). Its applicability has not been verified under different temperatures or exposure conditions - the model only "fine tuned" μ and σ in TR and IE experiments, but... Whether to still follow formula 15 without explanation.
4. The heuristic function for the tidal exposure effect lacks a mechanistic explanation (lines 225-228, formula 8, lines 605-618): the function g (h)=g₀+(1-g₀) · h²/(h²+k_h²) is purely a mathematical fit, with g₀ ranging from 0.4 to 0.5. Section 4.5 discusses possible mechanisms (CO2 availability, nutrient supply, sediment physical compaction), but: no additional experimental or literature quantitative data has been used to constrain g₀ and k_h; The response differences among the three IE cores are significant (core 8 decreases to<5%, core 7 remains almost unchanged), but the model only characterizes it with a single g ₀ ≈ 0.4-0.5, masking this heterogeneity; The treatment of compressing the diffusion layer (DBL) to zero under exposed conditions is too idealized.
5. The boundary description of the model is not prominent enough (lines 650-658): The model currently only considers molecular diffusion and ignores: wave driven pore water exchange in permeable sediments; Biological disturbance and biological irrigation; Horizontal processes and tidal gradients. These are mentioned as "future development" in lines 655-658, but the statement "predicting O2 dynamics in intertidal dimensions" in the abstract and introduction may lead readers to mistakenly believe that the model is universally applicable to intertidal zones. In fact, the model is strictly applicable to intertidal sediments dominated by mud, low permeability, and MPB.
6. Add references in lines 18-19 of the introduction（ https://doi.org/10.1016/j.scitotenv.2023.168299 ）
Citation: https://doi.org/10.5194/egusphere-2026-4452-RC2
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- 1
This manuscript presents an interesting experimental and modelling study of O2 dynamics in microphytobenthos-inhabited muddy intertidal sediments, examining responses to light, temperature, and immersion-emersion conditions. The combination of high-resolution microsensor measurements with reaction–transport modelling is potentially valuable, and I particularly found the experimental dataset and the observed responses across the different environmental treatments interesting.
However, in its current form, I think the manuscript places too much emphasis on the predictive and mechanistic interpretation of the model. The model contains a relatively large number of parameters, many of which are fitted to the experimental observations rather than independently constrained, while several important quantities show substantial variability or cannot be directly verified. This makes some of the mechanistic conclusions derived from the model difficult to evaluate independently. I therefore recommend refocusing the manuscript more strongly on the experimental observations, with the model primarily serving as a framework for interpreting these observations and identifying processes and hypotheses for future investigation. In fact, I think one of the important conclusions emerging from the study is that we are still far from a robust mechanistic understanding of the processes controlling the MPB associated O2 dynamics in intertidal sediments. Such a refocusing would also provide an opportunity to substantially shorten and streamline the manuscript, which I believe would make the main findings clearer and the study more accessible and appealing to a broader readership.
Major comments
1. Model claims. The model is technically sound and nicely designed, but the difficulty is that the model goes substantially beyond what the measurements independently constrain. It has 34 parameters, with most estimated by fitting the same experimental dataset that the model is subsequently used to interpret. Some particularly influential parameters are weakly constrained. For example, light attenuation and the vertical MPB distribution cannot be independently determined from the measurements, so several of these parameters are fitted simultaneously to the O2-production profiles. Deep ODU concentrations vary by about an order of magnitude between cores but were not measured directly. Photorespiration was introduced because simulations without it systematically overestimated measured O2- concentrations. That does not mean photorespiration is unimportant, but the present data do not independently demonstrate that it is the process responsible for the discrepancy. Also the parameterization based on on industrial studies focusing on microalgae culturing. Similarly, the model requires a fivefold change in the ODU reoxidation parameter between dark and light conditions, from which the manuscript then infers depth-dependent differences in reoxidation. These are useful hypotheses, but I would be cautious about presenting them as mechanistically demonstrated results.
2. Sediment characterization. Given the considerable variability observed among replicate cores, I find the characterization of the experimental sediments rather limited, and I do not consider references to previous studies sufficient in this context. Important sediment properties that could directly affect O2 dynamics and model parameterization, including porosity, grain size, organic matter content, and the concentration and distribution of reduced compounds, are either assumed, inferred through model fitting, or referred to previous studies. This makes it difficult to determine whether the observed differences among cores primarily reflect physiological responses of the MPB community or variability in sediment properties. I therefore suggest that the authors provide all available sediment characterization for the experimental cores and discuss this limitation more explicitly.
3. Microphytobenthos Physiology. The physiology of the microphytobenthos is represented in an overly simplified manner. To give just a few examples, microphytobenthos can assimilate substantial amounts of nitrate and ammonium and, together with their vertical migration and DNRA, drive a dynamic redistribution of ODUs within the sediment. This can shift hotspots of metabolic activity, and thus oxygen consumption, vertically over time. Relatedly, it is unclear what happens when cells experience, for example, extreme temperatures or prolonged emersion, potentially leading to physiological stress or cell lysis. I would not necessarily expect such processes to be explicitly represented if the model were more clearly framed as a simplified model applicable to a specific range of conditions, rather than as a broadly applicable model of oxygen dynamics in intertidal sediments.
4. Microscale heterogeneity. Recent and older studies have shown that microscale heterogeneity can play an important role in intertidal sediments, including processes and structures occurring at spatial scales smaller than those resolved by the microsensors used here. Examples include localized organic matter accumulations and associated anoxic hotspots, porosity variability, but also small-scale heterogeneity during emersion, such as changes in the light field caused by scattering around water droplets. These processes and their potential implications for the interpretation of the measurements and model results should at least be discussed.
5. Advective terms. The authors mention in several places that the model does not apply to permeable, advection-dominated sediments. I consider this an important limitation that should be reflected more clearly in the title, abstract, and conclusions. Although this limitation is acknowledged in the manuscript, it can easily get lost, particularly in some of the broader concluding statements. The applicability of the model is therefore restricted to some intertidal sediments in which molecular diffusion dominates solute transport, rather than intertidal sediments in general.